bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Detection of Multiple Faint Streak-like Space Targets from a Single Star Image
Authors: Han Yong , Wen Desheng , Li Jie , Qiao Zhangchi
Published: February 2025

With the increasing number of human space activities, space surveillance systems need to be developed to reduce the risk of collisions between space assets and space debris. In this context, optical surveillance systems have gradually become a significant means of space surveillance due to their various advantages. Generally, the sidereal tracking mode is used to search for unknown moving targets, which appear as streaks in the star image generated by the optical surveillance system. Typical matched filtering can detect faint streak-like targets in star images, but it generates more false alarms and must traverse all potential filters. In this paper, the layering approach is used to improve the environment for detecting faint targets, in which dual-threshold segmentation is proposed to separate bright objects while maintaining the completeness of faint targets. Second, a streak-like matched filter unit and a dual-step search approach are recommended to lower the computational cost of matched filtering. Finally, perpendicular cross filtering is provided to further eliminate false positives. Experiments performed with both simulated and real data demonstrate that the proposed method has excellent detection performance for detecting multiple faint streak-like targets in a single star image.
Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru
Authors: Quille-Mamani Javier , Ramos-Fernández Lia , Huanuqueño-Murillo José , Quispe-Tito David , Cruz-Villacorta Lena , Pino-Vargas Edwin , Flores del Pino Lisveth , Heros-Aguilar Elizabeth , Ángel Ruiz Luis
Published: February 2025

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R2 of 0.69 during the flowering and milk stages, and an R2 of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R2 of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning.
The Effect of Area Density of Polysilicon Thermocouples on Thermoelectric Performance
Authors: Yang Shih-Ming , Lai Zen-Wen , Liu Ai-Lin
Published: February 2025

Thermoelectric energy generators (TEGs) that can convert body heat into electricity are considered most promising to drive wearable devices. Many TEG designs with a polysilicon thermocouple have been proposed for implementation in high-yield semi-conductor foundry services. This study shows that the area density, defined by the number of thermocouples per mm2, is a better index than the fill factor in evaluating TEG performance. The effects of thermocouple length, width, and spacing (between the adjacent thermocouples) on area density, and hence on TEG performance, are analyzed. For a TEG with 33 × 1 μm (length × width) co-planar thermocouples (P- and N-thermoleg side by side) and 1 μm spacing between two adjacent thermocouples, the area density is 4902 thermocouples per mm2 and it can deliver a 0.110 μW/cm2K2 power factor and a 12.906 V/cm2K voltage factor. The performance can be improved further by 57 × 1 μm stacked thermocouples (P-thermoleg above N-thermoleg) with a higher area density 8621 to achieve results of 0.110 μW/cm2K2 and 22.638 V/cm2K. Such a high area density not only increases TEG performance, but also improves the DC–DC converter efficiency. A 5 × 5 mm2 TEG chip with co-planar or stacked thermocouples is shown to deliver above 3 μW and over 3 V when operating at a 10 °C temperature difference.
Jumping and Landing Kinematics in Spanish Female Soccer Players: A Comparison Between Elite and Non-Elite Athletes
Authors: Robles-Palazón Francisco Javier , Aparicio-Sarmiento Alba , Martínez-Romero María Teresa , De Ste Croix Mark , Ayala Francisco
Published: February 2025

Landing from a jump has been identified as a common situation of increased risk in sport and the tuck jump assessment (TJA) has been proposed for a comprehensive examination of landing mechanics. However, group-specific data on female athletes are limited. The purpose of this study was to examine the movement mechanics during a TJA in Spanish female soccer players and to explore potential differences between players of different performance levels. A total of 96 (elite and non-elite) female soccer players performed a TJA, and a rater visually graded the technique using the modified 10-item scoring system (0, 1, or 2 for “none”, “small”, or “large” flaws). Descriptive statistics were calculated. The association between the flaws and performance groups was assessed using the chi-square test. Almost 90% of all players involved were categorized with small and large flaws for the item “Lower extremity valgus at landing”. The proportion of players categorized with technical flaws was also high for “Foot contact timing not equal” (85%) and “Does not land in same footprint” (82%). Differences between elite and non-elite players were only found for “Foot placement not parallel” and “Excessive landing contact noise” (p < 0.008). These results reveal the importance of implementing training programs to reduce jumping and landing deficits in female soccer players, independently of the players’ level of performance.
Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet
Authors: Saleem Faisal , Ahmad Zahoor , Siddique Muhammad Farooq , Umar Muhammad , Kim Jong-Myon
Published: February 2025

Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline monitoring approach, integrating Empirical Wavelet Transform (EWT) for adaptive frequency decomposition with customized one-dimensional DenseNet architecture to achieve precise leak detection and size classification. The methodology begins with EWT-based signal segmentation, which isolates meaningful frequency bands to enhance leak-related feature extraction. To further improve signal quality, adaptive thresholding and denoising techniques are applied, filtering out low-amplitude noise while preserving critical diagnostic information. The denoised signals are processed using a DenseNet-based deep learning model, which combines convolutional layers and densely connected feature propagation to extract fine-grained temporal dependencies, ensuring the accurate classification of leak presence and severity. Experimental validation was conducted on real-world AE data collected under controlled leak and non-leak conditions at varying pressure levels. The proposed model achieved an exceptional leak detection accuracy of 99.76%, demonstrating its ability to reliably differentiate between normal operation and multiple leak severities. This method effectively reduces computational costs while maintaining robust performance across diverse operating environments.
Unmasking the Fungal Menace: A Case Report of Chronic Granulomatous Invasive Fungal Sinusitis of Maxilla
Authors: Krishna Swathi , Ashok Vivekanand , Abdulla Shahseena , John Rosmy , Ramalingam Prathap
Published: February 2025

Chronic granulomatous invasive fungal sinusitis (CGIFS) is an uncommon type of invasive sinusitis that is characteristically seen in immunocompetent individuals. Common clinical manifestations of this condition include proptosis, cheek swelling, and headache. The pathogenic organism is Aspergillus in the majority of reported cases. Diagnosis is made by histopathological and microbiological examination of tissue specimens. Due to its expansible nature, bone erosion is also associated with this condition. Treatment is surgical clearance/debulking followed by long-term antifungal therapy. Here, we report the case of a 31-year-old male presented with right-side cheek swelling. The patient underwent a medial maxillectomy and was diagnosed with CGIFS. The patient was put on oral voriconazole for 3 months. To conclude, CGIFS is a rare variant of invasive fungal sinusitis that may mimic malignancy or granulomatous diseases such as tuberculosis, rhinoscleroma, and syphilis. Accurate diagnosis is of utmost importance in providing management for CGIFS.
Hyaluronic Acid Rectal Spacer for Rectal Protection in Salvage Cystoprostatectomy
Authors: Wong Benjamin Ngie Xiong , Hong Anne , Alhamdani Zein , Bolton Damien , Woon Dixon Teck Sing
Published: February 2025

Background: In salvage cystoprostatectomies (SCPs), rectal injuries can occur at a rate of 1% to 10%. Factors including T3 disease and prior pelvic radiation can lead to complications such as bleeding, recurrent wound infections and the need for colonic diversion. Methods: We present a male patient in his late 70s with a new diagnosis of pT2 muscle-invasive bladder cancer (MIBC). This is on a background of Stage IIB prostate cancer 10 years ago, managed with external beam radiotherapy. He patient had hyaluronic acid (HA) rectal spacer infiltration into the Denonvilliers’ space two weeks prior, for rectal protection. HA rectal spacers are easily identifiable due to their anechoic appearance on ultrasound imaging, making them easily distinguishable when injected into the Denonvilliers’ space intraoperatively. Results: The patient did not experience any symptoms related to rectal injury and had full bowel continence postoperatively. Conclusion: Although approved for use in radiation treatment for prostate cancer, their role in aiding dissection during SCP remains unexplored. We exhibit the use of HA rectal spacers for rectal protection during SCP.
Considering Mountain Micro-Topographic Characteristics in Habitat Quality Assessments and Its Nonlinear Influencing Mechanism
Authors: Wang Fang , Li Zhe , Li Xiaoya , Li Zhaoyu , Qi Guangxiang , Wang Qi
Published: February 2025

Optimizing the habitat quality (HQ) assessment and revealing its nonlinear influence mechanisms, particularly by considering the mountain micro-topographic characteristics, are critically important for promoting sustainable development and safeguarding the ecological environment of mountain cities. Taking the Chongqing main city (CMC) as the study area, first, the Geomorphons algorithm was used to identify the mountain micro-topographic positions. On this basis, the HQ assessment of the InVEST model was optimized by collecting the multispectral data from UAV, and its spatiotemporal change trend was analyzed by the least-squares method. Secondly, hotspot analysis was used to explore the spatiotemporal differentiation of HQ on different land use and geomorphological types. Finally, based on the generalized additive model, the dominant influencing factors were determined, and their nonlinear effects were analyzed. The results showed the following: (1) The average HQ of the CMC showed an increasing trend from 2000 to 2020. The HQ of the four mountains and two rivers was higher, while it was lower in the central urban area. (2) The HQ hotspots were mainly distributed in parallel mountain areas and composed of forests, grasslands, and waters. The heterogeneity of HQ at the mountain micro-topographic scale was manifested in that the summits were always the hotspots of HQ. (3) HQ was influenced by a range of factors, including both natural environmental conditions and socio-economic drivers, among which the normalized difference vegetation index was the most important influencing factor.
Greater Sustainability in the Future of Hanjiang River Under Climate Change: The Case of Nitrogen
Authors: Zhang Yuchen , Zhao Yan , Chen Yiping
Published: February 2025

Water resources are essential for human survival and sustainable development. However, the global freshwater scarcity, exacerbated by climate change, presents significant sustainability challenges. Using the SWAT model, we simulated the spatiotemporal distribution of total nitrogen (TN) in the Hangjiang River Basin from 2005 to 2020. The average TN concentration was 2.16 mg/L, with the soil nitrogen pool contributing 92.78% of emissions, highlighting the need to address the soil nitrogen legacy. Sampling showed average concentrations of TN, nitrate, ammonium, nitrite, and dissolved organic nitrogen at 3.01 mg/L, 1.66 mg/L, 0.21 mg/L, 0.02 mg/L, and 1.11 mg/L, respectively. Precipitation accounted for 61.4% of nitrogen emission variability, indicating that water resource sustainability will be significantly influenced by climate change. Projections indicated that from 2020 to 2050, climate change will increase runoff by 6.19 m3/s and reduce TN concentration by 0.004 mg/L annually, potentially enhancing the overall sustainability of water resources. It’s necessary to continue strengthening the prevention and control of agricultural non-point source pollution and reduce nitrogen discharge to further enhance water resource security for the Beijing–Tianjin–Hebei development. The findings provide critical insights to inform policies aimed at protecting water sources and ensuring public water safety.
Application of Improved NSGA-II Multi-Objective Genetic Algorithm in Optimal Allocation of Water Resources in Main Tarim River Basin
Authors: Cheng Kaiyi , Li Donghao , Deng Mingjiang , Li Xin , Fang Guohua
Published: February 2025

As the longest inland river in China, the Tarim River is characterized by water shortage and ecological degradation in the basin, and water resources have become the most important factor restricting the sustainable economic and social development of the basin. In this paper, the optimal allocation model of water resources in the main Tarim River is constructed. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) as a classical multi-objective optimization algorithm suffers from the shortcomings of high computational complexity, long time-consuming non-dominated sorting, and difficulty in diversity preservation under high-dimensional objectives. To address these problems, good point set theory is introduced to improve the distributivity of the solution set, and a linear pressure selection mechanism is utilized to improve the convergence speed of the algorithm. The model is solved by using the improved NSGA-II, and the optimal allocation scheme of water resources in the main Tarim River is proposed. The results show that the total regional water supply remains unchanged under the optimal allocation scheme, while the guaranteed rate of agricultural water supply in the ALE-XQM Irrigation District, XQM-YBZ Irrigation District, YBZ-USM Irrigation District, and CAL-DXHZ Irrigation District is increased by 4.36%, 12.11%, 37.70%, and 0.36%, respectively. The guaranteed rate of ecological water supply is increased by 0.19%, 19.05%, 19.29%, and 36.05%, respectively. And the amount of water discharged from Daxihaizi increased by 0.51 billion m3. In addition, under the three typical hydrological frequency scenarios of moderate year, medium dry year, and extreme dry year in 2030, the guaranteed rate of agricultural and ecological water supply and the amount of water discharged from Daxihaizi can better meet the design requirements. In short, the improved algorithm has obvious superiority, which can make full use of the natural incoming water of the dry river to reduce the overall water shortage and improve the water supply guarantee rate of each region. The optimal allocation scheme can provide scientific reference for the rational allocation of water resources in the Tarim River basin and has an important application value for solving the problem of water resources shortage in Northwest Arid Region.
Machine Learning to Forecast Airborne Parietaria Pollen in the North-West of the Iberian Peninsula
Authors: Astray Gonzalo , Amigo Fernández Rubén , Fernández-González María , Dias-Lorenzo Duarte A. , Guada Guillermo , Rodríguez-Rajo Francisco Javier
Published: February 2025

Pollen forecasting models are helpful tools to predict environmental processes and allergenic risk events. Parietaria belongs to the Urticaceae family, and due to its high-level pollen production, is responsible for many cases of severe pollinosis reactions. This research aims to develop different machine learning models such as the random forest—RF, support vector machine—SVM, and artificial neural network—ANN models, to predict Parietaria pollen concentrations in the atmosphere of northwest Spain using 24 years of data from 1999 to 2022. The results obtained show an increase in the duration and intensity of the Parietaria main pollen season in the Mediterranean region (Ourense). Machine learning models exhibited their capacity to forecast Parietaria pollen concentrations at one, two, and three days ahead. The best selected models presented high correlation coefficients between 0.713 and 0.859, with root mean squared errors between 5.55 and 7.66 pollen grains·m−3 for the testing phase. The models developed could be improved by increasing the number of years, studying other hyperparameter ranges, or analyzing different data distributions.
Sustainable Consumption in Context: A Cross-Cultural Study of Social Representations
Authors: Weder Franzisca , Golob Urša , Podnar Klement
Published: February 2025

This study explores how consumers in developed and developing countries perceive sustainable food consumption and how these perceptions are shaped by local contexts. Using the theory of social representations, which frames sustainability as a system of values, ideas, and practices, we conducted a mixed-methods analysis of data from online focus groups in ten countries. The results reveal significant differences between sustainability frontrunners in developed countries and consumers in developing countries. Consumers in developed countries focus on immanent representations, linking sustainability to concrete practices such as recycling, buying organic products, and brand awareness. In contrast, consumers in developing countries adopt more transcendent views, emphasizing moral responsibility, tradition, and collective well-being, and often expecting government regulation to drive change. Barriers such as high cost, lack of knowledge, and skepticism reflect underlying socio-economic inequalities, particularly in developing contexts. The study highlights how global sustainability norms interact with local realities, revealing mismatches that limit the effectiveness of universal approaches. These findings underscore the need for context-sensitive policies and strategies that address local barriers while remaining consistent with global sustainability goals. More broadly, this research underscores the necessity of culturally tailored approaches to promote equitable and inclusive socio-ecological transformations.
Does Carbon Trading Policy Enhance the Autonomy and Controllability of Green Enterprises in Supply Chains? A Study of the Chain-Mediating Effects of Green Ambidextrous Innovation
Authors: Chen Wenjie , Yang Yisong
Published: February 2025

Enhancing the Autonomy and Controllability of green enterprises is crucial for promoting the sustainable development of a green, low-carbon economy. Carbon trading policy has garnered widespread attention across China, offering a novel approach to enhancing the capabilities of Chinese green enterprises. This study aims to explore the impact of carbon trading policies on the Autonomy and Controllability of green enterprises. By introducing the mediating variable of Green ambidextrous innovation, the action mechanism between carbon trading policies and the Autonomy and Controllability of green enterprises is analyzed. In addition, the internal action mechanism of Green ambidextrous innovation is revealed, providing a reference for improving the Autonomy and Controllability of green enterprises and building a global safe and controllable green supply chain. Drawing on data from 126 publicly-listed green companies in China, this study constructs a chain mediation framework based on the logic of “Carbon Trading Policy—Green ambidextrous innovation—Autonomy and Controllability of Green Enterprises.” Utilizing the DID methodology, the analysis explores the impact and mechanisms of the carbon trading policy on green enterprises’ Autonomy and Controllability. The findings indicate that the carbon trading policy significantly enhances the Autonomy and Controllability of green enterprises. Mechanistic analysis reveals that the policy boosts the controllability of green enterprise customers through Green ambidextrous innovation, although its effect is not significant. Additionally, the study identifies internal mechanisms within Green ambidextrous innovation that influence these capabilities. Heterogeneity analysis shows that the carbon trading policy has a particularly pronounced effect on the Autonomy and Controllability of green enterprises in western China and is more impactful for green factories compared to green supply chain enterprises.
HICA: A Hybrid Scientific Workflow Scheduling Algorithm for Symmetric Homogeneous Resource Cloud Environments
Authors: Hu Liang , Wu Xianwei , Che Xilong
Published: February 2025

With the increasing volume of scientific computation data and the advancement of computer performance, scientific computation is becoming more dependent on the powerful computing capabilities of cloud computing. On cloud platforms, tasks in scientific workflows are assigned to computational resources and executed according to specific strategies. Therefore, workflow scheduling has become a key factor affecting efficiency. This paper proposes a hybrid scientific workflow scheduling algorithm, HICA, to address the scheduling problem of scientific workflows in symmetric homogeneous cloud environments with optimization goals of makespan and cost. HICA combines the Imperialist Competitive Algorithm (ICA) with the HEFT algorithm, integrating HEFT into the initial population of the ICA to accelerate the convergence of the ICA. Experimental results show that the proposed hybrid approach outperforms other algorithms in real-world workflow applications. Specifically, when the workflow scale is 100, the average improvements in makespan and cost are 133.89 and 273.33, respectively; when the workflow scale is 1000, the improvements are 371.62 and 9178.98. The scheduling results for the Earth System Model parameter tuning workflow show that compared to the scenario without using a scheduling algorithm, the makespan and cost were improved by 13% and 21%, respectively.
Reliable Transmission of Energy Harvesting Full-Duplex Relay Systems with Short-Packet Communications
Authors: Yang Chenxi , Yu Mingkang , Huang Jinshu , Chen Dechuan , Li Jin , Jiang Pei
Published: February 2025

Energy harvesting (EH) from radio frequency (RF) signals provides a promising approach for supplying sustainable and convenient energy to low-power Internet of Things (IoT) devices. In this work, we investigate short-packet communications in a full-duplex (FD) relay system, where RF signals from a source are utilized to power an energy-constrained relay through the time switching protocol. Specifically, hardware impairments in each node and residual self-interference caused by FD are jointly considered. To ensure reliable transmission, two antennas are symmetrically arranged according to the position of the relay station, both of which are used for energy harvesting. Furthermore, we explored two practical schemes based on symmetric channel correlation, i.e., an independent channel for energy harvesting and an identical channel for energy harvesting. For both scenarios, we derive closed-form approximations for the overall average block error rate (BLER) and effective throughput. The validity of our analysis is confirmed through computer simulations, demonstrating that the proposed scheme enhances the reliability and throughput of the system compared with the existing scheme in the literature at low transmission rates and transmit signal-to-noise-ratios (SNRs).
A Probabilistic Approach to Overestimation by an Imperfect Inspector Subject to Random Defective Rates
Authors: Koo Kyo-Chan
Published: February 2025

This study investigates overestimations in defect inspections performed by imperfect inspectors, particularly in scenarios involving random defective rates. Mathematical models are developed under two key assumptions: (1) inspection errors are either constant or uniformly distributed and (2) defective rates follow a random uniform distribution. Four analytical models are used to evaluate the probability of overestimation (PO) and identify critical defect rate thresholds (CFBs). The findings reveal that the PO approaches 100% as defect rates approach zero, irrespective of inspection error characteristics. Sensitivity analysis demonstrates model robustness under varying error distributions and parameter changes. Addressing practical concerns, this research highlights the need to revise inspection schemes to mitigate biases, especially in industries with stringent quality control standards, such as electronics and pharmaceuticals. Recommendations include integrating probabilistic error models and adopting dynamic calibration systems to improve inspection accuracy. By providing a theoretical foundation for tackling overestimation, this study has significant implications for improving fairness and efficiency in global supply chains.
How Intelligent Transformation Empowers Innovation Quality Improvement in Manufacturing Enterprises: A Resource Orchestration Perspective
Authors: Liu Xinyi , Zheng Qinghao , Deng Yang , Wang Zongjun
Published: February 2025

The rise of Industry 4.0 has led more manufacturers to embrace the concept of intelligent transformation. Understanding this relationship improves both transformation efficiency and the quality of innovation. This study, based on resource orchestration theory, empirically tests a sample of Chinese A-share listed manufacturing companies from 2007 to 2022. The key findings are as follows: (1) intelligent transformation significantly enhances the innovation quality of manufacturing firms, particularly in the eastern region, non-state-owned enterprises, and non-high-tech enterprises; (2) intelligent transformation, as a vital means of resource orchestration, optimizes resource allocation, thereby strengthening corporate financing, organizational resilience, and risk-taking capabilities, ultimately empowering the improvement in innovation quality; (3) the three sub-dimensions of intelligent transformation—intelligent sensing technology, intelligent production and manufacturing, and intelligent strategic layout—all significantly contribute to the enhancement of innovation quality in manufacturing firms. These conclusions provide new insights for manufacturing enterprises to utilize intelligent transformation for resource orchestration and offer valuable implications for the improvement in their innovation quality.
Synergistic Evolution in the Digital Transformation of the Whole Rural E-Commerce Industry Chain: A Game Analysis Using Prospect Theory
Authors: Wang Yanling , Xu Junqian
Published: February 2025

In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in the digital transformation of platform enterprises and participating enterprises across the whole rural e-commerce industry chain. To achieve this, this paper combines prospect theory and evolutionary game theory, introduces the value function and decision weight of prospect theory, and constructs a two-party game model between platform enterprises and participating enterprises. Based on the demonstration of the impact of individual changes in major objective factors, such as the cooperative innovation benefit coefficient, as well as major behavioral characteristic factors, such as decision-makers’ risk attitude coefficients, on enterprises’ strategic choices, we further reveal the influence of the interaction of key factors on the evolutionary results through case simulations. The findings indicate that when the behavior characteristics of the players are introduced, the threshold interval of the cost–benefit ratio of the two sides to reach the optimal state of decision-making is obviously reduced. Under moderate risk attitudes and degrees of loss sensitivity, enhancing the resource absorption capacity of enterprises in the chain and reducing the potential risk loss of platform enterprises to alleviate the influence of subjective behavior characteristics on cooperation willingness are effective measures. Improving innovation ability is the key factor in alleviating the negative impact of uncertainty on the decision-making of both parties. This paper is one of the few studies to integrate prospect theory with evolutionary game analysis in examining the collaborative behaviors between platform enterprises and participating enterprises. Effective strategies are proposed to promote enterprises achieving synergy.
Automatic Robotic Ultrasound for 3D Musculoskeletal Reconstruction: A Comprehensive Framework
Authors: Sun Dezhi , Cappellari Alessandro , Lan Bangyu , Abayazid Momen , Stramigioli Stefano , Niu Kenan
Published: February 2025

Musculoskeletal ultrasound (US) imaging faces challenges such as operator experience, limited spatial flexibility, and high personnel costs. This study introduces an Automated Robotic Ultrasound Scanning (ARUS) system that integrates key technological advancements to automate the ultrasound scanning procedure with the robot, including anatomical target localization, automatic trajectory generation, deep-learning-based segmentation, and 3D reconstruction of musculoskeletal structures. The ARUS system consists of a robotic arm, ultrasound imaging, and stereo vision for precise anatomical area detection. A Graphical User Interface (GUI) facilitates a flexible selection of scanning trajectories, improving user interaction and enabling customized US scans. To handle complex and dynamic curvatures on the skin, together with anatomical area detection, the system employs a hybrid position–force control strategy based on the generated trajectory, ensuring stability and accuracy. Additionally, the utilized RA-UNet model offers multi-label segmentation on the bone and muscle tissues simultaneously, which incorporates residual blocks and attention mechanisms to enhance segmentation accuracy and robustness. A custom musculoskeletal phantom was used for validation. Compared to the reference 3D reconstruction result derived from the MRI scan, ARUS achieved a 3D reconstruction root mean square error (RMSE) of 1.22 mm, with a mean error of 0.94 mm and a standard deviation of 0.77 mm. The ARUS system extends 3D musculoskeletal imaging capacity by enabling both bones and muscles to be segmented and reconstructed into 3D shapes in real time and simultaneously. These features suggest significant potential as a cost-effective and reliable option for musculoskeletal examination and diagnosis in real-time applications.
Metabolomic Profiling of Human Urine Related to Mycotoxin Exposure
Authors: Dasí-Navarro Nuria , Lombardi Sonia , Vila-Donat Pilar , Llop Sabrina , Vioque Jesus , Soler-Blasco Raquel , Esplugues Ana , Manyes Lara , Lozano Manuel
Published: February 2025

Human exposure to mycotoxins is a global concern since several mycotoxins, such as enniatins and aflatoxins, have shown carcinogenic and neurotoxic effects, and the toxicologic mechanisms of most of them still need to be clarified. This study aims to investigate the metabolic pathways affected by mycotoxin exposure by evaluating metabolite alterations in urine. The participants were 540 women from the Spanish Childhood and Environment Project (INMA). For metabolite identification, a dilute and shoot extraction, followed by HPLC-Q-TOF-MS identification analysis, was performed. Data were processed using Agilent Mass Hunter Workstation with the METLIN database, Agilent Mass Profiler Professional 10.0, and Metaboanalyst 6.0. Over 2000 metabolites were obtained in each sample after feature extraction, and the most significant metabolites (p-value ≤ 0.05, fold change ≥ 2.0) were considered for pathway analysis. Enrichment analysis and topology showed that the most significantly affected pathway was the biosynthesis of unsaturated fatty acids (adjusted p-value = 0.007), with four metabolomic hits associated: linoleic acid, octadecanoic acid/stearic acid, an arachidonic acid metabolite, and (9Z)-octadecenoic acid/oleic acid. Other related pathways (unadjusted p-value ≤ 0.1) included fatty acid biosynthesis, glycerophospholipid metabolism, and ether lipid metabolism. The present study highlights the importance of metabolomics in increasing knowledge of the toxicity mechanisms and health effects of mycotoxins, especially emerging ones.
Fifty Years of Aflatoxin Research in Qidong, China: A Celebration of Team Science to Improve Public Health
Authors: Chen Jian-Guo , Zhu Yuan-Rong , Qian Geng-Sun , Wang Jin-Bing , Lu Jian-Hua , Kensler Thomas W. , Jacobson Lisa P. , Muñoz Alvaro , Groopman John D.
Published: February 2025

The Qidong Liver Cancer Institute (QDLCI) and the Qidong Cancer Registry were established in 1972 with input from doctors, other medical practitioners, and non-medical investigators arriving from urban centers such as Shanghai and Nanjing. Medical teams were established to quantify the extent of primary liver cancer in Qidong, a corn-growing peninsula on the north side of the Yangtze River. High rates of liver cancer were documented and linked to several etiologic agents, including aflatoxins. Local corn, the primary dietary staple, was found to be consistently contaminated with high levels of aflatoxins, and bioassays using this corn established its carcinogenicity in ducks and rats. Observational studies noted a positive association between levels of aflatoxin in corn and incidence of liver cancer across townships. Biomarker studies measuring aflatoxin B1 and its metabolite aflatoxin M1 in biofluids reflected the exposures. Approaches to decontamination of corn from aflatoxins were also studied. In 1993, investigators from Johns Hopkins University were invited to visit the QDLCI to discuss chemoprevention studies in some townships. A series of placebo-controlled clinical trials were conducted using oltipraz (a repurposed drug), chlorophyllin (an over-the-counter drug), and beverages prepared from 3-day-old broccoli sprouts (rich in the precursor phytochemical for sulforaphane). Modulation of biomarkers of aflatoxin DNA and albumin adducts established proof of principle for the efficacy of these agents in enhancing aflatoxin detoxication. Serendipitously, by 2012, aflatoxin exposures quantified using biomarker measurements documented a many hundred-fold reduction. In turn, the Cancer Registry documents that the age-standardized incidence rate of liver cancer is now 75% lower than that seen in the 1970s. This reduction is seen in Qidongese who have never received the hepatitis B vaccination. Aflatoxin mitigation driven by economic changes switched the dietary staple of contaminated corn to rice coupled with subsequent dietary diversity leading to lower aflatoxin exposures. This 50-year effort to understand the etiology of liver cancer in Qidong provides the strongest evidence for aflatoxin mitigation as a public health strategy for reducing liver cancer burden in exposed, high-risk populations. Also highlighted are the challenges and successes of international team science to solve pressing public health issues.
Effects of Dinoflagellate Toxins Okadaic Acid and Dinophysistoxin-1 and -2 on the Microcrustacean Artemia franciscana
Authors: Cavion Federica , Sosa Silvio , Kilcoyne Jane , D’Arelli Alessandra , Ponti Cristina , Carlin Michela , Tubaro Aurelia , Pelin Marco
Published: February 2025

Harmful algal blooms are an expanding phenomenon negatively impacting human health, socio-economic welfare, and ecosystems. Such events increase the risk of marine organisms’ exposure to algal toxins with consequent ecological effects. In this frame, the objective of this study was to investigate the ecotoxicological potential of three globally distributed dinoflagellate toxins (okadaic acid, OA; dinophysistoxin-1, DTX-1; dinophysistoxin-2, DTX-2) using Artemia franciscana as a model organism of marine zooplankton. Each toxin (0.1–100 nM) was evaluated for its toxic effects in terms of cyst hatching, mortality of nauplii Instar I and adults, and biochemical responses related to oxidative stress. At the highest concentration (100 nM), these toxins significantly increased adults’ mortality starting from 24 h (DTX-1), 48 h (OA), or 72 h (DTX-2) exposures, DTX-1 being the most potent one, followed by OA and DTX-2. The quantitation of oxidative stress biomarkers in adults, i.e., reactive oxygen species (ROS) production and activity of three endogenous antioxidant defense enzymes (glutathione S-transferase, superoxide dismutase, and catalase) showed that only DTX-2 significantly increased ROS production, whereas each toxin affected the antioxidant enzymes with a different activity profile. In general, the results indicate a negative impact of these toxins towards A. franciscana with potential consequences on the marine ecosystem.
Dark Energy and Cosmological Bounce Supported by an Unconventional Spinor Field
Authors: Fekecs Barna , Keresztes Zoltán
Published: February 2025

Alternative scenarios where the Big Bang singularity of the standard cosmological model is replaced by a bounce, or by an early almost static phase (known as emergent universe) have been frequently studied. We investigate the role of the spinor degrees of freedom in overcoming the initial singularity. We introduce a model which generalizes the Einstein–Cartan–Dirac theory, including local phase invariance of the spinor field supported by a gauge scalar field and certain couplings to the torsion. A natural gauge choice reduces the field equations to that of the Einstein–Dirac theory with a Dirac field potential that has polar and axial spinor currents. We identify a new potential term proportional to the square of the ratio of Dirac scalar and axial scalar, which provides a dark energy contribution dominating in the late-time Universe. In addition, the presence of spinor currents in the potential may induce the bounce of a contracting universe.
The Potential of Cannabidiol for Treating Canine Atopic Dermatitis
Authors: Bizarro Ana F. , Schmidt Vanessa M. , Fernandes Beatriz , Pinto Marta , Pereira Hugo , Marto Joana , Lourenço Ana M.
Published: February 2025

Atopic dermatitis is prevalent in humans (hAD) and dogs (cAD) and profoundly impacts the patients’ quality of life. The increasing number of new drugs in development for atopic dermatitis indicates both the need and potential for precision medicine to generate an optimised benefit–risk therapeutic plan. Cannabidiol (CBD), known for its potential anti-inflammatory and antipruritic properties, shows promise in hAD and cAD management, prompting the exploration of cannabinoids (CBs) and CBD as therapeutic tools. In fact, encouraging results on the benefits of using CBD in cAD have been published, along with safety evaluations that reveal that CBD is generally well tolerated in dogs. However, limited placebo-controlled trials and dosage variations in dogs pose barriers that hinder definitive conclusions. Challenges in product stability, inconsistent formulations, and legal ambiguities highlight the need for standardised CBD-based products for both research and commercial uses. The complex legal landscape further complicates accessibility and regulation. Despite these challenges, CBD is emerging as a potential avenue for cAD management, urging further high-quality research, standardised formulations, and legal clarity. This brief review provides valuable insights into the therapeutic potential of CBs and CBD in cAD, compared to hAD, emphasising the importance of rigorous research and unambiguous regulation for successful integration into veterinary dermatology.
Factors Affecting Transboundary Water Disputes: Nile, Indus, and Euphrates–Tigris River Basins
Authors: Azizi Mujib Ahmad , Leandro Jorge
Published: February 2025

Transboundary water disputes arise as nations compete over shared water resources, exacerbated by climate change, socio-economic inequalities, and geopolitical tensions. These disputes, prominent in river basins like the Nile, Euphrates–Tigris, and Indus, threaten regional stability, water security, and ecological sustainability. This study investigates the drivers of such conflicts, evaluates the role of historical treaties, and examines the socio-economic and political dynamics, including hydro-hegemony and power imbalances, that influence governance. The research addresses the two following key questions: what factors drive transboundary water disputes, and what lessons from past conflicts and cooperation can guide future governance frameworks? Using a descriptive approach, the study critically analyses the literature and case studies to identify actionable insights. Specific objectives include analyzing drivers of conflict, assessing treaty efficacy, and deriving sustainable water-sharing strategies. Findings reveal that climate variability, population growth, and outdated agreements intensify challenges. In the Nile River Basin, Ethiopia’s Grand Ethiopian Renaissance Dam (GERD) strains relations, while in the Indus Basin, the outdated Indus Waters Treaty struggles to address modern demands. Similarly, unilateral actions in the Euphrates–Tigris River Basin (ETRB) exacerbate socio-political instability. The study underscores the importance of inclusive governance, equitable treaties, and integrated strategies, such as integrated water resources management and climate adaptation, to balance ecological, socio-economic, and geopolitical priorities. Linking these efforts to the Sustainable Development Goals highlights their potential to transform conflict into opportunities for regional stability and shared prosperity.
Spatial Coupling Analysis of Urban Waterlogging Depth and Value Based on Land Use: Case Study of Beijing
Authors: Zhou Jinjun , Zhang Shuxun , Wang Hao , Ding Yi
Published: February 2025

With the acceleration of urbanization and due to the impact of climate warming, economic losses caused by urban waterlogging have become increasingly severe. To reduce urban waterlogging losses under the constraints of limited economic and time resources, it is essential to identify key waterlogging-prone areas for focused governance. Previous studies have often overlooked the spatial heterogeneity in the distribution of value and risk. Therefore, identifying the spatial distribution of land value and risk, and analyzing their spatial overlay effects, is crucial. This study constructs a “Waterlogging-Value-Loss” spatial analysis framework based on the hydrological and value attributes of land use. By developing a 1D–2D coupled hydrodynamic model, the study determines waterlogging risk distributions for different return periods. Combining these results with disaster loss curves, it evaluates land-use values and employs the bivariate local Moran’s I index to comprehensively assess waterlogging risk and land value, thereby identifying key areas. Finally, the SHAP method is used to quantify the contribution of water depth and value to waterlogging losses, and a Birch-K-means combined clustering algorithm is applied to identify dominant factors at the street scale. Using the central urban area of Beijing as a case study, the results reveal significant spatial heterogeneity in the distribution of urban waterlogging risks and values. Compared to traditional assessment methods that only consider waterlogging risk, the bivariate spatial correlation analysis method places greater emphasis on high-value areas, while reducing excessive attention to low-value, high-risk areas, significantly improving the accuracy of identifying key waterlogging-prone areas. Furthermore, the Birch-K-means combined clustering algorithm classifies streets into three types based on dominant factors of loss: water depth-dominated (W), value-dominated (V), and combined-dominated (WV). The study finds that as the return period increases, the dominant factors for 22.23% of streets change, with the proportion of W-type streets rising from 29% to 38%. This study provides a novel analytical framework that enhances the precision of urban flood prevention and disaster mitigation efforts. It helps decision-makers formulate more effective measures to prevent and reduce urban waterlogging disasters.
Exploring rural maternity clinicians’ views of an interprofessional education program
Authors: Zugna Stefanie , McLachlan Helen , Cullinane Meabh , Newton Michelle , Forster Della
Published: February 2025

Purpose
Maternity and Newborn Emergencies (MANE), a two-day face-to-face interprofessional education program, aimed to improve the safety culture of maternity services in rural and regional health services across Victoria, Australia. An evaluation of the program was conducted between 2017 and 2020. The aim of this study was to explore clinicians’ views of the impact of MANE on safety culture and clinical governance.
Methodology
This study used a qualitative design including, focus groups and an online survey at five health services – one in each Victorian rural health region. Data were thematically analysed using an inductive approach.
Findings
Data were collected between 2018 and 2020, via four focus groups and one online survey (due to COVID-19 restrictions), with a total of 46 participants. Three themes were identified: MANE was a high quality, professional education program; it led to changes in clinicians’ confidence, skills, and behaviour; and had a positive impact on clinical governance and safety culture.
Research implications
Clinicians reported that MANE improved safety culture and clinicians’ understanding of clinical governance, with the delivery by external experts considered key to program success. These findings support previously reported outcomes of the MANE evaluation in which participants reported an improvement in knowledge and confidence.
Practical implications
Education programs like MANE appear to improve clinical governance and safety culture in health services.
Value
This study adds to the evidence of strategies that health services can consider to strengthen their clinical governance structures and safety culture.
Limitations
Data from one health service were obtained by written response to the questions which did not allow for participants to respond to their colleagues’ views. Most focus group attendees were midwives or nurses, with only one doctor attending one focus group. This study was conducted only one month after MANE, therefore it is unknown whether these views were sustained.
Keywords: clinical governance, interprofessional education, emergencies, safety culture, maternity emergencies, neonatal emergencies