bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Multimorbidity Indexes Based on Common Mental Health Conditions
Authors: Kose Junko , Kesse-Guyot Emmanuelle , Duquenne Pauline , Hercberg Serge , Galan Pilar , Touvier Mathilde , Andreeva Valentina A. , Fezeu Léopold K.
Published: February 2025

Objectives

Numerous multimorbidity indexes exist, focused primarily or solely on somatic conditions. We developed mental multimorbidity indexes as epidemiological tools.

Methods

Participants in the French NutriNet-Santé cohort (73.5% women; mean age = 59.5 ± 13.7 years; index development N = 20,000; index comparison N = 7,259) completed self-report questionnaires (2020–2022) regarding depressive symptoms, anxiety, eating disorders, insomnia, alcohol use disorders, cognitive difficulties, and the WHO Disability Assessment Schedule 2.0 (WHODAS 2.0). Using established cutoffs, participants were split into 2 groups for each condition. Tweedie regression analyses were performed with the 6 mental health conditions as exposures and the WHODAS 2.0 score as the outcome. Performance (C-index) and calibration of the indexes were compared with a simple count.

Results

A general and a sex-specific mental multimorbidity indexes were developed; both were significantly associated with the disability score. The new indexes had slightly better predictive performance than simple counts of mental disorders.

Conclusion

We developed mental multimorbidity indexes as epidemiological research tools. Future prospective studies could investigate their predictive potential regarding outcomes such as medication use, healthcare utilization, and quality of life.
Case Report: Robotic PeTEP for Totally Extraperitoneal Repair of Incisional Hernia
Authors: Radu Victor G.
Published: February 2025

The development of minimally invasive surgical techniques for ventral hernias has significantly progressed, evolving from IPOM and IPOM + to advancements like eTEP/eTEP-TAR. These newer techniques have demonstrated their effectiveness by delivering excellent postoperative outcomes, despite being introduced less than a decade ago. Recreating traditional procedures, which are considered the “gold standard” in ventral hernia surgery, through minimally invasive methods—such as the Rives-Stoppa repair—could represent the next frontier in abdominal wall surgery. This publication focuses on replicating ventral incisional hernia repair using an extraperitoneal approach, as outlined by Todd Heniford, and taking inspiration from the PeTEP technique for primary ventral hernias, whether associated with diastasis recti or not, as described by Hector Valenzuela.
The Diagnostic Classification of the Pathological Image Using Computer Vision
Authors: Matsuzaka Yasunari , Yashiro Ryu
Published: February 2025

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown superior performance in tasks such as image classification, segmentation, and object detection in pathology. Computer vision has significantly improved the accuracy of disease diagnosis in healthcare. By leveraging advanced algorithms and machine learning techniques, computer vision systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep learning models have been trained on large datasets of annotated pathology images to perform tasks such as cancer diagnosis, grading, and prognostication. While deep learning approaches show great promise in diagnostic classification, challenges remain, including issues related to model interpretability, reliability, and generalization across diverse patient populations and imaging settings.
Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges
Authors: Aldemir Ceray , Uçma Uysal Tuğba
Published: February 2025

This study investigates the transformative capacity of artificial intelligence (AI) in improving financial accountability and governance in the public sector. The study aims to explore the strategic potential and constraints of AI integration, especially as fiscal systems become more complex and public expectations for transparency increase. This study employs a qualitative case study methodology to analyze three countries, which are Estonia, Singapore, and Finland. These countries are renowned for their innovative use of AI in public administration. The data collection tools included an extensive review of the literature, governmental publications, case studies, and public feedback. The study reveals that AI-driven solutions such as predictive analytics, fraud detection systems, and automated reporting significantly improve operational efficiency, transparency, and decision making. However, challenges such as algorithmic bias, data privacy issues, and the need for strong ethical guidelines still exist, and these could hinder the equitable use of AI. The study emphasizes the importance of aligning technological progress with democratic values and ethical governance by addressing these problems. The study also enhances the dialog around AI’s role in public administration. It provides practical recommendations for policymakers who seek to use AI wisely to promote public trust, improve efficiency, and ensure accountability in governance. Future research should focus on enhancing ethical frameworks and investigating scalable solutions to overcome the social and technical challenges of AI integration.
Research on Remote Sensing Monitoring of Key Indicators of Corn Growth Based on Double Red Edges
Authors: Yin Ying , Chen Chunling , Wang Zhuo , Chang Jie , Guo Sien , Li Wanning , Han Hao , Cai Yuanji , Feng Ziyi
Published: February 2025

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R2 of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R2 of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible.
High-Throughput 3D Rice Chalkiness Detection Based on Micro-CT and VSE-UNet
Authors: Cai Zhiqi , Deng Yangjun , Zhu Xinghui , Li Bo , Xu Chenglin , Li Donghui
Published: February 2025

Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hindering practical applications. To address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet. Our method begins with non-destructive 3D imaging of grains using micro-CT. For the accurate segmentation of kernels and chalky regions, we propose VSE-UNet, an improved VGG-UNet with an SE attention mechanism for enhanced feature learning. Through comprehensive training optimization strategies, including the Dice focal loss function and dropout technique, the model achieves robust and accurate segmentation of both kernels and chalky regions in continuous CT slices. To enable high-throughput 3D analysis, we developed a unified 3D detection framework integrating isosurface extraction, point cloud conversion, DBSCAN clustering, and Poisson reconstruction. This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. Finally, 3D morphological indicators of chalkiness are calculated using triangular mesh techniques. Experimental results demonstrate significant improvements in both 2D segmentation (7.31% improvement in chalkiness IoU, 2.54% in mIoU, 2.80% in mPA) and 3D phenotypic measurements, with VSE-UNet achieving more accurate volume and dimensional measurements compared with the baseline. These improvements provide a reliable foundation for studying chalkiness formation and enable high-throughput phenotyping.
Characterization of Olive-Resistant Genes Against Spilocaea oleagina, the Causal Agent of Scab
Authors: Estudillo Cristina , Pérez-Rial Adrián , Guerrero-Páez Francisco Abel , Díez Concepción M. , Moral Juan , Die José V.
Published: February 2025

The olive tree (Olea europaea subsp. europaea L.) is one of the most important perennial crops in the Mediterranean Basin. Olive Scab, caused by the fungal species Spilocaea oleagina, a member of the Venturiaceae family, is among the most significant diseases affecting olive cultivation, prompting farmers to spend millions of euros annually on fungicides for its control. The fungal genus Venturia includes highly specialized species responsible for diseases in other crops, such as Apple Scab, caused by V. inaequalis. One of the most effective control strategies for Apple Scab has been developing and using resistant varieties. However, in the case of Olive Scab, genetic resistance remains relatively underexplored. In apples, breeders have identified approximately 20 resistance genes against V. inaequalis, known as Rvi genes, over recent decades. In this study, we identified and characterized four homologous genes to the Rvi family in olive, analyzing their genomic organization and expression profiles in silico. A total of 14 homologous sequences were identified in the olive genome, all sharing conserved domains typical of the leucine-rich repeat (LRR) superfamily, widely associated with plant immune responses. Functional annotation using gene ontology indicated enrichment in categories related to stimulus response and diverse biological processes. Notably, homologous sequences corresponding to apple proteins linked to V. inaequalis resistance exhibited high expression levels in response to biotic and abiotic stresses. These results indicate that olive trees may harbor resistance mechanisms analogous to those observed in apples, providing a foundation for further research into olive disease resistance and breeding programs.
Educational Apps and Dog Behavioural Problem Prevention: Associations Between the Zigzag Dog-Training App and Behavioural Problems
Authors: Rowland Tom , de Assis Luciana , Menteith Carolyn , Winter Lorna , Zulch Helen , Mills Daniel S.
Published: February 2025

Behavioural problems in dogs are a major welfare concern for both dogs and owners, which can lead to relinquishment or euthanasia. As such, it is important to find ways to minimize the risk of development or severity of problematic behaviour. Such interventions should ideally occur early in the dog’s development and need to be widely and easily accessible. One way to implement such interventions, given modern smart phone use, is via an educational application (app). Here, we conducted a cross-sectional observational study where we sought to estimate associations between use of the Zigzag dog-training app (Zigzag Pet Care Services Ltd.) and the development and severity of problematic behaviours. We focused on a subset of the survey population (n = 367) who did no training at all (n = 194) or only used the app (did not attend formal classes or in person training; n = 173). Proportional odds ordinal logistic regression models were used to estimate odds ratios and 95% confidence intervals for a range of behaviours as a function of percentage completion of the first four chapters of the app while controlling for age, sex, health, and where the puppy was from. While sample sizes were relatively small and confidence intervals wide, 19 out of 21 odds ratio point estimates were in favour of severity being lower with increasing Zigzag app completion (the remaining 2 were marginally above 1). There was no good evidence that Zigzag increased the severity of any behaviours. On the contrary, there was reasonable evidence against the null hypothesis of no association in favour of Zigzag reducing the severity of familiar aggression, house soiling, chewing, barking, escaping, and noise fear. While causal effects cannot be claimed, overall, the reported associations are favourable and suggest that further study is warranted.
Comparison of the Efficacy of 15- and 19-Week Chemotherapy Protocols Based on Vincristine, L-Asparaginase, Doxorubicin, and Prednisolone for Dogs with Multicentric Lymphoma
Authors: Lin Yi-Chen , Wang Shang-Lin
Published: February 2025

Multicentric lymphoma is the most common type of hematopoietic neoplasm in dogs. Chemotherapeutic protocols typically consist of cyclophosphamide, doxorubicin, vincristine, and prednisolone (CHOP). The 15- and 19-week CHOP protocols exhibit similar outcomes, indicating that a shorter treatment duration may be a feasible treatment option. However, the 19-week LHOP protocol, which uses L-asparaginase instead of cyclophosphamide, results in a longer progression-free survival and a similar survival time compared to CHOP-based chemotherapy regimens. In this study, we conducted and historically compared the efficacy of the 15- and 19-week LHOP protocols to determine whether there are any differences in efficacy. Eighteen and twenty dogs underwent the 15- and 19-week LHOP protocols, respectively. No significant differences were found in age, body weight, sex, clinical stage, substage, T-cell phenotype, hypercalcemia status, and overall response rate between the two groups. The time to progression (TTP) and lymphoma-specific survival (LSS) for the 19-week LHOP protocol were significantly longer than those for the 15-week LHOP protocol (p = 0.004 and p = 0.008, respectively). Thus, the 19-week LHOP protocol may be a better treatment option for dogs with multicentric lymphoma. However, the 15-week LHOP protocol lowered the treatment cost and reduced the treatment time.
Antimicrobial Resistance in ESKAPE Pathogens: A Retrospective Epidemiological Study at the University Hospital of Palermo, Italy
Authors: Pipitò Luca , Rubino Raffaella , D’Agati Giulio , Bono Eleonora , Mazzola Chiara Vincenza , Urso Sofia , Zinna Giuseppe , Distefano Salvatore Antonino , Firenze Alberto , Bonura Celestino , Giammanco Giovanni M. , Cascio Antonio
Published: February 2025

Background: Antimicrobial resistance (AMR) is an escalating global health threat, projected to cause over 40 million deaths by 2050. ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) are major contributors to nosocomial infections and AMR. We evaluated the epidemiology and AMR prevalence of ESKAPE pathogens at the University Hospital in Palermo between January 2018 and July 2023, analyzing factors associated with mortality in patients with positive blood cultures. Methods: Microbiological data from all specimen types were collected using the Business Intelligence system Biwer, excluding duplicates. We assessed the prevalence and trends of ESKAPE isolates and AMR over time. Clinical data from hospital discharge forms were used to evaluate factors associated with mortality in patients with ESKAPE-positive blood cultures. Differences in AMR prevalence between blood and non-blood isolates were examined. Results: A total of 11,607 specimens from 4916 patients were analyzed. Most patients were admitted to Internal Medicine (19.4%), the ICU (13.2%), and General Surgery (9.9%). Additionally, 21.5% of the specimens were collected from ICU-admitted patients. Blood cultures accounted for 14.3% of the specimens, urine for 25.3%, respiratory secretions for 22.1%, and skin and mucosal swabs for 20.9%. The prevalence of all isolates increased progressively, peaking in 2021. The vancomycin-resistant E. faecium prevalence was 19.4%, with a significant upward trend, while oxacillin-resistant S. aureus prevalence was 35.0%, showing a significant decline. A. baumannii exhibited high resistance to all antibiotics tested except for colistin and cefiderocol. Carbapenemase resistance was 55.0% in K. pneumoniae, 20.4% in P. aeruginosa, and 4.6% in Enterobacter spp. P. aeruginosa showed a significant decrease in meropenem resistance. K. pneumoniae and A. baumannii bloodstream infections were linked to higher mortality risk.
Effects of Sulforaphane Treatment on Skeletal Muscle from Exhaustive Exercise-Induced Inflammation and Oxidative Stress Through the Nrf2/HO-1 Signaling Pathway
Authors: Ruhee Ruheea Taskin , Ma Sihui , Suzuki Katsuhiko
Published: February 2025

Skeletal muscle is primarily involved in exercise performance and health promotion. Sulforaphane (SFN) is a naturally occurring isothiocyanate that indirectly activates the transcription factor Nrf2 (nuclear factor erythroid 2-related factor 2), thus inducing the expression of Nrf2 target genes, including antioxidant enzymes. This study aimed to identify the effects of a single dose of SFN administration on exhaustive exercise-induced inflammation and oxidative stress in skeletal muscle tissue and elucidate the underlying mechanisms. Thirty-six mice were divided into four groups: control, SFN, exercise (Ex), and SFN + Ex. The SFN group and SFN + Ex group received SFN orally (50 mg/kg body weight) 2 h before the running test. Exercise significantly reduced plasma glucose levels, while the SFN-treated group exhibited a smaller reduction. Acute exhaustive exercise increased the expression of pro-inflammatory cytokines in muscle tissue, while the SFN + Ex group exhibited significantly reduced expression of pro-inflammatory cytokines. The gene expression of Nrf2 and its target enzymes, including heme oxygenase (HO)-1, superoxide dismutase (SOD)-1, catalase (CAT), and glutathione peroxidase (GPx)-1, was measured in the gastrocnemius and soleus muscle tissue. Compared with the Ex group, the SFN + Ex group showed upregulated expression of all these parameters, including Nrf2. SFN treatment reduced acute exhaustive exercise-induced oxidative stress and inflammation via activation of the Nrf2/HO-1 signaling pathway.
Protein Nitration in Patients with Mitochondrial Diseases
Authors: Livramento Jomênica B. , Rodrigues Gabriela S. , Faber Jean , de Souza Filho Luis A. , Moura Felipo V. , Barros Camila D. S. , Pinto Wladimir B. V. R. , Schmidt Beny , Oliveira Acary S. B. , Kiyomoto Beatriz H. , Tengan Célia H.
Published: February 2025

Mitochondrial diseases are complex disorders caused by nuclear or mitochondrial DNA mutations, leading to oxidative phosphorylation deficiency and excessive production of reactive oxygen species (ROS). While ROS have been well established in the pathogenesis of these diseases, the role of reactive nitrogen species (RNS) remains unclear. In this study, we performed a quantitative analysis of muscle fibers to investigate the relationship between protein nitration and mitochondrial abnormalities (mitochondrial proliferation and cytochrome-c oxidase (COX) deficiency) and factors like genotype, muscle damage, and age. A total of 1961 muscle fibers (303 from 4 controls and 1658 from 29 patients with mitochondrial diseases) were analyzed by immunostaining for nitro-tyrosine. Contrary to previous findings, which identified nitro-tyrosine only in small muscle vessels, we observed a broader distribution affecting the sarcolemma and sarcoplasm. Using multivariate techniques, we identified a significant correlation between protein nitration and mitochondrial proliferation but found no associations with COX deficiency, age, muscle damage, or genotype. These findings suggest that nitrative stress may contribute to mitochondrial dysfunction or play a role in signaling processes that induce mitochondrial biogenesis. Our results provide new insights into the molecular mechanisms of mitochondrial diseases and highlight the potential relevance of protein nitration.
Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
Authors: Li Wei , Lv Danju , Yu Yueyun , Zhang Yan , Gu Lianglian , Wang Ziqian , Zhu Zhicheng
Published: February 2025

Birds are significant bioindicators in the assessment of habitat biodiversity, ecological impacts and ecosystem health. Against the backdrop of easier bird vocalization data acquisition, and with deep learning and machine learning technologies as the technical support, exploring recognition and classification networks suitable for bird calls has become the focus of bioacoustics research. Due to the fact that the spectral differences among various bird calls are much greater than the differences between human languages, constructing birdsong classification networks based on human speech recognition networks does not yield satisfactory results. Effectively capturing the differences in birdsong across species is a crucial factor in improving recognition accuracy. To address the differences in features, this study proposes multi-scale deep features. At the same time, we separate the classification part from the deep network by using machine learning to adapt to classification with distinct feature differences in birdsong. We validate the effectiveness of multi-scale deep features on a publicly available dataset of 20 bird species. The experimental results show that the accuracy of the multi-scale deep features on a log-wavelet spectrum, log-Mel spectrum and log-power spectrum reaches 94.04%, 97.81% and 95.89%, respectively, achieving an improvement over single-scale deep features on these three spectrograms. Comparative experimental results show that the proposed multi-scale deep feature method is superior to five state-of-the-art birdsong identification methods, which provides new perspectives and tools for birdsong identification research, and is of great significance for ecological monitoring, biodiversity conservation and forest research.
How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
Authors: Xie Qisi , Chen Zihao , Luh Dingbang
Published: February 2025

As non-humanoid robots develop and become more involved in human life, emotional communication between humans and robots will become more common. Non-verbal communication, especially through body movements, plays a significant role in human–robot interaction. To enable non-humanoid robots to express a richer range of emotions, it is crucial to understand how humans recognize the emotional movements of robots. This study focuses on the underlying mechanisms by which humans perceive the motion arousal levels of non-humanoid robots. It proposes a general hypothesis: Human recognition of a robot’s emotional movements is based on the perception of overall motion, and is independent of the robot’s mechanical appearance. Based on physical motion constraints, non-humanoid robots are divided into two categories: those guided by inverse kinematics (IK) constraints and those guided by forward kinematics (FK) constraints. Through literature analysis, it is suggested that motion amplitude has the potential to be a common influencing factor. Two psychological measurement experiments combined with the PAD scale were conducted to analyze the subjects’ perception of the arousal expression effects of different types of non-humanoid robots at various motion amplitudes. The results show that amplitude can be used for expressing arousal across different types of non-humanoid robots. Additionally, for non-humanoid robots guided by FK constraints, the end position also has a certain impact. This validates the overall hypothesis of the paper. The expression patterns of emotional arousal through motion amplitude are roughly the same across different robots: the degree of motion amplitude corresponds closely to the degree of arousal. This research helps expand the boundaries of knowledge, uncover user cognitive patterns, and enhance the efficiency of expressing arousal in non-humanoid robots.
Neutralization of Toxic Malodorous Gases from Cattle Slurry
Authors: Kotarska Katarzyna , Dziemianowicz Wojciech , Świerczyńska Anna , Lach Michał , Sokołowska Barbara
Published: February 2025

This study investigated the ability of bacterial strains to neutralize odorous substances from cattle slurry (CS). The research was performed to develop a microbial preparation for the deodorization of CS. Among the strains of bacteria (Bacillus and Pseudomonas) isolated from natural environments, those with the highest ammonia and hydrogen sulfide reduction were selected, and the bacterial consortium was prepared. The biopreparation reduced ammonia by 98% in the unshaken culture and 100% in the aeration culture, after 10 days of incubation (compared to the initial sample). Complete elimination of hydrogen sulfide was noted on day 6 of the deodorization process for both cultures. The microbiological supplementation also had a positive effect on the chemical composition of the slurry, increasing its fertilizer value. The addition of biopreparation to the slurry resulted in a reduced loss of ammonium ions and increased nitrogen concentration by 29%. It was found that the use of the microbial consortium also increased the availability of potassium and phosphorus, which can be used in agricultural production. Nitrogen retention by microorganisms in the slurry increases its organic value and leads to a reduction in the use of mineral fertilizers.
Developing the Interdisciplinary Scope of Golf Strength and Conditioning Coaching: Learning from Practitioners’ Conceptualization and Use of Athletes’ Errors
Authors: Ferguson Callum , Collins Dave , Carson Howie J.
Published: February 2025

Interdisciplinarity, characterized by the cohesive integration of knowledge across disciplines, is increasingly recognized as a feature of expert sports coaching practice. Concurrently, the importance of coaches’ decision-making regarding the role and use of athletes’ errors during training has been emphasized from an increasingly interdisciplinary perspective. This study aimed to address the gap between current conceptualizations of effective training design and real-world application by seeking to (a) explore the current application of interdisciplinary knowledge and practice within golf strength and conditioning (S&C) support; (b) examine S&C coaches’ conceptualization of errors made by athletes in the golf S&C environment; (c) understand the role of golf S&C coaches in athlete development. This study used semi-structured interviews to explore eight golf S&C coaches’ (male = 7, female = 1, Mage = 37.8 ± 9.2 years, Mexperience = 9.3 ± 2.6 years) perspectives on and experiences of athletes’ errors and interdisciplinary integration. The data were analyzed using reflexive thematic analysis. The findings revealed that interdisciplinary knowledge integration is infrequent in golf S&C coaching despite practitioners’ recognition of the potential benefits for coaches and athletes. This lack of collaboration and knowledge across coaching domains is exemplified through practitioners’ simplistic conceptualization of athletes’ errors and narrow working scope regarding athlete support. In summary, the findings indicate a clear need for development within golf regarding interdisciplinary knowledge and practice pertaining to support provision. Specifically, encouraging a more developed conceptualization of athletes’ errors in alignment with advances in coaching science would be beneficial. Recommendations are made to support the integration of S&C knowledge across golf coaching domains and stress the importance of developing golf S&C practitioners’ psychosocial competencies during accreditation and professional development.
An Explainable XGBoost Model for International Roughness Index Prediction and Key Factor Identification
Authors: Lv Bin , Gong Haixia , Dong Bin , Wang Zixin , Guo Hongyu , Wang Jianzhu , Wu Jianqing
Published: February 2025

This study proposes an explainable extreme gradient boosting (XGBoost) model for predicting the international roughness index (IRI) and identifying the key influencing factors. A comprehensive dataset integrating multiple data sources, such as structure, climate and traffic load, is constructed. A voting-based feature selection strategy is adopted to identify the key influencing factors, which are used as inputs for the prediction model. Multiple machine learning (ML) models are trained to predict the IRI with the constructed dataset, and the XGBoost model performs the best with the coefficient of determination (R2) reaching 0.778. Finally, interpretable techniques including feature importance, Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are employed to reveal the mechanism of influencing factors on IRI. The results demonstrate that climate conditions and traffic load play a critical role in the deterioration of IRI. This study provides a relatively universal perspective for IRI prediction and key factor identification, and the outputs of the proposed method contribute to making scientific maintenance strategies of roads to some extent.
ECA-ATCNet: Efficient EEG Decoding with Spike Integrated Transformer Conversion for BCI Applications
Authors: Li Xuhang , Shen Qianzi , Wang Haitao , Wang Zijian
Published: February 2025

The Brain–Computer Interface (BCI) has applications in smart homes and healthcare by converting EEG signals into control commands. However, traditional EEG signal decoding methods are affected by individual differences, and although deep learning techniques have made significant breakthroughs, challenges such as high energy consumption and the processing of raw EEG data remain. This paper introduces the Efficient Channel Attention Temporal Convolutional Network (ECA-ATCNet) to enhance feature learning by applying Efficient Channel Attention Convolution (ECA-conv) across spatial and spectral dimensions. The model outperforms state-of-the-art methods in both within-subject and between-subject classification tasks on MI-EEG datasets (BCI-2a and PhysioNet), achieving accuracies of 87.89% and 71.88%, respectively. Additionally, the proposed Spike Integrated Transformer Conversion (SIT-conversion) method, based on Spiking–Softmax, converts the Transformer’s self-attention mechanism into Spiking Neural Networks (SNNs) in just 12 time steps. The accuracy loss of the converted ECA-ATCNet model is only 0.6% to 0.73%, while its energy consumption is reduced by 52.84% to 53.52%. SIT-conversion enables ultra-low-latency, near-lossless ANN-to-SNN conversion, with SNNs achieving similar accuracy to their ANN counterparts on image datasets. Inference energy consumption is reduced by 18.18% to 45.13%. This method offers a novel approach for low-power, portable BCI applications and contributes to the advancement of energy-efficient SNN algorithms.
Superior Photodegradation of Bentazon and Nile Blue and Their Binary Mixture Using Sol–Gel Synthesized TiO2 Nanoparticles Under UV and Sunlight Sources
Authors: Yasmeen Sadaf , Burratti Luca , Duranti Leonardo , Sgreccia Emanuela , Agresti Antonio , Prosposito Paolo
Published: February 2025

Herbicides and dyes in wastewater are considered serious water pollutants. These water pollutants have harmful effects on the ecosystem and due to this, the degradation of these pollutants is very important. In this article, titanium dioxide (TiO2) nanoparticles were synthesized by the sol–gel method and used as photocatalysts. TiO2 powder was characterized by using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and UV-Visible (UV-Vis) spectroscopy. The XRD analysis revealed the anatase phase for TiO2. The SEM investigation showed that TiO2 nanoparticles exhibit highly irregular block-shaped morphology. TiO2 nanoparticles degrade the organic pollutants under UV as well as sunlight. The photocatalytic activity of such prepared catalyst was carried out in solutions of bentazon herbicide (BZ) and Nile blue dye (NB) and in the mixture of these pollutants, under UV and sunlight. The degradation rate of both BZ and NB was very high in individual solutions as well as in the combination of them. The obtained results show that TiO2 photocatalyst is a potential candidate for the photocatalytic degradation of dyes and herbicides under UV and sunlight.
A Virtual Synchronous Generator Low-Voltage Ride-Through Control Strategy Considering Complex Grid Faults
Authors: Yin Jun , Chen Ziang , Qian Weichen , Zhou Shengyu
Published: February 2025

The Virtual Synchronous Generator (VSG) control strategy has garnered widespread application during the low-voltage ride-through (LVRT) of distributed energy resources integrated into power grids, primarily due to its inertia and damping properties. However, grid voltage dips frequently coincide with unbalanced conditions and harmonic disturbances, against which traditional VSG LVRT control strategies offer limited resilience, resulting in unbalanced and unstable grid-connected currents. Consequently, this impacts the VSG’s support for voltage at the point of common coupling (PCC) during LVRT, potentially leading to control system failure. To tackle this challenge, this paper introduces a VSG LVRT control strategy tailored for complex grid faults. Initially, a mathematical model is developed to analyze the impact on the VSG control system’s grid-connected current when the PCC voltage of the LC filter experiences harmonic-laden unbalanced dips. Subsequently, the traditional dual second-order generalized integrator phase-locked loop (DSOGI-PLL) is enhanced to bolster its filtering capabilities. Additionally, PCC voltage feedforward control is incorporated, with a meticulously derived feedforward function to counteract the disturbances caused by unbalanced grid voltage dips, thereby enhancing the VSG system’s anti-interference ability and stabilizing the grid-connected current. This enables effective VSG LVRT control in the face of complex grid faults and ensures successful LVRT of the grid-connected system. Ultimately, the efficacy of the proposed control strategy is confirmed through PSCAD simulations.
Who Will Be My Friend? The Role of the Liking Gap in Preschooler Friend Selection
Authors: Liu Jialu , Mo Kainian , Yan Zhiqiang , Zeng Xiao
Published: February 2025

Friend selection is a crucial aspect of social development, particularly during preschool years. This study investigated the role of the liking gap in shaping preschoolers’ friend selection mechanisms through two experiments. In Experiment 1 (N = 120), a liking gap task was used to examine whether preschoolers perceive a discrepancy between how much they like familiar friends and how much they believe those friends like them in return. The results indicated that preschoolers tend to assume that their most liked peers evaluate them less positively than they do in return, whereas they believe their least liked peers evaluate them more favorably than expected. In Experiment 2 (N = 117), a friend selection task was conducted to assess whether the liking gap influences preschoolers’ choices of friends among unfamiliar peers. The findings revealed that while preschoolers prefer to befriend individuals they like or who like them, their decisions also reflect an awareness of how they are perceived by others. These results suggest that by ages 5 to 6, preschoolers develop an emerging sensitivity to social evaluations, which plays a role in their friend selection process.
Teaching Communication and Functional Life Skills in Children Diagnosed with Autism Spectrum Disorder
Authors: Costa Juliana Ribeiro Rabelo , de Matos Daniel Carvalho
Published: February 2025

Children with autism spectrum disorder (ASD) commonly show difficulty in communication and daily functional skills. The use of scripts may help establish these repertoires. Scripts may be visual (e.g., pictures depicting actions), textual (e.g., printed or written sentences depicting actions), or auditory (e.g., recorded or dictated phrases depicting actions). Background/Objectives: The purpose was to assess the efficacy of script fading in establishing the vocal verbal emission of sentences under the control of pictures representing actions from four behavioral sequences (e.g., brushing teeth) in three children with ASD. The effects of the intervention on the emergence of related non-verbal repertoires were evaluated. During intervention, the scripts were textual for one participant, who initially read the sentences. For the remaining two participants, scripts were dictated to them so they could repeat them. Across sessions, scripts were faded out by gradually omitting the words from the sentences. Results: Script fading produced the emission of sentences solely in the presence of pictures (tacts according to a Skinnerian approach of language), replicating a previous study in which the same procedure also established the same type of repertoire. However, as an extension, in the current investigation, related non-verbal actions also emerged. Other previous studies into script fading were not specifically concerned with teaching tacts and probing the emergence of related non-verbal untaught repertoires. Conclusions: The data were interpreted as indicating correspondence between verbal and non-verbal behavior or “say-do” correspondence. The data were discussed in the sense that script fading, for some learners, may improve communication with sentences that impact the acquisition of related non-verbal behavioral chains. Limitations of the research were discussed.
An Evolutionary Game Theory-Based Framework for Analyzing Behavioral Strategies in Contractor–Owner Conflicts over Additional Construction Costs
Authors: Jeong Seong-Sik , Cha Hee-Sung , Yoon Jong-Han
Published: February 2025

The construction industry faces increasing conflicts over additional construction costs due to economic uncertainties, such as global pandemics and wars. These disputes often lead to project delays, legal actions, and even construction halts, causing significant financial and operational losses for stakeholders. To address these challenges, this study develops a simulation model based on evolutionary game theory (EGT) to identify the key influencing factors and applies the Analytic Hierarchy Process (AHP) to analyze and manage the conflicts between contractors and owners in private construction projects. The model quantifies decision-making dynamics by calculating the relative importance of various factors under different scenarios. A proof-of-concept simulation of the model reveals that cooperative evolution dynamics significantly decrease when the cost-sharing ratio reaches 0.5 for contractors and 0.9 for owners. Furthermore, the sensitivity analysis indicates that exceeding cost-sharing thresholds undermines cooperation, increasing the risk of disputes. Through this simulation, this study concludes that fostering mutual trust and informed decision-making on cost-sharing ratio significantly reduces project disputes and enhances the stakeholders’ profitability. The developed model and its framework serve as valuable tools for providing project stakeholders with actionable insights aimed at fostering strategic behaviors that minimize dispute-driven financial risks in construction projects.
Examining Shear Behavior in Sandy Gravel Interfaces: The Role of Relative Density and Material Interactions
Authors: Li Zhanhai , Li Jinxiao , Mao Xiang , Xie Xinyu , Zhu Jingze , Zheng Yang , Li Yuan , Ren Zhifeng , Sun Zhaohui , Liu Jiankun
Published: February 2025

Current research on soil–structure interface properties mainly focuses on sand, clay, and silt, with little attention given to sandy gravel. In order to study the effects of relative density and interface materials on the shear behavior of the sandy gravel–structure interface, a series of large-scale direct shear tests on sandy gravel were carried out, and stress–strain relationships, volume change curves, and shear strengths were investigated. The results show that the angle of internal friction of sandy gravel increases linearly with relative density (R2 is 0.998), from 43.0° to 48.0° when the relative density increases from 0.3 to 0.9. The growth trend of cohesion increases, the shear behavior transitions from strain hardening to strain softening, and the shear strength increases linearly with the increase in relative density. The interfacial shear strengths and interface adhesion of sandy gravel with steel and concrete interfaces increase linearly with relative density, and the shear curves are strain hardening. Furthermore, the interface friction angle of concrete increases linearly with relative density (R2 is 0.985), from 30.2° to 34.2°, while the interface friction angle of the steel interface remains relatively constant around 28.9°. Finally, relative density was introduced into the Mohr–Coulomb shear strength formula, and the relationship equations of relative density and normal pressure with the shear strength and interfacial shear strength of sandy gravel were established. The validation results show that the error margin of the formula is within 4%. This formula can be used to evaluate changes in the mechanical properties of sandy gravel formations and the bearing capacity of pile foundations after they have been disturbed by factors such as construction.
Ground Motion Recording-Based Validation of Capacity Curves for High-Rise Reinforced Concrete Structures in Romania Constructed Before 1977
Authors: Pavel Florin
Published: February 2025

Capacity curves are essential in the evaluation of seismic fragility of structures and especially in seismic risk assessments. The most widely used approach for evaluating capacity curves is based on static nonlinear analyses performed in dedicated software. However, a validation of such curves is necessary considering the uncertainties associated with the analyses. Overall, 18% of the population of Romania that lives in reinforced concrete buildings inhabits high-rise structures. In Bucharest, the percentage of people living in the same category of structures is more than 40%. A large part of these structures was built before the Vrancea 1977 earthquake. In this context, this paper presents a review of existing capacity curves for this category of high-rise reinforced concrete structures in Romania. Next, an analysis of relevant ground motions recorded on buildings during various Vrancea intermediate-depth earthquakes (1986 and 1990) is performed. In addition, results from various ambient vibration measurements are also used for a more thorough understanding of the dynamic characteristics of the buildings and of their stiffness. A comparison between the analytical capacity curves and the data from ground motion recordings is presented, highlighting significant differences, especially for reinforced concrete frame structures. Finally, updated capacity curves for high-rise residential structures constructed before 1977 are proposed by combining the analytical results with the earthquake data.
Diffusion-Improved Recrystallization of Ammonia Doping to Enhancing the Optoelectronic and Thermoelectric Effects of Multi-Junction Carbon Nanotube Paper Diodes
Authors: Liu Jih-Hsin , Yen Cheng-Jhe
Published: February 2025

This study focuses on fabricating flexible multi-junction diodes using carbon nanotubes (CNTs) as the base material, employing doping engineering and recrystallization-driven thermal diffusion techniques to enhance optoelectronic and thermoelectric properties. N-type CNTs are synthesized through ammonia doping and combined with intrinsic P-type CNTs to create PN multi-junction “buckypaper”. Post-diffusion processes improve junction crystallinity and doping gradients, significantly boosting the rectification ratio and optoelectronic and thermoelectric response. The device follows the superposition principle, achieving notable increases in thermoelectric and photovoltaic outputs, with the Seebeck coefficient rising from 5.7 μV/K to 24.4 μV/K. This study underscores the potential of flexible carbon-based devices for energy harvesting applications and advancing optoelectronic and thermoelectric systems.
Prevalence of Homologous Recombination Repair Mutations and Association with Clinical Outcomes in Patients with Solid Tumors: A Study Using the AACR Project GENIE Dataset
Authors: Shao Changxia , Zhou Heng , Chen Cai , Dettman Elisha J. , Ren Yixin , Cristescu Razvan , Gozman Alexander , Jin Fan
Published: February 2025

Background/Objectives: Mutations in BRCA1 and/or BRCA2 (BRCAm) and other homologous recombination repair genes (HRRm) are associated with several cancers. We evaluated the prevalence and association with overall survival (OS) of somatic BRCAm and HRRm among patients with advanced solid tumors. Methods: We used deidentified data from the AACR GENIE Biopharma Collaborative dataset derived from patients with tumors genotyped using next-generation sequencing between 1 January 2014 and 31 December 2017. Eligible patients were aged ≥18 years diagnosed with non-small-cell lung, colorectal, breast, bladder, prostate, or pancreatic cancer, with documented BRCA/HRR somatic mutation status. The primary analysis was OS (censored at the start of poly[ADP ribose] polymerase inhibitors [PARPi]/immunotherapy) after initiation of second-line therapy since most patients had sequencing after first-line therapy. Results: Among eligible patients, 242/7022 (3.4%) had BRCAm and 477/5474 (8.7%) had HRRm. Adjusted OS HRs (95% CI) for the primary analysis (using the initiation of second-line therapy as index date) were 0.79 (0.61–1.03) with/without BRCAm (n = 116/n = 3394) and 0.83 (0.69–0.99) with/without HRRm (n = 247/n = 2656); in sensitivity analysis of patients with stage IV disease, HRs were 0.97 (0.68–1.38) with/without BRCAm (n = 58/n = 1847) and 0.92 (0.73–1.18) with/without HRRm (n = 132/n = 1488). Conclusions: Overall, BRCAm and HRRm did not show a strong association with OS, with a trend toward longer OS among patients receiving standard second-line therapies excluding PARPi/immunotherapy.