bluuebunny/crossref_metadata_2025 huggingface dataset:
A Partial Least Squares Structural Equation Modelling Analysis of the Primary Barriers to Sustainable Construction in Iran
Authors: Kamranfar Saeed , Damirchi Farid , Pourvaziri Mitra , Abdunabi Xalikovich Pardayev , Mahmoudkelayeh Samira , Moezzi Reza , Vadiee Amir
Published: September 2023

This paper outlines the obstacles to sustainable construction growth in Iran and thereafter examines the effect and relation between these barriers and the direction of sustainable construction growth as one of the essential objectives for achieving sustainable cities and infrastructure. The study is applied for research purposes that are based on descriptive survey data gathering and correlational data analysis techniques. The statistical population for this study consists of 120 construction-related engineers and university professors who were assessed on a five-point Likert scale. Using SmartPLS software version 4, the responses to the questionnaire were examined. The Kolmogorov–Smirnov assessment was utilized to evaluate the normalcy of the variables, as this assessment is typically employed for this purpose. For data analysis, the PLS (partial least squares) method was used, while SEM (structural equation modeling) methods have been used to assess the study hypotheses. Cronbach’s alpha and the composite reliability coefficient (CR) were applied to determine the instrument’s viability, and the results show that the coefficient connected to all variables is above 7.0, which is an acceptable value. The AVE (average variance extracted) was also used to evaluate the questionnaire’s validity, which was greater than 0.4 and deemed acceptable for coefficients of significance (T-values), coefficient of predictive power (Q2), and coefficient of determination (R2). The obtained results support and confirm all research hypotheses, including that the identified obstacles directly affect the performance of sustainable construction. According to the results of the Friedman test, the legal restrictions variable (CL) is the most significant obstacle to sustainable construction in Iran, with a rank of 4.24. The indicators of political limits (CP) and social and cultural constraints (CSC) came in at second and third, respectively. The results could help government officials make better decisions about where to focus their attention and how to distribute scarce resources.
River Flood Risk Assessment in Communities of the Peruvian Andes: A Semiquantitative Application for Disaster Prevention
Authors: Ccopi-Trucios Dennis , Barzola-Rojas Brissette , Ruiz-Soto Sheyla , Gabriel-Campos Edwin , Ortega-Quispe Kevin , Cordova-Buiza Franklin
Published: September 2023

River floods are common natural phenomena that occur when the flow of water exceeds the capacity of a river due to excessive rainfall. In the Peruvian territory, the heavy rains of 2010 had consequences of great magnitude, leaving more than 5000 people affected and 25 dead in the Peruvian Andes. This research aimed to analyze and determine the level of risk due to river floods in communities of the Peruvian Andes in terms of hazard and vulnerability, using a semi-quantitative methodology and applying a multi-criteria analysis with vector information and raster from the national spatial data infrastructure that acted as triggering and conditioning factors, as well as conducting fieldwork with the application of targeted surveys. Then, the geoprocessing of thematic maps through GIS software was carried out. The research findings indicate that virtually the entire study area, approximately 99.26%, is at a high level of hazard, with only a small 0.74% classified as very high hazard. In other words, the entire studied territory is susceptible to floods. Furthermore, it is noteworthy that over 75% of the households in this area face vulnerability to floods, resulting in 99.15% of them being categorized at a high-risk level. It is concluded that river flooding represents a potential risk in large areas of the high Andean community due to the existence of various social, economic and environmental factors that make this phenomenon catastrophic.
Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data
Authors: Jenkins Eva L. , Lukose Dickson , Brennan Linda , Molenaar Annika , McCaffrey Tracy A.
Published: September 2023

Food waste is a complex issue requiring novel approaches to understand and identify areas that could be leveraged for food waste reduction. Data science techniques such as sentiment analysis, emotion analysis, and topic modelling could be used to explore big-picture themes of food waste discussions. This paper aimed to examine food waste discussions on Twitter and identify priority areas for future food waste communication campaigns and interventions. Australian tweets containing food-waste-related search terms were extracted from the Twitter Application Programming Interface from 2019–2021 and analysed using sentiment and emotion engines. Topic modelling was conducted using Latent Dirichlet Allocation. Engagement was calculated as the sum of likes, retweets, replies, and quotes. There were 39,449 tweets collected over three years. Tweets were mostly negative in sentiment and angry in emotion. The topic model identified 13 key topics such as eating to save food waste, morals, economics, and packaging. Engagement was higher for tweets with polarising sentiments and negative emotions. Overall, our interdisciplinary analysis highlighted the negative discourse surrounding food waste discussions and identified priority areas for food waste communication. Data science techniques should be used in the future to monitor public perceptions and understand priority areas for food waste reduction.
Cultural Competence among Healthcare Professional Educators: A Mixed-Methods Study
Authors: Mukhalalati Banan , Ahmed Aicha , Elshami Sara , Awaisu Ahmed
Published: September 2023

As cultural diversity gains global prominence, healthcare professional educators (HPEs) are expected to exhibit a high level of cultural competence in education. Responding to this necessity requires the establishment of healthcare education that is oriented toward sustainability. This study aimed to investigate HPEs’ perceptions of cultural competence at the Qatar University-Health Cluster (QU-HC). A convergent mixed-methods design was applied. The quantitative phase involved 118 HPEs at QU-HC responding to the Multicultural Teaching Competency Scale (MTCS). The qualitative phase included 3 focus groups (FGs) with 22 HPEs guided by Campinha-Bacote’s (1999) model of cultural competence. Thematic analysis was applied to analyze FGs data. Seventy-one educators responded to the MTCS (response rate was 60.2%), and twenty-two educators attended the FGs. HPEs demonstrated a moderate level of cultural awareness (total MTCS mean = 57 ± 7.8). The FGs revealed that the HPEs exhibited awareness and responsive teaching, but individual and institutional factors needed improvement. This study expands upon the existing literature concerning the cultural diversity impacts on the teaching and learning aspects of health profession programs, specifically within the Middle East context. It is recommended that health professional programs intensify the cultural orientation provided to educators, reanalyze the curricular content to serve diverse patients, and explore innovative approaches that embrace cultural diversity and sustainability.
Improving the Performance of Autonomous Driving through Deep Reinforcement Learning
Authors: Tammewar Akshaj , Chaudhari Nikita , Saini Bunny , Venkatesh Divya , Dharahas Ganpathiraju , Vora Deepali , Patil Shruti , Kotecha Ketan , Alfarhood Sultan
Published: September 2023

Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and significantly aiding in building autonomous systems with a higher level comprehension of the world as we observe it. Deep learning (DL) facilitates RL to scale and resolve previously intractable problems, for instance, allowing supervision principles designed for robots to be acquired directly from visual data, developing video game proficiency from pixel-level information, etc. Recent research shows that RL algorithms help represent problems dealing with high-dimensional, unprocessed data input and can have successful applications in computer vision, pattern identification, natural language analysis, and speech parsing. This research paper focuses on training a simulation model of a car to navigate autonomously on a racetrack using RL. The study explores several fundamental algorithms in Deep RL, namely Proximal Policy Optimization (PPO), Deep Q-network (DQN), and Deep Deterministic Policy Gradient (DDPG). The paper documents a comparative analysis of these three prominent algorithms—based on their speed, accuracy, and overall performance. After a thorough evaluation, the research indicates that the DQN surpassed the other existing algorithms. This study further examined the performance of the DQN with and without ε-decay and observed that the DQN with ε-decay is better suited for our objective and is significantly more stable than its non ε-decay counterpart. The findings from this research could assist in improving the performance and stability of autonomous vehicles using the DQN with ε -decay. It concludes by discussing the fine-tuning of the model for future real-world applications and the potential research areas within the field of autonomous driving.
Assessment of Machine Learning Algorithms for Predicting Air Entrainment Rates in a Confined Plunging Liquid Jet Reactor
Authors: Alazmi Asmaa , Al-Anzi Bader S.
Published: September 2023

A confined plunging liquid jet reactor (CPLJR) is an unconventional efficient and feasible aerator, mixer and brine dispenser that operates under many operating conditions. Such operating conditions could be challenging, and hence, utilizing prediction models built on machine learning (ML) approaches could be very helpful in giving reliable tools to manage highly non-linear problems related to experimental hydrodynamics such as CPLJRs. CPLJRs are vital in protecting the environment through preserving and sustaining the quality of water resources. In the current study, the effects of the main parameters on the air entrainment rate, Qa, were investigated experimentally in a confined plunging liquid jet reactor (CPLJR). Various downcomer diameters (Dc), jet lengths (Lj), liquid volumetric flow rates (Qj), nozzle diameters (dn), and jet velocities (Vj) were used to measure the air entrainment rate, Qa. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. In addition to the experimental work, machine learning (ML) algorithms were applied to the confined plunging jet reactor parameters to determine the parameter that predicts Qa the best. The results obtained from ML showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, the proportion of variance in the Qa that can be explained by the CPLJR parameter was 90%, the root mean square error (RMSE) = 0.069, and the mean absolute error (MAE) = 0.052. Sensitivity analysis was applied to determine the most effective predictor in predicting Qa. The Qj and Vj were the most influential among all the input variables. The sensitivity analysis shows that the lasso algorithm can create an effective air entrainment rate model with just two of the most crucial variables, Qj and Vj. The coefficient of determination (R2) was 82%. The present findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.
Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel
Authors: Kim Sunghun , Park Youngjin , Yoo Seungbeom , Lee Sejun , Chanda Uttam Kumar , Cho Wonjun , Lim Ocktaeck
Published: September 2023

The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.
Cyclic Fatigue Durability of Woven Geotextiles for Use in Sustainable Waste-Dewatering Systems
Authors: Guimarães Mag Geisielly Alves , Oliveira Pedro Victor Garcia de , Urashima Denise de Carvalho , Pereira Eleonardo Lucas , Urashima Beatriz Mydori Carvalho
Published: September 2023

Geosynthetics are increasingly used in geotechnical engineering to replace conventional solutions due to their cost-effective and environmental benefits. For example, geotextiles can be used in sustainable waste-dewatering systems to confine solid waste for final disposal. This study is presented to analyze the durability of a geotextile regarding cyclic fatigue induced during the pumping stages in these sustainable waste-dewatering systems. A polypropylene woven geotextile was used and subjected to cyclic tensile loading levels of 10%, 30% and 50% of the ultimate average tensile strength. We also used hysteresis loops with a frequency of 0.1 Hz at different numbers of cycles (10, 20, 30, and 90 cycles). With a 95% confidence level and response surface, the results show that increasing the tensile loading levels and the number of cycles made the geotextile lose its tensile strength. Laboratory experiments simulated scenarios where the geotextile was subjected to cyclic fatigue that might directly impact its strength–strain and hysteretic stiffness performance over its design lifetime. This study contributes to a better understanding of the durability of geotextiles to subsidize decision-making involving social, environmental, and technical impacts in waste-dewatering system applications.
Role of Digital Strategy in Managing the Planning Complexity of Mega Construction Projects
Authors: Abdullahi Iliyasu , Watters Casey , Kapogiannis Georgios , Lemański Michal K.
Published: September 2023

Background: This study investigates the potential of digital construction to enhance the planning competence of project managers in dealing with the complexities of mega construction projects. Traditional project strategies often struggle to adapt in dynamic situations, particularly evident in mega construction endeavours. Drawing inspiration from successful digital strategies in manufacturing, this research proposes that adopting digital techniques could bolster project managers’ ability to navigate complexity during construction, leading to improved infrastructure delivery within budget and on schedule. Methods: Employing a quantitative approach, this study utilized an online questionnaire to gather insights from project managers. The proposed hypothesis was assessed using a one-sample t-test. Additionally, Pearson’s correlation coefficient was employed to gauge the strength of the relationship between various constructs. This approach aimed to determine the extent to which digital construction can support effective complexity management during mega construction projects. Results: The results indicate that digital construction equips project managers with enhanced capabilities to efficiently coordinate and allocate resources in real-time within complex construction environments, thereby optimizing overall project performance. Despite these advantages, the findings also reveal that managers continue to encounter challenges overseeing numerous participants during infrastructure construction. This suggests that while digital construction contributes to improved planning against complexity, addressing the management of multiple stakeholders remains an ongoing challenge. Conclusions: This study presents a novel contribution to the construction industry by demonstrating the potential of synergizing various digital tools throughout construction processes to empower project managers in effectively addressing the complexities inherent in mega construction planning. Furthermore, it underscores how digital construction confers a dynamic advantage for project managers in navigating complexities and enhancing overall project performance.
Study on the Measurement Method of Wheat Volume Based on Binocular Structured Light
Authors: Zhao Zhike , Chang Hao , Wu Caizhang
Published: September 2023

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.
Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization
Authors: Alkahtani Mohammed , Abidi Mustufa Haider , Obaid Hamoud S. Bin , Alotaik Osama
Published: September 2023

Due to the emergence of technology, electric motors (EMs), an essential part of electric vehicles (which basically act as engines), have become a pivotal component in modern industries. Monitoring the spare parts of EMs is critical for stabilizing and managing industrial parts. Generally, the engine or motor parts are delivered to factories using packing boxes (PBs). This is mainly achieved via a pooling center that manages the operation and transportation costs. Nevertheless, this process has some drawbacks, such as a high power train, bad press, and greater energy and time consumption, resulting in performance degradation. Suppliers generally take the parts from one place and deliver them to the other, which leads to more operation and transportation costs. Instead, it requires pooling centers to act as hubs, at which every supplier collects the material. This can mitigate the cost level. Moreover, choosing the placement of pooling centers is quite a challenging task. Different methods have been implemented; however, optimal results are still required to achieve better objectives. This paper introduces a novel concept for pooling management and transport optimization of engine parts to overcome the issues in traditional solution methodologies. The primary intention of this model is to deduce the total cost of the system operation and construction. Programming techniques for transporting the PBs, as well as for locating the pooling center, are determined with the aid of an objective function as a cost function. The location of the pooling center’s cost is optimized, and a Modified Gannet Optimization Algorithm (MGOA) is proposed. Using this method, the proposed model is validated over various matrices, and the results demonstrate its better efficiency rate.
In Search of Equitable Resilience: Unravelling the Links between Urban Resilience Planning and Social Equity
Authors: Mohabat Doost Danial , Brunetta Grazia , Caldarice Ombretta
Published: September 2023

Building resilient cities is becoming increasingly vital due to the rise in urban challenges such as climate change, socioeconomic disparities, and pandemics. While the concept of resilience is gaining popularity, many scholars argue that existing resilience plans do not adequately address social equity issues. Therefore, this study investigates the incorporation of equity into resilience planning by conducting a case study analysis of ten European resilience strategies. The employed methodology is summative content analysis, and the approach is inductive. For each resilience strategy, the incorporation of three equity dimensions—distributional, procedural, and recognitional—is examined. The results show significant variation in addressing equity dimensions across the case studies. Although some plans do not effectively address equity, others integrate it more comprehensively and successfully. Thus, we argue that resilience planning can potentially contribute to social equity issues, although currently, this contribution is not sufficient. We recommend a number of strategies by which future resilience planning can enhance its contribution. These are: promoting structural transformations, considering the political processes of resilience building, adopting participatory approaches to co-create resilience plans, fostering trust and accountability between citizens and governing bodies, favouring a systemic view, and prioritising the upstream inequality factors for building capacity.
A Comprehensive Evaluation of the Friendliness of Urban Facilities for the Elderly in Taipei City and New Taipei City
Authors: Yang Ling , Chang Hsiao-Tung , Li Jian , Xu Xinyue , Qiu Zhi , Jiang Xiaomin
Published: September 2023

To address the topic of building age-friendly cities that better meet the needs of the elderly in a sustainable-city-oriented manner, this paper focuses on the interaction between the needs of the elderly and urban facilities in the urban built environment in order to propose a comprehensive evaluation method regarding the friendliness of urban facilities with respect to the elderly in large urban areas. The development of the proposed method was guided by the distribution characteristics of the elderly population and combines a spatial measurement evaluation, which is based on the spatial distribution characteristics of urban facilities for the elderly, and a post-use measurement evaluation, which is based on the characteristics of use by the elderly. Taipei City and New Taipei City are then taken as examples for evaluation. From the final evaluation results of the Boston four-quadrant analysis, the areas with higher spatial and post-use metric evaluation values were defined as areas of high concern, while those with lower spatial and higher post-use metric evaluation values were defined as advantage-maintained areas. These two types of areas accounted for about 58% of the total area, and are distributed in the Taipei urban area and northeast New Taipei City. The areas with higher spatial and lower post-use metric evaluation values were defined as priority improvement areas, while those with lower spatial and post-use metric evaluation values were defined as key complement areas. These two types of area accounted for about 42%, and are mainly distributed in the northwest part of Taipei City, as well as the western and southern mountainous areas of New Taipei City. Accordingly, region-specific planning policy recommendations were provided.
Machine Learning Prediction and Optimization of Performance and Emissions Characteristics of IC Engine
Authors: Sanjeevannavar Mallesh B. , Banapurmath Nagaraj R. , Kumar V. Dananjaya , Sajjan Ashok M. , Badruddin Irfan Anjum , Vadlamudi Chandramouli , Krishnappa Sanjay , Kamangar Sarfaraz , Baig Rahmath Ulla , Khan T. M. Yunus
Published: September 2023

In this work, a study was conducted to investigate the effects of different biodiesel blends with hydrogen peroxide additive on the performance and emissions of an internal combustion engine under various operating parameters. A CI engine was operated with diesel, four dissimilar biodiesels, and H2O2 at various proportions. The biodiesel blends used were Jatropha (D60JB30A10, D60JB34A6, D60JB38A2, D60JB40), Honge (D60HB30A10, D60HB34A6, D60HB38A2, D60HB40), Simarouba (D60SB30A10, D60SB34A6, D60SB38A2, D60SB40), and Neem (D60NB30A10, D60NB34A6, D60NB38A2, D60NB40). The engine was tested at different injection operating pressures (200, 205, and 210 bar), a speed of 1500 rpm, and a CR of 17.5:1. From the experiments conducted, it was highlighted that, under specific conditions, i.e., with an injection pressure of 205 bar, 80% load, a compression ratio of 17.5, an injection timing set at 230 before top dead center, and an engine speed of 1500 rpm, the biodiesel blends D60JB30A10, D60HB30A10, D60SB30A10, and D60NB30A10 achieved the highest brake thermal efficiencies of 24%, 23.9675%, 23.935%, and 23.9025%, respectively. Notably, the blend D60JB30A10 stood out with the highest brake thermal efficiency of 24% among these tested blends. Similarly, when evaluating emissions under the same operational conditions, the D60JB30A10 blend exhibited the lowest emissions levels: CO (0.16% Vol), CO2 (7.8% Vol), HC (59 PPM), and Smoke (60 HSU), while NOx (720 PPM) emissions showed a relative increase with higher concentrations of the hydrogen-based additive. The D60HB30A10, D60SB30A10, and D60NB30A10 blends showed higher emissions in comparison. Additionally, the study suggests that machine learning techniques can be employed to predict engine performance and emission characteristics, thereby cutting down on time and costs associated with traditional engine trials. Specifically, machine learning methods, like XG Boost, random forest regressor, decision tree regressor, and linear regression, were utilized for prediction purposes. Among these techniques, the XG Boost model demonstrated highly accurate predictions, followed by the random forest regressor, decision tree regressor, and linear regression models. The accuracy of the predictions for XG Boost model was assessed through evaluation metrics such as R2-Score (0.999), Root Mean Squared Error (0.540), Mean Squared Error (0.248), and Mean Absolute Error (0.292), which allowed for a thorough analysis of the algorithm’s performance compared to actual values.
Suppression of Meloidogyne javanica Infection in Peach (Prunus persica (L.) Batsch) Using Fungal Biocontrol Agents
Authors: Saeed Muhammad , Mukhtar Tariq , Ahmed Raees , Ahmad Tanveer , Iqbal Muhammad Aamir
Published: September 2023

In South Asian countries, the lucrative production of peaches has been seriously threatened by an assortment of biotic stresses especially nematodes. This situation compromises the achievement of sustainable development goals (SDGs) related to food security and zero hunger. Recently under changing climate, root-knot nematodes of the genus Meloidogyne have emerged as the most damaging phytopathogenic nematodes, while the efficacy of chemical control has remained limited. Therefore, a study was executed to assess the efficacy of four biocontrol agents including Pochonia chlamydosporia, Purpureocillum lilacinum, Trichoderma harzianum, and T. viride (at concentrations of 2.5 × 103, 5 × 103, 7.5 × 103, and 1 × 104) along with nematicide Rugby and a control treatment against Meloidogyne javanica on peach. The response variables included nematode infestations in terms of number of galls, egg masses, and reproductive factors. P. lilacinus and T. harzianum (1 × 104 concentration) reduced the number of galls by 18% and 16%, respectively, than the control. All biocontrol agents exhibited their effectiveness by significantly reducing number of egg masses, eggs per egg mass, and reproductive factors, while these remained statistically at par to each other. The study proved that application of these biocontrol agents holds potential for controlling root-knot nematodes and might be developed as a potent strategy to replace or at least reduce the use of traditional chemicals for avoiding environmental pollution and contamination.
How Much Financial Development Accentuates Income Inequality in Central and Eastern European Countries?
Authors: Manta Alina Georgiana , Badareu Gabriela , Florea Inocentiu Alexandru , Staicu Anamaria Liliana , Lepădat Cătălin Valentin Mihai
Published: September 2023

Financial development is often associated with significant economic growth, but studies have shown that a high level of financial development can be the cause of deepening income inequality in many countries. The main objective of the proposed study is to identify to what extent financial development influences income inequality in Central and Eastern European Countries (CEEC). Thus, for the model specification we used as dependent variable the Gini coefficient and as independent variable the financial development index. The sample period for the analysis was from 2004 to 2019, restricted by the lack of data on the Gini coefficient in CEECs. Data on the financial development index were collected from International Monetary Fund, and data on the Gini coefficient were extracted from the World Bank’s Poverty and Inequality Platform. The study unravels several contributions. First of all, the use of quantile regression allowed for the examination of the effects of financial development across the entire distribution of income inequality. Second of all, the use of a comprehensive financial development index offered a more robust and comprehensive measure of financial development compared to single indicators. Taking into account that the Gini coefficient must be close to zero, this result was a positive one with, in essence, financial development reducing income inequality in CEECs. Thirdly, the specific focus on CEECs fills a gap in the literature. Finally, the findings of this study have important policy implications. The obtained results indicate a negative causal relationship between financial development and income inequality, emphasizing the fact that the relationship between these two components cannot be generalized for all regions. These might include measures to promote financial inclusion, improve financial literacy, and enhance the stability and efficiency of financial systems. Supporting financial development in CEECs and similar transition economies can be an effective strategy for tackling income inequality.
Environmental Impact Assessment of Nesjavellir Geothermal Power Plant for Heat and Electricity Production
Authors: Mainar-Toledo María Dolores , Díaz-Ramírez Maryori , Egilsson Snorri J. , Zuffi Claudio , Manfrida Giampaolo , Leiva Héctor
Published: September 2023

This work is focused on presenting the main results and discussions concerning the environmental benefits of reducing the non-condensable gases emitted from the Nesjavellir geothermal power plant. The primary objective of this study is to conduct a life cycle evaluation to analyse the overall environmental benefit effects of producing 1 kWh of electricity and 1 kWh of thermal energy in the geothermal power plant at Nesjavellir, which is located in Iceland. The assessment is performed both before and after implementing an abatement system designed to reduce CO2 and H2S gases. The production of geothermal energy is increasing every year and, therefore, it is crucial to identify and quantify the key environmental factors of producing this type of energy and improvements for the future energy transition of the energy generation sector. Firstly, the results show that the environmental impact of electricity production is higher compared to heat production. More in detail, the emissions due to the nature of the geothermal fluid and the construction phase represent the most relevant environmental load for both electricity and heat production for nearly all the 18 environmental impact indicators studied. Furthermore, considering the abatement system for the non-condensable gas emissions, reductions of 78% and 60% in global warming potential is achieved for a production of 1 kWh of electricity and 1 kWh of thermal energy. In terms of external environmental costs, the implementation of an abatement system results in a reduction exceeding 95% for both electricity and thermal energy production per kilowatt-hour. The outcomes obtained from both the baseline scenario and the application of the abatement system undeniably prove that the latter results in a substantial decrease in the overall environmental impacts linked to the generation of 1 kWh of electricity and 1 kWh of heat, encompassing a notable reduction in external environmental costs (externalities).
Present Situation and Research Progress of Comprehensive Utilization of Antimony Tailings and Smelting Slag
Authors: Yu Zeen , Wang Lei , Zheng Qi , Che Xiaokui , Cui Xinglan , Wei Shenyu , Li Hongxia , Shi Xinyue
Published: September 2023

The production process of antimony produces a large amount of solid waste, such as waste rock in mining, tailings in the beneficiation, metallurgical slag in the smelting, and so on. At present, most of these solid wastes are currently in storage, and the storage of a large amount of solid wastes is not only harmful to the local ecological environment but also a waste of resources. In view of this situation, this paper will take antimony tailings and metallurgical slag as examples and summarize them according to their different treatment methods. The comprehensive utilization of antimony tailings is mainly recovering metals by beneficiation and metallurgy and using antimony tailings as building materials and underground filling materials, while the comprehensive utilization method of antimony metallurgical slag is mainly the recovery of valuable metals by pyrometallurgy or hydrometallurgy or the stabilization technology. This paper summarizes the advantages and disadvantages of different treatment methods and puts forward the prospect of future research directions for the treatment of different metallurgical slags and tailings.
Optimal Design and Mean Stress Estimation of Modular Metamaterials Inspired by Burr Puzzles
Authors: Yang Kuijian , Li Xiaoxun , Li Zhi , Zhu Weiyu , Yao Yingkang
Published: September 2023

Modular impact-resistant metamaterials inspired by burr puzzles were recently proposed to combine flexibility, efficiency and adaptivity, which were also beneficial for sustainability in engineering protection. However, the optimal design remains to be explored and the mean stress cannot be effectively estimated. To break these limits, a stiffness-enhanced strategy is implemented to enhance the crashworthiness, and the relation between the mechanical behavior of metamaterials and locking points is revealed. The average thickness of all modules in the metamaterial is denoted by tave, and the thickness ratio of axially loaded to laterally loaded modules is denoted by y. From the experimental and simulation results, the mean stress of the metamaterials significantly increases with tave and y, while the deformation mode is gradually transformed into an inefficient global buckling mode and impairs the crashworthiness when ψ≥4. ψ=3 can be taken as the optimal design of metamaterials, which can increase the specific energy absorption SEA, energy absorption efficiency h and mean stress sm, respectively, by 62.4%, 44.2% and 57.6% compared to the regular design (ψ=1). On this basis, we develop a universe method to estimate the mean stress of the metamaterials with a relative error less than 9.6%, and a guideline for their design and application in engineering fields is summarized. This research opens a new avenue for broadening the design and applications of modular metamaterials in engineering applications.
Supplier Selection for a Power Generator Sustainable Supplier Park: Interval-Valued Neutrosophic SWARA and EDAS Application
Authors: Cakmak Emre
Published: September 2023

Power generator manufacturers play a critical role in maintaining electric flow for sustainable product and service production. The aim of this study is to extract the criteria necessary for a generator manufacturer to evaluate and select its suppliers for its sustainable supplier park, and to prioritize them to form the supply network. The methodology of this research covers the phases as (i) extracting the criteria affecting the supplier selection decision process of a power generator company via an in-depth literature and industrial report review, (ii) evaluating these criteria by industry experts, (iii) identifying the weights of each criterion via SWARA (“step-wise weight assessment ratio analysis”), (iv) prioritizing the alternative suppliers fitting to the criteria so that the power generator company can construct its sustainable supplier park via IVN EDAS (“interval valued neutrosophic Evaluation Based on Distance from Average Solution”), (v) conducting a sensitivity analysis to check for the robustness of the results by changing the weights, and (vi) applying a comparative analysis to validate the methodology’s accuracy by comparing the results with IVN TOPSIS and IVN CODAS. Moreover, this paper contributes to the literature by elaborating on the integration details of the IVN SWARA and IVN EDAS as the first research paper of the author’ knowledge. A practitioner can understand which factors to consider prominently in forming a sustainable supplier park, or in deciding on which suppliers to select to plan the strategic operations of a power generator company.
Case Studies on Impacts of Climate Change on Smallholder Livestock Production in Egypt and Spain
Authors: Hashem Nesrein M. , Martinez-Ros Paula , Gonzalez-Bulnes Antonio , El-Raghi Ali Ali
Published: September 2023

Climate change is one of the hot topics of this decade and seriously affects all economic production sectors including the livestock farming sector. In many scenarios, the Mediterranean region is expected to experience unconventional and severe climate change that necessitates adopting effective strategies to improve the resilience of the livestock farming sector, particularly for smallholders. Here, we performed a cross-sectional survey of 277 smallholder livestock farmers in Egypt and 223 in Spain in order to (1) assess smallholder livestock farmers’ awareness of climate change and its potential impacts on animal performance, (2) identify climate change factors affecting animal productivity, and (3) identify the resilience of small-scale livestock farms in the region to the effects of climate change on animal productivity and existing and future needed adaptive measures. The results showed that just over 90% of respondents were aware of climate change and its potential effects on animal productivity, and just over 60% of smallholders in Spain obtained relevant knowledge through their own direct observation, while most smallholders in Egypt obtained knowledge through communication with other farmers and from the media. The role of extension advisors has diminished in the two countries, recording 0.36% in Egypt and 1.35% in Spain. The survey responses suggest that heat waves, humidity, and drought are the major climatic changes affecting smallholding animal production, representing 68.65, 16.34, and 15.01%, respectively. Climatic change appears to have affected primarily milk yield, wool production, and reproductive performance on the smallholding farms in our survey, while affecting meat production, mortality rate, and egg production to a smaller extent. As measures to buffer the effects of climate change, 25% of respondents in Egypt indicated that they have adopted nutritional strategies, 36% indicated that they manage housing conditions, and 6% indicated that they use genetically improved animal breeds. The corresponding percentages among respondents in Spain were 15%, 28%, and 4%, respectively. In conclusion, awareness about climate change as well as adaptation measures are the major axes to sustaining the growing demand for livestock products. Furthermore, mitigation strategies are keys to limiting the upcoming extent of climate change, and there are several adaptation strategies.
Consumer Awareness of Biodegradability of Food Products Packaging
Authors: Bojanowska Agnieszka , Sulimierska Agnieszka
Published: September 2023

This manuscript concerned customer recognition of biodegradable packaging. The authors of this study took up this issue due to its topicality and importance for enterprises and customers. The authors conducted a survey among consumers that showed how customers perceived 100% biodegradable packaging. It explored the role of these packages in marketing activities in the organic products market. The purpose of this article was to determine customer awareness of 100% biodegradable packaging. A diagnostic survey with a sample of 1000 respondents was used. The survey results indicated that customers declared environmentally friendly attitudes; however, they were not always able to recognize biodegradable packaging. Providing correct answers on this topic did not depend on gender, health status, or place of residence, only on the age and education of respondents. The research results may have both practical and theoretical implications. The results contribute to the development of management sciences. The conclusions from the manuscript allow producers, not only in food, to design new, biodegradable packaging in accordance with the feelings and expectations of consumers. Thus, it can contribute to increasing the demand for products in ecological packaging.
Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China
Authors: He Baozhong , Ding Jianli , Huang Wenjiang , Ma Xu
Published: September 2023

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.
Current Status and Prospects on High-Precision Quantum Tests of the Weak Equivalence Principle with Cold Atom Interferometry
Authors: Yuan Liang , Wu Jizhou , Yang Sheng-Jun
Published: September 2023

For a hundred years, general relativity has been the best theory to describe gravity and space–time and has successfully explained many physical phenomena. At the same time, quantum mechanics provides the most accurate description of the microscopic world, and quantum science technology has evoked a wide range of developments today. Merging these two very successful theories to form a grand unified theory is one of the most elusive challenges in physics. All the candidate theories that wish to unify gravity and quantum mechanics predict the breaking of the weak equivalence principle, which lies at the heart of general relativity. It is therefore imperative to experimentally verify the equivalence principle in the presence of significant quantum effects of matter. Cold atoms provide well-defined properties and potentially nonlocal correlations as the test masses and will also improve the limits reached by classical tests with macroscopic bodies. The results of rigorous tests using cold atoms may tell us whether and how the equivalence principle can be reformulated into a quantum version. In this paper, we review the principles and developments of the test of the equivalence principle with cold atoms. The status of the experiments and the key techniques involved are discussed in detail. Finally, we give an outlook on new questions and opportunities for further exploration of this topic.