Search results for: damage prediction models

Authors: Hossein Pourbashash

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Recent experimental studies have shown that HIV can be transmitted directly from cell to cell when structures called virological synapses form during interactions between T cells. In this article, we describe a new within-host model of HIV infection that incorporates two mechanisms: infection by free virions and the direct cell-to-cell transmission. We conduct the local and global stability analysis of the model. We show that if the basic reproduction number R0 1, the virus is cleared and the disease dies out; if R0 > 1, the virus persists in the host. We also prove that the unique positive equilibrium attracts all positive solutions under additional assumptions on the parameters.

Keywords: HIV virus model, cell-to-cell transmission, global stability, Lyapunov function, second compound matrices

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Authors: Marcela Dávalos López

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In Mexico, the distribution of hygienistarchitecture during the 20th century had different rhythms. The culmination of the urban hygiene system (from sewers to showers, passing through garbage collection) forced neighbors and citizens to participate in the common welfare. This turned the urban references and dissociated the ways of living and led to comfort and health. However, the contrast between two Mexicancities, Cuernavaca and Mexico City shows us very different cultural practices regarding the use of hygienicarchitectures: in the first, thenature of its deepravines marked the destiny of residential architecture, while in Mexico City, state participation alteredthelandscape and homogenized the architectural models of domestic and intímate spaces.

Keywords: Cultural Practices, Dissociated Ways To Comfort, Hygiene Architecture , Mexico

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Authors: Mehdi Moayyedian, Kazem Abhary, Romeo Marian

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This paper presents an evaluation of a plastic defect in injection molding before it occurs in the process; it is known as the short shot defect. The evaluation of different parameters which affect the possibility of short shot defect is the aim of this paper. The analysis of short shot possibility is conducted via SolidWorks Plastics and Taguchi method to determine the most significant parameters. Finite Element Method (FEM) is employed to analyze two circular flat polypropylene plates of 1 mm thickness. Filling time, part cooling time, pressure holding time, melt temperature and gate type are chosen as process and geometric parameters, respectively. A methodology is presented herein to predict the possibility of the short-shot occurrence. The analysis determined melt temperature is the most influential parameter affecting the possibility of short shot defect with a contribution of 74.25%, and filling time with a contribution of 22%, followed by gate type with a contribution of 3.69%. It was also determined the optimum level of each parameter leading to a reduction in the possibility of short shot are gate type at level 1, filling time at level 3 and melt temperature at level 3. Finally, the most significant parameters affecting the possibility of short shot were determined to be melt temperature, filling time, and gate type.

Keywords: injection molding, plastic defects, short shot, Taguchi method

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Authors: Gehan A. Hegazy, Hesham N. Mustafa, Sally A. El Awdan, Marawan AbdelBaset

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Doxorubicin (DOX) is a chemotherapeutic agent used for the treatment of different cancers and its clinical usage is hindered by the oxidative injury-related cardiotoxicity. This work aims to declare if the harmful effects of DOX on the heart can be alleviated with the use of Coenzyme Q10 (CoQ10) or L-carnitine. The study was performed on seventy-two female Wistar albino rats divided into six groups, 12 animals each: Control group; DOX group (10 mg/kg); CoQ10 group (200 mg/kg); L-carnitine group (100 mg/kg); DOX + CoQ10 group; DOX + L-carnitine group. CoQ10 and L-carnitine treatment orally started five days before a single dose of 10 mg/kg DOX that injected intraperitoneally (IP) then the treatment continued for ten days. At the end of the study, serum biochemical parameters of cardiac damage, oxidative stress indices, and histopathological changes were investigated. CoQ10 or L-carnitine showed noticeable effects in improving cardiac functions evidenced reducing serum enzymes as serum interleukin-1 beta (IL-1), tumor necrosis factor alpha (TNF-), leptin, lactate dehydrogenase (LDH), Cardiotrophin-1, Troponin-I and Troponin-T. Also, alleviate oxidative stress, decrease of cardiac Malondialdehyde (MDA), Nitric oxide (NO) and restoring cardiac reduced glutathione levels to normal levels. Both corrected the cardiac alterations histologically and ultrastructurally. With visible improvements in -SMA, vimentin and eNOS immunohistochemical markers. CoQ10 or L-carnitine supplementation improves the functional and structural integrity of the myocardium.

Keywords: CoQ10, doxorubicin, L-Carnitine, cardiotoxicity

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Authors: Harshit Agrawal, Salman Muhammad

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Degradation to reinforced concrete (RC), primarily due to corrosion of embedded reinforcement, has been a major cause of concern worldwide. Among several ways to control corrosion, the use of coated reinforcement has gained significant interest in field applications. However, the choice of proper coating material and the effect of damage over coating are yet to be addressed for effective application of coated reinforcements. The present study aims to investigate and compare the performance of three different types of coated reinforcements —Fusion-Bonded Epoxy Coating (FBEC), Cement Polymer Composite Coating (CPCC), and Dual Zinc-Epoxy Coating (DZEC) —in concrete structures. The aim is to assess their corrosion resistance, durability, and overall effectiveness as coated reinforcement materials both in undamaged and simulated damaged conditions. Through accelerated corrosion tests, electrochemical analysis, and exposure to aggressive marine environments, the study evaluates the long-term performance of each coating system. This research serves as a crucial guide for engineers and construction professionals in selecting the most suitable corrosion protection for reinforced concrete, thereby enhancing the durability and sustainability of infrastructure.

Keywords: corrosion, reinforced concrete, coated reinforcement, seawater exposure, electrochemical analysis, service life, corrosion prevention

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Authors: Esha Banerjee, Atul Kumar Ojha, Girish Nath Jha

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This study aims to develop an intonation labeled database for Hindi. Although no single standard for prosody labeling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behavior of prosody patterns in Hindi. Based on such existing research and largely agreed upon intonational theories in Hindi, this study attempts to develop a manually annotated prosodic corpus of Hindi speech data, which can be used for training speech models for natural-sounding speech in the future. 100 sentences ( 500 words) each for declarative and interrogative types have been labeled using Praat.

Keywords: speech dataset, Hindi, intonation, labeled corpus

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Authors: Laila Oubahman, Duleba Szabolcs

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Public transport stakeholders aim to increase the ridership ratio by encouraging citizens to use common transportation modes. For this sight, improving service quality is a crucial option to reach the quality desired by users and reduce the gap between desired and perceived quality. Multi-criteria decision aid has been applied in literature in recent decades because it provides efficient models to assess the most impacting criteria on the overall assessment. In this paper, the PROMETHEE method is combined with the best-worst approach to construct a consensual model that avoids rank reversal to support stakeholders in ameliorating service quality.

Keywords: best-worst method, MCDA, PROMETHEE, public transport

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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: Olfat A. Mohamed

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Rhamnolipids biosurfactants have distinct properties given them importance in many industrial applications, especially their great new future applications in cosmetic and pharmaceutical industries. These applications have encouraged the search for diverse and renewable resources to control the cost of production. The experimental results were then applied to find a suitable mathematical model for obtaining the design criteria of the batch bioreactor. This research aims to produce Rhamnolipids from different oily wastewater sources such as petroleum crude oil (PO) and vegetable oil (VO) by using Pseudomonas aeruginosa ATCC 9027. Different concentrations of the PO and the VO are added to the media broth separately are in arrangement (0.5 1, 1.5, 2, 2.5 % v/v) and (2, 4, 6, 8 and 10%v/v). The effect of the initial concentration of oil residues and the addition of glycerol and palmitic acid was investigated as an inducer in the production of rhamnolipid and the surface tension of the broth. It was found that 2% of the waste (PO) and 6% of the waste (VO) was the best initial substrate concentration for the production of rhamnolipids (2.71, 5.01 g rhamnolipid/l) as arrangement. Addition of glycerol (10-20% v glycerol/v PO) to the 2% PO fermentation broth led to increase the rhamnolipid production (about 1.8-2 times fold). However, the addition of palmitic acid (5 and 10 g/l) to fermentation broth contained 6% VO rarely enhanced the production rate. The experimental data for 2% initially (PO) was used to estimate the various kinetic parameters. The following results were obtained, maximum rate or velocity of reaction (Vmax) = 0.06417 g/l.hr), yield of cell weight per unit weight of substrate utilized (Yx/s = 0.324 g Cx/g Cs) maximum specific growth rate (μmax = 0.05791 hr⁻¹), yield of rhamnolipid weight per unit weight of substrate utilized (Yp/s)=0.2571gCp/g Cs), maintenance coefficient (Ms =0.002419), Michaelis-Menten constant, (Km=6.1237 gmol/l), endogenous decay coefficient (Kd=0.002375 hr⁻¹). Predictive parameters and advanced mathematical models were applied to evaluate the time of the batch bioreactor. The results were as follows: 123.37, 129 and 139.3 hours in respect of microbial biomass, substrate and product concentration, respectively compared with experimental batch time of 120 hours in all cases. The expected mathematical models are compatible with the laboratory results and can, therefore, be considered as tools for expressing the actual system.

Keywords: batch bioreactor design, glycerol, kinetic parameters, petroleum crude oil, Pseudomonas aeruginosa, rhamnolipids biosurfactants, vegetable oil

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Authors: K. R. Sultana, K. Pope, Y. S. Muzychka

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In recent years, the research of heat transfer or phase change phenomena of liquid water droplets experiences a growing interest in aircraft icing, power transmission line icing, marine icing and wind turbine icing applications. This growing interest speeding up the research from single to multiple droplet phenomena. Impingements of multiple droplets and the resulting solidification phenomena after impact on a very cold surface is computationally studied in this paper. The model used in the current study solves the flow equation, composed of energy balance and the volume fraction equations. The main aim of the study is to investigate the effects of several thermo-physical properties (density, thermal conductivity and specific heat) on droplets freezing. The outcome is examined by various important factors, for instance, liquid fraction, total freezing time, droplet temperature and total heat transfer rate in the interface region. The liquid fraction helps to understand the complete phase change phenomena during solidification. Temperature distribution and heat transfer rate help to demonstrate the overall thermal exchange behaviors between the droplets and substrate surface. Findings of this research provide an important technical achievement for ice modeling and prediction studies.

Keywords: droplets, CFD, thermos-physical properties, solidification

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Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

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The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

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Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

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Authors: Hugo Daneluzzo, Christelle Rabbat, Alan Jean-Marie

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Water electrolysis is one of the most advanced technologies for producing hydrogen and can be easily combined with electricity from different sources. Under the influence of electric current, water molecules can be split into oxygen and hydrogen. The production of hydrogen by water electrolysis favors the integration of renewable energy sources into the energy mix by compensating for their intermittence through the storage of the energy produced when production exceeds demand and its release during off-peak production periods. Among the various electrolysis technologies, anion exchange membrane (AEM) electrolyser cells are emerging as a reliable technology for water electrolysis. Modeling and simulation are effective tools to save time, money, and effort during the optimization of operating conditions and the investigation of the design. The modeling and simulation become even more important when dealing with multiphysics dynamic systems. One of those systems is the AEM electrolysis cell involving complex physico-chemical reactions. Once developed, models may be utilized to comprehend the mechanisms to control and detect flaws in the systems. Several modeling methods have been initiated by scientists. These methods can be separated into two main approaches, namely equation-based modeling and graph-based modeling. The former approach is less user-friendly and difficult to update as it is based on ordinary or partial differential equations to represent the systems. However, the latter approach is more user-friendly and allows a clear representation of physical phenomena. In this case, the system is depicted by connecting subsystems, so-called blocks, through ports based on their physical interactions, hence being suitable for multiphysics systems. Among the graphical modelling methods, the bond graph is receiving increasing attention as being domain-independent and relying on the energy exchange between the components of the system. At present, few studies have investigated the modelling of AEM systems. A mathematical model and a bond graph model were used in previous studies to model the electrolysis cell performance. In this study, experimental data from literature were simulated using OpenModelica using bond graphs and mathematical approaches. The polarization curves at different operating conditions obtained by both approaches were compared with experimental ones. It was stated that both models predicted satisfactorily the polarization curves with error margins lower than 2% for equation-based models and lower than 5% for the bond graph model. The activation polarization of hydrogen evolution reactions (HER) and oxygen evolution reactions (OER) were behind the voltage loss in the AEM electrolyzer, whereas ion conduction through the membrane resulted in the ohmic loss. Therefore, highly active electro-catalysts are required for both HER and OER while high-conductivity AEMs are needed for effectively lowering the ohmic losses. The bond graph simulation of the polarisation curve for operating conditions at various temperatures has illustrated that voltage increases with temperature owing to the technology of the membrane. Simulation of the polarisation curve can be tested virtually, hence resulting in reduced cost and time involved due to experimental testing and improved design optimization. Further improvements can be made by implementing the bond graph model in a real power-to-gas-to-power scenario.

Keywords: hydrogen production, anion-exchange membrane, electrolyzer, mathematical modeling, multiphysics modeling

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Authors: Ozcan Yavas, Berrak Erol Nalbur, Sermin Gunarslan

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A materiality matrix is a tool that organizations use to prioritize their activities and adapt to the increasing sustainability requirements in recent years. For the materiality index to move from business models to the sustainability business model stage, it must be done with all partners in the raw material, supply, production, product, and end-of-life product stages. Within the scope of this study, the Materiality Matrix was used to transform the business model into a sustainability business model and to create a sustainability roadmap in a factory producing electric buses. This matrix determines the necessary roadmap for all stakeholders to participate in the process, especially in sectors that produce sustainable products, such as the electric vehicle sector, and to act together with the cradle-to-cradle approach of sustainability roadmaps. Global Reporting Initiative analysis was used in the study conducted with 1150 stakeholders within the scope of the study, and 43 questions were asked to the stakeholders under the main headings of 'Legal Compliance Level,' 'Environmental Strategies,' 'Risk Management Activities,' 'Impact of Sustainability Activities on Products and Services,' 'Corporate Culture,' 'Responsible and Profitable Business Model Practices' and 'Achievements in Leading the Sector' and Economic, Governance, Environment, Social and Other. The results of the study aimed to include five 1st priority issues and four 2nd priority issues in the sustainability strategies of the organization in the short and medium term. When the studies carried out in the short term are evaluated in terms of Sustainability and Environmental Risk Management, it is seen that the studies are still limited to the level of legal legislation (60%) and individual studies in line with the strategies (20%). At the same time, the stakeholders expect the company to integrate sustainability activities into its business model within five years (35%) and to carry out projects to become the first company that comes to mind with its success leading the sector (20%). Another result obtained within the study's scope is identifying barriers to implementation. It is seen that the most critical obstacles identified by stakeholders with climate change and environmental impacts are financial deficiency and lack of infrastructure in the dissemination of sustainable products. These studies are critical for transitioning to sustainable business models for the electric vehicle sector to achieve the EU Green Deal and CBAM targets.

Keywords: sustainability business model, materiality matrix, electricity bus, carbon neutrality, sustainability management

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Authors: Hamza Javar Magnier, Robin Curtis

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There have been rapid advances in the upstream processing of protein therapeutics, which has shifted the bottleneck to downstream purification and formulation. Finding liquid formulations with shelf lives of up to two years is increasingly difficult for some of the newer therapeutics, which have been engineered for activity, but their formulations are often viscous, can phase separate, and have a high propensity for irreversible aggregation1. We explore means to develop improved predictive ability from a better understanding of how protein-protein interactions on formulation conditions (pH, ionic strength, buffer type, presence of excipients) and how these impact upon the initial steps in protein self-association and aggregation. In this work, we study the initial steps in the aggregation pathways using a minimal protein model based on square-well potentials and discontinuous molecular dynamics. The effect of model parameters, including range of interaction, stiffness, chain length, and chain sequence, implies that protein models fold according to various pathways. By reducing the range of interactions, the folding- and collapse- transition come together, and follow a single-step folding pathway from the denatured to the native state2. After parameterizing the model interaction-parameters, we developed an understanding of low-temperature conformational properties and fluctuations, and the correlation to the folding transition of proteins in isolation. The model fluctuations increase with temperature. We observe a low-temperature point, below which large fluctuations are frozen out. This implies that fluctuations at low-temperature can be correlated to the folding transition at the melting temperature. Because proteins “breath” at low temperatures, defining a native-state as a single structure with conserved contacts and a fixed three-dimensional structure is misleading. Rather, we introduce a new definition of a native-state ensemble based on our understanding of the core conservation, which takes into account the native fluctuations at low temperatures. This approach permits the study of a large range of length and time scales needed to link the molecular interactions to the macroscopically observed behaviour. In addition, these models studied are parameterized by fitting to experimentally observed protein-protein interactions characterized in terms of osmotic second virial coefficients.

Keywords: protein folding, native-ensemble, conformational fluctuation, aggregation

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Authors: Swanand Patil, Pankaj Agarwal

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In past few decades, seismic performance objectives have shifted from earthquake resistance to earthquake resilience of the structures, especially for the lifeline buildings. Features such as negligible post-earthquake damage and replaceable damaged components, makes energy dissipating systems a valid choice for a seismically resilient building. In this study, various energy dissipation devices are applied on an eight-storey moment resisting RC building model. The energy dissipating devices include both hysteresis-based and viscous type of devices. The seismic response of the building is obtained for different positioning and mechanical properties of the devices. The investigation is carried forward to the deficiently ductile RC frame also. The performance assessment is done on the basis of drift ratio, mode shapes and displacement response of the model structures. Nonlinear dynamic analysis shows largely improved displacement response. The damping devices improve displacement response more efficiently in the deficient ductile frames than that in the perfectly moment resisting frames. This finding is important considering the number of deficient buildings in India and the world. The placement and mechanical properties of the dampers prove to be a crucial part in modelling, analyzing and designing of the structures with supplemental energy dissipation.

Keywords: earthquake resilient structures, lifeline buildings, retrofitting of structures, supplemental energy dissipation

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Authors: Jerome Michael J. Sadullo, Jamaica Lois A. Torres, Trisha Muriel T. Valino

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In the Philippines, Central Luzon is one of the regions at high risk in terms of volcanic eruption. In fact, last June 15, 1991, which were the Mount Pinatubo has erupted, the most affected provinces were Zambales, Olangapo, Pampanga, Tarlac, Bataan, Bulacan and Nueva Ecija. During the Mount Pinatubo eruption, Castillejos, Zambales, has recorded the most significant damage to both commercial and residential structures. In this study, the researchers aim to determine and analyze the various impacts of ashfall on roof structures in Concepcion, Tarlac, during the event of a volcanic eruption. In able for the researcher to determine the sample size of the study, they have utilized Cochran's sample size formula. With the computed sample size, the researchers have gathered data through the distribution of survey forms, utilizing public records, and picture documentation of different roof structures in Concepcion, Tarlac. With the data collected, Chi-squared goodness of fit was done by the researcher in order to compare the data collected from the observed N (Concepcion, Tarlac) and expected N (Castillejos, Zambales). The results showed that when it comes to the roof constructions material used in Concepcion, Tarlac and Castillejos, Zambales. Structures in Concepcion, Tarlac were most likely to suffer worse when another eruption happens compared to the structures in Castillejos, Zambales. Yet, considering the current structural statuses of structure in Concepcion Tarlac and its location from Mount Pinatubo, they are less likely to experience ashfall.

Keywords: risk assessment, Concepcion, Tarlac, Volcano Pinatubo, roof structures, ashfall

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Authors: M. Mokhber, J. Hedayati

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From the managerial perspective, it is important to devise an operational plan based on top priorities due to limited resources, diversity of measures and high costs needed to improve safety in infrastructure. Dealing with the high-risk corridors across Iran, this study prioritized the corridors according to statistical data on accidents involving fatalities, injury or damage over three consecutive years. In collaboration with the Iranian Police Department, data were collected and modified. Then, the prioritization criteria were specified based on the expertise opinions and international standards. In this study, the prioritization criteria included accident severity and accident density. Finally, the criteria were standardized and weighted (equal weights) to score each high-risk corridor. The prioritization phase involved the scoring and weighting procedure. The high-risk corridors were divided into twelve groups out of 50. The results of data analysis for a three-year span suggested that the first three groups (150 corridors) along with a quarter of Iranian road network length account for nearly 60% of traffic accidents. In the next step, according to variables including weather conditions particular roads for the purpose of winter safety measures were extracted from the abovementioned categories. According to the results ranking, ‎‏9‏‎ roads with the overall ‎length of about ‎‎‏1000‏‎ Km of high-risk corridors are considered as preferences of ‎safety measures‎.

Keywords: high-risk corridors, HRCs, road safety rating, road scoring, winter safety measures

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Authors: S. R. Dehghani, G. F. Naterer, Y. S. Muzychka

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Wave-impact sea spray creates many droplets which form a spray cloud traveling over marine objects same as marine vessels and offshore structures. In cold climates such as Arctic reigns, sea spray icing, which is ice accretion on cold substrates, is strongly dependent on the wave-impact sea spray. The rate of cooling of droplets affects the process of icing that can yield to dry or wet ice accretion. Trajectories of droplets determine the potential places for ice accretion. Combining two models of trajectories and heat transfer for droplets can predict the risk of ice accretion reasonably. The majority of the cooling of droplets is because of droplet evaporations. In this study, a combined model using trajectory and heat transfer evaluate the situation of a cloud of spray from the generation to impingement. The model uses some known geometry and initial information from the previous case studies. The 3D model is solved numerically using a standard numerical scheme. Droplets are generated in various size ranges from 7 mm to 0.07 mm which is a suggested range for sea spray icing. The initial temperature of droplets is considered to be the sea water temperature. Wind velocities are assumed same as that of the field observations. Evaluations are conducted using some important heading angles and wind velocities. The characteristic of size-velocity dependence is used to establish a relation between initial sizes and velocities of droplets. Time intervals are chosen properly to maintain a stable and fast numerical solution. A statistical process is conducted to evaluate the probability of expected occurrences. The medium size droplets can reach the highest heights. Very small and very large droplets are limited to lower heights. Results show that higher initial velocities create the most expanded cloud of spray. Wind velocities affect the extent of the spray cloud. The rate of droplet cooling at the start of spray formation is higher than the rest of the process. This is because of higher relative velocities and also higher temperature differences. The amount of water delivery and overall temperature for some sample surfaces over a marine vessel are calculated. Comparing results and some field observations show that the model works accurately. This model is suggested as a primary model for ice accretion on marine vessels.

Keywords: evaporation, sea spray, marine icing, numerical solution, trajectory

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Authors: Melisa Mete, Gary Davies, Susan Whelan

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Maintaining a good employer brand image is crucial for companies since it has numerous advantages such as better recruitment, retention and employee engagement, and commitment. This study aims to understand the relationship between employer brand image and employee satisfaction and engagement in the British context. A panel survey data (N=228) is tested via the regression models from the Hayes (2012) PROCESS macro, in IBM SPSS 23.0. The results are statistically significant and proves that the more positive employer brand image, the greater employee’ engagement and satisfaction, and the greater is employee satisfaction, the greater their engagement.

Keywords: employer brand, employer brand image, employee engagement, employee satisfaction

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Authors: Muhammad Saad Aslam

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In 2021, our organization has launched our proprietary mobile App i.e. Farm Intelligence platform, an industrial-first precision agriculture solution, to Pakistan. It was piloted at 47 locations (spanning around 1,200 hectares of land), addressing growers’ pain points by bringing the benefits of precision agriculture to their doorsteps. This year, we have extended its reach by more than 10 times (nearly 130,000 hectares of land) in almost 600 locations across the country. The project team selected highly infested areas to set up traps, which then enabled the sales team to initiate evidence-based conversations with the grower community about preventive crop protection products that includes pesticides and insecticides. Mega farmer meeting field visits and demonstrations plots coupled with extensive marketing activities, were setup to include farmer community. With the help of App real-time pest monitoring (using heat maps and infestation prediction through predictive analytics) we have equipped our growers with on spot insights that will help them optimize pesticide applications. Heat maps allow growers to identify infestation hot spots to fine-tune pesticide delivery, while predictive analytics enable preventive application of pesticides before the situation escalates. Ultimately, they empower growers to keep their crops safe for a healthy harvest.

Keywords: precision pest management, precision agriculture, real time pest tracking, pest forecasting

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Laura Simmer, Maria Kalt, Oliver Schauer

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Freight transport is growing fast, but many vehicles are empty or just partially loaded. The vision and concepts of the Physical Internet (PI) proposes to eliminate these inefficiencies. Aiming for a radical sustainability improvement, the PI – inspired by the Digital Internet – is a hyperconnected global logistic system, enabling seamless asset sharing and flow consolidation. The implementation of a PI in its full expression will be a huge challenge: the industry needs innovation and implementation support including change management approaches, awareness creation and good practices diffusion, legislative actions to remove antitrust and international commerce barriers, standardization and public incentives policies. In order to take a step closer to this future the project ‘Atropine - Fast Track to the Physical Internet’ funded under the Strategic Economic and Research Program ‘Innovative Upper Austria 2020’ was set up. The two-year research project unites several research partners in this field, but also industrial partners and logistics service providers. With Atropine, the consortium wants to actively shape the mobility landscape in Upper Austria and make an innovative contribution to an energy-efficient, environmentally sound and sustainable development in the transport area. This paper should, on the one hand, clarify the questions what the project Atropine is about and, on the other hand, how a proof of concept will be reached. Awareness building plays an important role in the project as the PI requires a reorganization of the supply chain and the design of completely new forms of inter-company co-operation. New business models have to be developed and should be verified by simulation. After the simulation process one of these business models will be chosen and tested in real life with the partner companies. The developed results - simulation model and demonstrator - are used to determine how the concept of the PI can be applied in Upper Austria. Atropine shall pave the way for a full-scale development of the PI vision in the next few decades and provide the basis for pushing the industry toward a new level of co-operation with more shared resources and increased standardization.

Keywords: Atropine, inter-company co-operation, Physical Internet, shared resources, sustainable logistics

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Authors: H. Mohit

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In automotive air conditioning system, two heat exchangers are used as evaporator and condenser which are placed inside the bonnet of a car in a compact manner. The dust particles from outside and moisture content produced during the process leads to formation of impure particles on the surface of evaporator coil. But in condenser coil, the impure particles are settling down due to dust from atmosphere. The major problem of the heat exchanger used in automotive air conditioning is leakage of refrigerant due to corrosion. This effect of corrosion will lead to damage on the surface of heat exchanger and leakage of refrigerant from the system. To protect from corrosion, coatings are applied on its surfaces. Nowadays, to improve the corrosion resistance of these heat exchangers, hydrophilic coatings are used, which is very expensive. Cryogenic treatment is one method which involves the treatment of materials below -150 °C using the cryogenic fluid such as liquid nitrogen. In this project work, a study of improvement in corrosion resistance of materials of aluminium alloys of various grades as AA 1100, AA 6061, AA 6063 and AA 2024 that are mainly used for fin and tube heat exchangers in automotive air conditioning system is made. In total, five different processes are selected for these grades of aluminium alloy and various parameters like corrosion rate, dimensional stability, hardness and microstructure are measured. The improvements were observed in these parameters while comparing it with conventional heat treatment process.

Keywords: cryogenic treatment, corrosion resistance, dimensional stability, materials science

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Authors: Natasha Sharma, Kulbhushan Agnihotri

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Cellular Automata are discrete dynamical systems which are based on local character and spatial disparateness of the spreading process. These factors are generally neglected by traditional models based on differential equations for epidemic spread. The aim of this work is to introduce an SEIR model based on cellular automata on graphs to imitate epidemic spreading. Distinctively, it is an SEIR-type model where the population is divided into susceptible, exposed, infected and recovered individuals. The results obtained from simulations are in accordance with the spreading behavior of a real time epidemics.

Keywords: cellular automata, epidemic spread, graph, susceptible

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Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani

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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.

Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation

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Authors: Juan Sepúlveda

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Energy is a fundamental component in sustainability, the access and use of this resource is related with economic growth, social improvements, and environmental impacts. In this sense, energy efficiency has been studied as a factor that enhances the positive impacts of energy in communities; however, the implementation of efficiency requires strong policy and strategies that usually rely on individual measures focused in independent dimensions. In this paper, the problem of energy efficiency as a multi-objective problem is studied, using scientometric analysis to discover trends and patterns that allow to identify the main variables and study approximations related with a further development of models to integrate energy efficiency and MCA into policy making for small communities.

Keywords: energy efficiency, MCA, scientometric, trends

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Authors: Muhammad Randhawa, Muhammad Nadeem

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Guava (Psidium guajava L.) is a vital source of minerals, vitamins, dietary fiber and antioxidants. Owing to highly perishable nature and proning towards chilling injury, diseases, insect-pests and physical damage the main drawbacks of guava after harvesting, present study was designed. Due to its delicacy in physiology, economic importance, effects of pre and postharvest factors and maturity indices, guava fruits should be given prime importance for good quality attributes. In this study guava fruits were stored at 10°C with 80% relative humidity after treating with different levels of sulphate salt of magnesium followed by dipping in cellulose based edible coating hydroxy propyl methyl cellulose (HPMC). The main objective of this coating was to enhance the shelf life of guava by inhibiting the respiration and also by binding the dissolved solids with salt application. Characterization for quality attributes including physical, physiological and bio chemical analysis was performed after every 7 days interval till the fruit remains edible during the storage period of 4 weeks. Finally, data obtained was subjected to statistical analysis. It was concluded on statistical basis that Surahi variety (treated with 5% MgSO4) showed best storage stability and kept its original quality up to almost 23 days during storage.

Keywords: edible coating, guava cultivars, physicochemical attributes, storage

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Authors: Reza Akbari, Jalal MontazeriFashtali, PeymanMomeni Taromsari

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Pipeline systems, play an important role as a vital element in reducing or increasing the risk of earthquake damage and vulnerability. Pipelines are suitable, cheap, fast, and safe routes for transporting oil, gas, water, sewage, etc. The sepipelines must pass from a wide geographical area; hence they will structurally face different environmental and underground factors of earthquake forces’ effect. Therefore, structural engineering analysis and design for this type of lines requires the understanding of relevant parameters behavior and lack of familiarity with them can cause irreparable damages and risks to design and execution, especially in the face of earthquakes. Today, buried pipelines play an important role in human life cycle, thus, studying the vulnerability of pipeline systems is of particular importance. This study examines the behavior of buried pipelines affected by strike-slip fault. Studied fault is perpendicular to the tube axis and causes stress and deformation in the tube by sliding horizontally. In this study, the pipe-soil interaction is accurately simulated, so that one can examine the large displacements and strains, nonlinear material behavior and contact and friction conditions of soil and pipe. The results can be used for designing buried pipes and determining the amount of fault displacement that causes the failure of the buried pipes.

Keywords: pipe lines , earthquake , fault , soil-fault interaction

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Authors: Francesca Spagnoli, Shenja van der Graaf, Pieter Ballon

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Humans do not behave rationally. We are emotional, easily influenced by others, as well as by our context. The study of human behaviour became a supreme endeavour within many academic disciplines, including economics, sociology, and clinical and social psychology. Understanding what motivates humans and triggers them to perform certain activities, and what it takes to change their behaviour, is central both for researchers and companies, as well as policy makers to implement efficient public policies. While numerous theoretical approaches for diverse domains such as health, retail, environment have been developed, the methodological models guiding the evaluation of such research have reached for a long time their limits. Within this context, digitisation, the Information and communication technologies (ICT) and wearable, the Internet of Things (IoT) connecting networks of devices, and new possibilities to collect and analyse massive amounts of data made it possible to study behaviour from a realistic perspective, as never before. Digital technologies make it possible to (1) capture data in real-life settings, (2) regain control over data by capturing the context of behaviour, and (3) analyse huge set of information through continuous measurement. Within this complex context, this paper describes a new framework for initiating behavioural change, capitalising on the digital developments in applied research projects and applicable both to academia, enterprises and policy makers. By applying this model, behavioural research can be conducted to address the issues of different domains, such as mobility, environment, health or media. The Modular Behavioural Analysis Approach (MBAA) is here described and firstly validated through a concrete use case within the domain of health. The results gathered have proven that disclosing information about health in connection with the use of digital apps for health, can be a leverage for changing behaviour, but it is only a first component requiring further follow-up actions. To this end, a clear definition of different 'behavioural profiles', towards which addressing several typologies of interventions, it is essential to effectively enable behavioural change. In the refined version of the MBAA a strong focus will rely on defining a methodology for shaping 'behavioural profiles' and related interventions, as well as the evaluation of side-effects on the creation of new business models and sustainability plans.

Keywords: behavioural change, framework, health, nudging, sustainability

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