Search results for: educating for the future

Authors: Chang Li-Yu

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Purposes: The present research focuses on exploring the latent growth model of psychological capital in disadvantaged adolescents and assessing its relationship with subjective well-being. Methods: Longitudinal study design was utilized and the data was from Taiwan Database of Children and Youth in Poverty (TDCYP), using the student questionnaires from 2009, 2011, and 2013. Data analysis was conducted using both univariate and multivariate latent growth curve models. Results: This study finds that: (1) The initial state and growth rate of individual factors such as parent-child relationships, psychological capital, and subjective wellbeing in economically disadvantaged adolescents have a predictive impact; (2) There are positive interactive effects in the development among factors like parentchild relationships, psychological capital, and subjective well-being in economically disadvantaged adolescents; and (3) The initial state and growth rate of parent-child relationships and psychological capital in economically disadvantaged adolescents positively affect the initial state and growth rate of their subjective well-being. Recommendations: Based on these findings, this study concretely discusses the significance of psychological capital and family cohesion for the mental health of economically disadvantaged youth and offers suggestions for counseling, psychological therapy, and future research.

Keywords: economically disadvantaged adolescents, psychological capital, parent-child relationships, subjective well-beings

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Kousar Shaheen

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The electoral process of Pakistan always remains underestimated due to malpractices claimed by the political leaders. The democratic system relies on public decision, selectorial process, transparent arrangements made by public administration, and governance system. Political polarization plays a vital role in any democratic system, which depends upon the way of applying leadership capabilities. In modern societies, public engagement is playing a key role in changing political polarization and implementation of the newest technologies, e-leadership and e-governance to bring e-democracy. The Overseas Pakistanis are unable to cast their votes in the selectorial process of Pakistan. To align this issue with civil society, efforts were made to implement modernized services and facilities by intervening in the Supreme Court. However, the results were found insignificant because of ineffective citizen engagement, IT-based, governance and public administration. which proved that the shifting to advanced society is crucial in Pakistan due to the elected Officials of current democratic system. It is an empirical study to involve Pakistani nationals (overseas) in the democratic process by utilizing the digital facility of vote casting. The role of Government. The role of e-leadership in changing the political polarization for the implementation of e-election will be measured by collecting data from different sources.

Keywords: e-democracy, e-leadership, political polarization, public engagement

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Authors: Thinh Huu Nguyen, Shahab Sharfaei, Jindřich Soukup

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In the pursuit of reducing production costs, offshoring has been a major trend throughout global value chains for many decades. However, with the rise of advanced technologies, new opportunities to automate their production are changing the motivation of multinational firms to go offshore. Instead, many firms are working to relocate their offshored activities from developing economies back to their home countries. This phenomenon, known as reshoring, has recently garnered much attention as it becomes clear that automation in advanced countries might have major implications not only on their own economies but also through international trade on the economy of low-income countries, including their labor market outcomes and their comparative advantages. Thus, while using robots to substitute human labor may lower the relative costs of producing at home, it has the potential to decrease employment and demand for exports from developing economies through reshoring. In this paper, we investigate the recent literature to provide a further understanding of the relationships between robotization and the reshoring of production. Moreover, we analyze the impact of robot adoption on international trade in both developed and emerging markets. Finally, we identify the research gaps and provide avenues for future research in international economics. This study is a part of the project funded by the Internal Grant Agency (IGA) of the Faculty of Business Administration, Prague University of Economics and Business.

Keywords: automation, robotization, reshoring, international trade

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Authors: Talakokula Visalakshi

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Concrete is the most commonly used construction material across the globe, its usage is second only to water. It is prepared using ordinary Portland cement whose production contributes to 5-8% of total carbon emission in the world. On the other hand the fly ash by product from the power plants is produced in huge quantities is termed as waste and disposed in landfills. In order to address the above issues mentioned, it is essential that other forms of binding material must be developed in place of cement to make concrete. The geo polymer concrete is one such alternative developed by Davidovits in 1980’s. Geopolymer do not form calcium-silicate hydrates for matrix formation and strength but undergo polycondensation of silica and alumina precursors to attain structural strength. Its setting mechanism depends upon polymerization rather than hydration. As a result it is able to achieve its strength in 3-5 days whereas concrete requires about a month to do the same. The objective of this research is to assess the performance of geopolymer concrete under sulphate and acid attack. The assessment is done based on the experiments conducted on geopolymer concrete. The expected outcomes include that if geopolymer concrete is more durable than normal concrete, then it could be a competitive replacement option of concrete and can lead to significant reduction of carbon foot print and have a positive impact on the environment. Fly ash based geopolymer concrete offers an opportunity to completely remove the cement content from concrete thereby making the concrete a greener and future construction material.

Keywords: fly ash, geo polymer, geopolymer concrete, construction material

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Authors: Mira Ezora Zainal Abidin, Siti Fauzuna Othman, Zalina Harun, M. Hafiz M. Pikri

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In a fire incident involving a Nitrogen storage tank that over-pressured and exploded, resulting in a fire in one of the units in a refinery, lack of data and evidence hampered the investigation to determine the root cause. Instrumentation and fittings were destroyed in the fire. To make it worst, this incident occurred during the COVID-19 pandemic, making collecting and testing evidence delayed. In addition to that, the storage tank belonged to a third-party company which requires legal agreement prior to the refinery getting approval to test the remains. Despite all that, the investigation had to be carried out with stakeholders demanding answers. The investigation team had to devise alternative means to support whatever little evidence came out as the most probable root cause. International standards, practices, and previous incidents on similar tanks were referred. To narrow down to just one root cause from 8 possible causes, transient simulations were conducted to simulate the overpressure scenarios to prove and eliminate the other causes, leaving one root cause. This paper shares the methodology used and details how transient simulations were applied to help solve this. The experience and lessons learned gained from the event investigation and from numerous case studies via transient analysis in finding the root cause of the accident leads to the formulation of future mitigations and design modifications aiming at preventing such incidents or at least minimize the consequences from the fire incident.

Keywords: fire, transient, simulation, relief

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Hans-Peter Piepho

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We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions.

Keywords: bayesian state-space model, Markov Chain Monte Carlo, population dynamics, conservation

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Authors: Dr. Muhammad Idris Idris, Dr. Said Saeed Saeed

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Terrorism is the chronic issue of the hour. It is the disciplined practice of vicious activities like assassinating, slaughtering, mutilating, and frightening of the innocents to attain religious, fiscal, and political goals and to question the authority of the government. Leaders of the world promised to transform the planet by empowering community members and building community resilience (CR) against terrorism. This study concentrates to explore building community resilience against terrorism and empowering community members and implement strategies for strengthening community resilience. For data collection a mixed methods methodology will be used. Means, STD deviation, Pearson correlation, and thematic analysis will be employed to analyze the gathered data. The findings of the study will be interpreted and recommendations will be furnished accordingly. Study results will be disseminated to all concerned through conferences and seminar sessions. It is predicted that after the completion, the project team will be in a robust position to start writing the report that concentrates on strengthening community resilience, which is the crucial goal of this project. The publication will contribute effectively to all stakeholders and society, particularly to the lower rungs of social order. Moreover, it is expected that this project will contribute to future research in the domain of community resilience. This project will also reveal the remarkable potential of archival research on community resilience.

Keywords: Violent Extremism, community Role, community resilience, community empowerment, Leadership role

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Authors: V. Revathi, J. Thaarrini, M. Venkob Rao

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This paper presents a study on use of alkali activated bottom ash (BA) and ground granulated blast furnace slag (GGBS) blend in paver blocks. A preliminary effort on alkali-activated bottom ash, blast furnace slag based geopolymer (BA-GGBS-GP) mortar with river sand was carried out to identify the suitable mix for paver block. Several mixes were proposed based on the combination of BA-GGBS. The percentage ratio of BA:GGBS was selected as 100:0, 75:25, 50:50, 25:75 and 0:100 for the source material. Sodium based alkaline activators were used for activation. The molarity of NaOH was considered as 8M. The molar ratio of SiO2 to Na2O was varied from 1 to 4. Two curing mode such as ambient and steam curing 60°C for 24 hours were selected. The properties of paver block such as compressive strength split tensile strength, flexural strength and water absorption were evaluated as per IS15658:2006. Based on the preliminary study on BA-GGBS-GP mortar, the combinations of 25% BA with 75% GGBS mix for M30 and 75% BA with 25% GGBS mix for M35 grade were identified for paver block. Test results shows that the combination of BA-GGBS geopolymer paver blocks attained remarkable compressive strength under steam curing as well as in ambient mode at 3 days. It is noteworthy to know BA-GGBS-GP has promising future in the construction industry.

Keywords: bottom ash, GGBS, alkali activation, paver block

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Authors: Jagoda Przybysz

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The main objective of this paper is to provide a comprehensive description of the immigrants in Poland, especially situation at the labour market. The paper will provide descriptive information on the composition of immigrants in Poland, and how this has changed over time, their socio-economic characteristics, their industry allocation and their labour market outcomes. Then we will investigate various labour market performance indicators (labour force participation, employment, wages and self-employment) for immigrants of different origins based on reached statistics. Individual interviews with immigrants will indicate areas of problems of living in Poland, mostly on labour market. The article shows that immigrants from some ethnic minority groups are more active in selected sectors of labour market. The empirical basis for the work related to the situation on the labor market of foreigners who came to the Poland and live in Lodz. The studies assumed that foreigners work in Poland and operate in different ways being integrated / excluded in varying degrees. Theoretical framework for analysis are: concepts of inclusion and exclusion, the concept of a dual labour market and the concept of social anchors. Completed in the 2014-2016, a pilot study (The forms of individual interviews) with 32 foreigners arrived in the last decade to Lodz. Preliminary studies have enabled the formulation of research issues and have set the future direction of research revealing to the personal experiences of respondents, a group of factors hindering integration and exclusion areas.

Keywords: foreigners, immigrants, labour market, migration, Poland

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Authors: Jeonghun Lee

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Thyroid-stimulating hormone (TSH) is regulated by the negative feedback of T3 and T4 but is affected by cortisol and cytokines during allostasis. Hence, TSH levels can be influenced by stress through cortisol. In the present study, changes in TSH levels under stress and the potential of TSH as a stress marker were examined in patients lacking T3 or T4 feedback after thyroid surgery. The three stress questionnaires (Korean version of the Daily Stress Inventory, Social Readjustment Rating Scale, and Stress Overload Scale-Short [SOSS]), open-ended question (OQ), and thyroid function tests were performed twice in 106 patients enrolled from January 2019 to October 2020. Statistical analysis was performed using the generalized linear mixed effect model (GLMM) in R software version 4.1.0. In a multiple LMM involving 106 patients, T3, T4, SOSS (category), open-ended questions, the extent of thyroidectomy, and preoperative TSH were significantly correlated with lnTSH. T3 and T4 increased by 1 and lnTSH decreased by 0.03, 3.52, respectively. In case of a stressful event on OQ, lnTSH increased by 1.55. In the high-risk group, lnTSH increased by 0.79, compared with the low group (p<0.05). TSH had a significant relationship with stress, together with T3, T4, and the extent of thyroidectomy. As such, it has the potential to be used as a stress marker, though it showed a low correlation with other stress questionnaires. To address this limitation, questionnaires on various social environments and research on copy strategies are necessary for future studies.

Keywords: stress, surgery, thyroid stimulating hormone, thyroidectomy

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Authors: J. Scott Kenney

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Not all pain is created equal. In recent decades, the concept of Illegitimate pain has begun to shed light on the phenomena of emotional and physical pain that is misunderstood, neglected, or stigmatized, broadly conceptualized along dimensions of relative legitimation and physicality. Yet, beyond a pioneering study of the suffering of closeted LGBTQ + individuals, along with an analysis of the pains experienced by students at a religious boarding school, there has been insufficient attention to what lies behind such marginalized suffering beyond the original claim that it relates to broad interpretive standards and structured power relations, mediated through interaction in various groups/settings. This paper seeks to delve theoretically into this underdeveloped terrain. Building on earlier work, it takes direct aim at the definitional aspect that lies analytically prior to such matters, theoretically unpacking the role of ideology. Following a general introduction focused on theoretical relationships between social structure, power, and ideas, the paper reviews a range of sociological literature on relevant matters. After condensing the insights from these various literatures into a series of theoretical statements, the paper analytically engages with these to articulate a series of theoretical and methodological elaborations intended to practically assist researchers in empirically examining such matters in today's complex social environment.

Keywords: deviance, ideology, illegitimate pain, social theory, victimization

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Authors: Zahra Alipour, Amirreza Moheb Afzali

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In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience.

Keywords: YOLOv8, mediapipe, finger tracking, joint estimation, human-computer interaction (HCI)

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Authors: Ming-Han Tsai, Chihpin Huang

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Electrochemical oxidation of ammonia (AEO) is one of the highly efficient and environmentally friendly methods for NH₃ removal from wastewater. Recently, researchers have focused on non-Pt-based electrodes (n-PtE) for AEO, aiming to evaluate the feasibility of these low-cost electrodes for future practical applications. However, for most n-PtE, NH₃ is oxidized mainly to nitrate ion NO₃⁻ instead of the desired nitrogen gas N₂, which requires further treatment to remove excess NO₃⁻. Therefore, developing a high N₂ conversion electrode for AEO is highly urgent. In this study, we fabricated various Cu₂O/Ni foam (NF) electrodes by electrodeposition of Cu on NF. The Cu plating bath contained different additives, including cetyltrimethylammonium chloride (CTAC), sodium dodecyl sulfate (SDS), polyamide acid (PAA), and sodium alginate (SA). All the prepared electrodes were physically and electrochemically investigated. Batch AEO experiments were conducted for 3 h to clarify the relation between electrode structures and N₂ selectivity. The SEM and XRD results showed that crystalline platelets-like Cu₂O, particles-like Cu₂O, cracks-like Cu₂O, and sheets-like Cu₂O were formed in the Cu plating bath by adding CTAC, SDS, PAA, and SA, respectively. For electrochemical analysis, all Cu₂O/NF electrodes revealed a higher current density (2.5-3.2 mA/cm²) compared to that without additives modification (1.6 mA/cm²). At a constant applied potential of 0.95 V (vs Hg/HgO), the Cu₂O sheet (51%) showed the highest N₂ selectivity, followed by Cu₂O cracks (38%), Cu₂O particles (30%), and Cu₂O platelet (18%) after 3 h reaction. Our result demonstrated that the selectivity of N₂ during AEO was surface structural dependent.

Keywords: ammonia, electrooxidation, selectivity, cuprous oxide, Ni foam

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Authors: Anosh Arshad Sundhu, Francesco Giordano, Giacomo Della Croce, Maurizio Arnone

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Digital Twin (DT) is a technology that generates a virtual representation of a physical system or process, enabling real-time monitoring, analysis, and simulation. DT of an Electrical Distribution System (EDS) can perform online analysis by integrating the static and real-time data in order to show the current grid status and predictions about the future status to the Distribution System Operator (DSO), producers and consumers. DT technology for EDS also offers the opportunity to DSO to test hypothetical scenarios. This paper discusses the development of a DT of an EDS by Smart Grid Controller (SGC) application, which is developed using open-source libraries and languages. The developed application can be integrated with Supervisory Control and Data Acquisition System (SCADA) of any EDS for creating the DT. The paper shows the performance of developed tools inside the application, tested on real EDS for grid observability, Smart Recursive Load Flow (SRLF) calculation and state estimation of loads in MV feeders.

Keywords: digital twin, distributed energy resources, remote terminal units, supervisory control and data acquisition system, smart recursive load flow

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Authors: Mohamed Rasslan, Doaa Abdelrahman, Mahmoud M. Nasreldin, Ghada Farouk, Heba K. Aslan

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Blockchain is a distributed database that endorses transparency while bitcoin is a decentralized cryptocurrency (electronic cash) that endorses anonymity and is powered by blockchain technology. Smart contracts are programs that are stored on a blockchain. Smart contracts are executed when predetermined conditions are fulfilled. Smart contracts automate the agreement execution in order to make sure that all participants immediate-synchronism of the outcome-certainty, without any intermediary's involvement or time loss. Currently, the Bitcoin market worth billions of dollars. Bitcoin could be transferred from one purchaser to another without the need for an intermediary bank. Network nodes through cryptography verify bitcoin transactions, which are registered in a public-book called “blockchain”. Bitcoin could be replaced by other coins, merchandise, and services. Rapid growing of the bitcoin market-value, encourages its counterparts to make use of its weaknesses and exploit vulnerabilities for profit. Moreover, it motivates scientists to define known vulnerabilities, offer countermeasures, and predict future threats. In his paper, we study blockchain technology and bitcoin from the attacker’s point of view. Furthermore, mitigations for the attacks are suggested, and contemporary security solutions are discussed. Finally, research methods that achieve strict security and privacy protocol are elaborated.

Keywords: Cryptocurrencies, Blockchain, Bitcoin, Smart Contracts, Peer-to-Peer Network, Security Issues, Privacy Techniques

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Authors: Yu-Chen Chang, Yen-Ping Peng, Wei-Yu Chen, Ku-Fan Chen

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Contamination of soil and groundwater has become a serious and widespread environmental problem. In this study, sustained-release persulfate tablets were developed using persulfate powder and a modified cellulose binder for organic-contaminated groundwater remediation. Conventional cement-based persulfate-releasing materials were also synthesized for the comparison. The main objectives of this study were to: (1) evaluate the release rates of the remedial tablets; (2) obtain the optimal formulas of the tablets; and (3) evaluate the effects of the tablets on the subsurface environment. The results of batch experiments show that the optimal parameter for the preparation of the persulfate-releasing tablet was persulfate:cellulose = 1:1 (wt:wt) with a 5,000 kg F/cm2 of pressure application. The cellulose-based persulfate tablet was able to release 2,030 mg/L of persulfate per day for 10 days. Compared to cement-based persulfate-releasing materials, the persulfate release rates of the cellulose-based persulfate tablets were much more stable. Moreover, since the tablets are soluble in water, no waste will be produced in the subsurface. The results of column tests show that groundwater flow would shorten the release time of the tablets. This study successfully developed unique persulfate tablets based on green remediation perspective. The efficacy of the persulfate-releasing tablets on the removal of organic pollutants needs to be further evaluated. The persulfate tablets are expected to be applied for site remediation in the future.

Keywords: sustained-release persulfate tablet, modified cellulose, green remediation, groundwater

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Authors: Anita Kéri

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Satisfying foreign student needs has been in the center of research interest in the past several years. Higher education institutions have been exploring factors influencing foreign student satisfactionto stay competitive on the educational market. Even though foreign student satisfaction and loyalty are topics investigated deeply in the literature, the academic years of 2020 and 2021 have revealed challenges never experienced before. With the COVID-19 pandemic, new factors have emerged that might influence foreign student satisfaction and loyalty in higher education. The aim of the current research is to shed lights on what factors influence foreign student satisfaction and loyalty in the post-pandemic educational era and to reveal if the effects of factors influencing satisfaction and loyalty have changed compared to previous findings. Initial results show that students are less willing to participate in online surveys during and after the pandemic. The return rate of the survey instrument is below 5%. Results also reveal that there is a slight difference in what factors have significant effects on school-related and non-school-related satisfaction and overall loyalty, measured pre- and post-pandemic times. The results of the current study help us determine what factors higher education institutions need to consider when planning the future service affordances for their foreign students that might influence their satisfaction and loyalty.

Keywords: pandemic, COVID-19, satisfacion, loyalty, service quality, higher education

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Authors: Siddhartha Dhungana

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This article primarily explores dimensions in English language teacher's professionalism so that a teacher could reflect and make a strategic professional devotion to implement effective educational programs for the present and the future. The paper substantially incorporates the eastern Hindu practices, especially life values from the Bhagavad Gita, as a basis of teacher’s professional enrichment. Basically, it applies three categorical practices, i.e., Karma Yoga, Jnana Yoga, and Bhakti Yoga, in teachers’ professionality to illustrate, ignite further ahead and sharpen academic journey, professional journey, and professional devotion reflecting common practices. In this journey, a teacher comes to a stage of professional essence as s/he surpasses Karma Yoga, Jnana Yoga, and Bhakti Yoga with their basic quality formation. To illustrate their essence-making process, the three narrative stories for each category mentioned above are analyzed. The data collected from a research participant who has a high level of professional success and who inspires all English Language teachers in Nepal to develop stories for narrative analysis. The narrative analysis is based on eastern themes that are supported by Vygotsky's concept of developmental psychology. Moreover, the structural analysis is based on Gary Barkhuizen's narrative analysis.

Keywords: Karma Yoga, Jnana Yoga, Bhakti Yoga, Vygotsky's concepts, narrative analysis

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Authors: Yunpeng Xia, Jiajia Zha, Haoxin Huang, Hau Ping Chan, Chaoliang Tan

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Although the crystal phase of two-dimensional (2D) transition metal dichalcogenides (TMDs) has been proven to play an essential role in fabricating high-performance electronic devices in the past decade, its effect on the performance of 2D material-based flash memory devices still remains unclear. Here, we report the exploration of the effect of MoTe₂ in different phases as the charge trapping layer on the performance of 2D van der Waals (vdW) heterostructure-based flash memory devices, where the metallic 1T′-MoTe₂ or semiconducting 2H-MoTe₂ nanoflake is used as the floating gate. By conducting comprehensive measurements on the two kinds of vdW heterostructure-based devices, the memory device based on MoS2/h-BN/1T′-MoTe₂ presents much better performance, including a larger memory window, faster switching speed (100 ns) and higher extinction ratio (107), than that of the device based on MoS₂/h-BN/2H-MoTe₂ heterostructure. Moreover, the device based on MoS₂/h-BN/1T′-MoTe₂ heterostructure also shows a long cycle (>1200 cycles) and retention (>3000 s) stability. Our study clearly demonstrates that the crystal phase of 2D TMDs has a significant impact on the performance of nonvolatile flash memory devices based on 2D vdW heterostructures, which paves the way for the fabrication of future high-performance memory devices based on 2D materials.

Keywords: crystal Phase, 2D van der Waals heretostructure, flash memory device, floating gate

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Prih Bukhari

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The purpose of the proposed paper is to determine if the perception of online advertisement formed due to attitude and belief vary among two different countries or not. Specifically, it seeks to find out how people from China and Pakistan perceive online advertisement. Public attitude and belief towards advertising have been a focus of attention to explore a path to a better strategy of advertising. The ‘belief’ factor was analyzed through 4 items, i.e., product information, entertainment, and increase in economy’ whereas, the ‘attitude’ factor was analyzed thorough questions based on 4 items, i.e. ‘overall, I consider online advertising a good thing’; 'overall, I like online advertising’; ‘'I consider online advertising very essential’; and 'I would describe my overall attitude toward online advertising very favorably’. As such, it provides theoretical basis to explain similarities and differences of beliefs and attitude towards advertising across the two countries. Given its mixed method approach, both quantitative and qualitative method is used to carry out research. A questionnaire-based survey and focus group interviews were conducted. The sample size was of 500 participants. For analysis survey copies were then collected from which 497 were received whereas focus group interviews were collected from both nations. The findings showed that the belief factor among both countries had no significant relation with the perception of online advertisement. However, the attitude had a significant relation with the perception about online advertisement. Also it was observed that despite of different backgrounds, perception about online advertisement based on beliefs and attitude were found largely to be similar. Implications and future studies are provided.

Keywords: attitude, belief, online advertisement, perception

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Authors: Natcha Laethongkham, Gayan Ramanayake, Philip Charlesworth, Leshan Uggalla

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The throttling process in hybrid rocket engines (HREs) poses challenges due to inherent instability, impacting the engine’s reliability and robustness. Identifying and advancing existing technology is crucial to meet the demands of complex mission profiles required for next-generation launch vehicles. This paper reviews the current literature, focusing on oxidiser flow control for throttling purposes in HREs. Covered areas include oxidiser choices, commonly used throttle valves, and literature trends. Common oxidisers for throttling are hydrogen peroxide, nitrous oxide, and liquid oxygen. Two frequently chosen valves for throttling are the ball and variation pintle valves. The review identifies two primary research focuses: flow control valve studies and control system design. The current research stage is highlighted, and suggestions for future directions are proposed to advance thrust control systems in HREs. This includes further studies in existing research focuses and exploring new approaches such as system scheme design, numerical modelling, and applications.

Keywords: hybrid rocket engines, oxidiser flow control, thrust control, throttle valve, review

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Ahmed Al-Adaileh

Abstract:

Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Nayden Takev, Remy van Leeuwen, Shiva Chotoe, Hani Alers, Xiao Peng

Abstract:

Citizens, businesses, and public authorities all around the world are becoming aware of the impact that they have on the environment. Currently, climate change is an apparent cause to urge everyone to act and move to sustainable energy solutions. After the Paris Climate Agreement, every country has thought of a way to cut down carbon emissions. The Netherlands formulated the National Climate Agreement. “The government’s central goal with the National Climate Agreement is to reduce greenhouse gas emissions in the Netherlands by 49% compared to 1990 levels. At a European level, the government is advocating a 55% reduction of greenhouse gas emissions by 2030.” [5]. From a survey of the CBS, it is apparent that citizens are not putting in as much effort into the transition to sustainable energy as the government would like them to. After analysing the data, it became clear that the citizens miss the motivation to switch to sustainable energy because they do not believe it is urgent at this point and it is too expensive for them [2]. This needs to be changed. The citizens need to be aware of their impact on the climate and the advantages that this process will bring them. For example, the implementation of smart home displays 4 for real time energy measuring will give the citizens an overview of their energy usage so they are aware of the impact they have. Researchers have also found that the citizens must be included in the decision-making aimed at changing their behaviour [4, 3, 1]. In the future, the government will need to include the citizens when they create campaigns, strategies or introduce new policies [7, 6]. By including and informing the citizens about the policies it will be more attractive for them to choose sustainable energy. However, is all of this enough to motivate the citizens towards energy transition? Or are there other and better ways to do it?

Keywords: Awereness, Energy Transition, Netherlands, citizens

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Authors: Mimoun Melliti

Abstract:

In the wake of the global pandemic, educational systems worldwide faced unprecedented challenges and had to swiftly adapt to new conditions. This necessitated a fundamental shift in assessment processes, as traditional in-person exams became impractical. The present paper aims to investigate how educational systems have adapted to the new conditions imposed by the outbreak of the pandemic. This paper serves as a case study documenting the various decisions, conditions, experiments, and outcomes associated with transitioning the assessment processes of a higher education institution to a fully online format. The participants of this study consisted of 4666 students from health, engineering, science, and humanities disciplines, who were enrolled in general English (Eng101/104) and English for specific purposes (Eng102/113) courses at a preparatory year institution in Saudi Arabia. The findings of this study indicate that online assessment can be effectively implemented given the fulfillment of specific requirements. These prerequisites encompass the presence of competent staff, administrative flexibility, and the availability of necessary infrastructure and technological support. The significance of this case study lies in its comprehensive description of the various steps and measures undertaken to adapt to the "new normal" situation. Furthermore, it evaluates the impact of these measures and offers detailed recommendations for potential similar future scenarios.

Keywords: hybrid learning, testing, adaptive teaching, EFL

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: Reinhold Kosfeld, Andreas Gohs

Abstract:

In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

Procedia PDF Downloads 175