bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Drug Compounds Discovery for Gynecomastia
Authors: Lu Yeheng , Kim Byeong Seop , Zeng Junhao , Chen Zhiwei , Zhu Mengyu , Tang Yuxi , Pan Yuyan
Published: January 2025

Background: Gynecomastia, caused by an estrogen–testosterone imbalance, affects males across various age groups. With unclear mechanisms and no approved drugs, the condition underscores the need for efficient, innovative treatment strategies. Methods: This study utilized deep learning-based computational methods to discover potential drug compounds for gynecomastia. To identify genes and pathways associated with gynecomastia, initial analyses included text mining, biological process exploration, pathway enrichment and protein–protein interaction (PPI) network construction. Subsequently, drug–target interactions (DTIs) were examined to identify potential therapeutic compounds. The DeepPurpose toolkit was employed to predict interactions between these candidate drugs and gene targets, prioritizing compounds based on their predicted binding affinities. Results: Text mining identified 177 genes associated with gynecomastia. Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified critical genes and pathways, with notable involvement in signal transduction, cell proliferation and steroid hormone biosynthesis. PPI network analysis highlighted 10 crucial genes, such as IGF1, TGFB1 and AR. DTI analysis and DeepPurpose predictions identified 12 potential drugs, including conteltinib, yifenidone and vosilasarm, with high predicted binding affinities to the target genes. Conclusions: The study successfully identified potential drug compounds for gynecomastia using a deep learning-based approach. The findings highlight the effectiveness of combining text mining and artificial intelligence in drug discovery. This innovative method provides a new avenue for developing specific treatments for gynecomastia and underscores the need for further experimental validation and optimization of prediction models to support novel drug development.
Performance of a Two-Week Rehabilitation Improves Motor Function in Inpatients with Progressive Supranuclear Palsy: A Pre–Post Study
Authors: Matsuda Naomi , Takamatsu Yasuyuki , Sawada Makoto , Aiba Ikuko
Published: January 2025

Background: Progressive supranuclear palsy (PSP) is characterized by early postural instability and gait dysfunction, with frequent falls. Rehabilitation is an important therapeutic approach for motor dysfunction in patients with PSP. However, no conclusions have yet been drawn regarding the beneficial effects of rehabilitation in PSP, including the optimal duration of rehabilitation and differences in treatment effects among PSP subtypes. Herein, we investigated the effects of short-term rehabilitation and separately analyzed the effects on patients with PSP-Richardson’s syndrome (RS) and PSP-progressive gait freezing (PGF). Methods: The participants underwent several therapeutic exercise programs individualized for each participant, performed over 2 weeks. Analysis was performed on 25 patients with PSP-RS and eight with PSP-PGF. Results: Short-term rehabilitation improved the Berg Balance Scale score in both the PSP-RS and PSP-PGF groups, step length on the symptom-dominant side in PSP-RS, the coefficient of variation of step length on the symptom-dominant side, and the stance phase of the Symmetry Index in PSP-PGF. Conclusions: Overall, this 2-week short-term rehabilitation intervention was shown to have beneficial effects on balance in patients with PSP-RS and PSP-PGF.
The Body as a Vessel for Trauma: The Clinical Case Study of Aisha
Authors: Visco-Comandini Federica , Barbieri Alberto , Mancini Francesco , Ciolfi Alessandra
Published: January 2025

This case study examined the process of integration of cognitive behavioral therapy (CBT) and sensorimotor psychotherapy (SP) in three-phase trauma treatment with a 32-year-old female Bengali refugee with Complex Post Traumatic Disorder (C-PTSD). The treatment was provided in a Western country. The client’s improvement was assessed by using self-report assessments of post-traumatic, dissociative, and depressive symptoms. Psychodiagnostics’ scores gathered after 2 years of treatment showed significant improvements in post-traumatic, dissociative, and depressive symptoms. Although firm conclusions cannot be drawn due to the limitations of this study, results suggest that integrating CBT and SP in a phase-trauma approach may be an effective treatment for C-PTSD in a transcultural setting. However, replicating and standardizing such preliminary results on larger samples is necessary. Background/Objectives: SP is an innovative psychotherapeutic intervention aimed at treating trauma through a bottom-up approach, however, little research exists regarding the efficacy of this psychotherapy. This case study aims to investigate the efficacy of the combination of CBT and SP in treating a C-PTSD patient in a transcultural setting. Methods: Three questionnaires were administered to investigate post-traumatic, depressive, and dissociative symptoms. Results: Clinical and psychodiagnostic outcomes highlight significant psychological improvements in the patient. Conclusions: Although any firm conclusion cannot be drawn because of various intrinsic limitations (i.e., single case study) that straiten our ability to extend these results, this case study suggests that the integration of CBT and SP may be an effective treatment for C-PTSD in a transcultural setting.
Unlocking STEM Identities Through Family Conversations About Topics in and Beyond STEM: The Contributions of Family Communication Patterns
Authors: Dou Remy , Villa Nicole , Cian Heidi , Sunbury Susan , Sadler Philip M. , Sonnert Gerhard
Published: January 2025

Research shows that family conversations about STEM topics positively influence children’s STEM identity development. This study expands on these findings by exploring how family conversations beyond STEM content contribute to this development. Specifically, we focus on how non-academic forms of family support—as described by students who face systemic racial discrimination in STEM—shape these conversations. In this way, we extend existing work by exploring the extent to which families’ dispositions to talk about a wide range of topics—not just in STEM—might further support youth identification with STEM fields. Using Family Communication Patterns Theory (FCPT) to guide our analysis, we examined data from a survey of first-year college students (n = 1134) attending Minority-Serving Institutions and public universities in the United States. The survey asked students to reflect on their childhood conversations and their current sense of identity in STEM. Using structural equation modeling, we found that family disposition to engage in conversations about a broad range of topics was linked to more frequent STEM-related conversations during childhood and, in turn, greater identification as a “STEM person” in college. These findings highlight the complex ways that family communication patterns can support construction of an individual’s sense of themselves as a STEM person in later years. By interpreting these findings using FCPT, we highlight the nature of family communication patterns that can contribute to STEM identity formation.
Development and Validation of a Rapid Tool to Measure Pragmatic Abilities: The Brief Assessment of Pragmatic Abilities and Cognitive Substrates (APACS Brief)
Authors: Bischetti Luca , Frau Federico , Pucci Veronica , Agostoni Giulia , Pompei Chiara , Mangiaterra Veronica , Barattieri di San Pietro Chiara , Scalingi Biagio , Dall’Igna Francesca , Mangiaracina Ninni , Lago Sara , Montemurro Sonia , Mondini Sara , Bosia Marta , Arcara Giorgio , Bambini Valentina
Published: January 2025

Pragmatics is key to communicating effectively, and its assessment in vulnerable populations is of paramount importance. Although tools exist for this purpose, they are often effortful and time-consuming, with complex scoring procedures, which hampers their inclusion in clinical practice. To address these issues, we present the Brief Assessment of Pragmatic Abilities and Cognitive Substrates (APACS Brief), a rapid (10 min), easy-to-use and freely distributed tool for evaluating pragmatics in Italian, inspired by the existing APACS test and already validated in the remote version (APACS Brief Remote). The APACS Brief test measures–with a simplified scale–the domains of discourse production and figurative language understanding and is developed in two parallel forms, each including novel items differing from APACS. Psychometric properties, cut-off scores, and thresholds for change were computed on 287 adults. The analysis revealed satisfactory internal consistency, good test–retest reliability, and strong concurrent and construct validity. Moreover, APACS Brief showed excellent discriminant validity on a sample of 56 patients with schizophrenia, who were also cross-classified consistently by APACS Brief and APACS cut-off values. Overall, APACS Brief is a reliable tool for evaluating pragmatic skills and their breakdown, with brief administration time and simple scoring making it well-suited for screening in at-risk populations.
Raman Spectroscopy of Graphene/CNT Layers Deposited on Interdigit Sensors for Application in Gas Detection
Authors: Iordache Stefan-Marian , Iordache Ana-Maria , Florea (Raduta) Ana-Maria , Caramizoiu Stefan , Parvulescu Catalin , Baiasu Flaviu , Negut Irina , Bita Bogdan
Published: January 2025

Graphene/CNT layers were deposited onto platinum electrodes of an interdigitated sensor using radio-frequency magnetron sputtering. The graphene/CNTs were synthesized in an Argon atmosphere at a pressure of (2 × 10−2–5 × 10−3) mbar, with the substrate maintained at 300 °C either through continuous heating with an electronically controlled heater or by applying a −200 V bias using a direct current power supply throughout the deposition process. The study compares the surface morphology, carbon atom arrangement within the layer volumes, and electrical properties of the films as influenced by the different methods of substrate heating. X-ray diffraction and Raman spectroscopy confirmed the formation of CNTs within the graphene matrix. Additionally, scanning electron microscopy revealed that the carbon nanotubes are aligned and organized into cluster-like structure. The graphene/CNT layers produced at higher pressures present exponential I–V characteristics that ascertain the semiconducting character of the layers and their suitability for applications in gas sensing.
Normal Residual Lymphoid Cell Populations in Blood as Surrogate Biomarker of the Leukemia Cell Kinetics in CLL BinetA/Rai 0
Authors: Solano Fernando , Criado Ignacio , Moreno Nahir , Gomez-Gonzalez Carlos , Lerma-Verdejo Ana , Teodosio Cristina , Martinez-Moya María Dolores , Luts Iryna , Contreras Teresa , Oliva-Ariza Guillermo , Fuentes Herrero Blanca , Serrano-Lozano Jose Manuel , Almeida Julia , Orfao Alberto
Published: January 2025

Background/Objectives: Despite the current international prognostic index for chronic lymphocytic leukemia (CLL) being widely accepted and broadly used, it does not consider the kinetics of the B-cell clone over time. Here, we investigated the potential association between distinct features of leukemic cells and other immune cells in blood and the kinetics of clonal B-cells in CLL stage Binet A/Rai 0 (A/0) patients; Methods: Based on the leukemia cell kinetics, 69 CLL A/0 cases followed for a median of 105 months were classified as carrying stable (n = 53) vs. rapidly increasing in size (n = 16) CLL clones; Results: Patients with increasing CLL clones had a significantly higher risk of disease progression and shortened time to first therapy vs. those carrying stable B-cell clones (p ≤ 0.001). Strikingly, the distribution of various immune-cell populations in blood at diagnosis also differed significantly between the two groups, with lower Tαβ CD4+CD8lo cell counts (p = 0.03), a greater switched/unswitched memory B-cell ratio (p = 0.01), and higher plasma cell counts (p = 0.05) in CLL with increasing vs. stable clones. Multivariate analysis revealed that the number of circulating clonal B-cells (≥15 × 109/L) and Tαβ CD4+CD8lo cells (≤35 cells/µL), together with an IGHV unmutated gene status at diagnosis, were independent predictors of an increasing CLL clone; Conclusions: Altogether, these data suggest that the expansion of the CLL clone in stage A/0 patients may depend on both the intrinsic characteristics of CLL cells and the surrounding immune microenvironment.
Engineering of an Alkaline Feruloyl Esterase PhFAE for Enhanced Thermal Stability and Catalytic Efficiency Through Molecular Dynamics and FireProt
Authors: Yang Sheng , Lin Miaofang , Chen Jiyang , Liu Min , Chen Qi
Published: January 2025

Feruloyl esterases (FAEs) play critical roles in industrial applications such as food processing, pharmaceuticals, and paper production by breaking down plant cell walls and releasing ferulic acid. However, most bacterial FAEs function optimally in acidic environments, limiting their use in alkaline industrial processes. Additionally, FAEs with alkaline activity often lack the thermal stability required for demanding industrial conditions. In this study, an alkaline feruloyl esterase, PhFAE, from Pandoraea horticolens was identified that exhibits high catalytic activity but suffers from thermal instability, restricting its broader industrial applications. To address this limitation, molecular dynamics simulations were used to analyze enzyme stability, and FireProt, an automated computational tool, was employed to design stabilizing mutations. The engineered S155F mutant demonstrated a 7.8-fold increase in half-life at 60 °C and a 1.72-fold improvement in catalytic efficiency (Kcat/Km), corresponding to 680% and 72% enhancements, respectively, compared to the wild-type enzyme. Molecular docking and dynamics simulations revealed that these enhancements were likely due to increased hydrophobic interactions and altered surface charge, which stabilized the enzyme’s structure. This study provides an effective strategy for improving the functional properties of FAEs and other industrial enzymes, broadening their applicability in diverse industrial processes.
Design and Preparation of ZnIn2S4/g-C3N4 Z-Scheme Heterojunction for Enhanced Photocatalytic CO2 Reduction
Authors: Fang Jinghong , Wang Min , Yang Xiaotong , Sun Qiong , Yu Liyan
Published: January 2025

In this study, a novel Z-scheme heterojunction photocatalyst was developed by integrating g-C3N4 nanoplates into ZnIn2S4 microspheres. X-ray photoelectron spectroscopy analysis revealed a directional electron transfer from g-C3N4 to ZnIn2S4 upon heterojunction formation. Under irradiation, electrochemical tests and electron paramagnetic resonance spectroscopy demonstrated significantly enhanced charge generation and separation efficiencies in the ZnIn2S4/g-C3N4 composite, accompanied by reduced charge transfer resistance. In photocatalytic CO2 reduction, the ZnIn2S4/g-C3N4 composite achieved the highest CO yield, 1.92 and 5.83 times higher than those of pristine g-C3N4 and ZnIn2S4, respectively, with a notable CO selectivity of 91.3% compared to H2 (8.7%). The Z-scheme heterojunction mechanism, confirmed in this work, effectively preserved the strong redox capabilities of the photoinduced charge carriers, leading to superior photocatalytic performance and excellent long-term stability. This study offers valuable insights into the design and development of g-C3N4-based heterojunctions for efficient solar-driven CO2 reduction.
Complex Actions of FKBP12 on RyR1 Ion Channel Activity Consistent with Negative Co-Operativity in FKBP12 Binding to the RyR1 Tetramer
Authors: Richardson Spencer J. , Thekkedam Chris G. , Casarotto Marco G. , Beard Nicole A. , Dulhunty Angela F.
Published: January 2025

The association of the 12 KDa FK506 binding protein (FKBP12) with ryanodine receptor type 1 (RyR1) in skeletal muscle is thought to suppress RyR1 channel opening and contribute to healthy muscle function. The strongest evidence for this role is increased RyR1 channel activity following FKBP12 dissociation. However, the corollary that channel activity will decrease when FKBP12 is added back to FKBP12-depleted RyR1 is not well established, and when reported, the time- and concentration-dependence of inhibition vary over orders of magnitude. Here, we address this problem with an investigation of the molecular mechanisms of the FKBP12 regulation of RyR1. Muscle processing to obtain sarcoplasmic reticulum (SR) vesicle preparations enriched in RyR1 resulted in substantial FKBP12 dissociation from RyR1, indicating low-affinity binding. Conversely, high-affinity binding was indicated by some FKBP12 remaining bound to RyR1 after solubilization. We report, for the first time, an increase in the activity of FKBP12-depleted channels after the addition of exogenous FKBP12 (5 nM to 5 µM), followed by a reduction in activity consistent with inhibition after 20–30 min exposure to higher [FKBP12]s. Both the increase and later decline in activity were time- and concentration-dependent. The results suggest a high-affinity activation when FKBP12 binding sites on the RyR1 tetramer are partially occupied by FKBP12 and lower affinity inhibition as more RyR1 monomers become occupied. These novel results imply negative cooperativity in FKBP12 binding to RyR1 and a dynamic role for FKBP12/RyR1 interactions in intact muscle fibers.
Prostaglandins: Biological Action, Therapeutic Aspects, and Pathophysiology of Autism Spectrum Disorders
Authors: Yui Kunio , Imataka George , Ichihashi Mariko
Published: January 2025

Esterified ARA on the inner surface of the cell membrane is hydrolyzed to its free form by phospholipase A2 (PLA2), which is further metabolized by COXs and lipoxygenases (LOXs) and cytochrome P450 (CYP) enzymes. PGs produce detrimental effects due to their proinflammatory properties. The generation of prostaglandin (PG)G2 and PGH2 is triggered by cyclooxygenase (COX) isozymes such as COX-1 and COX-2. Prostaglandin E2 (PGE2) is significantly elevated in ASD. Considerable data indicate that COX enzymes and their metabolites of ARA play important roles in the initiation and development of human neurodevelopmental diseases. The involvement of disrupted COX2/PGE2 signaling in ASD pathology in changing neuronal cell behavior and the expression of ASD-related genes and proteins is due to disrupted COX2/PGE2 signaling. Prostacyclin (PGI2) is synthesized from arachidonic acid by metabolic-pathway-dependent cyclooxygenase (COX) and synthesized in a primary step of ARA transformation (PGG2, PGH2), by degradation of the abovementioned prostaglandins.
Using a Combined FE-CA Approach to Investigate Abnormally Large Grains Formed by the Limited Recrystallization Mechanism in a Powder Metallurgy Nickel-Based Superalloy
Authors: Yang Yanhui , Zhang Boyan , Chen Xiuquan , Wang Xiaoxuan , Feng Yankai , Su Lidong , Liang Zhengfei , Liu Yifan
Published: January 2025

Powder nickel-based superalloy is the key material for hot-end components such as turbine disks and gas engine disks in aeroengines, and its microstructure uniformity has an important influence on the disks’ service performance. However, thermomechanical treatments make it easy to produce abnormally large grains (ALGs) in powder superalloy disks. In order to investigate the relationship between the hot deformation conditions and ALGs of powder superalloys, isothermal compression experiments under various deformation conditions were carried out and a FE-CA method was constructed to investigate the ALGs formed by the limited recrystallization mechanism. The results indicate a close relationship between the ALGs formed after the supersolvus treatment of this alloy and the equivalent stress after thermal deformation, and the local dissolution of the γ′ phase in supersolvus heat treatment does not produce ALGs.
Material and Technique Analysis of Qing Dynasty Official Style Architectural Polychrome Paintings in Hangzhou, Zhejiang, China
Authors: Shen Ling , Hua Dan , Nan Baisu , Yao Yao , Duan Hong , Wang Jiakun
Published: January 2025

Hangzhou was the political and economic center of the Southern Song Dynasty (1127–1279 AD) and also the southern end of the Beijing-Hangzhou Grand Canal during the Ming and Qing Dynasties (1368–1644 AD). This historical position allowed the city’s economy to develop rapidly and influenced the form of its polychrome paintings with the imperial official style of the north China. However, due to the high temperature and rainy natural preservation conditions, southern polychrome paintings have always been a weak link in Chinese architectural polychrome painting craftsmanship. This study focuses on two well-preserved official-style architectural polychrome paintings in the grand halls from the late Qing period in Hangzhou. Through multi-techniques such as optical microscopy (OM), scanning electron microprobe with energy dispersive X-ray spectroscopy analysis (SEM-EDX), micro-Raman spectroscopy, micro-Fourier Transform Infrared spectroscopy (μ-FTIR), and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), it was found that there is a significant difference from the reported common non-ground architectural paintings in the south, typically having four-layer structures with a white base and ground plaster layer in preparation for painting. The appearance of pigments such as artificial ultramarine (Na6Al4Si6S4O20) and emerald green (Cu(C2H3O2)2·3Cu(AsO2)2) indicates that the paintings were made at least after the 1830s, and the use of malachite green dye and copper phthalocyanine blue (PB 15:X) suggests that unrecorded restorations were also performed after the 20th century. All samples are coated with a layer of alkyd resin, which may have been added during the repairs in the latter half of the 20th century, leading to the black discoloration of the present paintings, especially in areas where emerald green was used. This study provides an important case for the study of the official style of polychrome painting craftsmanship in the southern region of China and also offers important references for the future protection and restoration of traditional architectural polychrome painting.
Clinical Utility of Liquid Biopsy for the Early Diagnosis of EGFR-Mutant Advanced Lung Cancer Patients in a Real-Life Setting (CLEAR Study)
Authors: Samaha Ramy , El Sayed Rola , Alameddine Raafat , Florescu Marie , Tehfe Mustapha , Routy Bertrand , Elkrief Arielle , Belkaid Wiam , Desilets Antoine , Weng Xiaoduan , Nassabein Rami , Blanc-Durand Félix , Kenth Gurvinder , Kasymjanova Goulnar , Agulnik Jason , Blais Normand
Published: January 2025

Background: Lung cancer remains the leading cause of cancer mortality globally with EGFR mutations representing a significant driver in advanced non-small cell lung cancer (aNSCLC). The timely detection of these mutations is critical for initiating targeted therapy, yet tissue biopsy limitations often delay treatment. Methods: This multicenter prospective study evaluated the clinical utility of liquid biopsy (LBx) in real-life settings for the early diagnosis of EGFR mutations in patients with suspected aNSCLC. Circulating tumor DNA (ctDNA) was analyzed using the Cobas EGFR Mutation Test and compared to tissue-based next-generation sequencing (NGS). Results: Among 366 aNSCLC patients tested, LBx demonstrated a significantly shorter median turnaround time (TAT) of 3 days compared to 26 days for tissue NGS (p < 0.001) with 100% specificity and 65% sensitivity for EGFR mutation detection. LBx identified actionable EGFR mutations in cases where tissue biopsy was insufficient or unavailable, enabling 43.7% of patients to commence targeted therapy based on ctDNA results prior to biopsy confirmation. Conclusions: These findings highlight the potential of LBx to reduce diagnostic delays and improve access to personalized therapies in a real-world setting. Integrating LBx into routine diagnostic workflows may address current gaps in molecular testing, ensuring timely and precise treatment for aNSCLC patients.
Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations
Authors: Vaiyapuri Thavavel
Published: January 2025

The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand for cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data. Additionally, the interpretability of these models is a key concern. This study aims to bridge these gaps through two primary objectives: (1) modeling the co-gasification process using seven different ML algorithms, and (2) developing a framework for evaluating model interpretability, ultimately identifying the most suitable model for process optimization. A comprehensive set of experiments was conducted across three key dimensions, generalization ability, predictive accuracy, and interpretability, to thoroughly assess the models. Support Vector Regression (SVR) exhibited superior performance, achieving the highest coefficient of determination (R2) of 0.86. SVR outperformed other models in capturing non-linear dependencies and demonstrated effective overfitting mitigation. This study further highlights the limitations of other ML models, emphasizing the importance of regularization and hyperparameter tuning in improving model stability. By integrating Shapley Additive Explanations (SHAP) into model evaluation, this work is the first to provide detailed insights into feature importance and demonstrate the operational feasibility of ML models for industrial-scale hydrogen production in the co-gasification process. The findings contribute to the development of a robust framework for optimizing co-gasification, supporting the advancement of sustainable energy technologies and the reduction of greenhouse gas (GHG) emissions.
Discontinuous Structural Transitions in Fluids with Competing Interactions
Authors: Montero Ana M. , Yuste Santos B. , Santos Andrés , de Haro Mariano López
Published: January 2025

This paper explores how competing interactions in the intermolecular potential of fluids affect their structural transitions. This study employs a versatile potential model with a hard core followed by two constant steps, representing wells or shoulders, analyzed in both one-dimensional (1D) and three-dimensional (3D) systems. Comparing these dimensionalities highlights the effect of confinement on structural transitions. Exact results are derived for 1D systems, while the rational function approximation is used for unconfined 3D fluids. Both scenarios confirm that when the steps are repulsive, the wavelength of the oscillatory decay of the total correlation function evolves with temperature either continuously or discontinuously. In the latter case, a discontinuous oscillation crossover line emerges in the temperature–density plane. For an attractive first step and a repulsive second step, a Fisher–Widom line appears. Although the 1D and 3D results share common features, dimensionality introduces differences: these behaviors occur in distinct temperature ranges, require deeper wells, or become attenuated in 3D. Certain features observed in 1D may vanish in 3D. We conclude that fluids with competing interactions exhibit a rich and intricate pattern of structural transitions, demonstrating the significant influence of dimensionality and interaction features.
A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition
Authors: Li Jiawen , Feng Guanyuan , Ling Chen , Ren Ximing , Liu Xin , Zhang Shuang , Wang Leijun , Chen Yanmei , Zeng Xianxian , Chen Rongjun
Published: January 2025

Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage.
AI in the Classroom: Insights from Educators on Usage, Challenges, and Mental Health
Authors: Delello Julie A. , Sung Woonhee , Mokhtari Kouider , Hebert Julie , Bronson Amy , De Giuseppe Tonia
Published: January 2025

This study examines educators’ perceptions of artificial intelligence (AI) in educational settings, focusing on their familiarity with AI tools, integration into teaching practices, professional development needs, the influence of institutional policies, and impacts on mental health. Survey responses from 353 educators across various levels and countries revealed that 92% of respondents are familiar with AI, utilizing it to enhance teaching efficiency and streamline administrative tasks. Notably, many educators reported students using AI tools like ChatGPT for assignments, prompting adaptations in teaching methods to promote critical thinking and reduce dependency. Some educators saw AI’s potential to reduce stress through automation but others raised concerns about increased anxiety and social isolation from reduced interpersonal interactions. This study highlights a gap in institutional AI policies, leading some educators to establish their own guidelines, particularly for matters such as data privacy and plagiarism. Furthermore, respondents identified a significant need for professional development focused on AI literacy and ethical considerations. This study’s findings suggest the necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health and underscore the importance of incorporating student perspectives for a thorough understanding of AI’s role in education.
Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Comparative Analysis of Large Language Model Tools and Mental Health Professionals
Authors: Levkovich Inbar
Published: January 2025

Large language models (LLMs) offer promising possibilities in mental health, yet their ability to assess disorders and recommend treatments remains underexplored. This quantitative cross-sectional study evaluated four LLMs (Gemini (Gemini 2.0 Flash Experimental), Claude (Claude 3.5 Sonnet), ChatGPT-3.5, and ChatGPT-4) using text vignettes representing conditions such as depression, suicidal ideation, early and chronic schizophrenia, social phobia, and PTSD. Each model’s diagnostic accuracy, treatment recommendations, and predicted outcomes were compared with norms established by mental health professionals. Findings indicated that for certain conditions, including depression and PTSD, models like ChatGPT-4 achieved higher diagnostic accuracy compared to human professionals. However, in more complex cases, such as early schizophrenia, LLM performance varied, with ChatGPT-4 achieving only 55% accuracy, while other LLMs and professionals performed better. LLMs tended to suggest a broader range of proactive treatments, whereas professionals recommended more targeted psychiatric consultations and specific medications. In terms of outcome predictions, professionals were generally more optimistic regarding full recovery, especially with treatment, while LLMs predicted lower full recovery rates and higher partial recovery rates, particularly in untreated cases. While LLMs recommend a broader treatment range, their conservative recovery predictions, particularly for complex conditions, highlight the need for professional oversight. LLMs provide valuable support in diagnostics and treatment planning but cannot replace professional discretion.
MMFDetect: Webshell Evasion Detect Method Based on Multimodal Feature Fusion
Authors: Zhang Yifan , Kang Haiyan , Wang Qiang
Published: January 2025

In the context of escalating network adversarial challenges, effectively identifying a Webshell processed using evasion techniques such as encoding, obfuscation, and nesting remains a critical challenge in the field of cybersecurity. To address the poor detection performance of the existing Webshell detection methods for evasion samples, this study proposes a multimodal feature fusion-based evasion Webshell detection method (MMF-Detect). This method extracts RGB image features and textual vector features from two modalities: the visual and semantic modalities of Webshell file content. A multimodal feature fusion classification model was designed to classify features from both modalities to achieve Webshell detection. The multimodal feature fusion classification model consists of a text classifier based on a large language model (CodeBERT), an image classifier based on a convolutional neural network (CNN), and a decision-level feature fusion mechanism. The experimental results show that the MMF-Detect method not only demonstrated excellent performance in detecting a conventional Webshell but also achieved an accuracy of 99.47% in detecting an evasive Webshell, representing a significant improvement compared to traditional models.
Correction: Sobhaniasl et al. Fatigue Life Assessment for Power Cables in Floating Offshore Wind Turbines. Energies 2020, 13, 3096
Authors: Sobhaniasl Mohsen , Petrini Francesco , Karimirad Madjid , Bontempi Franco
Published: January 2025

There was an error in the original publication [...]
Investigation on MELCOR Code Capabilities for the Simulation of Lithium–Lead Chemical Interactions for Fusion Safety Applications
Authors: Galleni Francesco , Cossu Vittorio , Moscardini Marigrazia , Forgione Nicola
Published: January 2025

In the WCLL-BB liquid blanket concept, among the postulated accidental scenarios to be investigated regarding the PbLi system, a loss of liquid metal is one of the most crucial for safety purposes. As a consequence of this loss, chemical reactions might occur between lithium, air, and water. These reactions may lead to significant increases in temperature and pressure and to the formation of hydrogen inside the building; the evaluation of the impact of these events is essential to define future safety guidelines. Using MELCOR for fusion (v.1.8.6.), a first approach for investigating an out-vessel loss of PbLi was presented in this work. The temperature and pressure trends were investigated through the default MELCOR package for lithium air chemical reactions; however, this package works only with pure lithium. Therefore, a new model of the reaction was developed in this work using MELCOR Control Functions, which allows the simulation of the chemical reactions using PbLi as working fluid, in order to investigate a more realistic scenario. The results from different approaches were compared, and the limits of the code were outlined. It was found that the final pressure and temperature in the tokamak building might reach critical values at the end of the transient, if the energy released by the chemical reaction was entirely considered; however, it is important to note that, due to the assumptions and simplifications adopted, the results were very conservative in terms of temperature and pressure reached in the system, and further investigations were suggested in this work.
Dielectric and Gas Production Characteristics of Synthetic Esters Under Different Discharge Energies
Authors: Wang Huijuan , Chen Hua , Ke Junxin , Wang Jiaqi , Yu Huimin , Zhong Xvqin , Zhang Qi , Wang Tianjun , Tao Qiuli , Zhu Yuanwei , Li Shengtao
Published: January 2025

With the increasing demand for safety and sustainable development in the power industry, the research on synthetic esters as a new type of insulating oil is becoming more and more important. This article takes pentaerythritol ester as the research object, simulates its fault conditions under partial discharge (low-energy discharge) and spark discharge (high-energy discharge), studies the dielectric and gas production characteristics, and explores the applicability of the existing fault diagnosis methods for synthetic esters. The results show that during partial discharge, the dielectric constant and loss of the synthetic ester increase, and the resistivity decreases. With the increase of discharge time, the relative percentage of CO continuously decreases, while that of C2H6 and C2H4 continuously increases. Since the C2H4 content of the synthetic ester is significantly higher than that of the mineral oil, the commonly used diagnostic methods are not suitable for the partial discharge fault diagnosis of the synthetic ester. In spark discharge, the gap distance has little impact on the starting voltage but a great impact on the breakdown time, and the dielectric performance deteriorates seriously. C2H2 is the characteristic gas of the synthetic ester spark discharge, and a large amount is produced, but with the increase of the gap distance, its relative percentage continuously decreases. For the spark discharge fault diagnosis of the synthetic ester, the improved three-ratio method and DUVAL triangle method have certain applicability. Comparing different discharge energies, the increase of the discharge energy aggravates the deterioration of the synthetic ester. Under high-energy discharge, the dielectric constant increases significantly, the hydrocarbons increase rapidly, and the C2H2 content increases sharply. Finally, the Duval triangle was modified according to the gas generation characteristics. This research can provide data support for the application and fault diagnosis of synthetic esters in power transformers.
Efficient Online Inductance Tracking Algorithm for IPMSM Using Torque Estimation
Authors: Ahn Han-Woong , Bae Sungil , Park Hyun-Jong
Published: January 2025

This paper proposed a novel real-time inductance tracking algorithm. This algorithm estimates the inductance of interior permanent magnet synchronous motors (IPMSMs) using electrical and mechanical equations. Unlike the conventional algorithms, the proposed online inductance identification algorithm does not need information on magnetic flux. Therefore, the parameter can be estimated of the variation with the operating point and cross-saturation effect. In this paper, a novel real-time parameter tracking algorithm based on the electrical torque estimator is derived. The feasibility and usefulness of the proposed inductance estimation technique are verified by simulation and experimental results.
A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems
Authors: Cao Tiantian , Ye Zhengyang , Wu Qiong , Wan Xiaorong , Wang Jiangyun , Li Dayi
Published: January 2025

With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by new energy can be injected into the power grid in a stable and safe way, and its power grid adaptability has also received more and more close attention in the field of new energy research. This research focuses on the discussion of PV grid-connected inverters under the complex distribution network environment, introduces in detail the domestic and international standards and requirements on grid-connected inverter grid adaptability, and then analyzes in depth the impacts of the access point voltage changes, access point frequency changes, and access point harmonic changes on the inverters. In order to enhance the adaptability of grid-connected inverters under these abnormal conditions, this research systematically summarizes and concludes a series of inverter adaptive control strategies, which provide literature guidance to effectively reduce the probability of power system faults and improve the reliability of the power system. Finally, the future development direction of PV inverter technology is outlooked, pointing out that, with the increase in the proportion of PV power generation in the power system, PV inverters need to evolve gradually from adapting to the grid to supporting the grid and promote the transformation of PV power generation from the auxiliary power source to the main power source through the integration of PV and energy storage.
Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation
Authors: Si Ruihua , Yan Xintong , Liu Wanxun , Zhang Ping , Wang Mengdi , Li Fengyong , Yang Jiajia , Su Xiangjing
Published: January 2025

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.
Stand Age Affects Biomass Allocation and Allometric Models for Biomass Estimation: A Case Study of Two Eucalypts Hybrids
Authors: Huang Runxia , Zhu Wankuan , Du Apeng , Xu Yuxing , Wang Zhichao
Published: January 2025

We studied the effects of stand age on the allocation of biomass and allometric relationships among component biomass in five stands ages (1, 3, 5, 7, and 8 years old) of two eucalypts hybrids, including Eucalyptus urophylla × E. grandis and E. urophylla × E. tereticornis, in the Leizhou Peninsula, China. The stem, bark, branch, leaf, and root biomass from 60 destructively harvested trees were quantified. Allometric models were applied to examine the relationship between the tree component biomass and predictor variable (diameter at breast height, D, and height, H). Stand age was introduced into the allometric models to explore the effect of stand age on biomass estimation. The results showed the following: (1) Stand age significantly affected the distribution of biomass in each component. The proportion of stem biomass to total tree biomass increased with stand age, the proportions of bark, branch, and leaf biomass to total tree biomass decreased with stand age, and the proportion of root biomass to total tree biomass first decreased and then increased with stand age. (2) There were close allometric relationships between biomass (i.e., the components biomass, aboveground biomass, and total biomass per tree) and diameter at breast height (D), height (H), the product of diameter at breast height and tree height (DH), and the product of the square of the diameter at breast height and tree height (D2H). The allometric relationship between biomass and measurement parameters (D, H, DH, D2H) could be applied to the biomass assessment of eucalypts plantation. (3) Allometric equations that included stand age as a complementary variable significantly improved the fit and enhanced the accuracy of biomass estimates. The optimal independent variable for the biomass prediction model varied according to each organ. These results indicate that stand age has an important influence on biomass allocation. Allometric equations considering stand age could improve the accuracy of carbon sequestration estimates in plantations.
Relationships Between Saproxylic Beetle Microhabitat Occurrences and Forest State Indicators
Authors: Zoltán László , Németh Tamás , Horváth Soma , Bérces Sándor , Elek Zoltán , Standovár Tibor
Published: January 2025

The use of proxies in habitat assessments has become widespread in recent decades. We used forest state descriptive data from a large-scale project (SCP) as proxies to investigate the occurrence of habitats suitable for some protected saproxylic beetles. We searched for pre-defined tree-related microhabitats (TreMs) suitable for saproxylic beetles in 1 ha quadrats in the Börzsöny Mts., Hungary. We compared the frequency of each microhabitat type with the aggregated values of the forest state proxies. Our results suggest that the average number of snags with DBH = 21–50 cm and the frequency of lying deadwood with Ø > 35 cm can adequately represent the occurrence of all beetle microhabitats studied. In most cases, the frequency of plots with species richness of live canopy trees with DBH > 35 cm and the amount of lying dead wood were also good indicators. The TreM indicators of the SCP alone can detect the presence of specialist beetles requiring cavities. The stands with a better forest state had more protected saproxylic beetles. The practical implementation of our work is based on the optimization of the resources required for monitoring. In surveys prepared to cover large areas, it is easier to monitor the habitat of saproxylic beetles with the help of individual proxies.