bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Application Conditions on the Magnetic Properties of Recycled Polymer-Bonded Magnets Based on Thermoplastics and Ferrite-Based Fillers
Authors: Rösel Uta , Drummer Dietmar
Published: January 2025

Several application possibilities exist in terms of polymer-bonded magnets, such as the sensors or smaller motors, for example, in the window operators of cars. Increasing demand and rising environmental awareness necessitate a higher understanding in terms of the possible recycling strategies for polymer-bonded magnets, especially in the context of a circular economy; post-consumer materials are barely available in terms of polymer-bonded magnets. This paper investigates the focused aging of polymer-bonded magnets based on thermoplastics and ferrite-based fillers via temperature and chemical treatment to reveal the post-industrial products representative of post-consumer products via fast aging. The changes in the dimension and the magnetic properties via these treatments are analyzed. Further, new samples are produced with 50 weight-% of recycled material to investigate the impact of the treatment on the magnetic properties of recycled samples. It was proven that both the dimension and magnetic properties were affected by the treatments. Unexpectedly, the magnetic properties increased with the fast aging based on the temperature treatment due to a so-called “snowball effect”. In addition, only the magnetic properties changed within the recycled material, where they reached about 100 mT and were about the same to 20% less relative to the ideal remanence BR value. With these investigations, the usability of the recycling strategy of reusing shreds in terms of thermoplastic-based polymer-bonded magnets with ferrite-based fillers was proven in terms of the post-industrial products representative of post-consumer products via fast aging.
Antibodies Specific to Rheumatologic and Neurologic Pathologies Found in Patient with Long COVID
Authors: Timofeeva Anna M. , Klyaus Nataliya A. , Sedykh Sergey E. , Nevinsky Georgy A.
Published: January 2025

The SARS-CoV-2 virus can cause hyperstimulation of the immune system, sometimes leading to the production of various autoantibodies and increased levels of interferons and interleukins in blood plasma. Background/Objectives: Only a few studies are currently focusing on the dynamics of immunological indices after any transferred infectious disease encountered by an organism for the first time. The attention of researchers and clinicians is captured by the dynamics of antibody titers and immunologic markers (interferons and interleukins), as well as the correlation of immunologic indices with changes in the symptomatology of long COVID. This paper discusses the association of antibodies against various autoantigens with rheumatological and neurological manifestations of COVID-19. Our study patient was a 36-year-old man diagnosed with polyneuropathy, which developed after COVID-19. We conducted a dynamic follow-up of the patient for two years. Methods: The blood plasma samples collected were analyzed by ELISA for different autoantigens, IFN-γ, and a variety of interleukins. Results: An association between rheumatologic and neurologic markers in patients with long COVID symptoms was considered. Antibody titers for myelin basic protein (MBP), double-stranded DNA (dsDNA), single-stranded DNA (dsDNA), and IFN-γ, IL-1, IL-6, and IL-10 levels significantly increased during the posthospital period when the patient reported persistent symptoms of long COVID, with complaints decreasing after the symptoms were resolved. Conclusions: The findings of this study shed light on the dynamic alterations of immunological factors, and elucidate the mechanism by which SARS-CoV-2 infection disrupts immunotolerance and eventually restores equilibrium, leading to the rheumatological pathology. Significantly, the notable rise in antibody titers for various autoantigens was transient and did not lead to the progression of autoimmune pathology.
Combining Passive Infrared and Microwave Satellite Observations to Investigate Cloud Microphysical Properties: A Review
Authors: Viggiano Mariassunta , Cimini Domenico , De Natale Maria Pia , Di Paola Francesco , Gallucci Donatello , Larosa Salvatore , Marro Davide , Nilo Saverio Teodosio , Romano Filomena
Published: January 2025

Clouds play a key role in the Earth’s radiation budget, weather, and hydrological cycle, as well as the radiative and thermodynamic components of the climate system. Spaceborne observations are an essential tool to detect clouds, study cloud–radiation interactions, and explore their microphysical properties. Recent advancements in spatial, spectral, and temporal resolutions of satellite-borne measurements and the increasing variety of orbits and observing geometries offer the opportunity for more efficient and sophisticated retrieval procedures, leading to the more accurate estimation of cloud parameters. However, despite the availability of near-coincident observations of the same atmospheric state, the synergy between the whole set of acquired information is still largely underexplored. The use of synergy is often invoked to optimize the exploitation of the available information, but it is rarely implemented. Indeed, the strategy currently used in most cases is that retrievals are performed separately for each instrument and, only later, the retrieved products are combined. In this framework, therefore, there is a strong need to study and exploit the synergy potential of the instruments currently in orbit or that will be put in orbit in the next few years. This paper reviews the efforts already made in this direction, combining passive infrared and microwave to retrieve cloud microphysical properties. We provide readers with a framework to interpret the state of the art, highlighting the pros and cons of the various approaches currently used with a look to the most promising methodologies to be deployed to address the challenges of this field.
Novel Insights into Surface Energies and Enhanced Gas-Sensing Capabilities of ZnGa2O4(111) via Ab Initio Studies
Authors: Yu Cheng-Lung , Lin Yan-Cheng , Jhang Sheng-Yuan , Fu Jine-Du , Chen Yi-Chen , Liu Po-Liang
Published: January 2025

This study investigates the surface energies and work function changes in ZnGa2O4(111) surfaces with different atomic terminations using ab initio density functional theory. It explores the interactions of gas molecules such as NO, NO2, and CH3COCH3 with Ga-terminated, O-terminated, and Ga-Zn-O-terminated surfaces. This study reveals previously unreported insights into how O-terminated surfaces exhibit enhanced reactivity with NO, resulting in significant work function changes of +6.42 eV. In contrast, Ga-terminated surfaces demonstrate novel interactions with oxidizing gases, particularly NO2, with a notable reduction in work function change of −1.63 eV, offering potential gas sensor technology advancements. Particularly notable is the Ga-Zn-O-terminated surface, which presents mixed characteristics influenced by the interplay of oxygen and metallic elements (gallium and zinc), leading to substantial work function changes of +4.97 eV for NO and +1.82 eV for NO2, thereby significantly enhancing sensitivity. This study unveils the previously unexplored roles of Ga-Zn-O-terminated ZnGa2O4 surfaces in optimizing semiconductor-based gas sensors, offering both oxidative and reductive potentials and making them versatile for diverse applications.
A Deformation Reconstruction Strategy for Integrated Truss Structures Subjected to Thermal–Mechanical Load
Authors: Yu Zexing , Ma Xiaofei , Zhu Jialong , Zhang Dayu , Xue Yonggang , Huang Pengfei , Li Yichen , Li Hao
Published: January 2025

The deformation monitoring of integrated truss structures (ITSs) is essential for ensuring the reliable performance of mounted equipment in complex space environments. Reconstruction methods based on local strain information have been proven effective, yet the identification faces significant challenges due to variable thermal–mechanical loads, interactions among structural components, and special boundary conditions. This paper proposes a deformation reconstruction strategy tailored for ITSs under combined thermal–mechanical load scenarios wherein deformations of both the primary truss structures and the attached panel systems are investigated. The proposed approach utilizes Ko displacement theory as the core algorithm, while the least squares optimization method is employed to determine the integration with unknown initial values during the reconstruction process. Validation is conducted through diverse load scenarios, and the reconstruction results are evaluated using errors based on the root mean square. The result demonstrates that the proposed method can reconstruct deformations of truss structures under both mechanical and thermal loads. Furthermore, the optimization-based approach achieves accurate reconstructed results in the case of panels with two-point fixed boundary conditions. This study provides an effective strategy for in-orbit deformation reconstruction, addressing challenges posed by complex loads and structural configurations.
Assessment of Green Extraction Techniques in the Utilization of Oak Tree (Quercus robur) and Mugwort (Artemisia vulgaris) Biomass for the Production of Bioactive Extracts
Authors: Villanueva-Bermejo David , Hernández Diego Martín , Hernández Elvis Judith , Santoyo Susana , Fornari Tiziana
Published: January 2025

The aim of this study was to investigate the suitability of Pressurized Liquid Extraction (PLE) and Supercritical Fluid Extraction (SFE) for Quercus robur bark and Artemisia vulgaris extraction. PLE of Q. robur and A. vulgaris were carried out at different temperatures with water, ethanol and several hydroalcoholic mixtures. SFE of A. vulgaris was performed at different pressures and ethanol concentrations. The anti-inflammatory activity of Q. robur extracts, the antibacterial activity of A. vulgaris extracts and the antioxidant activity of the extracts from both materials were determined. The highest phenolic compound content and antioxidant activity of Q. robur extracts were achieved with water at 100 °C (487.97 mg GAE/g and 3741 µg trolox/g). The highest values for A. vulgaris extracts were obtained with ethanol at 200 °C (149.16 mg/g and 437.13 µmol/g). The ethanolic extract at 150 °C from A. vulgaris had a noticeable anti-inflammatory activity (inhibition of TNF-α and IL-6 secretion near basal values and inhibition of IL-1β higher than 80% at 20 µg/mL). A. vulgaris extracts obtained by SFE exerted antibacterial activity against E. coli (IC50 of 1388 µg/mL with neat SCCO2 at 15 MPa) and S. aureus (1406 µg/mL using SCCO2 with 10% ethanol).
Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks?
Authors: Khan Jowaria , Elfakharany Rana , Saleem Hiba , Pathan Mahira , Shahzad Emaan , Dhou Salam , Aloul Fadi
Published: January 2025

Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in the cybersecurity field. System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. This paper proposes a machine learning approach to identify and classify multi-step network intrusion attacks, with particular relevance to smart cities, where interconnected systems are highly vulnerable to cyber threats. Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). These models are trained on the Multi-Step Cyber-Attack Dataset (MSCAD), a recent dataset that is highly representative of real-world multi-step cyberattack scenarios, which increases the accuracy and efficiency of such systems. The experimental results show that the best performing model was XGB, which achieved a testing accuracy of 100% and an F1 Score of 88%. The proposed model is computationally efficient and easy to deploy, which ensures a fast, sustainable and low power-consuming intrusion detection system at the cutting edge.
Formation Mechanism of Traditional Chinese Health Food Purchase Intention Among Young People
Authors: Jin Rong , Liu Xuehua , Zhu Yong , Yang Xiao
Published: January 2025

The traditional Chinese health food consumption market in China has experienced significant growth in recent years, with rising popularity among young people. However, how young people form their intentions to purchase traditional Chinese health food is still unclear. This study aims to redefine the concept of traditional Chinese health food and provide a theoretical foundation for understanding consumer behavior within this context. A specialized survey was conducted to test the proposed conceptual model. The results indicate that both health anxiety and traditional cultural identity have a significantly positive influence on young people’s purchase intention for traditional Chinese health food. Perceived consumption risk negatively affects their purchase intention for such products. Perceived consumption risk plays a negative moderating role in the relationship between traditional cultural identity and young people’s purchase intention for traditional Chinese health food. This study offers novel insights and strategic directions for traditional Chinese health food industry professionals, while also serving as an illustrative case for other countries seeking to comprehend the health food consumption landscape.
Sustainable Industrial Energy Supply Systems with Integrated Renewable Energy, CCUS, and Energy Storage: A Comprehensive Evaluation
Authors: Yang Liujian , Wu Xingyu , Huang Beijia , Li Zeqiu
Published: January 2025

With the increasing emphasis on emission reduction targets, the low-carbon sustainable transformation of industrial energy supply systems is crucial. Addressing the urgent issue of reducing industrial carbon emissions, this study presents an integrated industrial energy supply system (IRE-CCUS-BESS-SPS) that incorporates renewable energy; calcium-based carbon capture, utilization, and storage (CCUS); and battery energy storage systems (BESSs) to improve energy efficiency and sustainability. The system model is designed to achieve a cost-effective and environmentally low-impact energy supply, validated through Aspen Plus V11.0 and Matlab R2019b simulations. The system’s performance is evaluated using a 4E index system encompassing economy, environment, energy, and exergy. The findings indicate that the system’s lifetime net present value (NPV) is positive, with a payback period of 6.09 years. Despite a 12.9% increase in the overall economic cost, carbon emissions are significantly reduced by 59.78%. The energy supply composition includes 48.60% from fuel oil and 22.10% from biomass, with an additional 270.04 kW of heat provided by waste heat boilers. The equalization costs for CO2 removal (LCCR) and methanation (LCOM) are 122.95 CNY/t and 10908.35 CNY/t, respectively, both exceeding current carbon emission trading costs and methane prices. This research offers a robust framework for designing sustainable industrial energy systems that integrate renewable energy, CCUS, and energy storage technologies for low-carbon operations. The analysis also suggests that government policies, such as direct financial subsidies or tax relief, are effective in accelerating the adoption of CCUS technology.
Historical Analysis of Real Energy Consumption and Indoor Conditions in Single-Family Passive Building
Authors: Firląg Szymon , Baig Abdullah Sikander , Koc Dariusz
Published: January 2025

The paper includes a historical analysis of real energy consumption and indoor conditions in a single-family passive building located in Warsaw, Poland. Passive houses have emerged as a sustainable alternative to the conventional construction of houses, having advantages such as low energy consumption, comfortable indoor temperatures, an environmentally friendly nature, and low carbon emissions. This research consists of indoor temperature assessments over a 5-year period (2018–2022) which include comfort assessments made in accordance with the standard EN 16798-1 and precise assessments made for extreme weather events over a two-week critical period including the heating and cooling seasons. The real energy consumption analysis, including electric heating, outdoor lighting, indoor lighting, ventilation, and domestic hot water, was compared against passive house and nearly-zero energy standards. The results of the study show that the building is thermally comfortable to live in, as it remained mainly in the first comfort category, IEQ I. There was no such issue as overheating and underheating even during extreme weather events. The energy need for heating remained very close to the passive standard, namely 15 kWh/(m2·year). The total primary energy consumption for heating, hot water, and electricity meets the standard required value of 120 kWh/(m2·year). These findings demonstrate the effectiveness of passive house design principles at achieving high levels of thermal comfort and energy efficiency in cold climates. In addition, it is demonstrated that it is possible to maintain comfortable indoor temperatures (even with outdoor air temperatures reaching 35 °C) without air conditioning or cooling systems. The integration of a photovoltaic system offers a viable pathway toward transforming the building into a zero-energy standard, contributing to sustainability goals and reducing carbon emissions.
Research on Impact of Digital Economy on Real Economy Based on Perspective of Coupling and Coordination of Manufacturing and Service Industries
Authors: He Fangli , Qin Hongzhen
Published: January 2025

Amid the global wave of digital transformation, advancing the sustainable growth of the real economy has emerged as a key strategic priority. Drawing on panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) between 2013 and 2021, this study utilizes fixed effects and mediation effect models to investigate both the direct and indirect pathways through which the digital economy drives the sustainable development of the real economy. The results indicate that (1) the digital economy exerts a significant direct positive influence on the real economy, demonstrating its role in spurring growth and innovation while injecting fresh momentum into sustainable development. (2) It also indirectly facilitates the real economy’s sustainability by promoting the coupling and coordination of the manufacturing and service sectors, emphasizing the importance of industrial synergy in achieving sustainable economic growth. (3) Regional analysis reveals that the digital economy’s positive direct effect on the real economy is particularly evident in North China and the Southeast and Southwest regions. Furthermore, in the Southeast and Southwest, the mediation effect of industrial coupling and coordination further strengthens the sustainability of the real economy. This study offers theoretical insights into the integration of the manufacturing and service industries and provides practical guidance for advancing the United Nations’ 2030 Agenda for Sustainable Development. It also highlights policy recommendations for China to build a modern industrial system and achieve high-quality economic growth.
A Sustainability-Oriented Spatial Multi-Criteria Decision Analysis Framework for Optimizing Recreational Ecological Park Development
Authors: Heydari Reza , Fathololoumi Solmaz , Soltanbeygi Mohammad , Firozjaei Mohammad Karimi
Published: January 2025

Recreational Ecological Parks (REPs) play a pivotal role in advancing sustainable development by promoting ecotourism, conserving biodiversity, and providing inclusive recreational opportunities with minimal environmental impact. Assessing the potential for REP development is crucial to optimize the sustainable use of natural resources and enhance ecological and socio-economic benefits. This study introduces a sustainability-oriented Spatial Multi-Criteria Decision Analysis (SMCDA) framework to evaluate and optimize REP development in Mazandaran Province, Iran. The framework incorporates 33 criteria across five categories: 13 for network connectivity and accessibility; 10 for facilities, services, and tourism potential; 4 for landform and land use; 3 for natural hazards; and 3 for climate conditions. Criteria were standardized using the minimum–maximum method and weighted based on expert input via the Best–Worst Method. A weighted linear combination approach was then applied to generate REP suitability maps. Existing recreational and tourism (R&T) sites were assessed against these maps, and a non-parametric bootstrapping method quantified uncertainties in suitability classifications. The results revealed that approximately 8%, 17%, 26%, 30%, and 19% of the study area fell into very low, low, moderate, high, and very high suitability classes, respectively. Among 151 R&T sites, 33% and 34% were situated in areas classified as very high and high suitability. Prediction rates were most accurate in the very high suitability category, while uncertainty analysis indicated a mean of 13% and a standard deviation of 1.7%, with uncertainties predominantly concentrated in lower suitability classes. The findings underscore the SMCDA framework’s efficacy in guiding sustainable REP development by identifying optimal sites and managing uncertainties. This study contributes to sustainability by integrating ecological, economic, and social dimensions into decision-making processes, thereby fostering resilience and long-term environmental stewardship in tourism planning.
Establishment and Application of Biomass Model for Vegetation Condition Assessment After Ecological Restoration—Yixing Quarry Case Study
Authors: Huang Chaokui , Wu Yueping , Yang Shaohui , Zhang Faming , Li Xiaokai , Zhang Huaqing , Zhang Xiaolong
Published: January 2025

Biomass is a vital index used to evaluate the vegetation rebuilding effect of mining slopes after restoration. It is essential to establish models for estimating the biomass and carbon storage of the vegetation community on mining slopes. Therefore, this paper establishes models for the biomass and carbon storage of such vegetation, taking an abandoned quarry after ecological restoration in Yixing City, Jiangsu Province, as the research object. Firstly, the variables of the biomass estimation models were determined based on the correlation analysis results; the vegetation biomass model was comprehensively selected, and the accuracy of the optimal models was verified. Meanwhile, the carbon storage calculation model was established in combination with the carbon content and the growth pattern of vegetation. The results showed that (1) the optimal models were the cubic and linear functions, respectively, for the shrubs and herbs, while the relevant variables of the shrub and the herb plants were the average height multiplied by the diameter of each shrub plant (DH) and the average height multiplied by the coverage rate (CH), respectively, with the verification results of R2 > 0.814, RS > 2.8%, and RMA > 6%; and (2) in the restored mining slopes, the vegetation biomass was 120.264 t, including 10.586 t of herbs and 109.678 t of shrubs, and the vegetation carbon storage was 50.585 t, including 3.705 t of herbs and 46.880 t of shrubs. The proposed models have good prediction accuracy and reliability after quantitative evaluation and can be applied to the biomass estimation and carbon storage calculation of restored mining slopes, providing a reference for the environmental sustainability of post-mining areas and other ecologically restored slopes.
Change of NDVI in the Upper Reaches of the Yangtze River and Its Influence on the Water–Sand Process in the Three Gorges Reservoir
Authors: Ma Yiming , Li Mingyue , Yao Huaming , Chen Peng , Pan Hongzhong
Published: January 2025

Vegetation coverage in the upper reaches of the Yangtze River is very important to the ecological balance in this area, and it also has an impact on the inflow runoff and sediment transport processes of the Three Gorges Reservoir. Based on the normalized vegetation index data (NDVI) with 250 m resolution in the upper reaches of the Yangtze River, annual runoff, sediment transport, land use, meteorology, and other data—and by using the methods of Sen + Mann–Kendall trend analysis, partial correlation analysis, and Hurst index—this paper analyzes the temporal and spatial variation characteristics, driving factors, and the influence on the water and sediment inflow processes of the Three Gorges Reservoir in each sub-basin in the upper reaches of the Yangtze River. The results show that (1) NDVI in the upper Yangtze River showed a fluctuating upward trend from 2001 to 2022, and the overall vegetation cover continued to increase, showing a spatial pattern of low in the west and high in the east. At the same time, the runoff volume of the upper reaches of the Yangtze River did not show a significant upward trend from 2006 to 2022, while the sand transport decreased significantly; (2) Among the NDVI-influencing factors in the upper reaches of the Yangtze River, the area driven by the land use factor accounts for about 43% of the whole study area, followed by precipitation; (3) Precipitation significantly affected runoff, and NDVI was negatively correlated with sand transport in most of the watersheds, suggesting that improved vegetation could help reduce sediment loss. In addition, the future trend of vegetation change was predicted to be dominated by improvement (Hurst > 0.5) based on the Hurst index, which will provide a reference for the NDVI change in the upper Yangtze River and the prediction of sediment inflow to the Three Gorges Reservoir.
A Study of Fish Community at the Obedska Bara Ramsar Site and Pathways to Sustainable Management
Authors: Nedić Zlatko , Simonović Predrag , Đikanović Vesna , Nicolae Raluca , Jurlina Dubravka Škraba , Nikolić Vera
Published: January 2025

The study was conducted on the Obedska Bara Ramsar site at two localities, Krstonošića okno and canal Vok, to provide insight into fish community status by analysing fish biomass, biodiversity, and changes after restoration measures. A total of 685 fish samples were examined across four periods, from 2011 to 2023. Biodiversity index, relative biomass, and non-native species were analysed to evaluate the restoration measures’ influence. All parameters showed a negative trend until the restoration works were implemented, after which they increased. This study also provides recommendations to improve the site considering the Mission Restore our Ocean and Waters by 2030.
An Evaluation and Correlation Analysis of Regional Development Under the Background of Chinese-Style Modernization
Authors: Wang Kaile , Chen Yunwei
Published: January 2025

Regional development represents a pivotal component in advancing the Chinese path to modernization. The pace of modernization is intrinsically linked to the effectiveness of regional development strategies. In this context, the innovative evaluation of regional development assumes critical importance. Understanding the status and dynamics of regional development offers valuable insights into the progress of Chinese-style modernization. This study, grounded in a scientific interpretation of the concept and distinctive characteristics of Chinese-style modernization, proposes an enhanced regional development evaluation indicator system within this conceptual framework. By employing dynamic comprehensive evaluation and gravitational models, this study examines the regional modernization process in China from 2012 to 2021, exploring development patterns and inter-regional relationships. The findings reveal that regional modernization has evolved into four distinct types of agglomerated areas, with a strong correlation observed between regional development patterns and geographic location. Based on these findings, targeted recommendations for optimizing regional development are provided.
Navigation Path Prediction for Farmland Road Intersections Based on Improved Context Guided Network
Authors: Li Xuyan , Wu Zhibo
Published: January 2025

Agricultural navigation, as an essential part of smart agriculture, is a crucial step in realizing intelligence and, compared with the structured features of urban roads, such as lane-keeping lines, traffic guidance lines, etc., the field environment is more complex. Especially at agricultural intersections, traditional navigation line extraction algorithms make it difficult to achieve the automatic prediction of multiple road navigation lines due to complex unstructured features such as weeds and trees. Therefore, this study proposed a field road navigation line prediction method based on an improved context guided network (CGNet), which can quickly, stably, and accurately detect intersection fields and promptly predict navigation lines for two different directional paths at intersections. Firstly, CGNet will be used to learn the local features of intersections and the joint features of video frames before and after the surrounding environment. Then, the CGNet with a self-attention block module is proposed by adding the self-attention mechanism to improve the semantic segmentation accuracy of CGNet in field road scenes, and the detection speed is not significantly reduced. The semantic segmentation accuracy mIoU is 0.89, and the processing speed is 104 FPS. Subsequently, a field road centerline extraction algorithm is proposed based on the partitioning idea, which can accurately obtain the centerlines of road intersections in the image. The average lateral deviation of each centerline is less than 4%. This study achieved the prediction of intersection navigation lines in mountainous field road scenes, which can provide technical support for field operation road planning of agricultural equipment such as plant protection and harvesting. At the same time, the research findings provide theoretical references for sustainable agricultural development.
Examining the Impact of Electric Bike-Sharing on For-Hire Vehicles in Medium-Sized Cities: An Empirical Study in Yancheng, China
Authors: Liu Xize , Hua Mingzhuang , Chen Xuewu , Chen Jingxu
Published: January 2025

Enabled by recent technological advances and the substantial growth of the sharing economy, electric bike-sharing (EBS) has experienced rapid growth in medium-sized Chinese cities, yet its impact on for-hire vehicle (FHV) services remains insufficiently studied. Using a six-month longitudinal dataset from Yancheng, a representative medium-sized city in China, we employ an instrumental variable method to address potential endogeneity and provide quantitative empirical analysis. The analysis identifies a significant substitution effect, where a 1% increase in EBS trips corresponds to a 0.810% decline in FHV ridership. Through heterogeneity analyses, this study reveals that the substitutive effect of EBS is stronger in central downtown, which has denser infrastructure, while its impact diminishes in peripheral districts. Furthermore, unfavorable weather conditions mitigate the substitutive effect, as users increasingly rely on FHVs for their reliability and comfort during unfavorable conditions. The findings of this study highlight the necessity of integrating EBS into the electrified shared mobility ecosystem in a balanced manner to prevent disruptions to the existing transportation network and provide valuable guidance for sustainable and stable transportation planning in medium-sized cities and similar urban contexts.
Circular Economy Solutions: The Role of Thermoplastic Waste in Material Innovation
Authors: Ochigue Princess Claire D. , Aguilos Maricar A. , Lubguban Arnold A. , Bacosa Hernando P.
Published: January 2025

Plastics play an indispensable role in modern society, yet their long-term durability poses severe environmental challenges, with mismanaged waste polluting ecosystems worldwide. The transition to a circular economy emphasizes the importance of recycling and resource recovery to mitigate these impacts. While conventional disposal methods like mechanical and chemical recycling or incineration face limitations such as quality degradation, high costs, or pollutant emissions, value-added approaches present an innovative solution. This review explores the potential of integrating recycled plastic waste into composite materials to enhance performance and sustainability. Focusing on diverse strategies, the paper highlights the use of recycled plastics in combination with fibers, wood, metal, concrete, glass, rubber, textiles, and foam. These composites demonstrate superior mechanical, thermal, and chemical properties, enabling applications across industries like construction, automotive, aerospace, and furniture. Furthermore, various roles of plastic waste—such as filler, reinforcement, matrix, or additive—are analyzed to showcase advancements in material innovation. By presenting methodologies and outcomes from recent research, this paper underscores the potential of recycled plastics in creating high-performance materials, supporting sustainable development and circular economic goals.
Research on Government Regulation, Agricultural Socialization Service and Green Treatment Behavior of Mushroom Residue by Mushroom Farmers—Based on Research Data from Gutian County, Fujian Province, China
Authors: Su Baocai , Zhao Zhenwen , Fan Shuisheng
Published: January 2025

Agricultural waste treatment in the post-production stage is a crucial component of agricultural green development. To achieve ecological revitalization, it is essential to address the pollution constraints posed by agricultural waste. This study is grounded in the survey data collected from 284 mushroom farmers in Gutian County, Fujian Province. Given that the explanatory variable pertains to the number of green treatment behavior adoptions by mushroom farmers regarding mushroom residue, which falls under the category of ordered discrete variables and exhibits an evident recurrence relationship, we opted to analyze the impacts of government regulation, agricultural socialized services, and their interaction terms on the green treatment behavior of mushroom farmers’ mushroom residue through the Ordered Probit model. This approach enabled us to uncover how the differentiation among mushroom farmers influences their green treatment behavior. The study yielded several significant findings. Firstly, both government regulation and agricultural socialized services can effectively drive mushroom farmers to adopt green treatment behaviors for mushroom residue. Secondly, there is an interactive effect between government regulation and agricultural socialized services in relation to the green treatment behavior of mushroom farmers’ residue, indicating a certain degree of complementarity between the two. Thirdly, the differentiation among mushroom farmers has a pronounced impact on the green treatment behavior of mushroom residue. Notably, there are distinct differences in the green treatment behavior of mushroom farmers with varying education levels and planting scales. Moreover, as the education level and planting scale increase, the influence of government regulation and agricultural socialized services on the behavior of mushroom farmers tends to strengthen. Consequently, in the process of promoting the green treatment behavior of mushroom farmers’ mushroom residue, we should maximize the utilization of the policy constraints and guiding measures of government regulation to enhance the normative role of mushroom farmers’ behavior. Simultaneously, we need to fully exploit the recycling and transportation support functions of agricultural socialized services. By grasping the complementarity between government regulation and agricultural socialized services in terms of both normative behavior and solution measures, we can effectively ensure the practical feasibility of the green treatment behavior of mushroom residue.
The Environmental and Economic Dynamics of Food Waste and Greenhouse Gas Emissions: A Causal Time Series Analysis from 2000 to 2022
Authors: Yılmaz Salim , Günal Ahmet Murat , Köse Gizem , Baş Murat
Published: January 2025

Food loss and waste pose significant social, economic, and environmental challenges worldwide, threatening food security and hindering sustainable development. While developing countries primarily face losses during production and storage, developed nations struggle with waste driven by consumer habits, spoilage, and overstocking, particularly in markets, restaurants, and homes. This study was aimed to analyze the complex relationships between food loss, waste, and various economic and environmental variables. The study examined the effects of variables such as education expenditures, food security, food prices, greenhouse gas emissions, and carbon emissions per capita on food losses and waste. These analyses shed light on the development of sustainable food policies at both national and global levels. Interventions to reduce food loss and waste will not only optimize food production and consumption processes but will also support a sustainable management of resources. As a result, this study aimed to understand the long-term effects of food loss and waste on economic growth, environmental sustainability, and social welfare. The findings of the study are of great importance in terms of directing future policies and aim to be an important guide in the transition to sustainable food systems.
Does Digital Transformation Affect Outward Foreign Direct Investment Performance? Evidence from China
Authors: Wu Si , Liu Xiaolong , Xiang Yuchen , Liu Zaiqi , Fan Minhao
Published: January 2025

Digital transformation has become a crucial strategic decision for enterprises to strengthen international competitiveness and achieve sustainable development. This study aims to investigate the impact of digital transformation on outward foreign direct investment (OFDI) performance and the conditions that influence this relationship using the ordinary least-squares regression estimation method and the data of Chinese A-share listed enterprises. The results show that digital transformation improves OFDI performance. The mechanism analysis verifies that digital transformation enhances OFDI performance by promoting corporate reputation and innovation. The moderating analysis demonstrates that the host country’s digital infrastructure negatively moderates the positive relationship between digital transformation and OFDI performance, while diplomatic relations between home and host countries play a positive moderating role. The heterogeneity analysis reveals that state-owned, labor-intensive, and technology-intensive enterprises and enterprises investing in non-Belt-and-Road countries benefit more from digital transformation to promote OFDI performance. This study extends the OFDI theory of emerging market enterprises in the context of digital transformation and provide practical implications for improving the OFDI performance of multinational enterprises.
Quantum-Chemical Investigations on the Structure and Stability of Mixed Trimers Containing HC3N in Combination with H2C2 and/or HCN Analyzed by QTAIM, NBO and SAPT Methods
Authors: Pietropolli Charmet Andrea , Stoppa Paolo , De Lorenzi Alessandra , Canton Patrizia
Published: January 2025

The present work deals with the computational study of HC3N··HCN··H2C2-, (HC3N)2··H2C2-, and HC3N··(H2C2)2-mixed trimers. The different equilibrium structures of the different low-lying minima on the corresponding potential energy surface (PES) were accurately determined, and the relative stabilities were computed by extrapolation procedures to the complete basis set limit. For each mixed trimer, the non-covalent interactions ruling the structure of the most stable isomer were analyzed using the QTAIM (Quantum Theory of Atoms in Molecules) approach. Additional insights into these interactions were provided by the Natural Bond Orbital (NBO) and Symmetry-Adapted Perturbation Theory (SAPT) methods. These results can be used to assist further theoretical investigations and experimental studies on the formation of larger molecules potentially relevant in astrochemistry.
Mathematical Models for Coverage with Star Tree Backbone Topology for 5G Millimeter Waves Networks
Authors: Cordero Sergio , Adasme Pablo , Dehghan Firoozabadi Ali , Rosa Renata Lopes , Rodríguez Demóstenes Zegarra
Published: January 2025

This paper proposes mathematical optimization models for solving the network planning problem using millimeter wave technology for 5G wireless communications networks. To this end, it is assumed that a set of users, M={1,…,m}, and a set of base stations, N={1,…,n}, are deployed randomly in a square area. In particular, the base stations should be connected, forming a star backbone so that users can connect to their nearest active base stations forming the backbone where the connections are symmetric. In particular, the first two models maximize the number of users connected to the backbone and minimize the distance costs of connecting users to the base stations, and distances of connecting the base stations themselves. Similarly, the last two models maximize and minimize the same objectives and the number of base stations to be activated to form the star backbone. Each user is allowed to connect to a unique active base station. In general, the millimeter wave technology presents a high path loss. Consequently, the transmission distances should be no larger than 300 m at most for different radial transmissions. Thus, a direct line of sight between users and base stations is assumed. Finally, we propose local search-based algorithms that allow finding near-optimal solutions for all our tested instances. Our numerical results indicate that we can solve network instances optimally with up to k=100, n=200, and m=5000 users.
On Fractional Discrete Memristive Model with Incommensurate Orders: Symmetry, Asymmetry, Hidden Chaos and Control Approaches
Authors: Al-Taani Hussein , Abu Hammad Ma’mon , Abudayah Mohammad , Diabi Louiza , Ouannas Adel
Published: January 2025

Memristives provide a high degree of non-linearity to the model. This property has led to many studies focusing on developing memristive models to provide more non-linearity. This article studies a novel fractional discrete memristive system with incommensurate orders using ϑi-th Caputo-like operator. Bifurcation, phase portraits and the computation of the maximum Lyapunov Exponent (LEmax) are used to demonstrate their impact on the system’s dynamics. Furthermore, we employ the sample entropy approach (SampEn), C0 complexity and the 0-1 test to quantify complexity and validate chaos in the incommensurate system. Studies indicate that the discrete memristive system with incommensurate fractional orders manifests diverse dynamical behaviors, including hidden chaos, symmetry, and asymmetry attractors, which are influenced by the incommensurate derivative values. Moreover, a 2D non-linear controller is presented to stabilize and synchronize the novel system. The work results are provided by numerical simulation obtained using MATLAB R2024a codes.
A Robust Large-Scale Multi-Criteria Decision Algorithm for Financial Risk Management with Interval-Valued Picture Fuzzy Information
Authors: Shang Na , Wang Hongfei , Fan Jie
Published: January 2025

Financial Risk Management (FRM) is crucial for organizations navigating complex and volatile economic conditions, as it aids in identifying and mitigating potential losses. Conventional FRM approaches are inadequate because they do not incorporate vagueness and variability in financial data. To overcome these challenges, this research presents interval-valued picture fuzzy measurement alternatives and rankings according to the Compromise Solution (IVPF-MARCOS) method. The IVPF-MARCOS method ranks investment strategies under uncertainty by assessing ten distinct investment options across seven key factors, including market risk and return on investment. It evidences its usefulness in enhancing decision-making, increasing accuracy in FRM, and developing Multi-Criteria Group Decision-Making (MCGDM) methodologies involving aggregation operators that are symmetric in nature. Consequently, this research establishes a compelling need to adopt improved fuzzy techniques in formulating the FRM to achieve more robust financial strategies.
Towards Smart and Resilient City Networks: Assessing the Network Structure and Resilience in Chengdu–Chongqing Smart Urban Agglomeration
Authors: Li Rui , Wang Yuhang , Zhang Zhiyue , Lu Yi
Published: January 2025

The mobility and openness of smart cities characterize them as particularly complex networks, necessitating the resilience enhancement of smart city regions from a network structure perspective. Taking the Chengdu–Chongqing urban agglomeration as a case study, this research constructs economic, information, population, and technological intercity networks based on the complex network theory and gravity model to evaluate their spatial structure and resilience over five years. The main conclusions are as follows: (1) subnetworks exhibit a ‘core/periphery’ structure with a significant evolution trend, particularly the metropolitan area integration degree of capital cities has significantly improved; (2) the technology network is the most resilient but was the most affected by COVID-19, while the population and information networks are the least resilient, resulting from poor hierarchy, disassortativity, and agglomeration; (3) network resilience can be improved through system optimization and node enhancement. System optimization should focus more on improving the coordinated development of population, information, and technology networks due to their low synergistic level of resilience, while node optimization should adjust strategies according to the dominance, redundancy, and network role of nodes. This study provides a reference framework to assess the resilience of smart cities, and the assessment results and enhancement strategies can provide valuable regional planning information for resilience building in smart city regions.