bluuebunny/crossref_metadata_2025 huggingface dataset:
What Is the Meaning of Reproduction for Individuals: An Attempted Explanation Based on Confucianism
Authors: Pi Mimi
Published: February 2025

In a modern world dominated by individualism, reproduction has become a difficult choice for individuals. The traditional reasons for reproduction have lost their appeal. For individuals, the meaning of reproduction has become a question. Confucian understandings of the universe and the individual provide insight to address the challenge. Dong Zhongshu’s theory of “humans are born of tian” and the concept of deriving benevolence from Heaven deeply appreciate the cosmic virtue of natural generation, establishing a meaningful connection between Heaven and humanity, and offering a cosmic perspective to understand the meaning of human life beyond blood relations. Mencius’s theory of human nature opens up multiple possibilities for the establishment of individual subjectivity, alleviating the potential tension in parent–child relationships from an individualistic perspective. Confucian rituals, through traceable bloodline genealogies, imbue individual life with meaning, and within this context, reproduction is given significance.
Public, Poor, and Promiscuous? Defining the Prostitute in Sixteenth-Century Zurich
Authors: Schulz Adrina
Published: February 2025

The impact of the Reformations, in Switzerland and elsewhere, on the regulation of sexuality has been extensively researched in recent decades. Laws against adultery and premarital fornication were strengthened and promulgated, leading to the decline of the late-medieval public brothel. However, the impact of these theological, legal, and institutional changes on the women who engaged in transactional or commercial sex outside of marriage has been harder to capture. Even the authorities’ definitions of such women remain difficult to pin down: were they viewed as promiscuous sinners to be punished, laborers in a dishonorable profession, poor vagrants to be exiled, or all or none of the above? This paper first discusses the historiography of prostitution to show how previous research terminology and definitions of the phenomenon are insufficient for the early modern context. It then analyses a case study from sixteenth-century Zurich in which different understandings about premodern prostitutes coexisted, intersected, and conflicted with the argument that these individuals were conceived of in terms of authorities’ fears rather than a specific set of characteristics.
From the Labor Question to the Murderous Economy: Catholic Approach to Economic Policy
Authors: Ujházi Lóránd , Jancsó András
Published: February 2025

From his election, it was clear that Pope Francis advocated for the poor and the protection of creation. However, both areas are linked to ethical aspects of the economy. This paper demonstrates that although new aspects of Catholic economic ethics emerged with Pope Francis, they show a continuity with the Church’s social doctrine. Pope Francis is under fire from two directions. He is under attack from both economically minded conservatives and liberals. This paradox is interesting since these areas indicate Pope Francis’ teaching is partly static and dynamic. It is here that the dichotomy of continuity and progression is most visible. The Catholic Church theologically reflected on economics and then gradually in a transdisciplinary perspective. In this paper, we examine both methodologically and substantively the static elements of the Church’s teaching on the economy. We point to the slow but firm critical articulation of the Church’s opposition to the ethics of the capitalist economy. A qualitative method was used for the research, so primary and secondary Church documents and the theological and economic literature were applied. These were compared, opinions were contrasted, and a conclusion was drawn to justify the hypothesis.
Hyperspectral Band Selection for Crop Identification and Mapping of Agriculture
Authors: Tan Yulei , Gu Jingtao , Lu Laijun , Zhang Liyuan , Huang Jianyu , Pan Lin , Lv Yan , Wang Yupeng , Chen Yang
Published: February 2025

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future.
Genetic Algorithm-Based Weighted Constraint Target Band Selection for Hyperspectral Target Detection
Authors: Chen Wenbin , Zhi Xiyang , Hu Jianming , Yu Lijian , Han Qichao , Zhang Wei
Published: February 2025

Hyperspectral imaging (HSI) data pose both opportunities and challenges for target detection due to the high spectral resolution and vast data volume. Traditional band selection methods for HSI often prioritize image quality or information content, neglecting target distinctiveness in specific detection tasks. To address this issue, this work proposes a novel band selection method, genetic algorithm-based weighted constraint target band selection (GA-WCTBS), which utilizes an improved genetic algorithm to optimize band subsets for small target detection. GA-WCTBS prioritizes target distinctiveness and background clutter fluctuations by a proposed spectral signal-to-clutter ratio (SCR) inspired by the constraint target method, even in bands with lower image quality. It employs a genetic algorithm to consider the combinatorial potential of bands for optimal detection. Additionally, a k-means and weight assignment strategy improves the background estimation for selecting a band subset with better clutter suppression capability. Experiments on widely used public ABU and AVIRIS datasets demonstrate that the band subset selected by GA-WCTBS significantly outperforms the existing methods in terms of detection capability.
The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
Authors: Zhao Nan , Pan Tianpeng , Li Zhitong , Chen Enke , Zhang Lili
Published: February 2025

In recent years, with the continuous development of deep learning, the scope of neural networks that can be expressed is becoming wider and their expressive ability stronger. Traditional deep learning methods based on extracting latent representations have achieved satisfactory results. However, in the field of hyperspectral image compression, the high computational cost and the degradation of their generalization ability reduce their application. We analyze the objective formulation of traditional learning-based methods and draw the conclusion that rather than treating the hyperspectral image as an entire tensor to extract the latent representation, it is preferred to view it as a stream of video data, where each spectral band represents a frame of information and variances between spectral bands represent transformations between frames. Moreover, in order to compress the hyperspectral image of this video representation, neural video representation that decouples the spectral and spatial dimensions from each other for representation learning is employed so that the information about the data is preserved in the neural network parameters. Specifically, the network utilizes the spectral band index and the spatial coordinate index encoded with positional encoding as its input to perform network overfitting, which can output the image information of the corresponding spectral band based on the index of that spectral band. The experimental results indicate that the proposed method achieves approximately a 5 dB higher PSNR compared with traditional deep learning-based compression methods and outperforms another neural video representation method by 0.5 dB when using only the spectral band index as input.
Exploiting Enhanced Altimetry for Constraining Mesoscale Variability in the Nordic Seas and Arctic Ocean
Authors: Bonaduce Antonio , Storto Andrea , Cipollone Andrea , Raj Roshin P. , Yang Chunxue
Published: February 2025

Recent advances in Arctic observational capabilities have revealed that the Arctic Ocean is highly turbulent in all seasons and have improved temporal and spatial sampling of sea level retrievals from remote sensing, even above 80°N. Such data are expected to be increasingly valuable in the future when the extent of sea ice in the Arctic Ocean is reduced. Assimilating this new data into ocean models, together with in situ observations, provides an enriched representation of the mesoscale population that induces new eddy-driven contributions to local dynamics and thermodynamics. To quantify the content of the new information, we compare three-year-long assimilative experiments at ¼° resolution incorporating in situ-only data, in situ and standard altimetry, and in situ and high-latitude-enhanced altimetry, respectively. The enhanced altimetry data lead to an increase in three-dimensional eddy kinetic energy, generated by coherent vortexes, of up to 20% in several areas. Robust ocean warming is generated in the Arctic sector down to 800 m. Via heat budget analysis, this warming can be ascribed to a local enhancement of vertical mixing, as well as an increase in meridional heat transport. The assimilation of enhanced altimetry amplifies the transport, compared to standard altimetry, especially north of 70°N.
Decoupling of Mechanical and Thermal Signals in OFDR Measurements with Integrated Fibres Based on Fibre Core Doping
Authors: Dittmar Clemens , Girmen Caroline , Gastens Markus , König Niels , Siedenburg Thorsten , Wlochal Michael , Schmitt Robert H. , Schael Stefan
Published: February 2025

In this paper, a new measurement principle for decoupling mechanical and thermal signals in an OFDR measurement with integrated optical fibres is investigated. Previous methods for decoupling require additional measuring equipment or knowledge about the substrate properties. This new method is based solely on simultaneous measurements of two fibres with different temperature sensitivities resulting from different core doping processes. By exposing both fibres to the same thermal and mechanical load, the signal could be differentiated through the signal variations caused by the thermo-optical effect. The two fibres used in the tests have a sufficient response difference in the cryogenic temperature range. Therefore, the method is suitable for various applications, such as high-temperature superconductors as well as cryogenic and space applications.
Design, Calibration, and Application of a Wide-Range Fiber Bragg Grating Strain Sensor
Authors: Wang Gang , Wang Jiajian , Meng Jian , Ren Liang , Fu Xing
Published: February 2025

To address the issue of extra-large structural deformation or strain in infrastructures such as bridges, buildings, railroads, and pipelines during catastrophic events, this study proposes a wide-range fiber Bragg grating (FBG) strain sensor utilizing a snake spring desensitization mechanism to share large parts of the strains. Initially, the axial stiffness of the snake spring desensitization mechanism was derived using the strain energy method, which was applied for stiffness calculation, range determination, and parameter design of the entire structure, where the snake spring and the FBG strain sensor were connected in series. Then, the snake springs were fabricated using 3D printing technology and assembled with the FBG sensor to construct a wide-range strain sensor. The wide-range sensor was subsequently calibrated, achieving a strain range of 10,000 με and a linearity coefficient above 0.9995. Finally, the sensor was installed in a pipeline for testing, yielding favorable results. These results demonstrate that the proposed sensor exhibits a wide strain monitoring range and can be effectively used for real-time structural safety analysis by continuously monitoring localized large structure strains.
Securing UAV Flying Ad Hoc Wireless Networks: Authentication Development for Robust Communications
Authors: Sen Muhammet A. , Al-Rubaye Saba , Tsourdos Antonios
Published: February 2025

Unmanned Aerial Vehicles (UAVs) have revolutionized numerous domains by introducing exceptional capabilities and efficiencies. As UAVs become increasingly integrated into critical operations, ensuring the security of their communication channels emerges as a paramount concern. This paper investigates the importance of safeguarding UAV communication against cyber threats, considering both intra-UAV and UAV–ground station interactions in the scope of the Flying Ad Hoc Networks (FANETs). To leverage the advancements in security methodologies, particularly focusing on Physical Unclonable Functions (PUFs), this paper proposes a novel authentication framework tailored for UAV networking systems. Investigating the existing literature, we categorize related studies into authentication strategies, illuminating the evolving landscape of UAV security. The proposed framework demonstrated a high level of security with lower communication and computation costs in comparison with selected studies with similar types of attacks. This paper highlights the urgent need for strong security measures to mitigate the increasing threats that UAVs encounter and ensure their sustained effectiveness in a variety of applications. The results indicate that the proposed protocol is sufficiently secure and, in terms of communication cost, achieves an 18% improvement compared to the best protocol in the referenced studies.
Spatial Blind Source Estimation of Respiratory Rate and Heart Rate Detection Based on Frequency-Modulated Continuous Wave Radar
Authors: Pei Tong , Liao Tao , Wan Xiangkui , Wang Binhui , Hao Danni
Published: February 2025

When detecting respiratory rate and heart rate in an FMCW radar room, there is a lot of static clutter and white Gaussian noise generated by hardware heat loss in the environment, which makes the separation of respiratory and heartbeat signals poor. At the same time, the harmonic component of the respiratory signal in the frequency domain will affect the estimation of heart rate. To solve the above problems, a spatial blind source estimation method was proposed to accurately estimate respiratory heart rate. Firstly, the weighted principal component analysis (WPCA) algorithm was used to extract the features of the target signal from the IF signal, and then the respiratory heart rate signal was reconstructed according to the different features. Then, the multi-signal classification (MUSIC) algorithm is used to convert the respiration and heartbeat signals into the zero domain to avoid the influence of the respective harmonic components on the detection results. The experimental results showed that the accuracy of respiratory rate detection and heart rate detection was 94.51% and 97.79%, respectively. Compared with the traditional algorithm, the proposed method is stable and has higher detection accuracy.
Integrating Artificial Intelligence into an Automated Irrigation System
Authors: Gaitan Nicoleta Cristina , Batinas Bianca Ioana , Ursu Calin , Crainiciuc Filaret Niculai
Published: February 2025

Climate change in Eastern Europe requires introducing automated irrigation systems and monitoring agricultural and climatic parameters to ensure food security. The automation of irrigation, together with the generation of climate reports based on AI (artificial intelligence) using OpenAI models for Internet of Things (IoT) data processing, contributes to the optimization of resources by reducing excessive water and energy consumption, supporting plant health through proper irrigation and increasing sustainable agricultural productivity by providing suggestions and statistics to streamline the agricultural process. In this paper, the authors present a system that allows continuous data collection of parameters such as temperature, humidity, and soil moisture, providing detailed information and advanced analytics for each device and area monitored using AI to generate predictive recommendations. The data transmission is performed wirelessly via WebSocket to the central database. This system uses data from all devices connected to the application to assess current climate conditions at a national level, identifying trends and generating reports that aid in adapting to extreme events. The integration of artificial intelligence in the context of monitoring and irrigation of agricultural areas is a step forward in the development of sustainable agriculture and for the adaptation of agriculture to increasingly aggressive climate phenomena, providing a replicable framework for vulnerable regions.
GazeCapsNet: A Lightweight Gaze Estimation Framework
Authors: Muksimova Shakhnoza , Valikhujaev Yakhyokhuja , Umirzakova Sabina , Baltayev Jushkin , Cho Young Im
Published: February 2025

Gaze estimation is increasingly pivotal in applications spanning virtual reality, augmented reality, and driver monitoring systems, necessitating efficient yet accurate models for mobile deployment. Current methodologies often fall short, particularly in mobile settings, due to their extensive computational requirements or reliance on intricate pre-processing. Addressing these limitations, we present Mobile-GazeCapsNet, an innovative gaze estimation framework that harnesses the strengths of capsule networks and integrates them with lightweight architectures such as MobileNet v2, MobileOne, and ResNet-18. This framework not only eliminates the need for facial landmark detection but also significantly enhances real-time operability on mobile devices. Through the innovative use of Self-Attention Routing, GazeCapsNet dynamically allocates computational resources, thereby improving both accuracy and efficiency. Our results demonstrate that GazeCapsNet achieves competitive performance by optimizing capsule networks for gaze estimation through Self-Attention Routing (SAR), which replaces iterative routing with a lightweight attention-based mechanism, improving computational efficiency. Our results show that GazeCapsNet achieves state-of-the-art (SOTA) performance on several benchmark datasets, including ETH-XGaze and Gaze360, achieving a mean angular error (MAE) reduction of up to 15% compared to existing models. Furthermore, the model maintains a real-time processing capability of 20 milliseconds per frame while requiring only 11.7 million parameters, making it exceptionally suitable for real-time applications in resource-constrained environments. These findings not only underscore the efficacy and practicality of GazeCapsNet but also establish a new standard for mobile gaze estimation technologies.
Advancements in Hydrogen Storage Vessels: A Bibliometric Analysis
Authors: del Bosque Antonio , Fernández-Arias Pablo , Vergara Diego
Published: February 2025

This bibliometric study examines the evolution of compressed-hydrogen storage technologies over the last 20 years, revealing exponential growth in research and highlighting key advancements in compressed-hydrogen storage, materials-based solutions and integration with renewable energy systems. The analysis highlights the pivotal role of composite material tanks and the filament-winding process in revolutionizing storage technology. These innovations have enhanced safety, reduced weight and facilitated adaptation for use in automotive and industrial applications. Global research efforts are characterized by substantial international collaboration, spearheaded by a small cohort of highly productive researchers and supported by a broader network of contributors. Notwithstanding the ongoing challenges pertaining to safety considerations and cost scalability, the potential of hydrogen as a clean energy carrier and its role in balancing renewable energy systems serve to reinforce its importance in the transition to sustainable energy.
Secure Electric Vehicle Charging Infrastructure in Smart Cities: A Blockchain-Based Smart Contract Approach
Authors: Chowdhury Abdullahi , Shafin Sakib Shahriar , Masum Saleh , Kamruzzaman Joarder , Dong Shi
Published: February 2025

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems.
‘Low-Level’ Social Care Needs of Adults in Prison (LOSCIP): A Scoping Review of the UK Literature
Authors: Buck Deborah , Ali Akash , Butt Noor , Chadwick Helen , Mulligan Lee D. , O’Neill Adam , Robinson Catherine , Shaw Jenny J. , Shepherd Andrew , Southworth Josh , Stalker Kate , Forsyth Katrina
Published: February 2025

In England, local authorities are responsible for providing social care in prison in accordance with the Care Act (implemented in 2015), but little is known about ‘low-level’ needs that do not meet eligibility criteria. These ‘low-level’ social care needs can greatly affect individuals living in prison with limited autonomy. This scoping review aimed to describe the nature, extent, and conceptualisation of ‘low-level’ social care in UK prisons, including prevention and promotion of independence. We adopted the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews checklist and Joanna Briggs Institute guidelines. Papers were screened by pairs of reviewers. In total, 31 papers were included. Findings are reported using descriptive techniques. Types of ‘low-level’ social care discussed included finances (n = 8 papers), feelings of safety (n = 8), and dignity (n = 8). Ten papers included prevention of social care needs and/or promoting independence in relation to social care needs. Evidence is limited, and further research is needed on ‘low-level’ support needs of people in prison. Consensus is needed on what constitutes ‘low-level’ social care needs; how they may vary depending on individual characteristics including age, gender, ethnicity, culture, and neurodiversity; and how individuals should be supported with these needs.
The Relationship Between Work Stress and Compensatory Tourism Consumption: Exploring New Directions for Individual Sustainable Tourism
Authors: Wang Xinzhu , Sim Jaeyeon
Published: February 2025

This study explores the impact of work stress on compensatory tourism consumption, with work stress categorized into challenge stressors and hindrance stressors. The research examines the potential for individuals to engage in regular compensatory tourism consumption. Grounded in the Challenge-Hindrance Stress framework, compensatory consumption theory, and the Stimulus-Organism-Response (S-O-R) model, the study employs structural equation modeling (SEM) to integrate these theories with the research model. Data were collected through an online survey, yielding 375 responses, of which 361 were valid. The sample was subjected to statistical analysis of sample characteristics, multifactorial analysis of variance, correlation analysis, convergent validity analysis, regression analysis, and Bootstrap mediation effect testing. The results indicate that work stress is positively correlated with compensatory tourism consumption, and emotional exhaustion partially mediates this relationship. Therefore, tourism industry enterprises can focus on maintaining relationships with customers who choose compensatory tourism consumption, thereby increasing the likelihood of sustainable, recurring compensatory tourism consumption among these customers.
Multi-Temporal Analysis of Cropping Patterns and Intensity Using Optical and SAR Satellite Data for Sustaining Agricultural Production in Tamil Nadu, India
Authors: Pazhanivelan Sellaperumal , Kumaraperumal Ramalingam , Vishnu Priya Manchuri , Rengabashyam Kalpana , Shankar Kanaka , Nivas Raj Moorthi , Yadav Manoj Kumar
Published: February 2025

Analyzing the spatial and temporal trends in cropping patterns and intensity on a larger scale is essential for implementing timely policy decisions and strategies in response to climate change and variability. By converting cropping intensity estimates, we can compute net and gross production values, indirectly indicating food security status in the study region. This study compared the utility of optical (MOD13Q1) and SAR (Sentinel 1A) datasets for determining cropping patterns and associated intensity estimates across multiple agricultural seasons from 2019 to 2023, with spatial resolutions of 250 m and 20 m, respectively. The analysis revealed that the highest and lowest gross cropped areas using Sentinel 1A data were 55.85 lakh hectares (2022–2023) and 52.88 lakh hectares (2019–2020), respectively. For MODIS data, the highest and lowest gross cropped areas were 62.07 lakh hectares (2022–2023) and 56.87 lakh hectares (2019–2020). Similarly, the highest and lowest net sown areas using Sentinel 1A data were 43.71 lakh hectares (2022–2023) and 41.76 lakh hectares (2019–2020), and for MODIS data, the values were 48.81 lakh hectares (2022–2023) and 46.39 lakh hectares (2019–2020), respectively. Regardless of the datasets used, the highest gross and net cropped areas were reported in Tiruvannamalai district and the lowest in Kanchipuram district. Thiruvarur district reported the highest cropping intensity, while Sivagangai district had the lowest. Among all seasons, the rabi season accounted for the maximum area, followed by the kharif and summer seasons. The study concluded that single cropping (51%) was the dominant cropping pattern in Tamil Nadu, followed by double cropping (31%) and triple cropping (17%) in both datasets. Sentinel 1A data showed better performance in estimating gross and net cropped areas than optical data, with deviations ranging from 7.02% to 11.01%, regardless of the year and cropping estimates derived. The results indicated that the spatial resolution of the datasets was not a significant factor in determining cropping patterns and intensity on a larger scale. However, this may differ for smaller study areas.
Normative Influences on Carbon Offset Behavior: Insights from Organic Farming Practices
Authors: Feng Yu , Feng Yi , Liu Ziyang
Published: February 2025

The production of green agricultural products and carbon sink compensation play a crucial role in mitigating climate change. Farmers’ behaviors are influenced by both social norms and personal norms. This study aims to explore how these norms shape farmers’ carbon sink compensation behaviors and to provide a theoretical basis for formulating effective policies and incentive mechanisms. A mixed-methods approach was adopted in this study, involving in-depth interviews with 13 agricultural workers and a survey of 409 individuals from China, Japan, and South Korea who are or were engaged in agriculture-related work. The results indicate that the activation of personal norms is primarily driven by economic costs rather than mere moral responsibility. Subjective norms serve as a significant mediator between personal norms and behavior. Social norms indirectly influence behavior through policy guidance and community support. Based on these findings, specific strategies to strengthen personal norms, optimize social norms, and improve policy incentives were proposed to enhance farmers’ willingness to participate in carbon sink compensation and promote sustainable low-carbon agriculture. To effectively promote farmers’ participation in carbon sink compensation, it is necessary to foster a positive social atmosphere at the community level while addressing farmers’ personal needs by enhancing environmental awareness and engagement through policy guidance and incentives. This study employs grounded theory, combining open, axial, and selective coding to thoroughly analyze the interaction between social and personal norms and their positive impact on farmers’ behavior, specifically regarding green agricultural product carbon sink compensation. Concrete policy and community-level pathways are proposed, providing clear guidance for both theory and practice.
Sustainable Agri-Food Systems: Environment, Economy, Society, and Policy—2nd Edition
Authors: Ben Hassen Tarek , El Bilali Hamid , Strassner Carola
Published: February 2025

Food systems encompass all elements and activities involved in producing, processing, distributing, and consuming food, along with their outputs [...]
Social Identification in Open Innovation Projects: Role of Knowledge Collaboration and Resource Interdependence
Authors: Fang Shaoze , Qin Yanjun
Published: February 2025

In open innovation projects (OIPs), project members from different participating organizations often have dual social identifications, namely, project identification and organizational identification. While such dual identifications may align or conflict in shaping collaborative behaviors, their micro-level psychological mechanisms and performance implications remain underexplored, as prior research predominantly adopts a firm-centric perspective. This study examines how dual social identifications affect OIP performance. The investigation considers mediating role of knowledge collaboration and the moderating role of resource interdependence. Data were collected through a questionnaire survey targeting the OIPs in major high-tech industrial development zones in China. The results show that project identification drives participating organizations to collaborate deeply (e.g., sharing core expertise) and broadly (e.g., involving diverse partners), both of which boost OIP performance. By contrast, organizational identification prioritizes expanding collaboration breadth over deep collaboration, which risks superficial engagement. Too many partners initially help but eventually harm OIP performance, whereas deeper collaboration consistently improves OIP performance. In addition, resource interdependence strengthens the positive effects of project identification on breadth and depth of knowledge collaboration but does little for organizational identification. This study extends social identity theory to the open innovation field and offers novel insights into practices of social psychological reality in OIPs, revealing how dual identifications interact with resource interdependence to shape OIP performance. Additionally, it offers actionable strategies for balancing knowledge collaboration breadth and depth while leveraging resource interdependence in OIP governance.
Strategic Management and Security Risk Assessment of Energy Systems
Authors: Koval Viktor , Atstāja Dzintra , Shmygol Nadiia , Udovychenko Viktoriia , Hrinchenko Hanna , Tsimoshynska Oksana
Published: February 2025

This study examines methods for assessing the risks of energy systems that supply urban areas based on an integrated approach in which a qualitative assessment takes into account internal and external impacts as an element of a multifactorial analysis. A risk matrix is offered for risk assessment, which was built for energy infrastructure facilities and takes into account the features of the functioning of energy equipment which is potentially dangerous due to the probability of failures on a five-point scale and the possible degree of severity of the consequences due to the possibility of restoring the systems. The study proposes two statistical models for the assessment of reliability parameters before the onset of a critical situation and calculations after a critical event, i.e., the probability of the restoration or adaptation of the energy system object being analyzed and the degree of such restoration. This approach allows for the consideration of risks, taking into account both the technical condition of power equipment and environmental aspects, including their mutual influence, for sustainable management. This approach evaluates the energy system’s failure intensity and recovery probability based on the parameters developed to assess the system functionality and resilience effectively. A risk assessment using reliability parameters based on maximum likelihood estimation (MLE) showed that when sampling statistical data with a total number of n = 10, the accuracy of risk assessment according to indicator parameters increases by 15%, which is especially relevant for the potential safety of urban energy systems. A list of threats is proposed for the multifactorial risk analysis of an energy company based on an expert assessment of their likelihood and consequences. The constructed risk matrix reduces the level of threat, which helps to increase the efficiency of risk management both at the stages of planning and forecasting and during operation, including the management of socio-economic consequences.
A Framework for Soil Moisture Downscaling on the Tibetan Plateau Based on Interpretable Deep Learning
Authors: Miao Zheyuan , Han Lei , Liu Zhao , Kang Hongliang , Tuo Fengwei , Zhang Han , Gan Shaoan , Ren Yuxuan , Hu Guiming
Published: February 2025

Soil moisture (SM) is crucial for agricultural production and ecological restoration, and high-resolution SM data can be efficiently acquired through downscaling remote sensing products. Although deep learning is widely used for downscaling, its mechanisms often lack interpretability. In this study, a downscaling model based on the architecture of the Kolmogorov–Arnold Network (KAN) was constructed using multi-source remote sensing data to generate SM data at 500 m resolution in the Tibetan Plateau. In addition, the interpretability of the model was effectively improved by fitting mathematical equations between SM and simplified variables. The results showed that (1) the downscaled SM of KANs demonstrated superior accuracy compared to ANN, significantly reducing the RMSE and ubRMSE of the SMAP product while closely aligning with station observations in temporal change trends; (2) correlation and SHAP analysis results showed that NDVI was the most important environmental factor affecting SM changes, followed by DEM and ET; (3) the symbolic equations of SM and simplified variables for each month were obtained by fitting the KAN model, whose downscaling accuracy was less than that of the KAN model but better than that of the SMAP product. Overall, this study explored the relationship between SM and environmental factors, and the obtained high-resolution SM data have good accuracy, which can meet the needs of small- or medium-scale research and provide a reference for subsequent downscaling studies.
Optical Characteristics of Dissolved, Particulate and Sedimentary Organic Matter in Coastal Porphyra Cultivation Zone: A Case Study of Dayu Bay, China
Authors: Wang Ting , Xu Jiajun , Qu Liyin
Published: February 2025

Seaweed plays a critical role in marine carbon sequestration due to its high release rate of organic matter. However, the impacts of Porphyra cultivation on the concentration and composition of dissolved, particulate and sedimentary organic matter (DOM, POM and SOM) in coastal cultivation zones remain unclear. Herein, we investigated the optical properties of DOM, POM and SOM along a transect from the subtropical Chi River to the adjacent Porphyra cultivation zone in Dayu Bay (southeast China) during the late cultivation stage. The results revealed that all types of organic matter in coastal cultivation zones were predominantly characterized by highly autochthonous sources, contrasting sharply with the allochthonous, terrestrial sources observed at freshwater sites. The estuarine mixing model and principal component analysis further indicated that the organic matter dynamics in the coastal zone are primarily controlled by Porphyra cultivation, with relatively limited contributions from riverine inputs, coastal sediment and porewater sources. Porphyra cultivation leads to significant additions of protein-like components in the coastal water and sediment. Microbial degradation incubations of DOM and POM further demonstrated that Porphyra cultivation promotes the in situ production of humic-like components (peak M) in coastal water. DOM exhibited a higher microbial transformation efficiency into refractory components than POM, suggesting a more substantial role of DOM in coastal carbon sequestration. Our findings underscore the potential of Porphyra cultivation to enhance the carbon sequestration of coastal ecosystems.
Assessment and Seasonal Monitoring of Groundwater Quality in Landfill-Affected Regions of China: Findings from Xiangyang
Authors: Du Juan , Yang Wenbing , Yang Qi , Li You , Wan Xiang , Zhu Anan , He Zhenzhu , Shrestha Rajendra Prasad , Razzaq Amar
Published: February 2025

Groundwater pollution in landfill-adjacent regions presents a critical environmental and public health issue. This study evaluates groundwater quality in Xiangyang City, focusing on drinking water sources and key pollution points near landfill sites. The investigation involved a comprehensive field survey, systematic sampling, and laboratory analysis to determine pollutant types, sources, and concentrations. A total of 13 landfill sites were examined, with 178 groundwater samples analyzed for physical, chemical, and biological indicators during both wet and dry seasons. The findings reveal that 27.0% of groundwater samples meet Class I standards, while 46.1% and 27.0% fall into Class IV and V categories, respectively, indicating a significant prevalence of poor-quality groundwater. Seasonal variations were observed, with both wet and dry seasons showing consistent distributions of Class I, IV, and V samples. Heavy metals such as lead and arsenic, along with organic pollutants like polychlorinated biphenyls and pesticides (e.g., hexachlorobenzene), were significant contaminants in several sites. Key indicators such as nitrate, ammonia nitrogen, manganese, and total hardness consistently exceeded standard limits, with the most affected sites including L4 and L5 in Xiangyang. This study identifies leachate infiltration as the primary cause of pollution, exacerbated by geological and agricultural non-point sources. Based on these findings, a robust framework for monitoring and controlling groundwater pollution is proposed, emphasizing stricter regulations, advanced monitoring systems, and cross-regional coordination. The results underscore the urgency of immediate intervention to safeguard groundwater quality in landfill-adjacent regions.
Research on Application of Convolutional Gated Recurrent Unit Combined with Attention Mechanism in Water Supply Pipeline Leakage Identification and Location Method
Authors: Jiang Zhu , Wang Yuchen , Ning Haiyan , Yang Yao
Published: February 2025

To improve the accuracy of leak identification and location of water supply pipelines, a novel convolution gated recurrent unit method based on the attention mechanism is proposed in this paper. Firstly, a convolutional neural network is used to capture the localspatio-temporal characteristics of the signal. Secondly, a gated recurrent unit is used to extract the signal’s long dependence relationship. Finally, an attention mechanism is combined to highlight the influence of key features in the learning process, so as to achieve accurate recognition of the pipeline pressure state. The accurate identification of leakage faults is expected to further improve the location accuracy of pipeline leakage points, which is very important for the practical application of the algorithm in engineering. In order to verify the effectiveness of the proposed method, a simulated leakage test platform is set up for the leakage simulation test. The test results of different leakage conditions show that the recognition accuracy of the proposed network structure is 98.75% for test samples, which is higher than other network structures of the same type. According to the identification results of leakage characteristics, the VMD method is used to extract the high-frequency components of the negative pressure wave signal, so as to obtain the inflection point of the negative pressure wave, so as to determine the arrival time difference of the signal, and the arrival time method based on the negative pressure wave is used to locate the leakage point. Across 12 leak locations, the maximum relative error is 7.67%, the minimum relative error is 0.86%, and the average relative error is only 2.97%, achieving the best performance among the various methods. The positioning accuracy meets the requirement of practical application and the algorithm has good robustness.
Retrato dos aquariofilistas do Estado do Pará: um breve panorama
Authors: Almeida Gabriele Santos de , Silva Erik Ramos da , Funck Ana Paula Monschau , Britto Elton Nunes
Published: February 2025

O Brasil tem um grande potencial para a produção de peixes ornamentais, pelo clima, disponibilidade de água e grande variedade de espécies ornamentais, e a indústria do aquarismo é um importante mercado mundial. Grande parte das espécies de peixes ornamentais do Brasil têm origem na Amazônia, região com a maior diversidade de peixes da ictiofauna brasileira. Dados estatísticos oficiais com o levantamento de demanda deste mercado impossibilitam dimensionar a real importância deste agronegócio. O Estado do Pará é uma região geográfica favorecida em termos de recursos hídricos com uma vasta diversidade de espécies de peixes. No entanto, em relação à cadeia de organismos aquáticos ornamentais há informações escassas e amparadas em dados subestimados comprometendo a atividade e inviabilizando-se incentivos, vindo a gerar prejuízos aos produtores. Assim, este estudo teve o objetivo geral de identificar e caracterizar os aquaristas de organismos aquáticos ornamentais de Santarém, Oeste do Estado do Pará utilizando a técnica de amostragem snowball e questionários enviados via google formulários com perguntas abertas e fechadas. Os resultados deste estudo revelaram que há um predomínio de aquariofilistas na maioria homens com mais experiência na atividade do que as mulheres, possuindo ensino médio e ensino médio técnico, que usam diversas artes de pesca, interagem com outros aquariofilistas locais para identificar os organismos aquáticos ornamentais, usam aquários, ração comercial e gastam com a atividade a cada três meses.
Fatores associados ao óbito por COVID-19 em Pernambuco, entre os anos 2020 e 2024: um estudo transversal
Authors: Araújo Cristie Aline Santos de , Marinho Ana Clara de Souza , Oliveira Ana Luiza de Menezes , Araújo Bruno José Santana de Freitas , Moraes Gabriela Kamilla Xavier de , Maciel Maria Eduarda Moura , Angelis Milena Maria Santiago de , Silva Renata Claudia Santos da
Published: February 2025

Justificativa/Problema: A pandemia de COVID-19 trouxe óbitos e consequências graves para a saúde física e mental da população mundial. Em 2020, o Nordeste brasileiro apresentou a segunda maior taxa de mortalidade pela doença entre as grandes regiões nacionais. Diante desse cenário epidemiológico, são necessários estudos que investiguem os fatores associados ao óbito por COVID-19, essa investigação tem relevância para a prática clínica, dirigida a grupos populacionais com maior chance de agravamento do desfecho por doenças infecto-contagiosas. Objetivo: Analisar condições clínicas e características sociodemográficas associadas ao óbito por COVID-19 em Recife, Pernambuco, Brasil. Métodos: estudo transversal, sobre casos síndrome respiratória aguda grave por COVID-19 através de dados secundários registrados nas plataformas de dados do DATA-SUS, Informs.saude.gov e SES-PE, para quantificação e delineamento da população atingida em PE entre os anos 2020 e 2024. Resultados: No período foram notificados 1.233.063 casos da COVID-19 na região metropolitana e interior do Estado, desse total 23.230 (1,88%) indivíduos vieram a óbito, sendo 12.280 (52,86%) correlacionados a alguma comorbidade. Conclusão: Diante do presente estudo observou-se que pacientes acometidos com a COVID-19 que possuiam uma ou mais comorbidades têm maior risco de óbito, sendo as doenças cardíacas e/ou cardiovasculares e diabetes as mais prevalentes. Apesar das limitações na coleta de dados, o Estado de Pernambuco, com grande número de óbitos não possuíam todas as informações acerca de comorbidades, o estudo apresenta um panorama geral dos óbitos por COVID-19 e sua letalidade. Os dados obtidos neste estudo reforçam o mau prognóstico da COVID-19 nesta população.
Metástase testicular como complicação rara em câncer de próstata: relato de caso e revisão de desafios diagnósticos e terapêuticos
Authors: Mira Lucas Mansani de , Petranhski Gabrielle , Galvão Mariana Luisa , Cavalli Rafael Cavalheiro
Published: February 2025

Introdução: o câncer de próstata é a segunda neoplasia mais comum em homens, e apesar de ser frequentemente diagnosticado, metástase em testículo são raramente descritas. Objetivo: reportar um caso de câncer de próstata com metástase testicular em paciente com mais de 15 anos de acompanhamento. Relato do Caso: paciente do sexo masculino, caucasiano, com diagnóstico de câncer de próstata em 2003 e metástase óssea em 2005. Após anos de acompanhamento e tratamento hormonal, evoluiu com retenção urinária. Foi submetido a prostatectomia transvesical e orquiectomia bilateral. O anatomopatológico indicou adenocarcinoma metastático em testículo direito. Conclusão: este caso demonstra que o câncer de próstata pode metastatizar para locais raros, como os testículos. A presença desses relatos auxilia a prática clínica e acrescentam a literatura, incentivando maiores estudos sobre as abordagens diagnósticas e tratamento.