Search results for: benchmark collection

Authors: Mohammad Ali Rajaeifar, Oliver Heidrich

Abstract:

Nowadays, Li-ion batteries are vastly disseminated and the battery market is expected to experience a huge growth during next decade especially in terms of traction batteries. As the automotive industry moving towards the electrification of the powertrain, more raw/critical materials and energy are extracted while on the other hand, concerns are made regarding the scarcity of the materials as well as environmental issues regarding the destiny of the spent batteries. In this regards, recycling could play a vital role in the supply chain, leading reutilization of key battery materials and also reducing environmental burden related to the use of batteries. The aim of this paper is to review the previous and state-of-the-art treatments for recycling of Li-ion batteries. All the treatments method from mechanical, mild-thermal, pyrometallurgical and hydrometallurgical as well as combined methods for recycling of Li-ion batteries were considered in the study. There are various treatment methods that are economical, but they are not environmentally friendly or vice versa. This is due to the fact that the benefits of the Li-ion batteries recycling could be affected by different factors such as the amount of spent batteries available, the quality of the recovered material, the energy and material consumption by the process itself and environmental burdens caused by required logistics. Finally, a preliminary work sheet of possible route for recycling of spent Li-ion batteries was presented through the course of this study. Overall, it is worth quoting that recycling processes generally consumes a great deal of energy and auxiliary materials. Moreover, the collection of spent products from waste streams represents additional environmental efforts. Therefore, developing and optimizing efficient collection and separation technologies is essential to achieve sustainability goals.

Keywords: hydrometallurgical treatment, Li-ion batteries, mild-thermal treatment, mechanical treatment, recycling, pyrometallurgical treatment

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Authors: Zainal Abu Zatim, Hafizah Omar Zaki

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Substantial research had been done on refining employee’s customer service performance. Thus, there were very limited empirical studies that are engage in an inter-organizational climate study in assessing employee’s customer service performance. With the current economic situation as well as emerging needs and requirements, all businesses either from public or private sector serving customers put greater attention on fulfilling those needs and requirements. In this state of affairs, the act of polishing its employees’ skills, knowledge, teamwork and passion is very important in ensuring better performance deliverance. A study conducted in one of the telecommunication service provider company in Malaysia had been done to test its inter-organizational climate study. The Internal Climate Study was done to benchmark opinions and perceptions of its employees. The study had provided baseline information about perceptions that exist in the internal environment and ways forward to improve customer service performance. The approach used is through the use of focus group and qualitative interview.

Keywords: employees, Customer Service Performance, inter-organizational climate study, public and private sector

Procedia PDF Downloads 380

Authors: Phenias Nsabimana, Jérôme W. Some, Hilda Vasanthakaalam, Stefaan De Henauw, Souheila Abbeddou

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Tools for the dietary assessment in adults are limited in low- and middle-income settings. The objective of this study was to develop and validate a semi-quantitative food frequency questionnaire (FFQ) against the multiple pass-24 h recall tool for use in urban and rural Rwanda. A total of 212 adults (154 females and 58 males), 18-49 aged, including 105 urban and 107 rural residents, from the four regions of Rwanda, were recruited in the present study. A multiple-pass 24- H recall technique was used to collect dietary data in both urban and rural areas in four different rounds, on different days (one weekday and one weekend day), separated by a period of three months, from November 2020 to October 2021. The details of all the foods and beverages consumed over the 24h period of the day prior to the interview day were collected during face-to-face interviews. A list of foods, beverages, and commonly consumed recipes was developed by the study researchers and ten research assistants from the different regions of Rwanda. Non-standard recipes were collected when the information was available. A single semi-quantitative FFQ was also developed in the same group discussion prior to the beginning of the data collection. The FFQ was collected at the beginning and the end of the data collection period. Data were collected digitally. The amount of energy and macro-nutrients contributed by each food, recipe, and beverage will be computed based on nutrient composition reported in food composition tables and weight consumed. Median energy and nutrient contents of different food intakes from FFQ and 24-hour recalls and median differences (24-hour recall –FFQ) will be calculated. Kappa, Spearman, Wilcoxon, and Bland-Altman plot statistics will be conducted to evaluate the correlation between estimated nutrient and energy intake found by the two methods. Differences will be tested for their significance and all analyses will be done with STATA 11. Data collection was completed in November 2021. Data cleaning is ongoing and the data analysis is expected to be completed by July 2022. A developed and validated semi-quantitative FFQ will be available for use in dietary assessment. The developed FFQ will help researchers to collect reliable data that will support policy makers to plan for proper dietary change intervention in Rwanda.

Keywords: food frequency questionnaire, reproducibility, 24-H recall questionnaire, validation

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

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Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

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Authors: Zhongmin Wang, Lin Wei, Hengshan Zhang, Tianhua Chen, Yimin Zhou

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The assembly line balancing problems are signficant in mass production systems. In order to deal with the uncertainties that practically exist but barely mentioned in the literature, this paper develops a mathematic model with an optimisation algorithm to solve the assembly line balancing problem with uncertainty operation time. The developed model is able to work with a variable number of workstations under the uncertain environment, aiming to obtain the minimal number of workstation and minimal idle time for each workstation. In particular, the proposed approach first introduces the concept of protection time that closely works with the uncertain operation time. Four dominance rules and the mechanism of determining up and low bounds are subsequently put forward, which serve as the basis for the proposed branch and bound algorithm. Experimental results show that the proposed work verified on a benchmark data set is able to solve the uncertainties efficiently.

Keywords: assembly lines, SALBP-UOT, uncertain operation time, branch and bound algorithm.

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Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: M. Yusuf Ansari, Asad Abbas

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Proton exchange membrane (PEM) fuel cell is a very efficient and promising energy conversion device. Although Nafion® is considered as benchmark materials for membrane used in PEM fuel cell, it has limitations that restrict its uses. Alternative materials for the membrane is always a challenging field for researchers. Sulfonated poly(ether ether ketone) (SPEEK) is one of the promising material for membrane due to its chemical and mechanical stability and lower cost. In this work, SPEEK is synthesized, and property booster such as silica nanoparticles and polyvinyl alcohol (PVA) are also added to analyse changes in properties such as water uptake, IEC, and conductivity. It has been found that adding PVA support high water uptake and proton conductivity but at large amount of PVA reduces the proton conductivity due to very high water uptake. Adding silica enhances water uptake and proton conductivity.

Keywords: PEM Membrane, sulfonated poly (ether ether ketone) (SPEEK), silica fumes (SiO2), polyvinyl alcohol (PVA)

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Bin Liu

Abstract:

As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors.

Keywords: protein remote homology detection, protein fold recognition, top frequency profile, support vector machines

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Jun-Ki Choi

Abstract:

The usage of valuable resources and the potential for waste generation at the end of the life cycle of photovoltaic (PV) technologies necessitate a proactive planning for a PV recycling infrastructure. To ensure the sustainability of PV in large scales of deployment, it is vital to develop and institute low-cost recycling technologies and infrastructure for the emerging PV industry in parallel with the rapid commercialization of these new technologies. There are various issues involved in the economics of PV recycling and this research examine those at macro and micro levels, developing a holistic interpretation of the economic viability of the PV recycling systems. This study developed mathematical models to analyze the profitability of recycling technologies and to guide tactical decisions for allocating optimal location of PV take-back centers (PVTBC), necessary for the collection of end of life products. The economic decision is usually based on the level of the marginal capital cost of each PVTBC, cost of reverse logistics, distance traveled, and the amount of PV waste collected from various locations. Results illustrated that the reverse logistics costs comprise a major portion of the cost of PVTBC; PV recycling centers can be constructed in the optimally selected locations to minimize the total reverse logistics cost for transporting the PV wastes from various collection facilities to the recycling center. In the micro- process level, automated recycling processes should be developed to handle the large amount of growing PV wastes economically. The market price of the reclaimed materials are important factors for deciding the profitability of the recycling process and this illustrates the importance of the recovering the glass and expensive metals from PV modules.

Keywords: photovoltaic, recycling, mathematical models, sustainability

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Authors: Usman Jilani, Ibad Khurram, Irshad Hussain

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Environmentally sustainable waste management is a challenging task as it involves multiple and diverse economic, environmental, technical and regulatory issues. Municipal Solid Waste Management (MSWM) is more challenging in developing countries like Pakistan due to lack of awareness, technology and human resources, insufficient funding, inefficient collection and transport mechanism resulting in the lack of a comprehensive waste management system. This work presents an overview of current MSWM practices in Peshawar, the provincial capital of Khyber Pakhtunkhwa, Pakistan and proposes a better and sustainable integrated solid waste management system with incineration (Waste to Energy) option. The diverted waste would otherwise generate revenue; minimize land fill requirement and negative impact on the environment. The proposed optimized solution utilizing scientific techniques (like mathematical modeling, optimization algorithms and GIS) as decision support tools enhances the technical & institutional efficiency leading towards a more sustainable waste management system through incorporating: - Improved collection mechanisms through optimized transportation / routing and, - Resource recovery through incineration and selection of most feasible sites for transfer stations, landfills and incineration plant. These proposed methods shift the linear waste management system towards a cyclic system and can also be used as a decision support tool by the WSSP (Water and Sanitation Services Peshawar), agency responsible for the MSWM in Peshawar.

Keywords: municipal solid waste management, incineration, mathematical modeling, optimization, GIS, Peshawar

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Authors: G. M. Komaki, S. Mobin, E. Teymourian, S. Sheikh

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This paper addresses minimizing the makespan of the distributed permutation flow shop scheduling problem. In this problem, there are several parallel identical factories or flowshops each with series of similar machines. Each job should be allocated to one of the factories and all of the operations of the jobs should be performed in the allocated factory. This problem has recently gained attention and due to NP-Hard nature of the problem, metaheuristic algorithms have been proposed to tackle it. Majority of the proposed algorithms require large computational time which is the main drawback. In this study, a general variable neighborhood search algorithm (GVNS) is proposed where several time-saving schemes have been incorporated into it. Also, the GVNS uses the sophisticated method to change the shaking procedure or perturbation depending on the progress of the incumbent solution to prevent stagnation of the search. The performance of the proposed algorithm is compared to the state-of-the-art algorithms based on standard benchmark instances.

Keywords: distributed permutation flow shop, scheduling, makespan, general variable neighborhood search algorithm

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Authors: Mahdi Mottahedi, Peter Zahn, Armin Lechler, Alexander Verl

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Optimal topology of components leads to the maximum stiffness with the minimum material use. For the generation of these topologies, normally algorithms are employed, which tackle manufacturing limitations, at the cost of the optimal result. The global optimum result with penalty factor one, however, cannot be fabricated with conventional methods. In this article, an additive manufacturing method is introduced, in order to enable the production of global topology optimization results. For a benchmark, topology optimization with higher and lower penalty factors are performed. Different algorithms are employed in order to interpret the results of topology optimization with lower factors in many microstructure layers. These layers are then joined to form the final geometry. The algorithms’ benefits are then compared experimentally and numerically for the best interpretation. The findings demonstrate that by implementation of the selected algorithm, the stiffness of the components produced with this method is higher than what could have been produced by conventional techniques.

Keywords: topology optimization, additive manufacturing, 3D-printer, laminated object manufacturing

Procedia PDF Downloads 318

Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Chen Chou, Feng-Tyan Lin

Abstract:

Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

Procedia PDF Downloads 255

Authors: Khitam Ibrahem Shlash Mohammad

Abstract:

Background: Postnatal depression is a focus of considerable research attention, but little is known about the pattern of stress across this period. Objective: to investigate the prevalence of stress after childbirth for Jordanian women and identify associated risk factors. Method: Design: A descriptive cross-sectional study. Participants were recruited six to eight weeks postpartum, provided personal, social and obstetric information, and completed the stress subscale of Depression Anxiety and Stress Scale (DASS-S), the Maternity Social Support Scale (MSSS), and Perceived Self-Efficacy Scale (PSES). Setting: maternal and child health care clinics in four health care centres in Maan city in Southern Jordan. Participants: Arabic speaking women (n = 324) between the ages of 18 and 45 years, six to eight weeks postpartum, primiparous or multiparous at low risk for obstetric complications. Data collection took place between October 2015 and January 2016. Ethical clearance was obtained prior to data collection. Results: The prevalence of postpartum stress among Jordanian women was 39.8 %. A regression analysis revealed that occupation, low social support, financial problems, difficult marital relationships, difficult relationship with family-in-law, giving birth to a female baby, difficult childbirth, and low self-efficacy were associated with postpartum stress. Conclusions and implications for practice: Jordanian women need support during pregnancy, during and after childbirth. Postpartum emotional support and assessment of symptoms of stress need to be incorporated into routine practice. The opportunity for open discussion along with increased awareness and clarification of common misconceptions about postpartum stress is necessary.

Keywords: prevalence, postpartum, stress, Jordanian women

Procedia PDF Downloads 332

Authors: R. Gounina-Allouane, N. Ouali, F. Z. Berrabah, A. Bentaleb

Abstract:

For a long time, the Gram-positive spore-forming bacteria Bacillus thuringiensis (Bt) has been widely used in biological control against devastating and disease vectors insects. This is due to the insecticidal activity of its crystalline parasporal inclusion (crystals) predominantly comprised of one or more proteins (Cry and Cyt proteins) also called δ-endotoxins, produced during sporulation. The shape and composition of Bt crystals vary among strains and crystalline proteins are extremely varied (more than 475 cry gene were discovered). The insecticidal activity of Bt crystals is very well studied, thus their insecticidal mode of action is well established, however, their antimicrobial effect is largely unknown. The lack of data on the antimicrobial effect of crystalline proteins of Bt and the need for searching new antimicrobial molecules encouraged us to carried out this study. The antibacterial effect of δ-endotoxines produced by two Bt stains; a strain isolated from soil at northern of Algeria (Bt 7.2.B), and a strain isolated from a bioinsecticide (Bacillus thuringiensis var aizawai), activated by proteolysis, was assayed on clinical bacterial strains and ATCC collection ones respectively. Gram positive and negative clinical bacterial strains (Escherichia coli, Klebsiella pneumonaie, Pseudomonas aeruginosa, Staphylococcus aureus) were sensitive to activated Bt 72B endotoxins. Similarly, bacterial strains from ATCC collection (Escherichia coli ATCC 25922, Pseudomonas aerugenosa ATCC 27853, Staphylococcus aureus ATCC 25923) were sensitive to activated B. thuringiensis var aizawai δ-endotoxines. The activated δ-endotoxins were separated by SDS-PAGE.

Keywords: Bacillus thuringiensis, crystals, cry proteins, δ-endotoxins, antibacterial activity

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Authors: Pinar Dogan, Merve Tarhan, Ahu Kurklu

Abstract:

Syrian refugees who sheltering due to war has protected by the Government of Turkey since 2011. Since Syria was a medium-low income country prior to the war, it is known that chronic health problems weren’t common among citizens. However, it is also known that they frequently use health services in our country because of the spread of infectious and acute diseases due to insufficient sanitation and crowding after the war. This study was planned to compare the social distances of the community against the Syrian refugees and the perceptions of accessing health care services. The descriptive-cross sectional study was carried out on 1262 individuals living in Istanbul. A questionnaire form consisted of Personal Information Form, The Bogardus Social Distance Scale (BSDS) and The Survey of Access to Healthcare Services (AHS) was used as data collection tool. Descriptive tests and chi-square test were used for statistical analysis. It was found that the majorities of participants was satisfied with the health services and were waiting for more than 40 minutes to be examined. It was determined that participants have high scores from BSDS. At the same time, the majority of participants stated that their level of access to health care is diminishing due to refugees. Participants who experienced disruption in access to health services due to refugees were found to have higher scores from BSDS. The data collection process in the study will continue until 2400 individuals are reached. With these conclusions, it is considered necessary that the effect of the presence of the refugees in reaching the health services and nursing care of the society should be revealed through extensive researches to be conducted in Turkey.

Keywords: health care services, nursing care, social distances, Syrian refugees

Procedia PDF Downloads 116

Authors: Neliswa Dyosi

Abstract:

Purpose - Using an integrated Technology-Organization-Environment (TOE) framework and the model of technology appropriation (MTA) as a theoretical lens, this interpretive qualitative study seeks to understand and explain the factors that influence the appropriation, non-appropriation, and disappropriation of bitcoin as a payment method by South African retailers. Design/methodology/approach –The study adopts the interpretivist philosophical paradigm. Multiple case studies will be adopted as a research strategy. For data collection, the study follows a qualitative approach. Qualitative data will be collected from the six retailers in various industries. Semi-structured interviews and documents will be used as the data collection techniques. Purposive and snowballing sampling techniques will be used to identify participants within the organizations. Data will be analyzed using thematic analysis. Originality/value - Using the deduction approach, the study seeks to provide a descriptive and explanatory contribution to theory. The study contributes to theory development by integrating the MTA and TOE frameworks as a means to understand technology adoption behaviors of organizations, in this case, retailers. This is also the first study that looks at an integrated approach of the Technology-Organization-Environment (TOE) framework and the MTA framework to understand the adoption and use of a payment method. South Africa is ranked amongst the top ten countries in the world on cryptocurrency adoption. There is, however, still a dearth of literature on the current state of adoption and usage of bitcoin as a payment method in South Africa. The study will contribute to the existing literature as bitcoin cryptocurrency is gaining popularity as an alternative payment method across the globe.

Keywords: cryptocurrency, bitcoin, payment methods, blockchain, appropriation, online retailers, TOE framework, disappropriation, non-appropriation

Procedia PDF Downloads 113

Authors: Benselama Amel, Ounane S. Mohamed, Bekki Abdelkader

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A collection of 20 isolates from fresh Nodules of the legume plant Lablab purpureus was isolated. These isolates have been authenticated by seedling inoculation grown in jars containing sand. The results obtained after two months of culture have revealed that the 20 isolates (100% of the isolates) are able to nodulate their host plants. The results obtained were analyzed statistically by ANOVA using the software statistica and had shown that the effect of the inoculation has significantly improved all the growth parameters (the height of the plant and the dry weight of the aerial parts and roots, and the number of nodules). We have evaluated the tolerance of all strains of the collection to the major stress factors as the salinity, pH and extreme temperature. The osmotolerance reached a concentration up to 1710mm of NaCl. The strains were also able to grow on a wide range of pH, ranging from 4.5 to 9.5, and temperature, between 4°C and 40°C. Also, we tested the effect of the acidity, aluminum and ferric deficit on the Lablab-rhizobia symbiosis. Lablab purpureus has not been affected by the presence of high concentrations of aluminum. On the other hand, iron deficiency has caused a net decrease in the dry biomass of the aerial part. The results of all the phenotypic characters have been treated by the statistical Minitab software, the numerical analysis had shown that these bacterial strains are divided into two distinct groups at a level of similarity of 86 %. The SDS-PAGE was carried out to determine the profile of the total protein of the strains. The coefficients of similarity of polypeptide bands between the isolates and strains reference (Bradyrhizobium, Mesorizobium sp.) confirm that our strain belongs to the groups of rhizobia.

Keywords: SDS-PAGE, rhizobia, symbiosis, phenotypic characterization, Lablab purpureus

Procedia PDF Downloads 284

Authors: R. Gounina-Allouane, N. Ouali, F. Z. Berrabah, A. Bentaleb

Abstract:

For a long time, the Gram-positive spore-forming bacteria Bacillus thuringiensis (Bt) has been widely used in biological control against devastating and disease vectors insects. This is due to the insecticidal activity of its crystalline parasporal inclusion (crystals) predominantly comprised of one or more proteins (Cry and Cyt proteins) also called δ-endotoxins, produced during sporulation. The shape and composition of Bt crystals vary among strains and crystalline proteins are extremely varied (more than 475 cry gene were discovered). The insecticidal activity of Bt crystals is very well studied, thus their insecticidal mode of action is well established, however, their antimicrobial effect is largely unknown. The lack of data on the antimicrobial effect of crystalline proteins of Bt and the need for searching new antimicrobial molecules encouraged us to carried out this study. The antibacterial effect of δ-endotoxines produced by two Bt stains; a strain isolated from soil at northern of Algeria (Bt 7.2.B), and a strain isolated from a bioinsecticide (Bacillus thuringiensis var aizawai), activated by proteolysis, was assayed on clinical bacterial strains and ATCC collection ones respectively. Gram positive and negative clinical bacterial strains (Escherichia coli, Klebsiella pneumonaie, Pseudomonas aeruginosa, Staphylococcus aureus) were sensitive to activated Bt 72B endotoxins. Similarly, bacterial strains from ATCC collection (Escherichia coli ATCC 25922, Pseudomonas aerugenosa ATCC 27853, Staphylococcus aureus ATCC 25923) were sensitive to activated B. thuringiensis var aizawai δ-endotoxines. The activated δ-endotoxins were separated by SDS-PAGE.

Keywords: Bacillus thuringiensis, crystals, cry proteins, δ-endotoxins, antibacterial activity

Procedia PDF Downloads 415

Authors: Agah Tugrul Korucu

Abstract:

Information and communication technologies have become an important part of our daily lives with significant investments in technology in the 21st century. Individuals are more willing to design and implement computer-related activities, and they are the main component of computer self-efficacy and self-efficacy related to the fact that the increase in information technology, with operations in parallel with these activities more successful. The Self-efficacy level is a significant factor which determines how individuals act in events, situations and difficult processes. It is observed that individuals with higher self-efficacy perception of computers who encounter problems related to computer use overcome them more easily. Therefore, this study aimed to examine self-efficacy perceptions of pre-service art and music teachers towards the use of information and communication technologies in terms of different variables. Research group consists of 60 pre-service teachers who are studying at Necmettin Erbakan University Ahmet Keleşoğlu Faculty of Education Art and Music department. As data collection tool of the study; “personal information form” developed by the researcher and used to collect demographic data and "the perception scale related to self-efficacy of informational technology" are used. The scale is 5-point Likert-type scale. It consists of 27 items. The Kaiser-Meyer-Olkin (KMO) sample compliance value is found 0.959. The Cronbach alpha reliability coefficient of the scale is found to be 0.97. computer-based statistical software package (SPSS 21.0) is used in order to analyze the data collected by data collection tools; descriptive statistics, t-test, analysis of variance are used as statistical techniques.

Keywords: self-efficacy perceptions, teacher candidate, information and communication technologies, art teacher

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Authors: L. Heidari, M. Jalili Ghazizade

Abstract:

This paper aims to determine the best environmental and economic scenario for Municipal Solid Waste (MSW) management of the Maku city by using Life Cycle Assessment (LCA) approach. The functional elements of this study are collection, transportation, and disposal of MSW in Maku city. Waste composition and density, as two key parameters of MSW, have been determined by field sampling, and then, the other important specifications of MSW like chemical formula, thermal energy and water content were calculated. These data beside other information related to collection and disposal facilities are used as a reliable source of data to assess the environmental impacts of different waste management options, including landfills, composting, recycling and energy recovery. The environmental impact of MSW management options has been investigated in 15 different scenarios by Integrated Waste Management (IWM) software. The photochemical smog, greenhouse gases, acid gases, toxic emissions, and energy consumption of each scenario are measured. Then, the environmental indices of each scenario are specified by weighting these parameters. Economic costs of scenarios have been also compared with each other based on literature. As final result, since the organic materials make more than 80% of the waste, compost can be a suitable method. Although the major part of the remaining 20% of waste can be recycled, due to the high cost of necessary equipment, the landfill option has been suggested. Therefore, the scenario with 80% composting and 20% landfilling is selected as superior environmental and economic scenario. This study shows that, to select a scenario with practical applications, simultaneously environmental and economic aspects of different scenarios must be considered.

Keywords: IWM software, life cycle assessment, Maku, municipal solid waste management

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Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

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Authors: Abdelghani Boudiaf

Abstract:

The profusion of studies relating to the concept of organizational learning shows the importance that has been given to this concept in the management sciences. A few years ago, companies leaned towards ISO 9001 certification; this requires the implementation of the quality management system (QMS). In order for this objective to be achieved, companies must have a set of skills, which pushes them to develop learning through continuous training. The results of empirical research have shown that implementation of the QMS in the company promotes the development of learning. It should also be noted that several types of learning are developed in this sense. Given the nature of skills development is normative in the context of the quality demarche, companies are obliged to qualify and improve the skills of their human resources. Continuous training is the keystone to develop the necessary learning. To carry out continuous training, companies need to be able to identify their real needs by developing training plans based on well-defined engineering. The training process goes obviously through several stages. Initially, training has a general aspect, that is to say, it focuses on topics and actions of a general nature. Subsequently, this is done in a more targeted and more precise way to accompany the evolution of the QMS and also to make the changes decided each time (change of working method, change of practices, change of objectives, change of mentality, etc.). To answer our problematic we opted for the method of qualitative research. It should be noted that the case study method crosses several data collection techniques to explain and understand a phenomenon. Three cases of companies were studied as part of this research work using different data collection techniques related to this method.

Keywords: changing mentalities, continuing training, organizational learning, quality management system, skills development

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Authors: D. Abdelrazek, M. NaguibAly, A. A. Badawi, Asmaa G. Abo Elnour, A. A. El-Kafas

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This work is developed within IAEA Coordinated Research Program 1496, “Innovative methods in research reactor analysis: Benchmark against experimental data on neutronics and thermal-hydraulic computational methods and tools for operation and safety analysis of research reactors.” The study investigates the capability of Code RELAP5/Mod3.4 to solve complex geometry complexity. Its results are compared to the results of PARET, a common code in thermal hydraulic analysis for research reactors, belonging to MTR-PC groups. The WWR-SM reactor at the Institute of Nuclear Physics (INP) in the Republic of Uzbekistan is simulated using both PARET and RELAP5 at steady state. Results from the two codes are compared. REALP5 code succeeded in solving the complex fuel geometry. The PARET code needed some calculations to obtain the final result. Although the final results from the PARET are more accurate, the small differences in both results makes using RELAP5 code recommended in case of complex fuel assemblies.

Keywords: complex fuel geometry, PARET, RELAP5, WWR-SM reactor

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

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The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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Authors: Raed Alnajjar, Mohd Zakree, Ahmad Nazri

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In this study, we apply Discrete Group Search Optimizer (DGSO) for solving Traveling Salesman Problem (TSP). The DGSO is a nature inspired optimization algorithm that imitates the animal behavior, especially animal searching behavior. The proposed DGSO uses a vector representation and some discrete operators, such as destruction, construction, differential evolution, swap and insert. The TSP is a well-known hard combinatorial optimization problem, which seeks to find the shortest path among numbers of cities. The performance of the proposed DGSO is evaluated and tested on benchmark instances which listed in LIBTSP dataset. The experimental results show that the performance of the proposed DGSO is comparable with the other methods in the state of the art for some instances. The results show that DGSO outperform Ant Colony System (ACS) in some instances whilst outperform other metaheuristic in most instances. In addition to that, the new results obtained a number of optimal solutions and some best known results. DGSO was able to obtain feasible and good quality solution across all dataset.

Keywords: discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)

Procedia PDF Downloads 304