Search results for: brute force feature extraction

Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

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Authors: Anderson Rocha-Buelvas, Jaqueline Mena Huertas, Edith Burbano Rosero, Arsenio Hidalgo Troya, Mauricio Casas Cruz

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COVID-19 is a disruptive pandemic for the public health and economic system of whole countries, including Colombia. Nariño Department is the southwest of the country and draws attention to being on the border with Ecuador, constantly facing demographic transition affecting infections between countries. In Nariño, the early routine diagnosis of SARS-CoV-2, which can be handled at BSL-2, has affected the transmission dynamics of COVID-19. However, new emerging and re-emerging viruses with biological flexibility classified as a Risk Group 3 agent can take advantage of epidemiological opportunities, generating the need to increase clinical diagnosis, mainly in border regions between countries. The overall objective of this project was to assure the quality of the analytical process in the diagnosis of high biological risk pathogens in Nariño by building a laboratory that includes biosafety level (BSL)-2 and (BSL)-3 containment zones. The delimitation of zones was carried out according to the Verification Tool of the National Health Institute of Colombia and following the standard requirements for the competence of testing and calibration laboratories of the International Organization for Standardization. This is achieved by harmonization of methods and equipment for effective and durable diagnostics of the large-scale spread of highly pathogenic microorganisms, employing negative-pressure containment systems and UV Systems in accordance with a finely controlled electrical system and PCR systems as new diagnostic tools. That increases laboratory capacity. Protection in BSL-3 zones will separate the handling of potentially infectious aerosols within the laboratory from the community and the environment. It will also allow the handling and inactivation of samples with suspected pathogens and the extraction of molecular material from them, allowing research with pathogens with high risks, such as SARS-CoV-2, Influenza, and syncytial virus, and malaria, among others. The diagnosis of these pathogens will be articulated across the spectrum of basic, applied, and translational research that could receive about 60 daily samples. It is expected that this project will be articulated with the health policies of neighboring countries to increase research capacity.

Keywords: medical laboratory science, SARS-CoV-2, public health surveillance, Colombia

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Authors: Johannes Barlang

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Adhesive bonding plays a pivotal role in various industrial applications, ranging from automotive manufacturing to aerospace engineering. The peel strength of adhesives, a critical parameter reflecting the ability of an adhesive to withstand external forces, is crucial for ensuring the integrity and durability of bonded joints. This study provides a synopsis of the methodologies, influencing factors, and significance of peel testing in the evaluation of adhesive performance. Peel testing involves the measurement of the force required to separate two bonded substrates under controlled conditions. This study systematically reviews the different testing techniques commonly applied in peel testing, including the widely used 180-degree peel test and the T-peel test. Emphasis is placed on the importance of selecting an appropriate testing method based on the specific characteristics of the adhesive and the application requirements. The influencing factors on peel strength are multifaceted, encompassing adhesive properties, substrate characteristics, environmental conditions, and test parameters. Through an in-depth analysis, this study explores how factors such as adhesive formulation, surface preparation, temperature, and peel rate can significantly impact the peel strength of adhesively bonded joints. Understanding these factors is essential for optimizing adhesive selection and application processes in real-world scenarios. Furthermore, the study highlights the role of peel testing in quality control and assurance, aiding manufacturers in maintaining consistent adhesive performance and ensuring the reliability of bonded structures. The correlation between peel strength and long-term durability is discussed, shedding light on the predictive capabilities of peel testing in assessing the service life of adhesive bonds. In conclusion, this study underscores the significance of peel testing as a fundamental tool for characterizing adhesive performance. By delving into testing methodologies, influencing factors, and practical implications, this study contributes to the broader understanding of adhesive behavior and fosters advancements in adhesive technology across diverse industrial sectors.

Keywords: adhesively bonded joints, cleavage resistance, elastic adhesives, peel strength

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Authors: T. Sarat Chandra, M. Maneesh Kumar, S. N. Mudliar, V. S. Chauhan, S. Mukherji, R. Sarada

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The life cycle assessment (LCA) of biodiesel production from freshwater microalgae Scenedesmus dimorphus cultivated in open raceway pond is performed. Various scenarios for biodiesel production were simulated using primary and secondary data. The parameters varied in the modelled scenarios were related to biomass productivity, mode of culture mixing and type of energy source. The process steps included algae cultivation in open raceway ponds, harvesting by chemical flocculation, dewatering by mechanical drying option (MDO) followed by extraction, reaction and purification. Anaerobic digestion of defatted algal biomass (DAB) for biogas generation is considered as a co-product allocation and the energy derived from DAB was thereby used in the upstream of the process. The scenarios were analysed for energy demand, emissions and environmental impacts within the boundary conditions grounded on "cradle to gate" inventory. Across all the Scenarios, cultivation via raceway pond was observed to be energy intensive process. The mode of culture mixing and biomass productivity determined the energy requirements of the cultivation step. Emissions to Freshwater were found to be maximum contributing to 93-97% of total emissions in all the scenarios. Global warming potential (GWP) was the found to be major environmental impact accounting to about 99% of total environmental impacts in all the modelled scenarios. It was noticed that overall emissions and impacts were directly related to energy demand and an inverse relationship was observed with biomass productivity. The geographic location of an energy source affected the environmental impact of a given process. The integration of defatted algal remnants derived electricity with the cultivation system resulted in a 2% reduction in overall energy demand. Direct biogas generation from microalgae post harvesting is also analysed. Energy surplus was observed after using part of the energy in upstream for biomass production. Results suggest biogas production from microalgae post harvesting as an environmentally viable and sustainable option compared to biodiesel production.

Keywords: biomass productivity, energy demand, energy source, Lifecycle Assessment (LCA), microalgae, open raceway pond

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Authors: Ahmed Auda, Manjree Agarwala, Giles Hardya, Yonglin Rena

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Botrytis cinerea is a major pest for many plants. It can attack a wide range of plant parts. It can attack buds, flowers, and leaves, stems, and fruit. However, B. cinerea can be mixed with other diseases that cause the same damage. There are many species of botrytis and more than one different strains of each. Botrytis might infect the foliage of nursery stock stored through winter in damp conditions. There are no known resistant plants. Botrytis must have nutrients or food source before it infests the plant. Nutrients leaking from wounded plant parts or dying tissue like old flower petals give the required nutrients. From this food, the fungus becomes more attackers and invades healthy tissue. Dark to light brown rot forms in the ill tissue. High humidity conditions support the growth of this fungus. However, we suppose that selection pressure can act on the morphological and neurophysiologic filter properties of the receiver and on both the biochemical and the physiological regulation of the signal. Communication is implied when signal and receiver evolves toward more and more specific matching, culminating. In other hand, receivers respond to portions of a body odor bouquet which is released to the environment not as an (intentional) signal but as an unavoidable consequence of metabolic activity or tissue damage. Each year Botrytis species can cause considerable economic losses to plant crops. Even with the application of strict quarantine and control measures, these fungi can still find their way into crops and cause the imposition of onerous restrictions on exports. Blueberry fruit mould caused by a fungal infection usually results in major losses during post-harvest storage. Therefore, the management of infection in early stages of disease development is necessary to minimize losses. The overall purpose of this study will develop sensitive, cheap, quick and robust diagnostic techniques for the detection of B. cinerea in blueberry. The specific aim was designed to investigate the performance of volatile organic compounds (VOCs) in the detection and discrimination of blueberry fruits infected by fungal pathogens with an emphasis on Botrytis in the early storage stage of post-harvest.

Keywords: botrytis cinerea, blueberry, GC/MS, VOCs

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This research is part of an interdisciplinary project, promoting the design of a light temporary installable textile defence system against flood. In case river water levels increase abruptly especially in winter time, one can expect massive extra load on a textile protective structure in term of impact as a result of floating debris and even tree trunks. Estimation of this impulsive force on such structures is of a great importance, as it can ensure the reliability of the design in critical cases. This fact provides the motivation for the numerical analysis of a fluid structure interaction application, comprising flexible slender shaped and free-surface water flow, where an accelerated heavy flotsam tends to approach the membrane. In this context, the analysis on both the behavior of the flexible membrane and its interaction with moving flotsam is conducted by finite elements based solvers of the explicit solver and implicit Abacus solver available as products of SIMULIA software. On the other hand, a study on how free surface water flow behaves in response to moving structures, has been investigated using the finite volume solver of Star CCM+ from Siemens PLM Software. An automatic communication tool (CSE, SIMULIA Co-Simulation Engine) and the implementation of an effective partitioned strategy in form of an implicit coupling algorithm makes it possible for partitioned domains to be interconnected powerfully. The applied procedure ensures stability and convergence in the solution of these complicated issues, albeit with high computational cost; however, the other complexity of this study stems from mesh criterion in the fluid domain, where the two structures approach each other. This contribution presents the approaches for the establishment of a convergent numerical solution and compares the results with experimental findings.

Keywords: co-simulation, flexible thin structure, fluid-structure interaction, implicit coupling algorithm, moving flotsam

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Authors: Ziwei Song, Junjun Fan, Jeremy Teo, Yang Yu, Yukun Ma, Jie Yan, Shupei Mo, Lisa Tucker-Kellogg, Peter So, Hanry Yu

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Liver is the center of detoxification and exposed to toxic metabolites all the time. It is highly regenerative after injury, with the ability to restore even after 70% partial hepatectomy. Most of the previous studies were using hepatectomy as injury models for liver regeneration study. There is limited understanding of small-scale liver injury, which can be caused by either low dose drug consumption or hepatocyte routine metabolism. Although these small in situ injuries do not cause immediate symptoms, repeated injuries will lead to aberrant wound healing in liver. Therefore, the cellular dynamics during liver regeneration is critical for our understanding of liver regeneration mechanism. We aim to study the liver regeneration of small-scale in situ liver injury in transgenic mice labeling actin (Lifeact-GFP). Previous studies have been using sample sections and biopsies of liver, which lack real-time information. In order to trace every individual hepatocyte during the regeneration process, we have developed and optimized an intravital imaging system that allows in vivo imaging of mouse liver for consecutive 5 days, allowing real-time cellular tracking and quantification of hepatocytes. We used femtosecond-laser ablation to make controlled and repeatable liver injury model, which mimics the real-life small in situ liver injury. This injury model is the first case of its kind for in vivo study on liver. We found that small-scale in situ liver injury is repaired by the coordination of hypertrophy and migration of hepatocytes. Hypertrophy is only transient at initial phase, while migration is the main driving force to complete the regeneration process. From cellular aspect, Akt/mTOR pathway is activated immediately after injury, which leads to transient hepatocyte hypertrophy. From mechano-sensing aspect, the actin cable, formed at apical surface of wound proximal hepatocytes, provides mechanical tension for hepatocyte migration. This study provides important information on both chemical and mechanical signals that promote liver regeneration of small in situ injury. We conclude that hypertrophy and migration play a dominant role at different stages of liver regeneration.

Keywords: hepatocyte, hypertrophy, intravital imaging, liver regeneration, migration

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Authors: Kruttika Bhuse

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Aim: Synergism between periodontists and orthodontists (periodontal accelerated osteogenic orthodontics- PAOO) creates crucial opportunities to enhance clinical outcomes of combined therapies regarding both disciplines and has made adult orthodontics a reality. Thus, understanding the biomechanics of bone remodelling may increase the clinical applications of corticotomy facilitated orthodontics with or without alveolar augmentation. Wilckodontics can be an attractive treatment option and be a “win-win” situation for both the dental surgeon and patient by reducing the orthodontic treatment time in adults. Materials and methods: In this review, data related to the clinical aspects, steps of procedure, biomechanics of bone, indications and contraindications and final outcome of wilckodontic shall be discussed. 50 supporting articles from various international journals and 70 clinical cases were reviewed to get a better understanding to design this wilckodontic - meta analysis. Various journals like the Journal Of Clinical And Diagnostic Research, Journal Of Indian Society Of Periodontology, Journal Of Periodontology, Pubmed, Boston Orthodontic University Journal, Good Practice Orthodontics Volume 2, have been referred to attain valuable information on wilckodontics which was then compiled in this single review study. Result: As a promising adjuvant technique based on the transient nature of demineralization-remineralisation process in healthy tissues, wilckodontics consists of regional acceleratory phenomenon by alveolar corticotomy and bone grafting of labial and palatal/lingual surfaces, followed by orthodontic force. The surgical wounding of alveolar bone potentiates tissue reorganization and healing by a way of transient burst of localized hard and soft tissue remodelling.This phenomenon causes bone healing to occur 10-50 times faster than normal bone turnover. Conclusion: This meta analysis helps understanding that the biomechanics of bone remodelling may increase the clinical applications of corticotomy facilitated orthodontics with or without alveolar augmentation. The main benefits being reduced orthodontic treatment time, increased bone volume and post-orthodontic stability.

Keywords: periodontal osteogenic accelerated orthodontics, alveolar corticotomy, bone augmentation, win-win situation

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Authors: Connor Dunlop, Bassim Abbassi, Richard G. Zytner

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Life cycle assessment (LCA) is a powerful tool established by the International Organization for Standardization (ISO) that can be used to assess the environmental impacts of a product or process from cradle to grave. Many studies utilize the LCA methodology within the site remediation field to compare various decontamination methods, including bioremediation, soil vapor extraction or excavation, and off-site disposal. However, with the authors' best knowledge, limited information is available in the literature on a sustainability tool that could be used to help with the selection of the optimal remediation technology. This tool, based on the LCA methodology, would consider site conditions like environmental, economic, and social impacts. Accordingly, this project was undertaken to develop a tool to assist with the selection of optimal sustainable technology. Developing a proper tool requires a large amount of data. As such, data was collected from previous LCA studies looking at site remediation technologies. This step identified knowledge gaps or limitations within project data. Next, utilizing the data obtained from the literature review and other organizations, an extensive LCA study is being completed following the ISO 14040 requirements. Initial technologies being compared include bioremediation, excavation with off-site disposal, and a no-remediation option for a generic gasoline-contaminated site. To complete the LCA study, the modelling software SimaPro is being utilized. A sensitivity analysis of the LCA results will also be incorporated to evaluate the impact on the overall results. Finally, the economic and social impacts associated with each option will then be reviewed to understand how they fluctuate at different sites. All the results will then be summarized, and an interactive tool using Excel will be developed to help select the best sustainable site remediation technology. Preliminary LCA results show improved sustainability for the decontamination of a gasoline-contaminated site for each technology compared to the no-remediation option. Sensitivity analyses are now being completed on on-site parameters to determine how the environmental impacts fluctuate at other contaminated gasoline locations as the parameters vary, including soil type and transportation distances. Additionally, the social improvements and overall economic costs associated with each technology are being reviewed. Utilizing these results, the sustainability tool created to assist in the selection of the overall best option will be refined.

Keywords: life cycle assessment, site remediation, sustainability tool, contaminated sites

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Authors: Y. Saylan, F. Yılmaz, A. Denizli

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Rheumatoid arthritis (RA) which is the most common autoimmune disorder of the body's own immune system attacking healthy cells. RA has both articular and systemic effects.Until now romatiod factor (RF) assay is used the most commonly diagnosed RA but it is not specific. Anti-cyclic citrullinated peptide (anti-CCP) antibodies are IgG autoantibodies which recognize citrullinated peptides and offer improved specificity in early diagnosis of RA compared to RF. Anti-CCP antibodies have specificity for the diagnosis of RA from 91 to 98% and the sensitivity rate of 41-68%. Molecularly imprinted polymers (MIP) are materials that are easy to prepare, less expensive, stable have a talent for molecular recognition and also can be manufactured in large quantities with good reproducibility. Molecular recognition-based adsorption techniques have received much attention in several fields because of their high selectivity for target molecules. Quartz crystal microbalance (QCM) is an effective, simple, inexpensive approach mass changes that can be converted into an electrical signal. The applications for specific determination of chemical substances or biomolecules, crystal electrodes, cover by the thin films for bind or adsorption of molecules. In this study, we have focused our attention on combining of molecular imprinting into nanofilms and QCM nanosensor approaches and producing QCM nanosensor for anti-CCP, chosen as a model protein, using anti-CCP imprinted nanofilms. For this aim, anti-CCP imprinted QCM nanosensor was characterized by Fourier transform infrared spectroscopy, atomic force microscopy, contact angle measurements and ellipsometry. The non-imprinted nanosensor was also prepared to evaluate the selectivity of the imprinted nanosensor. Anti-CCP imprinted QCM nanosensor was tested for real-time detection of anti-CCP from aqueous solution. The kinetic and affinity studies were determined by using anti-CCP solutions with different concentrations. The responses related with mass shifts (Δm) and frequency shifts (Δf) were used to evaluate adsorption properties and to calculate binding (Ka) and dissociation (Kd) constants. To show the selectivity of the anti-CCP imprinted QCM nanosensor, competitive adsorption of anti-CCP and IgM was investigated.The results indicate that anti-CCP imprinted QCM nanosensor has a higher adsorption capabilities for anti-CCP than for IgM, due to selective cavities in the polymer structure.

Keywords: anti-CCP, molecular imprinting, nanosensor, rheumatoid arthritis, QCM

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Ceylan Sozer, Ece Ceylan Baba

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The reforms and changes that began with industrialization in cities and continued with globalization in 1980’s, created many changes in urban environments. City centers which are desolated due to industrialization, began to get crowded with globalization and became the heart of technology, commerce and social activities. While the immediate and intense alterations are planned around rigorous visions in developed countries, several urban areas where the processes were underestimated and not taken precaution faced with irrevocable situations. When the effects of the globalization in the cities are examined, it is seen that there are some anticipatory system plans in the cities about the future problems. Several cities such as New York, London and Tokyo have planned to resolve probable future problems in a systematic scheme to decrease possible side effects during globalization. The decisions in urban planning and their applications are the main points in terms of sustainability and livability in such mega-cities. This article examines the effects of globalization on urban planning through 3 mega cities and the applications. When the applications of urban plannings of the three mega-cities are investigated, it is seen that the city plans are generated under light of past experiences and predictions of a certain future. In urban planning, past and present experiences of a city should have been examined and then future projections could be predicted together with current world dynamics by a systematic way. In this study, methods used in urban planning will be discussed and ‘Anticipatory System’ model will be explained and relations with global-urban planning will be discussed. The concept of ‘anticipation’ is a phenomenon that means creating foresights and predictions about the future by combining past, present and future within an action plan. The main distinctive feature that separates anticipatory systems from other systems is the combination of past, present and future and concluding with an act. Urban plans that consist of various parameters and interactions together are identified as ‘live’ and they have systematic integrities. Urban planning with an anticipatory system might be alive and can foresight some ‘side effects’ in design processes. After globalization, cities became more complex and should be designed within an anticipatory system model. These cities can be more livable and can have sustainable urban conditions for today and future.In this study, urban planning of Istanbul city is going to be analyzed with comparisons of New York, Tokyo and London city plans in terms of anticipatory system models. The lack of a system in İstanbul and its side effects will be discussed. When past and present actions in urban planning are approached through an anticipatory system, it can give more accurate and sustainable results in the future.

Keywords: globalization, urban planning, anticipatory system, New York, London, Tokyo, Istanbul

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Authors: Norah Alosayl

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English is considered the most important foreign language in the Kingdom of Saudi Arabia (KSA) because of the usefulness of English as a global language compared to Arabic. As students’ desire to improve their English language skills has grown, English writing has been identified as the most difficult problem for Saudi students in their language learning. Although the English language in Saudi Arabia is taught beginning in the seventh grade, many students have problems at the university level, especially in writing, due to a gap between what is taught in secondary and high schools and university expectations- pupils generally study English at school, based on one book with few exercises in vocabulary and grammar exercises, and there are no specific writing lessons. Moreover, from personal teaching experience at King Saud bin Abdulaziz University, students face real problems with their writing. This paper revolves around the blackboard-based collaborative writing to help the undergraduate Saudi EFL students, in their first year enrolled in two sections of ENGL 101 in the first semester of 2021 at King Saud bin Abdulaziz University, practice the most difficult skill they found in their writing through a small group. Therefore, a sequential mixed methods design will be suited. The first phase of the study aims to highlight the most difficult skill experienced by students from an official writing exam that is evaluated by their teachers through an official rubric used in King Saud bin Abdulaziz University. In the second phase, this study will intend to investigate the benefits of social interaction on the process of learning writing. Students will be provided with five collaborative writing tasks via discussion feature on Blackboard to practice a skill that they found difficult in writing. the tasks will be formed based on social constructivist theory and pedagogic frameworks. The interaction will take place between peers and their teachers. The frequencies of students’ participation and the quality of their interaction will be observed through manual counting, screenshotting. This will help the researcher understand how students actively work on the task through the amount of their participation and will also distinguish the type of interaction (on task, about task, or off-task). Semi-structured interviews will be conducted with students to understand their perceptions about the blackboard-based collaborative writing tasks, and questionnaires will be distributed to identify students’ attitudes with the tasks.

Keywords: writing difficulties, blackboard-based collaborative writing, process of learning writing, interaction, participations

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Authors: M. J. Gibbons, A. J. Robinson

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Increasing expectations on small form factor electronics to be more compact while increasing performance has driven conventional cooling technologies to a thermal management threshold. An emerging solution to this problem is electrospray (ES) cooling. ES cooling enables two phase cooling by utilising Coulomb forces for energy efficient fluid atomization. Generated charged droplets are accelerated to the grounded target surface by the applied electric field and surrounding gravitational force. While in transit the like charged droplets enable plume dispersion and inhibit droplet coalescence. If the electric field is increased in the cone-jet regime, a subsequent increase in the plume spray angle has been shown. Droplet segregation in the spray plume has been observed, with primary droplets in the plume core and satellite droplets positioned on the periphery of the plume. This segregation is facilitated by inertial and electrostatic effects. This result has been corroborated by numerous authors. These satellite droplets are usually more densely charged and move at a lower relative velocity to that of the spray core due to the radial decay of the electric field. Previous experimental research by Gomez and Tang has shown that the number of droplets deposited on the periphery can be up to twice that of the spray core. This result has been substantiated by a numerical models derived by Wilhelm et al., Oh et al. and Yang et al. Yang et al. showed from their numerical model, that by varying the extractor potential the dispersion radius of the plume also varies proportionally. This research aims to investigate this dispersion density and the role it plays in the local heat transfer coefficient profile (h) of ES cooling. This will be carried out for different extractor – target separation heights (H2), working fluid flow rates (Q), and extractor applied potential (V2). The plume dispersion will be recorded by spraying a 25 µm thick, joule heated steel foil and by recording the thermal footprint of the ES plume using a Flir A-40 thermal imaging camera. The recorded results will then be analysed by in-house developed MATLAB code.

Keywords: electronic cooling, electrospray, electrospray plume dispersion, spray cooling

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Authors: Tomoya Oshiro, Hiroko Ono

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Japan's aging population is increasing due to the decrease in birthrate. It causes various problems like the decrease in the gross domestic product of the country. The reason is why the local government of Japan has been on the way to a sustainable city recently. Then it is essential to get control of an urban structure to make the compact city successful. There are many kinds of paper about the compact city; however, the paper about a compact city of the island-type city is less. The purpose of this study is to clarify difference of urban structure between an island-type and a general city type. The method which has conducted in this research has two steps. First of all, by using evaluation indexes in the handbook, we evaluated the urban structures among each same -population-class cities from 50,000 to 100,000 people. Next, to clear the difference about the urban structure and feature between island-type and general-type cities compare the radar chart which is composed with each evaluation indexes of urban structure. Moreover, in order to clarify the relationship between evaluation indexes and the place of residence by using GIS software to show up population density on the map. As a result of this research, the management of local government and the local economy in evaluation indexes are indicated to be negative point in comparison of island-type cities with general cities. However, evaluation indexes of safety/security and low-carbon/energy are proved to be positive point. The research to find the difference features of the island-type of urban structure proves that the management of local government or the local economy is negative point in these island-type cities. In addition, the public transportation coverage in Miyako Island, Sado Island, and Amakusa Island show low value compare with other islands and average value. Relationship between evaluation indexes of an urban structure and the place of residence prove that the place of residence is related to public transportation coverage. If the place of residence is spread out, the public transportation coverage will be decreased. The results of this research reveal that the finances in island-type cities are negative point compare to general cities. This problem is caused by declining population. In addition, the place of residence is related to the public transportation coverage. Even though, it needs a much money to increase the public transportation coverage. It is possibly to cause other problems furthermore the aspect of finance is influenced by that as well. The conclusion in this research suggests that it is important for creating the compact city in island-type cities that we first need to address solving the problems about the management of local government and the local economy.

Keywords: sustainable city, comparative analysis, geographic information system, urban structure

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Authors: Sharmin Sultana, Reinhard Schlickeiser

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A degenerate relativistic quantum plasma (DRQP) system (containing relativistically degenerate electrons, degenerate/non-degenerate light nuclei, and non-degenerate heavy nuclei) is considered to investigate the propagation characteristics of electrostatic solitary waves (in the ionic scale length) theoretically and numerically. The ion-acoustic solitons are found to be associated with the modified ion-acoustic waves (MIAWs) in which inertia (restoring force) is provided by mass density of the light or heavy nuclei (degenerate pressure of the cold electrons). A mechanical-motion analog (Sagdeev-type) pseudo-potential approach is adopted to study the properties of large amplitude solitary waves. The basic properties of the large amplitude MIAWs and their existence domain in terms of soliton speed (Mach number) are examined. On the other hand, a multi-scale perturbation approach, leading to an evolution equation for the envelope dynamics, is adopted to derive the cubic nonlinear Schrödinger equation (NLSE). The criteria for the occurrence of modulational instability (MI) of the MIAWs are analyzed via the nonlinear dispersion relation of the NLSE. The possibility for the formation of highly energetic localized modes (e.g. peregrine solitons, rogue waves, etc.) is predicted in such DRQP medium. Peregrine solitons or rogue waves with amplitudes of several times of the background are observed to form in DRQP. The basic features of these modulated waves (e.g. envelope solitons, peregrine solitons, and rogue waves), which are found to form in DRQP, and their MI criteria (on the basis of different intrinsic plasma parameters), are investigated. It is emphasized that our results should be useful in understanding the propagation characteristics of localized disturbances and the modulation dynamics of envelope solitons, and their instability criteria in astrophysical DRQP system (e.g. white dwarfs, neutron stars, etc., where matters under extreme conditions are assumed to exist) and also in ultra-high density experimental plasmas.

Keywords: degenerate plasma, envelope solitons, modified ion-acoustic waves, modulational instability, rogue waves

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Authors: Pouyan Motamedi, Ken Bosnick, Ken Cadien, James Hogan

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Growth of conformal ultrathin metal films has attracted a considerable amount of attention recently. Plasma-enhanced atomic layer deposition (PEALD) is a method capable of growing conformal thin films at low temperatures, with an exemplary control over thickness. The authors have recently reported on growth of metastable epitaxial nickel thin films via PEALD, along with a comprehensive characterization of the films and a study on the relationship between the growth parameters and the film characteristics. The goal of the current study is to use the mentioned films as a case study to investigate the temperature-activated phase transformation and agglomeration in ultrathin metallic films. For this purpose, metastable hexagonal nickel thin films were annealed using a controlled heating/cooling apparatus. The transformations in the crystal structure were observed via in-situ synchrotron x-ray diffraction. The samples were annealed to various temperatures in the range of 400-1100° C. The onset and progression of particle formation were studied in-situ via laser measurements. In addition, a four-point probe measurement tool was used to record the changes in the resistivity of the films, which is affected by phase transformation, as well as roughening and agglomeration. Thin films annealed at various temperature steps were then studied via atomic force microscopy, scanning electron microscopy and high-resolution transmission electron microscopy, in order to get a better understanding of the correlated mechanisms, through which phase transformation and particle formation occur. The results indicate that the onset of hcp-to-bcc transformation is at 400°C, while particle formations commences at 590° C. If the annealed films are quenched after transformation, but prior to agglomeration, they show a noticeable drop in resistivity. This can be attributed to the fact that the hcp films are grown epitaxially, and are under severe tensile strain, and annealing leads to relaxation of the mismatch strain. In general, the results shed light on the nature of structural transformation in nickel thin films, as well as metallic thin films, in general.

Keywords: atomic layer deposition, metastable, nickel, phase transformation, thin film

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Authors: Ali Pourkazemi

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The transient radar method (TRM) is one of the non-destructive methods that was introduced by authors a few years ago. The transient radar method can be classified as a wave-based non destructive testing (NDT) method that can be used in a wide frequency range. Nevertheless, it requires a narrow band, ranging from a few GHz to a few THz, depending on the application. As a time-of-flight and real-time method, TRM can measure the electromagnetic properties of the sample under test not only quickly and accurately, but also blindly. This means that it requires no prior knowledge of the sample under test. For multi-layer structures, TRM is not only able to detect changes related to any parameter within the multi-layer structure but can also measure the electromagnetic properties of each layer and its thickness individually. Although the temperature, humidity, and general environmental conditions may affect the sample under test, they do not affect the accuracy of the Blind TRM algorithm. In this paper, the electromagnetic properties as well as the thickness of the individual building insulation materials - as a single-layer structure - are measured experimentally. Finally, the correlation between the reflection coefficients and some other technical parameters such as sound insulation, thermal resistance, thermal conductivity, compressive strength, and density is investigated. The sample to be studied is 30 cm x 50 cm and the thickness of the samples varies from a few millimeters to 6 centimeters. This experiment is performed with both biostatic and differential hardware at 10 GHz. Since it is a narrow-band system, high-speed computation for analysis, free-space application, and real-time sensor, it has a wide range of potential applications, e.g., in the construction industry, rubber industry, piping industry, wind energy industry, automotive industry, biotechnology, food industry, pharmaceuticals, etc. Detection of metallic, plastic pipes wires, etc. through or behind the walls are specific applications for the construction industry.

Keywords: transient radar method, blind electromagnetic geometrical parameter extraction technique, ultrafast nondestructive multilayer dielectric structure characterization, electronic measurement systems, illumination, data acquisition performance, submillimeter depth resolution, time-dependent reflected electromagnetic signal blind analysis method, EM signal blind analysis method, time domain reflectometer, microwave, milimeter wave frequencies

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: G. Krishnamoorthy, S. Anandhakumar

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The effect of D-Lysine (D-Lys) on collagen with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide(EDC)/N-hydroxysuccinimide(NHS) initiated cross linking using experimental and modelling tools are evaluated. The results of the Coll-D-Lys-EDC/NHS scaffold also indicate an increase in the tensile strength (TS), percentage of elongation (% E), denaturation temperature (Td), and decrease the decomposition rate compared to L-Lys-EDC/NHS. Scanning electron microscopic (SEM) and atomic force microscopic (AFM) analyses revealed a well ordered with properly oriented and well-aligned structure of scaffold. The D-Lys stabilizes the scaffold against degradation by collagenase than L-Lys. The cell assay showed more than 98% fibroblast viability (NIH3T3) and improved cell adhesions, protein adsorption after 72h of culture when compared with native scaffold. Cell attachment after 74h was robust, with cytoskeletal analysis showing that the attached cells were aligned along the fibers assuming a spindle-shape appearance, despite, gene expression analyses revealed no apparent alterations in mRNA levels, although cell proliferation was not adversely affected. D-Lysine (D-Lys) plays a pivotal role in the self-assembly and conformation of collagen fibrils. The D-Lys assisted EDC/NHS initiated cross-linking induces the formation of an carboxamide by the activation of the side chain -COOH group, followed by aminolysis of the O-iso acylurea intermediates by the -NH2 groups are directly joined via an isopeptides bond. This leads to the formation of intra- and inter-helical cross links. Modeling studies indicated that D-Lys bind with collagen-like peptide (CLP) through multiple H-bonding and hydrophobic interactions. Orientational changes in collagenase on CLP-D-Lys are observed which may decrease its accessibility to degradation and stabilize CLP against the action of the former. D-Lys has lowest binding energy and improved fibrillar-assembly and staggered alignment without the undesired structural stiffness and aggregations. The proteolytic machinery is not well equipped to deal with Coll-D-Lys than Coll-L-Lys scaffold. The information derived from the present study could help in designing collagenolytically stable heterochiral collagen based scaffold for biomedical applications.

Keywords: collagen, collagenase, collagen like peptide, D-lysine, heterochiral collagen scaffold

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Authors: Mokgadi Malatji, Tebogo Mothiba, Rambelani Malema

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Absenteeism is a global problem in the working force and this is no exception in the nursing profession. A lot of attention has been drawn to factors that contribute to absenteeism however little attention has been placed on the effects of absenteeism on the remaining workers/nurses being left behind in the workplace by their colleagues. Nurses absent themselves leaving behind their colleagues to do their work. Nurses who are committed to their work often find themselves working under strenuous conditions due to inadequate staff. These may lead to poor patient care provision, nurses feeling overworked and sick due to the increased workload. The purpose of this study was to investigate the effects of absenteeism on nurses that remained at work at Mankweng Hospital in the Capricorn District, Limpopo Province. A descriptive cross-sectional quantitative research design was conducted to determine if there were any effects of absenteeism on nurses remaining at work. Data collection was done using structured questionnaires. The respondents (n=107), consisted of different categories of registered nurses (professional nurses (n=43), auxiliary nurses (n=40) and staff nurses (n=24)) who participated in this study. The findings indicated that most nurses (76, 6%) are demotivated and they struggle with completion of duties when their colleagues are absent. Patient care that nurses provided when their colleagues were absent was of poor quality as set standards and principles were not adhered to. Individualized patient care was not being implemented due to absenteeism. This simply implies that routine work is being done to cover basic duties. Most nurses (74, 8%) believed that favoritism and lack of appreciation of nurse’s skills and capabilities are being displayed by managers and that this contributes to absenteeism. Nurses who are loyal sacrifice their time and work overtime for absent colleagues and this led to fatigue and stress. From the study findings, it is recommended that nurses be trained frequently to upgrade their studies to motivate them to work. The government can provide this training to improve their skills as this will motivate nurses to work harder and be committed to their work. Training can be offered after a stipulated period. For example, after every five years, a nurse can be provided with a new skill. Team building events must be encouraged for the whole hospital to motivate staff. In conclusion, the study revealed that absenteeism poses detrimental effects on nurses, the hospital and patients. More and more nurses end up changing workplace due to these effects.

Keywords: absenteeism, effects, nurses, remaining at work

Procedia PDF Downloads 236

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Pappu Kumar, Ajai Singh, Anshuman Singh

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There are different water user sectors such as rural, urban, mining, subsistence and commercial irrigated agriculture, commercial forestry, industry, power generation which are present in the catchment in Subarnarekha River Basin and Ranchi city. There is an inequity issue in the access to water. The development of the rural area, construction of new power generation plants, along with the population growth, the requirement of unmet water demand and the consideration of environmental flows, the revitalization of small-scale irrigation schemes is going to increase the water demands in almost all the water-stressed catchment. The WEAP Model was developed by the Stockholm Environment Institute (SEI) to enable evaluation of planning and management issues associated with water resources development. The WEAP model can be used for both urban and rural areas and can address a wide range of issues including sectoral demand analyses, water conservation, water rights and allocation priorities, river flow simulation, reservoir operation, ecosystem requirements and project cost-benefit analyses. This model is a tool for integrated water resource management and planning like, forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas and Hydropower generation, In the present study, efforts have been made to access the utility of the WEAP model for water supply and demand analysis for Ranchi city. A detailed works have been carried out and it was tried to ascertain that the WEAP model used for generating different scenario of water requirement, which could help for the future planning of water. The water supplied to Ranchi city was mostly contributed by our study river, Hatiya reservoir and ground water. Data was collected from various agencies like PHE Ranchi, census data of 2011, Doranda reservoir and meteorology department etc. This collected and generated data was given as input to the WEAP model. The model generated the trends for discharge of our study river up to next 2050 and same time also generated scenarios calculating our demand and supplies for feature. The results generated from the model outputs predicting the water require 12 million litter. The results will help in drafting policies for future regarding water supplies and demands under changing climatic scenarios.

Keywords: WEAP model, water demand analysis, Ranchi, scenarios

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Authors: Ignacio W. Loor

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In the context of informal settlements in Quito, this paper provides evidence that slopes and deep ravines typical of Andean cities, around which marginalized urban communities sit, constitute a platform for green infrastructure that supports mobility for pedestrians in an incremental fashion. This is informally shaped green infrastructure that provides connectivity to other mobility infrastructures such as roads and public transport, which permits relegated dwellers reach their daily destinations and reclaim their rights to the city. This is relevant in that walking has been increasingly neglected as a viable mean of transport in Latin American cities, in favor of rather motorized means, for which the mobility benefits of green infrastructure have remained invisible to policymakers, contributing to the progressive isolation of informal settlements. This research leverages greatly on an ecological rejuvenation programme led by the municipality of Quito and the Andean Corporation for Development (CAN) intended for rehabilitating the ecological functionalities of ravines. Accordingly, four ravines in different stages of rejuvenation were chosen, in order to through ethnographic methods, capture the practices they support to dwellers of informal settlements across different stages, particularly in terms of issues of mobility. Then, by presenting fragments of interviews, description of observed phenomena, photographs and narratives published in institutional reports and media, the production process of mobility infrastructure over unoccupied slopes and ravines, and the roles that this infrastructure plays in the mobility of dwellers and their quotidian practices are explained. For informal settlements, which normally feature scant urban infrastructure, mobility embodies an unfavourable driver for the possibilities of dwellers to actively participate in the social, economic and political dimensions of the city, for which their rights to the city are widely neglected. Nevertheless, informal green infrastructure for mobility provides some alleviation. This infrastructure is incremental, since its features and usability gradually evolves as users put into it knowledge, labour, devices, and connectivity to other infrastructures in different dimensions which increment its dependability. This is evidenced in the diffusion of knowledge of trails and routes of footpaths among users, the implementation of linking stairs and bridges, the improved access by producing public spaces adjacent to the ravines, the illuminating of surrounding roads, and ultimately, the restoring of ecological functions of ravines. However, the perpetuity of this type of infrastructure is also fragile and vulnerable to the course of urbanisation, densification, and expansion of gated privatised spaces.

Keywords: green infrastructure, informal settlements, urban mobility, walkability

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Authors: Mohamed Abdelkareem

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Remote sensing data contributed on predicting the prospective areas of water resources. Integration of microwave and multispectral data along with climatic, hydrologic, and geological data has been used here. In this article, Sentinel-2, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological, hydrologic and structural features of Wadi Asyuti which represents a defunct tributary of the Nile basin, in the eastern Sahara. The image transformation of Sentinel-2 and Landsat-8 data allowed characterizing the different varieties of rock units. Integration of microwave remotely-sensed data and GIS techniques provided information on physical characteristics of catchments and rainfall zones that are of a crucial role for mapping groundwater prospective zones. A fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal using optical data. Lineament extraction and interpretation indicated that the area is clearly shaped by the NE-SW graben that is cut by NW-SE trend. Such structures allowed the accumulation of thick sediments in the downstream area. Processing of recent OLI data acquired on March 15, 2014, verified the flood potential maps and offered the opportunity to extract the extent of the flooding zone of the recent flash flood event (March 9, 2014), as well as revealed infiltration characteristics. Several layers including geology, slope, topography, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics were combined after assigning a weight for each using a GIS-based knowledge-driven approach. The results revealed that the predicted groundwater potential zones (GPZs) can be arranged into six distinctive groups, depending on their probability for groundwater, namely very low, low, moderate, high very, high, and excellent. Field and well data validated the delineated zones.

Keywords: GIS, remote sensing, groundwater, Egypt

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Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Christine Demaecker

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Many researchers have underlined the importance of metaphors in specialised language. Their use of specific domains helps us understand the conceptualisations used to communicate new ideas or difficult topics. Within the wide area of specialised discourse, wine tasting is a very specific example because it is almost exclusively metaphoric. Wine tasting metaphors express various conceptualisations. They are not linguistic but rather conceptual, as defined by Lakoff & Johnson. They correspond to the linguistic expression of a mental projection from a well-known or more concrete source domain onto the target domain, which is the taste of wine. But unlike most specialised terminologies, the vocabulary is never clearly defined. When metaphorical terms are listed in dictionaries, their definitions remain vague, unclear, and circular. They cannot be replaced by literal linguistic expressions. This makes it impossible to transfer them into another language with the traditional linguistic translation methods. Qualitative research investigates whether wine tasting metaphors could rather be translated with the cognitive translation process, as well described by Nili Mandelblit (1995). The research is based on a corpus compiled from two high-profile wine guides; the Parker’s Wine Buyer’s Guide and its translation into French and the Guide Hachette des Vins and its translation into English. In this small corpus with a total of 68,826 words, 170 metaphoric expressions have been identified in the original English text and 180 in the original French text. They have been selected with the MIPVU Metaphor Identification Procedure developed at the Vrije Universiteit Amsterdam. The selection demonstrates that both languages use the same set of conceptualisations, which are often combined in wine tasting notes, creating conceptual integrations or blends. The comparison of expressions in the source and target texts also demonstrates the use of the cognitive translation approach. In accordance with the principle of relevance, the translation always uses target language conceptualisations, but compared to the original, the highlighting of the projection is often different. Also, when original metaphors are complex with a combination of conceptualisations, at least one element of the original metaphor underlies the target expression. This approach perfectly integrates into Lederer’s interpretative model of translation (2006). In this triangular model, the transfer of conceptualisation could be included at the level of ‘deverbalisation/reverbalisation’, the crucial stage of the model, where the extraction of meaning combines with the encyclopedic background to generate the target text.

Keywords: cognitive translation, conceptual integration, conceptual metaphor, interpretative model of translation, wine tasting metaphor

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Authors: Rutuja Upadhyay, Anurag Mehra

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Solid food foams/cellular foods are colloidal systems which impart structure, texture and mouthfeel to many food products such as bread, cakes, ice-cream, meringues, etc. Their heterogeneous morphology makes the quantification of structure/mechanical relationships complex. The porous structure of solid food foams is highly influenced by the processing conditions, ingredient composition, and their interactions. Sensory perceptions of food foams are dependent on bubble size, shape, orientation, quantity and distribution and determines the texture of foamed foods. The state and structure of the solid matrix control the deformation behavior of the food, such as elasticity/plasticity or fracture, which in turn has an effect on the force-deformation curves. The obvious step in obtaining the relationship between the mechanical properties and the porous structure is to quantify them simultaneously. Here, we attempt to research food foams such as bread dough, baked bread and steamed rice cakes to determine the link between ingredients and the corresponding effect of each of them on the rheology, microstructure, bubble size and texture of the final product. Dynamic rheometry (SAOS), confocal laser scanning microscopy, flatbed scanning, image analysis and texture profile analysis (TPA) has been used to characterize the foods studied. In all the above systems, there was a common observation that when the mean bubble diameter is smaller, the product becomes harder as evidenced by the increase in storage and loss modulus (G′, G″), whereas when the mean bubble diameter is large the product is softer with decrease in moduli values (G′, G″). Also, the bubble size distribution affects texture of foods. It was found that bread doughs with hydrocolloids (xanthan gum, alginate) aid a more uniform bubble size distribution. Bread baking experiments were done to study the rheological changes and mechanisms involved in the structural transition of dough to crumb. Steamed rice cakes with xanthan gum (XG) addition at 0.1% concentration resulted in lower hardness with a narrower pore size distribution and larger mean pore diameter. Thus, control of bubble size could be an important parameter defining final food texture.

Keywords: food foams, rheology, microstructure, texture

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

Procedia PDF Downloads 216