Search results for: Strategic performance assessment systems

Authors: Pascal Mwenge, Tumisang Seodigeng

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The combination of world population and the third industrial revolution led to high demand for fuels. On the other hand, the decrease of global fossil 8fuels deposits and the environmental air pollution caused by these fuels has compounded the challenges the world faces due to its need for energy. Therefore, new forms of environmentally friendly and renewable fuels such as biodiesel are needed. The primary analytical techniques for methanolysis yield monitoring have been chromatography and spectroscopy, these methods have been proven reliable but are more demanding, costly and do not provide real-time monitoring. In this work, the in situ monitoring of biodiesel from sunflower oil using FTIR (Fourier Transform Infrared) has been studied; the study was performed using EasyMax Mettler Toledo reactor equipped with a DiComp (Diamond) probe. The quantitative monitoring of methanolysis was performed by building a quantitative model with multivariate calibration using iC Quant module from iC IR 7.0 software. 15 samples of known concentrations were used for the modelling which were taken in duplicate for model calibration and cross-validation, data were pre-processed using mean centering and variance scale, spectrum math square root and solvent subtraction. These pre-processing methods improved the performance indexes from 7.98 to 0.0096, 11.2 to 3.41, 6.32 to 2.72, 0.9416 to 0.9999, RMSEC, RMSECV, RMSEP and R2Cum, respectively. The R² value of 1 (training), 0.9918 (test), 0.9946 (cross-validation) indicated the fitness of the model built. The model was tested against univariate model; small discrepancies were observed at low concentration due to unmodelled intermediates but were quite close at concentrations above 18%. The software eliminated the complexity of the Partial Least Square (PLS) chemometrics. It was concluded that the model obtained could be used to monitor methanol of sunflower oil at industrial and lab scale.

Keywords: Biodiesel, calibration, chemometrics, FTIR, methanolysis, multivariate analysis, transesterification.

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Authors: Mitansha, Suzanne Wilkinson, Regan Potangaroa

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The built environment is designed, maintained, operated, and decommissioned by construction organisations, which play a significant role in providing physical resources and rebuilding infrastructures during major crises and disasters. It is evident that enhancing the resilience of construction organisations allows better responding ability and speedy recovery from disasters and acts as a boon for the nation in the face of significant disruptions. As individuals are the integral component of any organisation, hence, individual resilience is considered a critical aspect, which may boost organisational resilience of construction sector. It has been observed that individual resilience is indirectly supported by organisation’s citizenship behaviour, job performance, and career success. Not only this, it also tends to hold a directly proportional relation with job satisfaction, physical and emotional well-being affected by organisation’s work culture, whereas the resilience of organisation increases as a result of positive adaption, growth and collective learning of the employees as an entity. Moreover, indicators like situation awareness in staff and crisis related issues, effective vulnerability management, organisational leadership and culture ensured by approachable, encouraging and people-oriented leaders, are prominent for achieving organisational resilience. It, thus, becomes perceptible that both, organisational and individual resiliencies, have the potential to influence each other. Consequently, it arises a major question that how these characteristics are associated and tend to behave with respect to each other. The study, thus, aims to explore the overlapping dimensions of organisational and individual resilience to determine the impact boundaries. The research methodology of the paper would be based on systematic literature review specifically focused on the resilience of construction industry. This would provide a direct comparison of characteristics influencing individual and organisational resilience and will present the most significant indicators of individual resilience that can eventually help to enhance the resilience of construction organisations amidst any disaster or crisis.

Keywords: Construction industry, individual resilience, organisational resilience, overlapping dimension.

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Authors: J. Miranda, D. Chavarría-Barrientos, M. Ramírez-Cadena, M. E. Macías, P. Ponce, J. Noguez, R. Pérez-Rodríguez, P. K. Wright, A. Molina

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Higher education methods need to evolve because the new generations of students are learning in different ways. One way is by adopting emergent technologies, new learning methods and promoting the maker movement. As a result, Tecnologico de Monterrey is developing Open Innovation Laboratories as an immediate response to educational challenges of the world. This paper presents an Open Innovation Laboratory for Rapid Realization of Sensing, Smart and Sustainable Products (S³ Products). The Open Innovation Laboratory is composed of a set of specific resources where students and teachers use them to provide solutions to current problems of priority sectors through the development of a new generation of products. This new generation of products considers the concepts Sensing, Smart, and Sustainable. The Open Innovation Laboratory has been implemented in different courses in the context of New Product Development (NPD) and Integrated Manufacturing Systems (IMS) at Tecnologico de Monterrey. The implementation consists of adapting this Open Innovation Laboratory within the course’s syllabus in combination with the implementation of specific methodologies for product development, learning methods (Active Learning and Blended Learning using Massive Open Online Courses MOOCs) and rapid product realization platforms. Using the concepts proposed it is possible to demonstrate that students can propose innovative and sustainable products, and demonstrate how the learning process could be improved using technological resources applied in the higher educational sector. Finally, examples of innovative S³ products developed at Tecnologico de Monterrey are presented.

Keywords: Active learning, blended learning, maker movement, new product development, open innovation laboratory.

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Authors: Kyung Jin Ryu, Makhsuda Juraeva, Sang-Hyun Jeong, Dong Joo Song

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The Platform Screen Doors improve Indoor Air Quality (IAQ) in the subway station; however, and the air quality is degraded in the subway tunnel. CO2 concentration and indoor particulate matter value are high in the tunnel. The IAQ level in subway tunnel degrades by increasing the train movements. Air-curtain installation reduces dusts, particles and moving toxic smokes and permits traffic by generating virtual wall. The ventilation systems of the subway tunnel need improvements to have better air-quality. Numerical analyses might be effective tools analyze the flowfield inside the air-curtain installed subway tunnel. The ANSYS CFX software is used for steady computations of the airflow inside the tunnel. The single-track subway tunnel has the natural shaft, the mechanical shaft, and the PSDs installed stations. The height and width of the tunnel are 6.0 m and 4.0 m respectively. The tunnel is 400 m long and the air-curtain is installed at the top of the tunnel. The thickness and the width of the air-curtain are 0.08 m and 4 m respectively. The velocity of the air-curtain changes between 20 - 30 m/s. Three cases are analyzed depending on the installing location of the air-curtain. The discharged-air through the natural shafts increases as the velocity of the air-curtain increases when the air-curtain is installed between the mechanical and the natural shafts. The pollutant-air is exhausted by the mechanical and the natural shafts and remained air is pushed toward tunnel end. The discharged-air through the natural shaft is low when the air-curtain installed before the natural shaft. The mass flow rate decreases in the tunnel after the mechanical shaft as the air-curtain velocity increases. The computational results of the air-curtain installed tunnel become basis for the optimum design study. The air-curtain installing location is chosen between the mechanical and the natural shafts. The velocity of the air-curtain is fixed as 25 m/s. The thickness and the blowing angles of the air-curtain are the design variables for the optimum design study. The object function of the design optimization is maximizing the discharged air through the natural shaft.

Keywords: air-curtain, indoor air quality, single-track subway tunnel

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Authors: Emre Kara, Şura Karakuzu, Ahmet F. Geylan, Metehan Demir, Kadir Koç, Halil Aykul

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The material selection in the design of the sandwich structures is very crucial aspect because of the positive or negative influences of the base materials to the mechanical properties of the entire panel. In the literature, it was presented that the selection of the skin and core materials plays very important role on the behavior of the sandwich. Beside this, the use of the correct adhesive can make the whole structure to show better mechanical results and behavior. In the present work, the static three-point bending tests were performed on the sandwiches having an aluminum alloy foam core, the skins made of three different types of fabrics and two different commercial adhesives (flexible polyurethane and toughened epoxy based) at different values of support span distances by aiming the analyses of their flexural performance in terms of absorbed energy, peak force values and collapse mechanisms. The main results of the flexural loading are: force-displacement curves obtained after the bending tests, peak force and absorbed energy values, collapse mechanisms and adhesion quality. The experimental results presented that the sandwiches with epoxy based toughened adhesive and the skins made of S-Glass Woven fabrics indicated the best adhesion quality and mechanical properties. The sandwiches with toughened adhesive exhibited higher peak force and energy absorption values compared to the sandwiches with flexible adhesive. The use of these sandwich structures can lead to a weight reduction of the transport vehicles, providing an adequate structural strength under operating conditions.

Keywords: Adhesive and adhesion, Aluminum foam, Bending, Collapse mechanisms.

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

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Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: Abeer Aljohani

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The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: Ololade M. Aminu

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Child abuse is a significant issue worldwide, affecting the socio-development and mental and physical health of young individuals. It is the maltreatment of a child by an adult or a child. This paper focuses on child abuse in Communities in Lagos State, Nigeria. The aim of this study is to investigate the extent of child abuse and its impact on the mood, social activities, self-worth, concentration, and academic performance of children in Communities in Lagos State. The primary research instrument used in this study was the interview (Forensic), which consisted of two sections. The first section gathered data on the details of the child and the forms and impacts of abuse experienced, while the second section focused on family structure and parental style. The study found that children who experienced various forms of abuse, such as emotional, neglect, physical, or sexual abuse, were hesitant to report it out of fear of threats or even death from the abuser. These abused children displayed withdrawn behaviour, depression, and low self-worth and underperformed academically compared to their peers who did not experience abuse. The findings align with socio-learning theory and intergenerational transmission of violence, which suggest that parents and caregivers who engage in child abuse often do so because they themselves experienced or witnessed abuse as children, thereby normalizing violence. The study highlights the prevalent issue of child abuse in Lagos State and emphasizes the need for advocacy programs and capacity building to raise awareness about child abuse and prevention. The distribution of the Child’s Rights Act/Child’s Right Law in various sectors is also recommended to underscore the importance of protecting the rights of children. Additionally, the inclusion of courses on child abuse in the school curriculum is proposed to ensure children are educated on recognizing and reporting abuse.

Keywords: Child abuse, physical ill-treatment, neglect, parental style, psychological effect, sexual offence, reporting.

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Authors: Peng Hao Wang, Garam Kim, Ronald Sterkenburg

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The applications of composite materials within the aviation industry has been increasing at a rapid pace.  However, the growing applications of composite materials have also led to growing demand for more tooling to support its manufacturing processes. Tooling and tooling maintenance represents a large portion of the composite manufacturing process and cost. Therefore, the industry’s adaptability to new techniques for fabricating high quality tools quickly and inexpensively will play a crucial role in composite material’s growing popularity in the aviation industry. One popular tool fabrication technique currently being developed involves additive manufacturing such as 3D printing. Although additive manufacturing and 3D printing are not entirely new concepts, the technique has been gaining popularity due to its ability to quickly fabricate components, maintain low material waste, and low cost. In this study, a team of Purdue University School of Aviation and Transportation Technology (SATT) faculty and students investigated the effectiveness of a 3D printed composite compression mold. A 3D printed composite compression mold was fabricated by 3D scanning a steel valve cover of an aircraft reciprocating engine. The 3D printed composite compression mold was used to fabricate carbon fiber versions of the aircraft reciprocating engine valve cover. The 3D printed composite compression mold was evaluated for its performance, durability, and dimensional stability while the fabricated carbon fiber valve covers were evaluated for its accuracy and quality. The results and data gathered from this study will determine the effectiveness of the 3D printed composite compression mold in a mass production environment and provide valuable information for future understanding, improvements, and design considerations of 3D printed composite molds.

Keywords: Additive manufacturing, carbon fiber, composite tooling, molds.

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Authors: Sotiria Tzivinikou, Dimitra Kagkara

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Teacher’s sense of self-efficacy can affect significantly both teacher’s and student’s performance. More specific, self-efficacy is associated with the learning outcomes as well as student’s motivation and self-efficacy. For example, teachers with high sense of self-efficacy are more open to innovations and invest more effort in teaching. In addition to this, effective inclusive education is associated with higher levels of teacher’s self-efficacy. Pre-service teachers with high levels of self-efficacy could handle student’s behavior better and more effectively assist students with special educational needs. Teacher preparation programs are also important, because teacher’s efficacy beliefs are shaped early in learning, as a result the quality of teacher’s education programs can affect the sense of self-efficacy of pre-service teachers. Usually, a number of pre-service teachers do not consider themselves well prepared to work with students with special educational needs and do not have the appropriate sense of self-efficacy. This study aims to investigate the factors that contribute to the improvement of the sense of self-efficacy of pre-service special educators by using an academic practicum training program. The sample of this study is 159 pre-service special educators, who also participated in the academic practicum training program. For the purpose of this study were used quantitative methods for data collection and analysis. Teacher’s self-efficacy was assessed by the teachers themselves with the completion of a questionnaire which was based on the scale of Teacher’s Sense of Efficacy Scale. Pre and post measurements of teacher’s self-efficacy were taken. The results of the survey are consistent with those of the international literature. The results indicate that a significant number of pre-service special educators do not hold the appropriate sense of self-efficacy regarding teaching students with special educational needs. Moreover, a quality academic training program constitutes a crucial factor for the improvement of the sense of self-efficacy of pre-service special educators, as additional for the provision of high quality inclusive education.

Keywords: Inclusive education, pre-service, self-efficacy, training program.

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Authors: Viviane Nascimento, Dania Mehmod

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Introduction: The Coronavirus (COVID-19) disease was firstly reported in Asia at the end of 2019 and became a pandemic at the beginning of 2020. It resulted in a significant impact over the global economy and the health care systems around the world. The immediate measure adopted worldwide to contain the virus was mainly the lockdown and curfews. This certainly had an important impact on expats workers due to the financial insecurity, culture barrier and distance from the family. Saudi Arabia has one of the largest flows of foreign workers in the world and expats are the majority of the workforce. The aim of this essay was assessing the psychological impact of COVID-19 in non-health care expats living in Saudi Arabia. Methods: The study was conducted in a construction company in Riyadh with non-health care employees. The cross-sectional study protocol was approved by the company's executive management. Employees who verbally agreed to participate in the study were asked to anonymously answer a questionnaire validated for behavioral research (DASS-21). In addition, a second questionnaire was created to assess feelings and emotions. Results: More than a third of participants screened positive for one or more psychological symptoms (depression, anxiety and stress) on the DASS-21 scale. Moreover, it was observed an increase on negative feelings on the additional questionnaire. Conclusion: This study reveals an increase on negative feelings and psychological symptoms among non-health care migrant workers during the COVID-19 pandemic. In light of this, it is crucial to understand the emotional effects caused by the pandemic on migrant workers in order to create supportive and informative strategies minimizing the emotional impact on this vulnerable group.

Keywords: COVID-19 pandemic, Saudi Arabia, psychological effects, migrant workers.

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Authors: L. Piñeiro, N. Cobas, L. Gómez-Limia, S. Martínez, I. Franco

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The swordfish (Xiphias gladius) is a large oceanic fish of high commercial value, which is widely distributed in waters of the world’s oceans. They are considered to be an important source of high quality proteins, vitamins and essential fatty acids, although only half of the population follows the recommendation of nutritionists to consume fish at least twice a week. Swordfish is consumed worldwide because of its low fat content and high protein content. It is generally sold as fresh, frozen, and as pieces or slices. The aim of this study was to evaluate the effect of salting and frying on the composition of the water-soluble vitamins (B₂, B₃, B₉ and B₁₂) and fat-soluble vitamins (A, D, and E) of swordfish. Three loins of swordfish from Pacific Ocean were analyzed. All the fishes had a weight between 50 and 70 kg and were transported to the laboratory frozen (-18 ºC). Before the processing, they were defrosted at 4 ºC. Each loin was sliced and salted in brine. After cleaning the slices, they were divided into portions (10×2 cm) and fried in olive oil. The identification and quantification of vitamins were carried out by high-performance liquid chromatography (HPLC), using methanol and 0.010% trifluoroacetic acid as mobile phases at a flow-rate of 0.7 mL min^-1. The UV-Vis detector was used for the detection of the water- and fat-soluble vitamins (A and D), as well as the fluorescence detector for the detection of the vitamin E. During salting, water and fat-soluble vitamin contents remained constant, observing an evident decrease in the values of vitamin B₂. The diffusion of salt into the interior of the pieces and the loss of constitution water that occur during this stage would be related to this significant decrease. In general, after frying water-soluble and fat-soluble vitamins showed a great thermolability with high percentages of retention with values among 50–100%. Vitamin B₃ is the one that exhibited higher percentages of retention with values close to 100%. However, vitamin B₉ presented the highest losses with a percentage of retention of less than 20%.

Keywords: Frying, HPLC, salting, swordfish, vitamins.

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Authors: Z. Abdollahi Biron, P. Pisu

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Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). A connected vehicle system is essentially a set of vehicles communicating through a network to exchange their information with each other and the infrastructure. Although this interconnection of the vehicles can be potentially beneficial in creating an efficient, sustainable, and green transportation system, a set of safety and reliability challenges come out with this technology. The first challenge arises from the information loss due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Such scenario may lead to degraded or even unsafe operation which could be potentially catastrophic. Secondly, faulty sensors and actuators can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Further, sending that faulty sensor information to other vehicles and failure in actuators may significantly affect the safe operation of the overall vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a fault diagnosis scheme that deals with these aforementioned challenges. Specifically, the conventional CACC algorithm is modified by adding a Kalman filter-based estimation algorithm to suppress the effect of lost information under unreliable network. Further, a sliding mode observer-based algorithm is used to improve the sensor reliability under faults. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

Keywords: Fault diagnostics, communication network, connected vehicles, packet drop out, platoon.

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Authors: B. Gechbaia, K. Goletiani, G. Gabedava, N. Mikeltadze

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At this stage, Georgia is a country which is actively involved in the European integration process, for which the primary priority is effective integration in the European education system. The modern Georgian higher education system is the process of establishing a new sociocultural reality, whose main priorities are determined by the Quality System as a continuous cycle of planning, implementation, checking and acting. Obviously, in this situation, the issue of management of education institutions comes out in the foreground, since the proper planning and implementation of personnel management processes is one of the main determinants of the company's performance. At the same time, one of the most important factors is the psychological comfort of the personnel, ensuring their protection and efficiency of stress management policy.

The purpose of this research is to determine how intensely the relationship is between the psychological comfort of the personnel and the efficiency of the quality system in the institution as the quality assurance mechanisms of educational institutions affect the stability of personnel, prevention and management of the stressful situation. The research was carried out within the framework of the Internal Grant Project «The Role of Organizational Culture in the Process of Settlement of Management of Stress and Conflict, Georgian Reality and European Experience » of the Batumi Navigation Teaching University, based on the analysis of the survey results of target groups. The small-scale research conducted by us has revealed that the introduction of quality assurance system and its active implementation increased the quality of management of Georgian educational institutions, increased the level of universal engagement in internal and external processes and as a result, it has improved the quality of education as well as social and psychological comfort indicators of the society.

Keywords: Quality assurance, effective management, stability of personnel, psychological comfort, stress management.

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Authors: Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

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Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are increasingly important in automated customer service. These models, adept at recognizing complex relationships between input and output sequences, are essential for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the model’s focus during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the context of chatbots utilizing the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Using the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k = 3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k = 3). These findings emphasize the crucial influence of selecting an appropriate attention-scoring function to enhance the performance of seq2seq models for chatbots, particularly highlighting the model integrating tanh activation as a promising approach to improving chatbot quality in customer support contexts.

Keywords: Attention weight, chatbot, encoder-decoder, neural generative attention, score function, sequence-to-sequence.

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Authors: Himanshu Shekhar Maharana, S. K .Dash

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Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution. 

Keywords: Economic load dispatch, constriction factor based particle swarm optimization, dispersed particle swarm optimization, weight improved particle swarm optimization, ramp rate and constriction factor based particle swarm optimization.

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Authors: Yongsheng Guo, Mirza Muhammad Naseer, Xiaoxian Zhu

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This study explores the perceptions and conducts of Chinese foreign direct investment (FDI) in Africa from ethical and cultural perspectives. It offers a better understanding of how ethical and cultural factors affect Chinese investment in Africa and how the investment projects performed in Africa from both Chinese investors and African stakeholders’ perceptions. It adopted a grounded theory approach and conducted 30 in-depth interviews with corporate managers. Grounded theory models are developed to link the ethical and cultural factors, actions, and consequences. Results reveal that some ethical concepts like the unity of humans and nature, benevolence, virtue and responsibility, and cultural traits including propriety, righteousness, sincerity, equilibrium, long-term orientation, and principles affect Chinese investors when making investments in Africa. Most Chinese investors harmonize with local managers, cooperate with each other, and are gentle and courteous to partners. They take stable and steady actions and invest in infrastructure and agriculture projects and adopt a virtue governance system in the organization. This study finds that consequently, Chinese investors and local partners take complementary advantages, make achievements sequentially, and therefore both sides can win. They recognize great potentials and make sustainable development in Africa to achieve the Great Together in the future. This study proposes a Chinese ethics and governance system including economic, social, and political perspectives and compares it with alternative systems. It makes implications to the world island theory and propose suggestions to solve the Clash of Civilizations problem.

Keywords: Foreign Direct Investment, Ethics, National Culture, China, Africa.

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Authors: Yongquan Zhao, Bo Huang

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Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.

Keywords: Hybrid spatial-temporal-spectral fusion, high resolution synthetic imagery, least square regression, sparse representation, spectral transformation.

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Authors: Palak J. Shukla, Atul K. Desai, Chentankumar D. Modhera

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For any country in the world, it has become a priority to protect the critical infrastructure from looming risks of terrorism. In any infrastructure system, the structural elements like lower floors, exterior columns, walls etc. are key elements which are the most susceptible to damage due to blast load. The present study revisits the state of art review of the design and analysis of reinforced concrete panels subjected to blast loading. Various aspects in association with blast loading on structure, i.e. estimation of blast load, experimental works carried out previously, the numerical simulation tools, various material models, etc. are considered for exploring the current practices adopted worldwide. Discussion on various parametric studies to investigate the effect of reinforcement ratios, thickness of slab, different charge weight and standoff distance is also made. It was observed that for the simulation of blast load, CONWEP blast function or equivalent numerical equations were successfully employed by many researchers. The study of literature indicates that the researches were carried out using experimental works and numerical simulation using well known generalized finite element methods, i.e. LS-DYNA, ABAQUS, AUTODYN. Many researchers recommended to use concrete damage model to represent concrete and plastic kinematic material model to represent steel under action of blast loads for most of the numerical simulations. Most of the studies reveal that the increase reinforcement ratio, thickness of slab, standoff distance was resulted in better blast resistance performance of reinforced concrete panel. The study summarizes the various research results and appends the present state of knowledge for the structures exposed to blast loading.

Keywords: Blast phenomenon, experimental methods, material models, numerical methods.

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Authors: M. A. Wijewardane, S. A. N. C. Sudasinghe, H. K. G. Punchihewa, W. K. D. L. Wickramasinghe, S. A. Philip, M. R. S. U. Kumara

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Visual comfort is one of the major parameters that can be taken to measure the human comfort in any environment. If the provided illuminance level in a working environment does not meet the workers visual comfort, it will lead to eye-strain, fatigue, headache, stress, accidents and finally, poor productivity. However, improvements in lighting do not necessarily mean that the workplace requires more light. Unnecessarily higher illuminance levels will also cause poor visual comfort and health risks. In addition, more power consumption on lighting will also result in higher energy costs. So, during this study, visual comfort and the illuminance requirement for the workers in textile/apparel industry were studied to perform different tasks (i.e. cutting, sewing and knitting) at their workplace. Experimental studies were designed to identify the optimum illuminance requirement depending upon the varied fabric colour and type and finally, energy saving potentials due to controlled illuminance level depending on the workforce requirement were analysed. Visual performance of workers during the sewing operation was studied using the ‘landolt ring experiment’. It was revealed that around 36.3% of the workers would like to work if the illuminance level varies from 601 lux to 850 lux illuminance level and 45.9% of the workers are not happy to work if the illuminance level reduces less than 600 lux and greater than 850 lux. Moreover, more than 65% of the workers who do not satisfy with the existing illuminance levels of the production floors suggested that they have headache, eye diseases, or both diseases due to poor visual comfort. In addition, findings of the energy analysis revealed that the energy-saving potential of 5%, 10%, 24%, 8% and 16% can be anticipated for fabric colours, red, blue, yellow, black and white respectively, when the 800 lux is the prevailing illuminance level for sewing operation.

Keywords: Landolt ring experiment, lighting energy consumption, illuminance, textile and apparel industry, visual comfort.

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Authors: Charles Chikwendu Okpala, Christopher Chukwutoo Ihueze

Abstract:

As a technique that reduces variation in a product by lessening the sensitivity of the design to sources of variation, rather than by controlling their sources, Taguchi Robust Design entails the designing of ideal goods, by developing a product that has minimal variance in its characteristics and also meets the desired exact performance. This paper examined the concept of the manufacturing approach and its application to brake pad product of an automotive parts manufacturing company. Although the firm claimed that only defects, excess inventory, and over-production were the few wastes that grossly affect their productivity and profitability, a careful study and analysis of their manufacturing processes with the application of Single Minute Exchange of Dies (SMED) tool showed that the waste of waiting is the fourth waste that bedevils the firm. The selection of the Taguchi L9 orthogonal array which is based on the four parameters and the three levels of variation for each parameter revealed that with a range of 2.17, that waiting is the major waste that the company must reduce in order to continue to be viable. Also, to enhance the company’s throughput and profitability, the wastes of over-production, excess inventory, and defects with ranges of 2.01, 1.46, and 0.82, ranking second, third, and fourth respectively must also be reduced to the barest minimum. After proposing -33.84 as the highest optimum Signal-to-Noise ratio to be maintained for the waste of waiting, the paper advocated for the adoption of all the tools and techniques of Lean Production System (LPS), and Continuous Improvement (CI), and concluded by recommending SMED in order to drastically reduce set up time which leads to unnecessary waiting.

Keywords: Taguchi Robust Design, signal to noise ratio, Single Minute Exchange of Dies, lean production system, waste.

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

Abstract:

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: Modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition.

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Authors: Bassam Istanbouli

Abstract:

With the technology evolving every day and with the increase in global competition, industries are always under the pressure to be the best. They need to provide good quality products at competitive prices, when and how the customer wants them.  In order to achieve this level of service, products and their respective supply chain processes need to be flexible and evolvable; otherwise changes will be extremely expensive, slow and with many combinatorial effects. Those combinatorial effects impact the whole organizational structure, from a management, financial, documentation, logistics and specially the information system Enterprise Requirement Planning (ERP) perspective. By applying the normalized system concept/theory to segments of the supply chain, we believe minimal effects, especially at the time of launching an organization global software project. The purpose of this paper is to point out that if an organization wants to develop a software from scratch or implement an existing ERP software for their business needs and if their business processes are normalized and modular then most probably this will yield to a normalized and modular software system that can be easily modified when the business evolves. Another important goal of this paper is to increase the awareness regarding the design of the business processes in a software implementation project. If the blueprints created are normalized then the software developers and configurators will use those modular blueprints to map them into modular software. This paper only prepares the ground for further studies;  the above concept will be supported by going through the steps of developing, configuring and/or implementing a software system for an organization by using two methods: The Software Development Lifecycle method (SDLC) and the Accelerated SAP implementation method (ASAP). Both methods start with the customer requirements, then blue printing of its business processes and finally mapping those processes into a software system.  Since those requirements and processes are the starting point of the implementation process, then normalizing those processes will end up in a normalizing software.

Keywords: Blueprint, ERP, SDLC, Modular.

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Authors: Mahmoud Roushdy, Mohamed El Naggar, Ahmed Yehia Abdelaziz

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Anchored sheet pile has been used worldwide as front quay walls for decades. With the increase in vessel drafts and weights, those sheet pile walls need to be upgraded by increasing the depth of the dredging line in front of the wall. One of the upgrades for the sheet pile wall is to add a separated platform to the system, where the platform is structurally separated from the front wall. The platform is structurally separated from the front wall. This paper presents a numerical investigation utilizing finite element analysis on the behavior of separated relieve platforms installed within existing anchored sheet pile quay walls. The investigation was done in two steps: a verification step followed by a parametric study. In the verification step, the numerical model was verified based on field measurements performed by others. The validated model was extended within the parametric study to a series of models with different backfill soils, separation gap width, and number of pile rows supporting the platform. The results of the numerical investigation show that using stiff clay as backfill soil (neglecting consolidation) gives better performance for the front wall and the first pile row adjacent to sandy backfills. The degree of compaction of the sandy backfill slightly increases lateral deformations but reduces bending moment acting on pile rows, while the effect is minor on the front wall. In addition, the increase in the separation gap width gradually increases bending moments on the front wall regardless of the backfill soil type, while this effect is reversed on pile rows (gradually decrease). Finally, the paper studies the possibility of deepening the basin along with the separation to take advantage of the positive separation effect on piles, and front wall.

Keywords: Anchored sheet pile, relieving platform, separation gap, upgrade quay wall.

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Authors: Stephanie Ho

Abstract:

This project utilizes principles derived from the Surrealist movement to prioritize creative and critical thinking in secondary English Language Arts (ELA). The implementation of Surrealist-style pedagogies within an ELA classroom will be rooted in critical, radical pedagogy, which addresses the injustices caused by economic-oriented educational systems. The use of critical pedagogy will enable the subversive artistic and political aims of Surrealism to be transmitted to a classroom context. Through aesthetic reading strategies, appreciative questioning and dialogue, students will actively critique the power dynamics which structure (and often restrict) their lives. Within the ELA domain, cost-effective approaches often replace the actual “arts” of ELA. This research will therefore explore how Surrealist-oriented pedagogies could restore imaginative freedom and deconstruct conceptual barriers (normative standards, curricular constraints, and status quo power relations) in secondary ELA. This research will also examine how Surrealism can be used as a political and pedagogical model to treat societal problems mirrored in ELA classrooms. The stakeholders are teachers, as they experience constant pressure within their practices. Similarly, students encounter rigorous, results-based pressures. These dynamics contribute to feelings of powerlessness, thus reinforcing a formulaic model of ELA. The ELA curriculum has potential to create laboratories for critical discussion and active movement towards social change. This proposed research strategy of Surrealist-oriented pedagogies could enable students to experiment with social issues and develop senses of agency and voice that reflect awareness of contemporary society while simultaneously building their ELA skills.

Keywords: Arts-informed pedagogies, language arts, literature, Surrealism.

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Authors: M. Govindarajan

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In decision tree approach is most useful in classification problem. With this technique, tree is constructed to model the classification process. There are two basic steps in the technique: building the tree and applying the tree to the database. This paper describes a proposed C5.0 classifier that performs rulesets, cross validation and boosting for original C5.0 in order to reduce the optimization of error ratio. The feasibility and the benefits of the proposed approach are demonstrated by means of medial data set like hypothyroid. It is shown that, the performance of a classifier on the training cases from which it was constructed gives a poor estimate by sampling or using a separate test file, either way, the classifier is evaluated on cases that were not used to build and evaluate the classifier are both are large. If the cases in hypothyroid.data and hypothyroid.test were to be shuffled and divided into a new 2772 case training set and a 1000 case test set, C5.0 might construct a different classifier with a lower or higher error rate on the test cases. An important feature of see5 is its ability to classifiers called rulesets. The ruleset has an error rate 0.5 % on the test cases. The standard errors of the means provide an estimate of the variability of results. One way to get a more reliable estimate of predictive is by f-fold –cross- validation. The error rate of a classifier produced from all the cases is estimated as the ratio of the total number of errors on the hold-out cases to the total number of cases. The Boost option with x trials instructs See5 to construct up to x classifiers in this manner. Trials over numerous datasets, large and small, show that on average 10-classifier boosting reduces the error rate for test cases by about 25%.

Keywords: C5.0, Error Ratio, text mining, training data, test data.

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo

Abstract:

Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.

Keywords: Convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model.

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Authors: B. Sadepovich Maikanov, Z. Shabanbayevich Adilbekov, R. Husainovna Mustafina, L. Tyulegenovna Auteleyeva

Abstract:

This study involves detailed information about contaminants of honey in the Republic of Kazakhstan. The requirements of the technical regulation ‘Requirements to safety of honey and bee products’ and GOST 19792-2001 were taken into account in this research. Contamination of honey by antibiotics wqs determined by the IEA (immune-enzyme analysis), Ridder analyzer and Tecna produced test systems. Voltammetry (TaLab device) was used to define contamination by salts of heavy metals and gamma-beta spectrometry, ‘Progress BG’ system, with preliminary ashing of the sample of honey was used to define radioactive contamination. This article pointed out that residues of chloramphenicol were detected in 24% of investigated products, in 22% of them –streptomycin, in 7.3% - sulfanilamide, in 2.4% - tylosin, and in 12% - combined contamination was noted. Geographically, the greatest degree of contamination of honey with antibiotics occurs in the Northern Kazakhstan – 54.4%, and Southern Kazakhstan - 50%, and the lowest in Central and Eastern Kazakhstan with 30% and 25%, respectively. Generally, pollution by heavy metals is within acceptable limits, but the contamination from lead is highest in the Akmola region. The level of radioactive cesium and strontium is also within acceptable concentrations. The highest radioactivity in terms of cesium was observed in the East Kazakhstan region - 49.00±10 Bq/kg, in Akmola, North Kazakhstan and Almaty - 12.00±5, 11.05±3 and 19.0±8 Bq/kg, respectively, while the norm is 100 Bq/kg. In terms of strontium, the radioactivity in the East Kazakhstan region is 25.03±15 Bq/kg, while in Akmola, North Kazakhstan and Almaty regions it is 12.00±3, 10.2±4 and 1.0±2 Bq/kg, respectively, with the norm of 80 Bq/kg. This accumulation is mainly associated with the environmental degradation, feeding and treating of bees. Moreover, in the process of collecting nectar, external substances can penetrate honey. Overall, this research determines factors and reasons of honey contamination.

Keywords: Antibiotics, contamination of honey, honey, radionuclides.

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Authors: Yan Su, L. C. Chan

Abstract:

A novel typical day prediction model have been built and validated by the measured data of a grid-connected solar photovoltaic (PV) system in Macau. Unlike conventional statistical method used by previous study on PV systems which get results by averaging nearby continuous points, the present typical day statistical method obtain the value at every minute in a typical day by averaging discontinuous points at the same minute in different days. This typical day statistical method based on discontinuous point averaging makes it possible for us to obtain the Gaussian shape dynamical distributions for solar irradiance and output power in a yearly or monthly typical day. Based on the yearly typical day statistical analysis results, the maximum possible accumulated output energy in a year with on site climate conditions and the corresponding optimal PV system running time are obtained. Periodic Gaussian shape prediction models for solar irradiance, output energy and system energy efficiency have been built and their coefficients have been determined based on the yearly, maximum and minimum monthly typical day Gaussian distribution parameters, which are obtained from iterations for minimum Root Mean Squared Deviation (RMSD). With the present model, the dynamical effects due to time difference in a day are kept and the day to day uncertainty due to weather changing are smoothed but still included. The periodic Gaussian shape correlations for solar irradiance, output power and system energy efficiency have been compared favorably with data of the PV system in Macau and proved to be an improvement than previous models.

Keywords: Grid Connected, RMSD, Solar PV System, Typical Day.

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Authors: Salam Alzubaidi, G. Fantoni, F. Failli, M. Frosolini

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The dynamic facility layout problem is a really critical issue in the competitive industrial market; thus, solving this problem requires robust design and effective simulation systems. The sustainable simulation requires inputting reliable and accurate data into the system. So this paper describes an automated system integrated into the real environment to measure the duration of the material handling operations, collect the data in real-time, and determine the variances between the actual and estimated time schedule of the operations in order to update the simulation software and redesign the facility layout periodically. The automated method- time measurement system collects the real data through using Radio Frequency-Identification (RFID) and Internet of Things (IoT) technologies. Hence, attaching RFID- antenna reader and RFID tags enables the system to identify the location of the objects and gathering the time data. The real duration gathered will be manipulated by calculating the moving average duration of the material handling operations, choosing the shortest material handling path, and then updating the simulation software to redesign the facility layout accommodating with the shortest/real operation schedule. The periodic simulation in real-time is more sustainable and reliable than the simulation system relying on an analysis of historical data. The case study of this methodology is in cooperation with a workshop team for producing mechanical parts. Although there are some technical limitations, this methodology is promising, and it can be significantly useful in the redesigning of the manufacturing layout.

Keywords: Dynamic facility layout problem, internet of things, method time measurement, radio frequency identification, simulation.

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