bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models
Authors: Contreras-Ramírez Michael , Sora-Cardenas Jhonathan , Colorado-Salamanca Claudia , Ovalle-Bracho Clemencia , Suárez Daniel R.
Published: December 2024

Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to detect Leishmania spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. Starting with acquiring and labeling 500 images, an experimental design was implemented, including preprocessing and segmentation techniques such as Otsu, local thresholding, and Iterative Global Minimum Search (IGMS) to improve parasite detection. The phenotypic features of the parasites were extracted, focusing on morphology, texture, and color. Machine learning models (ANN, SVM, and RF) optimized through Grid Search were applied for classification. The model with the best results was a Support Vector Machine (SVM), achieving a sensitivity of 91.87% and a specificity of 89.21% at the crop level. Compared with previous studies, these results highlight the relevance and consistency of the methodology used, supporting the initial hypothesis. This suggests that machine learning techniques offer a promising path toward improving the diagnosis of cutaneous leishmaniasis.
High-Performance CP Magneto-Electric Dipole Antenna Fed by Printed Ridge Gap Waveguide at Millimeter-Wave
Authors: Mousavirazi Zahra , Ali Mohamed Mamdouh M. , PourMohammadi Peyman , Fei Peng , Denidni Tayeb A.
Published: December 2024

This paper presents a high-performance circularly polarized (CP) magneto-electric (ME) dipole antenna optimized for wideband millimeter-wave (mm-wave) frequencies, specifically targeting advancements in 5G and 6G technologies. The CP antenna is excited through a transverse slot in a printed ridge gap waveguide (PRGW), which operates in a quasi-transverse electromagnetic (Q-TEM) mode. Fabricated on Rogers RT 3003 substrate, selected for its low-loss and cost-effective properties at high frequencies, the design significantly enhances both impedance and axial ratio (AR) bandwidths. The antenna achieves an impressive impedance bandwidth of 31% (25.24–34.50 GHz) and an AR bandwidth of 24.9% (26.40–33.91 GHz), with a peak gain of up to 8.4 dBic, demonstrating a high cross-polarization level. The experimental results validate the high-performance characteristics of the antenna, making it a robust candidate for next-generation wireless communication systems requiring CP capabilities.
Additive Manufacturing of a Frost-Detection Resistive Sensor for Optimizing Demand Defrost in Refrigeration Systems
Authors: Aguiar Martim Lima de , Gaspar Pedro Dinis , Silva Pedro Dinho da
Published: December 2024

This article presents the development of a resistive frost-detection sensor fabricated using Fused Filament Fabrication (FFF) with a conductive filament. This sensor was designed to enhance demand-defrost control in industrial refrigeration systems. Frost accumulation on evaporator surfaces blocks airflow and creates a thermal insulating barrier that reduces heat exchange efficiency, increasing energy consumption and operational costs. Traditional timed defrosting control methods can mitigate these effects but often lead to inefficiencies due to their inability to align with actual frost accumulation, which can vary according to system and environmental conditions. Frost-detection sensors aim to solve this problem by acting as a tool to support defrosting control. A series of tests were conducted to evaluate the sensor’s performance in detecting frost under controlled conditions on a heat exchanger (HX). The sensor reliably detected frost in all cases, demonstrating its effectiveness in real-time frost detection. The sensor measurements were validated by comparison with results obtained through a computer vision method, confirming its reliability. It was also found that the sensor can detect temperature changes. This advancement in sensor technology highlights the potential of additive manufacturing to provide cost-effective, customizable, replicable, and compact sensor designs, contributing to improved system performance and energy efficiency in refrigeration systems.
EGNet: 3D Semantic Segmentation Through Point–Voxel–Mesh Data for Euclidean–Geodesic Feature Fusion
Authors: Li Qi , Song Yu , Jin Xiaoqian , Wu Yan , Zhang Hang , Zhao Di
Published: December 2024

With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean–geodesic network (EGNet), which uses point cloud–voxel–mesh data to characterize detail, contour, and geodesic features, respectively. The EGNet performs feature fusion through Euclidean and geodesic branches. In the Euclidean branch, the features extracted from point cloud data compensate for the detail features lost by voxel data. In the geodesic branch, geodesic features from mesh data are extracted using inter-domain fusion and aggregation modules. These geodesic features are then combined with contextual features from the Euclidean branch, and the simplified trajectory map of the grid is used for up-sampling to produce the final semantic segmentation results. The Scannet and Matterport datasets were used to demonstrate the effectiveness of the EGNet through visual comparisons with other models. The results demonstrate the effectiveness of integrating Euclidean and geodesic features for improved semantic segmentation. This approach can inspire further research combining these feature types for enhanced segmentation accuracy.
A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas
Authors: Xiao Yun , Li Rongqiao , Li Jinyan
Published: December 2024

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures.
Knowledge Reasoning- and Progressive Distillation-Integrated Detection of Electrical Construction Violations
Authors: Ma Bin , Liang Gang , Rao Yufei , Guo Wei , Zheng Wenjie , Wang Qianming
Published: December 2024

To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject–predicate–object relationship. These triplets are embedded into the model using an adaptive connection network, which dynamically weights the relevance of external knowledge to enhance detection accuracy. Furthermore, to enhance the model’s performance, the paper designs a progressive multi-level distillation strategy. On one hand, knowledge transfer is conducted at the object level, region level, and global level, significantly reducing the loss of contextual information during distillation. On the other hand, two teacher models of different scales are introduced, employing a two-stage distillation strategy where the advanced teacher guides the primary teacher in the first stage, and the primary teacher subsequently distills this knowledge to the student model in the second stage, effectively bridging the scale differences between the teacher and student models. Experimental results demonstrate that under the proposed method, the model size is reduced from 14.5 MB to 3.8 MB, and the floating-point operations (FLOPs) are reduced from 15.8 GFLOPs to 5.9 GFLOPs. Despite these optimizations, the AP50 reaches 92.4%, showing a 1.8% improvement compared to the original model. These results highlight the method’s effectiveness in accurately detecting workers’ violation behaviors, providing a quantitative basis for its superiority and offering a novel approach for safety management and monitoring at construction sites.
Application of Machine Learning to Predict CO2 Emissions in Light-Duty Vehicles
Authors: Udoh Jeffrey , Lu Joan , Xu Qiang
Published: December 2024

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards. One of the primary methods employed by the VCA to measure vehicle emissions for light-duty vehicles is the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). The WLTP is a global standard for testing vehicle emissions and fuel consumption, and sensors are crucial in ensuring accurate, real-time data collection in laboratories. Using the data collected by the VCA, regression machine learning models were trained to predict CO2 emissions in light-duty vehicles. Among six regression models tested, the Decision Tree Regression model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%. It was then deployed as a web application that provides users with accurate CO2 emission estimates for vehicles, enabling informed decisions to reduce GHG emissions. This research demonstrates the efficacy of machine learning and AI-driven approaches in fostering sustainability within the transportation sector.
Activating Peroxymonosulfate by a NovelFe-MOF-C/N Catalyst to Effectively Degrade Sulfamethoxazole: Catalyst Performance and Its Mechanism
Authors: Wu Zhaowei , Hu Meiyi , Zhuo Pengfan , Wang Jie , Tong Shaoping
Published: December 2024

A composite Fe-MOF-C/N was prepared with Fe-MOF and g-C3N4 precursors, and its activation property of peroxymonosulfate (PMS) in the degradation of Sulfamethoxazole (SMX) was studied. XRD, SEM, and TEM analyses showed that Fe-MOF-C/N was porous and mainly composed of phases of Fe3N, C0.08Fe1.92, and graphite. XPS indicated that Fe-MOF-C/N mainly had Fe, C, N, and O elements, among which Fe existed in the form of Fe2+ and Fe3+. Fe-MOF-C/N was used to activate PMS to degrade SMX, and the result showed that Fe-MOF-C/N had excellent catalytic performance and 84.17% of SMX could be removed in 8 min. Free radical quenching experiments showed that 1O2 was the main active species in Fe-MOF-C/N/PMS. The stability experiment showed that Fe-MOF-C/N had good stability, and the degradation rate of SMX only decreased by 5.8% after five times of use.
Exploring How Educational Preparedness Affects the Attitudes of Nurses Regarding Sexual and Gender Minorities: A Systematic Review
Authors: Nikitara Monica , Kenteli Marileni , Latzourakis Evangelos , Constantinou Costas S.
Published: December 2024

Numerous sources report that patients belonging to sexual or gender minority (SGM) groups often do not receive proper healthcare services due to negative attitudes from healthcare providers, including nurses. This literature review aims to explore the gaps in the existing curriculum and educational preparedness, including ehealth and mhealth trainings and how such educational preparedness affects the attitudes of nurses toward SGM groups. The search strategy included the electronic databases CINHAL, PubMed, Medline, EBSCO, and ProQuest. After reviewing and removing duplicates and irrelevant studies, 21 articles were selected to be included in this literature review. The findings of this research indicate that the provision of educational resources, including ehealth and mhealth trainings, falls short of meeting the requirements of caring for a patient who identifies with a sexual and gender minority (SGM) group. Consequently, nurses feel less confident and prepared to provide care in such situations, resulting in low comfort levels and readiness. It also reveals the nurses’ attitudes towards SGM patients affected by the lack of preparation. Key educational interventions, including targeted seminars and online modules, are recommended to enhance nurse preparedness. Implementing these interventions can foster improved attitudes and cultural competence in healthcare settings.
Leisure Styles in Adults: Changes Throughout the COVID-19 Pandemic
Authors: Maciel Marcos Gonçalves , Madariaga Ortuzar Aurora , Martins José Clerton de Oliveira , Gutierrez Luz Anegla Árdila , Bendikova Elena , Assis Karina Lopes , Reis Renato Francisco , Mola Dessalegn Wase , Uvinha Ricardo Ricci
Published: December 2024

This study explored changes in leisure styles among adults during the periods before (2019), during (2020–2022), and after (2023) the COVID-19 pandemic. A quantitative, cross-sectional, and exploratory design was employed using a non-probabilistic sample of 300 participants aged 18–60 years (mean age = 31.7 ± 10.8). Data were collected via an online survey using the Leisure Practices Scale, which measures engagement in artistic, manual, physical activity, intellectual, social, touristic, virtual, and contemplative practices. The results showed a significant increase in intellectual activities between the pre- and during-pandemic periods (p = 0.05), with the level of engagement remaining high in the post-pandemic period. Similarly, virtual activities demonstrated a significant increase in engagement between the pre- and during-pandemic periods (p = 0.00), returning to pre-pandemic levels afterward. Artistic (p = 0.91), manual (p = 0.60), and contemplative (p = 0.52) activities showed no significant changes, remaining stable across the analyzed periods. Significant reductions were observed in physical (p = 0.00), social (p = 0.05), and touristic (p = 0.04) activities during the pandemic, with the latter two returning to pre-pandemic levels afterward. These findings underscore the pandemic’s differentiated impact on leisure practices.
Smart City as an Ecosystem to Foster Entrepreneurship and Well-Being: Current State and Future Directions
Authors: Bukhari Atiya , Alshibani Safiya Mukhtar , Ali Mohamed Abouelhassan
Published: December 2024

Entrepreneurial endeavors are essential for stimulating economic growth and rendering them is a primary concern for policymakers. In recent years, smart city ecosystems have garnered attention for enhancing urban living and tackling contemporary difficulties. The contribution of smart cities in promoting entrepreneurship and improving well-being has received little attention. This study aims at examining the potential of smart city as an ecosystem to promote entrepreneurship and enhance well-being and quality of life (QoL). This study uses a Fuzzy evaluation model and the Analytic Hierarchy Process (AHP) to evaluate essential determinants of smart cities and their significance. Data from sources such as the Smart City Index, Ease of Doing Business Ranking, Global Innovation Index, Sustainable Development Report, and Technological Readiness Ranking are utilized with normalization, guaranteeing a dependable evaluation. The findings underscore the significance of open data efforts and transparent governance in recruiting innovative enterprises and promoting entrepreneurship. The study highlights the necessity of cooperative urban planning and public participation in decision-making. Moreover, the authors propose a new definition of smart cities from citizens’ well-being perspective. This research enhances the comprehension of smart cities’ influence on entrepreneurial endeavors, pinpointing problems and prospects for future investigations focused on improving well-being through smart city advancement.
Seasonal Differences and Driving Factors of Microbial Community Structure in Wetlands Along Shores of Daihai Lake
Authors: Hou Chenli , Yu Shengqian , Yin Zhenyu , Yang Wenhuan , Bai Long , Yao Zhi , Li Yang , Li Weiping
Published: December 2024

Microorganisms play a crucial role in biogeochemical cycling within wetland ecosystems. Soil-environment-mediated seasonal changes in microbial structure require further investigation. However, the factors driving seasonal variations in the microbial abundance and diversity in the Daihai wetlands remain unclear. This study employed high-throughput sequencing technology to examine the bacterial community structure and diversity in the lakeshore zone of Daihai across different seasons and to explore the seasonal variations in the bacterial community structure and the influence of driving factors. The results indicated that Pseudomonadota and Bacteroidota dominate in spring and Pseudomonadota abundance further increases in summer, while organic-matter-decomposing microorganisms (e.g., Actinobacteriota and Bacteroidota) dominate in autumn. Microbial diversity indices indicated a significantly greater diversity in spring than in summer and autumn (p < 0.05). Changes in environmental variables were strongly associated with bacterial community changes, with variations in total nitrogen, ammonium nitrogen, and soil moisture content being identified as potential key factors influencing seasonal bacterial diversity trends. Furthermore, bacterial community assembly in spring is driven by both stochastic processes and environmental selection, suggesting that abundant nutrients and organic matter promote bacterial diversity. The findings of this study provide a theoretical basis for wetland ecosystem restoration and conservation.
Sustainable Cement Paste Development Using Wheat Straw Ash and Silica Fume Replacement Model
Authors: Bastías Bryan , González Marcelo , Rey-Rey Juan , Valerio Guillermo , Guindos Pablo
Published: December 2024

Conventional cement production is a major source of carbon dioxide emissions, which creates a significant environmental challenge. This research addresses the problem of how to reduce the carbon footprint of cement paste production using agricultural and industrial waste by-products, namely wheat straw ash (WSA) and silica fume (SF). Currently, accurate models that can predict the mechanical properties of cement pastes incorporating these waste materials are lacking. To fill this gap, our study proposes a model based on response surface methodology and Box-Behnken design, designed to predict the strength of cement pastes with partial substitutions of WSA and SF. Through mechanical and characterization tests, the model demonstrated high accuracy in predicting the strength of the pastes, validated with three mixes, which showed maximum errors of less than 6% at different ages (7, 28, and 56 days). Response surface analysis revealed that replacing cement with 0–20% WSA and more than 5% SF can effectively reduce the carbon footprint by maximizing waste incorporation. This model allows for the calculation of optimal cement substitution levels based on the required strength, thus promoting sustainability in the construction industry through the use of local waste/resources.
Freeze-Casting of Mining Wastes for Developing Sustainable Self-Supporting Ceramic Membranes
Authors: Pereira Deyse Celestte S. , Silva Vanderlane C. , Santos Filho Josenildo I. , Cartaxo Juliana M. , Santos Ieda Maria G. , Santana Lisiane N. L. , Neves Gelmires A. , Menezes Romualdo R.
Published: December 2024

In this work, kaolin processing waste (KW) and columbite–tantalite waste (CTW) from mining activities were used to manufacture sustainable self-supporting ceramic membranes using the freeze-casting technique. The wastes were characterized, and formulations using only wastes were developed. Gelatin was used in the freeze-casting as a processing aid to avoid dendritic or lamellar pores. The membranes were sintered at different temperatures (1100 °C, 1200 °C and 1300 °C) and analyzed by X-ray diffraction, scanning electron microscopy, flexural strength measurement, and mercury porosimetry. The flux through the membranes was measured using a gravity-driven dead-end filtration system. The membranes containing 80% KW and 20% CTW sintered at 1200 °C showed high porosity (59%), a water permeate flux of 126.5 L/hm2, and a mechanical strength of 1.5 MPa. Filtration tests demonstrated effective turbidity removal (>99%) for synthetic water consisting of tap water and bentonite, reaching 0.1 NTU. The use of mining waste has shown considerable promise for the development of sustainable and affordable membranes for water treatment applications.
Water as a Key Focus Point for Developing Education for Sustainable Development: Content Analysis Based on Curriculum Standards
Authors: Li Jiaqi , Wang Xiangdong , Liang Xiuhua , Song Xiaolan , Pan Li , Lu Linhe
Published: December 2024

Water, as the source of life, is a key element of environmental education. Geography education is strongly linked to the three pillars of sustainable development (SD). Firstly, this paper used the water-related standards of China’s national geography curriculum (GCSs) as the object of textual analysis. Two coders categorized the water-related GCSs, coding them with two distinct dimensions: geography core competencies and the SD literacy framework. Then, the coding results were analyzed by word frequency analysis to compare them with U.S. high school GCSs. Finally, the distribution of core competencies and the degree of SD literacy in China’s water-related GCSs were summarized and described. Finally, this study showed that China’s sustainable water education content is rich in content, yet fragmented. China’s water-related GCSs focus on the three dimensions of ESD, with an emphasis on the environmental dimension. GCSs in both countries emphasize human–Earth relations effectively.
Research on Disruptive Green Technological Innovation in Agriculture Driven by Low-Carbon Initiatives
Authors: Huang Shizheng , Ke Chunyuan
Published: December 2024

Advancing new productive forces in agriculture requires the adoption of disruptive green technological innovation by agricultural enterprises. This study analyzes the generative mechanisms for promoting disruptive green technological innovation based on the DSR model and examines the moderating role of green governance alliances. A total of 302 questionnaires were collected from agricultural enterprises in Guangxi, and structural equation modeling was employed for verification. The results indicate that both low-carbon transformation capability and carbon label credibility have a significantly positive impact on disruptive green technological innovation and organizational green learning, with organizational green learning playing a mediating role. Green governance alliances positively moderate the relationship between low-carbon transformation capability, carbon label credibility, and organizational green learning but do not moderate the relationship between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation. Agricultural enterprises can advance new productive forces and achieve high-quality agricultural economic development by enhancing their low-carbon transformation capability, co-constructing carbon label credibility, and engaging in disruptive green technological innovation. Agricultural enterprises should adopt disruptive green technological innovation, establish carbon certification systems, participate in green learning platforms, and strengthen green governance alliances to promote low-carbon development and enhance market competitiveness.
Soil Organic Carbon Storage and Stratification in Land Use Types in the Source Area of the Tarim River Basin
Authors: Zhang Qin , Yue Chunfang , Yu Pujia , Xu Hailiang , Wu Jie , Sheng Fangyu
Published: December 2024

Accurate analysis of soil organic carbon (SOC) under different land uses in ecologically fragile arid zones is essential for effective regulatory measures and improvement of ecological quality. This study selected the ecologically fragile Tarim River source area as an example, aiming to quantitatively assess the SOC content, storage, carbon sequestration potential, and stratification ratio (SR) of different ecological land use types. Soil depths from 0–50 cm were determined and analyzed using the K2Cr2O7-H2SO4 oxidation method, the equivalent soil mass method and mathematical statistics. Forest, shrubland, and grassland ecological land types were included. The results show the following: (1) Both SOC content and storage showed a decrease with increasing soil depth. The total SOC content and storage sequence from high to low were natural forest, grassland, and shrubland. (2) There are variations in the SOC sequestration potential among the different ecological land types and shrubland (40.64 Mg C ha−1) > grassland (37.69 Mg C ha−1). (3) The SRs of the SOC in the forest were significantly greater than those in the shrubland and grassland. The different ecological land types had significant impacts on SR2, SR3, and SR4. SR2 could serve as a reliable index for assessing the impact of management practices on soil quality. The study area has a high potential for soil carbon sequestration in the future under these ecological conservation and management measures.
Developing the Desert: How Qatar Achieved Dairy Self-Sufficiency Through Baladna
Authors: Al-Dobashi Hussein , Wright Steven
Published: December 2024

This case study analyzes how Qatar rapidly achieved dairy self-sufficiency through public–private partnerships following the 2017–2021 blockade. Specifically, it examines the role of Baladna, Qatar’s leading dairy company, in scaling up its domestic production through alignment with government policies. A mixed-methods approach was employed, combining semi-structured interviews with key stakeholders from the Qatari government and Baladna and a review of Baladna’s internal documents and reports. Thematic content analysis was used to identify key themes, and data from Baladna’s reports were triangulated to validate the findings. Collaboration between Qatar’s government and Baladna ensured the self-sufficiency of domestic dairy demand, expanding operations with new products and exports, and strengthening supply chains. However, reliance on government support raises sustainability concerns, highlighting the need for efficiency and diversification. This partnership aligns with national policies, such as the Qatar National Food Security Strategy 2018–2023, and offers insights into how public–private collaborations can promote growth and supply security while balancing state support with market dynamics. This case study highlights how the blockade crisis catalyzed effective public–private collaboration, driving rapid growth in Qatar’s dairy sector to meet domestic demand. The lessons from Qatar’s developmental approach can provide insights for resource-rich countries struggling with food insecurity.
Carbon Footprint of a Windshield Reinforcement Component for a Sport Utility Vehicle
Authors: Tedesco Michele Maria , Bruno Federico , Lazzari Silvia , Lattore Marco , Palumbo Mauro , Rizzi Paola , Baricco Marcello
Published: December 2024

In this study, the carbon footprint of a steel-based windshield reinforcement component assembled in a sport utility vehicle was calculated in three different stages: steelmaking, stamping, and middle-of-use. Possible solutions to decrease carbon emissions were evidenced, such as the purchasing of steel made through low-impact technologies and the exploitation of the green energy grid to power up stamping machines. The life cycle assessment methodology was used to calculate the carbon footprint. Four different steels provided by different suppliers were compared in order to highlight the greenest material for both the steelmaking and stamping processes and the best supplier from an environmental point of view. In addition, the carbon footprint related to the component weight in vehicles with different traction set-ups, i.e., internal combustion engine, mild hybrid electric, and battery electric, was reported. To reduce the carbon footprint, electric arc furnace-based steelmaking and cold stamping were the best options. In addition, component weight reduction (for instance, changing materials) allowed a decrease in fuel and/or energy consumption, with carbon footprint benefits.
The Decarbonizing Strategies of China’s Iron and Steelmaking Industry: A Comprehensive Perspective
Authors: Wei Zichao , Xue Kai , Hu Guangwen , Wu Yufeng , Wang Yanfen
Published: December 2024

Decarbonizing the iron and steelmaking industry is critical for China to pursue the net-zero emissions target and advance sustainable industrialization (SDG 9). This paper addresses the urgent need for decarbonization strategies in this sector, aiming to align with China’s carbon neutrality goals by 2060. By reviewing the current technological advancements and potential pathways for deep decarbonization, including process optimization, hydrogen-based direct reduction, and carbon capture, utilization, and storage (CCUS), these decarbonization technologies are categorized into six strategic approaches: systemic energy efficiency improvement, resource recycling, process optimization and innovation, breakthrough smelting, product iterative upgrading, and CCUS. These strategies also align with SDG 13 (climate action) by reducing greenhouse gas emissions and SDG 7 (affordable and clean energy) through the promotion of clean energy technologies. These strategies are evaluated for their emission reduction potential and technological maturity. The results indicate that, while efficiency improvements and resource recycling are currently the most mature and widely implemented strategy, significant breakthroughs in hydrogen metallurgy and CCUS are essential for achieving long-term carbon neutrality. Based on an analysis, a comprehensive roadmap is proposed, detailing the near-term to long-term actions required for the industry’s transition. The near-term focus (up to 2030) should be on enhancing energy efficiency and process optimization, whereas the mid-term (2030–2050) focus should emphasize the adoption of hydrogen-based technologies and CCUS. By the 2050–2060 period, the industry should achieve widespread commercialization of breakthrough smelting technologies and CCUS, ensuring the achievement of carbon neutrality. This study intends to provide a systematic framework and strategic recommendations for policymakers and industry stakeholders to guide the decarbonization of China’s iron and steelmaking sector, addressing both technological and economic challenges to achieve sustainable and low-carbon development.
Experimental Study on the Influences of the Fines Contents and Initial Moisture on the Water and Salt Migration of Coarse-Grained Saline Soil Subgrades
Authors: Yang Haoyuan , Liu Jie , Ma Jianyong , Wang Yong , Wang Bo , Zhang Jiangpeng
Published: December 2024

The construction of roads in saline soil areas usually involves using coarse-grained soil as roadbed fill material; studying the water–vapor–salt migration mechanism in coarse-grained saline soil subgrades is crucial for ensuring the stability of highway infrastructure. In order to clarify the influence of fines content and initial moisture on the water–salt migration and to clarify the water–vapor–salt migration patterns in coarse-grained saline soil, a model test of coarse-grained saline soil was conducted to study the response patterns of external water replenishment, final moisture content, final salt content, and liquid level height of coarse-grained saline soil. The results indicated that the water vapor migration amount only causes a change in the final moisture content, albeit not enough to cause salt redistribution. With increasing initial moisture content in coarse-grained saline soil, the migration characteristics of water vapor are weakened, and it imposes a significant inhibitory effect on liquid water migration at the same time. Increasing fines content in coarse-grained soil significantly inhibits water vapor migration, whereas liquid water migration is promoted. Water and salt accumulate in the liquid and vapor coupling migration mode at different heights. Based on the mechanisms of water vapor and salt transport characteristics, this study proposes a novel roadbed structure, which is vital for ensuring the long-term service performance of coarse-grained saline soil roadbeds in saline soil areas.
Pathways of the Digital Economy’s Impact on Green Total Factor Productivity in the Construction Industry
Authors: Li Zhijiang , Tang Decai
Published: December 2024

The rapid development of the digital economy (DE) has provided innovative solutions for the transformation and upgrade of the construction industry. Leveraging technologies such as intelligent management, the Internet of Things, and artificial intelligence effectively enhances the construction industry’s green total factor productivity (GTFP). Based on data from 30 Chinese provinces spanning 2012 to 2022, this paper systematically investigates the mechanisms through which the DE influences the GTFP of the construction industry from multiple dimensions, including direct effects, indirect effects, and threshold effects. The findings reveal that the DE significantly promotes the improvement of GTFP in the construction industry. The DE indirectly enhances GTFP through technological innovation and environmental regulation, with the mediating effect of technological innovation being more pronounced. Urbanization exhibits a significant single-threshold effect in moderating the relationship between the DE and GTFP, with the impact of the DE on GTFP following a “U-shaped” trajectory.
Investigating the Structural Framework Beneath Riyadh Region for Potential Geothermal Exploration Utilizing Geological and Geophysical Data
Authors: Alghamdi Abdulrhman H. , Alonaizi Faisal A. , Almalki Majed A.
Published: December 2024

With growing electricity demand and an increasing focus on renewable energy, geothermal energy exploration is gaining prominence in Saudi Arabia. This study investigates the geothermal potential of the Riyadh region by integrating various geophysical data with geological information, providing a detailed image of the structural framework and thermal distribution. We generated a 2D density and susceptibility model using gravity and aeromagnetic data, constrained by well-logs information for sedimentary rocks. Distinct salt layers with low densities (2.15 g/cm3) were introduced in the model overlying the basement rocks, as well as unexposed faults, which may play a crucial role in the thermal regime in the study area. We produced a comprehensive thermal model extracted from the density and susceptibility model, along with temperature measurements from the Minjur Aquifer. The presence of the salt layer not only affects the geothermal gradient, but also suggests the potential for enhanced surface heat flow in specific areas. The results highlight the presence of a promising geothermal reservoir, the Minjur Formation, which exhibits significant potential for geothermal energy extraction due to its porosity, permeability, and sufficient thermal gradient ranging from 80 °C to 120 °C at depths of 2 to 3 km. These findings offer significant implications for Saudi Arabia’s transition to cleaner energy sources and support the future development of geothermal infrastructure in urban areas like Riyadh.
Screening of Suitable Species Combinations for Vegetation Restoration in Ecologically Damaged Areas of Representative Coal Mines on the South Slope of Tianshan Mountain
Authors: Xiao Juncheng , Song Chunwu , Yan Cheng , Jin Zhengzhong , Duo Jia
Published: December 2024

Coal mining has significantly degraded the ecological environment, and the ecosystem structure of the southern slopes of the Tianshan Mountains is complex, with drought, low rainfall, and ecological fragility increasing the difficulty of ecological restoration. There are few studies on the selection of suitable pioneer species for the restoration of mining areas on the south slope of Tianshan Mountain. In this study, we investigated the artificial restoration of vegetation in the damaged areas of nine representative coal mines on the south slope of the Tianshan Mountains. We categorized the coal mines into three classes based on elevation. Using a combination of the entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS), indicators such as the diversity index and vegetation cover were selected to compare the recovery of vegetation in the mining area and screen for suitable pioneer species in ecologically damaged areas. The results show the following: (1) Based on the entropy weight TOPSIS findings, Run Hua coal mine, Da Wanqi coal mine, and Ke Xing coal mine are the highest-scoring coal mines among different types of coal mines. (2) Based on the score results and species rankings, species combinations including Agropyron cristatum, Achnatherum splendens, Medicago sativa Linn, and Halogeton glomeratus are recommended for altitudes below 1500 m. For altitudes between 1500 m and 2000 m, combinations including Festuca elata Keng ex E, Agropyron cristatum, Halogeton glomeratus, and Artemisia sieversiana Ehrhart ex Willd are recommended; similarly, combinations including Agropyron cristatum, Achnatherum splendens, Seriphidium kaschgaricum, and Halogeton glomeratus are recommended for elevations above 2000 m. This study discusses the recovery of artificial vegetation communities within 2 years, with a view to providing a theoretical basis and data support for the recovery of the remaining ecologically damaged areas of the same types.
Subway Multi-Station Coordinated Dynamic Control Method Considering Transfer Inbound Passenger Flow
Authors: Xu Linghui , Lu Jia , Zhang Shuichao , Ren Gang , He Kangkang
Published: December 2024

The prominent contradiction between passenger demand and capacity in rush hours at subway stations causes inconveniences to travel and even leads to safety risks. Existing research on the cooperative control of passenger flow at stations mostly focuses on a single direction, rarely considering transfer passenger flow control. This study formulated a coordinated dynamic control strategy for multiple stations in both directions as a deterministic mathematical programming model to optimise the crowded passenger flow. The optimisation objectives were set as the warning levels of crowded passenger flow and the detention time of all passengers. The constraints included limitations on station service capacity, train capacity, and the number of people boarding trains. Additionally, considering separate control over the transfer inbound passenger flow at transfer stations, an upward- and downward-direction coordinated dynamic control model was constructed. Numerical experiments based on real-world data from the Nanjing Metro Line 1 were conducted to investigate the effectiveness of the proposed cooperative control scheme and evaluate its performance.
LCA and Emergy Approach to Evaluate the Environmental Performance of Plastic Bags from Fossil and Renewable Sources with the Function of Conditioning MSW
Authors: Lacerda Matheus Tavares , Fiatkoski Marcelo Vitor , Maceno Marcell Mariano Corrêa , Agostinho Feni Dalano Roosevelt , Spier Michele Rigon , Kleina Mariana , Marques Marcos Augusto Mendes
Published: December 2024

This study aimed to compare the environmental performance of plastic bags made of three different polymers, considering two product functions: carrying goods and packing municipal solid waste. The three polymers studied were HDPE, LDPE, and thermoplastic starch (TPS). Life cycle assessment and emergy accounting were used to evaluate the environmental performance of each scenario in analysis. To develop this research, eight scenarios were created to represent the customs of use and consumption in the Brazilian population. The LCA results showed that, in general, the scenarios with HDPE plastic bags presented the best environmental performances, while those with TPS presented the worst. The processes that contributed most to these results, representing 70% or more of the environmental impact in each impact category, are related to the use of raw materials, electricity, and water for the manufacture of plastic bags and the treatment in landfills. In other words, the fact that TPS has a mass around six times greater than that of HDPE and two times greater than that of LDPE ends up leaving this type of polymer with the worst environmental performance. In the comparative analysis of scenarios for the same polymer, scenarios that involve the use and reuse of plastic bags present the lowest potential environmental impacts. In contrast, those related to the use and disposal in landfills present the highest possible environmental impacts. The results of emergy accounting showed that the HDPE scenarios had the lowest total emergy flow, ranging from 1.77 × 1013 seJ to 2.40 × 1013 seJ. In contrast, the LDPE scenarios had the highest total emergy flow, ranging from 1.15 × 1014 to 1.21 × 1014 seJ. Although LDPE had the highest total emergy flow values, these results are similar to those obtained by the fossil resource scarcity impact category, which focuses on resource consumption analysis. Thus, through a real approach to the use of plastic bags and solid waste management in the Brazilian context, this study brings essential insights to direct public policies related to the consumption of plastic bags.
Evaluation of the Impact of Plant Protection Products (PPPs) on Non-Target Soil Organisms in the Olive Orchard: Drone (Aerial) Spraying vs. Tractor (Ground) Spraying
Authors: D’Alessandro Aldo , Coletta Martina , Torresi Aurora , Dell’Ambrogio Gilda , Renaud Mathieu , Ferrari Benoît J. D. , La Terza Antonietta
Published: December 2024

Policies aimed at reducing plant protection products (PPPs) are part of the UN’s 2030 Agenda for Sustainable Development. Sustainable management of PPPs is crucial for soil health, biodiversity, and ecosystem services, including food provision. While PPPs can control pests and enhance agricultural yields, they also pose environmental and health risks by contaminating water, soil, and non-target organisms through airborne drift. Investigating innovative and more sustainable distribution methods can support sustainability goals. This study aimed to evaluate the potential impact of the pesticide Spintor® Fly on non-target soil organisms in olive orchards comparing two spraying methods: a traditional Casotti® pump mounted on a tractor and an innovative Unmanned Aerial Vehicle (UAV) developed for the project. The study was conducted in 2021 in an organic olive orchard, which was divided into two plots: a Casotti-treated plot (CAS) and a drone-treated plot (DRO). A strip of uncultivated land at the edge of the orchard was used as a (non-treated) control plot (CAP). The impact on native soil microarthropod communities was assessed using the arthropod-based Soil Biological Quality Index (QBS-ar) and Bait Lamina Test (BLT). Soil samples were collected for earthworm avoidance tests and soil chemical–physical analysis. The results obtained with QBS-ar and BLT indicated no significant differences between DRO and CAS, in both sampling periods (pre- and post-treatment). However, DRO generally exhibited slightly better performance than CAS. The avoidance behaviour was confirmed for both CAS and DRO, although it was lower for the latter. Overall, drone aerial spray performed slightly better, suggesting a potentially lower impact on soil communities. Our results provide initial clues for the sustainable use of drones in agriculture with no increased risks for soil health compared to traditional methods. Further long-term studies should be conducted to validate these findings and possibly confirm the long-term benefits of drone applications compared to traditional methods.
On Nilpotent Elements, Weak Symmetry and Related Properties of Skew Generalized Power Series Rings
Authors: Mazurek Ryszard
Published: December 2024

The skew generalized power series ring R[[S,ω]] is a ring construction involving a ring R, a strictly ordered monoid (S,≤), and a monoid homomorphism ω:S→End(R). The ring R[[S,ω]] is a common generalization of ring extensions such as (skew) polynomial rings, (skew) Laurent polynomial rings, (skew) power series rings, (skew) Laurent series rings, (skew) Mal’cev–Neumann series rings, and (skew) monoid rings. In this paper, we study the nilpotent elements of skew generalized power series rings and the relationships between the properties of the rings R and R[[S,ω]] expressed in terms of annihilators, such as weak symmetry, weak zip, and the nil-Armendariz and McCoy properties. We obtain results on transferring the weak symmetry and weak zip properties between the rings R and R[[S,ω]], as well as sufficient and necessary conditions for a ring R to be (S,ω)-nil-Armendariz or linearly (S,ω)-McCoy.
A Hybrid Food Recommendation System Based on MOEA/D Focusing on the Problem of Food Nutritional Balance and Symmetry
Authors: Huang Shuchang , Wang Cungang , Bian Wei
Published: December 2024

With the improvement of people’s living standards, the issue of dietary health has received extensive attention. In order to simultaneously meet people’s demands for dietary preferences and nutritional balance, we have conducted research on the issue of personalized food recommendations. For this purpose, we have proposed a hybrid food recommendation model, which can provide users with scientific, reasonable, and personalized dietary advice. Firstly, the collaborative filtering (CF) algorithm is adopted to recommend foods to users; then, the improved Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is used to adjust the nutritional balance and symmetry of the recommended foods. In view of the existing problems in the current nutritional balance algorithm, such as slow convergence speed and insufficient local search ability, the autonomous optimization (AO) adjustment strategy, the self-adaptive adjustment strategy, and the two-sided mirror principle to optimize boundary strategy are introduced in the MOEA/D. According to the characteristics of the food nutrition regulation problem, an adaptive food regulation (AFR) adjustment strategy is designed to achieve more accurate nutritional regulation. Based on the above improvements, a food nutritional recommendation algorithm based on MOEA/D (FNR-MOEA/D) is proposed. Experiments show that compared with MOPSO, MOABC, and RVEA, FNR-MOEA/D performs more superiorly in solving the problem of nutritional balance in food recommendation.