bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Raman Spectroscopy to Evaluate the Structure Changes of Lubricating Grease Modified with Montmorillonite after Tribological Tests
Authors: Kozdrach Rafal
Published: March 2024

This study shows the experimental data obtained by Raman spectroscopy to evaluate the structural changes of vegetable lubricants modified with montmorillonite after tribological tests. The analysis of the friction factor and limiting load of wear in the test for the examined grease compound shows a substantial effect on this parameter for grease. A change in the evaluated tribo-parameter results in a modification of the structure of the tested lubricant and changes in the protection efficiency of the tribological system. The amount of thickener, oil base and additive in the grease structure has an influence on the value of anti-wear properties, as shown by the data obtained in the tribological test described in this paper. The Raman spectroscopy tests showed that, in the tribological processes, some of the ingredients undergo an oxidative reaction, which leads to the formation of oxygenated organic substances that form an organic layer on the metal surface and counteract the wear of the lubricated contact surfaces. Other compounds come into close contact with the tribological layer, which increases its ability to resist wear and shear. The efficiency of the used additive is based on the formation (during friction) of a low-shear and high-plastic-strength thin film which is chemically highly bonded to the material and has a high level of durability against wear processes. As a consequence of the thermal decomposition of the additive, chemical interactions occur among the ingredients of the material of the substrate and the lubricants.
Performance Comparison of High-Temperature Heat Pumps with Different Vapor Refrigerant Injection Techniques
Authors: Yang Yuqiang , Wang Yu , Xu Zhaoyang , Xie Baojiang , Hu Yong , Yu Jiatao , Chen Yehong , Zhang Ting , Lu Zhenneng , Gong Yulie
Published: March 2024

In order to develop a highly efficient and stable high-temperature heat pump to realize high-efficient electrification in the industrial sector, performance of high-temperature heat pumps with a flash tank vapor injection and sub-cooler vapor injection are compared under different evaporation temperatures, condensation temperatures, compressor suction superheat degrees, subcooling degrees and compressor isentropic efficiencies. The results show that the COP, injection mass flow ratio and VHC of the FTVC are higher than those of the SVIC-0, SVIC-5, SVIC-10 and SVIC-20 under the same working conditions, while the discharge temperature of the FTVC is approximately equal to that of the SVIC-0 and lower than those of the SVIC-5, SVIC-10 and SVIC-20. When the evaporation temperature, the condensation temperature and injection pressure are 55 °C, 125 °C and 921.4 kPa, respectively, the system COP of the FTVC is 4.49, which is approximately 6.7%, 7.3%, 7.8% and 8.9% higher than those of the SVIC-0, SVIC-5, SVIC-10, and SVIC-20, respectively.
Exploring the REEs Energy Footprint: Interlocking AI/ML with an Empirical Approach for Analysis of Energy Consumption in REEs Production
Authors: Pathapati Subbu Venkata Satyasri Harsha , Singh Rahulkumar Sunil , Free Michael L. , Sarswat Prashant K.
Published: March 2024

Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint.
Qualifying Religious Truth and Ecclesial Unity: The Soteriological Significance of Difference
Authors: McAleer Ryan K.
Published: March 2024

The trans-phenomenology of Emmanuel Levinas has helped expose the totalising dynamic that has marked much of Western philosophy. The quest for a unity of knowledge in the truth assimilates any hint of otherness into more of the same. Plurality becomes a source of violence and dissent regarded as decay. Levinasian perspectives, however, and recent developments in magisterial teaching in the Roman Catholic Church point to a more ethical approach that can begin to escape the dialectic binary of the same and the other and so help avoid static conceptions of truth and unity. Religious truth and ecclesial unity, in other words, are explored in this paper for their ethical–dialogical quality. Indeed, the asymmetrical priority of dissent within this dialogical approach offers positive soteriological significance for the church rather than seeing dissent as a threat. Such an approach can enable the church to take plurality and diversity seriously in the current context.
Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails
Authors: Contreras-Valdez Mario Ivan , Sahu Sonal , Núñez-Mora José Antonio , Santillán-Salgado Roberto Joaquín
Published: March 2024

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.
Trajectory Tracking and Disturbance Rejection Performance Analysis of Classical and Advanced Controllers for a SCORBOT Robot
Authors: Kern John , Urrea Claudio , Verdejo Humberto , Agramonte Rayko , Becker Cristhian
Published: March 2024

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement.
Hyperspectral Image Classification with the Orthogonal Self-Attention ResNet and Two-Step Support Vector Machine
Authors: Sun Heting , Wang Liguo , Liu Haitao , Sun Yinbang
Published: March 2024

Hyperspectral image classification plays a crucial role in remote sensing image analysis by classifying pixels. However, the existing methods require more spatial–global information interaction and feature extraction capabilities. To overcome these challenges, this paper proposes a novel model for hyperspectral image classification using an orthogonal self-attention ResNet and a two-step support vector machine (OSANet-TSSVM). The OSANet-TSSVM model comprises two essential components: a deep feature extraction network and an improved support vector machine (SVM) classification module. The deep feature extraction network incorporates an orthogonal self-attention module (OSM) and a channel attention module (CAM) to enhance the spatial–spectral feature extraction. The OSM focuses on computing 2D self-attention weights for the orthogonal dimensions of an image, resulting in a reduced number of parameters while capturing comprehensive global contextual information. In contrast, the CAM independently learns attention weights along the channel dimension. The CAM autonomously learns attention weights along the channel dimension, enabling the deep network to emphasise crucial channel information and enhance the spectral feature extraction capability. In addition to the feature extraction network, the OSANet-TSSVM model leverages an improved SVM classification module known as the two-step support vector machine (TSSVM) model. This module preserves the discriminative outcomes of the first-level SVM subclassifier and remaps them as new features for the TSSVM training. By integrating the results of the two classifiers, the deficiencies of the individual classifiers were effectively compensated, resulting in significantly enhanced classification accuracy. The performance of the proposed OSANet-TSSVM model was thoroughly evaluated using public datasets. The experimental results demonstrated that the model performed well in both subjective and objective evaluation metrics. The superiority of this model highlights its potential for advancing hyperspectral image classification in remote sensing applications.
An Adaptive Polarimetric Target Decomposition Algorithm Based on the Anisotropic Degree
Authors: Huang Pingping , Li Baoyu , Li Xiujuan , Tan Weixian , Xu Wei , Chen Yuejuan
Published: March 2024

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data.
Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs
Authors: Rojhani Neda , Shaker George
Published: March 2024

Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current state-of-the-art in radar technology, focusing on multiple-input multiple-output (MIMO) and beamforming techniques, to address this growing concern. It explores the challenges associated with detecting UAVs in urban settings and adverse weather conditions, where traditional radar systems often do not succeed. This paper examines the existing literature and technological advancements to understand how these methodologies can significantly boost detection capabilities under the constraints of low RCS. In particular, MIMO technology, renowned for its spatial multiplexing, and beamforming, with its directional signal enhancement, are evaluated for their efficacy in the context of UAV surveillance and defense strategies. Ultimately, a comprehensive comparison is presented, drawing on a variety of studies to illustrate the combined potential of integrating these technologies, providing the way for future developments in radar system design and UAV detection.
Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners
Authors: Ali Moonis , Lohani Bharat , Hollaus Markus , Pfeifer Norbert
Published: March 2024

Terrestrial LiDAR scanning (TLS) has the potential to revolutionize forestry by enabling the precise estimation of aboveground biomass, vital for forest carbon management. This study addresses the lack of comprehensive benchmarking for leaf-filtering algorithms used in TLS data processing and evaluates four widely recognized geometry-based leaf-filtering algorithms (LeWoS, TLSeparation, CANUPO, and a novel random forest model) across openly accessible TLS datasets from diverse global locations. Multiple evaluation dimensions are considered, including pointwise classification accuracy, volume comparisons using a quantitative structure model applied to wood points, computational efficiency, and visual validation. The random forest model outperformed the other algorithms in pointwise classification accuracy (overall accuracy = 0.95 ± 0.04), volume comparison (R-squared = 0.96, slope value of 0.98 compared to destructive volume), and resilience to reduced point cloud density. In contrast, TLSeparation exhibits the lowest pointwise classification accuracy (overall accuracy = 0.81 ± 0.10), while LeWoS struggles with volume comparisons (mean absolute percentage deviation ranging from 32.14 ± 29.45% to 49.14 ± 25.06%) and point cloud density variations. All algorithms show decreased performance as data density decreases. LeWoS is the fastest in terms of processing time. This study provides valuable insights for researchers to choose appropriate leaf-filtering algorithms based on their research objectives and forest conditions. It also hints at future possibilities for improved algorithm design, potentially combining radiometry and geometry to enhance forest parameter estimation accuracy.
Does Playing Tennis with a Low-Compression Ball Effect Psychophysiological Responses and Match Characteristics in Recreational Adult Players?
Authors: Kilit Bulent , Arslan Ersan , Soylu Yusuf , Lane Andrew M.
Published: March 2024

This study aimed to compare the effects of playing tennis using low-compression balls (Lc-Balls) and standard balls (St-Balls) on psychophysiological responses and match characteristics among recreational adult tennis players. Participants (N = 24; age: 20.5 ± 1.3 years) were randomly matched to play two singles matches over three sets: one match was played with a Lc-Ball and one match was played with a St-Ball, resulting in twenty-four matches. Heart-rate responses and match characteristics were assessed during each match. Post-match measures included retrospective assessments of perceived exertion, ratings of enjoyment towards physical activity, and ratings of mental effort and mood. Results showed higher psychophysiological responses and more intensive play during the game when playing with the Lc-Ball (p ≤ 0.05, d values ranging from 0.24 to 1.93 [small to very large effect]). Further, playing with a Lc-Ball related to reporting a lower rating of perceived exertion (p = 0.00, d = 0.90 [moderate effect]) and greater physical enjoyment (p = 0.00, d = 1.73 [large effect]). Playing with the St-Ball was associated with higher unpleasant mood responses including depression, tension, anger, and fatigue. In conclusion, the results suggest that using the Lc-Ball may lead to better match performance with higher enjoyment in the tennis match-play in recreational adult tennis players.
Integrating Renewable Energy in Transportation: Challenges, Solutions, and Future Prospects on Photovoltaic Noise Barriers
Authors: Wu Qiong , Zhang Xiaofeng , Wang Qi
Published: March 2024

The photovoltaic noise barrier (PVNB), a solar noise barrier, is an innovative integration of transportation and renewable energy. It is primarily installed alongside roads near acoustic environmental protection targets in proximity to traffic lanes. PVNBs serve the dual purpose of reducing noise pollution and harnessing solar energy. The electricity generated is used for traffic lights, surveillance, and even feeding into the power grid. This helps to reduce pollution and carbon emissions and improve energy efficiency. This paper provides a comprehensive review of the current research and practical applications of PVNBs, focusing on their unique features. It systematically addresses challenges and proposes solutions concerning optimal site selection, safety standards, noise attenuation effectiveness, power generation efficiency, durability, operational maintenance, and collaborative efforts across various departments. Additionally, this paper highlights the importance of conducting advanced research into glare mechanisms, improving site selection processes, optimizing design strategies, enhancing management and maintenance systems, and conducting comprehensive life-cycle cost–benefit analyses. This research aims to offer scientific insights for designing and deploying PVNBs, thereby fostering the progressive adoption and application of distributed photovoltaics in transportation infrastructures.
Thirty Years of Research and Methodologies in Value Co-Creation and Co-Design
Authors: Avila-Garzon Cecilia , Bacca-Acosta Jorge
Published: March 2024

Value co-creation and co-design strengthen the development of projects and initiatives that involve working with people from interdisciplinary areas. Moreover, both concepts have been adopted in contexts such as health, marketing, education, social issues, software/hardware design, and economic contexts, among others. Co-creation can be one of the strategies to achieve the Sustainable Development Goal (SDG) 17 because co-creation can be a means to implement global partnerships for sustainable development. However, there is a lack of research on methodologies for co-design and co-creation. To fill this gap in the literature in this study, we review studies about value co-creation and co-design and present an overview of the methodologies reported in the literature. The first part of this review was conducted as a bibliometric analysis based on 3858 (co-design) and 3524 (co-creation) scientific documents retrieved from the Scopus database. For the second part, a scoping review was conducted based on the analysis of 71 studies related to co-creation methodologies and 96 in the case of co-design. As a result, we identified 32 methodologies for co-creation and 26 methodologies for co-design. Co-creation and co-design add value to processes with different stakeholders, such as implementing innovations to provide better service in information systems of a wide range of contexts.
Holistic Environmental Risk Index for Oil and Gas Industry in Colombia
Authors: De Luque-Villa Miguel A. , Robledo-Buitrago Daniel Armando , Gómez-Rendón Claudia Patricia
Published: March 2024

Risk management for technological hazards mainly focuses on the consequences for human lives. Although technological risk analysis evaluates environmental vulnerability, it does not reflect the consequences of environmentally exposed elements. This paper’s objective is to propose a conceptual framework and create a multidisciplinary evaluation model for environmental risk analysis in the oil and gas industry. A holistic assessment was carried out based on probabilistic risk analysis methodologies to obtain a holistic environmental risk index, HERi. Moncho’s Equation was adapted by combining ecological risk, ER, and an aggravating coefficient, F. Transformation functions were utilized to represent the risk probability distributions. The results from the holistic environment risk index were standardized in a sigmoidal function using the ALARP criteria. Finally, the methodology was applied in two case studies in Colombia, comparing the results with an alternative model. This study found that Colombian armed conflict is a key factor that increases environmental risk in oil and gas projects. The proposed methodology takes a holistic approach by integrating socioeconomic factors and resilience considerations into the risk assessment process. This approach provides a more comprehensive understanding of the environmental risks associated with oil and gas projects in Colombia and promotes more effective sustainable management actions.
Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Element Method and Machine Learning Models
Authors: Hu Xiao , Ishaq Azher , Khattak Afaq , Chen Feng
Published: March 2024

This study researches environmental factors, vehicle dynamics, and loading conditions on pavement structures, aiming to comprehend and predict their impact. The susceptibility of asphalt pavement to temperature variations, vehicle speed, and loading cycles is explored, with a particular focus on the lateral distribution of wheel tracks in driving and passing lanes. Utilizing video analysis and finite element modelling (FEM) through ABAQUS 2022 software, multiple input factors, such as speed (60, 80 and 100 km/h), loading cycles (100,000 to 500,000), and temperature range (0 °C to 50 °C), are applied to observe the maximum rutting (17.89 mm to 24.7 mm). It is observed that the rut depth exhibited is directly proportional to the loading cycles and temperature, but the opposite is true in the case of vehicle speed. Moreover, interpretable machine learning models, particularly the Bayesian-optimized light gradient boosting machine (LGBM) model, demonstrate superior predictive performance in rut depth. Insights from SHAP interpretation highlight the significant roles of temperature and loading frequency in pavement deformation. This study concludes with a comprehensive understanding of how these factors impact road structures in Pakistan. Its implications extend to valuable insights for optimizing road design, offering a significant contribution to enhancing the durability and sustainability of road infrastructure in the region.
Synthesis of Fe(III)-g-C3N4 and Applications of Synergistic Catalyzed PMS with Mn(VII) for Methylene Blue Degradation
Authors: Li Lin , Gu Huangling , Wang Qiong , Chen Meiyin , Ma Wenjing , Zhang Hongwei
Published: March 2024

Refractory organic pollutants pose a great threat to public health in water bodies due to their toxicity and low biodegradability. Developing a method of activating persulfate efficiently and in an environmentally friendly way has become a popular topic of research in current advanced oxidation water treatment technologies. Fe(III)-g-C3N4 was prepared by the calcination method. Fe(III) was anchored on the framework of g-C3N4. The characterization analysis indicated that Fe(III) was successfully loaded on g-C3N4. The best effect for MB degradation was Fe(III)-g-C3N4 (0.1 g/L) dosed with 30 µmol/L KMnO4 for synergistic catalyzed PMS (0.1 g/L), where the degradation rate could reach 95.4%. The optimum temperature for MB degradation was determined to be 10 °C. The optimum pH range of Fe(III)-g-C3N4/Mn(VII) synergistic catalyzed PMS for MB degradation was pH 4.4–6.6 under acidic conditions, and the optimum pH range for MB degradation was pH 8–10 under alkaline conditions. The Fe(III)-g-C3N4/Mn(VII) synergistic catalyzed PMS system was also tested for the degradation of methyl orange and rhodamine b, and good degradation results were obtained with the degradation rates of 87.37% and 84%, respectively. It facilitates the reduction in pollutant emissions, improves water quality and will have a positive impact on the sustainability of the environment.
The Impact of Capital Market Opening on Enterprise Green Technology Innovation: Insights from the Shanghai–Hong Kong Stock Connect
Authors: He Jianfei , Huang Rong , Ding Jiayu , Liu Yuting , Zhou Rongxi
Published: March 2024

China’s economy has experienced a period of remarkable growth and entered a stage of high-quality development, necessitating the implementation of innovative eco-friendly practices involving green technology innovation. The capital market environment plays a direct role in influencing the sources and scale of external financing for businesses, thus affecting their green technology innovation activities. The Shanghai–Hong Kong Stock Connect (SHSC) mechanism in China is the first official two-way opening of China’s capital market. Therefore, this paper regards the SHSC policy as a quasi-natural experiment platform for the opening of the capital market and employs a difference-in-difference methodology to investigate the impact of the SHSC policy on enterprise green technology innovation and its influence mechanisms. The overall sample covers Chinese A-share listed enterprises from 2012 to 2017. The findings demonstrate that the SHSC policy significantly enhances the level of green technology innovation by enhancing corporate governance and alleviating financing constraints. Non-state-owned enterprises are more affected than state-owned ones, and there is no significant difference in the impact between heavily polluting industries and non-heavily polluting ones.
Decision Change: The First Step to System Change
Authors: Bomans Arnold J. , Roessingh Peter
Published: March 2024

Global crises, such as climate change and ecological collapse, require changes in systemic factors that cause the crises. These factors include the economy, population growth, and decision-making in global affairs. Current decision-making processes have failed to accomplish the required system change, necessitating a change to these processes (‘decision change’) for meaningful progress. The key question is how a procedure for deciding on the required system change should be designed in this setting. In this essay, we propose a three-step approach. First, independent experts in collective decision-making should design this procedure under monitoring by auxiliary bodies that safeguard the design process; second, proposals for system change should be collected; third, based on these proposals, system change should be designed and decided upon using the new decision-making procedure. We argue that authority can be given to the new decision-making body that decides on system change. A global team must convene the decision-making experts and auxiliary bodies, collect system-change proposals, and guarantee that the decision-making process is facilitated. We call on individuals and independent organisations to form such a team or support its formation.
Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability
Authors: Ali Syed Mithun , Rahman Amanat Ur , Kabir Golam , Paul Sanjoy Kumar
Published: March 2024

The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.
Analysis of Knowledge Graph: Hotspots and Future Trends in Environmental Education Research
Authors: Tian Yongli , Jin Yuchang , Zhao Yadi , Du Yifan , Shen Sicen , An Junxiu
Published: March 2024

Currently, as human society develops, environmental issues have become one of the significant challenges faced by humanity. Consequently, environmental education has emerged in response to this need. Environmental education aims to cultivate citizens with knowledge about the environment, which is crucial in addressing global challenges, fostering human–nature sustainability knowledge, and cultivating resilient individuals and communities. A comprehensive analysis of the current state of environmental education and exploring how environmental education can enhance the sustainability of interactions between humans and nature is one of the focal points of contemporary research. This systematic review employs CiteSpace to visually analyze the advancements of environmental education research, aiming to uncover the current status, predominant themes, evolutionary trajectories, and emerging trends. The Web of Science core collection database was examined to retrieve environmental education research papers published from 2013 to 2022. A total of 1851 papers were included in the final analysis. CiteSpace was utilized for visualizing and analyzing environmental education researchers, keyword co-occurrences, and keyword clustering, and Timeview was employed to generate relevant maps. The analysis identified five major hotspots in environmental education research: environmental awareness and literacy, theories in environmental education, delivery modes and methods, developments and implementation of environmental education, and the intersection of climate change and environmental justice. Emerging research directions include environmental justice, climate change education, critical environmental education, and environmental citizenship education. The results show that environmental education is gradually transcending the natural realm currently and integrating into a holistic framework encompassing societal, economic, and political dimensions. Environmental citizenship education is increasingly attracting the attention of scholars. Solving environmental problems requires interdisciplinary dialogue.
Optimizing Distribution System Resilience in Extreme Weather Using Prosumer-Centric Microgrids with Integrated Distributed Energy Resources and Battery Electric Vehicles
Authors: Thirumalai Muthusamy , Hariharan Raju , Yuvaraj Thangaraj , Prabaharan Natarajan
Published: March 2024

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters.
Optimal Sizing and Placement of Distributed Generation under N-1 Contingency Using Hybrid Crow Search–Particle Swarm Algorithm
Authors: Hussein Farh Hassan M. , Al-Shamma’a Abdullrahman A. , Qamar Affaq , Saeed Fahman , Al-Shaalan Abdullah M.
Published: March 2024

Line outage contingencies in power distribution systems pose critical challenges, leading to disruptions, reduced reliability, and potential cascading failures. These problems include increased vulnerability, limited resilience, and the need for efficient mitigation strategies to enhance the overall system reliability and quality. This study aims to investigate, analyze, and evaluate the renewable distributed generator (RDG) allocation and sizing under N-1 line outage conditions in terms of the reliability and quality for the IEEE 30-bus benchmark power system as a case study. Under all possible N-1 line outage conditions, there were four critical N-1 line outage conditions, 19–20, 10–20, 27–29, and 27–30, which caused overloading on at least one line. The Severity Performance Index (SPI) recorded the highest value of 0.715 during the line 10–20 outage, followed by 0.683, 0.606, and 0.476 during the line F27–30 outage, line F19–20 outage, and line F27–29 outage, respectively. This indicates that the line 10–20 outage is the most critical among the line outages followed by the line 27–30 outage. During the line 10–20 outage, the crow search integrated with the particle swarm optimizer recommends allocating renewable distributed generators (RDGs) at optimal or feasible buses 14, 15, 17, 20, and 30, with suggested sizes of 26.8127 MW, 38.8986 MW, 27.9600 MW, 21.6300 MW, and 27.0184 MW, respectively. The obtained finding revealed that allocating five RDGs at optimal busbars helped keep the line loading below maximum limits and improved the voltage profiles during the N-1 line outages in the IEEE 30-bus benchmark power system. This approach enhanced the power system reliability and quality across all four N-1 scenarios.
Flame Heat Sources as a Sustainable Method of Protecting Fruit Orchards against Frost in Poland
Authors: Balawejder Maciej , Matłok Natalia , Piechowiak Tomasz , Puchalski Czesław
Published: March 2024

One of the key factors affecting the yield of fruit orchards is their protection against spring frosts. This paper presents the methods currently used (like anti-frost nets, chemical agents, etc.) and proposes an alternative method of raising the temperature around fruit trees using flame heat sources with a suitable heating power. Flame heat sources in the form of metal containers filled with paraffin were utilised in this experiment. The effect of the flame heat sources on the relative increase in the temperature of a wooden surface (simulating trees located in Rzeszow, Poland) depending on the distance from the heat source was measured and estimated based on mathematical models. Based on the results of experimental work, an optimal placement of flame heat sources (1.58 m from each tree) was proposed for orchards of semi-dwarf fruit trees. The proposed modifications of the flame heat sources to incorporate infrared radiation converters resulted in a greater increase in the temperature of the wooden surface. The analysis of the available economic and environmental data showed that the proposed method may be a competitive alternative to the standard method of protection. If the flame heat sources are filled with combustible materials derived from renewable sources, such as those produced from fats, the overall CO2 emission balance for this solution will be significantly more favourable. The use of flame heat sources does not necessitate the introduction of permanent changes in the environment, as is required in cases when irrigation infrastructure is installed.
Consumers’ Attitudes towards Differentiated Agricultural Products: The Case of Reduced-Salt Green Table Olives
Authors: Paltaki Aikaterini , Mantzouridou Fani Th , Loizou Efstratios , Chatzitheodoridis Fotios , Alvanoudi Panagiota , Choutas Stelios , Michailidis Anastasios
Published: March 2024

Contemporary healthy food issues and food safety concerns induce consumers to become more interested in a healthier diet such as foods reduced in salt. This study explores consumers’ behaviour, attitude, and expectations for the development of a new reduced-salt table olive product from Chalkidiki, an area of Greek. In this context, the main aim of this paper is to investigate the knowledge and attitudes of consumers about health and nutrition, reduced salt consumption, and consumption of Chalkidiki reduced-salt green table olives. Summary statistics and multivariate analysis were performed to examine consumers’ perceptions. The results of the research highlight a remarkable consumer interest in products with reduced salt content. Furthermore, the majority are willing to purchase such foods which is a possible action that can be taken to reduce salt intake. These outcomes emphasise that producing a new reduced-salt table olive product is promising, as the interest of consumers, industries, and the research community has turned to innovative actions that add nutritional value and meet the consumers’ expectations.
Performance and Surface Modification of Cast Iron Corrosion Products by a Green Rust Converter (Mimosa tenuiflora Extract)
Authors: Arceo-Gómez David Enrique , Reyes-Trujeque Javier , Balderas-Hernández Patricia , Carmona-Hernández Andrés , Espinoza-Vázquez Araceli , Galván-Martínez Ricardo , Orozco-Cruz Ricardo
Published: March 2024

One of the alternative materials used for conducting conservation treatment of iron artifacts is the rust converter, since it generates barrier properties and more stable oxides. The protective properties and surface modifications from using Mimosa tenuiflora extract as a green rust converter on a gray iron oxide layer were studied. The surface characterization was carried out using a Scanning Electron Microscope coupled to an energy-dispersive X-ray spectrometer (SEM-EDS), along with infrared spectroscopy (IR), Raman spectroscopy, X-ray Diffraction (XRD), and Water Contact Angle (WCA). Electrochemical characterization was performed with an Electrochemical Impedance Spectroscope (EIS) using 3.5 wt.% NaCl as the electrolyte. According to the results of the Raman spectroscopy and XRD, the layer of corrosion products formed after 90 days in the atmosphere was composed of goethite, lepidocrocite, maghemite, hematite, and magnetite. The surface of the corrosion products was transformed with the rust converter into an amorphous and microcracked layer. By IR, the Fe-O and C-O-Fe bonds associated with the iron chelate were found with absorption bands at 1540 and 1567 cm−1, respectively. By XRD, a modification of the magnetite crystallinity was observed. Finally, the Water Contact Angle and the protective capacity of the corrosion products were improved by the presence of the rust converter.
Theoretical Modeling and Experimental Verification of Elliptical Hyperbolic Hybrid Flexure Hinges
Authors: Wang Yan , Zhang Lizhong , Meng Lixin , Lu Hongjie , Ma Yongheng
Published: March 2024

A flexure hinge composed of elliptical and hyperbolic hybrid configurations is developed and analyzed in this paper. The analytical models of compliance, rotation accuracy, and maximum stress of the flexure hinge are established, and the correctness of the models is validated by finite element analysis and experiments. The influence of structural parameters on compliance and rotation accuracy is discussed. The concept of compliance stress ratio is proposed to assess the deformation capacity of flexure hinges when subjected to the same stress, which provides a basis for quantitatively comparing the comprehensive performance of flexure hinges. The performance of the hybrid flexure hinge is compared with that of elliptical, hyperbolic, and circular flexure hinges by taking the compliance accuracy ratio and the compliance stress ratio as the performance evaluation indexes. The results show that the hybrid flexure hinge combines the advantages of hyperbolic and elliptical hinges and has a balanced performance in compliance, rotation accuracy, and low stress. The designed hybrid flexure hinge is suitable for the support structure of fast steering mirrors, which provides a valuable reference for the engineering optimization design of flexure hinges.
Risk Perception-Based Project Contingency Management Framework
Authors: Ottaviani Filippo Maria , De Marco Alberto , Rafele Carlo , Castelblanco Gabriel
Published: March 2024

Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.
Navigating Resource Challenges in Health Emergencies: The Role of Information Diffusion and Virus Spread in Demand Dynamics
Authors: Zhou Yizhuo , Zhang Jianjun , Yang Yundan
Published: March 2024

The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are investigated using a novel three-layer coevolution “information–epidemic–resource” model through Markov process simulations. The study firstly identifies eight factors influencing demand fluctuations in terms of some city characteristics, such as media exposure, consistency of public opinion, self-protection level, and restrictive protection level, while categorizing resources into individual holdings and centralized storage. Then, extensive simulations are examined to elucidate the impact of these factors. The results reveal that various city characteristics can affect fluctuation in demand for both individual holdings and centralized storage. Inaccurate media information tends to inflate fluctuations, while higher public opinion consistency can reduce it. Reinforcing self-protection decreases the demand fluctuations of individuals, and effective restrictive protections can reduce fluctuations in centralized resource storage. Moreover, an analytical simulation of various city scenarios, underpinned by statistical data from selected Chinese and German cities, demonstrates that distinct city characteristics significantly influence medical resource demand changes during epidemics. This underscores the importance of tailoring emergency medical supply strategies to the specific developmental traits of different countries and cities. This study provides valuable insights to researchers, governments, and enterprises, enhancing their preparedness and response for emergency supply chain disruptions.
Riparian Soil Pollution Caused by Sediment Metal Transport: Seasonal Changes and Ecological Risk Assessment
Authors: Çelebi Ahmet , Şengörür Bülent , Torabi Haghighi Ali , Danandeh Mehr Ali
Published: March 2024

The accumulation of pollutants in the sediment along surface water may negatively affect riparian zones and increase ecological risk. This article investigates the effects of metal sediments on riparian soil via field monitoring and ICP-OES analysis. To this end, pollution levels, seasonal changes, and potential sources of the pollutants were determined for the Melen River watershed, Turkey. The ecological statuses (contamination factor, enrichment factor, index of geo-accumulation, pollution index, modified pollution index, and potential and modified ecological risk indexes) of the watershed were also analyzed. Although no significant seasonal differences in the metal sediments were observed, their spatial distribution in the sediments and riparian soils varied markedly. Cr (11.4 to 136), Co (7.7 to 21.52), Cu (11.4 to 76.6), and Ni (14.06 to 128.2) recorded as mg/kg significantly increased from the upstream to the downstream. The metals possessing the highest risk in the sediment and riparian soil regarding the river health were Cu, Co, and Ni. The risk values were found to be heavily polluted (PI > 3 and MPI > 10), and the risk indexes were above the “desired environment without the risk”. The risk index was found to be more than 50, and the modified risk indexes exceeded 200 at many points. The transportation of pollutants in surface water became evident in the sediment, resulting in adverse effects on the riparian zone and the ecological system.