Search results for: player performance prediction

Authors: Burak Erkayman

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The manufacturing and service organizations are in the need of using ERP systems to integrate many functions from purchasing to storage, production planning to calculation of costs. Using ERP systems by the integration in the level of information provides companies remarkable advantages in terms of profitability, productivity and efficiency in processes. Cloud computing is one of the most significant changes in information and communication technology. The developments in Cloud Computing attract business world to take advantage of this field. Cloud Computing means much more storage area, more cost saving and faster data transfer rate. In addition to these, it presents new business models, new field of study and practicable solutions for anyone’s use. These developments make inevitable the implementation of ERP systems to cloud environment. In this study, the performance of ERP systems in cloud environment is analyzed through various performance criteria and a comparison between traditional and cloud-ERP systems is presented. At the end of study the transformation and the future of ERP systems is discussed.

Keywords: cloud-ERP, ERP system performance, information system transformation

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Authors: G. Massaad, E. Roziere, A. Loukili, L. Izoret

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Based on the monitoring of mass, hydrostatic weighing, and the amount of leached OH- we deduced the nature of leached and precipitated minerals, the amount of lost aggregates and the evolution of porosity and cracking during the sulphate attack. Using these information, we are able to draw the volume / mass changes brought by mineralogical variations and cracking of the cement matrix. Then we defined a new performance indicator, the averaged density, capable to resume along the test of sulphate attack the occurred physicochemical variation occurred in the cementitious matrix and then highlight.

Keywords: monitoring strategy, performance indicator, sulphate attack, mechanism of degradation

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Authors: Neveen AlQasas, Daniel Johnson

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Membranes for water treatment are an established technology that attracts great attention due to its simplicity and cost effectiveness. However, membranes in operation suffer from the adverse effect of membrane fouling. Bio-fouling is a phenomenon that occurs at the water-membrane interface, and is a dynamic process that is initiated by the adsorption of dissolved organic material, including biomacromolecules, on the membrane surface. After initiation, attachment of microorganisms occurs, followed by biofilm growth. The biofilm blocks the pores of the membrane and consequently results in reducing the water flux. Moreover, the presence of a fouling layer can have a substantial impact on the membrane separation properties. Understanding the mechanism of the initiation phase of biofouling is a key point in eliminating the biofouling on membrane surfaces. The adhesion and attachment of different fouling materials is affected by the surface properties of the membrane materials. Therefore, surface properties of different polymeric materials had been studied in terms of their surface energies and Hansen solubility parameters (HSP). The difference between the combined HSP parameters (HSP distance) allows prediction of the affinity of two materials to each other. The possibilities of measuring the HSP of different polymer films via surface measurements, such as contact angle has been thoroughly investigated. Knowing the HSP of a membrane material and the HSP of a specific foulant, facilitate the estimation of the HSP distance between the two, and therefore the strength of attachment to the surface. Contact angle measurements using fourteen different solvents on five different polymeric films were carried out using the sessile drop method. Solvents were ranked as good or bad solvents using different ranking method and ranking was used to calculate the HSP of each polymeric film. Results clearly indicate the absence of a direct relation between contact angle values of each film and the HSP distance between each polymer film and the solvents used. Therefore, estimating HSP via contact angle alone is not sufficient. However, it was found if the surface tensions and viscosities of the used solvents are taken in to the account in the analysis of the contact angle values, a prediction of the HSP from contact angle measurements is possible. This was carried out via training of a neural network model. The trained neural network model has three inputs, contact angle value, surface tension and viscosity of solvent used. The model is able to predict the HSP distance between the used solvent and the tested polymer (material). The HSP distance prediction is further used to estimate the total and individual HSP parameters of each tested material. The results showed an accuracy of about 90% for all the five studied films

Keywords: surface characterization, hansen solubility parameter estimation, contact angle measurements, artificial neural network model, surface measurements

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Authors: Joaquin Ticzon, Christian Demition, Jaime Honra

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Additive manufacturing (AM) or 3D printing has been one of the emerging trends present in various industries, particularly in prototyping. This review focuses on the impact of additive manufacturing on a motor vehicle's performance aiming to investigate potential advancements to further revolutionize the way parts are manufactured. One of the most common problems faced in the automotive industry is carbon footprint emissions from motor vehicles, which was stated to be remedied by lightweight; additively manufactured parts helped reduce these emissions due to weight reduction provided by additively manufactured parts. Composed of various techniques for AM as well as materials utilized during the manufacturing process, which differ in terms of the quality and performance it provides during its application on the final product. Given this, the generative design will not be discussed in such a detailed manner because the focus will revolve around the effects on the performance of a vehicle due to additively manufactured parts.

Keywords: additive manufacturing (AM), automotive, computer aided design (CAD), generative design

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Authors: Okoro Ikechukwu Ralph, Fuat Kara

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The discoveries of hydrocarbon reserves has clearly drifted offshore, and in deeper waters - areas where the industry still has limited knowledge; and that were hitherto, regarded as being out of reach. This shift presents significant and increased challenges in technology requirements needed to guarantee safety of personnel, environment and equipment; ensure high reliability of installed equipment; and provide high level of confidence in security of investment and company reputation. Nowhere are these challenges more apparent than on subsea well integrity and production performance. The past two decades has witnessed enormous rise in deep and ultra-deep water offshore field developments for the recovery of hydrocarbons. Subsea installed equipment at the seabed has been the technology of choice for these developments. This paper discusses the role of Subsea Control module (SCM) as a vital factor for deep-water well integrity and production performance. A case study for Deep-water well integrity and production performance is analysed.

Keywords: offshore reliability, production performance, subsea control module, well integrity

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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

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Authors: Abdulaziz Alkathiry

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Concussion is a common brain injury that affect physical and cognitive performance. While several studies indicated that adolescents are more likely to develop concussion, in the last decade concussion has been mainly explored in adults. Migraine has been identified as a common symptom reported after concussion and was tied with worse prognoses. Hence, we aimed to investigate the effect of migraine on functional performance and self-reported symptoms in children with concussion. This cross-sectional study involved 35 symptomatic children aged 9 – 17 years recruited within 1 year from their concussion injury at a tertiary balance center. Participants’ symptoms and functional performance were assessed using the post-concussion symptoms scale (PCSS) and the functional gait assessment (FGA) respectively. Concussed children with migraine showed significantly worse symptoms including fatigue, sleeping impairment, difficulty concentrating, and visual problems (P < 0.05). Functional performance didn’t show differences between concussed children with and without migraine. Although concussed children with and without migraine didn’t show any differences on functional performance, worse cognitive symptoms were found in concussed children with migraine. A customized treatment approach is indicated in the presence of migraine for the management of children with concussion. Keywords: Concussion; Migraine; Balance; Post-Concussion Symptoms Scale; Functional Gait Assessment

Keywords: concussion, migraine, post-concussion symptoms scale, functional gait assessment, balance

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Authors: Qingyu Zhang, Helin Ma, Lili Weng, Mei Cao

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Green supply chain management has been an important organizational strategy to reduce environmental risks and improve financial performance. Firms have to adopt green supply chain practices to meet the official regulations and reduce peer pressure in China. This paper exhibits an empirical study of the drivers of green supply chain management practices and the environmental and economic performance of green supply chain management implementation in Chinese firms. While China is the fastest-growing emerging economy, it has paid a high ecological price. It is reported that China hosts 7 of the world’s 10 most polluted cities. The continued environmental deterioration and the resultant heightened regulatory control and public scrutiny have posed new operating challenges to firms conducting business in China. These challenges make the country an ideal setting to conduct the present study. A research questionnaire was developed to gather data in China. The questionnaire targeted managers and employees in Chinese companies. The data were collected in the last quarter of 2015, involving industries such as electronic & communicational equipment, textile & clothing, pharmaceutical & healthcare, and so on. This study confirms and validates that (1) both internal and external drivers play a significant role in the implementation of green supply chain management practices; (2) green purchase and investment recovery have a significant impact on firms’ environmental and economic performance; (3) with the improvement of the firms’ environmental performance, their economic performance will improve.

Keywords: economic performance, environmental performance, external driver, green supply chain management

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Authors: Amit Arora, P. M. V. Subbarao, R. S. Agarwal

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This study attempts to find promising locations of upwash delta winglets for an inline finned tube heat exchanger. Later, location of winglets that delivers highest improvement in thermal performance is identified. Numerical results clearly showed that optimally located upwash delta winglets not only improved the thermal performance of fin area in tube wake and tubes, but also improved overall thermal performance of heat exchanger.

Keywords: apparent friction factor, delta winglet, fin and tube heat exchanger, longitudinal vortices

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Authors: Natalia Strobel, Jan Kratzer

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Achieving effective innovation is a complex task and during this process firms (especially SMEs) often face obstacles. However, research into obstacles to innovation focusing on SMEs is very scarce. In this study, we propose a theoretical framework for describing these obstacles to innovation and investigate their influence on the innovative performance of SMEs. Data were collected in 2013 through face-to-face interviews with executives of 49 technology SMEs from Germany. The semi-structured interviews were designed on the basis of scales for measuring innovativeness, financial/competitive performance and obstacles to innovation, next to purely open questions. We find that the internal obstacles lack the know-how, capacity overloading, unclear roles and tasks, as well as the external obstacle governmental bureaucracy negatively influence the innovative performance of SMEs. However, in contrast to prior findings this study shows that cooperation ties of firms might also negatively influence the innovative performance.

Keywords: innovation, innovation process, obstacles, SME

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Authors: Fatemeh Sadat Sharifi

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In this research, a barotropic model was employed to consider the tidal studies in the Persian Gulf and Oman Sea, where the only sufficient force was the tidal force. To do that, a finite-difference, free-surface model called Regional Ocean Modeling System (ROMS), was employed on the data over the Persian Gulf and Oman Sea. To analyze flow patterns of the region, the results of limited size model of The Finite Volume Community Ocean Model (FVCOM) were appropriated. The two points were determined since both are one of the most critical water body in case of the economy, biology, fishery, Shipping, navigation, and petroleum extraction. The OSU Tidal Prediction Software (OTPS) tide and observation data validated the modeled result. Next, tidal elevation and speed, and tidal analysis were interpreted. Preliminary results determine a significant accuracy in the tidal height compared with observation and OTPS data, declaring that tidal currents are highest in Hormuz Strait and the narrow and shallow region between Iranian coasts and Islands. Furthermore, tidal analysis clarifies that the M_2 component has the most significant value. Finally, the Persian Gulf tidal currents are divided into two branches: the first branch converts from south to Qatar and via United Arab Emirate rotates to Hormuz Strait. The secondary branch, in north and west, extends up to the highest point in the Persian Gulf and in the head of Gulf turns counterclockwise.

Keywords: numerical model, barotropic tide, tidal currents, OSU tidal prediction software, OTPS

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Authors: Qingli Lei

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This study investigates the impact of family socioeconomic status and parental involvement on the academic performance of elementary school students, specifically focusing on migrant students in China. The findings reveal that gender has a stronger influence on academic performance compared to local status and parental tutoring time. Female students tend to achieve higher scores than males. Parental education level does not significantly predict academic performance, while parent tutoring time does have a significant impact. Furthermore, there is a significant interaction between local status and parental education level, indicating that migrant students with lower-educated parents perform better than their local counterparts, while local children excel when their parents' education levels are higher. These results emphasize the importance of parental involvement, particularly for immigrant students, and highlight the need for interventions that enhance parental engagement in education to improve academic outcomes.

Keywords: academic performance, family socioeconomic status, migrant students, parental involvement

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Razika Ihaddadene, Nabila Ihaddadene

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This paper aims to highlight the importance of the application of the IEC 61724 standard in the study of the performance analysis of photovoltaic power plants on a monthly and annual scale. Likewise, the comparison of two photovoltaic power plants with two different climates was carried out in order to determine the effect of climatic parameters on the analysis of photovoltaic performances. All data from the Ain Skhouna and Adrar photovoltaic power plants for 2018 and the data from the Saida1 field for one month in 2019 were used. The results of the performance analysis according to the indicated standard show that the Saida PV power plant performs better than the Adrar PV power plant, which is due to the effect of increasing the ambient temperature. Increasing ambient temperature increases losses decreases system efficiency and performance ratio. It presents a key element in the proper functioning of PV plants.

Keywords: pv power plants, IEC 61724 norm, grid connected pv, algeria

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Authors: Mukesh Kumar, Onkar Singh, Naveen Kumar, Amar Deep

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The waste cooking oil is taken as feedstock for biodiesel production. For this research, waste cooking oil is collected from many hotels and restaurants, and then biodiesel is prepared for experimentation purpose. The prepared biodiesel is mixed with mineral diesel in the proportion of 10%, 20%, and 30% to perform tests on a diesel engine. The experimental analysis is carried out at different load conditions to analyze the impact of the blending ratio on the performance and emission parameters. When the blending proportion of biodiesel is increased, then the highest pressure reduces due to the fall in the calorific value of the blended mixture. Experimental analysis shows a promising decrease in nitrogen oxides (NOx). A mixture of 20% biodiesel and mineral diesel is the best negotiation, mixing ratio, and beyond that, a remarkable reduction in the outcome of the performance has been observed.

Keywords: alternative sources, diesel engine, emissions, performance

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Authors: Abdolmajid Mosleh, Afzal Ghasimi

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The purpose of this research is to investigate and identify effective factors that can improve the quality of personal performance in industrial companies. In the present study, it was assumed that the hidden variables of talent management could be explained by an important part of the variance in improving the quality of employee performance. This research is targeted in terms of applied research. The statistical population of the research is SAIPA automobile company with a number (N=10291); the sample of 380 people was selected based on the Cochran formula in a random sampling method among employed people. The measurement tool in this research was a questionnaire of 33 items with a control questionnaire that included two talent management departments (talent identification and talent exploitation) and improvements in staff performance (enhancement of technical and specialized capabilities, managerial capability, organizational interaction, and communication). The reliability of the internal consistency method was confirmed by the Cronbach's alpha coefficient and the two half-ways. In order to determine the validity of the questionnaire structure, confirmatory factor analysis was used. Based on the results of the data analysis, the effect of talent management on improving the quality of staff performance was confirmed. Based on the results of inferential statistics and structural equations of the proposed model, it had high fitness.

Keywords: employee performance, talent management, performance improvement, SAIPA automobile manufacturing company

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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: Nurul Nadrah

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As a result of the COVID-19 pandemic, a movement control order was implemented, leading to the adoption of online learning as a substitute for conventional classroom instruction. Thus, this study aims to determine the differences in skill performance between online learning and conventional methods among nursing students. We employed a quasi-experimental design with purposive sampling, involving a total of 59 nursing students, and used online learning as the intervention. As a result, the study found there was a significant difference in student skill performance between online learning and conventional methods. As a conclusion, in times of hardship, it is necessary to implement alternative pedagogical approaches, especially in critical fields like nursing, to ensure the uninterrupted progression of educational programs. This study suggests that online learning can be effectively employed as a means of imparting knowledge to nursing students during their training.

Keywords: nursing education, online learning, skill performance, conventional learning method

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Authors: Eudjen Cinc

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Work with a human voice, especially with a child s voice and cultivating the sound of the choir, presents an area of crucial importance for a conductor. We use the term conductor because it needs to be understood that regardless of whether we have in front of us an amateur or a professional choir, whether they are singers with a wealth of experience or children who are still developing and educating their inner ear so that in the future they could contribute to the development of choir music, the person who stands in front of the group and works with them, needs to have the characteristics of a conductor. Voice formation is a long-term process, without which there is no success in both solo and collective music performance.

Keywords: music group, conductor, collective, performance

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Authors: Tugrul Oktay, Mehmet Konar, Mohamed Abdallah Mohamed, Murat Aydin, Firat Sal, Murat Onay, Mustafa Soylak

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In this paper, it is aimed to improve autonomous flight performance of a load-carrying (payload: 3 kg and total: 6kg) unmanned aerial vehicle (UAV) through active wing and horizontal tail active morphing and also integrated autopilot system parameters (i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing and horizontal tail during flight) design. For this purpose, a loadcarrying UAV (i.e. ZANKA-II) is manufactured in Erciyes University, College of Aviation, Model Aircraft Laboratory is benefited. Optimum values of UAV parameters and autopilot parameters are obtained using a stochastic optimization method. Using this approach autonomous flight performance of UAV is substantially improved and also in some adverse weather conditions an opportunity for safe flight is satisfied. Active morphing and integrated design approach gives confidence, high performance and easy-utility request of UAV users.

Keywords: unmanned aerial vehicles, morphing, autopilots, autonomous performance

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Authors: P. Chaipanya, R. Keawchai, W. Sombatsanongkhun, S. Jantaramporn

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To manage the demand of wireless communication that has been dramatically increased, switched beam antenna in smart antenna system is focused. Implementation of switched beam antennas at mobile terminals such as notebook or mobile handset is a preferable choice to increase the performance of the wireless communication systems. This paper proposes the low complexity switched beam antenna using single element of antenna which is suitable to implement at mobile terminal. Main beam direction is switched by changing the positions of short circuit on the radiating patch. There are four cases of switching that provide four different directions of main beam. Moreover, the performance in terms of Signal to Interference Ratio when utilizing the proposed antenna is compared with the one using omni-directional antenna to confirm the performance improvable.

Keywords: switched beam, shorted circuit, single element, signal to interference ratio

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Authors: Yusuf Ziya Ayık, Ferhat Kahveci

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Currently, database management systems have various tools such as backup and maintenance, and also provide statistical information such as resource usage and security. In terms of query performance, this paper covers query optimization, views, indexed tables, pre-computation materialized view, query performance analysis in which query plan alternatives can be created and the least costly one selected to optimize a query. Indexes and views can be created for related table columns. The literature review of this study showed that, in the course of time, despite the growing capabilities of the database management system, only database administrators are aware of the need for dealing with archival and transactional data types differently. These data may be constantly changing data used in everyday life, and also may be from the completed questionnaire whose data input was completed. For both types of data, the database uses its capabilities; but as shown in the findings section, instead of repeating similar heavy calculations which are carrying out same results with the same query over a survey results, using materialized view results can be in a more simple way. In this study, this performance difference was observed quantitatively considering the cost of the query.

Keywords: cost of query, database management systems, materialized view, query performance

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Mohamed El-Khateeb

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Encompassing inventory, warehousing, and transportation management, logistics is a crucial predictor of firm performance. This has been extensively proven by extant literature in business and operations management. Logistics is also a fundamental determinant of a country's ability to access international markets. Available studies in international and transport economics have shown that limited transport infrastructure and underperforming transport services can severely affect international competitiveness. However, the evidence lacks the overall impact of logistics performance-encompassing all inventory, warehousing, and transport components- on global trade. In order to fill this knowledge gap, the paper uses a gravitational trade model with 155 countries from all geographical regions between 2007 and 2018. Data on logistics performance is obtained from the World Bank's Logistics Performance Index (LPI). First, the relationship between logistics performance and a country’s total trade is estimated, followed by a breakdown by the economic sector. Then, the analysis is disaggregated according to the level of technological intensity of traded goods. Finally, after evaluating the intensive margin of trade, the relevance of logistics infrastructure and services for the extensive trade margin is assessed. Results suggest that: (i) improvements in both logistics infrastructure and services are associated with export growth; (ii) manufactured goods can significantly benefit from these improvements, especially when both exporting and importing countries increase their logistics performance; (iii) the quality of logistics infrastructure and services becomes more important as traded goods are technology-intensive; and (iv) improving the exporting country's logistics performance is essential in the intensive margin of trade while enhancing the importing country's logistics performance is more relevant in the extensive margin.

Keywords: low-cost, recycling, reuse, solid waste, wastewater treatment

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Authors: N. Hiranmai, M. L. S. Deva Kumar

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In this project, it is planned to carry out an experimental investigation on 4- stroke Direct Injection Diesel Engine, which is a single-cylinder, four-stroke, water-cooled, and constant speed engine capable of developing a power output of 3.7 kW at 1500 rpm, run with diesel fuel and also with different proportions of Safflower oil methyl esters, with a piston having five number of grooves on its crown to create turbulence. Various performance parameters, such as brake power, specific fuel consumption, and thermal efficiency, are calculated. At all the load conditions, the performance of the engine is obtained better for blend B40 (40% Safflower oil + 60% of Diesel). At different load conditions, Brake thermal Efficiency (ηbth) is comparatively more for all blends than that for Diesel. At different load conditions, ηith is less for blend B40.

Keywords: four-stroke engine, diesel, safflower oil, engine performance, emissions.

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Authors: Parham Bakhtiari

Abstract:

Music performance anxiety (MPA) is a prevalent concern among musicians, manifesting through cognitive, physiological, and behavioral symptoms that can severely impact performance quality and overall well-being. This systematic review synthesizes research on coping strategies employed by musicians to manage MPA from 2016 to 2023, identifying a range of psychological and physical interventions, including acceptance and commitment therapy (ACT), cognitive behavioral therapy (CBT), mindfulness, and yoga. Findings reveal that these interventions significantly reduce anxiety and enhance psychological resilience, with ACT showing notable improvements in psychological flexibility. Physical approaches also proved effective in mitigating physiological symptoms associated with MPA. However, challenges such as small sample sizes and methodological limitations hinder the generalizability of results. The review underscores the necessity for multi-faceted intervention strategies tailored to the unique needs of different musicians and emphasizes the importance of future research employing larger, randomized controlled designs to further validate these findings. Overall, this review serves as a comprehensive resource for musicians seeking effective coping strategies for managing performance anxiety, highlighting the critical interplay between mental and physical approaches in promoting optimal performance outcomes.

Keywords: anxiety, performance, coping, music, strategy

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Authors: N. K. Chougule, G. V. Parishwad

Abstract:

The pin fin heat sink is a novel heat transfer device to transfer large amount of heat through with very small temperature differences and it also possesses large uniform cooling characteristics. Pin fins are widely used as elements that provide increased cooling for electronic devices. Increasing demands regarding the performance of such devices can be observed due to the increasing heat production density of electronic components. For this reason, extensive work is being carried out to select and optimize pin fin elements for increased heat transfer. In this paper, the effects of design parameters and the optimum design parameters for a Pin-Fin heat sink (PFHS) under multi-jet impingement case with thermal performance characteristics have been investigated by using Taguchi methodology based on the L9 orthogonal arrays. Various design parameters, such as pin-fin array size, gap between nozzle exit to impingement target surface (Z/d) and air velocity are explored by numerical experiment. The average convective heat transfer coefficient is considered as the thermal performance characteristics. The analysis of variance (ANOVA) is applied to find the effect of each design parameter on the thermal performance characteristics. Then the results of confirmation test with the optimal level constitution of design parameters have obviously shown that this logic approach can effective in optimizing the PFHS with the thermal performance characteristics. The analysis of the Taguchi method reveals that, all the parameters mentioned above have equal contributions in the performance of heat sink efficiency. Experimental results are provided to validate the suitability of the proposed approach.

Keywords: Pin Fin Heat Sink (PFHS), Taguchi method, CFD, thermal performance

Procedia PDF Downloads 248