Search results for: compressive strength prediction

Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Seong-Jin Cho, Tae-Hyeon Oh, Dae-Kyu Park

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Sound absorption coefficient is considered important when designing because noise affects emotion quality of car. It is designed with lots of experiment tunings in the field because it is unreliable to predict it for multi-layer material. In this paper, we present the design of sound absorption for automotive interior material with multiple layers using estimation software of sound absorption coefficient for reverberation chamber. Additionally, we introduce the method for estimation of physical properties required to predict sound absorption coefficient of car interior materials with multiple layers too. It is calculated by inverse algorithm. It is very economical to get information about physical properties without expensive equipment. Correlation test is carried out to ensure reliability for accuracy. The data to be used for the correlation is sound absorption coefficient measured in the reverberation chamber. In this way, it is considered economical and efficient to design automotive interior materials. And design optimization for sound absorption coefficient is also easy to implement when it is designed.

Keywords: sound absorption coefficient, optimization design, inverse algorithm, automotive interior material, multiple layers nonwoven, scaled reverberation chamber, sound impedance tubes

Procedia PDF Downloads 303

Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

Procedia PDF Downloads 177

Authors: Vitalijs Lusis, Videvuds-Arijs Lapsa, Olga Kononova, Andrejs Krasnikovs

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Steel fibres use in a concrete, such way obtaining Steel Fibre Reinforced Concrete (SFRC), is an important technological direction in building industry. Steel fibers are substituting the steel bars in conventional concrete in another situation is possible to combine them in the concrete structures. Traditionally fibers are homogeneously dispersed in a concrete. At the same time in many situations fiber concrete with homogeneously dispersed fibers is not optimal (majority of added fibers are not participating in a load bearing process). It is obvious, that is possible to create constructions with oriented fibers distribution in them, in different ways. Present research is devoted to one of them. Acknowledgment: This work has been supported by the European Social Fund within the project «Support for the implementation of doctoral studies at Riga Technical University» and project No. 2013/0025/1DP/1.1.1.2.0/13/APIA/VIAA/019 “New “Smart” Nanocomposite Materials for Roads, Bridges, Buildings and Transport Vehicle”.

Keywords: fiber reinforced concrete, 4-point bending, steel fiber, SFRC

Procedia PDF Downloads 626

Authors: Masoud Ghermezi

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Studying the sheet metals is one of the most important research areas in the field of metal forming due to their extensive applications in the aerospace industries. A useful method for determining the forming limit of these materials and consequently preventing the rupture of sheet metals during the forming process is the use of the forming limit curve (FLC). In addition to specifying the forming limit, this curve also delineates a boundary for the allowed values of strain in sheet metal forming; these characteristics of the FLC along with its accuracy of computation and wide range of applications have made this curve the basis of research in the present paper. This study presents a new model that not only agrees with the results obtained from the above mentioned theory, but also eliminates its shortcomings. In this theory, like in the M-K theory, a thin sheet with an inhomogeneity as a gradient thickness reduction with a sinusoidal function has been chosen and subjected to two-dimensional stress. Through analytical evaluation, ultimately, a governing differential equation has been obtained. The numerical solution of this equation for the range of positive strains (stretched region) yields the results that agree with the results obtained from M-K theory. Also the solution of this equation for the range of negative strains (tension region) completes the FLC curve. The findings obtained by applying this equation on two alloys with the hardening exponents of 0.4 and 0.24 indicate the validity of the presented equation.

Keywords: sheet metal, metal forming, forming limit curve (FLC), M-K theory

Procedia PDF Downloads 362

Authors: Manoj Kumar, Preeti Goel

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National Capital Region of India is one of the most populous urban agglomerations in the world. This region has residents from all the parts of India, and their shopping behaviors are quite different. The region also has the substantial population of people from other countries. Due to high purchasing power of a large number of people, NCR is one the major markets for luxury fashion brands. Marketers of luxury fashion brands keep on adding innovative features to their products to attract the buyers. This research is an attempt to understand the major factors which impact the brand selection for these brands and other buying decisions like purchasing time and location. The research is based on primary data collected from potential buyers of luxury fashion brands and the people involved in the marketing of these brands in various roles. The research has tried to identify the relative strength of various factors on the shopping behavior for these brands.

Keywords: luxury brands, fashion, shopping, National Capital Region (NCR)

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Authors: Mohsen Shirani, Shadi Asadzandi, Micaela Demichela

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This paper presents the results of a study to assess crucial aspects and the strength of the scientific basis of a typically interdisciplinary, applied field: food supply chain risk assessment research. Our approach is based on an advanced scientometrics analysis with novel elements to assess the influence and dissemination of research results and to measure interdisciplinary. This paper aims to describe the quantity and quality of the publication trends in food supply chain risk assessment. The population under study was composed of 266 articles from database web of science. The results were analyzed based on date of publication, type of document, language of the documents, source of publications, subject areas, authors and their affiliations, and the countries involved in developing the articles.

Keywords: food supply chain, risk assessment, scientometrics, web of science

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

Procedia PDF Downloads 284

Authors: Nigus Maregu Demewoz, Shu-Kai Yeh

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Low-density nanocellular foam is a fascinating new-generation advanced material due to its mechanical strength and thermal insulation properties. In nanocellular foam, reducing the density increases the insulation ability. However, producing a nanocellular foam of densities less than 0.3 with a cell size of less than 100 nm is very challenging. In this study, poly (methyl methacrylate) (PMMA) was blended with Polyether block amide (PEBAX) to study the effects of PEBAX on the nanocellular foam structure of the PMMA matrix. We added 2 wt% of PEBAX in the PMMA matrix, and the PEBAX nanostructured domain size of 45 nm was well dispersed in the PMMA matrix. The foaming result produced a new generation special bouquet-like nanocellular foam of cell size less than 50 nm with a relative density of 0.24. Also, we were able to produce a nanocellular foam of a relative density of about 0.17. In addition to thermal insulation applications, bouquet-like nanocellular foam may be expected for filtration applications.

Keywords: nanocellular foam, low-density, cell size, relative density, PMMA/PEBAX blend

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Sheena K. J., Jyoti Badge, Sayed Mohammed Zeeshan

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A Comparative Study of Exponential and Logistic Population Growth Models in India India is the second most populous city in the world, just behind China, and is going to be in the first place by next year. The Indian population has remarkably at higher rate than the other countries from the past 20 years. There were many scientists and demographers who has formulated various models of population growth in order to study and predict the future population. Some of the models are Fibonacci population growth model, Exponential growth model, Logistic growth model, Lotka-Volterra model, etc. These models have been effective in the past to an extent in predicting the population. However, it is essential to have a detailed comparative study between the population models to come out with a more accurate one. Having said that, this research study helps to analyze and compare the two population models under consideration - exponential and logistic growth models, thereby identifying the most effective one. Using the census data of 2011, the approximate population for 2016 to 2031 are calculated for 20 Indian states using both the models, compared and recorded the data with the actual population. On comparing the results of both models, it is found that logistic population model is more accurate than the exponential model, and using this model, we can predict the future population in a more effective way. This will give an insight to the researchers about the effective models of population and how effective these population models are in predicting the future population.

Keywords: population growth, population models, exponential model, logistic model, fibonacci model, lotka-volterra model, future population prediction, demographers

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Authors: Anurag Jha

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Dharavi is the largest slum in Asia, for which many redevelopment projects have been put forth, to improve the housing conditions of the locals. And yet, these projects are met with much-unexpected resistance from the locals. The research analyses the why and the how of the resistances these projects face and analyses these programs and points out the flaws and benefits of such projects, by predicting its impact on the regulars of Dharavi. The research aims to analyze various aspects of Dharavi, which affect its socio-cultural backdrops, such as its history, and eventual growth into a mega slum. Through various surveys, the research aims to analyze the life of a slum dweller, the street life, and the effect of such settlement on the urban fabric. Various development projects such as Dharavi Museum Movement, are analyzed, and a feasibility and efficiency analysis of the proposals for redevelopment of Dharavi Slums has been theorized. Flaws and benefits of such projects, by predicting its impact on the regulars of Dharavi has been the major approach to the research. Also, prediction the implementation of these projects in another prominent slum area, Anand Nagar, Bhopal, with the use of generated hypothetical model has been done. The research provides a basic framework for a comparative analysis of various redevelopment projects and the effect of implementation of such projects on the general populace. Secondly, it proposes a hypothetical model for feasibility of such projects in certain slum areas.

Keywords: Anand Nagar, Bhopal slums, Dharavi, slum redevelopment programmes

Procedia PDF Downloads 327

Authors: Shef Amir Arasy

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In 2023, the world's population will be 7.8 billion, which has increased significantly in the last 20 years. Every country in the world is experiencing the impacts of climate change directly and indirectly. However, the community still needs to build massive infrastructure and buildings. The massive CO2 emissions which lead to climate change come from cement usage in construction activity. Bamboo is one of the most sustainable materials for reducing carbon emissions and releasing more than 30% oxygen compared to the mass of trees. Besides, bamboo harvest time is faster than other sustainable materials, around 3-4 years. Furthermore, Bamboo has a high tensile strength, which can provide ductility effectively to prevent damage to buildings during an earthquake. By the finite element method, this research analyzes bamboo configuration and connection for tall building structures under different earthquake frequencies and fire. The aim of this research is to provide proper design and connection of bamboo buildings that can be more reliable than concrete structures.

Keywords: bamboo, concrete, ductility, earthquake.

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Authors: Chisomo Patrick Kumbuyo, Katsuyuki Shimizu, Hiroshi Yasuda, Yoshinobu Kitamura

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This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. Zone 1 consisted of Karonga and Nkhatabay stations, located in northern Malawi; and Zone 2 consisted of Bolero, located in northern Malawi; Kasungu, Dedza, Salima, located in central Malawi; Mangochi, Makoka and Ngabu stations located in southern Malawi. Links between Malawi rainfall and SST based on statistical correlations were evaluated and significant results selected as predictors for the regression models. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Pacific Ocean. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r=0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa.

Keywords: Malawi rainfall, forecast model, predictors, SST

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

Procedia PDF Downloads 132

Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

Procedia PDF Downloads 447

Authors: H. Mahdavinezhad, M. Labbaf, H. R. Tavakoli

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In recent decades, the study and control of the destructive effects of explosive vibration in construction projects has received more attention, and several experimental equations in the field of vibration prediction as well as allowable vibration limit for various structures are presented. Researchers have developed a number of experimental equations to estimate the peak particle velocity (PPV), in which the experimental constants must be obtained at the site of the explosion by fitting the data from experimental explosions. In this study, the most important of these equations was evaluated for strong massive conglomerates around Dez Dam by collecting data on explosions, including 30 particle velocities, 27 displacements, 27 vibration frequencies and 27 acceleration of earth vibration at different distances; they were recorded in the form of two types of detonation systems, NUNEL and electric. Analysis showed that the data from the explosion had the best correlation with the cube root of the explosive, R2=0.8636, but overall the correlation coefficients are not much different. To estimate the vibration in this project, data regression was performed in the other formats, which resulted in the presentation of new equation with R2=0.904 correlation coefficient. Finally according to the importance of the studied structures in order to ensure maximum non damage to adjacent structures for each diagram, a range of application was defined so that for distances 0 to 70 meters from blast site, exponent n=0.33 and for distances more than 70 m, n =0.66 was suggested.

Keywords: blasting, blast-induced vibration, empirical equations, PPV, tunnel

Procedia PDF Downloads 123

Authors: Reshma Jolly, Mohammad Shakir, Mohammad Shoeb Khan, Noor E. Iram

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A nanocomposite system integrating hydroxyapatite, chitin and starch (n-HA/CT/ST) has been synthesized via co-precipitation approach at room temperature, addressing the issues of biocompatibility, mechanical strength and cytotoxicity required for Bone tissue engineering. The interactions, crystallite size and surface morphology against n-HA/CT (nano-hydroxyapatite/chitin) nanocomposite have been obtained by correlating and comparing the results of FTIR, SEM, TEM and XRD. The comparative study of the bioactivity of n-HA/CT and n-HA/CT/ST nanocomposites revealed that the incorporation of starch as templating agent improved these properties in n-HA/CT/ST nanocomposite. The rise in thermal stability in n-HA/CT/ST nanocomposite as compared to n-HA/CT has been observed by comparing the TGA results. The comparison of SEM images of both the scaffolds indicated that the addition of ST influenced the surface morphology of n-HA/CT scaffold which appeared to be rougher and porous. The MTT assay on murine fibroblast L929 cells and in-vitro bioactivity of n-HA/CT/ST matrix referred superior non-toxic property of n-HA/CT/ST nanocomposite and higher possibility of osteo-integration in-vivo, respectively.

Keywords: bioactive, chitin, hyroxyapatite, nanocomposite

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Authors: Courtney Evans, Yuto Morimitsu, Tsubasa Hisadome, Futo Inomoto, Masahiro Yoshida, Takayuki Takei

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Hydrogels are useful biomaterials due to their highly biocompatible nature and their ability to absorb large quantities of liquid and mimic soft tissue. They are often used as therapeutic drug delivery systems. However, it is sometimes difficult to sustain controlled release when using hydrophobic medicines, as hydrogels are frequently hydrophilic. As such, this research shows the success of chitosan hydrogels modified through hydrophobic interaction. This was done through the imide bonding of the alkyl groups in fatty aldehydes and the amino groups in chitosan, followed by reductive animation. The resulting cryogels could be optimized for strength as well as sorption and desorption (of a hydrophobic dye used to mimic hydrophobic medicine) by varying the alkyl chain length and the substitution degree of the fatty aldehyde. Optimized cryogels showed potential as biomedical materials, particularly as drug delivery systems.

Keywords: biomedical materials, chitosan, drug carriers, hydrophobic modification

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Authors: F. Brunke, L. Waalkes, C. Siemers

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Due to reduced stiffness, research on second generation titanium alloys for implant applications, like the metastable β-titanium alloy Ti-15Mo, become more and more important in the recent years. The machinability of these alloys is generally poor leading to problems during implant production and comparably large production costs. Therefore, in the present study, Ti 15Mo was alloyed with 0.8 wt.-% of the rare earth metals lanthanum (Ti-15Mo+0.8La) and neodymium (Ti-15Mo+0.8Nd) to improve its machinability. Their microstructure consisted of a titanium matrix and micrometer-size particles of the rare earth metals and two of their oxides. The particles stabilized the micro structure as grain growth was minimized. As especially the ductility might be affected by the precipitates, the behavior of Ti-15Mo+0.8La and Ti-15Mo+0.8Nd was investigated during static and dynamic deformation at elevated temperature to develop a processing route. The resulting mechanical properties (static strength and ductility) were similar in all investigated alloys.

Keywords: Ti 15Mo, titanium alloys, rare earth metals, free machining alloy

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Authors: Ahsan Abdullah, Ahmed A. S. Bakshwain

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Livestock is one of the fastest-growing sectors in agriculture. If carefully managed, have potential opportunities for economic growth, food sovereignty and food security. In this study we mainly analyse and compare long-term i.e. for year 2030 climate variability impact on predicted productivity of meat i.e. beef, mutton and poultry for the Kingdom of Saudi Arabia w.r.t three factors i.e. i) climatic-change vulnerability ii) CO2 fertilization and iii) water scarcity and compare the results with two countries of the region i.e. Iraq and Yemen. We do the analysis using data from diverse sources, which was extracted, transformed and integrated before usage. The collective impact of the three factors had an overall negative effect on the production of meat for all the three countries, with adverse impact on Iraq. High similarity was found between CO2 fertilization (effecting animal fodder) and water scarcity i.e. higher than that between production of beef and mutton for the three countries considered. Overall, the three factors do not seem to be favorable for the three Middle-East countries considered. This points to possibility of a vegetarian year 2030 based on dependency on indigenous live-stock population.

Keywords: prediction, animal-source foods, pastures, CO2 fertilization, climatic-change vulnerability, water scarcity

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Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: project profitability, multi-objective optimization, genetic algorithm, Pareto set, neural networks

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Tasir Khan, Yejuan Wang

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The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management.

Keywords: RAMSE, multiple frequency analysis, annual maximum rainfall, L-moments

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Authors: C. Guven, M. Tufekci, E. Bayik, O. Gedik, M. Tas

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A sliding door system is used in commercial vehicles and passenger cars to allow a larger unobstructed access to the interior for loading and unloading. The movement of a sliding door on vehicle body is ensured by mechanisms and tracks having special cross-section which is manufactured by roll forming and stretch bending process. There are three tracks and three mechanisms which are called upper, central and lower on a sliding door system. There are static requirements as strength on different directions, rigidity for mechanisms, and door drop off, door sag; dynamic requirements as high energy slam opening-closing and durability requirement to validate these products. In addition, there is a kinematic requirement to find out force values from door handle during manual operating. In this study, finite element analysis and physical test results which are realized for sliding door systems will be shared comparatively.

Keywords: finite element analysis, sliding door, experimental, verification, vehicle tests

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Authors: Aristeidis Samitas, Stathis Polyzos

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As the UK and the EU prepare to start negotiations for Brexit, it is important for both sides to comprehend the full extent of the consequences of this process. In this paper, we employ an object oriented simulation framework in order to test for the short-term and long-term effects of Brexit on both sides of the Channel. The relative strength of the UK economy and the banking sector vis-à-vis the EU is taken under consideration. Our results confirm predictions in the relevant literature regarding the output cost of Brexit, with particular emphasis on the EU. Furthermore, we show that financial stability is also an important issue on both sides, with the banking system suffering significant losses, particularly over the longer term. Our findings suggest that policymakers should be extremely careful in handling Brexit negotiations, making sure to consider dynamic effects that may be caused by UK bank assets moving to the EU after Brexit. The model results show that, as the UK banking system loses its assets, the end state of the UK economy is deteriorated while the end state of EU economy is improved.

Keywords: Banking Crises, Brexit, Financial Stability, VBanking

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Authors: Nanxi Miao, Junjie Wang

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The discovery of 2D materials with distinct compositions and properties has been a research aim since the report of graphene. One of the latest members of the 2D material family is MXene, which is produced from the topochemical deintercalation of the A layer from a laminate MAX phase. Recently, analogous 2D MBenes (transitional metal borides) have been predicted by theoretical calculations as excellent alternatives in applications such as metal-ion batteries, magnetic devices, and catalysts. However, the practical applications of two-dimensional (2D) transition-metal borides (MBenes) have been severely hindered by the lack of accessible MBenes because of the difficulties in the selective etching of traditional ternary MAB phases with orthorhombic symmetry (ort-MAB). Here, we discover a family of ternary hexagonal MAB (h-MAB) phases and 2D hexagonal MBenes (h-MBenes) by ab initio predictions and experiments. Calculations suggest that the ternary h-MAB phases are more suitable precursors for MBenes than the ort-MAB phases. Based on the prediction, we report the experimental synthesis of h-MBene HfBO by selective removal of in from h-MAB Hf2InB2. The synthesized 2D HfBO delivered a specific capacity of 420 mAh g-1 as an anode material in lithium-ion batteries, demonstrating the potential for energy-storage applications. The discovery of this h-MBene HfBO added a new member to the growing family of 2D materials and provided opportunities for a wide range of novel applications.

Keywords: 2D materials, DFT calculations, high-throughput screening, lithium-ion batteries

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Authors: Mohd Radzi Hilmi, Mohd Zulfaezal Che Azemin, Khairidzan Mohd Kamal, Azrin Esmady Ariffin, Mohd Izzuddin Mohd Tamrin, Norfazrina Abdul Gaffur, Tengku Mohd Tengku Sembok

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Purpose: The purpose of this study is to predict optical quality changes after pterygium surgery using tissue redness grading. Methods: Sixty-eight primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 68 images and its association with contrast sensitivity function (CSF) and visual acuity (VA) was examined. Results: The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra- and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p<0.01) and VA (p<0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA respectively. Conclusions: The new grading of pterygium fibrovascular redness can be reliably measured from digital images and show a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.

Keywords: contrast sensitivity, pterygium, redness, visual acuity

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Authors: Ilham Priadythama, Pringgo Widyo Laksono, Agung Pamungkas

Abstract:

Myoelectric is prosthetic, flexible, and offered industrial application has been highly developed and widely used. Myoelectric hand use myoelectric signal from muscle to activate and control the membrane part of hand. Commonly myoelectric signal is detected on human arm from skin surface. So that it only small magnitude signal captured. Detecting myoelectric signal on the skin surface takes proper and consistent procedure. This paper provides preliminary study of electrodes position which gives best signal strength for five basic grasping. Two-position scenario used to place three channel electrodes set. A bi-potential amplifier based on AD620 used to amplify the signal. Finally, the signal was analyzed using DSSF3 software. From this study, we found that grasp type was stronger using first scenario electrode placement while the rest type better with another scenario.

Keywords: myoelectric signal, basic grasp, DSSF3, electrode, bi-potential amplifier

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Authors: Rafia Rafique, Mutmina Zainab

Abstract:

The present study investigated the relationship between fear of negative evaluation (FNE), social support and well-being in people with Vitiligo. It was hypothesized that low level of FNE and greater social support is likely to predict well-being. It was also hypothesized that social support is likely to moderate the relationship between FNE and well-being. Correlational research design was used for the present study. Non-probability purposive sampling technique was used to collect a sample (N=122) of people with Vitiligo. Hierarchical Moderated Regression analysis was used to test prediction and moderation. Brief Fear of Negative Evaluation Scale, Multidimensional Scale of Perceived Social Support (MSPSS) and Mental Health Continuum-Short form (MHC-SF) were used to evaluate the study variables. Fear of negative evaluation negatively predicted well-being (emotional and psychological). Social support from significant others and friends predicted social well-being. Social Support from family predicted emotional and psychological well-being. It was found that social support from significant others moderated the relationship between FNE and emotional well-being and social support from family moderated the relationship between FNE and social well-being. Dermatologists treating people with Vitiligo need to educate them and their families about the buffering role of social support (family and significant others). Future studies need to focus on other important mediating factors that can possibly explain the relationship between fear of negative evaluation and wellbeing.

Keywords: fear of negative evaluation, hierarchical moderated regression, vitiligo, well-being

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Authors: Nathan Labiosa, Aryan Kohli

Abstract:

This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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