bluuebunny/crossref_metadata_2025 huggingface dataset:
Social robots in adult psychiatry: a summary of utilisation and impact
Authors: Kling Maja , Haeussl Alfred , Dalkner Nina , Fellendorf Frederike T. , Lenger Melanie , Finner Alexander , Ilic Julia , Smolak Irina S. , Stojec Lena , Zwigl Ina , Reininghaus Eva Z.
Published: February 2025

Social robots are increasingly becoming more prevalent in healthcare, including nursing, geriatric care, and treatment for children on the autism spectrum. Their assistance is believed to hold promise in mitigating the effects of staffing shortages and enhancing current mental health treatment. Nevertheless, the application of social robotics in psychiatry remains restricted and controversial. This scoping review aims to provide an overview of the literature on social robots in adult psychiatry concerning their use, effects, and acceptability. We conducted a literature search, including PubMed and PsycINFO, to identify literature on robot interventions for adult psychiatric patients. Methodological quality was assessed using the ‘Mixed Methods Appraisal Tool’. Usage and target variables were unique to every included original study (N = 7) and suggested a wide range of possible implications for patient treatment and care. Social robots were used to reduce symptoms, improve functioning, and gain insights into characteristic features of specific mental health conditions. The included studies were concerned with the following diagnoses: schizophrenia (N = 3), autism spectrum disorder (N = 2), and intellectual disability (N = 2). The sample sizes were too small to generalise the outcome, but overall trends showed some positive effects on the selected symptoms. Observations and participant feedback suggested high acceptance and enjoyment of the users. Although the evidence regarding the benefits of robotic interventions in adult psychiatry is still low, it suffices to assume that investing in larger, randomised, and controlled trials is worthwhile and promising.

Systematic review registration

asprediced.org, identifier 128766.
Perception of phage therapy and research across selected professional and social groups in Poland
Authors: Żaczek Maciej , Zieliński Marcin W. , Górski Andrzej , Weber-Dąbrowska Beata , Międzybrodzki Ryszard
Published: February 2025

There is no doubt that in the last 15 years phage therapy has re-emerged from the shadow of antibiotics, from the perspective of both scientists and various patient advocacy groups. Despite some important progress, there is little to no data on phage therapy perceptions in key groups, i.e., patients and their relatives, physicians and anyone who could potentially become infected with antibiotic-resistant bacteria. To the best of our knowledge, with 1,098 collected questionnaires, this article constitutes the first large-scale analysis on phage therapy perceptions, interest and knowledge among ordinary people in a country with a long, internationally recognized, tradition of treating patients with phages. In addition to 36 general questions addressed to everyone (including lay people), representatives of the health care sector and science and research sector received individually selected questions. Further, each participant had a chance to take part in a short quiz (consisting of 12 questions) verifying their basic knowledge about bacteriophages, their history, biology and therapeutic connotations. Awareness of antibiotic resistance was very high (above 90%) but contradicted the low level of knowledge about associated risks (12%). Consciousness of phage therapy varied between 8.9% (people taking care of household chores as their primary activity) to 37.7% (people with higher education) and 39.7% (inhabitants of large cities) while the readiness to use such treatments was very high (84.4%) despite the need to pay for it. The level of awareness of bacteriophages and phage therapy was clearly correlated with the acceptance of this type of treatment and the social acceptance to allocate further funds for the development of phage research. Interestingly, physicians were quite reluctant to deepen their knowledge in the field of phage therapy with just over one third (37.5%) ready to do so. With the COVID-19 pandemic in the background, we also explored how the pandemic influenced the interest in experimental therapies in general, which makes this article a potential universal compendium on perceptions of experimental therapies in the coming years.
One size fits all: Enhanced zero-shot text classification for patient listening on social media
Authors: Matoshi Veton , De Vuono Maria Carmela , Gaspari Roberto , Kröll Mark , Jantscher Michael , Nicolardi Sara Lucia , Mazzola Giuseppe , Rauch Manuela , Sabol Vedran , Salhofer Eileen , Mariani Riccardo
Published: February 2025

Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have enabled the analysis of vast social media datasets, also called Social Media Listening (SML), providing insights not only into patient perspectives but also into those of other interest groups such as caregivers. In this method study, we propose an NLP framework that—given a particular disease—is designed to extract pertinent information related to three primary research topics: identification of interest groups, understanding of challenges, and assessing treatments and support systems. Leveraging external resources like ontologies and employing various NLP techniques, particularly zero-shot text classification, the presented framework yields initial meaningful insights into these research topics with minimal annotation effort.
Caste, local governance effectiveness, and multidimensional poverty in rural India: some evidences
Authors: Ogbonna Amarachi Onyeyirichi , Ekuban Charles Aaron Adams , Muralee Krishnan C. , Viswanathan P. K.
Published: February 2025

Introduction

This study explores the relationships between local governance effectiveness, population densities of Scheduled Castes (SC) and Scheduled Tribes (ST), and rural multidimensional poverty in India, using data from the 2011 Indian Census, 2011 Socio-Economic and Caste Census (SECC), 2020 Mission Antyodaya, and 2019/21 National Family and Health Survey (NFHS).

Methods

The research examines how SC/ST population densities and local governance effectiveness are associated with the Multidimensional Poverty Index (MPI) across Indian states through regression and correlation analysis.

Results

The study’s findings reveal a national, rural MPI of 0.110, with 26% of the rural population experiencing multidimensional poverty. The study’s results also show that a statistically significant negative correlation exists between rural MPI and local governance effectiveness, with a 0.32% reduction in MPI for every 1% improvement in governance effectiveness. Conversely, higher SC and ST population densities are associated with increased MPI, with a 0.14% rise in MPI for each 1% increase in these densities. The study also highlights that ST population density has a stronger association with MPI than SC population density, indicating greater vulnerability to multidimensional poverty in areas with higher ST populations.

Discussion

Overall, the study underscores the importance of effective local governance in reducing rural poverty and suggests targeted efforts in areas with high SC and ST densities, particularly ST, to alleviate poverty. It also emphasizes the need for up-to-date data to understand and address rural poverty comprehensively.
The promised potential of blockchain technology for transparency and fairness in agri-food chains: insights from the coffee sector
Authors: Samoggia Antonella , Fantini Andrea , Ghelfi Rino
Published: February 2025

In recent years, blockchain technology has emerged as a tool with the potential to enhance transparency, fairness and sustainability within agrifood supply chains. This research contributes to the ongoing discourse on the implications of adopting blockchain, by addressing the issue of the potential of blockchain technology to contribute to fairness within the coffee chains. Starting from a theoretical framework that conceptualizes agrifood fairness and its relationship with blockchain, the research proceeds with an exploration of adopted blockchains’ fairness relevant information in 47 coffee products commercialized by 25 coffee roaster companies. The objective is to assess how specific characteristics associated with both roaster companies and products influence the quantity and quality of fairness relevant information disclosed through blockchain. Data elaboration includes linear multivariate regressions processing information related to coffee roaster companies and products, and assessing the types of fairness information conveyed through the blockchain. By establishing correlations between these characteristics and specific types of fairness, this study reveals that some companies’ characteristics—such as company size and strength of commitment—and some coffee products’ characteristics—such as product storytelling, existence of certifications and presence of blockchain information on the coffee packaging—influence the amount of fairness relevant information displayed on the blockchain platforms available to end users. This suggests that blockchain technology can aid in increasing transparency in supply chains and conveying fairness relevant information to end users. Its effectiveness is particularly significant in companies adopting sustainability oriented measures and appropriate company policies. In these contexts, blockchain can serve to increase visibility of ongoing fairness oriented processes.
Epidemiological investigation of poultry infectious in Kazakhstan (2021–2024)
Authors: Zikibayeva Karlygash B. , Svanbayev Asset A. , Akhmetsadykov Nurlan N. , Kudaibergenova Kamshat N. , Akhmetsadykova Shynar N. , Nurolda Ernur N. , Kydyrmanov Aidyn I.
Published: February 2025

Introduction

This study examines the epidemiological dynamics and genetic diversity of major avian infectious diseases in Kazakhstan, including highly pathogenic avian influenza (HPAI), Newcastle disease virus (NDV), and others. Using official data, laboratory diagnostics, and surveys, we identified high prevalence rates and virulent strains, exposing gaps in vaccination coverage and biosecurity practices. Continuous monitoring, improved vaccination strategies, and robust biosecurity measures are essential to reduce disease impact and ensure sustainable poultry farming.

Methods

A cross-sectional study was conducted to assess the prevalence and genetic diversity of major avian infectious diseases in Kazakhstan. Data sources included official reports, laboratory diagnostics (RT-PCR, ELISA, and sequencing), and a survey of veterinary specialists. Serum samples were analyzed to evaluate antibody responses and vaccine efficacy. Genetic and phylogenetic analyses were conducted for key pathogens, while a questionnaire provided insights into farm-level disease control practices.

Results

Analysis of official data recorded 27 outbreaks of avian diseases in Kazakhstan from 2005 to 2023, primarily involving HPAI and NDV. Our research further identified virulent strains such as NDV genotype VII and infectious bursal disease virus (IBDV) variants linked to global lineages. Serological studies revealed widespread exposure to pathogens, including Mycoplasma gallisepticum (MG), Mycoplasma synoviae (MS), chicken anemia virus (CAV), Ornithobacterium rhinotracheale (ORT), and low-pathogenic avian influenza (LPAI) H9, underscoring deficiencies in vaccination coverage. Farm surveys also identified weaknesses in biosecurity measures and inconsistencies in vaccination protocols.

Discussion

The findings underscore the urgent need for enhanced biosecurity measures, standardized vaccination programs, and routine monitoring to mitigate the impact of avian infectious diseases. This integrated approach offers valuable insights to support evidence-based decision-making for effective poultry health management in Kazakhstan.
Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
Authors: Samaroo Kyle , Awan Abdul Waheed , Marimuthu Siva , Iqbal Muhammad Naveed , Daniel Kamran , Shabbir Noman
Published: February 2025

In this paper, a semi-active and fully active suspension system using a PID controller were designed and tuned in MATLAB/Simulink to achieve simultaneous optimisation of comfort and road holding ability. This was performed in order to quantify and observe the trends of both the semi-active and active suspension, which can then influence the choice of controlled suspension systems used for different applications. The response of the controlled suspensions was compared to a traditional passive setup in terms of the sprung mass displacement and acceleration, tyre deflection, and suspension working space for three different road profile inputs. It was found that across all road profiles, the usage of a semi-active or fully active suspension system offered notable improvements over a passive suspension in terms of comfort and road-holding ability. Specifically, the rms sprung mass displacement was reduced by a maximum of 44% and 56% over the passive suspension when using the semi-active and fully active suspension, respectively. Notably, in terms of sprung mass acceleration, the semi-active suspension offered better performance with a 65% reduction in the passive rms sprung mass acceleration compared to a 40% reduction for the fully active suspension. The tyre deflection of the passive suspension was also reduced by a maximum of 6% when using either the semi-active or fully active suspension. Furthermore, both the semi-active and fully active suspensions increased the suspension working space by 17% and 9%, respectively, over the passive suspension system, which represents a decreased level of performance. In summary, the choice between a semi-active or fully active suspension should be carefully considered based on the level of ride comfort and handling performance that is needed and the suspension working space that is available in the particular application. However, the results of this paper show that the performance gap between the semi-active and fully active suspension is quite small, and the semi-active suspension is mostly able to match and sometimes outperform the fully active suspension n in certain metrics. When considering other factors, such as weight, power requirements, and complexity, the semi-active suspension represents a better choice over the fully active suspension, in the author’s opinion. As such, future work will look at utilising more robust control methods and tuning procedures that may further improve the performance of the semi-active suspension.
A Lightweight Deep Learning Approach for Detecting External Intrusion Signals from Optical Fiber Sensing System Based on Temporal Efficient Residual Network
Authors: Wang Yizhao , Guo Ziye , Luo Haitao , Liu Jing , Zhou Ruohua
Published: February 2025

Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems.
Efficacy of Entomopathogenic Fungi Against Bruchus rufimanus (Coleoptera: Chrysomelidae) in Laboratory and Field Trials Using Dropleg Spraying Technique
Authors: Lugendo Adrien Rodrigue , Ben Fekih Ibtissem , Caparros Megido Rudy , Pierreux Jérôme , Francis Frédéric , Segers Arnaud
Published: February 2025

Entomopathogenic fungi (EPF)-based biopesticides have attracted growing interest in pest management as alternatives to neurotoxic insecticides. Their potential was evaluated against various pests, including the broad bean weevil (Bruchus rufimanus Boheman 1833), a significant threat to faba bean (Vicia faba L.) crops. This study examined the entomotoxic effects and sublethal impacts (on oviposition) of three fungal strains under laboratory conditions: Beauveria bassiana (GHA), Metarhizium brunneum (USDA 4556), and M. brunneum (V275) on B. rufimanus adults. Subsequently, a large-scale field trial assessed the efficacy of B. bassiana (GHA) against B. rufimanus infestations using conventional anti-drift and dropleg spraying methods. The laboratory LT50 values ranged from four days for B. bassiana to eight days for M. brunneum (V275). The mortality rates recorded after ten days ranged from 86.6% for M. brunneum (V275) to 96.6% for B. bassiana (GHA). The inhibition of oviposition rates ranged from 12% for M. brunneum (USDA 4556) to 36% for B. bassiana (GHA). Field trials showed that the dropleg nozzles targeted faba bean pods, the oviposition sites of B. rufimanus, more effectively than the anti-drift nozzles. However, both fungal and chemical treatments applied via dropleg nozzles offered limited protection, reducing the infestation rates by 7% and 14%, respectively, with only a 3% improvement over anti-drift nozzles. This suggests that the large-scale spraying of chemical or fungal agents, including B. bassiana GHA, is not an optimal IPM strategy for managing B. rufimanus in faba beans. These laboratory and field results highlight the potential of EPF for managing B. rufimanus. However, the limitations of spray-applied plant protection methods underscore the need to redirect research toward more targeted strategies, such as attract-and-infect or endophytic EPF approaches.
Basal Cut Prediction of a Sugarcane Culm via Numerical Simulations
Authors: Santos Alexandre Assis Rezende , Costa Andre Luiz Gonçalves , de Andrade Ednilton Tavares , Santos Fabio Lucio , Magalhães Ricardo Rodrigues
Published: February 2025

Brazil is the world’s largest sugarcane producer, with 45.71 million tons of sugar and 29.7 billion liters of ethanol in 2023. To meet growing demand while addressing environmental concerns, sugarcane mechanical harvesting demands new research in the area. This work took into account the anisotropy and heterogeneity of sugarcane stems. Base cutting was simulated using the finite element method. For this, mechanical properties were obtained by using sugarcane common grade RB966928. The simulation was based on a rotational speed of 640 rpm and a cutting angle of 18° of the cutting blade. The results demonstrate that the cutting angle has a significant contribution to the stress level. Results found a maximum stress in the sugarcane skin around 18.2 MPa and 14.1 MPa in the sugarcane pulp. Compared to other results in the literature, the stress value difference was around 12%. Based on that, this work highlights the importance of sugarcane mechanization based on finite element analysis.
VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels
Authors: Jiménez-Gaona Yuliana , Vivanco-Galván Oscar , Castillo-Malla Darwin , Vivanco-Gualán Israel , Díaz-Guzmán Patricia
Published: February 2025

Background: Vitamin D deficiency is a significant risk factor for several chronic conditions. This study aims to predict vitamin D deficiency levels in a private database, collected from the southern part of Loja-Ecuador using a graphical web interface tool based on artificial intelligence algorithms. Methods: Two databases were processed using ML training models: SVM, Random Forest (RF), Linear Regression (LR). (i) Private data collection was undertaken on 465 patients from a local university, where vitamin D levels were measured through a blood sample collection to calculate the concentration of 25-hydroxy vitamin D in plasma and determine it by enzyme-linked immunosorbent assay, and (ii) public data collection was obtained from the FigShare database. Then, a survey was conducted from April 2022 to June 2023, identifying 157 variables, 18 of which were used for ML training models. Results: Vitamin D deficiency levels in private patients reached 18.10 ng/mL and 20.42 ng/mL in the public. The RF algorithm achieved (87.73%) accuracy, the SVM (80.0%), and LR (70.70%). RF was selected as the best performance model for web application design in binary levels classification: deficiency (Class 0) indicates vitamin D levels below 15 ng/mL, and sufficiency (Class 1) indicates vitamin D levels above 15 ng/mL. Conclusions: The “VITA-D” web application was used to monitor and predict vitamin D levels and deficiency factor risk based on clinical and sociodemographic data, providing an efficient and cost-effective alternative to traditional vitamin D testing methods.
Rendezvous Missions to Systems of Small Solar System Bodies Using the Suboptimal SDRE Control Approach
Authors: Cereja Edson , Balthazar José Manoel , Tusset Angelo Marcelo , Razoumny Vladimir , Prado Antonio Fernando Bertachini de Almeida
Published: February 2025

In this work, we analyze the suitability of the State-Dependent Riccati Equation (SDRE) suboptimal nonlinear control formulation for the implementation of body-fixed hovering of a spacecraft in the highly nonlinear environment engendered by the faint force fields around single- and multi-body Near-Earth Objects (NEOs), a class of Small Solar System Bodies with high relevance either in scientific, economic, or planetary defense-related aspects. Our results, addressing the hovering of a spacecraft around relative equilibrium points on the effective potential of the Near-Earth Asteroid (16) Psyche and of the much smaller main body (called Alpha) of the triple NEA system (153591) 2001SN263, show that the known effectiveness offered by the flexibility engendered by state-dependent factorization of nonlinear models is also effective when applied in these faint and highly nonlinear force fields. In fact, this work is a qualitative evaluation of the suitability of using SDRE in the highly disturbed environment around Small Solar System Bodies, which has never been undertaken before. We intend to prove that this method is adequate. For real missions, it is necessary to make deeper studies. In particular, our results show the flexibility granted by the SDRE approach in the trade off between maneuvering time against fuel consumption, a central aspect in such space missions. For instance, our simulations showed control effort and time of convergence for two controlled trajectories around (16) Psyche ranging from a half-time convergence with ∼20 times lower cost. Analogously, for the much smaller bodies in the (153591) 2001SN263 triple system, we got two trajectories in which one of them may converge ∼10 times faster but with up to ∼100 times higher cost.
Prediction of Electrophysiological Severity and Carpal Tunnel Syndrome Instrument Changes After Carpal Tunnel Release Using Machine Learning Model
Authors: Inui Atsuyuki , Takase Fumiaki , Lucchina Stefano , Kanatani Takako
Published: February 2025

Introduction: The severity of carpal tunnel syndrome (CTS) is evaluated by electrophysiological examination as well as a patient-oriented questionnaire. We hypothesized that machine learning could predict postoperative electrophysiological severity as well as the scores of patient-oriented questionnaires. In this study, we developed machine learning models to predict postoperative changes in electrophysiological severity and changes in the Carpal Tunnel Syndrome Instrument (CTSI). Materials and Methods: Data from four hundred and twenty hands of individuals who had been diagnosed with CTS and undergone carpal tunnel release were used. The features used for the machine learning model were preoperative age, gender, distal motor latency (DML) value, sensory nerve conduction velocity (SCV) value, preoperative electrophysiological severity stage, CTSI-SS value, and CTSI-FS value. Logistic Regression (LR), ElesticNet (EN), Support Vector Machine (SVM), Random Forest (RF), and LightGBM (LGBM) were used as machine learning algorithms. A machine learning model was created to binary classify the electrophysiologic severity at one year postoperatively. In the second experiment, regression models were created to predict the change in CTSI-SS and CTSI-FS at one year postoperatively. Results: In the electrophysiological severity classification model, LGBM showed the highest score (AUC = 0.802). Preoperative DML, age, and preoperative electrophysiological severity were important factors for model prediction. RF model showed the best performance. In the regression model predicting the change in CTSI-SS or CTSI-FS (RMSE: 0.418, 0.333, respectively), preoperative age and CTSI-SS or CTSI-FS scores were important factors for model prediction. Conclusions: The machine learning model can predict postoperative electrophysiological severity and CTSI score with high accuracy.
Damage Characteristics and Fracture Patterns of Sandstone Under the Coupled Effects of Blasting Stress and In Situ Stress
Authors: Ding Chenxi , Guo Xu , Xu Jiye , Sui Zhe , You Yuanyuan , Zhao Deyang
Published: February 2025

To investigate the influence of the in situ stress on the damage characteristics and fracture patterns of sandstone, this study designed a confining pressure loading device that simulates a deep high in situ stress environment. It allows for the coupled loading of blasting stress and in situ stress on rock specimens. Based on this, experimental research on three-dimensional rock blasting was conducted. A segmental analysis method was then applied, dividing the sandstone into stemming, charge, and bottom segments to refine the study. Using CT scanning, three-dimensional reconstruction, multifractal methods, and SEM scanning technology, the damage characteristics and failure modes of sandstone under the coupling of blasting stress and in situ stress were investigated from macroscopic, mesoscopic, and microscopic perspectives. The experimental research indicates that, under in situ stress, the rock’s ability to resist the effects of blasting stress waves and blasting gas is enhanced. With increasing in situ stress, the overall damage level of sandstone specimens gradually decreases and the damage characteristics in different segments of the specimen show significant variation. Under the same in situ stress, the damage level of the sandstone specimen decreases as the axial position shifts from the stemming segment to the charge segment, and then to the bottom segment. Additionally, with increasing in situ stress, shear fractures increase, the blasting fracture surfaces become progressively rougher, and the microscopic fractures in the sandstone transition from brittle cleavage fractures to brittle–plastic quasi-cleavage fractures. In some areas, the energy is insufficient to penetrate the grain boundaries, leading to a transition from transgranular fractures to coupled fractures and intergranular fractures.
Evaluation of the Potential for Estimating Backscattering Coefficients over Bare Agricultural Soils at the Intra-Plot Scale
Authors: Fieuzal Remy , Baup Frédéric
Published: February 2025

The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m2), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from ~80 to ~2800 m2), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m2 area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm.
CNN-LSTM Based Automatic Classification Network for Multi-Type with Multi-Energy Charged Particle Identification
Authors: Mo Xishuo , Zhang Xin , Bai Chaoping , Wang Ziting , Zhang Shuai , Sun Yueqiang
Published: February 2025

Particle identification is a critical component of space environment detection. In this study, we propose a CNN-LSTM based multi-class automatic particle classification network, which employs 1-D CNN to extract more discriminative local features of waveforms produced by particle detector, and utilizes the ability of LSTM to capture long-range dependencies for extracting global features with stronger generalization capability. The proposed network can effectively classify the waveforms from multi-type with multi-energy charged particles, achieving an accuracy of 91.72% in simultaneously classifying H, B, C, O, and Al ions across three distinct energy levels. Furthermore, the proposed network achieves an accuracy of 73.60% in classifying particles with unseen energy levels during training, significantly surpassing the accuracy of previous methods. Extensive analysis experiments demonstrate the robustness and effectiveness of the proposed network.
Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
Authors: Aljamal Mohammed , Patel Sarosh , Mahmood Ausif
Published: February 2025

Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. The Robotics Operating System (ROS) and Reinforcement Learning (RL) are two promising approaches that help accomplish precise control, seamless integration of sensors-actuators, and exhibit learned behavior. The ROS enables seamless communication between heterogeneous components, while RL focuses on learning optimal behaviors through trial-and-error scenarios. Combining the ROS and RL offers superior decision making, improved perception, enhanced automation, and reliability. This work focuses on investigating ROS-based RL applications across various domains, aiming to enhance understanding through comprehensive discussion, analysis, and summarization. We base our evaluation on the application area, type of RL algorithm used, and degree of ROS–RL integration. Additionally, we provide summary of seminal works that define the current state of the art, along with GitHub repositories and resources for research purposes. Based on the review of successfully implemented projects, we make recommendations highlighting the advantages and limitations of RL techniques for specific applications and environments. The ultimate goal of this work is to advance the robotics field by providing a comprehensive overview of the recent important works that incorporate both the ROS and RL, thereby improving the adaptability of these emerging techniques.
Stabilization of a Class of Fractional-Order Nonlinear Systems Subject to Actuator Saturation and Time Delay
Authors: Alaviyan Shahri Esmat Sadat , Pariz Naser , Chen Yangquan
Published: February 2025

Actuator saturation and time delay are practical issues in practical control systems, significantly affecting their performance and stability. This paper addresses, for the first time, the stabilization problem of fractional-order (FO) nonlinear systems under these two practical constraints. Two primary methodologies are employed: the vector Lyapunov function method, integrated with the M-matrix approach, and the second one is the Lyapunov-like function method, which incorporates diffusive realization and the Lipchitz condition. An optimization framework is proposed to design stabilizing controllers based on the derived stability conditions. The proposed methods are validated numerically through their application to the FO Lorenz and Liu systems, demonstrating their effectiveness in handling actuator saturation and time delay.
An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
Authors: Negri Edoardo , Fuscaldo Walter , Burghignoli Paolo , Galli Alessandro
Published: February 2025

Two-dimensional leaky-wave antennas offer effective, compact, single-feeder, easy-to-fabricate solutions to the longstanding problem of realizing a simultaneously directive and low-profile radiating device. These traveling-wave antennas have been thus proposed as wideband, reconfigurable, or frequency-scanning radiating structures in different application contexts, spacing from the microwave to terahertz frequency range. These diverse contexts call for a comprehensive guide to characterizing and designing two-dimensional leaky-wave antennas. In this work, a review of numerical techniques for the analysis of either quasi-uniform or radially periodic leaky-wave antennas is proposed in order to provide the reader with straightforward yet effective design guidelines. Theoretical results are corroborated through full-wave simulations of realistic three-dimensional models of the considered devices, thus demonstrating the effectiveness of the proposed methods.
Sparse Indoor Camera Positioning with Fiducial Markers
Authors: García-Ruiz Pablo , Romero-Ramirez Francisco J. , Muñoz-Salinas Rafael , Marín-Jiménez Manuel J. , Medina-Carnicer Rafael
Published: February 2025

Accurately estimating the pose of large arrays of fixed indoor cameras presents a significant challenge in computer vision, especially since traditional methods predominantly rely on overlapping camera views. Existing approaches for positioning non-overlapping cameras are scarce and generally limited to simplistic scenarios dependent on specific environmental features, thereby leaving a significant gap in applications for large and complex settings. To bridge this gap, this paper introduces a novel methodology that effectively positions cameras with and without overlapping views in complex indoor scenarios. This approach leverages a subset of fiducial markers printed on regular paper, strategically placed and relocated across the environment and recorded by an additional mobile camera to progressively establish connections among all fixed cameras without necessitating overlapping views. Our method employs a comprehensive optimization process that minimizes the reprojection errors of observed markers while applying physical constraints such as camera and marker coplanarity and the use of a set of control points. To validate our approach, we have developed novel datasets specifically designed to assess the performance of our system in positioning cameras without overlapping fields of view. Demonstrating superior performance over existing techniques, our methodology establishes a new state-of-the-art for positioning cameras with and without overlapping views. This system not only expands the applicability of camera pose estimation technologies but also provides a practical solution for indoor settings without the need for overlapping views, supported by accessible resources, including code, datasets, and a tutorial to facilitate its deployment and adaptation.
A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
Authors: Kurmanbekova Mainur , Du Jiangtao , Sharples Stephen
Published: February 2025

This review focuses on the indoor air quality (IAQ) in social housing across developing nations to identify the key factors that influence IAQ, research gaps, and opportunities for future interventions. A total of 43 studies from 2000 to 2024 were identified and categorized into the following five themes: Health and Comfort, Direct Measurements, Retrofitting and Maintenance, Comparative Studies, and Housing Conditions and Socio-Economic Factors. Residents of social housing in low- and middle-income countries (LMICs) face disproportionately higher exposure to harmful pollutants, including PM2.5 (up to 1360 µg/m3 during cooking), CO (with peaks at 150 ppm), and formaldehyde (up to 4.802 ppm), often exceeding World Health Organization (WHO) guidelines. Approximately 76% of the studies were published post-2019, reflecting increased global awareness of IAQ’s health implications, improved measurement and simulation technologies, and heightened attention to indoor environments following the COVID-19 pandemic. Despite the growing body of research in this direction, most of the found studies focus on warm and humid climates, leaving gaps in the understanding of IAQ issues in other, colder climate regions. Cost-effective solutions, such as natural ventilation and adaptive architectural design, show promise, but empirical testing is lacking, especially in diverse geographical contexts. In addition, culturally driven occupant behaviors significantly influence IAQ, but remain understudied in the context of low-income residents in developing countries. There is a need for expanded research that considers regional climate differences, socio-economic factors, and occupant behavior to develop effective interventions to improve IAQ and health outcomes in these settings.
Diagnosis of Winter Wheat Nitrogen Status Using Unmanned Aerial Vehicle-Based Hyperspectral Remote Sensing
Authors: Huangfu Liyang , Jiao Jundang , Chen Zhichao , Guo Lixiao , Lou Weidong , Zhang Zheng
Published: February 2025

The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450–830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD666, FD826), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded R2 values of 0.85 and 0.75, respectively, and may be represented by the equation y = −2.0655x + 0.156. The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat.
Assessment of Limb Imbalance in Professional Soccer Players
Authors: Moreno-Villanueva Adrián , Soler-López Alejandro , Cuartero-Martínez Jose Carlos , Pino-Ortega Jose
Published: February 2025

Although it has been found that soccer produces limb imbalance, it has not been rigorously determined how to evaluate it in soccer players or which low-cost tests are the most effective for its analysis. Therefore, the objective of this systematic review was to identify and examine the evidence and evaluations of limb imbalance produced in professional soccer players. A systematic search was conducted in three databases (PubMed, Web of Sciences, and Scopus) to identify relevant studies published before 23 May 2022. Of the 2364 studies identified initially, only 12 articles were included in the systematic review. The results revealed that injury risks can be detected in professional soccer players through the YBT (Y Balance Test). The PSLR (Passive Straight Leg Raise) test, as well as the evaluation of the ROM (range of motion) in movements of adduction and internal hip rotation, seem to be two reliable tests to detect imbalances in the flexibility of the extremities. The FMS (Functional Motion Screen) test is inversely related to the performance in jump tests; thus, its combination can help to detect asymmetries in power generation. Finally, strength assessment tests in soccer players can negatively alter the flexibility values of agonist/antagonist muscles. Therefore, it is advisable to monitor both strength and flexibility tests synergistically to obtain a comprehensive evaluation.
Bio-Inspired Sutures: Localizing Damage by Isolating Strain Energy
Authors: Chen Diana A. , Gibbons Melissa M.
Published: February 2025

This study draws upon bio-inspiration from anatomical sutures found in hard structures, such as turtle shells, to explore if impact energy can be dissipated through geometric parameterization rather than relying on energy-absorbing materials. While previous finite element analysis studies identified optimal dovetail suture geometries for maximizing the global stiffness and toughness of archway structures, this paper explores how different suture geometries might optimize localization effects through segmentation to isolate damage caused by the propagation of strain energy. We compare the global toughness of each suture geometry to its scaling factor, defined as the ratio of strain energy in the center segment(s) of the archway over the total strain energy absorbed during deformation, normalized by the expected strain energy consistent with uniform volumetric distribution. Our findings indicate that the scaling factor tended to correlate positively with global toughness, suggesting that suture geometries that performed well globally would also exhibit the localization effect. However, there is some nuance in selecting suture geometries that perform well for both metrics, as well as ensuring that geometries that perform well for one type of segmentation are still structurally sound in others, due to little control over where impact may occur, relative to the location of a suture, in real scenarios.
Academic Anxiety in Spanish Higher Education: A Systematic Review
Authors: Idoiaga-Mondragon Nahia , Gaztañaga Mirari , Yarritu Ion , Pascual-Sagastizabal Eider
Published: February 2025

Background: Anxiety disorders are among the most prevalent mental health issues of the 21st century, significantly impacting individuals and healthcare systems worldwide. Within higher education research, academic anxiety is particularly significant, as it encompasses the specific anxieties students face within academic environments, such as exams and public speaking. This study aims to provide a contemporary overview of academic anxiety within Spanish universities by addressing three key questions: (1) How has research on academic anxiety evolved in Spain? (2) What tools have been used to measure academic anxiety? (3) What factors and variables have been analyzed in relation to academic anxiety, and what are the main findings? Methods: A systematic review was conducted following PRISMA guidelines for study selection, data extraction, and synthesis. The analysis focused on PubMed, PsycINFO, and Web of Science databases, examining 25 eligible articles published before January 2023. The objective was to evaluate, organize, and synthesize the evidence presented in these articles. Results: The findings revealed that the majority of studies were conducted in the last decade, employing 20 distinct measurement tools and examining more than 40 associated variables. The academic anxieties investigated included various types such as test anxiety, language learning anxiety, math anxiety, public speaking anxiety, and discipline-specific anxieties like dissection or music performance anxiety. Additionally, the studies explored the relationships between these academic anxieties and other variables such as gender and age. Conclusions: The implications of these findings for education and potential avenues for future research are discussed.
Experimental Study on Trenchless Treatment Technology of Differential Settlement of In-Service Highway Subgrade in Deep Soft Soil Area
Authors: Xu Jianqiang , Zhang Tiangong , Zhang Shasha , Wang Mengjie , Yang Xiaohua , Xu Feng
Published: February 2025

The highway network is densely distributed in the southeast coast of China. Highway subgrades passing through soft soil areas often produce large settlements, resulting in pavement cracking, bridgehead jumping, and other diseases. In order to study the effect of three trenchless treatment technologies of oblique jet grouting pile (JGP), lateral displacement limiting pile (LDLP), and load reducing pipe (LRP), centrifugal model tests were carried out under three treated conditions and without treatment. Based on the data of pore water pressure and settlement in the range of the half embankment model and outside the embankment, the settlement characteristics of highway soft soil foundation during the test simulation were studied, and the characteristics of different treatment methods were compared. The high level of pore water pressure corresponds to the rapid development of settlement. The average settlement during the existing operation period accounts for 96.7% of the total settlement of the simulation period, and the settlement does not converge. The methods can effectively inhibit the development of settlement, and each has its own characteristics: the LRP method does not involve foundation treatment, so its settlement characteristics are closest to that without treatment. The LDLP method can obviously limit the settlement within the embankment range and the pore water dissipation. The JGP method enhances the synergistic deformation ability of the embankment and significantly decreases the differential settlement.