bluuebunny/crossref_metadata_2025 huggingface dataset:
Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing
Authors: Paulo Rodrigo Leme de , Garcia Angel Pontin , Umezu Claudio Kiyoshi , Camargo Antonio Pires de , Soares Fabrício Theodoro , Albiero Daniel
Published: January 2023

Precision Irrigation (PI) is a promising technique for monitoring and controlling water use that allows for meeting crop water requirements based on site-specific data. However, implementing the PI needs precise data on water evapotranspiration. The detection and monitoring of crop water stress can be achieved by several methods, one of the most interesting being the use of infra-red (IR) thermometry combined with the estimate of the Crop Water Stress Index (CWSI). However, conventional IR equipment is expensive, so the objective of this paper is to present the development of a new low-cost water stress detection system using TL indices obtained by crossing the responses of infrared sensors with image processing. The results demonstrated that it is possible to use low-cost IR sensors with a directional Field of Vision (FoV) to measure plant temperature, generate thermal maps, and identify water stress conditions. The Leaf Temperature Maps, generated by the IR sensor readings of the plant segmentation in the RGB image, were validated by thermal images. Furthermore, the estimated CWSI is consistent with the literature results.
GRU-SVM Based Threat Detection in Cognitive Radio Network
Authors: I Evelyn Ezhilarasi , Clement J Christopher
Published: January 2023

Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study’s output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.
Estimation of Fast and Slow Adaptions in the Tactile Sensation of Mechanoreceptors Mimicked by Hybrid Fluid (HF) Rubber with Equivalent Electric Circuits and Properties
Authors: Shimada Kunio
Published: January 2023

In order to advance engineering applications of robotics such as wearable health-monitoring devices, humanoid robots, etc., it is essential to investigate the tactile sensations of artificial haptic sensors mimicking bioinspired human cutaneous mechanoreceptors such as free nerve endings, Merkel’s cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles. The generated receptor’s potential response to extraneous stimuli, categorized as slow adaption (SA) or fast adaption (FA), is particularly significant as a typical property. The present study addressed the estimation of SA and FA by utilizing morphologically fabricated mechanoreceptors made of our proposed magnetically responsive intelligent fluid, hybrid fluid (HF), and by applying our proposed electrolytic polymerization. Electric circuit models of the mechanoreceptors were generated using experimental data on capacitance and inductance on the basis of the electric characteristics of impedance. The present results regarding equivalent firing rates based on FA and SA are consistent with the FA and SA findings of vital mechanoreceptors by biomedical analysis. The present investigative process is useful to clarify the time of response to a force on the fabricated artificial mechanoreceptor.
Enabling Artificial Intelligent Virtual Sensors in an IoT Environment
Authors: Stavropoulos Georgios , Violos John , Tsanakas Stylianos , Leivadeas Aris
Published: January 2023

The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology.
DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment
Authors: Kolokas Nikolaos , Ioannidis Dimosthenis , Tzovaras Dimitrios
Published: January 2023

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called “DSF Core”. The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30–40%.
Failure Reason of PI Test Samples of Neural Implants
Authors: Guljakow Jürgen , Lang Walter
Published: January 2023

Samples that were meant to simulate the behavior of neural implants were put into Ringer’s solution, and the occurring damage was assessed. The samples consist of an interdigitated gold-structure and two contact pads embedded between two Polyimide layers, resulting in free-floating structures. The two parts of the interdigitated structure have no electric contacts and are submerged in the solution during the experiment. The samples were held at temperatures of 37 and 57 ∘C in order to undergo an accelerated lifetime test and to compare the results. During the course of the experiment, a voltage was applied and measured over a resistance of 1 kOhm over time. Arduinos were used as measuring devices. As the intact samples are insulating, a sudden rise in voltage indicates a sample failure due to liquid leaking in between the two polyimide layers. Once a short-circuit occurred and a sample broke down, the samples were taken out of the vial and examined under a microscope. In virtually all cases, delamination was observable, with variation in the extent of the delaminated area. A comparison between measured voltages after failure and damage did not show a correlation between voltage and area affected by delamination. However, at a temperature of 37 ∘C, voltage remained constant most of the time after delamination, and a pin-hole lead to a lower measured voltage and strong fluctuations. Visually, no difference in damage between the 37 and the 57 ∘C samples was observed, although fluctuations of measured voltage occurred in numerous samples at a higher temperature. This difference hints at differences in the reasons for failure and thus limited applicability of accelerated lifetime tests.
Factors Influencing the Prediction Accuracy of Model Peptides in Reversed-Phase Liquid Chromatography
Authors: Al Musaimi Othman , Mercado-Valenzo Oscar M. , Williams Daryl R.
Published: January 2023

Hydrophobicity is an important physicochemical property of peptides in solution. As well as being strongly associated with peptide stability and aggregation, hydrophobicity governs the solution based chromatographic separation processes, specifically reversed-phase liquid chromatography (RPLC). In addition, hydrophobicity is a major physicochemical property of peptides in comparison to H-bonding, electrostatic, and aromatic properties in intermolecular interactions. However, a wide range of molecular factors can influence peptide hydrophobicity, with accurate predictions depending on specific peptide amino acid compositions, structure, and conformation. It is noticeable that peptide composition, the position of the amino acid, and its neighbouring groups play a crucial role in the elution process. In light of this, the same amino acid behaved differently depending on its position and neighbouring amino acid in the peptide chain. Extra attention should be paid to the denaturation process during the course of elution, as it has been shown to complicate and alter the elution pattern. This paper reports on the key peptide properties that can alter hydrophobicity and, consequently, the RPLC elution behaviour of the peptides, and it will conclude by proposing improved prediction algorithms for peptide elution in RPLC.
An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements
Authors: Syrmos Evangelos , Sidiropoulos Vasileios , Bechtsis Dimitrios , Stergiopoulos Fotis , Aivazidou Eirini , Vrakas Dimitris , Vezinias Prodromos , Vlahavas Ioannis
Published: January 2023

This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.
The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective
Authors: Laureti Lucio , Massaro Alessandro , Costantiello Alberto , Leogrande Angelo
Published: January 2023

In this article, we investigate the impact of “Renewable Electricity Output” on the green economy in the context of the circular economy for 193 countries in the period 2011–2020. We use data from the World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Last Squares-WLS, and Pooled Ordinary Least Squares-OLS. Our results show that Renewable Electricity Output is positively associated, among others, with “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, with “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. In adjunct, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries. Furthermore, we improve the machine learning analysis with a Deep Learning approach using Convolutional Neural Network-CNN but the algorithm is not appropriate for the analyzed dataset. Less complex machine learning algorithms show better statistical results.
Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review
Authors: Satyro Walter Cardoso , Contador Jose Celso , Monken Sonia Francisca de Paula , Lima Anderson Ferreira de , Soares Junior Gilberto Gomes , Gomes Jansen Anderson , Neves João Victor Silva , do Nascimento José Roberto , de Araújo Josiane Lima , Correa Eduardo de Siqueira , Silva Leandro Simplício
Published: January 2023

The industrial impacts on the environment need to be minimized to reduce climate change, which will benefit human beings. Industry 4.0, the new production paradigm, promises productivity gains for companies that manage to implement it, but it is also dependent on natural resources, impacting the environment. The aim of this study is to identify and analyze possible cleaner production strategies associated with Industry 4.0 to optimize manufacturing systems in Industry 4.0 implementation projects, in addition to reducing the environmental impacts of these companies. Through a literature search, cleaner production strategies associated with Industry 4.0 were identified and classified into ten dimensions (strategy, waste, recycling, life cycle, resources, energy, production, work, performance and environment) contributing to the theory. The possibilities of using Industry 4.0 technologies were analyzed to meet each dimension. The relevance of this study lies in presenting possibilities for using and developing technologies and applications to meet these dimensions of cleaner production and helping those involved in Industry 4.0 projects to implement it more stably, contributing to the theory and practice.
Study on Quality Measurement and Influencing Factors of Russian Wood Forest Products Imported from China under the Background of High-Quality Development
Authors: Chen Hui , Zhang Yue , Tian Gang
Published: January 2023

Taking the trade flow of main wood forest products imported from Russia from 2011 to 2021 as the data source, this study measures the quality of the main wood forest products imported from Russia by China through the regression inference method of demand information and describes the changing trend of the quality of the wood forest products within the measurement range. In addition, the study also analyzes the influencing factors from the perspective of the high-quality development of China’s foreign trade. The results show that the quality of major wood forest products imported from Russia by China ranks at the top in the measurement range, among which the quality of logs shows a downward trend, while the quality of converted timber and wood pulp shows an upward trend; China’s customs convenience, import amount, and economic development level all play a significant positive correlation on the import quality of wood forest products, and the total population of China and the distance between the two countries have a significant negative correlation on the import quality of wood forest products. Some suggestions are put forward for expanding and strengthening the in-depth integration of forestry and economic cooperation between China and Russia, continuously deepening and strengthening the cooperation between the Far East and Siberia, and actively promoting the construction of the Sino-Russia free trade area.
Reliable Integration of Neural Network and Internet of Things for Forecasting, Controlling, and Monitoring of Experimental Building Management System
Authors: Essa Mohamed El-Sayed M. , El-shafeey Ahmed M. , Omar Amna Hassan , Fathi Adel Essa , Maref Ahmed Sabry Abo El , Lotfy Joseph Victor W. , El-Sayed Mohamed Saleh
Published: January 2023

In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment based on smart decisions. As a result, this paper introduces the design and investigation of an experimental building management system (BMS)-based IoT approach to monitor status of sensors and control operation of loads to reduce energy consumption. The proposed BMS is built on integration between a programmable logic controller (PLC), a Node MCU ESP8266, and an Arduino Mega 2560 to perform the roles of transferring and processing data as well as decision-making. The system employs a variety of sensors, including a DHT11 sensor, an IR sensor, a smoke sensor, and an ultrasonic sensor. The collected IoT data from temperature sensors are used to build an artificial neural network (ANN) model to forecast the temperature inside the laboratory. The proposed IoT platform is created by the ThingSpeak platform, the Bylink dashboard, and a mobile application. The experimental results show that the experimental BMS can monitor the sensor data and publish the data on different IoT platforms. In addition, the results demonstrate that operation of the air-conditioning, lighting, firefighting, and ventilation systems could be optimally monitored and managed for a smart system with an architectural design. Furthermore, the results prove that the ANN model can perform a distinct temperature forecasting process based on IoT data.
Long-Term Stochastic Modeling of Monthly Streamflow in River Nile
Authors: Abdelaziz Shokry , Mahmoud Ahmed Ahmed Mohamed , Eltahan Abdelhamid Mohamed , Abd Elhamid Ahmed Medhat Ismail
Published: January 2023

Synthetic time series created from historical streamflow data are thought of as substitute events with a similar likelihood of recurrence to the real event. This technique has the potential to greatly reduce the uncertainty surrounding measured streamflow. The goal of this study is to create a synthetic streamflow model using a combination of Markov chain and Fourier transform techniques based on long-term historical data for the Nile River. First, the Markov chain’s auto-regression is applied, in which the data’s trend and seasonality are discovered and eliminated before applying the Pearson III distribution function. The Pearson III distribution function is substituted by a discrete Fourier transform (DFT) technique in the second approach. The applicability of the two techniques to simulate the streamflow between 1900 and 1999 is evaluated. The ability of the generated series to maintain the four most important statistical properties of the samples of monthly flows, i.e., the mean, standard deviation, autocorrelation lag coefficient, and cumulative distribution, was used to assess the quality of the series. The results reveal that the two techniques, with small differences in accuracy, reflect the monthly variation in streamflow well in terms of the three mentioned parameters. According to the coefficient of determination (R2) and normalized root mean square error (NRMSE) statistics, the discrete Fourier transform (DFT) approach is somewhat superior for simulating the monthly predicted discharge.
Exploring the Relationships between Pre-Service Preparation and Student Teachers’ Social-Emotional Competence in Teacher Education: Evidence from China
Authors: Wu Siping , Zhu Xiaoshuang , Tian Guoxiu , Kang Xiaowei
Published: January 2023

The role of social-emotional competence in sustaining teachers’ professional development has been increasingly gaining prominence. Using Bronfenbrenner’s ecological systems theory, this study attempted to explore the deep internal mechanisms of the influence of university climate on student teachers’ social-emotional competence in the context of China. A cluster sampling method was used to conduct a questionnaire survey on 1776 student teachers from 20 universities in 17 provinces of China. This study uses a structural equation model to analyze the effect of university climate and basic psychological needs on social-emotional competence, which is moderated by relative deprivation. This study found that university climate has a significant positive effect on social-emotional competence; the association between university climate and social-emotional competence is mediated by basic psychological needs; relative deprivation plays a moderating role. The direct effect of university climate on social-emotional competence and the path from university climate to basic psychological needs were moderated by relative deprivation. Specifically, compared with low relative deprivation individuals, the university climate had a weaker positive effect on social-emotional competence and basic psychological needs in high relative deprivation individuals. Based on above empirical evidence, this study shed light on the mechanism for cultivating student teachers’ social-emotional competence, thus improving our understanding of the sustainable professional development of teachers from an emotional perspective.
A Novel Multiport Hybrid Wave Energy System for Grid-Connected and Off-Grid Applications
Authors: Yu Wei , Ma Ruiyang , Xu Darui , Huang Lei , Wang Shixiang
Published: January 2023

Direct drive wave energy converters (DDWECs) have gradually become the mainstream of wave energy converters (WECs). In order to make better use of wave energy, energy storage devices and other renewable energy sources are often used to suppress power fluctuation in DDWECs. However, the addition of other energy sources will increase the complexity of the converter system and the number of power switches. Considering the flexibility of nine-switch converters (NSCs), this paper proposes a novel nine-switch grid-connected/off-grid multiport hybrid wave energy system (HWES). First, the system structure and modulation principle are described. Then, a model for a generator, a grid and energy storage are built, including a control strategy of each part. Finally, a simulation for the grid-connected/off-grid application and an experiment on the off-grid HWES are carried out. The results show that the multiport wave energy system can achieve the objective of stable and reliable power transmission by reducing power devices.
Willingness to Pay for Environmental Quality Improvement Programs and Its Determinants: Empirical Analysis in Western Nepal
Authors: Paudel Uttam , Adhikari Shiva Raj , Pant Krishna Prasad
Published: January 2023

Environmental conditions in western Nepal are experiencing a possible threat to economic losses and sustainability, especially due to decreased productivity and increased health risks. This research investigates the maximum willingness to pay (WTP) of the local community for environmental quality improvement programs by using the contingent valuation technique. It also explores socio-economic and behavioral determinants that influence the maximum WTP for environmental quality improvement. A cross-sectional analytical design is employed using primary data obtained through in-depth face-to-face interviews with people in the community, interviews with key informants, focus group discussions and direct observations. Of the total of 420 households sampled, 72% were willing to pay for the environmental improvement program. The average WTP of households per annum for environmental protection at the community level is given as Nepalese rupees (NPR) 1909 (confidence interval—CI: 1796–2022). Environmental factors (prolonged drought, sporadic rains and drying sprout), socio-economic factors (family size, occupation, regular saving habits in microfinance, distance to the nearest health facility, health insurance enrollment, owning a home and owning arable land) and behavioral factors (cleanliness of the toilet) are the major factors influencing the household’s WTP decision. The findings of this study provide an important guideline and basis for the implementation of cost sharing in environmental quality improvement programs among the community, governments and other stakeholders in this sector.
Exploring Skills in Observing Teaching Competency through Video Evaluation of Class Demonstrations by Pre-Service Physical Education Teachers
Authors: Kim Chul-Min , Kim Min-Jun , Youn Hyun-Su , Jung Joo-Hyug
Published: January 2023

This study aimed to evaluate the level of skill in observing teaching competency in pre-service physical education (PE) teachers and provide basic data to cultivate teaching competency. A total of 21 third-year pre-service PE teachers were selected as participants. The study derived descriptive statistics to analyze the participants’ observation skills regarding teaching competency in different class stages using evaluation results of class demonstrations by pre-service teachers. The Mann–Whitney U test was conducted to verify the statistical significance of the evaluation between in-service and pre-service teachers. Compared with in-service teachers, pre-service teachers reported high agreement with intuitive teaching behaviors. However, pre-service teachers indicated low agreement with skills that required complex teaching behaviors depending on various situations. Based on theories by Schon (1983), pre-service teachers showed high “knowledge for practice” and low “knowledge in practice”. The present study confirmed that pre-service PE teachers tended to have high propositional knowledge and low methodological knowledge regarding teaching. Pre-service teacher education requires an approach that harmonizes theory- and practice-centered education. This study provided directions for pre-service teacher education to enhance the competency of pre-service teachers for leading and analyzing classes.
Influence of Rural Out-Migration on Household Participation in Community Forest Management? Evidence from the Middle Hills of Nepal
Authors: Bista Rajesh , Graybill Sophia , Zhang Qi , Bilsborrow Richard E. , Song Conghe
Published: January 2023

Rural out-migration was a rare socio-economic phenomenon when community forestry began in the 1980s in Nepal. Now, out-migration significantly influences nearly every aspect of rural livelihoods in the country. However, it is unclear how out-migration affects community forestry governance, which is essential for sustainable rural development. Therefore, this paper addresses the following research question: Does rural out-migration affect forest users’ participation in community forestry decision-making and management practices? This paper draws on data collected from an extensive survey of 415 households from 15 community forest user groups in 2 Mid-Hill districts of Nepal. The research used ordered-logit regression to model the impacts of out-migration on participation in forest management and decision-making, while controlling for a number of other socio-economic factors. The model results show that total household size and number of internal migrants, together with multiple resource characteristics and institutional attributes, were major factors affecting participation in decision-making and forest management. However, the number of international migrants did not have a significant role in determining the levels of the participation. This study provides valuable insights for future community forestry policymaking that aims to address the effects of out-migration on community forest management in Nepal.
Designing IAQ-Resilient Post-Pandemic Buildings
Authors: Lingua Carola , Crespi Giulia , Becchio Cristina , Corgnati Stefano Paolo
Published: January 2023

The COVID-19 pandemic has led to significant changes to human life and habits. There is an increasing urgency to promote occupants’ health and well-being in the built environment where they spend most of their lives, putting indoor air quality (IAQ) in the spotlight. This study fits into this context, aiming to provide useful information about the design, construction, and operation of an IAQ-resilient building in the post-pandemic era for it to ensure a good trade-off between energy- and health-related objectives. The PRISMA guidelines were adopted to conducting a systematic review obtaining 58 studies that offered relevant results on two main research areas: (i) the concept of resilience, focusing on its definition in relation to the built environment and to pandemic-related disruptions; and (ii) the building design strategies that are able to increase buildings’ resilience, focusing on the preventive measures involving engineering control. In addition, the metrics and the decision-making tools able to make IAQ-resilient buildings attractive to the investors, focusing on the cost-benefit analysis (CBA) technique, were discussed. The research supported the transition of the building sector to a human-centered approach that is able to include IAQ resilience among the main priorities of future buildings to guarantee the occupants’ health and well-being.
Simultaneous Inequity of Elderly Residents in Melbourne Metropolitan
Authors: Bokhari Ali , Sharifi Farahnaz
Published: January 2023

The importance of proper access to urban amenities for elderly residents is evidenced in the literature. Among them, mobility infrastructure, particularly public transport (PT), is of pivotal significance due to its intermediary role in access to other amenities such as healthcare or urban green space (UGS). Given this, the inequity in access to PT could lead to more adverse impacts on society, especially if it coincides with inequities in other amenities. In response, we calculate local indicators of spatial association (LISA) between the elderly population and urban amenities of PT, healthcare, and UGS at the suburban level of Melbourne Metropolitan. We, then, introduce and develop a LISA-on-LISA model to analyze and reveal the coexistence of inequities at the suburban level. The results evidence the existence of inequity in access to PT for elderly cohorts. We further reveal the clusters of PT inequity which are, at the same time, experiencing healthcare and UGS inequities. The implications of the study for resource allocation and distribution in areas suffering from simultaneous inequity are discussed.
Tone at the Top for Sustainable Corporate Governance to Prevent Fraud
Authors: Onesti Gianni , Palumbo Riccardo
Published: January 2023

This paper aims to provide a systematic literature review of sustainable corporate governance to prevent fraud through the tone at the top perspective. In recent years, studies on corporate governance and sustainability have considerably increased. The main aspects of the intersection of these fields were analyzed, especially considering the role of fraud and risk management. Indeed, corporate fraud can hinder corporate sustainability goals. However, to remove fraudulent behavior, rules of conduct, formal recommendations, or the implementation of business ethics programs are often insufficient. In this sense, corporate ethics linked to governance has been referred to as “tone at the top”. Given the significant progress in this area and the lack of a generally accepted theory, an exhaustive organization of the research is needed. Based on these assumptions, this study employed text network analysis to systematically analyze the research contributions collected from the Scopus database for the period 1990–2021. This study establishes networks using the main information of scientific contributions, such as “Abstract”, “Title”, and “Keywords”, and performs analyses, such as co-occurrence and content analyses. The main findings highlight the growing importance of corporate governance in sustainability and propose the emerging role of tone at the top as one of the main drivers of corporate governance sustainability to prevent fraud. We conclude by suggesting some insights derived from the study. The results could be useful for both the academic and professional communities, offering an opportunity for future research.
Assessment of Automated Parking Garage Services as a Means to Sustainable Traffic Development in a Mid-Sized City
Authors: Mikšíková Simona , Ulčák David , Kutá Dagmar
Published: January 2023

The research in this article deals with verifying the deficit of parking spaces from model examples in the city of Ostrava, Czech Republic. Specifically, it deals with the possibilities of solving these deficits using automated parking systems. The main data collection took place between 2010 and 2019; later, supplemental lockdown data (up until May 2022) were obtained. The main objective of this article was to use data to determine the profitability and functionality of automated parking systems in mid-sized cities such as Ostrava. The RING system was chosen as a suitable model for the automated parking system. The data (using a least-squares approximation) were used via statistical methods to make predictions for future years, including the construction of confidence limits for a given significance level. Based on data from 2011–2019, we found that the RING system would be profitable with a probability of 92.45% in the following years. We compared these predictions with the actual data and made a new prediction.
Insights on Harnessing Domestic Biowaste for Greening the Green City of Benguerir in Morocco
Authors: Chirinda Ngonidzashe , Metougui Mohamed Louay , El Gharous Mohamed
Published: January 2023

As the human population grows, the amount of household-generated waste is projected to grow. However, data on the amounts of household-generated waste are lacking in many regions. Uncertainties in the amounts and fractions of biowaste make it challenging to manage actions that reduce its environmental impacts and generate value. The current observational study sought to quantify and characterize the household waste generated in the green city of Benguerir. To achieve this objective, we surveyed waste generation rates in 68 randomly selected households. The results showed that, on average, 0.69 kg of waste was generated per person per day. An average of 80% of the household-generated waste was biowaste, which, if managed appropriately, represents a valuable source of plant nutrients. We estimated that shifting from the current landfill disposal of biowaste to its utilization for compost and biogas production will result in up to an 88% reduction in greenhouse gas emissions. These findings suggest that managing household waste will be vital for reducing city-wide carbon emissions and simultaneously creating employment in the waste management sector.
Quaternized Polyethersulfone (QPES) Membrane with Imidazole Functionalized Graphene Oxide (ImGO) for Alkaline Anion Exchange Fuel Cell Application
Authors: Mothupi Moshito Lethabo , Msomi Phumlani Fortune
Published: January 2023

Membranes made up of composite materials have shown promising advantages for application in alkaline fuel cell anion exchange membranes. In this study, a general method is employed to improve the overall performance and properties of alkaline anion exchange membranes by making use of polyethersulfone quaternized with imidazolium groups and blended with imidazolium functionalized graphene oxide inorganic filler (ImGO). The inorganic filler blended with the polymer matrix yielded better ionic transport, with 73.2 mS·cm−1 being the highest ion conductivity for the polymer membrane with 0.5% ImGO content, which is higher than that of the QPES parent material. The 0.5% ImGO content also showed better swelling ratio, water uptake, alkaline stability, ion exchange capacity and alkaline stability in comparison to other membranes. Furthermore, it also exhibited 130 mW·cm−2 peak power.
Monitoring of Performance-Based Environmental Impacts of Substituting Soybean Meal with Rapeseed Meal in the Rye-Based Diet of Weaned Pigs
Authors: Wilke Volker , Gickel Julia , Visscher Christian
Published: January 2023

Due to its favorable properties, soybean meal (SBM) is used especially in young growing animals. In terms of sustainability, there are various efforts to reduce the amounts of SBM in compound feeds and to increase the use of regional protein sources. This paper focuses on the effects of a partial to total substitution of SBM by regionally produced rapeseed meal (RSM) in different piglet diets regarding 10 important factors having an impact on the environment. Four diets, characterized by different shares (%) of both protein-rich ingredients (SBM/RSM: 18.1/0; 13.6/6.70; 8.10/16.1; 0/28.0), were fed to four groups of 10 piglets each in two runs. The impact was calculated related to feed (per t) and was performance-based for every piglet (impact·kg weight gain−1) for each factor using methods according to life-cycle-analyses (LCA). Although feed intake and weight gains were not affected negatively, higher feed conversion ratios occurred, with high amounts of rapeseed inclusion. Nevertheless, the performance-based negative influence on climate change (kg CO2 eq·kg weight gain−1) was nearly halved when SBM was replaced by RSM. Since performance was not negatively affected, the use of RSM instead of SBM in piglet diets could be a viable tool for markedly reducing the negative impact on climate change.
Reconstruction of Scalar-Torsion Gravity Theories from the Physical Potential of a Scalar Field
Authors: Chervon Sergei V. , Fomin Igor V.
Published: January 2023

We consider scalar-torsion gravity theories based on the exact solutions of a physical type of potential for cosmological inflationary models based on the non-minimal coupling of a scalar field and torsion. We analyzed the inflationary models with different types of inflationary dynamics and corresponding scalar field parameters. Such an approach allows us to consider different physical potentials and types of scalar-torsion gravity theories in the context of the realization of both stages of accelerated expansion of the universe. We also considered the correspondence surrounding the proposed inflationary models and the observational constraints on the parameters of cosmological perturbations.
Observational and Energetic Properties of Astrophysical and Galactic Black Holes
Authors: Narzilloev Bakhtiyor , Ahmedov Bobomurat
Published: January 2023

The work reviews the investigation of electromagnetic, optical, and energetic properties of astrophysical and galactic black holes and surrounding matter. The astrophysical applications of the theoretical models of black hole environment to the description of various observed phenomena, such as cosmic rays of the ultra-high-energy, black hole shadow, gravitational lensing, quasinormal modes, jets showing relativistic effects such as the Doppler beaming, thermal radiation from the accretion discs, quasiperiodic oscillations are discussed. It has been demonstrated that the observational data strongly depends on the structure and evolution of the accretion disk surrounding the central black hole. It has been shown that the simulated images of supermassive black holes obtained are in agreement with the observational images obtained by event horizon telescope collaboration. High energetic activity from supermassive black holes due to the magnetic Penrose process discussed in the work is in agreement with the highly energetic cosmic rays observed. The astronomical observation of black holes provides rich fundamental physics laboratories for experimental tests and verification of various models of black hole accretion and different theories of gravity in the regime of strong gravity.
The Astroparticle Detectors Array—An Educational Project in Cosmic Ray Physics
Authors: Arcani Marco , Conte Elio , Monte Omar Del , Frassati Alessandra , Grana Andrea , Guaita Cesare , Liguori Domenico , Nemolato Altea Renata Maria , Pigato Daniele , Rubino Elia
Published: January 2023

ADA, short for Astroparticle Detectors Array, is an educational project aiming to detect cosmic radiation and possibly high-energy particles known as ultra-high-energy cosmic rays (UHECRs) or even to spot a supernova event. Its working process is the same as that used in professional cosmic ray observatories: it consists of simple detectors spread over the entire Italian territory and beyond. The detectors are hosted among high schools, associations, and private astronomical observatories. ADA has been operating since 2013 and was brought about with the intention of promoting astroparticle physics to any given level of outreach. Furthermore, ADA is becoming an interesting tool not only for teachers but also for independent and keen scientists. Over the years, we have discovered the importance of having a long series of data for studying the relation between and among cosmic rays, weather, and space weather and to investigate the main cause of oscillations in cosmic ray data. In this paper, we show what we find to be the most compelling results, such as the beautiful symmetry of the behavior between muons and the atmospheric temperature and, likewise, the evident anti-correlation between the intensity of the muons at ground level compared with solar activity.
HyproBert: A Fake News Detection Model Based on Deep Hypercontext
Authors: Nadeem Muhammad Imran , Mohsan Syed Agha Hassnain , Ahmed Kanwal , Li Dun , Zheng Zhiyun , Shafiq Muhammad , Karim Faten Khalid , Mostafa Samih M.
Published: January 2023

News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models.
Ruled and Quadric Surfaces Satisfying ΔIIN = ΛN
Authors: Al-Zoubi Hassan , Hamadneh Tareq , Abu Hammad Ma’mon , Al-Sabbagh Mutaz , Ozdemir Mehmet
Published: January 2023

In the 3-dimensional Euclidean space E3, a quadric surface is either ruled or of one of the following two kinds z2=as2+bt2+c,abc≠0 or z=a2s2+b2t2,a>0,b>0. In the present paper, we investigate these three kinds of surfaces whose Gauss map N satisfies the property ΔIIN=ΛN, where Λ is a square symmetric matrix of order 3, and ΔII denotes the Laplace operator of the second fundamental form II of the surface. We prove that spheres with the nonzero symmetric matrix Λ, and helicoids with Λ as the corresponding zero matrix, are the only classes of surfaces satisfying the above given property.
Bonding of Dissimilar Metals in the Interlayer Region in Al-Based Composites: Molecular Dynamics
Authors: Polyakova Polina V. , Baimova Julia A. , Mulyukov Radik R.
Published: January 2023

The aluminum–matrix composites possess are very important for future applications because they have unique mechanical properties. Here, molecular dynamics is used to analyze the bonding of dissimilar metals on the interface of Al/Mg, Al/Ti, and Al/Cu interfaces during deformation treatment–compression combined with shear at room temperature. The terminal-mechanical treatment used in this simulation is a variant of the experimental technique applied to fabricate Al/Metal composites. It is found that there is a critical value of compressive and shear strain required to obtain the strong mixed Al/Metal interface. The diffusion depth of atoms of both components is dependent on the applied strain: (i) linear relationship for Al/Mg; (ii) logarithmic relationship for Al/Ti and Al/Cu. The mechanical behavior under tension and fracture analysis of the obtained interfaces are discussed in terms of atomic-level structural features which allow an understanding of the interconnection between the mechanical behavior and structure mixture near the interface. One of the important criteria for atomic mixing is the symmetry of the interface. After atomic mixing, Al/Ti composite has the highest ultimate tensile strength, Al/Cu—the average, and Al/Mg—the lowest between the considered interfaces, while the fracture strain of Al/Mg and Al/Ti composites are very close and higher than for Al/Cu. The obtained results are significant for the development of fabrication of Al/Metal interface by high-pressure torsion in practice.
A Novel Three-Step Numerical Solver for Physical Models under Fractal Behavior
Authors: Awadalla Muath , Qureshi Sania , Soomro Amanullah , Abuasbeh Kinda
Published: January 2023

In this paper, we suggest an iterative method for solving nonlinear equations that can be used in the physical sciences. This response is broken down into three parts. Our methodology is inspired by both the standard Taylor’s method and an earlier Halley’s method. Three evaluations of the given function and two evaluations of its first derivative are all that are needed for each iteration with this method. Because of this, the unique methodology can complete its goal far more quickly than many of the other methods currently in use. We looked at several additional practical research models, including population growth, blood rheology, and neurophysiology. Polynomiographs can be used to show the convergence zones of certain polynomials with complex values. Polynomiographs are produced as a byproduct, and these end up having an appealing look and being artistically engaging. The twisting of polynomiographs is symmetric when the parameters are all real and asymmetric when some of the parameters are imaginary.
Popcorn Transitions and Approach to Conformality in Homogeneous Holographic Nuclear Matter
Authors: Cruz Rojas Jesús , Demircik Tuna , Järvinen Matti
Published: January 2023

We study cold and dense nuclear matter by using the gauge/gravity duality. To this end, we use the Witten–Sakai–Sugimoto model and the V-QCD models with an approach where the nuclear matter is taken to be spatially homogeneous. We focus on the “popcorn” transitions, which are phase transitions in the nuclear matter phases induced by changes in the layer structure of the configuration on the gravity side. We demonstrate that the equation of state for the homogeneous nuclear matter becomes approximately conformal at high densities, and compare our results to other approaches.