bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Bacillus anthracis Strains from Animal Cases in Ethiopia and Genetic Characterization by Whole-Genome Sequencing
Authors: Olani Abebe , Galante Domenico , Lakew Matios , Wakjira Bayeta Senbeta , Mekonnen Getnet Abie , Rufael Tesfaye , Teklemariam Tsegaye , Kumilachew Wondwosen , Dejene Shimalis , Woldemeskel Ayele , Wakjira Adanech , Abichu Getachew , Ashenafi Baye , Kebede Nigatu , Feleke Haile Aklilu , Bari Fufa Dawo , Del Sambro Laura , Eguale Tadesse
Published: January 2025

Anthrax is a zoonotic disease characterized by rapid onset with usual fatal outcomes in livestock and wildlife. In Ethiopia, anthrax is a persistent disease; however, there are limited data on the isolation and molecular characterization of Bacillus anthracis strains. This study aimed to characterize B. anthracis isolated from animal anthrax outbreaks between 2019 and 2024, from different localities in Ethiopia. B. anthracis was identified using standard microbiology techniques and confirmed by real-time PCR. For the first time in Ethiopia, the genetic diversity of five Bacillus anthracis strains, isolated from dead cattle and goats, was investigated by Whole Genome Sequencing (WGS) and bioinformatics analyses. The five sequenced strains were compared to one Ethiopian B. anthracis genome and the other 29 B. anthracis genomes available in the global genetic databases to determine their phylogeny. The genomes of the strains were also analyzed to detect the presence of antimicrobial resistance and virulence genes. The whole genome SNP analysis showed that the Ethiopian B. anthracis strains were grouped in the A clade. Three strains (BA2, BA5, and BA6) belonged to the A.Br.034 subgroup (A.Br.005/006), and two strains (BA1 and BA4) belonged to the A.Br.161 (Heroin) clade of the Trans-Eurasian (TEA) group. The findings of this study will contribute to expanding the current understanding of the anthrax hotspots in Ethiopia, and the phylogenetic correlation and/or diversity of the circulating strains.
Current Insight of Peptide-Based Hydrogels for Chronic Wound Healing Applications: A Concise Review
Authors: Nizam Aifa Asyhira Khairul , Masri Syafira , Fadilah Nur Izzah Md , Maarof Manira , Fauzi Mh Busra
Published: January 2025

Chronic wounds present a substantial healthcare obstacle, marked by an extended healing period that can persist for weeks, months, or even years. Typically, they do not progress through the usual phases of healing, which include hemostasis, inflammation, proliferation, and remodeling, within the expected timeframe. Therefore, to address the socioeconomic burden in taking care of chronic wounds, hydrogel-based therapeutic materials have been proposed. Hydrogels are hydrophilic polymer networks with a 3D structure which allows them to become skin substitutes for chronic wounds. Knowing that peptides are abundant in the human body and possess distinct biological functionality, activity, and selectivity, their adaptability as peptide-based hydrogels to individual therapeutic requirements has made them a significant potential biomaterial for the treatment of chronic wounds. Peptide-based hydrogels possess excellent physicochemical and mechanical characteristics such as biodegradability and swelling, and suitable rheological properties as well great biocompatibility. Moreover, they interact with cells, promoting adhesion, migration, and proliferation. These characteristics and cellular interactions have driven peptide-based hydrogels to be applied in chronic wound healing.
Assessment of the Photosynthetic Response of Potato Plants Inoculated with Rhizoctonia solani and Treated with Flesh-Colored Potato Extracts Nanoencapsulated with Solid Lipid Nanoparticles
Authors: Rivas Sheina , Fincheira Paola , González Felipe , Santander Christian , Meier Sebastián , Santos Cledir , Contreras Boris , Ruiz Antonieta
Published: January 2025

Potato has great nutritional and economic importance in agriculture. However, Rhizoctonia solani represents a significant risk, reducing the yield and quality of potato production. Flesh-colored potato (FCP) extracts show in vitro inhibitory effects against R. solani, although environmental factors may reduce their stability. Solid lipid nanoparticles (SNLs) offer a solution by encapsulating these compounds, preventing degradation, and improving delivery, positioning solid lipid nanoparticles as a promising technology for sustainable extract application. A greenhouse potato assay at two phenological stages under R. solani inoculation was used to evaluate the photosynthetic response (photosynthetic parameters and pigments) to two doses of the nanoencapsulated extract (SNL + FCP). During inoculation and commercial fungicide application, stomatal conductance, the photosynthetic rate, and the internal CO2 concentration increased compared with those of the non-inoculated control (NT), whereas the nanoencapsulated extract maintained levels similar to those of the NT, suggesting the possible regulation of the photosynthetic defense system. In terms of photosynthetic pigments, SLN + FCP maintained chlorophyll concentrations, unlike those in inoculated plants, which significantly decreased. Component analysis revealed that a lower dose primarily increased chlorophyll B synthesis, whereas a higher dose increased chlorophyll A compared with the inoculated control. These findings suggest an improved response from SLN + FCP to commercial fungicides, particularly with respect to photosynthetic pigments. However, further research is needed, and the results indicate promising potential for the eco-friendly control of phytopathogenic fungi in agriculture.
‘Messianic Fraternity’: Anticommunism in the General Conferences of the Latin American and Caribbean Episcopate
Authors: Piccone-Camere Carlos
Published: January 2025

This paper analyzes the development and consolidation of anticommunist discourse in the General Conferences of the Latin American Episcopate (CELAM), from Rio de Janeiro to Aparecida. It adopts a critical perspective to examine the construction of the “messianic fraternity” myth—an ideological narrative contrasting Christian ideals of community and redemption with Marxist principles of class struggle and revolution, which served as a central axis for the Church’s rejection of communism in Latin America. Grounded in a critical analysis of the CELAM’s final documents, this study identifies the theological, political, and social underpinnings of this stance, situating it within the historical and geopolitical dynamics that positioned the Church as a key counterforce to Marxism in the region. It also examines how anticommunist positions shaped pastoral strategies, particularly in relation to social movements like liberation theology, and reinforced an episcopal identity centered on defending Christian values against a perceived global ideological threat. This analysis highlights the Church’s internal tensions and contradictions and the broader impact of its anticommunist stance on Latin America’s sociopolitical and religious dynamics in the twentieth century.
Using OCO-2 Observations to Constrain Regional CO2 Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model
Authors: Konovalov Igor B. , Golovushkin Nikolai A. , Mareev Evgeny A.
Published: January 2025

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO2) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO2 fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO2. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO2 data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO2 with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO2 data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO2 fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO2 ecosystem fluxes from satellite XCO2 observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO2 fluxes are scarce.
A Line Feature-Based Rotation Invariant Method for Pre- and Post-Damage Remote Sensing Image Registration
Authors: Zhao Yalun , Chen Derong , Gong Jiulu
Published: January 2025

The accurate registration of pre- and post-damage images plays a vital role in the change analysis of the target area and the subsequent work of damage effect assessment. However, due to the impact of shooting time and damaged areas, there are large background and regional differences between pre- and post-damage remote sensing images, and the existing image registration methods do not perform well. In this paper, a line feature-based rotation invariant image registration method is proposed for pre- and post-damage remote sensing images. First, we extract and screen straight line segments from the images before and after damage. Then, we design a new method to calculate the main direction of each line segment and rotate the image based on the current line segment’s main direction and the center coordinates. According to the spatial distribution (distance and angle) of the reference line segment relative to the remaining line segments, a line feature descriptor vector is constructed and matched for each line segment on the rotated image. Since the main edge contour can preserve more invariant features, this descriptor can be better applied to the registration of pre- and post-damage remote sensing images. Finally, we cross-pair the midpoints and endpoints of the matched line segments to improve the accuracy of subsequent affine transformation parameter calculations. In remote sensing images with large background and regional differences, the average registration precision of our method is close to 100%, and the root mean square error is about 1 pixel. At the same time, the rotation invariance of our method is verified by rotating the test images. In addition, the results of the comparative experiments show that the registration precision and error of the proposed method are better than those of the existing typical representative algorithms.
Prototype of a Spring-Loaded Module for Axillary Crutches
Authors: Méndez-Gómez Dalia Danely , Minor-Martínez Arturo , Montoya-Alvarez Salvador , Pérez-Escamirosa Fernando , Cantillo-Negrete Jessica
Published: January 2025

Axillary crutches assist people with lower limb injuries but can lead to upper limb strain with extended use. Spring-loaded crutches offer a potential solution, yet they are rarely tested in clinical settings. This study developed spring-loaded crutches with an integrated force-measuring system to analyze gait dynamics. Three prototypes, each with different spring constants (k), were tested. To measure ground reaction force (GRF), a Nylamid cover was around the crutch stem. Two participants with different weights completed a 15-m route using both the designed spring-loaded and standard crutches. Findings showed that spring-loaded crutches increased mean GFR and impulse, with the prototype matched to the user’s weight yielding the best results. The study’s findings suggest that when properly adjusted to the user’s weight, spring-loaded crutches can offer significant improvements in gait, which may have important implications for the design of mobility assistive devices.
Adaptive Filtering for Channel Estimation in RIS-Assisted mmWave Systems
Authors: Shao Shuying , Lv Tiejun , Huang Pingmu
Published: January 2025

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA).
Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition
Authors: Guo Xin , Kim Young , Ning Xueli , Min Se Dong
Published: January 2025

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). CFEB enables the Transformer model to leverage both local and global time series features for activity classification. Meanwhile, the absolute position embedding (APE) in existing Transformer models cannot accurately represent the distance relationship between individuals at different time points. To further explore positional correlations in temporal signals, this paper introduces the Vector-based Relative Position Embedding (vRPE), aiming to provide more relative temporal position information within sensor signals for the Transformer model. Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR.
A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features
Authors: Ma Ziyao , Deng Zhongliang , Tian Zidu , Zhang Yingjian , Wang Jizhou , Guo Jilong
Published: January 2025

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods.
Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data
Authors: Rex Franciel Eduardo , Silva Carlos Alberto , Broadbent Eben North , Dalla Corte Ana Paula , Leite Rodrigo , Hudak Andrew , Hamamura Caio , Latifi Hooman , Xiao Jingfeng , Atkins Jeff W. , Amaral Cibele , Cunha Neto Ernandes Macedo da , Cardil Adrian , Almeyda Zambrano Angelica M. Almeyda , Liesenberg Veraldo , Liang Jingjing , De Almeida Danilo Roberti Alves , Klauberg Carine
Published: January 2025

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R2 = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.
STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning
Authors: Lai Yi , Zhu Ye , Li Li , Lan Qing , Zuo Yizheng
Published: January 2025

Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97.
Research on the Estimation Model of Electrical Parameters of Silver-Based Contacts Based on Surface Morphology
Authors: Wang Chao , Wang Xiancheng , Guo Chengjun
Published: January 2025

The quality of surface morphology can reflect the electrical performance of silver-based contacts. Existing research on the correlation of morphological–electrical performance is based solely on empirical models from traditional visual inspections and only considers the impact of visually observable macro-textural features on electrical performance. However, the influence of micro-textural features on electrical performance should not be overlooked. This paper establishes a contactless surface morphology acquisition device based on a laser profilometer to address the assembly characteristics of contact components. Various original profile signals such as surface roughness, waviness, and surface form error are calculated using wavelet transformation, and a robust weight function is introduced to separate micro-textural features from macro-textural features. After the morphological parameters affecting electrical performance are quantified, the variation laws of single and composite morphological parameters on electrical performance are clarified. Parameter optimization iterations and parameter space distribution optimization are performed using a decision tree, and the optimized predictive model forecasts specific electrical parameter values. The predicted results are quantitatively evaluated, establishing evaluation metrics that reflect the errors and degree of fit between the model predictions and actual values from different perspectives. From the experimental results, the accuracy of the predictive model established in this study exceeds 97%.
Event-Trigger Reinforcement Learning-Based Coordinate Control of Modular Unmanned System via Nonzero-Sum Game
Authors: Liu Yebao , An Tianjiao , Chen Jianguo , Zhong Luyang , Qian Yuhan
Published: January 2025

Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system (MUS) via the nonzero-sum game (NZSG) strategy. The dynamic model of the MUS is established via joint torque feedback (JTF) technology. Based on the NZSG strategy, the existing coordinate control problem is transformed into an RL issue. With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. The stability of the closed-loop system is verified via Lyapunov’s theorem. Experiment results demonstrate the validity of the proposed method. The experimental results show that the proposed method reduces the position error by 30% and control torque by 10% compared with the existing control methods.
Mapping Evidence on Strategies Used That Encourage Pre-Exposure Prophylaxis (PrEP) Uptake and Adherence Amongst Female Sex Workers in South Africa
Authors: Makhakhe Nosipho Faith , Khumalo Gift
Published: January 2025

Female sex workers (FSWs) in South Africa have a high HIV prevalence. To reduce the rate of new infections, pre-exposure prophylaxis (PrEP) was introduced to FSWs; however, studies show that FSWs’ uptake and adherence to PrEP has been inconsistent. This study explored existing evidence related to strategies used to encourage PrEP uptake and adherence. Utilizing the scoping review framework, seven peer-reviewed articles were analyzed thematically. The themes were (1) PrEP promotion and distribution, (2) PrEP counselling and using educational resources, and (3) using instant messaging and rewards programs. The suggestions from the literature include a generalized PrEP promotion and distribution approach not aimed towards high-risk groups to avoid stigma. PrEP pick-up points should include sex work-friendly healthcare facilities as well as community-based venues. PrEP counselling should be conducted prior to PrEP administration to prepare users on how to deal with side effects. Information, education, and communication materials should be colorful and consist of catchy phrases targeted to the whole population. Instant messaging has been found to be effective in encouraging adherence. FSWs should be instrumental in informing intervention best practices. Future interventions aimed at FSWs should focus on holistic wellness that incorporates the psychosocial aspects of HIV prevention.
Leveraging Smart City Technologies for Enhanced Real Estate Development: An Integrative Review
Authors: Al-Rimawi Tarek , Nadler Michael
Published: January 2025

This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The research followed an integrative review methodology; the review is based on 168 publications. The compiled results based on metadata analysis displayed the state of each technology’s added values and usage in both scales. A total of 131 added values were identified. These added values were categorized based on the real estate life cycle sub-phases and processes. Moreover, the value of the integration between these technologies was revealed. The review and results proved that these technologies are mature enough for practical use; therefore, real estate developers, city management, planners, and experts should focus on implementing them. City management should invest in Big Data and geodata and adopt several technologies based on the aspects required for development. This study can influence stakeholders, enhance their decision-making on which technology would suit their needs, and provide recommendations on who to utilize them. Also, it provides a starting point for stakeholders who aim to establish a road map for incorporating smart technologies in future smart real estate.
Invisible Men: Unlocking Compassion and Understanding the Needs of Older Men Behind Bars
Authors: Ridley Louise , Waldegrave Kathryn E.
Published: January 2025

Men over 50 years of age are the fastest-growing population group in the prison system, leading to the prison service of England and Wales now becoming recognised as the largest provider of residential care for older men. Roughly one in five prisoners (18%) is over 50 years of age. This paper focuses on the impact on both staff and prisoners of developing an understanding of the needs and concerns of older prisoners during prison officer training. The authors approach the study of the older prison population from their respective disciplines—criminology and health. Both authors are aware of the necessity for custodial staff to be prepared and equipped to respond and manage a growing number of individuals who are vulnerable and appreciate the need for prison staff to be supported in this process, with an understanding of the complexities that come with housing large numbers of ageing men within a custodial setting. This article explores the provision of training for newly recruited prison officers and considers what good practice might look like.
Reducing Food Waste in Campus Dining: A Data-Driven Approach to Demand Prediction and Sustainability
Authors: Turker Gul Fatma
Published: January 2025

Tracking density in universities is essential for planning services like food, transportation, and social activities on campus. However, food waste remains a critical challenge in campus dining operations, leading to significant environmental and economic consequences. Addressing this issue is crucial not only for minimizing environmental impact but also for achieving sustainable operational efficiency. Campus food services significantly influence students’ university choices; thus, forecasting meal consumption and preferences enables effective planning. This study tackles food waste by analyzing daily campus data with machine learning, revealing strategic insights related to food variety and sustainability. The algorithms Linear Regression, Extra Tree Regressor, Lasso, Decision Tree Regressor, XGBoost Regressor, and Gradient Boosting Regressor were used to predict food preferences and daily meal counts. Among these, the Lasso algorithm demonstrated the highest accuracy with an R2 metric value of 0.999, while the XGBRegressor also performed well with an R2 metric value of 0.882. The results underline that factors such as meal variety, counts, revenue, campus mobility, and temperature effectively influence food preferences. By balancing production with demand, this model significantly reduced food waste to 28%. This achievement highlights the potential for machine learning models to enhance sustainable dining services and operational efficiency on university campuses.
Do European Social Funds Matter in Achieving the Sustainable Development Goals?
Authors: Bădîrcea Roxana Maria , Doran Nicoleta Mihaela , Manta Alina Georgiana , Cercel (Zamfirache) Camelia
Published: January 2025

The aim and novelty of this study lie in analyzing the role of the European Social Fund (ESF) in supporting the implementation of the Sustainable Development Goals (SDGs) within European Union (EU) countries over the 2015–2023 period. EU Member States were grouped into two clusters: Cluster A (15 lower-income states) and Cluster B (12 higher-income states). The study used ESF payments as the explanatory variable and 17 SDG indicators as dependent variables. The methodology combined exploratory factor analysis (EFA) and robust regression to analyze the relationship between the ESF and the SDGs. The ESF has a significant impact on reducing poverty (SDG1), fostering economic growth (SDG8), and enhancing industry and innovation (SDG9), particularly in less-developed regions where its interventions address structural challenges through vocational training, job creation, and infrastructure development. However, its contributions to sustainability goals (SDGs 7, 12, and 13) are limited or even negative in some cases, as economic priorities often overshadow environmental objectives, especially in more-developed regions where climate and energy transitions rely on other funding sources. The ESF excels in fostering partnerships (SDG17) in less-developed regions by mobilizing resources and promoting collaboration, but its role is less impactful in developed regions where institutional frameworks are already well-established.
Modeling and Numerical Analysis of the Severobaikalsk Section of the Baikal–Amur Mainline Considering Environmental Points
Authors: Bychkov Igor , Kazakov Alexander , Lempert Anna , Zharkov Maxim
Published: January 2025

The article addresses the problem of increasing the capacity of the Baikal–Amur Mainline, which is part of the Northern Eurasian Corridor and provides the shortest route from Europe to the countries of the Asia-Pacific region via the ports of the Russian Far East. A key focus of the study is the Severobaikalsk section, which currently operates as a single-track system and is located in a specially protected natural area. In this regard, it is a limiting element since its reconstruction projects require environmental expertise. To tackle this issue, we develop a mathematical model based on semi-open queuing networks as a research tool. This modeling apparatus allows us to describe the features of the considered transport system, including single-track traffic, long sections between stations, which leads to the appearance of additional sidings, the presence of through and ring routes, and the significant influence of random factors on train operation. Based on the results of a numerical study of the model, we evaluate the current and maximum permissible section loads and point out bottlenecks in its structure. We compare two alternative ways of increasing the section capacity: creating a double-track connection on the most problematic sections and using a partially batch train schedule. Based on the results of the calculations, we conclude about the possibility and conditions of their implementation.
A Multi-Model Gap-Filling Strategy Increases the Accuracy of GPP Estimation from Periodic Chamber-Based Flux Measurements on Sphagnum-Dominated Peatland
Authors: Albert-Saiz Mar , Stróżecki Marcin , Rastogi Anshu , Juszczak Radosław
Published: January 2025

Gross primary productivity (GPP), the primary driver of carbon accumulation, governs the sequestration of atmospheric CO2 into biomass. However, GPP cannot be measured directly, as photosynthesis and respiration are simultaneous. At canopy level in plot-scale studies, GPP can be estimated through the closed chamber-based measurements of net ecosystem exchange (NEE) and ecosystem respiration (Reco). This technique is cost-effective and widely used in small-scale studies with short vegetation, but measurements are periodic-based and require temporal interpolations. The rectangular hyperbolic model (RH) was the basis of this study, developing two temperature-dependent factors following a linear and exponential shift in GPP when the temperature oscillates from the optimum for vegetation performance. Additionally, a water table depth (WTD)-dependent model and an exponential model were tested. In the peak season, modified RH models showed the best performance, while for the rest of the year, the best model varied for each subplot. The statistical results demonstrate the limitations of assuming the light-use efficiency as a fixed shape mechanism (using only one model). Therefore, a multi-model approach with the best performance model selected for each period is proposed to improve GPP estimations for peatlands.
Predicting the Spatial Distribution of Soil Organic Carbon in the Black Soil Area of Northeast Plain, China
Authors: Li Yunfeng , Chen Zhuo , Chen Yang , Li Taotao , Wang Cen , Li Chaoteng
Published: January 2025

The accurate prediction of the spatial distribution of soil organic carbon (SOC) and the identification of the mechanisms underlying its spatial differentiation are of paramount significance for the conservation and utilization of land and regional sustainable development. A total of 512 soil samples were collected from Wuchang and Shuangcheng County in Harbin City, Heilongjiang Province, China, which served as the study area. Six machine learning models, including Random Forest (RF), AdaBoost, Support Vector Regression (SVR), weighted average, Stacking, and Blending, were utilized to predict the spatial distribution of SOC and analyze its spatial differentiation. The result reveals that 12 environmental variables, including soil type, bulk density, pH, average annual precipitation, average annual temperature, net primary productivity (NPP), land use type, normalized difference vegetation index (NDVI), slope, elevation, soil parent material, and distance to rivers, are effective influencing factors on SOC in the study area. It turns out that the Stacking model, with an R2 of 0.4327, performed the best in this study, followed by the weighted average, Blending, RF, AdaBoost, and SVR models; a heterogeneous integrated learning model may be more robust than an individual learner. The predicted SOC content is generally lower in the northwestern arable land and higher in the southeastern forest land. In addition, SOC differentiation shows that forest land and grass land with dark brown soil or swamp soil, soil covering igneous and metamorphic rocks with various minerals, higher elevation and slope, and suitable water-thermal and soil intrinsic conditions for aerobic microbial activity benefit the enrichment of SOC in the study area. The enrichment and depletion of SOC are jointly influenced by pedogenesis, microbial activity, and biodiversity.
Asymmetric Transformations of Levulinic Acid to γ-Valerolactone and 5-Methylpyrrolidin-2-one Derivatives: Chiral Compounds with Biological Potential
Authors: Łastawiecka Elżbieta , Szwaczko Katarzyna
Published: January 2025

Levulinic acid is a key platform molecule derived from biomass and readily available from natural sources, making it an attractive starting material for the synthesis of high-value chiral compounds. Among them, γ-valerolactone and 5-methylpyrrolidin-2-one derivatives are notable for their widespread occurrence and biological importance. This review paper highlights the importance of γ-valerolactone and 5-methylpyrrolidin-2-one derivatives as frameworks found in biologically active compounds and pharmaceuticals. It focuses on the asymmetric synthesis of these chiral building blocks from levulinic acid, highlighting recent advances in catalytic transformations that allow for efficient and selective transformations. The potential applications of these chiral molecules in medicine and industry underscore the importance of developing sustainable and scalable processes for their production. This review also examines future directions in the field, given the growing demand for green chemistry approaches and the increasing importance of chiral molecules in drug development.
A Dual-Layer Symmetric Multi-Robot Path Planning System Based on an Improved Neural Network-DWA Algorithm
Authors: Teng Yangxin , Feng Tingping , Li Junmin , Chen Siyu , Tang Xinchen
Published: January 2025

Path planning for multi-robot systems in complex dynamic environments is a key issue in autonomous robotics research. In response to the challenges posed by such environments, this paper proposes a dual-layer symmetric path planning algorithm that integrates an improved Glasius bio-inspired neural network (GBNN) and an enhanced dynamic window approach (DWA). This algorithm enables real-time obstacle avoidance for multi-robots in dynamic environments while effectively addressing robot-to-robot conflict issues. First, to address the low global optimization capability of the GBNN algorithm in the first layer, a signal waveform propagation model for single-neuron signals is established, enhancing the global optimization ability of the algorithm. Additionally, a path optimization function is developed to remove redundant points along the path, improving its efficiency. In the second layer, based on the global path, a reward function is introduced into the DWA. The Score function within the DWA algorithm is also modified to enable symmetric path adjustments, effectively reducing detour paths and minimizing the probability of deviation from the planned trajectory while ensuring real-time obstacle avoidance under the condition of maintaining the global path’s optimality. Next, to address conflicts arising from multi-robot encounters, a dynamic priority method based on distance is proposed. Finally, through multi-dimensional comparative experiments, the superiority of the proposed method is validated. Experimental results show that, compared with other algorithms, the improved neural network-DWA algorithm significantly reduces path length and the number of turns. This research contributes to enhancing the efficiency, adaptability, and safety of multi-robot systems.
Research on the Cross-Regional Traveling Welcome Short Messaging Service During the COVID-19 Pandemic: A Survey from Mobile Users’ Perspective
Authors: Yu Zhiyuan , Zhang Chi
Published: January 2025

Based on spatiotemporal sensing techniques, the cross-regional traveling welcome short messaging service (TW-SMS) has been adopted in China and has become popular, typically being used when travelers pass through or arrive in cities. In this service, governmental institutions in combination with telecom operators send welcome messages with the local characteristics. As a typical location-based service for mobile users, the TW-SMS includes reminders or alerts related to COVID-19 prevention and control. In this paper, we investigate the perceptions and behavior of mobile users regarding this special TW-SMS through mixed-methods research. An online survey was conducted among mobile users who engaged in intercity travel. After analyzing samples of TW-SMS data collected during the COVID-19 pandemic, we found that the respondents exhibited a relatively positive overall attitudes and recognized the necessity and helpfulness of the TW-SMS with its trusted content. For content analysis, we found that more than 70% of the messages transmitted by the TW-SMS were released by official departments (e.g., the COVID-19 Prevention and Control Office). Reminders about traveling registration and nucleic acid testing were assigned the highest importance, as they offer convenience in communicating the most up-to-date prevention and control information to mobile users during intercity travel. Through this study, we provide insights into epidemic prevention and control experiences during public health emergencies in cities.
TLR Agonist Immunoadjuvants Provide Effective Protection Against PCV2 and PRV Infections in a Bivalent Subunit Vaccine for PCV2 and PRV
Authors: Yu Fulai , Xiang Wei , Ou Weiye , Li Yang , Shu Xinbiao , Li Xiaoliang
Published: January 2025

Diseases associated with porcine circovirus type 2 (PCV2) and pseudorabies virus (PRV) significantly affect the economy of pig farms, particularly when combined infections lead to bacterial co-infections. Antigens from the pseudorabies variant strain gB and gD proteins and PCV2 (genotyped) Cap protein were mixed with the pattern recognition receptor (PRR) agonist FLICd as adjuvants and formulated with a micro-hydrogel adjuvant into PCV2 and PRV bivalent subunit vaccines. Twenty pigs, aged 30–35 days, were divided into groups A (received bivalent subunit vaccine) and B (received bivalent subunit vaccines with recombinant FLICd adjuvant), as well as C (non-vaccinated challenge control) and D (blank control). Groups A and B showed no significant difference in average daily weight gain compared to the unvaccinated controls. Fourteen days post-second vaccination, groups A and B exhibited significantly higher levels of PRV and PCV2 antibodies than groups C and D. Group B showed significantly higher average titers of PRV-specific neutralizing antibodies than group A. Fourteen days post-second vaccination, a PRV (ZJM-1 strain) challenge test was conducted. The vaccinated group achieved 100% protection. Vaccination effectively reduced virus load post-challenge and shortened the PRV shedding period. Vaccination with PCV2 and PRV bivalent subunit vaccines effectively prevents the onset of PCV2-related diseases and infections by wild pseudorabies strains.
Modelling Peatland Productivity by Water Table Depth or Near-Surface Water Contents via the DIMONA Online Platform
Authors: Dimitrov Dimitre D. , Lafleur Peter M.
Published: January 2025

This study extends our previous work showing that a process-based (PB) model, the DIMONA PB model, could accurately simulate peatland soil water dynamics when driven by water table depth, dWT, or by near surface soil water contents, θ. Here, we explore the model’s ability to simulate the peatland canopy photosynthesis, growth, biomass, height, and gross primary productivity (GPP) of vascular plants and bryophytes—thus ecosystem GPP—using either of these drivers. The DIMONA PB model is embedded into the DIMONA online modelling platform, a web application capable of ingesting data from the Internet and performing machine learning (ML) modelling and Internet of Things (IoT) modelling complementary to PB modelling. We test whether the DIMONA PB model, driven by dWT (Hypothesis 1) and by near-surface θ (Hypothesis 2), can successfully simulate peatland ecosystem GPP at the Mer Bleue bog (Ontario, Canada). Two model runs were generated, one driven by dWT and another by near-surface θ. Both model runs performed with similar accuracy. Data fit for simulated on observed GPP reached 0.72 for R2, 1.7 umol CO2 m−2 s−1 for RMSE, and 0.88 for Willmott’s index of agreement at an hourly time step and 0.91, 0.8 g C m−2 d−1, and 0.92, respectively, at a daily time step. We use the output from the two model runs to examine whether the model’s modifiers (i.e., equations) for water control can capture the specifics of contrasting hydrological conditions on peatland GPP (Hypothesis 3). Both model runs closely simulated the observed GPP to contrasting peatland hydrological conditions under similar meteorological forcing. We illustrate the ability of the DIMONA platform to facilitate the parameterization of DIMONA models for any geographic location, as well as its ability to perform IoT modelling of real-time photosynthesis at two site locations and ML modelling for ecosystem GPP as a complementary tool to PB modelling.