Search results for: Eight Series Research Module

Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of carbon dioxide (CO2) emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, and electricity), CO2 emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey-Fuller (ADF) test for stationarity, Johansen’s maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests no effects of the CO2 emissions and energy use on the GDP in Turkey. There exists a short-run bidirectional relationship between the electricity and natural gas consumption, and also there is a negative unidirectional causality running from the GDP to electricity use. Overall, the results partly support arguments that there are relationships between energy use and economic output; however, the effects may differ due to the source of energy such as in the case of Turkey for the period of 1980-2010. However, there is no significant relationship between the CO2 emissions and the GDP and between the CO2 emissions and the energy use both in the short term and long term.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

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Authors: Rolla Monib, Chris I. Goodier, Alistair Gibbs

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The aim of this paper is to investigate the key drivers in planning the construction phase for infrastructure projects to reduce project delays. To achieve this aim, the research conducted three case studies using semi-structured and unstructured interviews (n=36). The results conclude that a lack of modularisation awareness is among the key factors attributed to project delays. The current emotive and ill-informed approach to decision-making, coupled with the lack of knowledge regarding appropriate construction method selection, prevents the potential benefits of modularisation being fully realised. To assist with decision-making for the best construction method, the research presents project management tools to help decision makers to choose the most appropriate construction approach through optimising the use of modularisation in EC. A decision-making checklist and diagram are presented in this paper. These checklist tools and diagrams assist the project team in determining the best construction method, taking into consideration the module type.

Keywords: infrastructure, modularization, decision support, decision-making

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

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Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

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Authors: Martina I. Reinhold, Theresa Bacon-Baguley

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Background: Medical knowledge continuously evolves and to help health care providers to stay up-to-date, evidence-based medicine (EBM) has emerged as a model. The practice of EBM requires new skills of the health care provider, including directed literature searches, the critical evaluation of research studies, and the direct application of the findings to patient care. This paper describes the integration and evaluation of an evidence-based medicine course sequence into a Physician Assistant curriculum. This course sequence teaches students to manage and use the best clinical research evidence to competently practice medicine. A survey was developed to assess the outcomes of the EBM course sequence. Methodology: The cornerstone of the three-semester sequence of EBM are interactive small group discussions that are designed to introduce students to the most clinically applicable skills to identify, manage and use the best clinical research evidence to improve the health of their patients. During the three-semester sequence, the students are assigned each semester to participate in small group discussions that are facilitated by faculty with varying background and expertise. Prior to the start of the first EBM course in the winter semester, PA students complete a knowledge-based survey that was developed by the authors to assess the effectiveness of the course series. The survey consists of 53 Likert scale questions that address the nine objectives for the course series. At the end of the three semester course series, the same survey was given to all students in the program and the results from before, and after the sequence of EBM courses are compared. Specific attention is paid to overall performance of students in the nine course objectives. Results: We find that students from the Class of 2016 and 2017 consistently improve (as measured by percent correct responses on the survey tool) after the EBM course series (Class of 2016: Pre- 62% Post- 75%; Class of 2017: Pre- 61 % Post-70%). The biggest increase in knowledge was observed in the areas of finding and evaluating the evidence, with asking concise clinical questions (Class of 2016: Pre- 61% Post- 81%; Class of 2017: Pre- 61 % Post-75%) and searching the medical database (Class of 2016: Pre- 24% Post- 65%; Class of 2017: Pre- 35 % Post-66 %). Questions requiring students to analyze, evaluate and report on the available clinical evidence regarding diagnosis showed improvement, but to a lesser extend (Class of 2016: Pre- 56% Post- 77%; Class of 2017: Pre- 56 % Post-61%). Conclusions: Outcomes identified that students did gain skills which will allow them to apply EBM principles. In addition, the outcomes of the knowledge-based survey allowed the faculty to focus on areas needing improvement, specifically the translation of best evidence into patient care. To address this area, the clinical faculty developed case scenarios that were incorporated into the lecture and discussion sessions, allowing students to better connect the research studies with patient care. Students commented that ‘class discussion and case examples’ contributed most to their learning and that ‘it was helpful to learn how to develop research questions and how to analyze studies and their significance to a potential client’. As evident by the outcomes, the EBM courses achieved the goals of the course and were well received by the students. 

Keywords: evidence-based medicine, clinical education, assessment tool, physician assistant

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Authors: Tayab D. Memon, Shahji Farooque, Marvi Deshi, Imtiaz Hussain Kalwar, B. S. Chowdhry

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Recently single-bit ternary FIR-like filter (SBTFF) hardware synthesize in FPGA is reported and compared with multi-bit FIR filter on similar spectral characteristics. Results shows that SBTFF dominates upon multi-bit filter overall. In this paper, an optimized adder module for ternary quantized sigma-delta modulated signal is presented. The adder is simulated using ModelSim for functional verification the area-performance of the proposed adder were obtained through synthesis in Xilinx and compared to conventional adder trees. The synthesis results show that the proposed adder tree achieves higher clock rates and lower chip area at higher inputs to the adder block; whereas conventional adder tree achieves better performance and lower chip area at lower number of inputs to the same adder block. These results enhance the usefulness of existing short word length DSP algorithms for fast and efficient mobile communication.

Keywords: short word length (SWL), DSP algorithms, FPGA, SBTFF, VHDL

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Authors: Eyal Rosen

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This study is aimed to report a possible effect of the presence of dental implants on the development of crown or root fractures in adjacent natural teeth. A series of 26 cases of teeth diagnosed with crown or root fractures following the placement of adjacent dental implants is presented. In addition, a comprehensive systematic review of the literature was performed to detect other studies that evaluated this possible complication. The case series analysis revealed that all crown-fractured teeth were non-endodontically treated teeth (n=18), and all root fractured teeth were endodontically treated teeth (n=8). The time from implant loading to the diagnosis of a fracture in an adjacent tooth was longer than 1 year in 78% of cases. The majority of crown or root fractures occurred in female patients, over 50 years of age, with an average age of 59 in the crown fractures group, and 54 in the root fractures group. Most of the patients received 2 or more implants. Nine (50%) of the teeth with crown fracture were molars, 7 (39%) were mandibular premolars, and 2 (11%) were incisor teeth. The majority of teeth with root fracture were premolar or mandibular molar teeth (6 (75%)). The systematic review of the literature did not reveal additional studies that reported on this possible complication. To the best of the author’s knowledge this case series, although limited in its extent, is the first clinical report of a possible serious complication of implants, associated fractures in adjacent endodontically and non-endodontically treated natural teeth. The most common patient profile found in this series was a woman over 50 years of age, having a fractured premolar tooth, which was diagnosed more than 1 year after reconstruction that was based on multiple adjacent implants. Additional clinical studies are required in order to shed light on this potential serious complication.

Keywords: complications, dental implants, endodontics, fractured teeth

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

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Authors: Radomir Perzina, Jaroslav Ramik

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The Analytic Hierarchy Process is frequently used approach for solving decision making problems. There exists wide range of software programs utilizing that approach. Their main disadvantage is that they are relatively expensive and missing intermediate calculations. This work introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work with scenarios or multiple decision makers and displays intermediate calculations. Users can structure their decision models into three levels – scenarios/users, criteria and variants. Items on all levels can be evaluated either by weights or pair-wise comparisons. There are provided three different methods for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. The proposed software package is demonstrated on couple of illustrating examples of real life decision problems.

Keywords: analytic hierarchy process, multi-criteria decision making, pair-wise comparisons, Microsoft Excel, scenarios

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Authors: Shih-Ting Yang, Chih-Yun Lin, Ming-Yu Li, Jhong-Ting Syue, Wei-Ming Huang

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Global positioning system (GPS) has become increasing precise in recent years, and the location-based service (LBS) has developed rapidly. Take the example of finding a parking lot (such as Parking apps). The location-based service can offer immediate information about a nearby parking lot, including the information about remaining parking spaces. However, it cannot provide expected search results according to the requirement situations of users. For that reason, this paper develops a “Location-based Search Approach according to Users’ Application Scenario” according to the location-based search and demand determination to help users obtain the information consistent with their requirements. The “Location-based Search Approach based on Users’ Application Scenario” of this paper consists of one mechanism and three kernel modules. First, in the Information Pre-processing Mechanism (IPM), this paper uses the cosine theorem to categorize the locations of users. Then, in the Information Category Evaluation Module (ICEM), the kNN (k-Nearest Neighbor) is employed to classify the browsing records of users. After that, in the Information Volume Level Determination Module (IVLDM), this paper makes a comparison between the number of users’ clicking the information at different locations and the average number of users’ clicking the information at a specific location, so as to evaluate the urgency of demand; then, the two-dimensional space is used to estimate the application situations of users. For the last step, in the Location-based Search Module (LBSM), this paper compares all search results and the average number of characters of the search results, categorizes the search results with the Manhattan Distance, and selects the results according to the application scenario of users. Additionally, this paper develops a Web-based system according to the methodology to demonstrate practical application of this paper. The application scenario-based estimate and the location-based search are used to evaluate the type and abundance of the information expected by the public at specific location, so that information demanders can obtain the information consistent with their application situations at specific location.

Keywords: data mining, knowledge management, location-based service, user application scenario

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Authors: Deepa Vasudevan, Thomas Northrup, Angela Stotts, Michelle Klawans

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To date, we have conducted three studies on this subject. The research done to date is through three studies. The initial study was to document that modified criteria independently identified higher numbers of overweight/obese South Asian Indians. The second study was to document physician knowledge of appropriate diagnosis of obesity among South Asian Indians. The final study was an intervention to evaluate the efficacy of a training module on improving physician diagnosis and counseling of overweight/obese Asian patients.

Keywords: South Asian Indians, obesity, physicians, BMI and waist circumference

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Authors: Tsai Yu-hsin, Chung Yi-Hsin

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The employment of an HSR system, resulting in elevation in the inter-city/-region accessibility, is likely to promote spatial interaction between places in the HSR and extended territory. The inter-city/-region travel via HSR could be, among others, affected by the land use, transportation, and location of the HSR station at both trip origin and destination ends. However, relatively few insights have been shed on these impacts and spatial patterns of the HSR travel. The research purposes, as phase one of a series of HSR related research, of this study are threefold: to analyze the general spatial patterns of HSR trips, such as the spatial distribution of trip origins and destinations; to analyze if specific land use, transportation characteristics, and trip characteristics affect HSR trips in terms of the use of HSR, the distribution of trip origins and destinations, and; to analyze the socio-economic characteristics of HSR travelers. With the Taiwan HSR starting operation in 2007, this study emphasizes on the intermediate-term impact of HSR, which is made possible with the population and housing census and industry and commercial census data and a station area intercept survey conducted in the summer 2014. The analysis will be conducted at the city, inter-city, and inter-region spatial levels, as necessary and required. The analysis tools include descriptive statistics and multivariate analysis with the assistance of SPSS, HLM and ArcGIS. The findings, on the one hand, can provide policy implications for associated land use, transportation plan and the site selection of HSR station. On the other hand, on the travel the findings are expected to provide insights that can help explain how land use and real estate values could be affected by HSR in following phases of this series of research.

Keywords: high speed rail, land use, travel, spatial pattern

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Authors: K. Drabczyk, Z. Starowicz, S. Maleczek, P. Zieba

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The army, police and fire brigade commonly use dedicated equipment based on special textile materials. The properties of these textiles should ensure human life and health protection. Equally important is the ability to use electronic equipment and this requires access to the source of electricity. Photovoltaic cells integrated with such textiles can be solution for this problem in the most of outdoor circumstances. One idea may be to laminate the cells to textile without changing their properties. The main goal of this work was analyzed lamination quality of special designed semi-flexible solar module with special textile materials as a backsheet. In the first step of investigation, the quality of lamination was determined using device equipped with dynamometer. In this work, the crystalline silicon solar cells 50 x 50 mm and thin chemical tempered glass - 62 x 62 mm and 0.8 mm thick - were used. The obtained results showed the correlation between breaking force and type of textile weave and fiber. The breaking force was in the ranges: 4.5-5.5 N, 15-20 N and 30-33 N depending on the type of wave and fiber type. To verify these observations the microscopic and FTIR analysis of fibers was performed. The studies showed the special textile can be used as a backsheet of semi-flexible solar modules. This work presents a new composition of solar module with special textile layer which, to our best knowledge, has not been published so far. Moreover, the work presents original investigations on adhesion of EVA (ethylene-vinyl acetate) polymer to textile with respect to fiber structure of laminated substrate. This work is realized for the GEKON project (No. GEKON2/O4/268473/23/2016) sponsored by The National Centre for Research and Development and The National Fund for Environmental Protection and Water Management.

Keywords: flexible solar modules, lamination process, solar cells, textile for photovoltaics

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Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

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Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: S. D. Kadir

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Background: Antibiotics have always been at the centrum of the revolution of modern microbiology. Micro-organisms and its pathogenicity, resistant organisms, inappropriate or over usage of various types of antibiotic agents are fuelled multidrug-resistant pathogenic organisms. Our present time review report mainly focuses on the therapeutic condition of antibiotic resistance and the possible roots behind the development of antibiotic resistance in Bangladesh in 2019. Methodology: The systemic review has progressed through a series of research analyses on various manuscripts published on Google Scholar, PubMed, Research Gate, and collected relevant information from established popular healthcare and diagnostic center and its subdivisions all over Bangladesh. Our research analysis on the possible assurance of antibiotic resistance been ensured by the selective medical reports and on random assay on the extent of individual antibiotic in 2019. Results: 5 research articles, 50 medical report summary, and around 5 patients have been interviewed while going through the estimation process. We have prioritized research articles where the research analysis been performed by the appropriate use of the Kirby-Bauer method. Kirby-Bauer technique is preferred as it provides greater efficiency, ensures lower performance expenditure, and supplies greater convenience and simplification in the application. In most of the reviews, clinical and laboratory standards institute guidelines were strictly followed. Most of our reports indicate significant resistance shown by the Beta-lactam drugs. Specifically by the derivatives of Penicillin's, Cephalosporin's (rare use of the first generation Cephalosporin and overuse of the second and third generation of Cephalosporin and misuse of the fourth generation of Cephalosporin), which are responsible for almost 67 percent of the bacterial resistance. Moreover, approximately 20 percent of the resistance was due to the fact of drug pumping from the bacterial cell by tetracycline and sulphonamides and their derivatives. Conclusion: 90 percent of the approximate antibiotic resistance is due to the usage of relative and true broad-spectrum antibiotics. The environment has been created by the following circumstances where; the excessive usage of broad-spectrum antibiotics had led to a condition where the disruption of native bacteria and a series of anti-microbial resistance causing a disturbance of the surrounding environments in medium, leading to a state of super-infection.

Keywords: antibiotics, antibiotic resistance, Kirby Bauer method, microbiology

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Frederik Calsius, Mirela Popa, Alexia Briassouli

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In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module.

Keywords: activity recognition, fused deep representations, fine-grained dataset, temporal modeling

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Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

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Authors: Ahmad Zahran, Ahmed Herzallah, Ahmad Ahmad, Mahran Quraan

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Generation of high DC voltages is necessary for testing the insulation material of high voltage AC transmission lines with long lengths. The harmonic and ripple contents of the output DC voltage supplied by high voltage DC circuits require the use of costly capacitors to smooth the output voltage after rectification. This paper proposes a new modular multiplier high voltage DC generator with embedded Cockcroft-Walton circuits that achieve a negligible harmonic and ripple contents of the output DC voltage without the need for costly filters to produce a nearly constant output voltage. In this new topology, Cockcroft-Walton modules are connected in series to produce a high DC output voltage. The modules are supplied by low input AC voltage sources that have the same magnitude and frequency and shifted from each other by a certain angle to eliminate the harmonics from the output voltage. The small ripple factor is provided by the smoothing column capacitors and the phase shifted input voltages of the cascaded modules. The constituent harmonics within each module are determined using Fourier analysis. The viability of the proposed DC generator for testing purposes and the effectiveness of the cascaded connection are confirmed by numerical simulations using MATLAB/Simulink.

Keywords: Cockcroft-Walton circuit, harmonics, ripple factor, HVDC generator

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Authors: Swee Kun Yap, Sachin Jangam, Suraj Vasudevan

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Experiential learning is dubbed as a highly effective way to enhance learning. Virtual reality (VR) is thus a helpful tool in providing a safe, memorable, and interactive learning environment. A class of 49 fluid mechanics students participated in starting up a pump, one of the most used equipment in the chemical industry, in VR. They experience the process in VR to familiarize themselves with the safety training and the standard operating procedure (SOP) in guided mode. Students subsequently observe their peers (in groups of 4 to 5) complete the same training. The training first brings each user through the personal protection equipment (PPE) selection, before guiding the user through a series of steps for pump startup. One of the most common feedback given by industries include the weakness of our graduates in pump design and operation. Traditional fluid mechanics is a highly theoretical module loaded with engineering equations, providing limited opportunity for visualization and operation. With VR pump, students can now learn to startup, shutdown, troubleshoot and observe the intricacies of a centrifugal pump in a safe and controlled environment, thereby bridging the gap between theory and practical application. Following the completion of the guided mode operation, students then individually complete the VR assessment for pump startup on the same day, which requires students to complete the same series of steps, without any cues given in VR to test their recollection rate. While most students miss out a few minor steps such as the checking of lubrication oil and the closing of minor drain valves before pump priming, all the students scored full marks in the PPE selection, and over 80% of the students were able to complete all the critical steps that are required to startup a pump safely. The students were subsequently tested for their recollection rate by means of an online quiz 3 weeks later, and it is again found that over 80% of the students were able to complete the critical steps in the correct order. In the survey conducted, students reported that the VR experience has been enjoyable and enriching, and 79.5% of the students voted to include VR as a positive supplementary exercise in addition to traditional teaching methods. One of the more notable feedback is the higher ease of noticing and learning from mistakes as an observer rather than as a VR participant. Thus, the cycling between being a VR participant and an observer has helped tremendously in their knowledge retention. This reinforces the positive impact VR has on learning.

Keywords: experiential learning, learning by doing, pump, unit operations, virtual reality

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Authors: Abhishek Kumar Chandra, Kaushal Kishor, Wasim Khan, Dhananjay Singh, M. S. Alam

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Single phase fluid flow through series of uniform microchannels connected via transition section (converging-diverging section with or without throat) was analytically and numerically studied to characterize the flow within the channel and in the transition sections. Three sets of microchannels of diameters 100, 184, and 249 μm were considered for investigation. Each set contains 10 numbers of microchannels of length 20 mm, connected to each other in series via transition sections. Transition section consists of either converging-diverging section with throat or without throat. The effect of non-uniformity in microchannels on pressure drop was determined by passing water/air through the set of channels for Reynolds number 50 to 1000. Compressibility and rarefaction effects in transition sections were also tested analytically and numerically for air flow. The analytical and numerical results show that these configurations can be used in enhancement of transport processes. However, converging-diverging section without throat shows superior performance over with throat configuration.

Keywords: contraction-expansion flow, integrated microchannel, microchannel network, single phase flow

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Authors: Palwinder Singh, Munish Sandhir, Tejinder Singh

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The ordinary differential equations (ODE) represent a natural framework for mathematical modeling of many real-life situations in the field of engineering, control systems, physics, chemistry and astronomy etc. Such type of differential equations can be solved by analytical methods or by numerical methods. If the solution is calculated using analytical methods, it is done through calculus theories, and thus requires a longer time to solve. In this paper, we compare the numerical accuracy of the solutions given by the three main types of one-step initial value solvers: Taylor’s Series Method, Euler’s Method and Runge-Kutta Fourth Order Method (RK4). The comparison of accuracy is obtained through comparing the solutions of ordinary differential equation given by these three methods. Furthermore, to verify the accuracy; we compare these numerical solutions with the exact solutions.

Keywords: Ordinary differential equations (ODE), Taylor’s Series Method, Euler’s Method, Runge-Kutta Fourth Order Method

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Jelena Popović, Andrijana Mićanović

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Officially, 60% of Montenegrin territory is covered in forests, but they are continually being destroyed by illegal cutting, concession politics and wildfires. Montenegrin Ecologists Society started the first dendrochronology lab in Montenegro, and data collection began in the Summer of 2021. The cores were taken from 3 localities around the peak Lisac on the mt. Prekornica, where biggest P.heldreichii forests existed until recent huge wildfires. This research is the first step towards comprehensive dendrochronology research in Montenegro. It will show how old certain forest stands of Pinus heldreichii on mountain Prekornica are, that were not destroyed in huge wildfires from the recent years. It will also show how do they correlate between each other. Per locality 15 trees were sampled. Electric sanders (150 - 2000) were used for preparation. Cores were scanned, then measured in CooRecorder. Analysis is done in Cofecha. Process will be repeated with Lintab 6 and TSAP (Time Series Analysis and Presentation for Dendrochronology and Related Applications) - Win Scientific software by Rinntech. Since this is the first dendrochronology research entirely done in Montenegro it is a foundation for the dendroclimatology research. Besides, it’ll contribute to the understanding of the life of these forests in this part of its areal, and in designing good management practices.

Keywords: dendrochronology, bosnian pine, pinus heldreichii, montenegro, forests

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Authors: Antoni Grau, Yolanda Bolea, Alberto Sanfeliu

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Mobile robots can be used in many different applications, including mapping, search, rescue, reconnaissance, hazard detection, and carpet cleaning, exploration, etc. However, they are limited due to their reliance on traditional energy sources such as electricity and oil which cannot always provide a convenient energy source in all situations. In an ever more eco-conscious world, solar energy offers the most environmentally clean option of all energy sources. Electricity presents threats of pollution resulting from its production process, and oil poses a huge threat to the environment. Not only does it pose harm by the toxic emissions (for instance CO2 emissions), it produces the combustion process necessary to produce energy, but there is the ever present risk of oil spillages and damages to ecosystems. Solar energy can help to mitigate carbon emissions by replacing more carbon intensive sources of heat and power. The challenge of this work is to propose the design and the implementation of electric battery recharge stations. Those recharge docks are based on the use of renewable energy such as solar energy (with photovoltaic panels) with the object to reduce the CO2 emissions. In this paper, a comparative study of the CO2 emission productions (from the use of different energy sources: natural gas, gas oil, fuel and solar panels) in the charging process of the Segway PT batteries is carried out. To make the study with solar energy, a photovoltaic panel, and a Buck-Boost DC/DC block has been used. Specifically, the STP005S-12/Db solar panel has been used to carry out our experiments. This module is a 5Wp-photovoltaic (PV) module, configured with 36 monocrystalline cells serially connected. With those elements, a battery recharge station is made to recharge the robot batteries. For the energy storage DC/DC block, a series of ultracapacitors have been used. Due to the variation of the PV panel with the temperature and irradiation, and the non-integer behavior of the ultracapacitors as well as the non-linearities of the whole system, authors have been used a fractional control method to achieve that solar panels supply the maximum allowed power to recharge the robots in the lesser time. Greenhouse gas emissions for production of electricity vary due to regional differences in source fuel. The impact of an energy technology on the climate can be characterised by its carbon emission intensity, a measure of the amount of CO2, or CO2 equivalent emitted by unit of energy generated. In our work, the coal is the fossil energy more hazardous, providing a 53% more of gas emissions than natural gas and a 30% more than fuel. Moreover, it is remarkable that existing fossil fuel technologies produce high carbon emission intensity through the combustion of carbon-rich fuels, whilst renewable technologies such as solar produce little or no emissions during operation, but may incur emissions during manufacture. The solar energy thus can help to mitigate carbon emissions.

Keywords: autonomous robots, CO2 emissions, DC/DC buck-boost, solar energy

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Authors: Padmini Raman

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The architecture, engineering, and construction (AEC) industry is facing enormous technological and institutional changes and challenges including the information technology and appropriate application of sustainable practices. The engineer and architect must be able to handle with a rapid pace of technological change. BIM is a unique process of producing and managing a building by exploring a digital module before the actual project is constructed and later during its construction, facility operation and maintenance. BIM has been Adopted by construction contractors and architects in the western country mostly in US and UK to improve the planning and management of construction projects. In India, BIM is a basic stage of adoption only, several issues about data acquisition and management comes during the design formation and planning of a construction project due to the complexity, ambiguity, and fragmented nature of the Indian construction industry. This paper tells about the view a strategy for India’s AEC firms to successfully implement BIM in their current working processes. By surveying and collecting data about problems faced by these architectural firms, it will be analysed how to avoid those situations from rising and, thus, introducing BIM Capabilities in such firms in the most effective way. while this application is widely accepted throughout the industry in many countries for managing project information for cost control and facilities management.

Keywords: AEC industry, building information module, Indian industry, new technology, BIM implementation in India

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Authors: H. Hussain, N. H. Mohamad

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Practical experiences have been one of the successful criteria for the Project Management course for the art and design students. There are series of events that the students have to undergo as part of their practical exercises in the learning context for Project Management courses. These series have been divided into few mini programs that involved the whole individual in each group. Therefore, the events have been one of the bench marks for these students. Through the practical experience, the task that has been given to individual has been performed according to the needs of professional practice and ethics.

Keywords: practical experience, project management, art and design students, events, programs

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Authors: Pablo Martin, Jorge Olivares, Fernando Maass

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A method to obtain analytic approximations for special function of interest in engineering and physics is described here. Each approximate function will be valid for every positive value of the variable and accuracy will be high and increasing with the number of parameters to determine. The general technique will be shown through an application to the modified Bessel function of order zero, I₀(x). The form and the calculation of the parameters are performed with the simultaneous use of the power series and asymptotic expansion. As in Padé method rational functions are used, but now they are combined with other elementary functions as; fractional powers, hyperbolic, trigonometric and exponential functions, and others. The elementary function is determined, considering that the approximate function should be a bridge between the power series and the asymptotic expansion. In the case of the I₀(x) function two analytic approximations have been already determined. The simplest one is (1+x²/4)⁻¹/⁴(1+0.24273x²) cosh(x)/(1+0.43023x²). The parameters of I₀(x) were determined using the leading term of the asymptotic expansion and two coefficients of the power series, and the maximum relative error is 0.05. In a second case, two terms of the asymptotic expansion were used and 4 of the power series and the maximum relative error is 0.001 at x≈9.5. Approximations with much higher accuracy will be also shown. In conclusion a new technique is described to obtain analytic approximations to some functions of interest in sciences, such that they have a high accuracy, they are valid for every positive value of the variable, they can be integrated and differentiated as the usual, functions, and furthermore they can be calculated easily even with a regular pocket calculator.

Keywords: analytic approximations, mathematical-physics applications, quasi-rational functions, special functions

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