bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Applications of Tyre-Derived Aggregates for Railway Transportation Infrastructure
Authors: Farooq Mohammad Adnan , Nimbalkar Sanjay , Fatahi Behzad
Published: September 2022

Scrap tyres are used to produce tyre-derived aggregates (TDA), which can be used as fill material, backfill material, drainage layers, and vibration-damping material, among other uses. This study presents a comprehensive review of TDA applications in civil engineering with a specific focus on railway projects. A review of the existing literature reveals the lack of sufficient knowledge on the use of TDA in slab tracks. This article also analyses the adequacy of different constitutive models to properly simulate the performance of TDA while highlighting the importance of adopting the most suitable constitutive model. The variations in shear stresses and displacements with depth below ballasted and slab tracks in the presence and absence of TDA are discussed. It is shown that TDA effectively reduces the shear stresses for the subgrade layer of both track types. Moreover, the impact of TDA on stress transfer in the vertical and lateral track directions is assessed. The findings from the present analysis reveal that TDA helps in reducing the vertical and lateral stresses near its placement position in ballasted and slab tracks.
Effects of Potential Support Program on Drowning Prevention among Primary School Students in Nakhon Si Thammarat Province, Thailand
Authors: Kusol Kiatkamjorn , Phromphen Chuda , Eksirinimit Thidarat
Published: September 2022

In this study, we aimed to see how a student potential support program affected drowning prevention among primary school students in Nakhon Si Thammarat. This study was quasi-experimental. The sample consisted of students in Grades 1–6 who were randomly selected based on the inclusion criteria. The experimental and control groups had 120 participants. The experimental group was given the program (House, 1981), while the control group was to resume normal activities. The program lasted 8 weeks. The research instruments and data collection included the program and assessment forms on knowledge, attitudes, and behaviors in drowning prevention. Descriptive statistics, Chi-square statistics, paired sample t-tests, and independent t-tests were employed for the analysis of the data. The results show that the samples were 7–12 years of age (M = 9.50, SD = 1.72), with male and female students accounting for 37.5, and 62.5 percent, respectively; and 73.33 percent of them lived with their parents. The mean scores of the students in the control and experimental groups before using the program had no statistically significant differences in knowledge, attitudes, and behaviors (p > 0.05). As for the mean scores of the students’ potential for drowning prevention in the experimental group before and after using the program, it was found that after the program there was a marked increase in all aspects with a statistically significant difference (p < 0.001): before and after in knowledge (M = 17.08, SD = 3.22; M = 19.15, SD = 1.79, respectively), before and after in attitudes (M = 48.48, SD = 6.24; M = 55.23, SD = 4.66, respectively), and before and after in behaviors (M = 17.97, SD = 3.13; M = 21.00, SD = 2.50, respectively). The mean scores of the students’ potential for drowning prevention in the experimental group after using the program was higher than in the control group, with statistically significant differences in all aspects (p < 0.001): in the experimental group, knowledge, attitudes, and behaviors (M = 19.15, SD = 1.79; M = 55.23, SD = 4.66; M = 21.00, SD = 2.50; respectively); and in the control group, knowledge, attitudes, and behaviors (M = 16.15, SD = 3.22; M = 48.37, SD = 3.61; M = 17.85, SD = 2.64; respectively). The program can help students develop better drowning prevention knowledge, attitudes, and behaviors. It can assist students in being able to protect themselves from drowning in risky situations.
Application Progress of O3/PMS Advanced Oxidation Technology in the Treatment of Organic Pollutants in Drinking Water
Authors: Lu Hai , Li Qingpo , Feng Weihao , Zhang Xiaoyu
Published: September 2022

In recent years, due to the abuse of pharmaceuticals and personal care products (PPCPs), many refractory trace organic compounds (TrOCs) have been transferred into natural water bodies, posing significant challenges to the water environment. On the other hand, advanced oxidation processes (AOPs) are cleaner and more efficient than traditional biochemical degradation processes. Among them, the combined ozone/persulfate advanced oxidation process (O3/PMS) based on sulfate radicals (SO4•−) and hydroxyl radicals (•OH) has developed rapidly in recent years. Thus, this paper summarised the reaction mechanism of O3/PMS and analysed its research and application progress in drinking water treatment. In addition, the process’s operation characteristics and current application scope were discussed, and the generation ways and inhibition methods of bromate and halogenates, by-products in the oxidation process, were summarised, which had a specific reference value for further research on O3/PMS process.
Housing and Setting Constraints: The Portuguese Evidence
Authors: Santos António Duarte , Castro Hélio
Published: September 2022

In the last few decades, Portugal has witnessed an extraordinary quantitative and qualitative transformation in housing provision. The pace of housing construction was so extensive that the contemporary real estate market is currently characterized by an excessive supply, vis-à-vis the resident population. In this study, we discuss the impact of the financial process on the housing sector in comparison with tenancy. We consider transaction prices of the housing assignments, either through acquisition or through tenancy. The recent shock resulting from the pandemic situation did not slow down house prices but caused a slight drop in rents. The model used proposes to analyze the fluctuations in prices and rents in the face of external shocks. In the residential market, the estimation is complex due to the many heterogeneous attributes of residential assets. Non-fluctuating variables, such as size, location, and external demand for homes, explain a large part of the variation in price levels included in the model.
Efficient Reuse of Railway Track Waste Materials
Authors: Sañudo Roberto , Goswami Rohit Raj , Ricci Stefano , Miranda Marina
Published: September 2022

Some of the most important materials that need recycling are generated by the construction industry. This waste has a multitude of disposal problems. In the specific case of railways, the treatment of materials taken from track maintenance and renewal operations is even more challenging. Every year, tons of track materials are replaced on rail tracks all over the world. These kilometres of rails, sleepers, and tons of ballast can be reused for other purposes. However, sometimes the environmental cost generated by their secondary use is worse than the problems involved in their disposal. This work describes a revised methodology to improve the recycling process of these waste track materials and considers the carbon footprint generated during the process along with important advantages and benefits for the economy and the environment. The reuse of these worn track materials is important to extend their life cycle and reduce environmental and economic costs in the long term. This research aims to analyse dismantled track material and evaluate possible second uses, taking into account the carbon footprint generated. Special attention has been placed on environmentally friendly uses such as fencing protected areas or green routes, among others.
Analysis of the Cognitive Load of Employees Working from Home and the Construction of the Telecommuting Experience Balance Model
Authors: Wei Ting , Wang Weiwei , Yu Suihuai
Published: September 2022

Adapting to working from home caused physical and psychological difficulties, leading to work–family imbalance and lower employee performance during the COVID-19 pandemic. This study intends to identify the relationship between variables affecting telecommuting experience and improve employees’ perceived organizational support by constructing a balanced model of telecommuting experience. An online questionnaire survey was conducted with 142 employees from different organizations telecommuting during the epidemic in Xi’an. The NASA-TLX scale was used to quantitatively evaluate the cognitive load of employees working from home, and the Analytic Hierarchy Process method was applied to map negative experience factors with cognitive load to obtain the weight value of each factor. Finally, a balanced model of telecommuting experience was constructed through a system map. The results show that mental demand was the key factor affecting employees’ telecommuting experience. A good telecollaboration system could effectively manage work tasks and reduce the psychological load of employees. Frustration and temporal demand also significantly affected employees’ telecommuting experience, mainly due to work–family conflict. Adopting flexible work hours and organizing online sharing activities could reshape employees’ social relationships with their families and colleagues, effectively improving the telecommuting experience. The empirical study validated the effectiveness of the telecommuting experience balance model.
Counteracting French Fake News on Climate Change Using Language Models
Authors: Meddeb Paul , Ruseti Stefan , Dascalu Mihai , Terian Simina-Maria , Travadel Sebastien
Published: September 2022

The unprecedented scale of disinformation on the Internet for more than a decade represents a serious challenge for democratic societies. When this process is focused on a well-established subject such as climate change, it can subvert measures and policies that various governmental bodies have taken to mitigate the phenomenon. It is therefore essential to effectively identify and counteract fake news on climate change. To do this, our main contribution represents a novel dataset with more than 2300 articles written in French, gathered using web scraping from all types of media dealing with climate change. Manual labeling was performed by two annotators with three classes: “fake”, “biased”, and “true”. Machine Learning models ranging from bag-of-words representations used by an SVM to Transformer-based architectures built on top of CamemBERT were built to automatically classify the articles. Our results, with an F1-score of 84.75% using the BERT-based model at the article level coupled with hand-crafted features specifically tailored for this task, represent a strong baseline. At the same time, we highlight perceptual properties as text sequences (i.e., fake, biased, and irrelevant text fragments) at the sentence level, with a macro F1 of 45.01% and a micro F1 of 78.11%. Based on these results, our proposed method facilitates the identification of fake news, and thus contributes to better education of the public.
Can Preschool Teachers’ Accurate Analysis of the Development Trajectories of Children’s Preconceptions Ensure Their Effective Response? Evidence from Situational Judgement Tests
Authors: Li Li , Lv Xue , Xi Yaqin , Guo Liping
Published: September 2022

In early childhood science education, analyzing and responding to children’s preconceptions are essential professional skills possessed by preschool teachers. This study aims to evaluate the level of preschool teachers’ skills of analyzing and responding to the development trajectories of children’s preconceptions (DTCP) and explores the relationship between them in different science disciplines as well as between teachers with different teaching experiences from a Chinese teachers perspective. A newly developed and validated instrument, the Situational Judgement Tests of Preschool Teachers’ Skills to Analyze and Respond (SJTs-PTSAR), is adopted. Altogether, 1084 Chinese teachers from three cities in China were surveyed, and analysis of the psychometric properties indicated that SJTs-PTSAR was a reliable and valid scale. The means and standard deviations of preschool teachers’ analysis skills were 1.04 and 0.31, and those for responding were 1.02 and 0.26. There was no significant difference between the scores of the two skills (t=−1.842,p>0.01, Cohen’s d = 0.068). Correlation analysis showed that the preschool teachers’ analysis skills were positively related to their responding (r=0.353,p<0.001), and there was a significant correlation between the skills of teachers of different teaching ages. These results showed that preschool teachers’ skills to analyze and respond to the DTCP were at a medium level, and an accurate analysis could not guarantee a high-level response based on the DTCP. The correlation coefficient between these two skills with teachers of different teaching experience was nonlinear. A number of suggestions for teacher training and professional development are provided to promote the sustainable development of teachers’ analysis and response skills.
How Do Perceived Value and Risk Affect Purchase Intention toward Second-Hand Luxury Goods? An Empirical Study of U.S. Consumers
Authors: Lou Xingqiu , Chi Ting , Janke Justin , Desch Gianna
Published: September 2022

The second-hand luxury market is currently experiencing rapid growth. Despite the existing knowledge on consumer second-hand luxury shopping behavior, little is known about second-hand luxury consumption in the U.S. The purpose of this study is to investigate how U.S. consumers perceive second-hand luxury goods and the various factors that influence their purchase intentions, including economic, emotional, social, quality, green values, and perceived risk. An online survey was conducted to test the proposed relationships between the related variables in the conceptual model. A total of 340 eligible responses was collected. The psychometric properties of the proposed model were examined, and the multiple regression method was applied to test the hypotheses. Results of this study indicated that perceived emotional value, perceived social value, perceived quality value, and perceived green value positively influence U.S. consumers’ intention to purchase second-hand luxury products. The proposed research model shows a good explanatory power, accounting for 75.4% of variance in U.S. consumers’ purchase intention towards second-hand luxury goods. This research is one of the first attempts to understand the second-hand luxury consumption of U.S. consumers, a critical segment in the luxury resale market. Understanding consumers’ desired values regarding second-hand luxury goods will enable retailers and brands to develop effective marketing strategies and promote sustainable consumption.
A Feedback Analysis Automation Using Business Intelligence Technology in Companies Organizing Urban Public Transport
Authors: Bańka Michał , Daniłowski Jakub , Czerliński Mirosław , Murawski Jakub , Żochowska Renata , Sobota Aleksander
Published: September 2022

In companies organizing urban public transport, passengers’ feedback is usually collected personally, by phone, by e-mail, or via the Internet. Business Intelligence tools enable this process by automating the data flow between systems used to collect, process, and analyse data by applying analytical Business Intelligence tools. The most important advantage resulting from implementing the tool is facilitating contact between the company and passengers, which allows for an immediate response to the information reported by passengers by introducing the changes in passenger service offered by the company. Another advantage of the discussed tools is the ability to analyse the collected data quickly and easily. Due to the low implementation costs, the developed tool is dedicated mainly to the companies organizing urban public transport. The article presents an example of the use of Power BI tools in the Metropolitan Transport Authority, a company that is the largest organizer of public transport in Poland.
Optimal-Quality Choice and Committed Delivery Time in Build-To-Order Supply Chain
Authors: Wen Xiaoqin , Wang Chenhanzhi
Published: September 2022

This paper studies a build-to-order supply chain (BTO-SC), which consists of one contract manufacturer (CM) and one original equipment manufacturer (OEM). The CM commits to the delivery time and the OEM determines the quality level and the selling price of the supply chain product. We present a three-stage Stackelberg game model and identify a Nash equilibrium solution for the decisions of the CM and the OEM. We conduct a sensitivity analysis to provide insights into the roles of the CM and the OEM. Our main research findings are as follows: The CM’s profit increases while the OEM’s profit first decreases and then increases (non-monotonic) as the committed delivery time sensitivity of demand increases. Interestingly, this study finds that the OEM’s profit decreases, whereas the CM’s profit first increases and then decreases (non-monotonic) in the unit production subsidy paid by the OEM to the CM. Our work shows that the high-quality and fast-delivery product policy is worthwhile in a quality-sensitive or delivery time-sensitive market, which leads to a triple-win outcome. Counterintuitively, a high production capacity is not always advantageous for the supply chain product, even for the CM.
A Novel Fault Detection and Classification Strategy for Photovoltaic Distribution Network Using Improved Hilbert–Huang Transform and Ensemble Learning Technique
Authors: Nsaif Younis M. , Hossain Lipu Molla Shahadat , Hussain Aini , Ayob Afida , Yusof Yushaizad , Zainuri Muhammad Ammirrul A. M.
Published: September 2022

Due to the increased integration of distributed generations in distributed networks, their development and operation are facing protection challenges that traditional protection systems are incapable of addressing. These problems include variations in the fault current during various operation modes, diverse distributed network topology, and high impedance faults. Therefore, appropriate and reasonable fault detection is highly encouraged to improve the protection and dependability of the distributed network. This paper proposes a novel technique that employs an improved Hilbert–Huang Transform (HHT) and ensemble learning techniques to resolve these challenges in a photovoltaic distributed network. First, improved HHT is utilized to extract energy features from the current signal. Second, variational mode decomposition (VMD) is applied to extract the intrinsic mode function from the zero component of the current signal. Then, the acquired energy feature and intrinsic mode function are input to the ensemble learning technique for fault detection and classification. The proposed technique is implemented using MATLAB software environment, including a classification learner app and SIMULINK. An evaluation of the results is conducted under normal connected mode (NCM) and island mode (ISM) for radial and mesh-soft normally open point (SNOP) configurations. The accuracy of the ensemble bagged trees technique is higher when compared to the narrow-neural network, fine tree, quadratic SVM, fine-gaussian SVM, and wide-neural network. The presented technique depends only on local variables and has no requirements for connection latency. Consequently, the detection and classification of faults using the proposed technology are reasonable. The simulation results demonstrate that the proposed technique is superior to the neural network and support vector machine, achieving 100%, 99.2% and 99.7% accurate symmetrical and unsymmetrical fault detection and classification throughout NCM, ISM, and dynamic operation mode, respectively. Moreover, the developed technique protects DN effectively in radial and mesh-SNOP topologies. The suggested strategy can detect and classify faults accurately in DN with/without DGs. Additionally, this technique can precisely detect high and low impedance faults within 4.8 ms.
Household E-Waste Management: A Case Study of Wroclaw, Poland
Authors: Banaszkiewicz Kamil , Pasiecznik Iwona , Cieżak Wojciech , Boer Emilia den
Published: September 2022

Improper collection and processing of waste electrical and electronic equipment (WEEE) pose a serious threat to the environment and prevent the recovery of valuable materials. Due to the decreasing availability of resources and production materials, the relevance of WEEE recycling has increased. WEEE should be seen as an important source of raw materials for European economies. Moreover, e-waste recycling has a positive impact on the environment by limiting energy use and CO2 emissions during ore processing. The objective of this study was the analysis of the product use times, the reasons for purchasing new devices, and the consumers’ behavior, knowledge, and awareness concerning WEEE collection and treatment. This study discusses these issues based on a survey on electronic waste management in Wroclaw (Poland). The results from 495 questionnaire respondents indicated a shortening use time of many types of electronic and electric equipment (EEE). The “failure of the device” was the primary reason respondents replaced their products. It was indicated by 90% of the respondents. In many cases, the repair of devices was unprofitable. The most important factor determining the purchase of new devices was their price. The environmental factors, mainly those giving economic benefits, were also considered. Despite the implementation of the extended producer responsibility, the functioning model of WEEE collection has proven to be ineffective. A significant amount of small WEEE ended up in waste bins. For this reason, the willingness of residents to pay an additional fee for WEEE collection was also estimated. The results indicated that campaigns to increase residents’ awareness of WEEE management should continue.
How Blockchain Facilitates the Transition toward Circular Economy in the Food Chain?
Authors: Pakseresht Ashkan , Ahmadi Kaliji Sina , Xhakollari Vilma
Published: September 2022

Food loss and waste are two of the many problems that modern society is facing. To date, among many solutions, the circular economy is the one prevailing. A successful transition toward a circular economy (CE) requires the food sector to overcome the challenges of today’s complex food supply chains such as information asymmetry, poor cooperation among stakeholders, and concerns about food safety. Blockchain, a form of distributed ledger technology, has been progressively gaining traction in supply chains in areas like data management, certifying product provenance and tracking products. Despite its importance, knowledge around the potential of the blockchain technology in facilitating the transition towards a circular economy in the agri-food sector is fragmented. This review provides evidence-based insights into the blockchain implementations in the food supply chains and the implications for CE. Our findings indicated four major areas that blockchain could accelerate CE in the agri-food sector: improving data utility; supply chain management efficacy; enhanced eco-efficiency; and superior traceability.
Towards Sustainable Business Model Innovation for the Pharmaceutical Industry
Authors: Chuang Li-Min , Lee Yu-Po , Liu Te-Hui
Published: September 2022

This study examines pharmaceutical companies in the context of BMI. The purpose is to develop an SBM for the pharmaceutical industry and then to validate the causal relationships of the variables in such a business model. This study used purposive sampling by issuing questionnaires to 12 companies. The research consisted of the following four studies: Study 1: construction of dimension conceptualization. The conceptualization of BMI consists of three dimensions, i.e., technological, social, and organizational. Study 2 and study 3 are about process development and the construction of a unique BM. Study 2 explores the evolution of innovations in an SBM on the basis of a balance scorecard. Study 3 develops a unique SBM by referring to a focus group comprised of senior executives. Study 4: model validation. This stage is about the synthesis of research frameworks in the literature on BMI and an empirical study on the causal relationships in the context of SBMI.
Investigating the Determinants of the Adoption of Solar Photovoltaic Systems—Citizen’s Perspectives of Two Developing Countries
Authors: Ahmed Yunis Ali , Rashid Ammar , Khurshid Muhammad Mahboob
Published: September 2022

The adoption of solar photovoltaic (PV) systems is seen as an important part of the sustainable energy transition. In this regard, it is crucial to identify the determinants of solar (PV) systems’ adoption to facilitate this process. Therefore, this article aims to examine the determinants of SPVS adoption by contrasting the relationships in a cross-cultural environment. For the accomplishment of the purpose, this paper follows a quantitative method in which data is analysed by adopting the PLS-SEM approach using SmartPLS 3.3.9. After analysing the collected data of 464 consumers from Somalia and Pakistan, it is found that perceived usefulness, perceived ease-of-use, compatibility, observability, and perceived trust are significant predictors. However, no significant difference in influencing determinants has been observed between the two cultures using multi-group analysis. Further, perceived trust is not revealed as a significant determinant of behavioural intention in the Somalian context. The strongest relationship is found between attitude and behavioural intention in both cultures. In Somalia, the results reveal a variance of 49% in attitudes and 51% in intention to adopt SPVSs. In Pakistan, a variance of 60.1% in attitudes and 76.8% in intention to adopt SPVSs is found. Implications for both academics and managers to scale-up the adoption of SPVSs are made.
Defining a Digital Strategy in a BIM Environment to Manage Existing Reinforced Concrete Bridges in the Context of Italian Regulation
Authors: Ciccone Angelo , Suglia Pompilio , Asprone Domenico , Salzano Antonio , Nicolella Maurizio
Published: September 2022

Regulatory activity concerning the management of existing bridges has recently been affected by updates, for instance, in Italy, which calls for a speedy and pragmatic approach based on new technologies such as building information modeling (BIM), when dealing with the survey and risk classification as well as the evaluation and monitoring of structural safety. This paper focuses on the development and integration of a digital solution, based principally on the specific framework developed by the authors, which supports BIM modeling and information management activities, in the structural setting under investigation, through the use of several technologies and tools, namely BIM-authoring, CDE platform and visual programming, in addition to programming in Python. Starting from the organization of a specific BIM object library and the initial data, inserted by means of a custom-made input environment, it was possible to reproduce digital models of bridges in accordance with specific information requirements following the new Level of Information Need setting. The applicability of the proposal is tested on two judiciously chosen real-life cases with different characteristics. Through this implementation, a series of advantages emerge, including expediting traditional procedures for BIM modeling, accessibility and traceability of information—which are constantly updated to support the monitoring of structural safety over time—and the decision-making process related to the bridge management context.
Flexural Strength of Concrete Beams Made of Recycled Aggregates: An Experimental and Soft Computing-Based Study
Authors: Momeni Ehsan , Omidinasab Fereydoon , Dalvand Ahmad , Goodarzimehr Vahid , Eskandari Abas
Published: September 2022

The implementation of recycled concrete aggregates (RCAs) in the construction industry has been highlighted in the literature recently. This study aimed to propose an intelligent model for predicting the ultimate flexural strength of recycled reinforced concrete (RRC) beams. For this reason, a database comprising experimental tests on concrete beams was compiled from the literature. Additionally, two experimental tests were performed in the laboratory to enhance the aforementioned database. The flexural test results showed a 10% reduction in flexural strength when the RRC beam was tested instead of a conventional beam (constructed with natural aggregates). Nevertheless, an artificial neural network (ANN) improved by particle swarm optimization (PSO), as well as an imperialist competitive algorithm (ICA), were utilized for developing the predictive model. The inputs of the hybrid predictive models of flexural strength were the beam geometrical properties, reinforcement ratio, RCA percentage, compressive strength of concrete, and the yield strength of steel. The overall findings (e.g., correlation coefficient values of 0.997 and 0.994 for the testing data) showed the feasibility of the PSO-based ANN predictive model, as well as the ICA-based ANN predictive model in the flexural assessment of RRC beams. Furthermore, comparing the prediction performances of PSO-based ANN with ICA-based ANN and the conventional ANN showed that the PSO-based ANN model outperformed the predictive model built with the conventional ANN and the ICA-ANN.
Experimental Study on Microbial-Induced Calcium Carbonate Precipitation Repairing Fractured Rock under Different Temperatures
Authors: Deng Junren , Deng Hongwei , Zhang Yanan , Luo Yilin
Published: September 2022

Microbial-induced calcium carbonate precipitation (MICP) technology mainly uses carbonates produced by the reaction of microbial activities to repair rocks and soils. Temperature influences microbial metabolism and the kinetics of chemical reactions. In this study, microbial repair experiments on fractured sandstone under different temperatures are carried out. The repair effects are tested with nuclear magnetic resonance (NMR), an X-ray automatic diffractometer (XRD), uniaxial compressive strength (UCS), and a scanning electron microscope (SEM) test. The influence of the temperature on the restorative effects of MICP was discussed. The results show that the repair effect of the Sporosarcina pasteurii is significantly better as the temperature increases. When the temperature reaches 33 °C, the porosity and permeability of fractured sandstone can be reduced by 55.174% and 98.761%, respectively. The average uniaxial compressive strength can be restored to 6.24 MPa. The repair effect gradually weakens with the increase in temperature. However, the Sporosarcina pasteurii can still maintain relatively good biological activity at temperatures from 33 °C to 39 °C. The main form of CaCO3 produced in the process of MICP is calcite. It can fill in the rock pores, and result in reducing the size and number of large pores and improving the impermeability and strength of fractured yellow sandstone.
Microscopic Damage to Limestone under Acidic Conditions: Phenomena and Mechanisms
Authors: Chen Xingming , Liu Xiaoping , Luo Haoming , Long Linjian , Liu Chuanju
Published: September 2022

In an acidic environment, the mineral components in rock begin to break down. As a result, the microstructure will be damaged, and then the mechanical properties will deteriorate, which will eventually have a negative effect on engineering stability. In order to study acid damage’s effect on this kind of rock, limestone samples were acidified for 0 days, 5 days, 10 days, 15 days, and 20 days. The microstructure changes in the limestone after acidification were studied via the wave velocity test and electron microscope scanning, and the damage deterioration mechanism was revealed. The results show that the acoustic signal of acidified samples has an obvious absorption effect at high frequency, and the surface pore structure of acidified samples shows fractal characteristics. The P-wave velocity, main peak amplitude, and fractal dimension of the acidified samples did not gradually decrease with time; however, there was a short-term strengthening phenomenon during immersion, which was mainly caused by the formation of CaSO4 crystals.
Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency
Authors: Zhao Dong , Sun Shuyan , Mohammadzadeh Ardashir , Mosavi Amir
Published: September 2022

In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results.
Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method
Authors: Alilou Masoud , Gharehpetian Gevork B. , Ahmadiahangar Roya , Rosin Argo , Anvari-Moghaddam Amjad
Published: September 2022

The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system.
Optimizing the Systematic Characteristics of Online Learning Systems to Enhance the Continuance Intention of Chinese College Students
Authors: Li Mengfan , Wang Ting , Lu Wei , Wang Mengke
Published: September 2022

Different from systems that directly provide online shared courses such as MOOC, online learning systems such as Tencent Classroom simulate a real classroom environment for students and teachers to realize online face-to-face teaching, utilized during the COVID-19 pandemic. Nevertheless, due to the limitation of physical distance, the intelligent design of online learning systems is necessary to provide students with a good learning experience. This study notes that an unexpected optimization effect is the impact of system characteristics on the flow experience of online learning systems, which has not been studied, but plays a vital role in the effectiveness of online learning systems. In the study, a questionnaire was created and multi-stage sampling was used to investigate 623 college students. Based on the DeLone and McLean model of IS success and flow theory, a model for optimizing system characteristics and flow experience was constructed and its effectiveness was tested. The results reveal that system characteristics have a positive impact on continuance intention and flow experience. Additionally, flow experience and learning effect have a positive impact on continuance intention. Furthermore, flow experience has a positive impact on the learning effect. This study emphasizes the flow experience of online learning systems and reveals the optimization direction of online virtual face-to-face classrooms to provide references for the Ministry of Education, schools, and enterprises providing education systems.
Evaluation of the Incorporation of Tire-Derived Aggregates (TDA) in Rigid Pavement Mix Designs
Authors: Abu Abdo Ahmad M. , El Naggar Hany
Published: September 2022

Owing to the extensive worldwide generation of solid wastes, such as rubber tires, and the resulting adverse environmental impacts, the incorporation of these waste materials in construction projects has become a widespread aim. However, concerns have arisen regarding the effects of rubber waste on the mechanical properties of Portland cement concrete (PCC) mixes. Thus, this study investigates the effects of replacing natural coarse aggregates with tire-derived aggregates (TDA). In PCC mixes, natural aggregates were replaced by 0, 10, 20, 40, 60, 80, and 100% TDA by volume, and the properties of these specimens were tested in the laboratory. The results obtained were then used as inputs for the KENPAVE software, to evaluate induced stresses, deflections, and cracking indices in rigid pavement slabs, with eleven different thicknesses, ranging from 200 to 300 mm in 10 mm increments. Stresses under different loading conditions decreased as PCC slab thickness and TDA content increased. Increased deflection and cracking indices resulting from adding TDA could be counteracted by increasing the PCC slab thickness by 10 mm. Moreover, environmental impacts and cost analyses were examined via PaLATE 2.0, which showed that the use of TDA could reduce energy consumption, harmful emissions, and material costs. Overall, this study indicates that the use of TDA in PCC mixes has benefits that can make it a good candidate for sustainable, ecofriendly rigid pavement construction projects.
A Schematic Model for Implementing Industries High Risk Projects through the Agent of Human Capital
Authors: Soloveva Irina , Mostovshchikova Irina , Baeva Darya , Alola Andrew
Published: September 2022

Given the global drive for more sustainable industrial practices, the goal of this paper is to develop and test an algorithm to diagnose the readiness of industrial enterprises to implement team management in innovation projects. Our model was tested at a large industrial enterprise in the food industry. To study the peculiar features of the perception of a company image by different functional groups of participants (within the framework of the proposed structural model of the image), we used the in-depth interview method. The study involved a total of 60 people making up three groups of company employees (managers, brand sellers, workers). The data were complemented by a documentary analysis. Because of the chosen research approach, the research results may lack generalizability, thus researchers are encouraged to test the proposed propositions further. The results reveal significant discrepancies in the company image formed among different groups of current and potential employees. An analysis showed that the enterprise under study is not ready for the implementation of team management, and additional training is required. The proposed approach will allow us to identify the factors that somewhat (inwardly) impede the implementation of investments at the enterprise and restrain its sustainable innovation and development. The scientific and business significance of the developed correctional and diagnostic model lies in its ability to assess the level of consistency in the perception of the goals and development prospects of the company. As such, the potential participants in project teams are identified, and, through the implementation of corrective measures, we create a basis for the formation of effective teams.
Virtual Carbon Flow in China’s Capital Economic Circle: A Multi-Regional Input–Output Approach
Authors: Yin Chong , Liu Yue , Cui Yingxin
Published: September 2022

The Capital Economic Circle (CEC) is the area with the largest economic aggregate in northern China and has a strong status in driving the economic development of China. However, the industrial structure dominated by high energy consuming industries leads to a large number of carbon dioxide emissions, and the imbalance between economic development and carbon emissions in CEC is serious; therefore, it is necessary to explore how to solve the carbon imbalance problem of the CEC by relying on interregional cooperation. Based on China’s multi-regional input–output tables of 2012, 2015 and 2017, this paper proposes the CEC carbon-extended, multi-regional input–output model to measure virtual carbon flow and analyze how the industrial structure leads to the imbalance of carbon flow distribution in CEC. Indicators such as direct carbon emission coefficients, complete carbon emission coefficients and carbon emissions pull coefficients of the industrial sectors in CEC are calculated and the physical carbon emission and virtual carbon flows among the industrial sectors and the regions are evaluated. The results show that there are potential constraints from the uncoordinated configuration of industrial innovation chains among the CEC, and the “carbon imbalance” of CEC is mainly reflected in the backward production technology of Hebei and its inefficient connection with the industrial innovation chain of Beijing and Tianjin. It is suggested that policymakers should promote the low-carbon production system and strengthen green energy development and utilization to enhance green development in CEC. In future research, we should pay attention to the updating method of the input–output table and the development of carbon circular networks. This study has implications for some areas of China and developing countries in Asia, which also have an imbalance between industrial economy development and carbon emissions, and a similarity in space structure and industry layout with CEC.
Implementation of Magnetic Nanostructured Adsorbents for Heavy Metals Separation from Textile Wastewater
Authors: Barozzi Marco , Copelli Sabrina , Russo Eleonora , Sgarbossa Paolo , Lavagnolo Maria Cristina , Sandon Annalisa , Morosini Cristiana , Sieni Elisabetta
Published: September 2022

In the framework of sustainability, water shortages and water pollution are two important aspects to be considered. Proposing efficient and low-impact technologies is of paramount importance to promote circular economies associated with the use of water in the industrial context, especially in the textile industry. In this work, the application of a set of magnetic nanostructured adsorbents (MNAs) to cleanse metal ions from textile wastewaters was studied and analyzed. MNAs were generated with a low-cost process, involving iron (II/III) salts (e.g., chlorides), sodium or ammonium hydroxide solutions, and graphene oxide, obtained from graphite by a modified Hummers’ method at room temperature. The shape and the size were studied with transmission electron microscopy. Adsorbents were tested with different metal ions (e.g., copper, chromium (III), and nickel). Metal ion concentrations were analyzed by means of inductively coupled plasma optical emission spectroscopy (ICP-OES), and adsorption isotherms were characterized. From the results, the MNAs exhibited the capability of removing metal ions up to a yield of 99% for Cr3+, 94.7% for Cu2+, and 91.4% for Ni2+, along with adsorption loads up to 4.56 mg/g of MNAs.
Mediating Effects of Self-Efficacy, Resilience and Job Satisfaction on the Relationship between Person–Organisation Fit and Employee Work Adjustment
Authors: Wongsuwan Natthaya , Na-Nan Khahan
Published: September 2022

The purposes of this study were to test the direct and indirect effects of person–organisation fit (POF) and examine how self-efficacy (SE), resilience (RES) and job satisfaction (JS) acted as full or partial mediations between POF and employee work adjustment (EWA). A survey was conducted on 317 new graduates from the Rajamangala University of Technology Thanyaburi, with analysis of direct effects, indirect effects and mediators performed using the Hayes Process Macro Model 81. Results showed that POF had a direct effect on EWA with statistical significance and an indirect effect through self-efficacy, RES and JS, while SE, RES and JS acted as partial mediators between EWA with statistical significance. The mediating effects of SE, RES and JS were helpful in explaining the theory of POF on the work adjustment of new graduates. Results contribute to the development and expansion of POF in various industrial contexts. Human resource department managers can apply the study results as guidelines for selecting suitable employees as well as enhancing SE, RES and JS, leading to fast EWA. This study contributes to POF literature by clarifying the mediating effects of SE, RES and JS and clearly explaining the relationship between POF and EWA.
The Effects of Shareholding of the National Pension Fund on Environmental, Social, Governance, and Financial Performance: Evidence from the Korean Manufacturing Industry
Authors: Kim Jootae , Son Sungjin , Jin Ick
Published: September 2022

With the growing number of environmental, social, and governance (ESG) problems, many companies have begun to implement more sustainable business practices. In the midst of this change, institutional shareholders declare and adopt socially responsible investment procedures, which is a way of engaging in investor activism. Despite the growing interest in investor activism following the introduction of the stewardship code, little attention has been paid to how socially responsible investment practices of institutional investors affect the non-financial value of the pillars of environmental, social, and governance as well as financial performance, including short-term accounting (ROE, ROA) and long-term market performance (Tobin q). The current study examines whether the national pension fund (NPF), the world’s third-largest Korean pension fund, can increase the ESG performance of investee firms in addition to accounting and market performance through institutional investors’ shareholding. This study, by applying path analysis, attempts to explore the relationship between the NPF’s socially responsible investing, ESG, and the financial performance of the investee firms. This research offers evidence that ESG performance acts as a moderator or a mediator between NPF’s shareholding and financial performance.