Search results for: Polling Applications

Authors: Ahmad Mohammad Sharqi, Mohamed Hechmi El Ouni, Saleh Alsulamy

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Due to the emerging technologies revolution worldwide, the need to exploit and employ innovative technologies for other functions and purposes in different aspects has become a remarkable matter. Saudi Arabia is considered one of the most powerful economic countries in the world, where the construction sector participates effectively in its economy. Thus, the construction sector in KSA should convoy the rapid digital revolution and transformation and implement smart devices on sites. A Smart Construction Site (SCS) includes smart devices, artificial intelligence, the internet of things, augmented reality, building information modeling, geographical information systems, and cloud information. This paper aims to study the level of implementation of SCS in KSA, analyze the obstacles and challenges of adopting SCS and find out critical success factors for its implementation. A survey of close-ended questions (scale and multi-choices) has been conducted on professionals in the construction sector of Saudi Arabia. A total number of twenty-nine questions has been prepared for respondents. Twenty-four scale questions were established, and those questions were categorized into several themes: quality, scheduling, cost, occupational safety and health, technologies and applications, and general perception. Consequently, the 5-point Likert scale tool (very low to very high) was adopted for this survey. In addition, five close-ended questions with multi-choice types have also been prepared; these questions have been derived from a previous study implemented in the United Kingdom (UK) and the Dominic Republic (DR), these questions have been rearranged and organized to fit the structured survey in order to place the Kingdom of Saudi Arabia in comparison with the United Kingdom (UK) as well as the Dominican Republic (DR). A total number of one hundred respondents have participated in this survey from all regions of the Kingdom of Saudi Arabia: southern, central, western, eastern, and northern regions. The drivers, obstacles, and success factors for implementing smart devices and technologies in KSA’s construction sector have been investigated and analyzed. Besides, it has been concluded that KSA is on the right path toward adopting smart construction sites with attractive results comparable to and even better than the UK in some factors.

Keywords: artificial intelligence, construction projects management, internet of things, smart construction sites, smart devices

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Authors: Ylva Fältholm, Cathrine Norberg

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One of the major threats identified to successful future mining is that women do not find the industry attractive. Many attempts have been made, for example in Sweden and Australia, to create organizational structures and mining communities attractive to both genders. Despite such initiatives, many mining areas are developing into gender-segregated fly-in/fly out communities dominated by men with both social and economic consequences. One of the challenges facing many mining companies is thus to break traditional gender patterns and structures. To do this increased knowledge about gender in the context of mining is needed. Since language both constitutes and reproduces knowledge, increased knowledge can be gained through an exploration and description of the mining discourse from a gender perspective. The aim of this study is to explore what conceptual ideas are activated in connection to the physical/geographical mining area and to work within the mining industry. We use a combination of critical discourse analysis implying close reading of selected texts, such as policy documents, interview materials, applications and research and innovation agendas, and analyses of linguistic patterns found in large language corpora covering millions of words of contemporary language production. The quantitative corpus data serves as a point of departure for the qualitative analysis of the texts, that is, suggests what patterns to explore further. The study shows that despite technological and organizational development, one of the most persistent discourses about mining is the conception of dangerous and unfriendly areas infused with traditional notions of masculinity ideals and manual hard work. Although some of the texts analyzed highlight gender issues, and describe gender-equalizing initiatives, such as wage-mapping systems, female networks and recruitment efforts for women executives, and thereby render the discourse less straightforward, it is shown that these texts are not unambiguous examples of a counter-discourse. They rather illustrate that discourses are not stable but include opposing discourses, in dialogue with each other. For example, many texts highlight why and how women are important to mining, at the same time as they suggest that gender and diversity are all about women: why mining is a problem for them, how they should be, and what they should do to fit in. Drawing on a constitutive view of discourse, knowledge about such conflicting perceptions of women is a prerequisite for succeeding in attracting women to the mining industry and thereby contributing to the development of future mining.

Keywords: discourse, corpus linguistics, gender, mining

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Authors: Zane Brough, Claudio Paoloni

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Modern low earth orbit (LEO) satellites that require multi-mission flexibility are highly likely to be repositioned between different operational orbits. While executing this process the satellite may experience high levels of vibration and environmental hazards, exposing the deployed solar panel to dangerous stress levels, fatigue and space debris, hence it is desirable to retract the solar array before satellite repositioning to avoid damage or failure. Furthermore, to accommodate for today's technological world, the power demand of a modern LEO satellite is rapidly increasing, which consequently provides pressure upon the design of the satellites solar array system to conform to the strict volume and mass limitations. A novel concept of deployable/retractable hybrid solar array system, aimed to provide a greater power to volume ratio while dramatically reducing the disadvantages of system mass and cost is proposed. Taking advantage of the new lightweight technology in solar panels, a mechanical system composed of both rigid and flexible solar panels arranged within a petal formation is proposed to yield a stowed to deployment area ratio up to at least 1:7, which improves the power density dramatically. The system consists of five subsystems, the outer ones based on a novel eight-petal configuration that provides a large surface and supports the flexible solar panels. A single cable and spool based hinge mechanism were designed to synchronously deploy/retract the panels in a safe, simple and efficient manner while the mass compared to the previous systems is considerably reduced. The relevant challenge to assure a smooth movement is resolved by a proper minimization of the gearing system and the use of a micro-controller system. A prototype was designed by 3D simulators and successfully constructed and tested. Further design works are in progress to implement an epicyclical gear hinge mechanism, which will further reduce the volume, mass and complexity of the system significantly. The proposed system due to an effective and reliable mechanism provides a large active surface, whilst being very compact. It could be extremely advantageous for use as ground portable solar panel system.

Keywords: mechatronic engineering, satellite, solar panel, deployable/retractable mechanism

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Authors: Rui Lin, Jimmy Xiangji Huang, Gary Spraakman

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This forthcoming systematic review will underscore the imperative of leveraging technology to mitigate social isolation in older adults, particularly in the context of unprecedented global challenges such as the COVID-19 pandemic. With the continual evolution of technology, it becomes crucial to scrutinize the efficacy of interventions and discern how they can alleviate social isolation and augment social well-being among the elderly. This review will strive to clarify the best methods for older adults to utilize cost-effective and user-friendly technology and will investigate how the adaptation and execution of such interventions can be fine-tuned to maximize their positive outcomes. The study will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to filter pertinent studies. We foresee conducting an analysis of articles and executing a narrative analysis to discover themes and indicators related to quality of life and, technology use and well-being. The review will examine how involving older adults at the community level, applying top practices from community-based participatory research, can establish efficient strategies to implement technology-based interventions designed to diminish social isolation and boost digital use self-efficacy. Applications based on mobile technology and virtual platforms are set to assume a crucial role not only in enhancing connections within families but also in connecting older adults to vital healthcare resources, fostering both physical and mental well-being. The review will investigate how technological devices and platforms can address the cognitive, visual, and auditory requirements of older adults, thus strengthening their confidence and proficiency in digital use—a crucial factor during enforced social distancing or self-isolation periods during pandemics. This review will endeavor to provide insights into the multifaceted benefits of technology for older adults, focusing on how tailored technological interventions can be a beacon of social and mental wellness in times of social restrictions. It will contribute to the growing body of knowledge on the intersection of technology and elderly well-being, offering nuanced understandings and practical implications for developing user-centric, effective, and inclusive technological solutions for older populations.

Keywords: older adults, health service delivery, digital health, social isolation, social well-being

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Authors: Matthias Walluch, José Alberto Rodríguez, Christopher Giehl, Gunther Arnold, Daniela Ehgartner

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Dynamic mechanical analysis (DMA) is a powerful method to characterize viscoelastic properties and phase transitions for a wide range of materials. It is often used to characterize polymers and their temperature-dependent behavior, including thermal transitions like the glass transition temperature Tg, via determination of storage and loss moduli in tension (Young’s modulus, E) and shear or torsion (shear modulus, G) or other testing modes. While production and application temperatures for polymers are often limited to several hundred degrees, material properties of glasses usually require characterization at temperatures exceeding 600 °C. This contribution highlights a high temperature setup for rotational and oscillatory rheometry as well as for DMA in different modes. The implemented standard convection oven enables the characterization of glass in different loading modes at temperatures up to 950 °C. Three-point bending, tension and torsional measurements on different glasses, with E and G moduli as a function of frequency and temperature, are presented. Additional tests include superimposing several frequencies in a single temperature sweep (“multiwave”). This type of test results in a considerable reduction of the experiment time and allows to evaluate structural changes of the material and their frequency dependence. Furthermore, DMA in torsion and tension was performed to determine the complex Poisson’s ratio as a function of frequency and temperature within a single test definition. Tests were performed in a frequency range from 0.1 to 10 Hz and temperatures up to the glass transition. While variations in the frequency did not reveal significant changes of the complex Poisson’s ratio of the glass, a monotonic increase of this parameter was observed when increasing the temperature. This contribution outlines the possibilities of DMA in bending, tension and torsion for an extended temperature range. It allows the precise mechanical characterization of material behavior from room temperature up to the glass transition and the softening temperature interval. Compared to other thermo-analytical methods, like Dynamic Scanning Calorimetry (DSC) where mechanical stress is neglected, the frequency-dependence links measurement results (e.g. relaxation times) to real applications

Keywords: dynamic mechanical analysis, oscillatory rheometry, Poisson's ratio, solid glass, viscoelasticity

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi

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Background:-Due to the essential role of the foot in movement, foot disorders (FDs) have significant impacts on activity and quality of life. Many studies confirmed the association between FDs and demographic characteristics. On the other hand, recent advances in data collection and statistical analysis led to an increase in the volume of databases. Analysis of patient’s data through the decision tree can be used to explore the relationship between demographic characteristics and FDs. Significance of the study: This study aimed to investigate the relationship between demographic characteristics with common FDs. The second purpose is to better inform foot intervention, we classify FDs based on demographic variables. Methodologies: We analyzed 2323 subjects with pes-planus (PP), pes-cavus (PC), hallux-valgus (HV) and plantar-fasciitis (PF) who were referred to a foot therapy clinic between 2015 and 2021. Subjects had to fulfill the following inclusion criteria: (1) weight between 14 to 150 kilogram, (2) height between 30 to 220, (3) age between 3 to 100 years old, and (4) BMI between 12 to 35. Medical archives of 2323 subjects were recorded retrospectively and all the subjects examined by an experienced physician. Age and BMI were classified into five and four groups, respectively. 80% of the data were randomly selected as training data and 20% tested. We build a decision tree model to classify FDs using demographic characteristics. Findings: Results demonstrated 981 subjects from 2323 (41.9%) of people who were referred to the clinic with FDs were diagnosed as PP, 657 (28.2%) PC, 628 (27%) HV and 213 (9%) identified with PF. The results revealed that the prevalence of PP decreased in people over 18 years of age and in children over 7 years. In adults, the prevalence depends first on BMI and then on gender. About 10% of adults and 81% of children with low BMI have PP. There is no relationship between gender and PP. PC is more dependent on age and gender. In children under 7 years, the prevalence was twice in girls (10%) than boys (5%) and in adults over 18 years slightly higher in men (62% vs 57%). HV increased with age in women and decreased in men. Aging and obesity have increased the prevalence of PF. We conclude that the accuracy of our approach is sufficient for most research applications in FDs. Conclusion:-The increased prevalence of PP in children is probably due to the formation of the arch of the foot at this age. Increasing BMI by applying high pressure on the foot can increase the prevalence of this disorder in the foot. In PC, the Increasing prevalence of PC from women to men with age may be due to genetics and innate susceptibility of men to this disorder. HV is more common in adult women, which may be due to environmental reasons such as shoes, and the prevalence of PF in obese adult women may also be due to higher foot pressure and housekeeping activities.

Keywords: decision tree, demographic characteristics, foot disorders, machine learning

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Authors: Ruchi Pathania, Ahmad Ahmad, Shireesh Srivastava

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Cyanobacteria is a promising microbe that can capture and convert atmospheric CO₂ and light into valuable industrial bio-products like biofuels, biodegradable plastics, etc. Among their most attractive traits are faster autotrophic growth, whole year cultivation using non-arable land, high photosynthetic activity, much greater biomass and productivity and easy for genetic manipulations. Cyanobacteria store carbon in the form of glycogen which can be hydrolyzed to release glucose and fermented to form bioethanol or other valuable products. Marine cyanobacterial species are especially attractive for countries with scarcity of freshwater. We recently identified a marine native cyanobacterium Synechococcus sp. BDU 130192 which has good growth rate and high level of polyglucans accumulation compared to Synechococcus PCC 7002. In this study, firstly we sequenced the whole genome and the sequences were annotated using the RAST server. Genome scale metabolic model (GSMM) was reconstructed through COBRA toolbox. GSMM is a computational representation of the metabolic reactions and metabolites of the target strain. GSMMs construction through the application of Flux Balance Analysis (FBA), which uses external nutrient uptake rates and estimate steady state intracellular and extracellular reaction fluxes, including maximization of cell growth. The model, which we have named isyn942, includes 942 reactions and 913 metabolites having 831 metabolic, 78 transport and 33 exchange reactions. The phylogenetic tree obtained by BLAST search revealed that the strain was a close relative of Synechococcus PCC 7002. The flux balance analysis (FBA) was applied on the model iSyn942 to predict the theoretical yields (mol product produced/mol CO₂ consumed) for native and non-native products like acetone, butanol, etc. under phototrophic condition by applying metabolic engineering strategies. The reported strain can be a viable strain for biotechnological applications, and the model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for enhanced production of various bioproducts.

Keywords: cyanobacteria, flux balance analysis, genome scale metabolic model, metabolic engineering

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Authors: Boce Zhang

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Strategic innovation of nanotechnology to promote food safety has drawn tremendous attentions among research groups, which includes the need for research support during the implementation of the Food Safety Modernization Act (FSMA) in the United States. There are urgent demands and knowledge gaps to the understanding of a) food-water-bacteria interface as for how pathogens persist and transmit during food processing and storage; b) minimum processing requirement needed to prevent pathogen cross-contamination in the food system. These knowledge gaps are of critical importance to the food industry. However, the lack of knowledge is largely hindered by the limitations of research tools. Our groups recently endeavored two novel engineering systems with biomimetics and microfluidics as a holistic approach to hazard analysis and risk mitigation, which provided unprecedented research opportunities to study pathogen behavior, in particular, contamination, and cross-contamination, at the critical food-water-pathogen interface. First, biomimetically-patterned surfaces (BPS) were developed to replicate the identical surface topography and chemistry of a natural food surface. We demonstrated that BPS is a superior research tool that empowers the study of a) how pathogens persist through sanitizer treatment, b) how to apply fluidic shear-force and surface tension to increase the vulnerability of the bacterial cells, by detaching them from a protected area, etc. Secondly, microfluidic devices were designed and fabricated to study the bactericidal kinetics in the sub-second time frame (0.1~1 second). The sub-second kinetics is critical because the cross-contamination process, which includes detachment, migration, and reattachment, can occur in a very short timeframe. With this microfluidic device, we were able to simulate and study these sub-second cross-contamination scenarios, and to further investigate the minimum sanitizer concentration needed to sufficiently prevent pathogen cross-contamination during the food processing. We anticipate that the findings from these studies will provide critical insight on bacterial behavior at the food-water-cell interface, and the kinetics of bacterial inactivation from a broad range of sanitizers and processing conditions, thus facilitating the development and implementation of science-based food safety regulations and practices to mitigate the food safety risks.

Keywords: biomimetic materials, microbial food safety, microfluidic device, nanotechnology

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Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

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Authors: Asena Demirci, Gozen Guner Aktaş, Nur Ayalp

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Nature provides a living space for humans, also in contrast it is destroyed by humans for their personal needs and ambitions. For decreasing these damages against nature, solutions are started to generate and to develop. Moreover, precautions are implemented. After 1960s, especially when the ozone layer got harmed and got thinner by toxic substances coming from man made structures, environmental problems which effected human’s activities of daily living. Thus, this subject about environmental solutions and precautions is becoming a priority issue for scientists. Most of the environmental problems are caused by buildings and factories which are built without any concerns about protecting nature. This situation creates awareness about environmental issues and also the terms like sustainability, Renewable energy show up in building, Construction and architecture sectors to provide environmental protection. In this perspective, the design disciplines also should be respectful to nature and the sustainability. Designs which involve the features like sustainability, renewability and being ecologic have specialties to be less detrimental to the environment rather than the designs which do not involve. Furthermore, these designs produce their own energy for consuming, So they do not use the natural resources. They do not contain harmful substances and they are made of recyclable materials. Thus, they are becoming environmentally friendly structures. There is a common concern among designers about the issue of sustainable design. They believe that the idea of sustainability inhibits the creativity. All works of design resemble each other from the point of aesthetics and technological matters. In addition, there is a concern about design ethics which aesthetic designs cannot be accepted as a priority. For these reasons, there are few designs included the features of being eco-friendly and well-designed and also had design concerns around the world. Despite the other design disciplines, The concept of sustainability is getting more important each day in interior architecture and interior design. As it is known that human being spends 90 % of his life in interior spaces, The importance of that concept in interior spaces is obvious. Aesthetic is another vital concern in interior space design also. Most of the time sustainable materials and sustainable interior design applications conflicts with personal aesthetic parameters. This study aims to discuss the great paradox between the design aesthetic and the sustainable design. Does the sustainable approach in interior design disturbs the design aesthetic? This is one of the most popular questions that have been discussed for a while. With this paper this question will be evaluated with a case study which analyzes the aesthetic perceptions and preferences of the users and designers in sustainable interior spaces.

Keywords: aesthetics, interior design, sustainable design, sustainability

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Authors: Ariadne C. Catto, Luis F. da Silva, Khalifa Aguir, Valmor Roberto Mastelaro

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Zinc oxide (ZnO) pure or doped are one of the most promising metal oxide semiconductors for gas sensing applications due to the well-known high surface-to-volume area and surface conductivity. It was shown that ZnO is an excellent gas-sensing material for different gases such as CO, O2, NO2 and ethanol. In this context, pure and doped ZnO exhibiting different morphologies and a high surface/volume ratio can be a good option regarding the limitations of the current commercial sensors. Different studies showed that the sensitivity of metal-doped ZnO (e.g. Co, Fe, Mn,) enhanced its gas sensing properties. Motivated by these considerations, the aim of this study consisted on the investigation of the role of Co ions on structural, morphological and the gas sensing properties of nanostructured ZnO samples. ZnO and Zn1-xCoxO (0 < x < 5 wt%) thin films were obtained via the polymeric precursor method. The sensitivity, selectivity, response time and long-term stability gas sensing properties were investigated when the sample was exposed to a different concentration range of ozone (O3) at different working temperatures. The gas sensing property was probed by electrical resistance measurements. The long and short-range order structure around Zn and Co atoms were investigated by X-ray diffraction and X-ray absorption spectroscopy. X-ray photoelectron spectroscopy measurement was performed in order to identify the elements present on the film surface as well as to determine the sample composition. Microstructural characteristics of the films were analyzed by a field-emission scanning electron microscope (FE-SEM). Zn1-xCoxO XRD patterns were indexed to the wurtzite ZnO structure and any second phase was observed even at a higher cobalt content. Co-K edge XANES spectra revealed the predominance of Co2+ ions. XPS characterization revealed that Co-doped ZnO samples possessed a higher percentage of oxygen vacancies than the ZnO samples, which also contributed to their excellent gas sensing performance. Gas sensor measurements pointed out that ZnO and Co-doped ZnO samples exhibit a good gas sensing performance concerning the reproducibility and a fast response time (around 10 s). Furthermore, the Co addition contributed to reduce the working temperature for ozone detection and improve the selective sensing properties.

Keywords: cobalt-doped ZnO, nanostructured, ozone gas sensor, polymeric precursor method

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Authors: Genevieve Lim, Arthur Shelley, Dongcheol Heo

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Abstract—Mobile technologies can enhance the learning process as it enables social engagement around concepts beyond the classroom and the curriculum. Early results in this ongoing research is showing that when learning interventions are designed specifically to generate new insights, mobile devices support regulated learning and encourage learners to collaborate, socialize and co-create new knowledge. As students navigate across the space and time boundaries, the fundamental social nature of learning transforms into mobile computer supported collaborative learning (mCSCL). The metacognitive interaction in mCSCL via mobile applications reflects the regulation of learning among the students. These metacognitive experiences whether self-, co- or shared-regulated are significant to the learning outcomes. Despite some insightful empirical studies, there has not yet been significant research that investigates the actual practice and processes of the new knowledge co-creation. This leads to question as to whether mobile learning provides a new channel to leverage learning? Alternatively, does mobile interaction create new types of learning experiences and how do these experiences co-create new knowledge. The purpose of this research is to explore these questions and seek evidence to support one or the other. This paper addresses these questions from the students’ perspective to understand how students interact when constructing knowledge in mCSCL and how students’ self-regulated learning (SRL) strategies support the co-creation of new knowledge in mCSCL. A pilot study has been conducted among international undergraduates to understand students’ perspective of mobile learning and concurrently develops a definition in an appropriate context. Using classroom action research (CAR) with multiple case studies, this study is being carried out in a private university in Thailand to narrow the research gaps in mCSCL and SRL. The findings will allow teachers to see the importance of social interaction for meaningful student engagement and envisage learning outcomes from a knowledge management perspective and what role mobile devices can play in these. The findings will signify important indicators for academics to rethink what is to be learned and how it should be learned. Ultimately, the study will bring new light into the co-creation of new knowledge in a social interactive learning environment and challenges teachers to embrace the 21st century of learning with mobile technologies to deepen and extend learning opportunities.

Keywords: mobile computer supported collaborative learning, mobile instant messaging, mobile learning, new knowledge co-creation, self-regulated learning

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Authors: Gayathri Devi V, Aravamudan Kannan, Amit Sircar

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In the inner fuel cycle system of a nuclear fusion reactor, the Hydrogen Isotopes Removal System (HIRS) plays a pivoted role. It enables the effective extraction of the hydrogen isotopes from the breeder purge gas which helps to maintain the tritium breeding ratio and sustain the fusion reaction. One of the components of HIRS, Cryogenic Molecular Sieve Bed (CMSB) columns with zeolites adsorbents are considered for the physisorption of hydrogen isotopes at 1 bar and 77 K. Even though zeolites have good thermal stability and reduced activation properties making them ideal for use in nuclear reactor applications, their modest capacity for hydrogen isotopes adsorption is a cause of concern. In order to enhance the adsorbent capacity in an informed manner, it is helpful to understand the adsorption phenomena at the quantum electronic structure level. Physicochemical modifications of the adsorbent material enhances the adsorption capacity through the incorporation of active sites. This may be accomplished through the incorporation of suitable metal ions in the zeolite framework. In this work, molecular hydrogen isotopes adsorption on the active sites of functionalized zeolites are investigated in detail using Density Functional Theory (DFT) study. This involves the utilization of hybrid Generalized Gradient Approximation (GGA) with dispersion correction to account for the exchange and correlation functional of DFT. The electronic energies, adsorption enthalpy, adsorption free energy, Highest Occupied Molecular Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO) energies are computed on the stable 8T zeolite clusters as well as the periodic structure functionalized with different active sites. The characteristics of the dihydrogen bond with the active metal sites and the isotopic effects are also studied in detail. Validation studies with DFT will also be presented for adsorption of hydrogen on metal ion functionalized zeolites. The ab-inito screening analysis gave insights regarding the mechanism of hydrogen interaction with the zeolites under study and also the effect of the metal ion on adsorption. This detailed study provides guidelines for selection of the appropriate metal ions that may be incorporated in the zeolites framework for effective adsorption of hydrogen isotopes in the HIRS.

Keywords: adsorption enthalpy, functionalized zeolites, hydrogen isotopes, nuclear fusion, physisorption

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Authors: Inigo Beckett

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In forensic investigations, it is often the case that the most potentially useful recorded evidence derives from coincidental imagery, recorded immediately before or during an incident, and that during the incident (e.g. a ‘failure’ or fire event), the evidence is changed or destroyed. To an image analysis expert involved in photogrammetric analysis for Civil or Criminal Proceedings, traditional computer vision methods involving calibrated cameras is often not appropriate because image metadata cannot be relied upon. This paper presents an approach for resolving this problem, considering in particular and by way of a case study, the angle of a simple ladder shown in a photograph. The UK Health and Safety Executive (HSE) guidance document published in 2014 (INDG455) advises that a leaning ladder should be erected at 75 degrees to the horizontal axis. Personal injury cases can arise in the construction industry because a ladder is too steep or too shallow. Ad-hoc photographs of such ladders in their incident position provide a basis for analysis of their angle. This paper presents a direct approach for ascertaining the position of the camera and the angle of the ladder simultaneously from the photograph(s) by way of a workflow that encompasses a novel application of projective geometry and matrix analysis. Mathematical analysis shows that for a given pixel ratio of directly measured collinear points (i.e. features that lie on the same line segment) from the 2D digital photograph with respect to a given viewing point, we can constrain the 3D camera position to a surface of a sphere in the scene. Depending on what we know about the ladder, we can enforce another independent constraint on the possible camera positions which enables us to constrain the possible positions even further. Experiments were conducted using synthetic and real-world data. The synthetic data modeled a vertical plane with a ladder on a horizontally flat plane resting against a vertical wall. The real-world data was captured using an Apple iPhone 13 Pro and 3D laser scan survey data whereby a ladder was placed in a known location and angle to the vertical axis. For each case, we calculated camera positions and the ladder angles using this method and cross-compared them against their respective ‘true’ values.

Keywords: image analysis, projective geometry, homography, photogrammetry, ladders, Forensics, Mathematical modeling, planar geometry, matrix analysis, collinear, cameras, photographs

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Authors: Zichao Zhang, Yifan Zhao, Samuel Court

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The carbon emission problem of the traditional construction industry has long been a pressing issue. With the growing emphasis on environmental protection and advancement of science and technology, the organic integration of digital technology and emission reduction has gradually become a mainstream solution. Among various sophisticated digital technologies, digital twins, which involve creating virtual replicas of physical systems or objects, have gained enormous attention in recent years as tools to improve productivity, optimize management and reduce carbon emissions. However, the relatively high implementation costs including finances, time, and manpower associated with digital twins have limited their widespread adoption. As a result, most of the current applications are primarily concentrated within a few industries. In addition, the creation of digital twins relies on a large amount of data and requires designers to possess exceptional skills in information collection, organization, and analysis. Unfortunately, these capabilities are often lacking in the traditional construction industry. Furthermore, as a relatively new concept, digital twins have different expressions and usage methods across different industries. This lack of standardized practices poses a challenge in creating a high-quality digital twin framework for construction. This paper firstly reviews the current academic studies and industrial practices focused on reducing greenhouse gas emissions in the construction industry using digital twins. Additionally, it identifies the challenges that may be encountered during the design and implementation of a digital twin framework specific to this industry and proposes potential directions for future research. This study shows that digital twins possess substantial potential and significance in enhancing the working environment within the traditional construction industry, particularly in their ability to support decision-making processes. It proves that digital twins can improve the work efficiency and energy utilization of related machinery while helping this industry save energy and reduce emissions. This work will help scholars in this field to better understand the relationship between digital twins and energy conservation and emission reduction, and it also serves as a conceptual reference for practitioners to implement related technologies.

Keywords: digital twins, emission reduction, construction industry, energy saving, life cycle, sustainability

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Authors: V. Reyes, P. Ferreira

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In response to high levels of competitiveness, industrial systems have evolved to improve productivity. As a consequence, a rapid increase in volume production and simultaneously, a customization process require lower costs, more variety, and accurate quality of products. Reducing time-cycle production, enabling customizability, and ensure continuous quality improvement are key features in advance intelligent industry. In this scenario, customers and producers will be able to participate in the ongoing production life cycle through real-time interaction. To achieve this vision, transparency, predictability, and adaptability are key features that provide the industrial systems the capability to adapt to customer demands modifying the manufacturing process through an autonomous response and acting preventively to avoid errors. The industrial system incorporates a diversified number of components that in advanced industry are expected to be decentralized, end to end communicating, and with the capability to make own decisions through feedback. The evolving process towards advanced intelligent industry defines a set of stages to empower components of intelligence and enhancing efficiency to achieve the decision-making stage. The integrated system follows an industrial cyber-physical system (CPS) architecture whose real-time integration, based on a set of enabler technologies, links the physical and virtual world generating the digital twin (DT). This instance allows incorporating sensor data from real to virtual world and the required transparency for real-time monitoring and control, contributing to address important features of the advanced intelligent industry and simultaneously improve sustainability. Assuming the industrial CPS as the core technology toward the latest advanced intelligent industry stage, this paper reviews and highlights the correlation and contributions of the enabler technologies for the operationalization of each stage in the path toward advanced intelligent industry. From this research, a real-time integration architecture for a cyber-physical system with applications to collaborative robotics is proposed. The required functionalities and issues to endow the industrial system of adaptability are identified.

Keywords: cyber-physical systems, digital twin, sensor data, system integration, virtual model

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Authors: Tayyaba Bari, Muhammad Hamza Anjum, Samra Kanwal, Fakhera Ikram

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Osteoarthritis, a degenerative condition, affects more than 213 million individuals globally. Since articular cartilage has no or limited vessels, therefore, after deteriorating, it is unable to rejuvenate. Traditional approaches for cartilage repair, like autologous chondrocyte implantation, microfracture and cartilage transplantation are often associated with postoperative complications and lead to further degradation. Decellularized human umbilical cord has gained interest as a viable treatment for cartilage repair. Decellularization removes all cellular contents as well as debris, leaving a biologically active 3D network known as extracellular matrix (ECM). This matrix is biodegradable, non-immunogenic and provides a microenvironment for homeostasis, growth and repair. UC derived bioink function as 3D scaffolding material, not only mediates cell-matrix interactions but also adherence, proliferation and propagation of cells for 3D organoids. This study comprises different physical, chemical and biological approaches to optimize the decellularization of human umbilical cord (UC) tissues followed by the solubilization of these tissues to bioink formation. The decellularization process consisted of two cycles of freeze thaw where the umbilical cord at -20˚C was thawed at room temperature followed by dissection in small sections from 0.5 to 1cm. Similarly decellularization with ionic and non-ionic detergents Sodium dodecyl sulfate (SDS) and Triton-X 100 revealed that both concentrations of SDS i.e 0.1% and 1% were effective in complete removal of cells from the small UC tissues. The results of decellularization was further confirmed by running them on 1% agarose gel. Histological analysis revealed the efficacy of decellularization, which involves paraffin embedded samples of 4μm processed for Hematoxylin-eosin-safran and 4,6-diamidino-2-phenylindole (DAPI). ECM preservation was confirmed by Alcian Blue, and Masson’s trichrome staining on consecutive sections and images were obtained. Sulfated GAG’s content were determined by 1,9-dimethyl-methylene blue (DMMB) assay, similarly collagen quantification was done by hydroxy proline assay. This 3D bioengineered scaffold will provide a typical atmosphere as in the extracellular matrix of the tissue, which would be seeded with the mesenchymal cells to generate the desired 3D ink for in vitro and in vivo cartilage regeneration applications.

Keywords: umbilical cord, 3d printing, bioink, tissue engineering, cartilage regeneration

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: Xinghui Wu, Chun Yang

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Inertial microfluidics is a competitive fluidic method with applications in separation of particles, cells and bacteria. In contrast to traditional microfluidic devices with low Reynolds number, inertial microfluidics works in the intermediate Re number range which brings about several intriguing inertial effects on particle separation/focusing to meet the throughput requirement in the real-world. Geometric modifications to make channels become irregular shapes can leverage fluid inertia to create complex secondary flow for adjusting the particle equilibrium positions and thus enhance the separation resolution and throughput. Although inertial microfluidics has been extensively studied by experiments, our current understanding of its mechanisms is poor, making it extremely difficult to build rational-design guidelines for the particle focusing locations, especially for irregularly shaped microfluidic channels. Inertial particle microfluidics in irregularly shaped channels were investigated in our group. There are several fundamental issues that require us to address. One of them is about the balance between the inertial lift forces and the secondary drag forces. Also, it is critical to quantitatively describe the dependence of the life forces on particle-particle interactions in irregularly shaped channels, such as a rectangular one. To provide physical insights into the inertial microfluidics in channels of irregular shapes, in this work the immersed boundary-lattice Boltzmann method (IB-LBM) was introduced and validated to explore the transport characteristics and the underlying mechanisms of an inertial focusing single particle in a rectangular microchannel. The transport dynamics of a finitesized particle were investigated over wide ranges of Reynolds number (20 < Re < 500) and particle size. The results show that the inner equilibrium positions are more difficult to occur in the rectangular channel, which can be explained by the secondary flow caused by the presence of a finite-sized particle. Furthermore, force decoupling analysis was utilized to study the effect of each type of lift force on the inertia migration, and a theoretical model for the lateral lift force of a finite-sized particle in the rectangular channel was established. Such theoretical model can be used to provide theoretical guidance for the design and operation of inertial microfluidics.

Keywords: inertial microfluidics, particle focuse, life force, IB-LBM

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Authors: Hiba Naccache, Rima Hleiss

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Additive manufacturing AM or 3D printing is one of the pillars of Industry 4.0. Compared to traditional manufacturing, AM provides a prototype before production in order to optimize the design and avoid the stock market and uses strictly necessary material which can be recyclable, for the benefit of leaning towards local production, saving money, time and resources. Different types of AM exist and it has a broad range of applications across several industries like aerospace, automotive, medicine, education and else. The Laser Powder Bed Fusion (LPBF) is a metal AM technique that uses a laser to liquefy metal powder, layer by layer, to build a three-dimensional (3D) object. In industry 4.0 and aligned with the numbers 9 (Industry, Innovation and Infrastructure) and 12 (Responsible Production and Consumption) of the Sustainable Development Goals of the UNESCO 2030 Agenda, the AM’s manufacturers committed to minimizing the environmental impact by being sustainable in every production. The LPBF has several environmental advantages, like reduced waste production, lower energy consumption, and greater flexibility in creating components with lightweight and complex geometries. However, LPBF also have environmental drawbacks, like energy consumption, gas consumption and emissions. It is critical to recognize the environmental impacts of LPBF in order to mitigate them. To increase awareness and promote sustainable practices regarding LPBF, the researchers use the Elaboration Likelihood Model (ELM) theory where people from multiple universities in France and Qatar process information in two ways: peripherally and centrally. The peripheral campaigns use superficial cues to get attention, and the central campaigns provide clear and concise information. The authors created a seminar including a video showing LPBF production and a website with educational resources. The data is collected using questionnaire to test attitude about the public awareness before and after the seminar. The results reflected a great shift on the awareness toward LPBF and its impact on the environment. With no presence of similar research, to our best knowledge, this study will add to the literature on the sustainability of the LPBF production technique.

Keywords: additive manufacturing, laser powder bed fusion, elaboration likelihood model theory, sustainable development goals, education-awareness, France, Qatar, specific energy consumption, environmental impact, lightweight components

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Authors: Nikolai N. Bogolubov, Andrey V. Soldatov

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Terahertz radiation occupies a range of frequencies somewhere from 100 GHz to approximately 10 THz, just between microwaves and infrared waves. This range of frequencies holds promise for many useful applications in experimental applied physics and technology. At the same time, reliable, simple techniques for generation, amplification, and modulation of electromagnetic radiation in this range are far from been developed enough to meet the requirements of its practical usage, especially in comparison to the level of technological abilities already achieved for other domains of the electromagnetic spectrum. This situation of relative underdevelopment of this potentially very important range of electromagnetic spectrum is known under the name of the 'terahertz gap.' Among other things, technological progress in the terahertz area has been impeded by the lack of compact, low energy consumption, easily controlled and continuously radiating terahertz radiation sources. Therefore, development of new techniques serving this purpose as well as various devices based on them is of obvious necessity. No doubt, it would be highly advantageous to employ the simplest of suitable physical systems as major critical components in these techniques and devices. The purpose of the present research was to show by means of conventional methods of non-equilibrium statistical mechanics and the theory of open quantum systems, that a thoroughly studied two-level quantum system, also known as an one-electron two-level 'atom', being driven by external classical monochromatic high-frequency (e.g. laser) field, can radiate continuously at much lower (e.g. terahertz) frequency in the fluorescent regime if the transition dipole moment operator of this 'atom' possesses permanent non-equal diagonal matrix elements. This assumption contradicts conventional assumption routinely made in quantum optics that only the non-diagonal matrix elements persist. The conventional assumption is pertinent to natural atoms and molecules and stems from the property of spatial inversion symmetry of their eigenstates. At the same time, such an assumption is justified no more in regard to artificially manufactured quantum systems of reduced dimensionality, such as, for example, quantum dots, which are often nicknamed 'artificial atoms' due to striking similarity of their optical properties to those ones of the real atoms. Possible ways to experimental observation and practical implementation of the predicted effect are discussed too.

Keywords: terahertz gap, two-level atom, resonant fluorescence, quantum dot, resonant fluorescence, two-level atom

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Authors: Dr. Dionysis Panos Dpt. Communication, Internet Studies Cyprus University of Technology

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The widespread use of New Media and particularly Social Media, through fixed or mobile devices, has changed in a staggering way our perception about what is “intimate" and "safe" and what is not, in interpersonal communication and social relationships. The distribution of self and identity-related information in communication now evolves under new and different conditions and contexts. Consequently, this new framework forces us to rethink processes and mechanisms, such as what "exposure" means in interpersonal communication contexts, how the distinction between the "private" and the "public" nature of information is being negotiated online, how the "audiences" we interact with are understood and constructed. Drawing from an interdisciplinary perspective that combines sociology, communication psychology, media theory, new media and social networks research, as well as from the empirical findings of a longitudinal comparative research, this work proposes an integrative model for comprehending mechanisms of personal information management in interpersonal communication, which can be applied to both types of online (Computer-Mediated) and offline (Face-To-Face) communication. The presentation is based on conclusions drawn from a longitudinal qualitative research study with 458 new media users from 24 countries for almost over a decade. Some of these main conclusions include: (1) There is a clear and evidenced shift in users’ perception about the degree of "security" and "familiarity" of the Web, between the pre- and the post- Web 2.0 era. The role of Social Media in this shift was catalytic. (2) Basic Web 2.0 applications changed dramatically the nature of the Internet itself, transforming it from a place reserved for “elite users / technical knowledge keepers" into a place of "open sociability” for anyone. (3) Web 2.0 and Social Media brought about a significant change in the concept of “audience” we address in interpersonal communication. The previous "general and unknown audience" of personal home pages, converted into an "individual & personal" audience chosen by the user under various criteria. (4) The way we negotiate the nature of 'private' and 'public' of the Personal Information, has changed in a fundamental way. (5) The different features of the mediated environment of online communication and the critical changes occurred since the Web 2.0 advance, lead to the need of reconsideration and updating the theoretical models and analysis tools we use in our effort to comprehend the mechanisms of interpersonal communication and personal information management. Therefore, is proposed here a new model for understanding the way interpersonal communication evolves, based on a revision of social penetration theory.

Keywords: new media, interpersonal communication, social penetration theory, communication exposure, private information, public information

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Kai-Chieh Lin, Chih-Fu Wu, Hsiang Ling Hsu, Yung-Hsiang Tu, Chia-Chen Wu

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With the rapid development of computer technology, the design of computers and keyboards moves towards a trend of slimness. The change of mobile input devices directly influences users’ behavior. Although multi-touch applications allow entering texts through a virtual keyboard, the performance, feedback, and comfortableness of the technology is inferior to traditional keyboard, and while manufacturers launch mobile touch keyboards and projection keyboards, the performance has not been satisfying. Therefore, this study discussed the design factors of slim pressure-sensitive keyboards. The factors were evaluated with an objective (accuracy and speed) and a subjective evaluation (operability, recognition, feedback, and difficulty) depending on the shape (circle, rectangle, and L-shaped), thickness (flat, 3mm, and 6mm), and force (35±10g, 60±10g, and 85±10g) of the keyboard. Moreover, MANOVA and Taguchi methods (regarding signal-to-noise ratios) were conducted to find the optimal level of each design factor. The research participants, by their typing speed (30 words/ minute), were divided in two groups. Considering the multitude of variables and levels, the experiments were implemented using the fractional factorial design. A representative model of the research samples were established for input task testing. The findings of this study showed that participants with low typing speed primarily relied on vision to recognize the keys, and those with high typing speed relied on tactile feedback that was affected by the thickness and force of the keys. In the objective and subjective evaluation, a combination of keyboard design factors that might result in higher performance and satisfaction was identified (L-shaped, 3mm, and 60±10g) as the optimal combination. The learning curve was analyzed to make a comparison with a traditional standard keyboard to investigate the influence of user experience on keyboard operation. The research results indicated the optimal combination provided input performance to inferior to a standard keyboard. The results could serve as a reference for the development of related products in industry and for applying comprehensively to touch devices and input interfaces which are interacted with people.

Keywords: input performance, mobile device, slim keyboard, tactile feedback

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Authors: Muhammad Saleem

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Background: The teaching of sports sciences encompasses a broad spectrum of disciplines, including biomechanics, physiology, psychology, and coaching. Effective pedagogical techniques are crucial in imparting both theoretical knowledge and practical skills necessary for students to excel in the field. The impact of these techniques on students’ learning outcomes, engagement, and professional preparedness remains a vital area of study. Objective: This study aims to evaluate the effectiveness of various pedagogical techniques used in the teaching of sports sciences. It seeks to identify which methods most significantly enhance student learning, retention, engagement, and practical application of knowledge. Methods: A mixed-methods approach was employed, including both quantitative and qualitative analyses. The study involved a comparative analysis of traditional lecture-based teaching, experiential learning, problem-based learning (PBL), and technology-enhanced learning (TEL). Data were collected through surveys, interviews, and academic performance assessments from students enrolled in sports sciences programs at multiple universities. Statistical analysis was used to evaluate academic performance, while thematic analysis was applied to qualitative data to capture student experiences and perceptions. Results: The findings indicate that experiential learning and PBL significantly improve students' understanding and retention of complex sports science concepts compared to traditional lectures. TEL was found to enhance engagement and provide students with flexible learning opportunities, but its impact on deep learning varied depending on the quality of the digital resources. Overall, a combination of experiential learning, PBL, and TEL was identified as the most effective pedagogical approach, leading to higher student satisfaction and better preparedness for real-world applications. Conclusion: The study underscores the importance of adopting diverse and student-centered pedagogical techniques in the teaching of sports sciences. While traditional lectures remain useful for foundational knowledge, integrating experiential learning, PBL, and TEL can substantially improve student outcomes. These findings suggest that educators should consider a blended approach to pedagogy to maximize the effectiveness of sports science education.

Keywords: sport sciences, pedagogical techniques, health and physical education, problem-based learning, student engagement

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Authors: Maria Antonieta Erna Castillo Holly

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During the last decade, data analysis, strategic decision making, and the use of artificial intelligence (AI) tools in Latin American agriculture have been a challenge. In some countries, the availability, quality, and reliability of historical data, in addition to the current data recording methodology in the field, makes it difficult to use information systems, complete data analysis, and their support for making the right strategic decisions. This is something essential in Agriculture 4.0. where the increase in the global demand for fresh agricultural products of tropical origin, during all the seasons of the year requires a change in the production model and greater agility in the responses to the consumer market demands of quality, quantity, traceability, and sustainability –that means extensive data-. Having quality information available and updated in real-time on what, how much, how, when, where, at what cost, and the compliance with production quality standards represents the greatest challenge for sustainable and profitable agriculture in the region. The objective of this work is to present a methodological proposal for the collection of georeferenced data from the protected agriculture sector, specifically in production units (UP) with tall structures (Greenhouses), initially for Venezuela, taking the state of Mérida as the geographical framework, and horticultural products as target crops. The document presents some background information and explains the methodology and tools used in the 3 phases of the work: diagnosis, data collection, and analysis. As a result, an evaluation of the process is carried out, relevant data and dashboards are displayed, and the first satellite maps integrated with layers of information in a geographic information system are presented. Finally, some improvement proposals and tentatively recommended applications are added to the process, understanding that their objective is to provide better qualified and traceable georeferenced data for subsequent analysis of the information and more agile and accurate strategic decision making. One of the main points of this study is the lack of quality data treatment in the Latin America area and especially in the Caribbean basin, being one of the most important points how to manage the lack of complete official data. The methodology has been tested with horticultural products, but it can be extended to other tropical crops.

Keywords: greenhouses, protected agriculture, data analysis, geographic information systems, Venezuela

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Authors: Okpala Charles Chikwendu, Ezeanyim Okechukwu Chiedu, Onukwuli Somto Kenneth

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The demand for lightweight and fuel-efficient automobiles has led to the use of fiber-reinforced polymer composites in place of traditional metal parts. Coir, a natural fiber, offers qualities such as low cost, good tensile strength, and biodegradability, making it a potential filler material for automotive components. However, poor interfacial adhesion between coir and polymeric matrices has been a challenge. To address poor interfacial adhesion with polymeric matrices due to their moisture content and method of preparation, the extracted coir was chemically treated using NaOH. To develop a side view mirror encasement by investigating the mechanical effect of fiber percentage composition, fiber length and percentage composition of Epoxy in a coir fiber reinforced composite, polyester was adopted as the resin for the mold, while that of the product is Epoxy. Coir served as the filler material for the product. Specimens with varied compositions of fiber loading (15, 30 and 45) %, length (10, 15, 20, 30 and 45) mm, and (55, 70, 85) % weight of epoxy resin were fabricated using hand lay-up technique, while those specimens were later subjected to mechanical tests (Tensile, Flexural and Impact test). The results of the mechanical test showed that the optimal solution for the input factors is coir at 45%, epoxy at 54.543%, and 45mm coir length, which was used for the development of a vehicle’s side view mirror encasement. The optimal solutions for the response parameters are 49.333 Mpa for tensile strength, flexural for 57.118 Mpa, impact strength for 34.787 KJ/M2, young modulus for 4.788 GPa, stress for 4.534 KN, and 20.483 mm for strain. The models that were developed using Design Expert software revealed that the input factors can achieve the response parameters in the system with 94% desirability. The study showed that coir is quite durable for filler material in an epoxy composite for automobile applications and that fiber loading and length have a significant effect on the mechanical behavior of coir fiber-reinforced epoxy composites. The coir's low density, considerable tensile strength, and bio-degradability contribute to its eco-friendliness and potential for reducing the environmental hazards of synthetic automotive components.

Keywords: coir, composite, coir fiber, coconut husk, polymer, automobile, mechanical test

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Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

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Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

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Authors: Deborah Eric, Abbas Ahmad Khan

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Lead halide perovskites quantum dots have attracted immense scientific and technological interest for successful photovoltaic applications because of their remarkable optoelectronic properties. In this paper, we have simulated CsMAPbI3-xBrx based quantum dots to implement their use in intermediate band solar cells (IBSC). These types of materials exhibit optical and electrical properties distinct from their bulk counterparts due to quantum confinement. The conceptual framework provides a route to analyze the electronic properties of quantum dots. This layer of quantum dots optimizes the position and bandwidth of IB that lies in the forbidden region of the conventional bandgap. A three-dimensional MAPbI3 quantum dot (QD) with geometries including spherical, cubic, and conical has been embedded in the CsPbBr3 matrix. Bound energy wavefunction gives rise to miniband, which results in the formation of IB. If there is more than one miniband, then there is a possibility of having more than one IB. The optimization of QD size results in more IBs in the forbidden region. One band time-independent Schrödinger equation using the effective mass approximation with step potential barrier is solved to compute the electronic states. Envelope function approximation with BenDaniel-Duke boundary condition is used in combination with the Schrödinger equation for the calculation of eigen energies and Eigen energies are solved for the quasi-bound states using an eigenvalue study. The transfer matrix method is used to study the quantum tunneling of MAPbI3 QD through neighbor barriers of CsPbI3. Electronic states are computed using Schrödinger equation with effective mass approximation by considering quantum dot and wetting layer assembly. Results have shown the varying the quantum dot size affects the energy pinning of QD. Changes in the ground, first, second state energies have been observed. The QD is non-zero at the center and decays exponentially to zero at boundaries. Quasi-bound states are characterized by envelope functions. It has been observed that conical quantum dots have maximum ground state energy at a small radius. Increasing the wetting layer thickness exhibits energy signatures similar to bulk material for each QD size.

Keywords: perovskite, intermediate bandgap, quantum dots, miniband formation

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Authors: Ariadna Manresa, Ines Ferrer

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Introduction: Ultrasonic moulding process (USM) is a recent injection technology used to manufacture micro components. It is able to melt small amounts of material so the waste of material is certainly reduced comparing to microinjection molding. This is an important advantage when the materials are expensive like medical biopolymers. Micro-scaled components are involved in a variety of uses, such as biomedical applications. It is required replication fidelity so it is important to stabilize the process and minimize the variability of the responses. The aim of this research is to investigate the influence of the main process parameters on the filling behaviour, the dimensional accuracy and the cavity pressure when a micro-plate is manufactured by biomaterials such as PLA and PCL. Methodology or Experimental Procedure: The specimens are manufactured using a Sonorus 1G Ultrasound Micro Molding Machine. The used geometry is a rectangular micro-plate of 15x5mm and 1mm of thickness. The materials used for the investigation are PLA and PCL due to biocompatible and degradation properties. The experimentation is divided into two phases. Firstly, the influence of process parameters (vibration amplitude, sonotrodo velocity, ultrasound time and compaction force) on filling behavior is analysed, in Phase 1. Next, when filling cavity is assured, the influence of both cooling time and force compaction on the cavity pressure, part temperature and dimensional accuracy is instigated, which is done in Phase. Results and Discussion: Filling behavior depends on sonotrodo velocity and vibration amplitude. When the ultrasonic time is higher, more ultrasonic energy is applied and the polymer temperature increases. Depending on the cooling time, it is possible that when mold is opened, the micro-plate temperature is too warm. Consequently, the polymer relieve its stored internal energy (ultrasonic and thermal) expanding through the easier direction. This fact is reflected on dimensional accuracy, causing micro-plates thicker than the mold. It has also been observed the most important fact that affects cavity pressure is the compaction configuration during the manufacturing cycle. Conclusions: This research demonstrated the influence of process parameters on the final micro-plated manufactured. Future works will be focused in manufacturing other geometries and analysing the mechanical properties of the specimens.

Keywords: biomaterial, biopolymer, micro injection molding, ultrasound

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