bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Habitat Suitability for Oedaleus decorus asiaticus Using MaxEnt and Frequency Ratio Model in Xilingol League, China
Authors: Ahmed Raza , Huang Wenjiang , Dong Yingying , Guo Jing , Dildar Zeenat , Rahman Zahid Ur , Zhang Yan , Zhang Xianwei , Du Bobo , Yue Fangzheng
Published: February 2025

Grasshoppers can significantly disrupt agricultural and livestock management because they reproduce and develop quickly in friendly environments. Xilingol League is the region most severely affected by grasshopper infestations. The region’s extensive grasslands are considered valuable, a critical component of the local ecosystem, a vital resource for the region’s key economic activity of livestock farming, and crucial for supporting diverse flora and fauna, carbon sequestration, and climate regulation. Oedaleus decorus asiaticus (O. d. asiaticus) is highly harmful in Xilingol League in the Inner Mongolia Autonomous Region of China. Therefore, early warning is crucial for projecting O. d. asiaticus’s regional spread and detecting the impacts of critical environmental elements. We systematically identified 26 major contributing elements by examining four categories of environmental factors— meteorology, vegetation, soil, and topography—encompassing the three growth phases of grasshoppers. Furthermore, the MaxEnt and frequency ratio (FR) approaches, coupled with multisource remote sensing data, were used to predict a potentially appropriate distribution (habitat suitability) of O. d. asiaticus in Xilingol League. The research found nine key habitat factors influencing O. d. asiaticus distribution: the mean specific humidity during the adult stage (ASH), vegetation type (VT), above-ground biomass during the nymph stage (NAB), soil sand content (SSAND), mean precipitation during the egg stage (EP), mean precipitation during the nymph stage (NP), soil bulk density (SBD), elevation, and soil type (ST). Additionally, our analysis revealed that the most suitable and moderately suitable habitats for O. d. asiaticus are predominantly located in the southern and eastern parts of Xilingol League, with significant concentrations in West Ujumqin, East Ujumqin, Xilinhot, Zhenglan, Zheng Xiangbai, Duolun, and Taipusi. Based on the suitable habitat results, policymakers may make judgments about future management actions to preserve the ecological security of grasslands and their sustainable growth. This study indicates that the Maxent approach exhibited superior accuracy (receiver operating characteristic) compared to the FR approach for assessing the habitat suitability for O. d. asiaticus in Xilingol League.
Optimising Satellite-Derived Bathymetry Using Optical Imagery over the Adelaide Metropolitan Coast
Authors: Downes Joram , Bruce David , Miot da Silva Graziela , Hesp Patrick A.
Published: February 2025

This study enhances the accuracy of optical satellite-derived bathymetric datasets in a shallow, mixed-bottom, low-wave-energy coastal environment by identifying the optimal combination of input satellite imagery, spectral bands, and empirical derivation techniques. A total of 109 unique derivations were performed based on an exhaustive combination of these variables. These derivations were calibrated and validated using 1,064,536 ground truth observations. The results revealed that the multiband linear technique consistently outperformed the band ratio technique, achieving the best results with input bands from PlanetScope SuperDove imagery. The top-performing derivation attained an R2 value of 0.94 and an RMSE of 0.41 m when compared with the ground truth data, surpassing the published RMSE values in similar environments. Further validation beyond the calibration site confirmed its effectiveness within depths of 0.5 m to 5 m, demonstrating an RMSE of 0.51 m, albeit with a gradual reduction in accuracy with increasing depth. This research not only identifies the optimal combination of variables but also provides valuable insights into how the number of input bands, their spatial resolution, and their specific spectral properties (central wavelength and bandwidth) influence the quality of satellite-derived bathymetry datasets. Challenges remain in accounting for mixed bottom types and their variable albedos.
Optimization of Orthogonal Waveform Using Memetic Algorithm with Iterative Greedy Code Search
Authors: Wang Wanbin , Qian Lu , Zhou Yun
Published: February 2025

The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an “individual uniqueness strategy” and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS.
Multi-Level Spectral Attention Network for Hyperspectral BRDF Reconstruction from Multi-Angle Multi-Spectral Images
Authors: Song Liyao , Li Haiwei
Published: February 2025

With the rapid development of hyperspectral applications using unmanned aerial vehicles (UAVs), the traditional assumption that ground objects exhibit Lambertian reflectance is no longer sufficient to meet the high-precision requirements for quantitative inversion and airborne hyperspectral data applications. Therefore, it is necessary to establish a hyperspectral bidirectional reflectance distribution function (BRDF) model suitable for the area of imaging. However, obtaining multi-angle information from UAV push-broom hyperspectral data is difficult. Achieving uniform push-broom imaging and flexibly acquiring multi-angle data is challenging due to spatial distortions, particularly under heightened roll or pitch angles, and the need for multiple flights; this extends acquisition time and exacerbates uneven illumination, introducing errors in BRDF model construction. To address these issues, we propose leveraging the advantages of multi-spectral cameras, such as their compact size, lightweight design, and high signal-to-noise ratio (SNR) to reconstruct hyperspectral multi-angle data. This approach enhances spectral resolution and the number of bands while mitigating spatial distortions and effectively captures the multi-angle characteristics of ground objects. In this study, we collected UAV hyperspectral multi-angle data, corresponding illumination information, and atmospheric parameter data, which can solve the problem of existing BRDF modeling not considering outdoor ambient illumination changes, as this limits modeling accuracy. Based on this dataset, we propose an improved Walthall model, considering illumination variation. Then, the radiance consistency of BRDF multi-angle data is effectively optimized, the error caused by illumination variation in BRDF modeling is reduced, and the accuracy of BRDF modeling is improved. In addition, we adopted Transformer for spectral reconstruction, increased the number of bands on the basis of spectral dimension enhancement, and conducted BRDF modeling based on the spectral reconstruction results. For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. In order to evaluate BRDF modeling and quantitative application potential from the reconstruction results, we conducted comparison and ablation experiments. Finally, we solved the problem of difficulty in obtaining multi-angle information due to the limitation of hyperspectral imaging equipment, and we provide a new solution for obtaining multi-angle features of objects with higher spectral resolution using low-cost imaging equipment.
Influences of Global Warming and Upwelling on the Acidification in the Beaufort Sea
Authors: Jin Meibing , Chen Zijie , Lin Xia , Li Chenglong , Qi Di
Published: February 2025

Over the past three decades, increasing atmospheric CO2 (AtmCO2) has led to climate warming, sea ice reduction and ocean acidification in the Beaufort Sea (BS). Additionally, the effects of upwelling on the carbon cycle and acidification in the BS are still unknown. The Regional Arctic System Model (RASM) adequately reflects the observed long-term trends and interannual variations in summer sea ice concentration (SIC), temperature, partial pressure of CO2 (pCO2) and pH from 1990 to 2020. Multiple linear regression results from a control case show that surface (0–20 m) pH decline is significantly driven by AtmCO2 and SIC, while AtmCO2 dominates in subsurface (20–50 m) and deep layers (50–120 m). Regression results from a sensitivity case show that even if the AtmCO2 concentration remained at 1990 levels, the pH would still exhibit a long-term decline trend, being significantly driven by SIC only in the surface layers and by SIC and net primary production (NPP) in the subsurface layers. In contrast to the nearly linearly increasing AtmCO2 over the last three decades, the ocean pH shows more interannual variations that are significantly affected by SIC and mixed layer depth (MLD) in the surface, NPP and Ekman pumping velocity (EPV) in the subsurface and EPV only in the deep layer. The comparison of results from high and low SIC years reveals that areas with notable pH differences are overlapping regions with the largest differences in both SIC and MLD, and both cause a statistically significant increase in pCO2 and decrease in pH. Comparison of results from high and low EPV years reveals that although stronger upwelling can lift up more nutrient-rich seawater in the subsurface and deep layers and lead to higher NPP and pH, this effect is more than offset by the higher DIC lifted up from deep water, leading to generally lower pH in most regions.
Evaluation of the Impact of Climate Change on Fagus sylvatica Dieback—A Combined Approach with ERA5-Land Data and Landsat Imagery
Authors: Longo-Minnolo Giuseppe , Consoli Simona , Tessitori Matilde
Published: February 2025

Widespread dieback of Fagus sylvatica has been observed in several areas of Sicily (Italy) in recent decades, often associated with Biscogniauxia nummularia infections. However, the primary drivers of this decline remain debated, with climate change increasingly recognized as a key factor not only in exacerbating tree physiological stress but also in enhancing susceptibility to pathogens. This study addresses this gap by quantifying the impact of climate change on beech decline in the Nebrodi Regional Park using an integrated approach that combines climate reanalysis data (ERA5-Land) and remote sensing (Landsat imagery). Analysis of climatic trends between two climate normals (1961–1990 and 1991–2020) revealed significant increases in temperature, evapotranspiration, and solar radiation, coupled with a decline in relative humidity. NDVI trends indicate a progressive loss of beech vigor since 2009, strongly correlated with decreasing soil moisture and precipitation. Although forest cover has expanded, this does not necessarily indicate improved forest health, as persistent climate stress may compromise tree vitality and increase vulnerability to secondary pathogens such as B. nummularia. These findings highlight the need for adaptive forest management strategies, including selective thinning and species diversification, to enhance resilience against climate change. Future research should prioritize high-resolution satellite imagery (e.g., Sentinel-2) and in situ physiological measurements (e.g., leaf water potential and sap flow) to refine early detection of climate-induced stress and improve conservation strategies for Mediterranean beech forests.
Deriving Coastal Sea Surface Current by Integrating a Tide Model and Hourly Ocean Color Satellite Data
Authors: Chen Songyu , Shen Fang , Li Renhu , Zhang Yuan , Li Zhaoxin
Published: February 2025

Sea surface currents (SSCs) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSCs, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSCs from sequential ocean color data using maximum cross-correlation (MCC) has emerged as a promising approach. In this study, we proposed a novel SSC estimation method, called tide-restricted maximum cross-correlation (TRMCC), and implemented it on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) and the global tide model FES2014 to derive SSCs in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that TRMCC is capable of obtaining high-resolution SSCs with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and normalized cross-correlation in the Fourier domain technique. Seasonal SSC structure in the Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via the satellite method, for the first time. Our study highlights the vast potential of TRMCC to improve the understanding of current dynamics in complex coastal regions.
Switchable and Tunable Terahertz Metamaterial Absorber with Ultra-Broadband and Multi-Band Response for Cancer Detection
Authors: Abdulkarim Yadgar I.
Published: February 2025

This paper proposes a switchable and tunable terahertz metamaterial absorber utilizing a graphene-VO2 layered structure. The design employs reconfigurable seven-layer architecture from top to bottom as (topaz/VO2/topaz/Si/graphene/topaz/Au). CST software 2018 was used to simulate the absorption properties of terahertz waves (0–14 THz). The proposed metamaterial exhibits dual functionalities depending on the VO2 phase state. In the insulating state, the design achieves a tri-band response with distinct peaks at 3.12 THz, 5.65 THz, and 7.24 THz. Conversely, the VO2’s conducting state enables ultra-broadband absorption from 2.52 THz to 11.62 THz. Extensive simulations were conducted to demonstrate the tunability of absorption: Simulated absorption spectra were obtained for broadband and multi-band states. Electric field distributions were analyzed at resonance frequencies for both conducting and insulating states. The impact was studied of VO2 conductivity, loss tangent, and graphene’s chemical potential on absorption. The influence was investigated of topaz layer thickness on the absorption spectrum. Absorption behavior was examined of VO2 under different states and layer configurations. Variations were analyzed of absorption spectra with frequency, polarization angle, and incident angle. The proposed design used for the detection of cervical and breast cancer detection and the sensitivity is about is 0.2489 THz/RIU. The proposed design holds significant promise for real-world applications due to its reconfigurability. This tunability allows for tailoring absorption properties across a broad terahertz range, making it suitable for advanced devices like filters, modulators, and perfect absorbers.
Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches
Authors: Seredynski Franciszek , Szaban Miroslaw , Skaruz Jaroslaw , Switalski Piotr , Seredynski Michal
Published: February 2025

In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). For this purpose, we apply a game-theoretical framework based on an application of a variant of the Spatial Prisoner’s Dilemma game. The framework is used to build a multi-agent system, where agent-players in the process of iterated games tend to achieve a Nash equilibrium, providing them the possible maximal values of payoffs. A reached equilibrium corresponds to a global solution for the coverage problem represented by the following two objectives: coverage and the corresponding number of sensors that need to be turned on. A multi-agent system using the game-theoretic framework assumes the creation of a graph model of WSNs and the further interpretation of nodes of the WSN graph as agents participating in iterated games. We use the following two types of reinforcement learning machines as agents: Learning Automata (LA) and Cellular Automata (CA). The main novelty of the paper is the development of a specialized reinforcement learning machine based on the application of (ϵ,h)-learning automata. As the second model of an agent, we use the adaptive CA that we recently proposed. While both agent models operate in discrete time, they differ in the way they store and use available information. LA-based agents store in their memories the current information obtained in the last h-time steps and only use this information to make a decision in the next time step. CA-based agents only retain information from the last time step. To make a decision in the next time step, they participate in local evolutionary competitions that determine their subsequent actions. We show that agent-players reaching the Nash equilibria corresponds to the system achieving a global optimization criterion related to the coverage problem, in a fully distributed way, without the agents’ knowledge of the global optimization criterion and without any central coordinator. We perform an extensive experimental study of both models and show that the proposed learning automata-based model significantly outperforms the cellular automata-based model.
An NDT Method for Measuring the Diameter and Embedment Depth of the Main Rebar in Cement Poles Based on Rotating Permanent Magnet Excitation
Authors: Wang Hejia , Xiong Lan , Zhang Zhanlong , Liu Zhenyou , Yang Hanyu , Wu Hao
Published: February 2025

Cement poles serve as supporting components for transmission lines and are widely used in medium- and low-voltage transmission networks. The main rebar is the primary load-bearing structure of the pole, and the accurate measurement of its diameter and embedment depth is crucial for quality control and safety assessment. However, existing non-destructive testing methods lack the accuracy of quantifying the internal main rebar of cement poles, and the measurement process is complex, cumbersome, and inefficient. To address this issue, this paper proposes a magnetic rotation-based detection method for measuring the diameter and embedment depth of the main rebar within cement poles. A specially designed H-type magnetic excitation structure is proposed, coupled with a detection technique utilizing rotating permanent magnets. The magnetic induction intensity data were acquired at seven distinct rotation angles using sensors, and the collected data were subsequently combined with a CNN-LSTM model to invert the diameter and embedment depth of the main rebar. The experimental results indicate that the method significantly improved the measurement accuracy compared with the condition of fixed magnetic excitation, with reductions in root mean square error (RMSE) of 46.71% and 35.57% for the diameter and embedment depth measurements, respectively. This method provides a robust, efficient, and accurate solution for quantifying the main rebar within cement poles, addressing the challenge associated with the quality assessment and health monitoring of these structures.
DCAN: Dynamic Channel Attention Network for Multi-Scale Distortion Correction
Authors: Zhang Jianhua , Peng Saijie , Liu Jingjing , Guo Aiying
Published: February 2025

Image distortion correction is a fundamental yet challenging task in image restoration, especially in scenarios with complex distortions and fine details. Existing methods often rely on fixed-scale feature extraction, which struggles to capture multi-scale distortions. This limitation results in difficulties in achieving a balance between global structural consistency and local detail preservation on distorted images with varying levels of complexity, resulting in suboptimal restoration quality for highly complex distortions. To address these challenges, this paper proposes a dynamic channel attention network (DCAN) for multi-scale distortion correction. Firstly, DCAN employs a multi-scale design and utilizes the optical flow network for distortion feature extraction, effectively balancing global structural consistency and local detail preservation under varying levels of distortion. Secondly, we present the channel attention and fusion selective module (CAFSM), which dynamically recalibrates feature importance across multi-scale distortions. By embedding CAFSM into the upsampling stage, the network enhances its ability to refine local features while preserving global structural integrity. Moreover, to further improve detail preservation and structural consistency, a comprehensive loss function is designed, incorporating structural similarity loss (SSIM Loss) to balance local and global optimization. Experimental results on the widely used Places2 dataset demonstrate that DCAN achieves state-of-the-art performance, with an average improvement of 1.55 dB in PSNR and 0.06 in SSIM compared with existing methods.
Interpretable Machine Learning for Explaining and Predicting Collapse Hazards in the Changbai Mountain Region
Authors: He Xiangyang , Lang Qiuling , Zhang Jiquan , Zhang Yichen , Jin Qingze , Xu Jinyuan
Published: February 2025

This study analyzes collapse hazards for complex interactions between geology, meteorology, and human activities in the Changbai Mountain region, focusing on how to cope with these features through machine learning. Using a dataset of 651 collapse events, this study evaluates four machine learning methods, Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to deal with complex nonlinear data structures. To overcome the limitations of a single-feature selection method, a variance inflation factor is introduced to optimize the selection of collapse risk factors. The transparency and interpretability of the modeling results are enhanced by combining the Shapley Additive Explanations (SHAP) with interpretable artificial intelligence. Model performance is evaluated on a test set by several statistical metrics, which shows that the optimized random forest model performs best and outperforms SVM, XGBoost, and LightGBM. The SHAP analysis results indicate that distance from the road is a key factor for collapse hazard. This study emphasizes the need for collapse management strategies that provide interpretable solutions for collapse hazard assessment.
The Role of Affect Regulation in Linking Mindfulness in Teaching and Teachers’ Job Satisfaction: Contrasting One-to-One and Classroom Teaching
Authors: Barata-Gonçalves Miguel , Carona Carlos , Pires Luís , Gaspar Pedro , Pedroso de Lima Margarida , L. Oliveira Albertina
Published: February 2025

The teaching profession and its associated conditions are demanding, exposing teachers to significant stress that adversely impacts mental health and job satisfaction. Moreover, research indicates that mindfulness in teaching correlates with positive classroom emotions and work engagement, suggesting a potential link to job satisfaction. However, the mechanisms underlying this relationship remain underexplored. Recognizing the critical role of affect regulation in teachers’ professional adaptation, this study examined the direct and indirect effects of mindfulness in teaching on job satisfaction via positive and negative affect. A sample of 1098 teachers (906 classroom teachers and 191 one-to-one instrumental/vocal teachers; Mage = 48.4 years; 77% women) completed self-report measures of mindfulness in teaching, positive and negative affect, and job satisfaction. Mediation analysis using SPSS Process macro demonstrated that mindfulness in teaching enhances job satisfaction by increasing positive affect and reducing negative affect. Moderated mediation analysis revealed these findings were consistent across both teaching modalities. These results suggest that lifelong education for teachers through mindfulness-based professional development programs within a broader affect regulation framework may effectively enhance job satisfaction in both classroom and one-to-one teaching contexts.
Staying or Moving: Racial Differences in Single Mothers’ Residential Stability
Authors: Gabriel Ryan , Polhill Peter , Waite Adrienne
Published: February 2025

In this study, we investigate the residential stability and mobility patterns of Black single mothers compared to White single mothers. Using data from the Panel Study of Income Dynamics from 1970 to 2015, linked to the U.S. Census for contextual characteristics, our multilevel linear probability models reveal substantial racial disparities. Black single mothers have a lower probability of remaining in non-poor neighborhoods rather than migrating to poor neighborhoods relative to White single mothers. Conversely, Black single mothers possess a higher probability of remaining in poor neighborhoods instead of moving to non-poor ones in relation to White single mothers. When economic resources are allowed to vary between Black and White single mothers, even higher-income Black single mothers cannot convert these resources into remaining in or migrating to non-poor neighborhoods at the same rate as White single mothers.
On the Execution and Runtime Verification of UML Activity Diagrams
Authors: Siewe François , Ngounou Guy Merlin
Published: February 2025

The unified modelling language (UML) is an industrial de facto standard for system modelling. It consists of a set of graphical notations (also known as diagrams) and has been used widely in many industrial applications. Although the graphical nature of UML is appealing to system developers, the official documentation of UML does not provide formal semantics for UML diagrams. This makes UML unsuitable for formal verification and, therefore, limited when it comes to the development of safety/security-critical systems where faults can cause damage to people, properties, or the environment. The UML activity diagram is an important UML graphical notation, which is effective in modelling the dynamic aspects of a system. This paper proposes a formal semantics for UML activity diagrams based on the calculus of context-aware ambients (CCA). An algorithm (semantic function) is proposed that maps any activity diagram onto a process in CCA, which describes the behaviours of the UML activity diagram. This process can then be executed and formally verified using the CCA simulation tool ccaPL and the CCA runtime verification tool ccaRV. Hence, design flaws can be detected and fixed early during the system development lifecycle. The pragmatics of the proposed approach are demonstrated using a case study in e-commerce.
The Structural Equation Model of Factors Affecting Decision-Making on Low-Carbon Tourist Destinations
Authors: Janchai Napaporn , Suvittawat Adisak
Published: February 2025

Low-carbon tourism (LCT) has emerged as a pivotal approach to mitigate the environmental impact of tourism, particularly its contribution to greenhouse gas emissions. This study develops a structural equation model (SEM) to investigate the factors influencing decision-making in selecting low-carbon tourism destinations. The research integrates key variables including destination characteristics, marketing strategies, and tourist perceptions, grounded in the Theory of Planned Behavior (TPB) and consumer behavior theory. Data were collected from 405 tourists visiting Khaoyai National Park, one of Thailand’s most popular nature-based destinations, utilizing structured questionnaires and a robust sampling strategy. Findings reveal significant interrelationships among destination characteristics, marketing strategies, and tourists’ perceptions of LCT, which collectively shape decision-making processes. Enhanced perceptions of LCT are mediated by innovative marketing communications and sustainable destination features, emphasizing the role of eco-friendly practices in fostering responsible tourism. The study underscores the importance of aligning policy, business strategies, and tourist education to promote sustainable travel behaviors. These insights provide practical guidelines for stakeholders to advance low-carbon tourism initiatives while preserving the integrity of tourist experiences and environmental stewardship.
AI-Enabled Supply Chain Management: A Bibliometric Analysis Using VOSviewer and RStudio Bibliometrix Software Tools
Authors: Belu Mihaela Gabriela , Marinoiu Ana Maria
Published: February 2025

Artificial intelligence (AI) is fundamentally transforming the management of supply chain activities, offering companies the opportunity to configure resilient, transparent, and sustainable supply chains. Given its importance, this paper presents aspects of the implementation of artificial intelligence in supply chain management by performing a bibliometric analysis of 400 scientific papers published between 2010 and 2024 and indexed in the Scopus database. The analysis was based on the Bibliometrix 4.4.2 and VOSviewer 1.6.19 software to identify the most important authors and journals of interest for the researched topic. Keyword co-occurrence and co-citation analyses were used to map intellectual networks and highlight themes of interest. The research results confirm the increase in scientific interest in the field of applying AI in supply chain management, highlighting the advantages of implementing this technology in supply chain management. At the same time, the recommendations and conclusions of this paper will be useful to both academic researchers and business professionals to identify potential areas of collaboration with the aim of developing supply chain strategies that contribute to the competitiveness of companies that are part of the network.
Effect of No-Tillage on Soil Bacterial Community Structure in the Black Soil Region of Northeast China
Authors: Liu Chuan , Liu Gang , Gao Hui , Xie Yun
Published: February 2025

To assess the effects of prolonged no-tillage practices on soil health and crop output, an 18-year field study was carried out in the black soil region of Northeast China. We investigated the variations in soil physicochemical properties, bacterial community structure, and soybean yield under different no-tillage (NT) durations from year 10 to 18 and conventional tillage (CT) treatments for 18 years. The findings indicated that the 18-year no-tillage (NT18) treatment resulted in significantly greater levels of soil organic matter, total nitrogen, and available phosphorus—18.3%, 30.4%, and 65.8% higher, respectively (p < 0.05)—compared to the traditional tillage (CT18) treatment. In the 0–30 cm soil layer, the relative abundance of Acidobacteriota had risen with the duration of no-tillage, whereas Proteobacteria, Gemmatimonadota, and Verrucomicrobiota had shown a decline. In addition, no-tillage treatments increased network complexity, with longer durations of no-tillage leading to higher levels of complexity. Soybean yield increased by 8.5% under NT18 compared to CT18 (p < 0.05). These findings provide insights into the interaction between no-tillage treatments and soil bacterial microbial communities within the black soil region, thereby establishing a solid foundation for developing efficient, ecological, and sustainable conservation tillage systems in Northeast China.
About a Classical Gravitational Interaction in a General Non-Inertial Reference Frame: Applications on Celestial Mechanics and Astrodynamics
Authors: Condurache Daniel , Cojocari Mihail , Popa Ionuț
Published: February 2025

This paper offers new insights into gravitational interactions within a general non-inertial reference frame. By utilizing symbolic tensor calculus, the study establishes a unified framework that connects time derivatives in non-inertial frames to those in inertial frames. The research introduces new first integrals of motion for a system of many particles in arbitrary non-inertial and barycentric rotating reference frames. These first integrals provide a kinematic and geometric visualization of motion in non-inertial frames. Additionally, a generalized potential energy function is presented for broader applicability. For the gravitational two-body problem, the paper delivers a closed-form, coordinate-free solution for the motion of each body relative to the original frame. Consequently, sufficient conditions for stability against collisions are established within the context of the two-body problem in a non-inertial reference frame. Furthermore, the paper examines the relative orbital motion of spacecraft, presenting a closed-form and coordinate-free solution in the local vertical local horizontal (LVLH) non-inertial frame, which is centered on the center of mass of the main spacecraft.
Analysis of Transverse Vibration in a Concentrated Mass Rayleigh Pipe
Authors: Ergut Abdulkerim
Published: February 2025

The dynamic behavior of pipelines subjected to additional masses is crucial for optimizing the design and reliability of engineering systems, particularly in offshore and industrial applications. This study investigates the effect of slenderness on the dynamic response of a pipe with one or more additional masses placed at different positions along its length, considering the symmetry of the system in mass distribution. The aim is to analyze how mass placement influences vibration characteristics under fluid–structure interaction (FSI) conditions. The pipe is modeled as a Rayleigh beam, and the governing equations of motion are derived using Hamilton’s principle while preserving the inherent symmetry of the system. A non-dimensionalized approach is employed to ensure broad applicability across different geometric and material configurations. The vibration frequencies are obtained using the Galerkin method (GM) and validated via a two-way FSI technique, integrating computational fluid dynamics (CFDs) and structural mechanics using ANSYS 2022 software. The results demonstrate the relationship between the concentrated mass ratio and vibration frequency for the first three modes, highlighting the influence of slenderness ratio on system stability. These findings provide valuable insights for the engineering design of pipeline systems subjected to dynamic loading.
MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer
Authors: Li Wen , Onishi Natsuko , Gibbs Jessica E. , Wilmes Lisa J. , Le Nu N. , Metanat Pouya , Price Elissa R. , Joe Bonnie N. , Kornak John , Yau Christina , Wolf Denise M. , Magbanua Mark Jesus M. , LeStage Barbara , van ’t Veer Laura J. , DeMichele Angela M. , Esserman Laura J. , Hylton Nola M.
Published: February 2025

Background: Functional tumor volume (FTV), measured from dynamic contrast-enhanced MRI, is an imaging biomarker that can predict treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The FTV-based predictive model, combined with core biopsy, informed treatment decisions of recommending patients with excellent responses to proceed to surgery early in a large NAC clinical trial. Methods: In this retrospective study, we constructed models using FTV measurements. We analyzed performance tradeoffs when a probability threshold was used to identify excellent responders through the prediction of pathology complete response (pCR). Individual models were developed within cohorts defined by the hormone receptor and human epidermal growth factor receptor 2 (HR/HER2) subtype. Results: A total of 814 patients enrolled in the I-SPY 2 trial between 2010 and 2016 were included with a mean age of 49 years (range: 24 to 77). Among these patients, 289 (36%) achieved pCR. The area under the ROC curve (AUC) ranged from 0.68 to 0.74 for individual HR/HER2 subtypes. When probability thresholds were chosen based on minimum positive predictive value (PPV) levels of 50%, 70%, and 90%, the PPV-sensitivity tradeoff varied among subtypes. The highest sensitivities (100%, 87%, 45%) were found in the HR−/HER2+ sub-cohort for probability thresholds of 0, 0.62, and 0.72; followed by the triple-negative sub-cohort (98%, 52%, 4%) at thresholds of 0.13, 0.58, and 0.67; and HR+/HER2+ (78%, 16%, 8%) at thresholds of 0.34, 0.57, and 0.60. The lowest sensitivities (20%, 0%, 0%) occurred in the HR+/HER2− sub-cohort. Conclusions: Predictive models developed using imaging biomarkers, alongside clinically validated probability thresholds, can be incorporated into decision-making for precision oncology.
An Ex Ante Approach to the Resilience and Recovery Plan’s Impacts on Sustainable Tourism in Algarve and Alentejo
Authors: Valente Bernardo , Uršič Erika Džajić
Published: February 2025

The European Union has been trying to adjust its tourism policy in response to the challenges posed by the recent COVID-19 pandemic. The funding of the EU Cohesion Policy has been one of the primary mechanisms guaranteeing that all regions can be prepared to receive tourists and cope with the sustainability challenges the pandemic has raised. The recovery and resilience plan (PRR) is the most recent instrument created to help economic growth in most European Union countries. The implementation of the PRR in Algarve and Alentejo has been fundamental to understanding the Portuguese tourism sector, as these regions are heavily dependent on tourism sector revenue and were hit hard by the consecutive lockdowns in recent years. Therefore, this policy, in brief, critically assesses the tourism-related projects that received funding from the PRR until November 2023 (ex ante) and their potential to guarantee long-term tourism sustainability in Algarve and Alentejo. Despite not achieving the expected efficiency results due to delays in project execution and low investment, the PRR is still a positive upgrade for tourism-related policy in Algarve and Alentejo.
Lead Causes Lipid Droplet Accumulation by Impairing Lysosomal Function and Autophagic Flux in Testicular Sertoli Cells
Authors: Guo Chengwei , Wang Lingqiao , Cui Ke , Zhang Guowei , Tan Yao , Chen Weiyan , Wang Yiqi , Liu Jijun , Liu Wenbin , Zhang Guanghui , Zhou Ziyuan
Published: February 2025

Lead (Pb) is one of the most common environmental pollutants that negatively impacts male reproductive health. Thus far, the underlying molecular mechanisms of Pb-induced reproductive toxicity are still not well understood. In this study, 64 male ICR mice were given drinking water with Pb (0, 100, 200, and 300 mg/L) for 90 days. We found that exposure to 300 mg/L Pb resulted in reduced sperm quality and elevated autophagy-related protein levels in the mouse testes. Our findings indicate that the Pb hindered the autophagic clearance by impairing the lysosomes’ function and then obstructing the fusion of lysosomes and autophagosomes. The autophagy cycle obstruction prevented the lipid droplets from breakdown and led to their accumulation in the Sertoli cells. In turn, the ccytotoxic effects that resulted from the interruption of the autophagy maturation stage, instead of the elongation phase, could be alleviated by either Chloroquine or Bafilomycin A1. Furthermore, exposure to 400 μM Pb initiated the TFE3 nuclear translocation and caused the increased expression of its target genes. Then, the knockdown of TFE3 reduced the formation of the autophagosome. In addition, the use of the antioxidant NAC notably enhanced the autophagic activity and reduced the occurrence of lipid droplets in the Sertoli cells. This study demonstrated that Pb disrupted the autophagic flow, which caused lipid droplet accumulation in the TM4 cells. Consequently, focusing on the maturation stage of autophagy might offer a potential therapeutic approach to alleviate male reproductive toxicity caused by Pb exposure.
Could Mussel Populations Be Differentially Threatened by the Presence of Microplastics and Related Chemicals?
Authors: Borges Filipe , Freitas Rosa , Silva Ana L. Patrício , Soliz Rojas Dulce Lucy , Paniagua González Gema , Solé Montserrat
Published: February 2025

Mussels serve as indicators of anthropogenic chemical pollution; however, the effects of microplastics and plastic-related chemicals on their health performance remain an emerging issue. In this study, mussels were exposed to a polyamide (PA; 5 μg/L) and tricresyl phosphate (TCP; 1 μg/L) for 28 days. The exposures to the two contaminants were performed independently or in combination and lasted 28 days. The results showed that the independent exposure altered enzyme activities more significantly than the combined one. Exposure to the PA significantly (p < 0.05) inhibited the antioxidant enzyme catalase (CAT) by 43.5% and the neurotransmitter enzyme acetylcholinesterase (AChE) by 40.6%, while TCP specifically inhibited carboxylesterase (CE) activity by 38.5%, all in respect to the solvent control. When both pollutants were combined, most biomarker responses were similar to control levels. To further investigate if the mussels’ response to contaminants (here, chemical compounds only) could be population-specific, a comparative study between Atlantic and Mediterranean mussels was included. Firstly, baseline detoxification defenses were contrasted in the digestive glands of each mussel population, followed by an assessment of in vitro responses to a wide range of plastic additives. The results revealed that Mediterranean mussels expressed higher baseline activities for most detoxification enzymes, although the in vitro sensitivity to the targeted chemicals was similar in both populations. Of all the plastic additives tested, TCP significantly inhibited CE activity both in vivo and in vitro. The in vitro screening also indicated that other plastic additives could act as strong inhibitors of CE. However, additional in vivo exposures in mussels are needed to confirm CE suitability as a biomarker of these chemical exposures. All together, these results also suggest critical population-level differences in susceptibility to microplastic pollution, highlighting a need for targeted conservation efforts.
Non-Inferiority of Dual Kidney Transplantation: A Retrospective Matched Study
Authors: Quaresma Vasco , Carneiro Margarida , Marconi Lorenzo , Tavares da Silva Edgar , Jarimba Roberto , Eliseu Miguel , Nunes Pedro , Figueiredo Arnaldo
Published: February 2025

Background/Objectives: Dual kidney transplantation is a potential technique to reduce the number of discarded kidneys from expanded-criteria donors. Due to allegedly poor outcomes, some centres have abandoned this technique. We aimed to compare dual versus single kidney transplantation. Methods: This retrospective, propensity score-matched, non-inferiority study compared dual kidney transplantation and single kidney transplantation results. Matching was performed based on key donor characteristics, including age, sex, serum creatinine levels, and cause of death due to cerebrovascular accident. The primary outcome was graft survival at ten years post-transplant. Secondary outcomes included overall survival and perioperative complications. Non-inferiority of dual kidney transplantation was defined as a difference in graft survival within a 10% margin. Results: After propensity score, 39 dual kidney transplant recipients were matched with 78 single kidney transplants. Five-year graft survival was 66.1% for dual kidney transplants and 81.3% for single kidney transplants (p = 0.228), and 9-year graft survival was 54.1% dual transplant and 60.8% for single transplant (p = 0.961). There was no significant difference in terms of 10-year overall survival (p = 0.912) either. Surgical times were greater during dual kidney transplants (199.31 ± 49.12 min vs. 129.37 ± 42.11 min, p < 0.001). There were more overall complications associated with dual kidney transplants (35.9% vs. 17.9%, p < 0.05). Conclusions: Dual kidney transplantation achieved non-inferiority for ten-year graft and overall survival, despite higher incidence of complications and longer surgical times. Dual kidney transplantation can be a viable alternative to single kidney transplantation and may increase the pool of potential donors, reducing renal transplant waiting lists.
Neutralizing Antibody Screening Using NanoBiT-Based Virus-like Particles of Foot-and-Mouth Disease Type Asia1 Enhances Biosafety and Sensitivity
Authors: Kim Hyejin , Kim Dong-Wan , Cho Giyoun , Hwang Ji-Hyeon , Chae Yeonrae , Kim Taejun , Kim Jae Young , Ko Young-Joon , Park Jong-Hyeon , Lee Yoon-Hee , Park Sung-Han
Published: February 2025

Background/Objectives: Foot-and-mouth disease (FMD) is a highly contagious class 1 animal disease that affects cloven-hoofed animals, such as cattle, pigs, and goats. Diagnosis and research on live FMD virus (FMDV) typically require biosafety level 3 facilities, which are challenging to maintain due to strict protocols and high costs. The development of NanoBiT-based assays has accelerated in response to the coronavirus disease pandemic, providing safer alternatives for viral research, and is now applicable for general laboratories. This study aimed to develop a NanoBiT-based virus-like particle (VLP) assay for the rapid and safe screening of neutralizing antibodies against FMDV Asia1 Shamir (AS). Methods: We developed an AS VLP with an inserted HiBiT tag that enabled the detection of entry into LgBiT cells through luminescence signals. Results: HiBiT-tagged AS VLPs mixed with anti-serum and introduced into LgBiT-expressing cells led to a reduction in luciferase activity. Therefore, we established a NanoBiT-based viral neutralizing antibody test (VNT) that demonstrated a high correlation (R2 = 0.881) with the traditional gold standard VNT. Conclusions: The assay demonstrated high sensitivity and could be performed in BL-2 facilities, offering a safer and more efficient alternative to traditional assays while reducing the need to handle live viruses in high-containment facilities. This method provides a valuable tool for rapid screening of neutralizing antibodies and can be adapted for broader applications in FMDV research.