Search results for: sustainable business model

Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Nady Farag Faragalla Hanna

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In the car dealership industry in Japan, the sales specialist is a key factor in the success of the company. I hypothesize that when a company understands the characteristics of sales professionals in its industry, it is easier to recruit and train salespeople effectively. Lean human resources management ensures the economic success and performance of companies, especially small and medium-sized companies.The purpose of the article is to determine the characteristics of sales specialists for small and medium-sized car dealerships using the chi-square test and the proximate variable model. Accordingly, the results show that career change experience, learning ability and product knowledge are important, while university education, career building through internal transfer, leadership experience and people development are not important for becoming a sales professional. I also show that the characteristics of sales specialists are perseverance, humility, improvisation and passion for business.

Keywords: electronics engineering, marketing, sales, E-commerce digitalization, interactive systems, sales process ARIMA models, sales demand forecasting, time series, R codetraits of sales professionals, variable precision rough sets theory, sales professional, sales professionals

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Authors: Mohamed Abd Esselem Dems, Souhila Laib, Nadjia Latelli, Nadia Ouddai

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In this work, we have developed QSAR model for Relative Binding Affinity (RBA) of a large diverse set of estradiol among these derivatives, the organometallic derivatives. By dividing the dataset into a training set of 24 compounds and a test set of 6 compounds. The DFT method was used to calculate quantum chemical descriptors and physicochemical descriptors (MR and MLOGP) were performed using E-Dragon. All the validations indicated that the QSAR model built was robust and satisfactory (R2 = 90.12, Q2LOO = 86.61, RMSE = 0.272, F = 60.6473, Q2ext =86.07). We have therefore apply this model to predict the RBA, for two isomers β and α wherein Mn(CO)3 complex with the aromatic ring of estradiol, and the two isomers show little appreciation for the estrogenic receptor (RBAβ = 1.812 and RBAα = 1.741).

Keywords: DFT, estradiol, haptotropic rearrangement, QSAR, relative binding affinity

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Authors: Sandani Muthukumarana, Malith Shehan Keraminiyage, Pavithra Rathnasiri

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Climate change is one of the most pressing issues facing our generation. However, it also presents an opportunity to grow the economy using biotechnology. Biotechnology offers a variety of solutions that can help mitigate the effects of global warming. Despite this, there is a lack of research on the potential and challenges associated with the further use of biotechnology to combat the impacts of climate change. To address this gap, it is essential to investigate the current context surrounding the use of biotechnology for climate change mitigation, including potential applications, current practices, and existing challenges. By reviewing the existing literature on these perspectives, this paper aims to provide a comprehensive understanding of the potential for biotechnology to mitigate the hazards of climate change. The use of biotechnology to mitigate the effects of climate change will be made easier as a result, and this will lay the groundwork for further study and actual initiatives in this field. Biotechnology can play a crucial role in mitigating the impacts of climate change. It offers a range of solutions, such as genetically modified crops, bioremediation, and bioenergy, that can help reduce greenhouse gas emissions, enhance carbon sequestration, and increase climate resilience. By utilizing biotechnology, we can reduce the negative impacts of climate change and create a more sustainable future. According to this knowledge, researchers can harness the potential of biotechnology to fight climate change and build a more sustainable future for future generations.

Keywords: biotechnology, impact, solutions, climate changes

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Authors: Kadrefi Athanasia, Fronimaki Evgenia, Mavri Maria

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3D Printing (3DP) is a distinct, disruptive technology that belongs to a wider group of manufacturing technologies, Additive Manufacturing (AM). In 3DP, a custom digital file turns into a solid object using a single computer and a 3D printer. Among multiple advantages, 3DP offers production with fewer steps compared to conventional manufacturing, lower production costs, and customizable designs. 3DP can be performed by several techniques, while the most common is Fused Deposition Modeling (FDM). FDM belongs to a wider group of AM techniques, material extrusion, where a digital file converts into a solid object using raw material (called filament) melted in high temperatures. As in most manufacturing procedures, environmental issues have been raised here, too. This study aims to review the literature on issues that determine technical and mechanical factors that affect the sustainability and resilience of a final 3D-printed object. The research focuses on the collection of papers that deal with 3D printing techniques and use keywords or phrases like ‘3D printed objects’, ‘factors of 3DP sustainability’, ‘waste materials,’ ‘infill patterns,’ and ‘support structures.’ After determining factors, a pilot survey will be conducted at the 3D Printing Lab in order to define the significance of each factor in the final 3D printed object.

Keywords: additive manufacturing, 3D printing, sustainable manufacturing, sustainable production

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Authors: Ha Van Trung, Suchint Simaraks

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Agro-tourism is a form of rural tourism that has actively developed in recent years. Tra Que vegetable village has developed this type of tourism to meet the needs of visitors to visit and experience. However, in the process of agricultural tourism development, Tra Que village is facing many issues related to the agricultural way of life, affecting the attraction of tourists. The purpose of this study is to find those issues. The survey questionnaire of 71 households and a semi-structured group interview of 30 households has been applied for the data collection. Research results show that there is a shortage of young workers, lack of training in tourism and agricultural production, and households only exploit a few agricultural activities for tourism. The number of households receiving tourists tends to decrease, and the number of households selling products to tourists at farms accounts for a small proportion. These will affect sustainable agro-tourism development in the future. Focusing on training local households in tourism and agricultural production, encourage young generation to preserve the agricultural way of life, upgrade infrastructure and public services, develop agro-products and tourism services will contribute to the sustainable development of agro-tourism in Tra Que vegetable village in the future.

Keywords: agro-tourism, way of life, Vietnamese tourists, Tra Que vegetable village

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Authors: Betty Johanna Garzon Rozo, Jonathan Crook, Fernando Moreira

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Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. We propose a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severity distributions, and to calculate the capital for Operational Risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mix distribution (the Lognormal for the body of losses and the Generalized Pareto Distribution for the tail) via extreme value theory using SAS®, (ii) the multivariate skew t-copula applied for the first time for operational losses and (iii) Bayesian theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyses a newly operational loss data set, SAS Global Operational Risk Data [SAS OpRisk], to model operational risk at international financial institutions. All the severity models are constructed in SAS® 9.2. We implement the procedure PROC SEVERITY and PROC NLMIXED. This paper focuses in describing this implementation.

Keywords: operational risk, loss distribution approach, extreme value theory, copulas

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Authors: Fatema-Tuz-Zahura, Raquib Ahsan

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Punching shear failure is usually the governing failure mode of flat plate structures. Punching failure is brittle in nature which induces more vulnerability to this type of structure. In the present study, a 3D finite element model of a flat plate with low reinforcement ratio and without any transverse reinforcement has been developed. Punching shear stress and the deflection data were obtained on the surface of the flat plate as well as through the thickness of the model from numerical simulations. The obtained data were compared with the experimental results. Variation of punching stress with respect to deflection as obtained from numerical results is found to be in good agreement with the experimental results; the range of variation of punching stress is within 5%. The numerical simulation shows an early and gradual onset of nonlinearity, whereas the same is late and abrupt as observed in the experimental results. The range of variation of punching stress for different slab thicknesses between experimental and numerical results is less than 15%. The developed numerical model is useful to complement available punching test series performed in the past. The results obtained from the numerical model will be helpful for designing retrofitting schemes of flat plates.

Keywords: flat plate, finite element model, punching shear, reinforcement ratio

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Authors: Javad Khaligh, Aghileh Heydari, Ali Akbar Heydari

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Mathematical epidemiological models for the spread of disease through a population are used to predict the prevalence of a disease or to study the impacts of treatment or prevention measures. Initial conditions for these models are measured from statistical data collected from a population since these initial conditions can never be exact, the presence of chaos in mathematical models has serious implications for the accuracy of the models as well as how epidemiologists interpret their findings. This paper confirms the chaotic behavior of a model for dengue fever and SI by investigating sensitive dependence, bifurcation, and 0-1 test under a variety of initial conditions.

Keywords: epidemiological models, SEIR disease model, bifurcation, chaotic behavior, 0-1 test

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Authors: Prakash Kumar Nayak, Avinash Kumar, Uma Dash, Kalpana Rayaguru

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Soybean oil is one of the most widely consumed cooking oils, worldwide. Deep-fat frying of foods at higher temperatures adds unique flavour, golden brown colour and crispy texture to foods. But it brings in various changes like hydrolysis, oxidation, hydrogenation and thermal alteration to oil. The presence of Peroxide value (PV) is one of the most important factors affecting the quality of the deep-fat fried oil. Using bentonite as an adsorbent, the PV can be reduced, thereby improving the quality of the soybean oil. In this study, operating parameters like heating time of oil (10, 15, 20, 25 & 30 h), contact time ( 5, 10, 15, 20, 25 h) and concentration of adsorbent (0.25, 0.5, 0.75, 1.0 and 1.25 g/ 100 ml of oil) have been optimized by response surface methodology (RSM) considering percentage reduction of PV as a response. Adsorption data were analysed by fitting with Langmuir and Freundlich isotherm model. The results show that the Langmuir model shows the best fit compared to the Freundlich model. The adsorption process was also found to follow a pseudo-second-order kinetic model.

Keywords: bentonite, Langmuir isotherm, peroxide value, RSM, soybean oil

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Ivana Bulog

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Decision making is a vital part of the business world and any other field of human endeavor. Which way a business organization will take, and where that way will lead it, depends on broad range of decisions made by managers in the managerial structure. Strategic decisions are of the greatest importance for organizational success. Although much empirical research has been done trying to describe and explain its nature and effectiveness, knowledge about strategic decision making is still incomplete. This paper explores the nature of strategic decision making in particular setting - in Croatian companies. The main focus of this research is on the style that decision makers on strategic management level are following when making decisions of life importance for their companies. Two main decision making style that explain the way decision maker collects and processes available information and performs all the activities in strategic decision making process were empirical tested: rational and intuitive one. Besides analyzing their existence on strategic management level in Croatian companies, their effectiveness is analyzed as well. Results showed that decision makers at strategic management level are following both styles somewhat equally in order to function effectively, and that intuitive style is more effective when considering decisions outcomes.

Keywords: decision making style, decision making effectiveness, strategic decisions, management sciences

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Authors: Anusha Vaddiraj Pallapu

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Measuring urban sustainable development is at the forefront in contributing to overall sustainability, and it refers to attaining social equity, environmental protection and minimizing the impacts of urbanization. Assessing performance of urban issues ranging from larger consumption of natural resources by humans in terms of lifestyle to creating a polluted nearby environment, social and even economic dimensions of sustainability major issues observed such as water quality, transportation, management of solid waste and traffic pollution. However, relying on the framework of the project to do the goals of sustainable development or minimization of urban impacts through management practices is not enough to deal with the present urban issues. The aim of the sustainability is to know how severely the resources are depleted because of human consumption and how issues are characterized. The paper aims to assign benchmarks for the selected sustainability indicators for research, and analysis is done through multivariate analysis in Indian context a case of Nagpur city to identify the play role of each urban issues in the overall sustainability. The main objectives of this paper are to examine the indicators over by time basis on various scenarios and how benchmarking is used, what and which categories of values should be considered as the performance of indicators function.

Keywords: environmental sustainability indicators, principal component analysis, urban sustainability, urban clusters, benchmarking

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Authors: Rizwan Arshad, Alessio Panza, Nimra Inayat, Syeda Mariam Batool Kazmi, Shawana Azmat

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The rapidly expanding use of immunotherapy for a wide range of cancers from late to early stages has, predictably, been accompanied by evidence of inequities in access to these highly effective but costly treatments. In this survey-based case study, we aimed to develop a One-window services model (OWSM) based on Anderson’s behavioral model to enhance competence in accessing cancer medications, particularly immunotherapies, through the analysis of 20 patient surveys conducted in the Armed forces bone marrow transplant center of the district, Rawalpindi from November to December 2022. The purposive sampling technique was used. Cronbach’s alpha coefficient was found to be 0.71. It was analyzed using SPSS version 26 with descriptive analysis, and results showed that the majority of the cancer patients were non-competent to access their prescribed cancer immunotherapy because of individual-level, socioeconomic, and organizational barriers.

Keywords: cancer immunotherapy, one-window services model, accessibility, competence

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Authors: Salim Khan

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The attainment of the carbon neutrality objective and Sustainable Development Goal 13 (SDG-13) target, which pertains to climate actions, received widespread attention in developing and emerging nations. Given the increasing pace of urbanization, technological advancements, and rapid growth, it is imperative to examine the linear and nonlinear effects of urbanization and economic growth and the linear impact of information and communication technology (ICT) on carbon emissions (CO2e). This study employs the Dynamic System GMM (DSGMM) and Panel Quantile Regression (PQR) methodologies to investigate the causal relationship between urbanization, ICT, economic growth, and their interplay on CO2e in 39 BRI countries from 2001 to 2020. The study's findings indicate that the impact of urbanization on CO2e exhibits linear and nonlinear patterns. The specific nonlinear impact of urbanization leads to a decrease in CO2e, hence facilitating the achievement of carbon neutrality and contributing to SDG-13. The study highlights the importance of ICT in achieving SDG-13 by reducing CO2e, emphasizing the need for informatization. Simultaneously, the findings support the Environmental Kuznets Curve (EKC) hypothesis and support the pollution haven theory. Finally, based on empirical findings, significant policy implications are suggested for achieving SGD 13 and carbon neutrality.

Keywords: urbanization, ICT, CO2 emission, EKC, pollution haven, BRI

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Authors: Mustafa Tufekci, Caner Guven

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In Automotive Industry, sliding door systems that are also used as body closures, are safety members. Extreme product tests are realized to prevent failures in a design process, but these tests realized experimentally result in high costs. Finite element analysis is an effective tool used for the design process. These analyses are used before production of a prototype for validation of design according to customer requirement. In result of this, the substantial amount of time and cost is saved. Finite element model is created for geometries that are designed in 3D CAD programs. Different element types as bar, shell and solid, can be used for creating mesh model. The cheaper model can be created by the selection of element type, but combination of element type that was used in model, number and geometry of element and degrees of freedom affects the analysis result. Sliding door system is a good example which used these methods for this study. Structural analysis was realized for sliding door mechanism by using FE models. As well, physical tests that have same boundary conditions with FE models were realized. Comparison study for these element types, were done regarding test and analyses results then the optimum combination was achieved.

Keywords: finite element analysis, sliding door mechanism, element type, structural analysis

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Authors: Petra Hofmann, Dana Koniel, Jussi Luukkanen, Walter Nieminen, Lea Hannola, Ilkka Donoghue

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The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic hasnot only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of the normal design, construction, and operation of cities provides a unique opportunity to improve connection between people. Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the researchcontribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create a competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business.

Keywords: internet of things, data analytics, smart cities, competitive advantage

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Authors: Feng-Ming Fan, Xiu-Hui Wen

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Shortage of water resources is a crucial problem to be solved in Taiwan. However, lack of effective and mandatory regulation on water recovery and recycling leads to no effective water resource controls currently. Although existing legislation sets standards regarding water recovery, implementation and enforcement of legislation are facing challenges. In order to break through the dilemma, this study aims to find enforcement tools, improve inspection skills, develop an inspection system, to achieve sustainable development of precious water resources. The Taiwan Environmental Impact Assessment Act (EIA Act) was announced on 1994. The aim of EIA Act is to protect the environment by preventing and mitigating the adverse impact of development activity on the environment. During the EIA process, we can set standards that require enterprises to reach a certain percentage of water recycling based on different case characteristics, to promote sewage source reduction and water saving benefits. Next, we have to inspect how the enterprises handle their waste water and perform water recovery based on environmental assessment commitments, for the purpose of reviewing and measuring the implementation efficiency of water recycling and reuse, an eco-friendly measure. We invited leading experts in related fields to provide lecture on water recycling, strengthen law enforcement officials’ inspection knowledge, and write inspection reference manual to be used as basis of enforcement. Then we finalized the manual by reaching mutual agreement between the experts and relevant agencies. We then inspected 65 high-tech companies whose daily water consumption is over 1,000 tons individually, located at 3 science parks, set up by Ministry of Science and Technology. Great achievement on water recycling was achieved at an amount of 400 million tons per year, equivalent to 2.5 months water usage for general public in Taiwan. The amount is equal to 710 billion bottles of 600 ml cola, 170 thousand international standard swimming pools of 2,500 tons, irrigation water applied to 40 thousand hectares of rice fields, or 1.7 Taipei Feitsui Reservoir of reservoir storage. This study demonstrated promoting effects of environmental impact assessment commitments on water recycling, and therefore water resource sustainable development. It also confirms the value of EIA Act for environmental protection. Economic development should go hand in hand with environmental protection, and it’s a mainstream. It clearly shows the EIA regulation can minimize harmful effects caused by development activity to the environment, as well as pursuit water resources sustainable development.

Keywords: the environmental impact assessment act, water recycling environmental assessment commitment, water resource sustainable development, water recycling, water reuse

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Authors: Ayşe Çoban, Orhan Çoban, Murat Birekul

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In this study, the effects of radio frequency identification system on the retailing sector were theoretically analysed. The technology of Radio Frequency Identification (RFID) is a method enabling to identify the objects individually and automatically, using radio frequency. RFID generally consists of a tag and reader. RFID tags can be programmed to receive, store, and send the information of object such as Electronic Product Code (EPC). Having read the tags placed on product by the reader, the information associated with the management of supply chain can be automatically recorded and replaced. Recently, RFID technology used in many areas has particularly important effects on the businesses that are active in the retailing sector. The most important disadvantage of this technology is that the cost of installation and operation is higher compared to its alternatives. However, it provides important advantages to the business enterprises in the application process. At present, it is especially adopted by the large sized enterprises and with chain stores in the international areas. The application results point out that RFID technology provides business enterprises with the important competitive advantage.

Keywords: RFID, retailing sector, RFID technologies, electronic product code

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Authors: Seyedsamad Tahani, Samira Ghorbanpour

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The global warming crisis forced humans to consider their place in the world and the earth's future. In this regard, Electric Vehicles (EVs) are a significant step toward protecting the environment. By identifying factors that influence people's behavior intentions toward using Electric Vehicles (EV), we proposed a theoretical model by extending the Technology Acceptance Model (TAM), including three more concepts, Subjective Norm (SN), Self-Efficacy (SE), and Perceived Behavior Control (PBC). The study was conducted in South Korea, and a random sample was taken at a specific time. In order to collect data, a questionnaire was created in a Google Form and sent via Kakao Talk, a popular social media application used in Korea. There were about 220 participants in this survey. However, 201 surveys were completely done. The findings revealed that all factors in the TAM model and the other added concepts such as subjective norms, self-efficacy and perceived behavior control significantly affect the behavioral intention of using EVs.

Keywords: electric vehicles, behavioral intention, perceived trust, perceived enjoyment, self-efficacy

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Authors: Jia Li, Huacong Li, Xiaobao Han

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Due to the more variable geometry parameters of VCE (variable cycle aircraft engine), presents an adaptive controller method based on the full-state linear model of VCE and has simulated to solve the multivariate controller design problem of the whole flight envelops. First, analyzes the static and dynamic performances of bypass ratio and other state parameters caused by variable geometric components, and develops nonlinear component model of VCE. Then based on the component model, through small deviation linearization of main fuel (Wf), the area of tail nozzle throat (A8) and the angle of rear bypass ejector (A163), setting up multiple linear model which variable geometric parameters can be inputs. Second, designs the adaptive controllers for VCE linear models of different nominal points. Among them, considering of modeling uncertainties and external disturbances, derives the adaptive law by lyapunov function. The simulation results showed that, the adaptive controller method based on full-state linear model used the angle of rear bypass ejector as input and effectively solved the multivariate control problems of VCE. The performance of all nominal points could track the desired closed-loop reference instructions. The adjust time was less than 1.2s, and the system overshoot was less than 1%, at the same time, the errors of steady states were less than 0.5% and the dynamic tracking errors were less than 1%. In addition, the designed controller could effectively suppress interference and reached the desired commands with different external random noise signals.

Keywords: variable cycle engine (VCE), full-state linear model, adaptive control, by-pass ratio

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Authors: Niladri Sekhar Barma, Satish Dhandole

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Damage identification in the context of safety, nowadays, has become a fundamental research interest area in the field of mechanical, civil, and aerospace engineering structures. The following research is aimed to identify damage in a mechanical beam structure and quantify the severity or extent of damage in terms of loss of stiffness, and obtain an updated analytical Finite Element (FE) model. An FE model is used for analysis, and the location of damage for single and multiple damage cases is identified numerically using the modal strain energy method and mode shape curvature method. Experimental data has been acquired with the help of an accelerometer. Fast Fourier Transform (FFT) algorithm is applied to the measured signal, and subsequently, post-processing is done in MEscopeVes software. The two sets of data, the numerical FE model and experimental results, are compared to locate the damage accurately. The extent of the damage is identified via modal frequencies using a mixed numerical-experimental technique. Mode shape comparison is performed by Modal Assurance Criteria (MAC). The analytical FE model is adjusted by the direct method of model updating. The same study has been extended to some real-life structures such as plate and GARTEUR structures.

Keywords: damage identification, damage quantification, damage detection using modal analysis, structural damage identification

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Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: auto-encoder, behavior model simulation, digital hardware design, pre-route simulation, Unsupervised feature learning

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Authors: Amin Ajabi, Chinchu Cherian, Sumi Siddiqua

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Rammed earth (RE) is a traditional soil-based building material made by compressing a mixture of natural earth and binder ingredients such as chalk or lime, in temporary formworks. However, the modern RE uses 5 to 10% cement as a binder in order to meet the strength and durability requirements as per the standard specifications and guidelines. RE construction is considered to be an energy-efficient and environmental-friendly approach when compared to conventional concrete systems, which use 20 to 30% cement. The present study aimed to develop RE mix designs by utilizing non-hazardous wood-based fly ash generated by pulp and paper mills as a partial replacement for cement. The pulp mill fly ash (PPFA)-stabilized RE is considered to be a sustainable approach keeping in view of the massive carbon footprints associated with cement production as well as the adverse environmental impacts due to disposal of PPFA in landfills. For the experimental study, as-received PPFA, as well as PPFA-based geopolymer (synthesized by alkaline activation method), were incorporated as cement substitutes in the RE mixtures. Initially, local soil was collected and characterized by index and engineering properties. The PPFA was procured from a pulp manufacturing mill, and its physicochemical, mineralogical and morphological characterization, as well as environmental impact assessment, was conducted. Further, the various mix designs of RE material incorporating local soil and different proportions of cement, PPFA, and alkaline activator (a mixture of sodium silicate and sodium hydroxide solutions) were developed. The compacted RE specimens were cured and tested for 7-day and 28-day unconfined compressive strength (UCS) variations. Based on UCS results, the optimum mix design was identified corresponding to maximum strength improvement. Further, the cured RE specimens were subjected to freeze-thaw cycle testing for evaluating its performance and durability as a sustainable construction technique under extreme climatic conditions.

Keywords: sustainability, rammed earth, stabilization, pulp mill fly ash, geopolymer, alkaline activation, strength, durability

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Authors: Philomena Ibuh Adzongo, Margaret U. Oluwole, Justina Nguveren Jor.

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This paper investigated the need for the development of entrepreneurial skills for Benue State University students. The population consisted of all 1,500 final year students in Benue State University. A sample of 100 students was selected using simple random sampling. A 12-item self-constructed and content validated questionnaire by research experts titled, the Need for the Development of Entrepreneurial Skills in Benue State University Students (NDECBSUS) was used to collect the data. The questionnaire items were rated using a 4-point modified rating scale of Strongly Agree, Agree, Disagree and Strongly Disagree, assigned the following scores of 4,3,2 and 1, respectively. The questionnaire was administered by the researcher with the help of two research assistants through the primary source. Simple percentages and chi-square were used to answer the research questions and test the hypotheses, respectively. The findings revealed that in business management, business management skills, personal skills, and technical skills need to be developed in students for them to become effective and efficient entrepreneurs and concluded that the acquisition of these skills will reduce the challenge of unemployment. The study recommended that funds should be made available by all education stakeholders for such programmes to remain functional.

Keywords: entrepreneurial skill, entrepreneurship, need for development, university students

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Thanasuwit Thabhiranrak

Abstract:

This research aims to study factors that affected the operations of the cassava industrial enterprises in northeast of Thailand. Hypothesis was tested by regress analysis and also the analysis in order to determine the relationship between variables with Pearson correlation and show a class action in cassava process including the owner of business executives and supervisors. The research samples were 400 people in northeast region of Thailand. The research results revealed that success of entrepreneurs related to transformation leadership and knowledge management in a positive way at statistical significance level of 0.01 and respondents also emphasized on the importance of transformational leadership factors. The individual and the use of intelligence affect the success of entrepreneurs in cassava industry at statistical significance level of 0.05. The qualitative data were also collected by interviewing with operational level staff, supervisors, executives, and enterprise owners in the northeast of Thailand. The result was found that knowledge management was important in their business operations. Personnel in the organizations should learn from working experience, develop their skills, and increase knowledge from education.

Keywords: transformational leadership, knowledge management (KM), cassava, northeast of Thailand, industrial

Procedia PDF Downloads 308

Authors: Karim Kheloufi, El Hachemi Amara

Abstract:

In the present study, a three-dimensional transient numerical model was developed to study the temperature field and cutting kerf shape during laser fusion cutting. The finite volume model has been constructed, based on the Navier–Stokes equations and energy conservation equation for the description of momentum and heat transport phenomena, and the Volume of Fluid (VOF) method for free surface tracking. The Fresnel absorption model is used to handle the absorption of the incident wave by the surface of the liquid metal and the enthalpy-porosity technique is employed to account for the latent heat during melting and solidification of the material. To model the physical phenomena occurring at the liquid film/gas interface, including momentum/heat transfer, a new approach is proposed which consists of treating friction force, pressure force applied by the gas jet and the heat absorbed by the cutting front surface as source terms incorporated into the governing equations. All these physics are coupled and solved simultaneously in Fluent CFD®. The main objective of using a transient phase change model in the current case is to simulate the dynamics and geometry of a growing laser-cutting generated kerf until it becomes fully developed. The model is used to investigate the effect of some process parameters on temperature fields and the formed kerf geometry.

Keywords: laser cutting, numerical simulation, heat transfer, fluid flow

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

Abstract:

Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

Procedia PDF Downloads 126

Authors: A. Nabhan, Mohamed R. El-Sharkawy, A. Rashed

Abstract:

The main aim of this paper is to dedicate the belt drive system faults like cogs missing, misalignment and belt worm using vibration analysis technique. Experimentally, the belt drive test-rig is equipped to measure vibrations signals under different operating conditions. Finite element 3D model of belt drive system is created and vibration response analyzed using commercial finite element software ABAQUS/CAE.  Root mean square (RMS) and Crest Factor will serve as indicators of average amplitude of envelope analysis signals. The vibration signals pattern obtained from the simulation model and experimental data have the same characteristics. It can be concluded that each case of the RMS is more effective in detecting the defect for acceleration response. While Crest Factor parameter has a response with the displacement and velocity of vibration signals. Also it can be noticed that the model has difficulty in completing the solution when the misalignment angle is higher than 1 degree.

Keywords: simulation model, misalignment, cogs missing, vibration analysis

Procedia PDF Downloads 287