Search results for: reduced modeling

Authors: Caroline Lindlar, Eva Jakob

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Female entrepreneurs often face significant barriers in accessing funding due to biases from business angels, venture capitalists, and financial institutions, which tend to favor male entrepreneurs. These biases contribute to persistent funding disparities, with female entrepreneurs receiving less financial support than their male counterparts. The situation worsens when female entrepreneurs have prior experiences with venture failure, which diminishes their attractiveness to traditional investors. Venture failure, defined as the cessation of operations due to declining revenues, rising costs, or ownership changes, plays a substantial role in shaping funding opportunities. In response, female entrepreneurs frequently turn to alternative funding sources such as crowdlending, where gender biases are often reversed in favor of women, particularly when their ventures emphasize social value creation. While existing research highlights the positive impact of gender on crowdfunding success, it remains unclear how venture failure, known to negatively bias female entrepreneurs in traditional funding contexts, interacts with the positive effects of gender in crowdlending. This interaction is particularly relevant because crowdlending often involves non-professional funders who make repeated investment decisions under uncertainty, based on limited information and past experiences. Given that approximately one-third of ventures fail to deliver promised returns, the role of gender bias after failure in crowdlending is an important area of investigation. This study addresses How failure affects crowd funders’ gender bias in future funding decisions? Drawing on social role and role congruity theory, we posit that societal perceptions of women as more communal conflict with the agentic qualities traditionally associated with entrepreneurship. This incongruence may result in reduced confidence in the success of female entrepreneurs after failure, limiting their access to future funding. However, we also hypothesize that social framing may mitigate this bias by aligning perceptions of female entrepreneurs with traits such as warmth and caring, enhancing their appeal after failure. To test these assertions, it conducted a between-subject audio vignette experiment with 155 participants who listened to entrepreneur pitches manipulated by gender (male vs. female) and venture framing (social vs. commercial). Participants made initial investment decisions, received failure-related news about the venture, and then made subsequent investment decisions. Pre-tests with 159 participants ensured the validity and reliability of the experimental manipulations. Moreover, we did a metric conjoint analysis with 100 participants, and they had to decide between different crowdfunding campaigns based on the attributes of previous failure, gender, and venture mission. it findings reveal that failure activates gender biases in crowdlending. Female-led ventures receive significantly less funding after failure compared to male-led ventures, suggesting the positive bias toward female entrepreneurs in the pre-funding phase does not persist post-failure. Moreover, framing a venture as socially oriented exacerbates the negative effect of failure for female entrepreneurs, as they secure fewer funds after failure compared to male entrepreneurs leading similar social ventures. This indicates that role-congruent framing does not mitigate gender bias after failure. This study contributes to research on gender in entrepreneurship by exploring how failure impacts future funding for female entrepreneurs. It also expands social crowdfunding literature by examining social value framing and adds to the entrepreneurial failure literature by focusing on crowd funders’ post-failure behavior.

Keywords: gender bias, crowdfunding, investment failure, investment behavior, social entrepreneurship

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Authors: Rosimeire Araújo Silva, Philip Martin Fearnside

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Extreme droughts and floods in the Amazon have serious consequences for natural ecosystems and the human population in the region. The frequency of these events has increased in recent years, and projections of climate change predict greater frequency and intensity of these events. Understanding the links between these extreme events and different patterns of sea surface temperature in the Atlantic and Pacific Oceans is essential, both to improve the modeling of climate change and its consequences and to support efforts of adaptation in the region. The relationship between sea temperatures and events in the Amazon is much more complex than is usually assumed in climatic models. Warming and cooling of different parts of the oceans, as well as the interaction between simultaneous temperature changes in different parts of each ocean and between the two oceans, have specific consequences for the Amazon, with effects on precipitation that vary in different parts of the region. Simplistic generalities, such as the association between El Niño events and droughts in the Amazon, do not capture this complexity. We investigated the variability of Sea Surface Temperature (SST) in the Tropical Pacific Ocean during the period 1950-2022, using Empirical Orthogonal Functions (FOE), spectral analysis coherence and wavelet phase. The two were identified as the main modes of variability, which explain about 53,9% and 13,3%, respectively, of the total variance of the data. The spectral and coherence analysis and wavelets phase showed that the first selected mode represents the warming in the central part of the Pacific Ocean (the “Central El Niño”), while the second mode represents warming in the eastern part of the Pacific (the “Eastern El Niño The effects of the 1982-1983 and 1976-1977 El Niño events in the Amazon, although both events were characterized by an increase in sea surface temperatures in the Equatorial Pacific, the impact on rainfall in the Amazon was distinct. In the rainy season, from December to March, the sub-basins of the Japurá, Jutaí, Jatapu, Tapajós, Trombetas and Xingu rivers were the regions that showed the greatest reductions in rainfall associated with El Niño Central (1982-1983), while the sub-basins of the Javari, Purus, Negro and Madeira rivers had the most pronounced reductions in the year of Eastern El Niño (1976-1977). In the transition to the dry season, in April, the greatest reductions were associated with the Eastern El Niño year for the majority of the study region, with the exception only of the sub-basins of the Madeira, Trombetas and Xingu rivers, which had their associated reductions to Central El Niño. In the dry season from July to September, the sub-basins of the Japurá Jutaí Jatapu Javari Trombetas and Madeira rivers were the rivers that showed the greatest reductions in rainfall associated with El Niño Central, while the sub-basins of the Tapajós Purus Negro and Xingu rivers had the most pronounced reductions. In the Eastern El Niño year this season. In this way, it is possible to conclude that the Central (Eastern) El Niño controlled the reductions in soil moisture in the dry (rainy) season for all sub-basins shown in this study. Extreme drought events associated with these meteorological phenomena can lead to a significant increase in the occurrence of forest fires. These fires have a devastating impact on Amazonian vegetation, resulting in the irreparable loss of biodiversity and the release of large amounts of carbon stored in the forest, contributing to the increase in the greenhouse effect and global climate change.

Keywords: sea surface temperature, variability, climate, Amazon

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Authors: Jun Wang, Chenglei Diao, Emanuele Pagone, Jialuo Ding, Stewart Williams

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As a crucial component in aircraft, landing gear systems are responsible for supporting the plane during parking, taxiing, takeoff, and landing. Given the need for high load-bearing capacity over extended periods, 300M ultra-high strength steel (UHSS) is often the material of choice for crafting these systems due to its exceptional strength, toughness, and fatigue resistance. In the quest for cost-effective and sustainable manufacturing solutions, Wire Arc Additive Manufacturing (WAAM) emerges as a promising alternative for fabricating 300M UHSS landing gears. This is due to its advantages in near-net-shape forming of large components, cost-efficiency, and reduced lead times. Cranfield University has conducted an extensive preliminary study on WAAM 300M UHSS, covering feature deposition, interface analysis, and post-heat treatment. Both Gas Metal Arc (GMA) and Plasma Transferred Arc (PTA)-based WAAM methods were explored, revealing their feasibility for defect-free manufacturing. However, as-deposited 300M features showed lower strength but higher ductility compared to their forged counterparts. Subsequent post-heat treatments were effective in normalising the microstructure and mechanical properties, meeting qualification standards. A 300M UHSS landing gear demonstrator was successfully created using PTA-based WAAM, showcasing the method's precision and cost-effectiveness. The demonstrator, measuring Ф200mm x 700mm, was completed in 16 hours, using 7 kg of material at a deposition rate of 1.3kg/hr. This resulted in a significant reduction in the Buy-to-Fly (BTF) ratio compared to traditional manufacturing methods, further validating WAAM's potential for this application. A "cradle-to-gate" environmental impact assessment, which considers the cumulative effects from raw material extraction to customer shipment, has revealed promising outcomes. Utilising Wire Arc Additive Manufacturing (WAAM) for landing gear components significantly reduces the need for raw material extraction and refinement compared to traditional subtractive methods. This, in turn, lessens the burden on subsequent manufacturing processes, including heat treatment, machining, and transportation. Our estimates indicate that the carbon footprint of the component could be halved when switching from traditional machining to WAAM. Similar reductions are observed in embodied energy consumption and other environmental impact indicators, such as emissions to air, water, and land. Additionally, WAAM offers the unique advantage of part repair by redepositing only the necessary material, a capability not available through conventional methods. Our research shows that WAAM-based repairs can drastically reduce environmental impact, even when accounting for additional transportation for repairs. Consequently, WAAM emerges as a pivotal technology for reducing environmental impact in manufacturing, aiding the industry in its crucial and ambitious journey towards Net Zero. This study paves the way for transformative benefits across the aerospace industry, as we integrate manufacturing into a hybrid solution that offers substantial savings and access to more sustainable technologies for critical component production.

Keywords: WAAM, aircraft landing gear, microstructure, mechanical performance, life cycle assessment

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Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

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Authors: Wonil Nam, Xiang Ren, Inyoung Kim, Masoud Agah, Wei Zhou

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Despite its ultrasensitive detection capability, surface-enhanced Raman spectroscopy (SERS) faces challenges as a quantitative biochemical analysis tool due to the significant dependence of local field intensity in hotspots on nanoscale geometric variations of plasmonic nanostructures. Therefore, despite enormous progress in plasmonic nanoengineering of high-performance SERS devices, it is still challenging to quantitatively correlate the measured SERS signals with the actual molecule concentrations at hotspots. A significant effort has been devoted to developing SERS calibration methods by introducing internal standards. It has been achieved by placing Raman tags at plasmonic hotspots. Raman tags undergo similar SERS enhancement at the same hotspots, and ratiometric SERS signals for analytes of interest can be generated with reduced dependence on geometrical variations. However, using Raman tags still faces challenges for real-world applications, including spatial competition between the analyte and tags in hotspots, spectral interference, laser-induced degradation/desorption due to plasmon-enhanced photochemical/photothermal effects. We show that electronic Raman scattering (ERS) signals from metallic nanostructures at hotspots can serve as the internal calibration standard to enable quantitative SERS analysis and improve biostatistical analysis. We perform SERS with Au-SiO₂ multilayered metal-insulator-metal nano laminated plasmonic nanostructures. Since the ERS signal is proportional to the volume density of electron-hole occupation in hotspots, the ERS signals exponentially increase when the wavenumber is approaching the zero value. By a long-pass filter, generally used in backscattered SERS configurations, to chop the ERS background continuum, we can observe an ERS pseudo-peak, IERS. Both ERS and SERS processes experience the |E|⁴ local enhancements during the excitation and inelastic scattering transitions. We calibrated IMRS of 10 μM Rhodamine 6G in solution by IERS. The results show that ERS calibration generates a new analytical value, ISERS/IERS, insensitive to variations from different hotspots and thus can quantitatively reflect the molecular concentration information. Given the calibration capability of ERS signals, we performed label-free SERS analysis of living biological systems using four different breast normal and cancer cell lines cultured on nano-laminated SERS devices. 2D Raman mapping over 100 μm × 100 μm, containing several cells, was conducted. The SERS spectra were subsequently analyzed by multivariate analysis using partial least square discriminant analysis. Remarkably, after ERS calibration, MCF-10A and MCF-7 cells are further separated while the two triple-negative breast cancer cells (MDA-MB-231 and HCC-1806) are more overlapped, in good agreement with the well-known cancer categorization regarding the degree of malignancy. To assess the strength of ERS calibration, we further carried out a drug efficacy study using MDA-MB-231 and different concentrations of anti-cancer drug paclitaxel (PTX). After ERS calibration, we can more clearly segregate the control/low-dosage groups (0 and 1.5 nM), the middle-dosage group (5 nM), and the group treated with half-maximal inhibitory concentration (IC50, 15 nM). Therefore, we envision that ERS calibrated SERS can find crucial opportunities in label-free molecular profiling of complicated biological systems.

Keywords: cancer cell drug efficacy, plasmonics, surface-enhanced Raman spectroscopy (SERS), SERS calibration

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Authors: Martin Cvek, Miroslav Mrlik, Michal Sedlacik, Tomas Plachy

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Magnetorheological elastomers (MREs) are multi-phase composite materials containing micron-sized ferromagnetic particles dispersed in an elastomeric matrix. Their properties such as modulus, damping, magneto-striction, and electrical conductivity can be controlled by an external magnetic field and/or pressure. These features of the MREs are used in the development of damping devices, shock attenuators, artificial muscles, sensors or active elements of electric circuits. However, imperfections on the particle/matrix interfaces result in the lower performance of the MREs when compared with theoretical values. Moreover, magnetic particles are susceptible to corrosion agents such as acid rains or sea humidity. Therefore, the modification of particles is an effective tool for the improvement of MRE performance due to enhanced compatibility between particles and matrix as well as improvements of their thermo-oxidation and chemical stability. In this study, the carbonyl iron (CI) particles were controllably modified with poly(trimethylsilyloxyethyl methacrylate) (PHEMATMS) nano-grafts to develop magnetic core–shell structures exhibiting proper wetting with various elastomeric matrices resulting in improved performance within a frame of rheological, magneto-piezoresistance, pressure-piezoresistance, or radio-absorbing properties. The desired molecular weight of PHEMATMS nano-grafts was precisely tailored using surface-initiated atom transfer radical polymerization (ATRP). The CI particles were firstly functionalized using a 3-aminopropyltriethoxysilane agent, followed by esterification reaction with α-bromoisobutyryl bromide. The ATRP was performed in the anisole medium using ethyl α-bromoisobutyrate as a macroinitiator, N, N´, N´´, N´´-pentamethyldiethylenetriamine as a ligand, and copper bromide as an initiator. To explore the effect PHEMATMS molecular weights on final properties, two variants of core-shell structures with different nano-graft lengths were synthesized, while the reaction kinetics were designed through proper reactant feed ratios and polymerization times. The PHEMATMS nano-grafts were characterized by nuclear magnetic resonance and gel permeation chromatography proving information to their monomer conversions, molecular chain lengths, and low polydispersity indexes (1.28 and 1.35) as the results of the executed ATRP. The successful modifications were confirmed via Fourier transform infrared- and energy-dispersive spectroscopies while expected wavenumber outputs and element presences, respectively, of constituted PHEMATMS nano-grafts, were occurring in the spectra. The surface morphology of bare CI and their PHEMATMS-grafted analogues was further studied by scanning electron microscopy, and the thicknesses of grafted polymeric layers were directly observed by transmission electron microscopy. The contact angles as a measure of particle/matrix compatibility were investigated employing the static sessile drop method. The PHEMATMS nano-grafts enhanced compatibility of hydrophilic CI with low-surface-energy hydrophobic polymer matrix in terms of their wettability and dispersibility in an elastomeric matrix. Thus, the presence of possible defects at the particle/matrix interface is reduced, and higher performance of modified MREs is expected.

Keywords: atom transfer radical polymerization, core-shell, particle modification, wettability

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Authors: Samira Hamiditehrani

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Automated vehicles (AVs) are emerging technologies that could potentially offer a wide range of opportunities and challenges for the transportation sector. The advent of AV technology has also resulted in new business models in shared mobility services where many ride hailing and car sharing companies are developing on-demand AVs including shared automated vehicles (SAVs) and pooled automated vehicles (Pooled AVs). SAVs and Pooled AVs could provide alternative shared mobility services which encourage sustainable transport systems, mitigate traffic congestion, and reduce automobile dependency. However, the success of on-demand AVs in addressing major transportation policy issues depends on whether and how the public adopts them as regular travel modes. To identify conditions under which individuals may adopt on-demand AVs, previous studies have applied human behavior and technology acceptance theories, where Theory of Planned Behavior (TPB) has been validated and is among the most tested in on-demand AV research. In this respect, this study has three objectives: (a) to propose and validate a theoretical model for behavioral intention to use SAVs and Pooled AVs by extending the original TPB model; (b) to identify the characteristics of early adopters of SAVs, who prefer to have a shorter and private ride, versus prospective users of Pooled AVs, who choose more affordable but longer and shared trips; and (c) to investigate Canadians’ intentions to adopt on-demand AVs for regular trips. Toward this end, this study uses data from an online survey (n = 3,622) of workers or adult students (18 to 75 years old) conducted in October and November 2021 for six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa, Montreal, Calgary, and Hamilton. To accomplish the goals of this study, a base bivariate ordered probit model, in which both SAV and Pooled AV adoptions are estimated as ordered dependent variables, alongside a full structural equation modeling (SEM) system are estimated. The findings of this study indicate that affective motivations such as attitude towards AV technology, perceived privacy, and subjective norms, matter more than sociodemographic and travel behavior characteristic in adopting on-demand AVs. Also, the results of second objective provide evidence that although there are a few affective motivations, such as subjective norms and having ample knowledge, that are common between early adopters of SAVs and PooledAVs, many examined motivations differ among SAV and Pooled AV adoption factors. In other words, motivations influencing intention to use on-demand AVs differ among the service types. Likewise, depending on the types of on-demand AVs, the sociodemographic characteristics of early adopters differ significantly. In general, findings paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Findings from the final objective suggest that policymakers, planners, the vehicle and technology industries, and the public at large should moderate their expectations that on-demand AVs may suddenly transform the entire transportation sector. Instead, this study suggests that SAVs and Pooled AVs (when they entire the Canadian market) are likely to be adopted as supplementary mobility tools rather than substitutions for current travel modes

Keywords: automated vehicles, Canadian perception, theory of planned behavior, on-demand AVs

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Authors: John E. Boberg, Steven J. Bourgeois

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The decline in student engagement and motivation through the middle years is well documented and clearly associated with a decline in mathematics achievement that persists through high school. To combat this trend and, very often, to meet high-stakes accountability standards, a growing number of parents, teachers, and schools have implemented various methods to incentivize learning. However, according to Self-Determination Theory, forms of incentivized learning such as public praise, tangible rewards, or threats of punishment tend to undermine intrinsic motivation and learning. By focusing on external forms of motivation that thwart autonomy in children, adults also potentially threaten relatedness measures such as trust and emotional engagement. Furthermore, these controlling motivational techniques tend to promote shallow forms of cognitive engagement at the expense of more effective deep processing strategies. Therefore, any short-term gains in apparent engagement or test scores are overshadowed by long-term diminished motivation, resulting in inauthentic approaches to learning and lower achievement. The current study focuses on the relationships between student trust, engagement, and motivation during these crucial years as students transition from elementary to middle school. In order to test the effects of controlled motivational techniques on achievement in mathematics, this quantitative study was conducted on a convenience sample of 22 elementary and middle schools from a single public charter school district in the south-central United States. The study employed multi-source data from students (N = 1,054), parents (N = 7,166), and teachers (N = 356), along with student achievement data and contextual campus variables. Cross-sectional questionnaires were used to measure the students’ self-regulated learning, emotional and cognitive engagement, and trust in teachers. Parents responded to a single item on incentivizing the academic performance of their child, and teachers responded to a series of questions about their acceptance of various incentive strategies. Structural equation modeling (SEM) was used to evaluate model fit and analyze the direct and indirect effects of the predictor variables on achievement. Although a student’s trust in teacher positively predicted both emotional and cognitive engagement, none of these three predictors accounted for any variance in achievement in mathematics. The parents’ use of incentives, on the other hand, predicted a student’s perception of his or her controlled motivation, and these two variables had significant negative effects on achievement. While controlled motivation had the greatest effects on achievement, parental incentives demonstrated both direct and indirect effects on achievement through the students’ self-reported controlled motivation. Comparing upper elementary student data with middle-school student data revealed that controlling forms of motivation may be taking their toll on student trust and engagement over time. While parental incentives positively predicted both cognitive and emotional engagement in the younger sub-group, such forms of controlling motivation negatively predicted both trust in teachers and emotional engagement in the middle-school sub-group. These findings support the claims, posited by Self-Determination Theory, about the dangers of incentivizing learning. Short-term gains belie the underlying damage to motivational processes that lead to decreased intrinsic motivation and achievement. Such practices also appear to thwart basic human needs such as relatedness.

Keywords: controlled motivation, student engagement, incentivized learning, mathematics achievement, self-determination theory, student trust

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Authors: Catherine Vasnetsov, Victor Vasnetsov

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Context: Thermo-responsive polymers are materials that undergo significant changes in their physical properties in response to temperature changes. These polymers have gained significant attention in research due to their potential applications in various industries and medicine. However, the molecular mechanisms underlying their behavior are not well understood, particularly in relation to cosolvency, which is crucial for practical applications. Research Aim: This study aimed to theoretically investigate the phenomenon of cosolvency in long-chain polymers using the Flory-Huggins statistical-mechanical framework. The main objective was to understand the interactions between the polymer, solvent, and cosolvent under different conditions. Methodology: The research employed a combination of Monte Carlo computer simulations and advanced machine-learning methods. The Flory-Huggins mean field theory was used as the basis for the simulations. Spinodal graphs and ternary plots were utilized to develop an initial computer model for predicting polymer behavior. Molecular dynamic simulations were conducted to mimic real-life polymer systems. Machine learning techniques were incorporated to enhance the accuracy and reliability of the simulations. Findings: The simulations revealed that the addition of very low or very high volumes of cosolvent molecules resulted in smaller radii of gyration for the polymer, indicating poor miscibility. However, intermediate volume fractions of cosolvent led to higher radii of gyration, suggesting improved miscibility. These findings provide a possible microscopic explanation for the cosolvency phenomenon in polymer systems. Theoretical Importance: This research contributes to a better understanding of the behavior of thermo-responsive polymers and the role of cosolvency. The findings provide insights into the molecular mechanisms underlying cosolvency and offer specific predictions for future experimental investigations. The study also presents a more rigorous analysis of the Flory-Huggins free energy theory in the context of polymer systems. Data Collection and Analysis Procedures: The data for this study was collected through Monte Carlo computer simulations and molecular dynamic simulations. The interactions between the polymer, solvent, and cosolvent were analyzed using the Flory-Huggins mean field theory. Machine learning techniques were employed to enhance the accuracy of the simulations. The collected data was then analyzed to determine the impact of cosolvent volume fractions on the radii of gyration of the polymer. Question Addressed: The research addressed the question of how cosolvency affects the behavior of long-chain polymers. Specifically, the study aimed to investigate the interactions between the polymer, solvent, and cosolvent under different volume fractions and understand the resulting changes in the radii of gyration. Conclusion: In conclusion, this study utilized theoretical modeling and computer simulations to investigate the phenomenon of cosolvency in long-chain polymers. The findings suggest that moderate cosolvent volume fractions can lead to improved miscibility, as indicated by higher radii of gyration. These insights contribute to a better understanding of the molecular mechanisms underlying cosolvency in polymer systems and provide predictions for future experimental studies. The research also enhances the theoretical analysis of the Flory-Huggins free energy theory.

Keywords: molecular modelling, flory-huggins, cosolvency, stimuli-responsive polymers

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Authors: K. Karapetyan, F. Tkhruni, A. Aghajanyan, T. S. Balabekyan, L. Arstamyan

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Introduction: Globalization of the food supply has created the conditions favorable for the emergence and spread of food-borne and especially dangerous pathogens (EDP) in developing countries. The fresh-cut fruit and vegetable industry is searching for alternatives to replace chemical treatments with biopreservative approaches that ensure the safety of the processed foods product. Antimicrobial compounds of lactic acid bacteria (LAB) possess bactericidal or bacteriostatic activity against intestinal pathogens, spoilage organisms and food-borne pathogens such as Listeria monocytogenes, Staphylococcus aureus and Salmonella. Endemic strains of LAB were isolated. The strains, showing broad spectrum of antimicrobial activity against food spoiling microorganisms, were selected. The genotyping by 16S rRNA sequencing, GS-PCR, RAPD PCR methods showed that they were presented by Lactobacillus rhamnosus109, L.plantarum 65, L.plantarum 66 and Enterococcus faecium 64 species. LAB are deposited in "Microbial Depository Center" (MDC) SPC "Armbiotechnology". Methods: LAB strains were isolated from different dairy products from rural households from the highland regions of Armenia. Serially diluted samples were spread on MRS (Merck, Germany) and hydrolyzed milk agar (1,2 % w/v). Single colonies from each LAB were individually inoculated in liquid MRS medium and incubated at 37oC for 24 hours. Culture broth with biomass was centrifuged at 10,000 g during 20 min for obtaining of cell free culture broth (CFC). The antimicrobial substances from CFC broth were purified by the combination of adsorption-desorption and ion-exchange chromatography methods. Separation of bacteriocins was performed using a HPLC method on "Avex ODS" C18 column. Mass analysis of peptides recorded on the device API 4000 in the electron ionization mode. The spot-on-lawn method on the test culture plated in the solid medium was applied. The antimicrobial activity is expressed in arbitrary units (AU/ml). Results. Purification of CFC broth of LAB allowed to obtain partially purified antimicrobial preparations which contains bacteriocins with broad spectrum of antimicrobial activity. Investigation of their main biochemical properties shown, that inhibitory activity of preparations is partially reduced after treatment with proteinase K, trypsin, pepsin, suggesting a proteinaceous nature of bacteriocin-like substances containing in CFC broth. Preparations preserved their activity after heat treatment (50-121 oC, 20 min) and were stable in the pH range 3–8. The results of SDS PAAG electrophoresis show that L.plantarum 66 and Ent.faecium 64 strains have one bacteriocin (BCN) with maximal antimicrobial activity with approximate molecular weight 2.0-3.0 kDa. From L.rhamnosus 109 two BCNs were obtained. Mass spectral analysis indicates that these bacteriocins have peptide bonds and molecular weight of BCN 1 and BCN 2 are approximately 1.5 kDa and 700 Da. Discussion: Thus, our experimental data shown, that isolated endemic strains of LAB are able to produce bacteriocins with high and different inhibitory activity against broad spectrum of microorganisms of different taxonomic group, such as Salmonella sp., Esherichia coli, Bacillus sp., L.monocytogenes, Proteus mirabilis, Staph. aureus, Ps. aeruginosa. Obtained results proved the perspectives for use of endemic strains in the preservation of foodstuffs. Acknowledgments: This work was realized with financial support of the Project Global Initiatives for Preliferation Prevention (GIPP) T2- 298, ISTC A-1866.

Keywords: antimicrobial activity, bacteriocins, endemic strains, food safety

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Authors: Tobias Huwer, Thomas Bobek, Gunter Spöcker

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Driving forces for enhancements in production are trends like digitalization and individualized production. Currently, such developments are restricted to assembly parts. Thus, complex freeform surfaces are not addressed in this context. The need for efficient use of resources and near-net-shape production will require individualized production of complex shaped workpieces. Due to variations between nominal model and actual geometry, this can lead to changes in operations in Computer-aided process planning (CAPP) to make CAPP manageable for an adaptive serial production. In this context, 3D CAD data can be a key to realizing that objective. Along with developments in the geometrical adaptation, a preceding inspection method based on CAD data is required to support the process planner by finding objective criteria to make decisions about the adaptive manufacturability of workpieces. Nowadays, this kind of decisions is depending on the experience-based knowledge of humans (e.g. process planners) and results in subjective decisions – leading to a variability of workpiece quality and potential failure in production. In this paper, we present an automatic part inspection method, based on design and measurement data, which evaluates actual geometries of single workpiece preforms. The aim is to automatically determine the suitability of the current shape for further machining, and to provide a basis for an objective decision about subsequent adaptive manufacturability. The proposed method is realized by a workflow-based approach, keeping in mind the requirements of industrial applications. Workflows are a well-known design method of standardized processes. Especially in applications like aerospace industry standardization and certification of processes are an important aspect. Function blocks, providing a standardized, event-driven abstraction to algorithms and data exchange, will be used for modeling and execution of inspection workflows. Each analysis step of the inspection, such as positioning of measurement data or checking of geometrical criteria, will be carried out by function blocks. One advantage of this approach is its flexibility to design workflows and to adapt algorithms specific to the application domain. In general, within the specified tolerance range it will be checked if a geometrical adaption is possible. The development of particular function blocks is predicated on workpiece specific information e.g. design data. Furthermore, for different product lifecycle phases, appropriate logics and decision criteria have to be considered. For example, tolerances for geometric deviations are different in type and size for new-part production compared to repair processes. In addition to function blocks, appropriate referencing systems are important. They need to support exact determination of position and orientation of the actual geometries to provide a basis for precise analysis. The presented approach provides an inspection methodology for adaptive and part-individual process chains. The analysis of each workpiece results in an inspection protocol and an objective decision about further manufacturability. A representative application domain is the product lifecycle of turbine blades containing a new-part production and a maintenance process. In both cases, a geometrical adaptation is required to calculate individual production data. In contrast to existing approaches, the proposed initial inspection method provides information to decide between different potential adaptive machining processes.

Keywords: adaptive, CAx, function blocks, turbomachinery

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Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

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It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

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Authors: Lionel Trovalet, Lisa Liu, Dimitri Bigot, Nadia Hammami, Jean-Pierre Habas, Bruno Malet-Damour

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The MCP-iBAT1 project is carried out to study the behavior of Phase Change Materials (PCM) integrated in building envelopes in a tropical environment. Through the phase transitions (melting and freezing) of the material, thermal energy can be absorbed or released. This process enables the regulation of indoor temperatures and the improvement of thermal comfort for the occupants. Most of the commercially available PCMs are more suitable to temperate climates than to tropical climates. The case of Reunion Island is noteworthy as there are multiple micro-climates. This leads to our key question: developing one or multiple bio-based PCMs that cover the thermal needs of the different locations of the island. The present paper focuses on the numerical approach to select the PCM properties relevant to tropical areas. Numerical simulations have been carried out with two softwares: EnergyPlusTM and Isolab. The latter has been developed in the laboratory, with the implicit Finite Difference Method, in order to evaluate different physical models. Both are Thermal Dynamic Simulation (TDS) softwares that predict the building’s thermal behavior with one-dimensional heat transfers. The parameters used in this study are the construction’s characteristics (dimensions and materials) and the environment’s description (meteorological data and building surroundings). The building is modeled in accordance with the experimental setup. It is divided into two rooms, cells A and B, with same dimensions. Cell A is the reference, while in cell B, a layer of commercial PCM (Thermo Confort of MCI Technologies) has been applied to the inner surface of the North wall. Sensors are installed in each room to retrieve temperatures, heat flows, and humidity rates. The collected data are used for the comparison with the numerical results. Our strategy is to implement two similar buildings at different altitudes (Saint-Pierre: 70m and Le Tampon: 520m) to measure different temperature ranges. Therefore, we are able to collect data for various seasons during a condensed time period. The following methodology is used to validate the numerical models: calibration of the thermal and PCM models in EnergyPlusTM and Isolab based on experimental measures, then numerical testing with a sensitivity analysis of the parameters to reach the targeted indoor temperatures. The calibration relies on the past ten months’ measures (from September 2020 to June 2021), with a focus on one-week study on November (beginning of summer) when the effect of PCM on inner surface temperatures is more visible. A first simulation with the PCM model of EnergyPlus gave results approaching the measurements with a mean error of 5%. The studied property in this paper is the melting temperature of the PCM. By determining the representative temperature of winter, summer and inter-seasons with past annual’s weather data, it is possible to build a numerical model of multi-layered PCM. Hence, the combined properties of the materials will provide an optimal scenario for the application on PCM in tropical areas. Future works will focus on the development of bio-based PCMs with the selected properties followed by experimental and numerical validation of the materials. 1Materiaux ´ a Changement de Phase, une innovation pour le B ` ati Tropical

Keywords: energyplus, multi-layer of PCM, phase changing materials, tropical area

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Authors: Tuji Jemal Ahmed, Hussen Beker Yusuf

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Food safety is a major concern worldwide, with microbial contamination being one of the leading causes of foodborne illnesses. In Ethiopia, drinking water and untreated groundwater are a primary source of microbial contamination, leading to significant health risks. East Hararghe, Oromia, is one of the regions in Ethiopia that has been affected by this problem. This paper provides an overview of the impact of untreated groundwater on human health in Haramaya Rural District, East Hararghe and highlights the urgent need for sustained efforts to address the water sanitation supply problem. The use of untreated groundwater for drinking and household purposes in Haramaya Rural District, East Hararghe is prevalent, leading to high rates of waterborne illnesses such as diarrhea, typhoid fever, and cholera. The impact of these illnesses on human health is significant, resulting in significant morbidity and mortality, especially among vulnerable populations such as children and the elderly. In addition to the direct health impacts, waterborne illnesses also have indirect impacts on human health, such as reduced productivity and increased healthcare costs. Groundwater sources are susceptible to microbial contamination due to the infiltration of surface water, human and animal waste, and agricultural runoff. In Haramaya Rural District, East Hararghe, poor water management practices, inadequate sanitation facilities, and limited access to clean water sources contribute to the prevalence of untreated groundwater as a primary source of drinking water. These underlying causes of microbial contamination highlight the need for improved water sanitation infrastructure, including better access to safe drinking water sources and the implementation of effective treatment methods. The paper emphasizes the need for regular water quality monitoring, especially for untreated groundwater sources, to ensure safe drinking water for the population. The implementation of effective preventive measures, such as the use of effective disinfectants, proper waste disposal methods, and regular water quality monitoring, is crucial to reducing the risk of contamination and improving public health outcomes in the region. Community education and awareness-raising campaigns can also play a critical role in promoting safe water practices and reducing the risk of contamination. These campaigns can include educating the population on the importance of boiling water before drinking, the use of water filters, and proper sanitation practices. In conclusion, the use of untreated groundwater as a primary source of drinking water in East Hararghe, Oromia, Ethiopia, has significant impacts on human health, leading to widespread waterborne illnesses and posing a significant threat to public health. Sustained efforts are urgently needed to address the root causes of contamination, such as poor sanitation and hygiene practices, improper waste management, and the water sanitation supply problem, including the implementation of effective preventive measures and community-based education programs, ultimately improving public health outcomes in the region. A comprehensive approach that involves community-based water management systems, point-of-use water treatment methods, and awareness-raising campaigns can contribute to reducing the incidence of microbial contamination in the region.

Keywords: food safety, health risks, microbial contamination, untreated groundwater

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Authors: Siti Rahayu, Khairulniza Mohd Puat, Kesavan R., Mohammad Harris A., Jalila, Kunalan G., Fazir Mohamad

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The greatest challenge has been in establishing and maintaining the dedicated nursing team. Continuity is served when nurses are assigned exclusively for managing wound, where they can continue to build expertise and skills. In addition, there is a growing incidence of chronic wounds and recognition of the complexity involved in caring for these patients. We would like to share 4 cases with different techniques of wound management. 1st case, 39 years old gentleman with underlying rheumatoid arthritis with chronic periprosthetic joint infection of right total knee replacement presented with persistent drainage over right knee. Patient was consulted for two stage revision total knee replacement. However, patient only agreed for debridement and retention of implant. After debridement, large medial and lateral wound was treated with Instillation Negative Pressure Wound Therapy Dressings. After several cycle, the wound size reduced, and conventional dressing was applied. 2nd case, 58 years old gentleman with underlying diabetes presented with right foot necrotizing fasciitis with gangrene of 5th toe. He underwent extensive debridement of foot with rays’ amputation of 5th toe. Post debridement patient was started on Instillation Negative Pressure Wound Therapy Dressings. After several cycle of VAC, the wound bed was prepared, and he underwent split skin graft over right foot. 3 rd case, 60 years old gentleman with underlying diabetes mellitus presented with right foot necrotizing soft tissue infection. He underwent rays’ amputation and extensive wound debridement. Upon stabilization of general condition, patient was discharge with regular wound dressing by same nurse and doctor during each visit to clinic follow up. After 6 months of follow up, the wound healed well. 4th case, 38-year-old gentleman had alleged motor vehicle accident and sustained closed fracture right tibial plateau. Open reduction and proximal tibial locking plate were done. At 2 weeks post-surgery, the patient presented with warm, erythematous leg and pus discharge from the surgical site. Empirical antibiotic was started, and wound debridement was done. Intraoperatively, 50cc pus was evacuated, unhealthy muscle and tissue debrided. No loosening of the implant. Patient underwent multiple wound debridement. At 2 weeks post debridement wound healed well, but the proximal aspect was unable to close immediately. This left the proximal part of the implant to be exposed. Patient was then put on VAC dressing for 3 weeks until healthy granulation tissue closes the implant. Meanwhile, antibiotic was change according to culture and sensitivity. At 6 weeks post the first debridement, the wound was completely close, and patient was discharge home well. At 3 months post operatively, patient wound and fracture healed uneventfully and able to ambulate independently. Complex wounds are too serious to be dealt with. Team managing complex wound need continuous support through the provision of educational tools to support their professional development, engagement with local and international expert, as well as highquality products that increase efficiencies in services

Keywords: VAC (Vacuum Assisted Closure), empirical- initial antibiotics, NPWT- negative pressure wound therapy, NF- necrotizing fasciitis, gangrene- blackish discoloration due to poor blood supply

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Authors: Zhiwu Zheng, Prakhar Kumar, Sigurd Wagner, Naveen Verma, James C. Sturm

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Soft robots have drawn great interest recently due to a rich range of possible shapes and motions they can take on to address new applications, compared to traditional rigid robots. Large-area electronics (LAE) provides a unique platform for creating soft robots by leveraging thin-film technology to enable the integration of a large number of actuators, sensors, and control circuits on flexible sheets. However, the rich shapes and motions possible, especially when interacting with complex environments, pose significant challenges to forming well-generalized and robust models necessary for robot design and control. In this work, we describe an analytical model for predicting the shape and locomotion of a flexible (steel-foil-based) piezoelectric-actuated 2D robot based on Euler-Bernoulli beam theory. It is nominally (unpowered) lying flat on the ground, and when powered, its shape is controlled by an array of piezoelectric thin-film actuators. Key features of the models are its ability to incorporate the significant effects of gravity on the shape and to precisely predict the spatial distribution of friction against the contacting surfaces, necessary for determining inchworm-type motion. We verified the model by developing a distributed discrete element representation of a continuous piezoelectric actuator and by comparing its analytical predictions to discrete-element robot simulations using PyBullet. Without gravity, predicting the shape of a sheet with a linear array of piezoelectric actuators at arbitrary voltages is straightforward. However, gravity significantly distorts the shape of the sheet, causing some segments to flatten against the ground. Our work includes the following contributions: (i) A self-consistent approach was developed to exactly determine which parts of the soft robot are lifted off the ground, and the exact shape of these sections, for an arbitrary array of piezoelectric voltages and configurations. (ii) Inchworm-type motion relies on controlling the relative friction with the ground surface in different sections of the robot. By adding torque-balance to our model and analyzing shear forces, the model can then determine the exact spatial distribution of the vertical force that the ground is exerting on the soft robot. Through this, the spatial distribution of friction forces between ground and robot can be determined. (iii) By combining this spatial friction distribution with the shape of the soft robot, in the function of time as piezoelectric actuator voltages are changed, the inchworm-type locomotion of the robot can be determined. As a practical example, we calculated the performance of a 5-actuator system on a 50-µm thick steel foil. Piezoelectric properties of commercially available thin-film piezoelectric actuators were assumed. The model predicted inchworm motion of up to 200 µm per step. For independent verification, we also modelled the system using PyBullet, a discrete-element robot simulator. To model a continuous thin-film piezoelectric actuator, we broke each actuator into multiple segments, each of which consisted of two rigid arms with appropriate mass connected with a 'motor' whose torque was set by the applied actuator voltage. Excellent agreement between our analytical model and the discrete-element simulator was shown for both for the full deformation shape and motion of the robot.

Keywords: analytical modeling, piezoelectric actuators, soft robot locomotion, thin-film technology

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: K. O'Malley

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Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Luisa Barreira, Viana Castaneda, Maria J. Rodrigues, Florinda Gama, Tamara Santos, Marta Oliveira, Catarina Pereira, Maribela Pestana, Pedro Correia, Miguel Salazar, Carla Nunes, Luisa Custodio, Joao Varela

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In the last century, the world witnessed an exponential demographic increase that has put an enormous pressure on agriculture and food production. Associated also with climate changes, there has been a decrease in the amount of available freshwater and an increased salinization of soils which can affect the production of most food crops. Halophytes, however, are plants able to withstand high salinities while maintaining a good growth productivity. To cope with the excess salt, they produce secondary metabolites (e.g. vitamins and phenolic compounds) which, along with the natural presence of some minerals, makes them not only nutritionally rich but also functional foods. Some halophytes, as quinoa or salicornia, are already used in some countries, mostly as gourmet food. Hydroponic cultivation of halophytes using seawater or diluted seawater for watering can decrease the pressure on freshwater resources while producing a nutritional and functional food. The XtremeGourmet project funded by the EU aims to develop and optimize the production of different halophytes by hydroponics. One of the more specific objectives of this project is the study of halophytes’ productivity and chemical composition under different abiotic conditions, e.g. salt and nutrient concentration and light intensity. Three species of halophytes commonly occurring in saltmarshes of the South of Portugal (Inula chrithmoides, Salicornia ramosissima and Mesembryanthemum nodiflorum) were cultivated using hydroponics under different salinities, ranging from 5 to 45 dS/m. For each condition, several parameters were assessed namely: total and commercial productivity, electrical conductivity, total soluble solids, proximal composition, mineral profile, total phenolics, flavonoids and condensed tannins content and antioxidant activity. Results show that productivity was significantly reduced for all plants with increasing salinity up to salinity 29 dS/m and remained low onwards. Oppositely, the electrical conductivity and the total soluble solids content of the produced plants increased with salinity, reaching a plateau at 29 dS/m. It seems that plants reflect the salt concentration of the water up to some point, being able to regulate their salt content for higher salinities. The same tendency was observed for the ash content of these plants, which is related to the mineral uptake from the cultivating media and the plants’ capacity to both accumulate and regulate ions’ concentration in their tissues. Nonetheless, this comes with a metabolic cost which is observed by a decrease in productivity. The mineral profile of these plants shows high concentrations of sodium but also high amounts of potassium. In what concerns the microelements, these plants appear to be a good source of manganese and iron and the low amounts of toxic metals account for their safe consumption in moderate amounts. Concerning the phenolics composition, plants presented moderate concentrations of phenolics but high amounts of condensed tannins, particularly I. crithmoides which accounts for its characteristic sour and spicy taste. Contrary to some studies in which higher amounts of phenolics were found in plants cultivated under higher salinities, in this study, the highest amount of phenolic compounds were found in plants grown at the lowest or intermediate salinities. Nonetheless, there was a positive correlation between the concentration of these compounds and the antioxidant capacity of the plants’ extracts.

Keywords: functional properties, halophytes, hydroponics, nutritional composition, salinity effect

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Authors: Adewale Tolulope Irewale, Elias Emeka Elemike, Christian O. Dimkpa, Emeka Emmanuel Oguzie

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As the global population steadily approaches the 10billion mark, the world is currently faced with two major challenges among others – accessing sustainable and clean energy, and food security. Accessing cleaner and sustainable energy sources to drive global economy and technological advancement, and feeding the teeming human population require sustainable, innovative, and smart solutions. To solve the food production problem, producers have relied on fertilizers as a way of improving crop productivity. Commercial inorganic fertilizers, which is employed to boost agricultural food production, however, pose significant ecological sustainability and economic problems including soil and water pollution, reduced input efficiency, development of highly resistant weeds, micronutrient deficiency, soil degradation, and increased soil toxicity. These ecological and sustainability concerns have raised uncertainties about the continued effectiveness of conventional fertilizers. With the application of nanotechnology, plant biomass upcycling offers several advantages in greener energy production and sustainable agriculture through reduction of environmental pollution, increasing soil microbial activity, recycling carbon thereby reducing GHG emission, and so forth. This innovative technology has the potential for a circular economy and creating a sustainable agricultural practice. Nanomaterials have the potential to greatly enhance the quality and nutrient composition of organic biomass which in turn, allows for the conversion of biomass into nanofertilizers that are potentially more efficient. Water hyacinth plant harvested from an inland water at Warri, Delta State Nigeria were air-dried and milled into powder form. The dry biomass were used to prepare biochar at a pre-determined temperature in an oxygen deficient atmosphere. Physicochemical analysis of the resulting biochar was carried out to determine its porosity and general morphology using the Scanning Transmission Electron Microscopy (STEM). The functional groups (-COOH, -OH, -NH2, -CN, -C=O) were assessed using the Fourier Transform InfraRed Spectroscopy (FTIR) while the heavy metals (Cr, Cu, Fe, Pb, Mg, Mn) were analyzed using Inductively Coupled Plasma – Optical Emission Spectrometry (ICP-OES). Impregnation of the biochar with nanonutrients were achieved under varied conditions of pH, temperature, nanonutrient concentrations and resident time to achieve optimum adsorption. Adsorption and desorption studies were carried out on the resulting nanofertilizer to determine kinetics for the potential nutrients’ bio-availability to plants when used as green fertilizers. Water hyacinth (Eichhornia crassipes) which is an aggressively invasive aquatic plant known for its rapid growth and profusion is being examined in this research to harness its biomass as a sustainable feedstock to formulate functionalized nano-biochar fertilizers, offering various benefits including water hyacinth biomass upcycling, improved nutrient delivery to crops and aquatic ecosystem remediation. Altogether, this work aims to create output values in the three dimensions of environmental, economic, and social benefits.

Keywords: biochar-based nanofertilizers, eichhornia crassipes, greener agriculture, sustainable ecosystem, water hyacinth

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Authors: Bidzina Matsaberidze, Anna Sikharulidze, Gia Sirbiladze, Bezhan Ghvaberidze

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In the modern world emergency management decision support systems are actively used by state organizations, which are interested in extreme and abnormal processes and provide optimal and safe management of supply needed for the civil and military facilities in geographical areas, affected by disasters, earthquakes, fires and other accidents, weapons of mass destruction, terrorist attacks, etc. Obviously, these kinds of extreme events cause significant losses and damages to the infrastructure. In such cases, usage of intelligent support technologies is very important for quick and optimal location-transportation of emergency service in order to avoid new losses caused by these events. Timely servicing from emergency service centers to the affected disaster regions (response phase) is a key task of the emergency management system. Scientific research of this field takes the important place in decision-making problems. Our goal was to create an expert knowledge-based intelligent support system, which will serve as an assistant tool to provide optimal solutions for the above-mentioned problem. The inputs to the mathematical model of the system are objective data, as well as expert evaluations. The outputs of the system are solutions for Fuzzy Multi-Objective Emergency Location-Transportation Problem (FMOELTP) for disasters’ regions. The development and testing of the Intelligent Support System were done on the example of an experimental disaster region (for some geographical zone of Georgia) which was generated using a simulation modeling. Four objectives are considered in our model. The first objective is to minimize an expectation of total transportation duration of needed products. The second objective is to minimize the total selection unreliability index of opened humanitarian aid distribution centers (HADCs). The third objective minimizes the number of agents needed to operate the opened HADCs. The fourth objective minimizes the non-covered demand for all demand points. Possibility chance constraints and objective constraints were constructed based on objective-subjective data. The FMOELTP was constructed in a static and fuzzy environment since the decisions to be made are taken immediately after the disaster (during few hours) with the information available at that moment. It is assumed that the requests for products are estimated by homeland security organizations, or their experts, based upon their experience and their evaluation of the disaster’s seriousness. Estimated transportation times are considered to take into account routing access difficulty of the region and the infrastructure conditions. We propose an epsilon-constraint method for finding the exact solutions for the problem. It is proved that this approach generates the exact Pareto front of the multi-objective location-transportation problem addressed. Sometimes for large dimensions of the problem, the exact method requires long computing times. Thus, we propose an approximate method that imposes a number of stopping criteria on the exact method. For large dimensions of the FMOELTP the Estimation of Distribution Algorithm’s (EDA) approach is developed.

Keywords: epsilon-constraint method, estimation of distribution algorithm, fuzzy multi-objective combinatorial programming problem, fuzzy multi-objective emergency location/transportation problem

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Catherine P. Bradshaw, Elise T. Pas, Katrina J. Debnam, Jessika H. Bottiani, Michael Rosenberg

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Worldwide, ethnically and culturally diverse students are at increased risk for school failure, discipline problems, and dropout. Despite decades of concern about this issue of disparities in education and other fields (e.g., 'school to prison pipeline'), there has been limited empirical examination of models that can actually reduce these gaps in schools. Moreover, few studies have examined the effectiveness of in-service teacher interventions and supports specifically designed to reduce discipline disparities and improve student engagement. This session provides an overview of the evidence-based Double Check model which serves as a framework for teachers to use culturally-responsive strategies to engage ethnically and culturally diverse students in the classroom and reduce discipline problems. Specifically, Double Check is a school-based prevention program which includes three core components: (a) enhancements to the school-wide Positive Behavioral Interventions and Supports (PBIS) tier-1 level of support; (b) five one-hour professional development training sessions, each of which addresses five domains of cultural competence (i.e., connection to the curriculum, authentic relationships, reflective thinking, effective communication, and sensitivity to students’ culture); and (c) coaching of classroom teachers using an adapted version of the Classroom Check-Up, which intends to increase teachers’ use of effective classroom management and culturally-responsive strategies using research-based motivational interviewing and data-informed problem-solving approaches. This paper presents findings from a randomized controlled trial (RCT) testing the impact of Double Check, on office discipline referrals (disaggregated by race) and independently observed and self-reported culturally-responsive practices and classroom behavior management. The RCT included 12 elementary and middle schools; 159 classroom teachers were randomized either to receive coaching or serve as comparisons. Specifically, multilevel analyses indicated that teacher self-reported culturally responsive behavior management improved over the course of the school year for teachers who received the coaching and professional development. However, the average annual office discipline referrals issued to black students were reduced among teachers who were randomly assigned to receive coaching relative to comparison teachers. Similarly, observations conducted by trained external raters indicated significantly more teacher proactive behavior management and anticipation of student problems, higher student compliance, less student non-compliance, and less socially disruptive behaviors in classrooms led by coached teachers than classrooms led teachers randomly assigned to the non-coached condition. These findings indicated promising effects of the Double Check model on a range of teacher and student outcomes, including disproportionality in office discipline referrals among Black students. These results also suggest that the Double Check model is one of only a few systematic approaches to promoting culturally-responsive behavior management which has been rigorously tested and shown to be associated with improvements in either student or staff outcomes indicated significant reductions in discipline problems and improvements in behavior management. Implications of these findings are considered within the broader context of globalization and demographic shifts, and their impacts on schools. These issues are particularly timely, given growing concerns about immigration policies in the U.S. and abroad.

Keywords: ethnically and culturally diverse students, student engagement, school-based prevention, academic achievement

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Authors: Chenyi Ma

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Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

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Authors: Alejandra Castillo-Arias, Carlos A. Fuenmayor, Carlos M. Zuluaga-Domínguez

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The connection between health and nutrition has driven the food industry to explore healthier and more sustainable alternatives. Key strategies to enhance nutritional quality and extend shelf life include reducing saturated fats and incorporating natural ingredients. One area of focus is the use of modified starch in baked goods, which has attracted significant interest in food science and industry due to its functional benefits. Modified starches are commonly used for their gelling, thickening, and water-retention properties. Derived from sources like waxy corn, potatoes, tapioca, or rice, these polysaccharides improve thermal stability and resistance to dough. The use of modified starch enhances the texture and structure of baked goods, which is crucial for consumer acceptance. In this study, it was evaluated the effects of modified starch inclusion on dough used for puff pastry elaboration, measured with Mixolab analysis. This technique assesses flour quality by examining its behavior under varying conditions, providing a comprehensive profile of its baking properties. The analysis included measurements of water absorption capacity, dough development time, dough stability, softening, final consistency, and starch gelatinization. Each of these parameters offers insights into how the flour will perform during baking and the quality of the final product. The performance of wheat flour with varying levels of modified starch inclusion (10%, 20%, 30%, and 40%) was evaluated through Mixolab analysis, with a control sample consisting of 100% wheat flour. Water absorption, gluten content, and retrogradation indices were analyzed to understand how modified starch affects dough properties. The results showed that the inclusion of modified starch increased the absorption index, especially at levels above 30%, indicating a dough with better handling qualities and potentially improved texture in the final baked product. However, the reduction in wheat flour resulted in a lower kneading index, affecting dough strength. Conversely, incorporating more than 20% modified starch reduced the retrogradation index, indicating improved stability and resistance to crystallization after cooling. Additionally, the modified starch improved the gluten index, contributing to better dough elasticity and stability, providing good structural support and resistance to deformation during mixing and baking. As expected, the control sample exhibited a higher amylase index, due to the presence of enzymes in wheat flour. However, this is of low concern in puff pastry dough, as amylase activity is more relevant in fermented doughs, which is not the case here. Overall, the use of modified starch in puff pastry enhanced product quality by improving texture, structure, and shelf life, particularly when used at levels between 30% and 40%. This research underscores the potential of modified starches to address health concerns associated with traditional starches and to contribute to the development of higher-quality, consumer-friendly baked products. Furthermore, the findings suggest that modified starches could play a pivotal role in future innovations within the baking industry, particularly in products aiming to balance healthfulness with sensory appeal. By incorporating modified starch into their formulations, bakeries can meet the growing demand for healthier, more sustainable products while maintaining the indulgent qualities that consumers expect from baked goods.

Keywords: baking quality, dough properties, modified starch, puff pastry

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Authors: Mohammadreza Kashizadeh, Mohammadamin Hashemi

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This paper explores a kinetic building facade designed for optimal energy capture and architectural expression. The system integrates photovoltaic panels with soft robotic actuators for precise solar tracking, resulting in enhanced electricity generation compared to static facades. Driven by the growing interest in dynamic building envelopes, the exploration of facade systems are necessitated. Increased energy generation and regulation of energy flow within buildings are potential benefits offered by integrating photovoltaic (PV) panels as kinetic elements. However, incorporating these technologies into mainstream architecture presents challenges due to the complexity of coordinating multiple systems. To address this, the design leverages soft robotic actuators, known for their compliance, resilience, and ease of integration. Additionally, the project investigates the potential for employing Large Language Models (LLMs) to streamline the design process. The research methodology involved design development, material selection, component fabrication, and system assembly. Grasshopper (GH) was employed within the digital design environment for parametric modeling and scripting logic, and an LLM was experimented with to generate Python code for the creation of a random surface with user-defined parameters. Various techniques, including casting, Three-dimensional 3D printing, and laser cutting, were utilized to fabricate physical components. A modular assembly approach was adopted to facilitate installation and maintenance. A case study focusing on the application of this facade system to an existing library building at Polytechnic University of Milan is presented. The system is divided into sub-frames to optimize solar exposure while maintaining a visually appealing aesthetic. Preliminary structural analyses were conducted using Karamba3D to assess deflection behavior and axial loads within the cable net structure. Additionally, Finite Element (FE) simulations were performed in Abaqus to evaluate the mechanical response of the soft robotic actuators under pneumatic pressure. To validate the design, a physical prototype was created using a mold adapted for a 3D printer's limitations. Casting Silicone Rubber Sil 15 was used for its flexibility and durability. The 3D-printed mold components were assembled, filled with the silicone mixture, and cured. After demolding, nodes and cables were 3D-printed and connected to form the structure, demonstrating the feasibility of the design. This work demonstrates the potential of soft robotics and Artificial Intelligence (AI) for advancements in sustainable building design and construction. The project successfully integrates these technologies to create a dynamic facade system that optimizes energy generation and architectural expression. While limitations exist, this approach paves the way for future advancements in energy-efficient facade design. Continued research efforts will focus on cost reduction, improved system performance, and broader applicability.

Keywords: artificial intelligence, energy efficiency, kinetic photovoltaics, pneumatic control, soft robotics, sustainable building

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Authors: G. K. Viswanadh, B. Rajasekhar, G. Venkata Ramana

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Water distribution networks play a vital role in ensuring a reliable supply of clean water to urban areas. However, they face several challenges, including pressure control, pump speed optimization, and burst event detection. This paper combines insights from two studies to address these critical issues in Water distribution networks, focusing on the specific context of Kapra Municipality, India. The first part of this research concentrates on optimizing pressure control and pump speed in complex Water distribution networks. It utilizes the EPANET- MATLAB Toolkit to integrate EPANET functionalities into the MATLAB environment, offering a comprehensive approach to network analysis. By optimizing Pressure Reduce Valves (PRVs) and variable speed pumps (VSPs), this study achieves remarkable results. In the Benchmark Water Distribution System (WDS), the proposed PRV optimization algorithm reduces average leakage by 20.64%, surpassing the previous achievement of 16.07%. When applied to the South-Central and East zone WDS of Kapra Municipality, it identifies PRV locations that were previously missed by existing algorithms, resulting in average leakage reductions of 22.04% and 10.47%. These reductions translate to significant daily Water savings, enhancing Water supply reliability and reducing energy consumption. The second part of this research addresses the pressing issue of burst event detection and localization within the Water Distribution System. Burst events are a major contributor to Water losses and repair expenses. The study employs wireless sensor technology to monitor pressure and flow rate in real time, enabling the detection of pipeline abnormalities, particularly burst events. The methodology relies on transient analysis of pressure signals, utilizing Cumulative Sum and Wavelet analysis techniques to robustly identify burst occurrences. To enhance precision, burst event localization is achieved through meticulous analysis of time differentials in the arrival of negative pressure waveforms across distinct pressure sensing points, aided by nodal matrix analysis. To evaluate the effectiveness of this methodology, a PVC Water pipeline test bed is employed, demonstrating the algorithm's success in detecting pipeline burst events at flow rates of 2-3 l/s. Remarkably, the algorithm achieves a localization error of merely 3 meters, outperforming previously established algorithms. This research presents a significant advancement in efficient burst event detection and localization within Water pipelines, holding the potential to markedly curtail Water losses and the concomitant financial implications. In conclusion, this combined research addresses critical challenges in Water distribution networks, offering solutions for optimizing pressure control, pump speed, burst event detection, and localization. These findings contribute to the enhancement of Water Distribution System, resulting in improved Water supply reliability, reduced Water losses, and substantial cost savings. The integrated approach presented in this paper holds promise for municipalities and utilities seeking to improve the efficiency and sustainability of their Water distribution networks.

Keywords: pressure reduce valve, complex networks, variable speed pump, wavelet transform, burst detection, CUSUM (Cumulative Sum), water pipeline monitoring

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Authors: Ching-Sung Lee, Wei-Chou Hsu, Han-Yin Liu, Hung-Hsi Huang, Si-Fu Chen, Yun-Jung Yang, Bo-Chun Chiang, Yu-Chuang Chen, Shen-Tin Yang

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AlGaN/GaN high electron mobility transistors (HEMTs) have been intensively studied due to their intrinsic advantages of high breakdown electric field, high electron saturation velocity, and excellent chemical stability. They are also suitable for ultra-violet (UV) photodetection due to the corresponding wavelengths of GaN bandgap. To improve the optical responsivity by decreasing the dark current due to gate leakage problems and limited Schottky barrier heights in GaN-based HEMT devices, various metal-oxide-semiconductor HEMTs (MOS-HEMTs) have been devised by using atomic layer deposition (ALD), molecular beam epitaxy (MBE), metal-organic chemical vapor deposition (MOCVD), liquid phase deposition (LPD), and RF sputtering. The gate dielectrics include MgO, HfO2, Al2O3, La2O3, and TiO2. In order to provide complementary circuit operation, enhancement-mode (E-mode) devices have been lately studied using techniques of fluorine treatment, p-type capper, piezoneutralization layer, and MOS-gate structure. This work reports an Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMT design by using a cost-effective ozone water oxidization technique. The present ozone oxidization method advantages of low cost processing facility, processing simplicity, compatibility to device fabrication, and room-temperature operation under atmospheric pressure. It can further reduce the gate-to-channel distance and improve the transocnductance (gm) gain for a specific oxide thickness, since the formation of the Al2O3 will consume part of the AlGaN barrier at the same time. The epitaxial structure of the studied devices was grown by using the MOCVD technique. On a Si substrate, the layer structures include a 3.9 m C-doped GaN buffer, a 300 nm GaN channel layer, and a 5 nm Al0.25Ga0.75N barrier layer. Mesa etching was performed to provide electrical isolation by using an inductively coupled-plasma reactive ion etcher (ICP-RIE). Ti/Al/Au were thermally evaporated and annealed to form the source and drain ohmic contacts. The device was immersed into the H2O2 solution pumped with ozone gas generated by using an OW-K2 ozone generator. Ni/Au were deposited as the gate electrode to complete device fabrication of MOS-HEMT. The formed Al2O3 oxide thickness 7 nm and the remained AlGaN barrier thickness is 2 nm. A reference HEMT device has also been fabricated in comparison on the same epitaxial structure. The gate dimensions are 1.2 × 100 µm 2 with a source-to-drain spacing of 5 μm for both devices. The dielectric constant (k) of Al2O3 was characterized to be 9.2 by using C-V measurement. Reduced interface state density after oxidization has been verified by the low-frequency noise spectra, Hooge coefficients, and pulse I-V measurement. Improved device characteristics at temperatures of 300 K-450 K have been achieved for the present MOS-HEMT design. Consequently, Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMTs by using the ozone water oxidization method are reported. In comparison with a conventional Schottky-gate HEMT, the MOS-HEMT design has demonstrated excellent enhancements of 138% (176%) in gm, max, 118% (139%) in IDS, max, 53% (62%) in BVGD, 3 (2)-order reduction in IG leakage at VGD = -60 V at 300 (450) K. This work is promising for millimeter-wave integrated circuit (MMIC) and three-terminal active UV photodetector applications.

Keywords: MOS-HEMT, enhancement mode, AlGaN/GaN, passivation, ozone water oxidation, gate leakage

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Authors: Abdulkarim Nasir, Alhassan T. Yahaya, Hauwa T. Abdulkarim, Abdussalam El-Suleiman, Yakubu K. Abubakar

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The need for sustainable and reliable electricity supply in rural communities of Nigeria remains a pressing issue, given the country's vast energy deficit and the significant number of inhabitants lacking access to electricity. This research focuses on the development of a portable hybrid renewable energy system designed to provide a sustainable and efficient electricity supply to these underserved regions. The proposed system integrates multiple renewable energy sources, specifically solar and wind, to harness the abundant natural resources available in Nigeria. The design and development process involves the selection and optimization of components such as photovoltaic panels, wind turbines, energy storage units (batteries), and power management systems. These components are chosen based on their suitability for rural environments, cost-effectiveness, and ease of maintenance. The hybrid system is designed to be portable, allowing for easy transportation and deployment in remote locations with limited infrastructure. Key to the system's effectiveness is its hybrid nature, which ensures continuous power supply by compensating for the intermittent nature of individual renewable sources. Solar energy is harnessed during the day, while wind energy is captured whenever wind conditions are favourable, thus ensuring a more stable and reliable energy output. Energy storage units are critical in this setup, storing excess energy generated during peak production times and supplying power during periods of low renewable generation. These studies include assessing the solar irradiance, wind speed patterns, and energy consumption needs of rural communities. The simulation results inform the optimization of the system's design to maximize energy efficiency and reliability. This paper presents the development and evaluation of a 4 kW standalone hybrid system combining wind and solar power. The portable device measures approximately 8 feet 5 inches in width, 8 inches 4 inches in depth, and around 38 feet in height. It includes four solar panels with a capacity of 120 watts each, a 1.5 kW wind turbine, a solar charge controller, remote power storage, batteries, and battery control mechanisms. Designed to operate independently of the grid, this hybrid device offers versatility for use in highways and various other applications. It also presents a summary and characterization of the device, along with photovoltaic data collected in Nigeria during the month of April. The construction plan for the hybrid energy tower is outlined, which involves combining a vertical-axis wind turbine with solar panels to harness both wind and solar energy. Positioned between the roadway divider and automobiles, the tower takes advantage of the air velocity generated by passing vehicles. The solar panels are strategically mounted to deflect air toward the turbine while generating energy. Generators and gear systems attached to the turbine shaft enable power generation, offering a portable solution to energy challenges in Nigerian communities. The study also addresses the economic feasibility of the system, considering the initial investment costs, maintenance, and potential savings from reduced fossil fuel use. A comparative analysis with traditional energy supply methods highlights the long-term benefits and sustainability of the hybrid system.

Keywords: renewable energy, solar panel, wind turbine, hybrid system, generator

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Authors: Camille Perréard, Yoann Ladner, Fanny D'Orlyé, Stéphanie Descroix, Vélan Taniga, Anne Varenne, Cédric Guyon, Michael. Tatoulian, Frédéric Kanoufi, Cyrine Slim, Sophie Griveau, Fethi Bedioui

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The development of micro total analysis systems is of major interest for contaminant and biomarker analysis. As a lab-on-chip integrates all steps of an analysis procedure in a single device, analysis can be performed in an automated format with reduced time and cost, while maintaining performances comparable to those of conventional chromatographic systems. Moreover, these miniaturized systems are either compatible with field work or glovebox manipulations. This work is aimed at developing an analytical microsystem for trace and ultra trace quantitation in complex matrices. The strategy consists in the integration of a sample pretreatment step within the lab-on-chip by a confinement zone where selective ligands are immobilized for target extraction and preconcentration. Aptamers were chosen as selective ligands, because of their high affinity for all types of targets (from small ions to viruses and cells) and their ease of synthesis and functionalization. This integrated target extraction and concentration step will be followed in the microdevice by an electrokinetic separation step and an on-line detection. Polymers consisting of cyclic olefin copolymer (COC) or fluoropolymer (Dyneon THV) were selected as they are easy to mold, transparent in UV-visible and have high resistance towards solvents and extreme pH conditions. However, because of their low chemical reactivity, surface treatments are necessary. For the design of this miniaturized diagnostics, we aimed at modifying the microfluidic system at two scales : (1) on the entire surface of the microsystem to control the surface hydrophobicity (so as to avoid any sample wall adsorption) and the fluid flows during electrokinetic separation, or (2) locally so as to immobilize selective ligands (aptamers) on restricted areas for target extraction and preconcentration. We developed different novel strategies for the surface functionalization of COC and Dyneon, based on plasma, chemical and /or electrochemical approaches. In a first approach, a plasma-induced immobilization of brominated derivatives was performed on the entire surface. Further substitution of the bromine by an azide functional group led to covalent immobilization of ligands through “click” chemistry reaction between azides and terminal alkynes. COC and Dyneon materials were characterized at each step of the surface functionalization procedure by various complementary techniques to evaluate the quality and homogeneity of the functionalization (contact angle, XPS, ATR). With the objective of local (micrometric scale) aptamer immobilization, we developed an original electrochemical strategy on engraved Dyneon THV microchannel. Through local electrochemical carbonization followed by adsorption of azide-bearing diazonium moieties and covalent linkage of alkyne-bearing aptamers through click chemistry reaction, typical dimensions of immobilization zones reached the 50 µm range. Other functionalization strategies, such as sol-gel encapsulation of aptamers, are currently investigated and may also be suitable for the development of the analytical microdevice. The development of these functionalization strategies is the first crucial step in the design of the entire microdevice. These strategies allow the grafting of a large number of molecules for the development of new analytical tools in various domains like environment or healthcare.

Keywords: alkyne-azide click chemistry (CuAAC), electrochemical modification, microsystem, plasma bromination, surface functionalization, thermoplastic polymers

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