bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of Drone and Satellite Imagery Improves Agricultural Management Agility
Authors: Ogungbuyi Michael Gbenga , Mohammed Caroline , Fischer Andrew M. , Turner Darren , Whitehead Jason , Harrison Matthew Tom
Published: December 2024

Effective agricultural management hinges upon timely decision-making. Here, we evaluated whether drone and satellite imagery could improve real-time and remote monitoring of pasture management. Using unmanned aerial systems (UAS), we quantified grassland biomass through changes in sward height pre- and post-grazing by sheep. As optical spectral data from Sentinel-2 satellite imagery is often hindered by cloud contamination, we assessed whether machine learning could help improve the accuracy of pasture biomass prognostics. The calibration of UAS biomass using field measurements from sward height change through 3D photogrammetry resulted in an improved regression (R2 = 0.75, RMSE = 1240 kg DM/ha, and MAE = 980 kg DM/ha) compared with using the same field measurements with random forest-machine learning and Sentinel-2 imagery (R2 = 0.56, RMSE = 2140 kg DM/ha, and MAE = 1585 kg DM/ha). The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. When UAS data were integrated with the Sentinel-2-random forest model, SEM reduced from 1642 kg DM/ha to 1473 kg DM/ha, demonstrating that integration of UAS data improved model accuracy. We show that modelled biomass from 3D photogrammetry has significantly higher accuracy than that predicted from Sentinel-2 imagery with random forest modelling (S2-RF). Our study demonstrates that timely, accurate quantification of pasture biomass is conducive to improved decision-making agility, and that coupling of UAS with satellite imagery may improve the accuracy and timeliness of agricultural biomass prognostics.
Detection of the Contribution of Vegetation Change to Global Net Primary Productivity: A Satellite Perspective
Authors: Hu Xiaoqing , Feng Huihui , Tang Yingying , Wang Shu , Wang Shihan , Wang Wei , Huang Jixian
Published: December 2024

Exploring NPP changes and their corresponding drivers is significant for the achievement of sustainable ecosystem management and in addressing climate change. This study aimed to explore the spatiotemporal variation in NPP and analyze the effects of vegetation and climate change on the global NPP from 2003 to 2020. Methodologically, the Theil–Sen and Mann–Kendall methods were used to study the spatiotemporal characteristics of global NPP change. Moreover, a ridge regression model was built by selecting the vegetation indicators of the leaf area index (LAI) and fraction vegetation coverage (FVC) and the climate factors of CO2, shortwave downward solar radiation (Rsd), precipitation (P), and temperature (T). Then, the relative contributions of each factor were evaluated. The results showed that, over the previous two decades, the global mean NPP reached 503.43 g C m−2 yr−1, with a fluctuating upward trend of 1.52 g C m−2 yr−1. The regions with a significant increase in NPP (9.22 g C m−2 yr−1) were mainly located in Central Africa, while the regions with decreasing NPP (−3.21 g C m−2 yr−1) were primarily in the Amazon Rainforest in northern South America. Additionally, CO2, the LAI, and the FVC exhibited positive contributions to the NPP trend, with the predominant factors being CO2 (relative contribution of 32.22%) and the LAI (relative contribution of 21.96%). In contrast, the contributions of Rsd and precipitation were relatively low (<10%). In addition, the contributions varied at different land cover and climate zone scales. The CO2, LAI, FVC, and temperature were the predominant factors affecting NPP across the vegetation types. At the scale of climate zones, CO2 was the predominant factor influencing changes in vegetation NPP. As the climate gradually transitioned towards temperate and cold regions, the contribution of the LAI to NPP increased. The findings of this study help to clarify the effects of vegetation and climate change on the ecosystem, providing theoretical support for ecological environmental protection and other related initiatives.
Multi-Modal Prototypes for Few-Shot Object Detection in Remote Sensing Images
Authors: Liu Yanxing , Pan Zongxu , Yang Jianwei , Zhou Peiling , Zhang Bingchen
Published: December 2024

Few-shot object detection has attracted extensive attention due to the abomination of time-consuming or even impractical large-scale data labeling. Current studies attempted to employ prototype-matching approaches for object detection, constructing class prototypes from textual or visual features. However, single visual prototypes exhibit limited generalization in few-shot scenarios, while single textual prototypes lack the spatial details of remote sensing targets. Therefore, to achieve the best of both worlds, we propose a prototype aggregating module to integrate textual and visual prototypes, leveraging both semantics from textual prototypes and spatial details from visual prototypes. In addition, the transferability of multi-modal few-shot detectors from natural scenarios to remote sensing scenarios remains unexplored, and previous training strategies for FSOD do not adequately consider the characteristics of text encoders. To address the issue, we have conducted extensive ablation studies on different feature extractors of the detector and propose an efficient two-stage training strategy, which takes the characteristics of the text feature extractor into account. Experiments on two common few-shot detection benchmarks demonstrate the effectiveness of our proposed method. In four widely used data splits of DIOR, our method significantly outperforms previous state-of-the-art methods by at most 8.7%.
Downscaling Land Surface Temperature via Assimilation of LandSat 8/9 OLI and TIRS Data and Hypersharpening
Authors: Alparone Luciano , Garzelli Andrea
Published: December 2024

Land surface temperature (LST) plays a pivotal role in many environmental sectors. Unfortunately, thermal bands produced by instruments that are onboard satellites have limited spatial resolutions; this seriously impairs their potential usefulness. In this study, we propose an automatic procedure for the spatial downscaling of the two 100 m thermal infrared (TIR) bands of LandSat 8/9, captured by the TIR spectrometer (TIRS), by exploiting the bands of the optical instrument. The problem of fusion of heterogeneous data is approached as hypersharpening: each of the two sharpening images is synthesized following data assimilation concepts, with the linear combination of 30 m optical bands and the 15 m panchromatic (Pan) image that maximizes the correlation with each thermal channel at its native 100 m scale. The TIR bands resampled at 15 m are sharpened, each by its own synthetic Pan. On two different scenes of an OLI-TIRS image, the proposed approach is compared with 100 m to 15 m pansharpening, carried out uniquely by means of the Pan image of OLI and with the two high-resolution assimilated thermal images that are used for hypersharpening the two TIRS bands. Besides visual evaluations of the temperature maps, statistical indexes measuring radiometric and spatial consistencies are provided and discussed. The superiority of the proposed approach is highlighted: the classical pansharpening approach is radiometrically accurate but weak in the consistency of spatial enhancement. Conversely, the assimilated TIR bands, though adequately sharp, lose more than 20% of radiometric consistency. Our proposal trades off the benefits of its counterparts in a unique method.
Deep Learning for Hyperspectral Image Classification: A Critical Evaluation via Mutation Testing
Authors: Chen Zhifei , Hao Yang , Liu Qichao , Liu Yuyong , Zhu Mingyang , Xiao Liang
Published: December 2024

Recently, there has been a surge in the adoption of deep learning (DL) techniques, especially convolutional neural networks (CNNs), to perform hyperspectral image (HSI) classification. Although deep learners have been shown to achieve impressive performance in HSI classification, they are known to be extremely sensitive to even slight perturbations to their inputs and models. When applied in safety-critical applications, it is crucial to know how robust they really are against perturbations. However, there is still limited tool support for DL testing in terms of their robustness, nor are the existing RGB testing approaches able to address the HSI-specific challenges. In this paper, we propose a mutation analysis framework specialized for DL models trained to classify HSIs, which facilitates a critical evaluation of the robustness of DL-based HSI classifiers. First, we introduce a set of mutation operators to inject faults into the inputs and models to simulate distortions of remote sensing HSI classifiers. By utilizing the mutation testing technique, we implement a novel framework which supports the multidimensional evaluation of individual DL-based classifiers. Finally, a comparative study of the robustness of seven popular CNN-based HSI classifiers (i.e., 3D-CNN, FDSSC, HybridSN, MCNN, FC3DCNN, DWTDENSE, and Tri-CNN) on six HSI datasets is provided. Results show that FDSSC and Tri-CNN achieve higher robustness in the presence of distortions, and FDSSC maintains a relatively stable level of robustness even with few training samples. These empirical findings can be partly explained by the characteristics of the classifiers’ architectures. The results substantiate the efficacy of our evaluation framework in assessing the robustness of HSI classifiers and thus confirm its contribution to the field of remote sensing image classification.
Conservation Strategies for Endangered Species in the Forests Utilizing Landscape Connectivity Models
Authors: Park Hyomin , Jeong Ahmee , Koo Seulki , Lee Sangdon
Published: December 2024

Urban expansion leads to changes in land use, and the resulting habitat fragmentation increases the risk of species extinction. Therefore, strategies to connect fragmented habitats for wildlife conservation are required, but past research has focused mainly on large mammals and specific species, and there has been a lack of research on habitat connectivity in Korea. In the present study, we sought to design an ecological network for the conservation of endangered forest wildlife (leopard cat, yellow-throated marten, and Siberian flying squirrel) in Pyeongchang, Gangwon State, Korea. The InVEST habitat quality and MaxEnt models were used to predict forest areas with excellent habitat quality and a high probability of the occurrence of endangered wildlife. We then used Linkage Mapper to identify corridors and bottlenecks that connect fragmented habitats within the study area. The quality of these corridors and the environmental features of the pinch points were also analyzed. The results showed that the area outside of Pyeongchang is the most likely area for endangered forest wildlife habitats and occurrence. A total of seven core areas were identified, and 12 corridors connecting the core areas were identified. The highest quality corridors were those connecting forest areas outside of Pyeongchang because they had a high habitat quality with alternative paths of least resistance. We also identified sections with high pinch points in all corridors, and these points tended to have high elevation, a southern aspect, a long distance from agricultural land and water bodies, low traffic density, and low building density. ANOVA revealed that the environmental variables associated with high pinch points, least-cost paths, and Pyeongchang in general exhibited statistically significant differences. These results demonstrate that the proposed conservation planning model can be applied to multiple species using a corridor-integrated mapping approach and produces quantitative figures for the targeted improvement of ecological connectivity in forests according to local characteristics, including biodiversity. As such, this approach can be utilized as the basis for the selection and management of protected forest areas and for environmental impact assessment. However, because this study had data limitations, field surveys and the monitoring of target species are needed. Once these limitations are addressed, a quantitative conservation plan can be established based on the ecological characteristics of endangered forest wildlife.
Traffic Safety Footprint in Sustainability Practices and Reporting—Exploring the Views of Companies
Authors: Wennberg Hanna , Hyllenius Mattisson Pernilla
Published: December 2024

Road traffic accidents cause nearly 1.3 million deaths and around 50 million injuries each year globally. Most organisations generate travel and transport, and influence thereby traffic safety. In Sweden, 47 percent of fatal accidents in road traffic are work-related, and 36 percent are linked to a procured transport service. Effective approaches to managing traffic safety in organisations have not yet been established to advance traffic safety implementation, especially through sustainability practices and reporting. This study explores the untapped potential of improving traffic safety by addressing the traffic safety impact of organisations within the context of sustainability. Firstly, this study examines organisations’ views on traffic safety as a sustainability issue, and the status and driving forces in handling traffic safety as an integrated part of sustainability practices and reporting. Secondly, it identifies enablers for advancing traffic safety implementation in organisations with a focus on the sustainability context. The study is based on interviews with 22 organisations (mainly private companies) and analysis of 23 sustainability reports. It is concluded that sustainability is a relevant context for traffic safety for all organisations that consider traffic safety as a significant sustainability issue due to the travel and transport generated, directly or indirectly. However, traffic safety is generally not viewed as a sustainability issue and is rarely included in sustainability reports. Transport companies are more likely to consider traffic safety in the context of sustainability. Enablers concern the necessity to communicate traffic safety as a sustainability issue and to raise awareness of traffic safety as part of the 2030 Agenda for Sustainable Development. Communication is needed to raise awareness among private and public organisations on their traffic safety footprint and possibilities to influence traffic safety. Furthermore, legislative directives and standards on sustainability reporting should explicitly include traffic safety. By integrating traffic safety in sustainability practices and reporting, organisations can more clearly draw from and utilise the positive synergies between traffic safety and other sustainability goals. Furthermore, such integration is a way to bring traffic safety issues up to the management level, facilitating leadership for traffic safety.
Unraveling the Potential of Baru (Dipteryx alata Vog.) Fruit Fractions as a Sustainable Food Ingredient: Chemical and Technological Characteristics and Prebiotic Potential
Authors: Sousa Tainara Leal de , Filho Josemar Gonçalves de Oliveira , Cabassa Isabelly de Campos Carvalho , Lemes Ailton Cesar , Egea Mariana Buranelo
Published: December 2024

The baru is a native Brazilian Cerrado fruit known for its high nutritional and techno-functional value, with the potential to generate new food ingredients. Utilizing the entire baru fruit maximizes the use of all its components, reduces the environmental impact, minimizes waste, and contributes to the region’s sustainable development. In this context, this study aimed to evaluate the nutritional composition, bioactive compounds, technological properties, and prebiotic potential of baru by-products and nuts, aiming at their integral and sustainable utilization. For this purpose, baru fruits were collected, characterized in terms of biometrics, and separated into pericarp (by-product) and nuts. The nutritional composition, fatty acid profile, bioactive compounds, techno-functional properties, and prebiotic potential were characterized in the roasted almonds and the by-products. The results indicated that the baru meal had a high protein content (33.09 g/100 g), the lowest atherogenic and thrombogenic indices (0.09 and 0.10, respectively), and the highest antioxidant capacity in the DPPH, ABTS, and FRAP assays. Additionally, the baru meal showed high levels of potassium (14.05 g/100 g), magnesium (2.20 g/100 g), and iron (61.80 g/100 g). The mesocarp stood out for its higher total phenolic content (157.48 mg GAE/100 g) and enhanced water solubility (63.90 g/g). The baru almond flour showed the best results for water absorption capacity (3.62 g/g), oil absorption capacity (3.28 g/g), emulsifying activity (71.94%), emulsion stability (87.69%), and foaming capacity (8.17%). In terms of prebiotic activity, the endocarp fermented by Lactobacillus acidophilus (0.76 log CFU/mL) and the mesocarp fermented by Bifidobacterium animalis (0.62 log CFU/mL) presented the best results. These findings demonstrate that baru by-products have the potential for full utilization, contributing to the sustainable development of underexplored plant species.
Enhancing Supportive and Adaptive Environments for Aging Populations in Jordan: Examining Location Dynamics
Authors: Al-Homoud Majd
Published: December 2024

This study explores home modification and personalization as mechanisms to promote aging in place and independence for older adults in Jordan. Personalization and home modification are important because they help older people age in place and promote independence. Thus, this study investigated elderly adaptability and housing alteration methods in Jordan. As the first study in Jordan, this research examined the meaning of aging and cultural variations in aging in place. The study tested the following hypotheses for different governorates, all showing significant effects: (I) overall, the sense of control is different in different locations, (II) space personalization is different in different locations, (III) home modification is associated with location, (IV) the association between space personalization and overall sense of control varies across locations, and (V) home modification is associated with the overall sense of control differently in different locations. Additionally, the study highlights the importance of regulated and well-organized home care services to ensure seniors can receive the necessary care while remaining in their families and communities. This research offers valuable insights into the cultural variations surrounding visions of aging in place and emphasizes the need for further research.
Agricultural Land Price Dynamics in Europe: Convergence, Divergence, and Policy Impacts Across EU Member States
Authors: Wasilewski Adam , Gospodarowicz Marcin , Wasilewska Anna
Published: December 2024

This study investigates the dynamics of agricultural land prices across the European Union (EU) from 2006 to 2022, focusing on trends of convergence and divergence among member states. Employing a mixed-methods approach, including econometric analysis of price convergence models and comparative policy evaluation, the research identifies critical economic, policy, and environmental factors influencing land markets. The results indicate price convergence in Eastern European countries, such as Romania and Poland, driven by EU policies, including the Common Agricultural Policy (CAP), and increased foreign investment. In contrast, Western European countries like the Netherlands and Luxembourg show price stability at elevated levels due to mature markets and limited land availability. The findings reveal a nuanced interaction between EU-wide and national policies, with CAP fostering convergence in emerging markets while maintaining price stability in developed ones. This study underscores the importance of tailored policy measures to address market disparities, support smaller farmers, and promote sustainable rural development across diverse EU regions.
Research on Multimodal Adaptive In-Vehicle Interface Interaction Design Strategies for Hearing-Impaired Drivers in Fatigue Driving Scenarios
Authors: Wei Dapeng , Zhang Chi , Fan Miaomiao , Ge Shijun , Mi Zhaoyang
Published: December 2024

With the advancement of autonomous driving technology, especially the growing adoption of SAE Level 3 and above systems, drivers are transitioning from active controllers to supervisors who must take over in emergencies. For hearing-impaired drivers in a fatigued state, conventional voice alert systems often fail to provide timely and effective warnings, increasing safety risks. This study proposes an adaptive in-vehicle interface that combines visual and tactile feedback to address these challenges. Experiments were conducted to evaluate response accuracy, reaction time, and cognitive load under varying levels of driver fatigue. The findings show that the integration of visual and tactile cues significantly improves takeover efficiency and reduces mental strain in fatigued drivers. These results highlight the potential of multimodal designs in enhancing the safety and driving experience for hearing-impaired individuals. By providing practical strategies and evidence-based insights, this research contributes to the development of more inclusive and effective interaction designs for future autonomous driving systems.
Inclusive and Sustainable Supply Chain Strategies in Live E-Commerce: The Role of Influencer Marketing and Spillover Effects
Authors: Zhu Xiaodong , Deng Ruiting
Published: December 2024

Live e-commerce has proliferated as a new business model in recent years. Live-streaming channel selection strategies and behavioral decisions are critical considerations for retailers to enhance business performance by increasing engagement and profitability. This study introduces the spillover effect between influencer and self-live-streaming channels, offering new theoretical insights into sustainable supply chain management from multichannel retailing, game theory, and spillover effects for channel selection and pricing, optimizing dual-channel tactics for inclusive marketing, and challenging pricing and channel effectiveness beliefs. Hence, inclusive and sustainable business practices in live e-commerce have been examined, focusing on dual-digital-channel strategies involving influencer marketing and self-live-streaming. This paper investigates three digital supply chain structures for sustainability, which are based on influencer characteristics: the single digital channel (S), the dual-digital-channel structure without spillover effect (DN), and the dual-digital-channel structure with spillover effect (DS). This study employs Stackelberg game models to analyze behavioral, pricing, and channel preferences, and it also extends the model to offline channels. This study finds that (a) influencer effort affects influencer live-streaming pricing, which may not always be cheaper than self-streaming digital channels; (b) commission rates significantly impact influencer pricing strategies, leading retailers to use skimming and penetration pricing strategies when commission rates are low and high; and (c) retailers may develop influencer live-streaming channels if the cross-price coefficient is medium and low but prefer it when the coefficient and spillover impact are large. By analyzing how these digital channels contribute to societal sustainability through reduced environmental impact and enhanced social inclusion, this research highlights key marketing decisions that optimize business performance and social responsibility in the digital economy. It offers live e-commerce managers strategic channel selection, pricing, and sustainability advice and recommendations for future empirical validation to enhance practical applicability.
Influence of Environmental Conditions on the Behaviour of Tailings from Tungsten Mining for Sustainable Geotechnical Applications and Storage
Authors: Oliveira João Pedro , Araújo Santos Luís , Ribeiro Joana , Coelho Paulo , Pedro António M. G.
Published: December 2024

Modern societies require increasingly large amounts of minerals and metals for their development. Therefore, huge amounts of waste must be stored in safe and cost-effective massive tailing storage facilities that would benefit from using tailings in sustainable geotechnical applications within the context of the circular economy. However, to consider tailings as assets, the long-term behaviour of these unconventional geomaterials under realistic environmental conditions must be assessed. This paper focuses on the effects of the environmental conditions on the behaviour of tailings from tungsten mining by experimentally determining their major physical and mechanical properties for three different conditions: twenty-months-aged undisturbed samples and reconstituted samples, with the latter being fresh and three months. The results confirm that twenty-months-aged undisturbed and fresh reconstituted tailings have significantly different mechanical behaviour, while three-months-aged reconstituted samples show an in-between behaviour as if the material regenerates and improved its behaviour with time due to physical and chemical processes. These ageing processes are experimentally confirmed by measuring the electrical conductivity in the samples. The results confirm that optimising the design of tailing storage facilities and using these geomaterials in sustainable geotechnical applications must consider the existing environmental conditions and the potential tailings’ mechanical changes due to ageing.
Inequity Aversion Selection Under Revenue-Sharing in a Reservation Commuting Platform for Urban–Rural Agglomerations
Authors: Zang Jiapu , Liu Xun , Xu Mengyu
Published: December 2024

The intelligent design of a transportation system management platform offers a compelling solution for optimizing traffic flow in urban–rural agglomerations. While previous studies have concentrated on the technical aspects of addressing transportation challenges in these areas, there has been a paucity of attention paid to the economic dimensions. This study explores the economic viability of such a system by examining the revenue-sharing mechanism. Moreover, it examines the principal cost elements within the construction context and develops a mathematical model based on game theory and considerations of inequity aversion. A case study of the Guangdong–Hong Kong–Macao Greater Bay Area serves to demonstrate the model’s validity. From the perspective of the service provider, it is advantageous for the provider to pursue fairness, or at the very least, to disregard any unfairness, if the owner pursues absolute fairness. In an ideal scenario, where both parties prioritize fairness, optimal or near-optimal solutions can be reached based on their fairness preferences. This approach allows for the maximization of overall benefits without incurring losses. The research findings not only offer guidance to policymakers in the realm of traffic management but also present novel ideas and methodologies for scholarly exploration in related fields.
Solid-State Battery Developments: A Cross-Sectional Patent Analysis
Authors: Bridgelall Raj
Published: December 2024

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This study systematically reviews the global SSB patent landscape using a cross-sectional bibliometric and thematic analysis to identify innovations addressing key technical challenges. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including year, geographic distribution, inventor engagement, award latency, and technological focus. The analysis reveals significant advancements in electrolyte materials, electrode designs, and manufacturability. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions.
Study on the Coupled and Coordinated Development of Climate Investment and Financing and Green Finance of China
Authors: Shen Danhong , Guo Xiaorong , Ma Shenglin
Published: December 2024

The growth of climate investment and financing highlights the growing global focus on climate change. In China, fostering the coordinated development of climate investment and financing and green finance is a crucial step toward accomplishing sustainable development objectives and addressing climate challenges effectively. By constructing the indicator system of climate investment and financing and green finance, and using the entropy method and the coupled coordination model, we comprehensively explored the development level and the coupling coordination relationship between climate investment and financing and green finance in 30 provinces in China during the period of 2013–2022. The findings of the research indicate that the development of climate investment and financing and green finance in China as a whole shows a growth trend, but the development of climate investment and financing remains relatively underdeveloped, and noticeable variations can be observed among regions. In respect to the level of coupling coordination, the overall coordination level of each province has not yet attained the desired level of optimality, and despite the yearly increase in the coordination degree at the national level, it is still in the state of mild dislocation. The eastern coastal region has the highest level of coupling coordination and is in the stage of verging on dislocation. And the great northwestern region has the lowest level of coordination and is in the stage of moderate dislocation. These findings provide an important reference for the formulation and execution of climate investment and financing and green finance policies in China.
Life Cycle Assessment of Greenhouse Gas Emissions in Hydrogen Production via Water Electrolysis in South Korea
Authors: Kim Kyeong-Mi , Kim Dongwoo
Published: December 2024

This study evaluated the greenhouse gas (GHG) emissions associated with hydrogen production in South Korea (hereafter referred to as Korea) using water electrolysis. Korea aims to advance hydrogen as a clean fuel for transportation and power generation. To support this goal, we employed a life cycle assessment (LCA) approach to evaluate the emissions across the hydrogen supply chain in a well-to-pump framework, using the Korean clean hydrogen certification tiers. Our assessment covered seven stages, from raw material extraction for power plant construction to hydrogen production, liquefaction, storage, and distribution to refueling stations. Our findings revealed that, among the sixteen power sources evaluated, hydroelectric and onshore wind power exhibited the lowest emissions, qualifying as the Tier 2 category of emissions between 0.11 and 1.00 kgCO2e/kg H2 under a well-to-pump framework and Tier 1 category of emissions below 0.10 kgCO2e/kg H2 under a well-to-gate framework. They were followed by photovoltaics, nuclear energy, and offshore wind, all of which are highly dependent on electrolysis efficiency and construction inputs. Additionally, the study uncovered a significant impact of electrolyzer type on GHG emissions, demonstrating that improvements in electrolyzer efficiency could substantially lower GHG outputs. We further explored the potential of future energy mixes for 2036, 2040, and 2050, as projected by Korea’s energy and environmental authorities, in supporting clean hydrogen production. The results suggested that with progressive decarbonization of the power sector, grid electricity could meet Tier 2 certification for hydrogen production through electrolysis, and potentially reach Tier 1 when considering well-to-gate GHG emissions.
Exploring Environmental, Social, and Governance Factors Affecting Supply Chain Resilience: From Employees’ Perspectives
Authors: Zheng Lingyu , Chen Han , Zheng Wenjia
Published: December 2024

In the domain of supply chain resilience (SCR), research has disproportionately emphasized the environmental dimensions of environmental, social, and governance (ESG) factors, leading to an oversight regarding the roles played by social and governance factors. To address this gap, a questionnaire survey was conducted among 313 employees from five Chinese supply chain enterprises. Through factor analysis, this study identified four latent variables associated with environmental factors, three with social factors, and four with governance factors. A structural equation model was then developed to present a comprehensive analysis of the impacts of the three ESG dimensions and digital intelligence on SCR, while also examining the interplay among these ESG factors. The findings reveal that environmental factors positively influence SCR, whereas social and governance factors exert a negative impact. This study further observes that digital intelligence enhances ESG factors but does not directly influence SCR. These results underscore the intricate dynamics between ESG indicators, digital intelligence, and SCR, highlighting the imperative for supply chain entities to make balanced decisions. This research offers novel insights into the effects of ESG factors from the employees’ viewpoint, providing implications and recommendations for supply chain management.
Revolutionizing End-of-Life Product Recovery with Product 4.0: An Examination of Intelligent Products in Industry 4.0
Authors: Popolo Valentina , Vespoli Silvestro , Gallo Mosè , Grassi Andrea
Published: December 2024

In the context of growing environmental concerns and the increasing impact of the manufacturing sector on sustainability, this paper introduces the concept of “Product 4.0” (P4.0) as a novel approach to harnessing the potential of Artificial Intelligence (AI) within Industry 4.0 (I4.0) technologies. P4.0 focuses on optimizing the performance of the product throughout its lifecycle and improving recovery strategies at End of Use (EoU) and End of Life (EoL) stages. Through a comprehensive review of the literature, this study identifies critical gaps in the current application of AI within I4.0 for sustainable manufacturing, particularly in regard to smart product systems and their interactions with external environments. To address these gaps, the paper proposes a holistic approach for the P4.0 that leverages AI-driven data analysis and decision making to facilitate efficient product recovery and resource utilization. Additionally, a Causal Loop Diagram (CLD) model is developed to illustrate the relationships between sustainability dimensions—environmental, economic, and social—and product demand influenced by P4.0, while also discussing the challenges and limitations associated with its implementation. By bridging theoretical insights with practical recovery solutions, this research contributes to the sustainable manufacturing discourse and offers actionable directions for future investigations into AI-enhanced P4.0 applications within the manufacturing industry.
Sustainable Removal of Phenol Dye-Containing Wastewater by Composite Incorporating ZnFe2O4/Nanocellulose Photocatalysts
Authors: Li Zan , Gao Kun , Jiang Wenrui , Xu Jiao , Lushchyk Pavel
Published: December 2024

The escalating issue of phenol-containing wastewater necessitates the development of efficient and sustainable treatment methods. In this context, we present a novel composite photocatalyst comprising ZnFe2O4 (ZFO) nanoparticles supported on nanocellulose (NC), aimed at addressing this environmental challenge. The synthesis involved a facile hydrothermal method followed by the impregnation of ZFO nanoparticles onto the NC matrix. The morphology and structure of ZFO, NC, and ZFO/NC were investigated by TEM, SEM-EDX, UV–vis, FT-IR, XRD, and XPS analyses. ZFO, as a weakly magnetic semiconductor catalytic material, was utilized in photocatalytic experiments under magnetic field conditions. By controlling the electron spin states through the magnetic field, electron–hole recombination was suppressed, resulting in improved photocatalytic performance. The results demonstrated that 43% and 76% degradation was achieved after 120 min of irradiation due to ZFO and 0.5ZFO/NC treatment. Furthermore, the composite 0.5ZFO/NC demonstrated the highest photocatalytic efficiency, showing promising recyclability by maintaining its activity after three cycles of use. This study underscores the potential of the ZFO/NC composite for sustainable wastewater treatment, offering a promising avenue for environmental remediation.
Food Security and Food Sovereignty: A Review of Commonly Used Indicators and Consideration of Environmental Sustainability Aspects
Authors: Cadavid Luis , Arulnathan Vivek , Pelletier Nathan
Published: December 2024

Agriculture and food systems are central to human well-being. As a result, food production and consumption are at the heart of every discourse on sustainable development, both from an environmental and a socio-economic perspective. The concepts of food security and food sovereignty, both significant within the discourse on sustainable food systems, have seen increased interest over recent decades. Despite the importance and relevance of these concepts to one another within the agri-food systems discourse, they are rarely discussed in concert. This literature review explores the definitions, synergies, and potential trade-offs between food security, food sovereignty, and environmental sustainability objectives, as assessed using Life Cycle Assessment—today’s pre-eminent tool for environmental impact measurement and management of products and services. Food cost, adequate caloric/nutrient intake, and access to food were identified as the most used food security indicators, and resource availability, food consumption, and characterization of food production were observed to be the most common food sovereignty indicators. Simultaneous consideration of both food security and environmental sustainability was surprisingly rare in the reviewed literature and was mostly limited to no more than mentions of the terms “environment” or “sustainability”. Joint consideration of these aspects might enable understanding of potential tensions as well as a means to identify strategies that best satisfy what may be viewed as competing objectives.
The Study of Differential Game on Government Carbon Trading Policy and Firms’ Production Strategies Under Different Market Competition Levels
Authors: Zheng Jialin , Zhou Ya , Li Keqiang , Zeng Yang , Wang Ruining , Zhang Canmin
Published: December 2024

We construct a Stackelberg differential game model in which industries with varying degrees of market rivalry are the followers, and the government is the leader, describing their long-term dynamic optimal decision-making behaviors, and we research how the government establishes the unit carbon trading price based on feedback from various industries in the early stages of carbon trading market establishment. We also examine how to decrease the peaking period and lower the peak value of the industry’s overall carbon emissions by modifying the pivotal parameters. Furthermore, the impacts of relevant parameters on the optimal feedback strategy are also analyzed. In light of this, policymakers may obtain the optimal carbon trading price for various industries based on the actual market competition levels in those sectors, achieving the goal of reducing carbon emissions while growing the economy. The government can incentivize companies to reduce carbon emissions by reasonably allocating carbon emission quotas. This approach helps achieve a win-win situation for environmental protection and economic development, thereby promoting sustainable societal progress.
Development of a Deep Learning-Based Flooding Region Segmentation Model for Recognizing Urban Flooding Situations
Authors: Yoo Jaeeun , Lee Jungmin , Jeung Sejin , Jung Seungkwon , Kim Myeongin
Published: December 2024

Urban flooding has become increasingly frequent due to the rising intensity of rainfall driven by urban development and climate change. Effective prevention measures are crucial to mitigate the significant human and material damages caused by such events. Rapid and accurate pre-detection techniques can help to reduce the impacts of urban flooding. With the advancement of deep learning, deep neural networks (DNNs) have been successfully applied across various domains, including computer vision and speech recognition. In particular, DNNs for computer vision demonstrate high performance with relatively low computational costs. In this paper, we propose a flooding region segmentation model for urban underpasses based on the U-Net architecture. To train and evaluate the model, we collected datasets from the Mannyeon, Oryang, and Daedong underpasses in Daejeon. The proposed method achieved Dice coefficients of 98.8%, 94.03%, and 93.85%, respectively. This model demonstrates high segmentation performance in detecting flooded regions and can be integrated into continuous flood monitoring systems.
Golden Meta-Spring Inspired by Bone Fractal Operators
Authors: Jian Zhimo , Yin Yajun
Published: December 2024

In recent years, our research on bone mechanics has revealed that the fundamental laws in physical fractal space can be characterized by fractal operators. Based on the invariant properties of bone fractal operators, we used the error function as the core and derived the fractional-order correlation between different special functions. This paper is a continuation of the previous work. Inspired by bone fractal operators, we aim to logically construct a Golden Meta-Spring to illustrate the interconnections between various disciplines. Specifically, the following contents are included: (1) originating from the Golden Ratio, we present the construction process of Golden Meta-Spring; (2) based on the continued fraction theory, we discuss the properties, characteristics, and interdisciplinary insights provided by various types of Meta-Springs; (3) using the bone fractal operators as the link, we demonstrate the correlations between different disciplines.
Weighted Linear Discriminant Analysis: An Effective Feature Extraction Method for Multi-Class Imbalanced Datasets
Authors: Liu Yuhan , Guo Shuangle
Published: December 2024

In high-dimensional machine learning tasks, supervised feature extraction is essential for improving model performance, with Linear Discriminant Analysis (LDA) being a common approach. However, LDA tends to deliver suboptimal performance when dealing with class imbalance. To address this issue, we propose a novel feature extraction model, Weighted Linear Discriminant Analysis (WLDA), which integrates cost-sensitive techniques into the traditional LDA framework. By assigning weights inversely proportional to class sample sizes, WLDA achieves effective feature extraction under imbalanced sample conditions. We introduce an efficient solution algorithm for the proposed model and provide a thorough complexity analysis. Experimental results demonstrate the superior performance of WLDA in handling imbalanced datasets, confirming its potential as a robust tool for high-dimensional data scenarios. Overall, WLDA not only improves feature extraction for imbalanced datasets but also enhances classification accuracy across diverse applications.
Dynamic Survivability Centrality in Nonlinear Oscillator Systems
Authors: Wang Yuexin , Sun Zhongkui , Ye Sijun , Zhao Tao , Zhang Xinshuai , Xu Wei
Published: December 2024

In light of the fact that existing centrality indexes disregard the influence of dynamic characteristics and lack generalizability due to standard diversification, this study investigates dynamic survivability centrality, which enables quantification of oscillators’ capacity to impact the dynamic survivability of nonlinear oscillator systems. Taking an Erdős–Rényi random graph system consisting of Stuart–Landau oscillators as an illustrative example, the typical symmetry synchronization is considered as the key mission to be accomplished in light of the study and the dynamic survivability centrality value is found to be dependent on both the system size and connection density. Starting with a small scale system, the correctness of the theoretical results and the superiority in comparison to traditional indexes are verified. Further, we present the quantitative results by means of error analysis, distribution comparison of various indexes and relationship with system structure exploration, and give the position of the key oscillator. The results demonstrate a negligible error between the theoretical and numerical outcomes, and highlighting that the distribution of dynamic survivability centrality closely resembles the distribution of system state changes. The conclusions serve as evidence for the accuracy and validity of the proposed index. The findings provide an effective approach to protect systems to improve dynamic survivability.
Spatial Entanglement Between Electrons Confined to Rings
Authors: Ciftja Orion , Batle Josep , Abdel-Aty Mahmoud , Hafez Mohamed Ahmed , Alkhazaleh Shawkat
Published: December 2024

We study systems of two and three electrons confined to circular rings. The electrons are considered spinless, and we assume that one electron occupies a single ring. We use the framework of such a model to calculate the linear entropy and, thus, the spatial entanglement between the confined electrons. The geometry of the problem for the case of two electrons incorporates situations in which the planes of the two rings form an arbitrary angle with each other. The resulting Schrödinger’s equation is solved numerically with very high accuracy by means of the exact diagonalization method. We compute the ground state energy and entanglement for all configurations under consideration. We also study the case of three electrons confined to identical, parallel and concentric rings which are located in three different equidistant planes. The vertically separated system of rings is allowed to gradually merge into a single ring geometry, which would represent the equivalent system of a ring with three electrons. It is observed that the system of three electrons gives rise to a richer structure, as the three rings merge into a single one.
Annealing Effects on Microstructure and Texture in KOBO-Processed LPBF AlSi10Mg Alloy: Elucidating CSL Boundary Formation
Authors: Snopiński Przemysław , Liverić Lovro , Beniak Juraj
Published: December 2024

This study introduces a strain-annealing approach to tailor the grain boundary characteristics of additively manufactured AlSi10Mg alloy produced by Laser Powder Bed Fusion (LPBF). By combining KOBO extrusion and subsequent annealing treatments, we aim to increase the proportion of low-Σ coincident site lattice (CSL) grain boundaries, particularly Σ3 boundaries. Through grain boundary engineering (GBE), specifically focused on inducing a high fraction of symmetrical CSL boundaries, our approach allows for the optimization of microstructural features that inhibit defect propagation and improve material stability. Microstructural analysis using electron backscatter diffraction (EBSD) revealed a substantial increase in Σ3 boundaries (60° <111> twin relationship) in the early recrystallization stages of the KOBO-processed LPBF AlSi10Mg alloy, demonstrating the effectiveness of this method. The findings presented in this manuscript highlight a new strategy for advancing the microstructural characteristics of LPBF AlSi10Mg alloy, with promising implications for applications requiring high-performance materials, such as in the aerospace, nuclear, and automotive industries.