bluuebunny/crossref_metadata_2025 huggingface dataset:
Alternaria alternata JTF001 Metabolites Recruit Beneficial Microorganisms to Reduce the Parasitism of Orobanche aegyptiaca in Tomato
Authors: Luo Wenfang , Ping Xingxing , Zhou Junhui , Gao Shuaijun , Huang Xin , Song Suqin , Xu Jianjun , He Wei
Published: January 2025

Orobanche aegyptiaca is a holoparasitic weed that extracts water, nutrients, and growth regulators from host plants, leading to significant yield and quality losses. Biocontrol microbial metabolites have been shown to enhance plant resistance against parasitic plants, yet the underlying microbial mechanisms remain poorly understood. In this study, we investigated the role of Alternaria alternata JTF001 (J1) microbial metabolites in recruiting beneficial microbes to the tomato rhizosphere and promoting the establishment of a disease-suppressive microbiome. Pot experiments revealed that J1 metabolite application significantly reduced O. aegyptiaca parasitism. High-throughput sequencing of full-length 16S rRNA genes and ITS regions, along with in vitro culture assays, demonstrated an increase in the abundance of plant-beneficial bacteria, particularly Pseudomonas spp. The three candidate beneficial strains (zOTU_388, zOTU_533, and zOTU_2335) showed an increase of 5.7-fold, 5.4-fold, and 4.7-fold, respectively. These results indicate that J1 metabolites induce the recruitment of a disease-suppressive microbiome in tomato seedlings, effectively inhibiting O. aegyptiaca parasitism. Our findings suggest that microbial metabolites represent a promising strategy for managing parasitic plant infestations through microbial community modulation, offering significant implications for sustainable agricultural practices.
Anterior Petrosectomy vs. Retrosigmoid Approach—Surgical Anatomy and Navigation-Augmented Morphometric Analysis: A Comparative Study in Cadaveric Laboratory Setting
Authors: Signoretti Stefano , Signorelli Francesco , Pesce Alessandro , Delitala Alberto , Visocchi Massimiliano
Published: January 2025

Background: Different lateral and postero-lateral cranial approaches to the petroclival region and to the mid-upper brain stem have been described so far, some of which require extensive osseous demolition and possible damage of neurovascular structures. Neuronavigational systems are now extensively available for preoperative planning and intraoperative navigation to assist the surgeons in choosing the optimally invasive approach for each pathology. Herein, we describe a detailed navigation-augmented morphometric analysis to bring insight into the usefulness of an anterior petrosectomy (AP) to handle lesions in the petroclival region. Methods: Eight cadaveric, silicone injected heads were used. A total of 14 approaches (AP, n = 8; retrosigmoid, RS, n = 6) using a standard microsurgical dissection technique were performed. All specimens had preoperative CT and MRI scans, as well as a post-dissection CT. The neuronavigational system was used for distance measurements, craniotomy sizes and surgical corridor volumes, for each approach. Results: The distance from the skull surface to the petrous apex was significantly shorter in the AP approach when compared with the RS (46.0 ± 1.9 mm versus 71.3 ± 1.8 mm, respectively, p < 0.001). Although the craniotomy size was not different, the volume of the surgical corridor was significantly larger with the AP approach (21.31 ± 1.91 cm3 vs. 13.39 ± 1.8 cm3). The AP approach increased the length of the basilar artery exposure from 6.9 ± 1.5 mm (obtained with a standard subtemporal approach) to 22.1 ± 1.7 mm (p < 0.0001). Conclusions: The surgical corridor to the petroclival region achieved by virtue of an AP was significantly larger and featured shorter working distances, resulting in a higher degree of surgical freedom. Although significant individual anatomical variations of fundamental neurovascular and bony structures were found, these difficulties were overcome by careful pre- and intraoperative use of neuronavigation.
Neural Activity for Uninvolved Knee Motor Control After ACL Reconstruction Differs from Healthy Controls
Authors: Chaput Meredith , Criss Cody R. , Onate James A. , Simon Janet E. , Grooms Dustin R.
Published: January 2025

Recovery from anterior cruciate ligament reconstruction (ACLR) induces bilateral functional and physiological adaptations. Neurophysiologic measures of motor control have focused on the involved knee joint, limiting understanding regarding the extent of bilateral neural adaptations. Therefore, the aim of this study was to investigate differences in neural activity during uninvolved-limb motor control after ACLR compared to healthy controls. Methods: Fifteen participants with left ACLR (8 female and 7 male, 21.53 ± 2.7 years, 173.22 ± 10.0 cm, 72.15 ± 16.1 kg, Tegner 7.40 ± 1.1, 43.33 ± 33.1 mo. post-surgery, 2 patellar tendon, and 13 hamstring) and 15 matched controls (8 female, 23.33 ± 2.7 years, 174.92 ± 9.7 cm, 72.14 ± 15.4 kg, Tegner 7.33 ± 1.0) participated. Neural activity was evaluated using functional magnetic resonance imaging on a 3T Siemens Magnetom scanner during four 30-s cycles of a right (uninvolved) knee flexion-extension task paced with a metronome (1.2 Hz) and was completed interspersed with 30 s of rest. A significance threshold of p < 0.05 was used for all analyses, cluster corrected for multiple comparisons, and z-thresholds of >3.1 (subject level), and >2.3 (group level). Results: The ACLR group had greater neural activity in one statistically significant cluster corresponding to the left middle frontal gyrus (MFG) (834 voxels, z = 3.81, p < 0.01 multiple comparisons corrected) compared to controls. Conclusions: These data indicate a potential contribution to uninvolved-knee neuromuscular deficits after injury and support the limitations of using the uninvolved side as a clinical reference. Uninvolved knee motor control after ACLR may require greater cognitive demand. Clinicians should be aware that the uninvolved limb might also demonstrate whole brain alterations limiting clinical inference from functional symmetry.
The Association Between Internet Addiction and Adolescents’ Mental Health: A Meta-Analytic Review
Authors: Soriano-Molina Elena , Limiñana-Gras Rosa , Patró-Hernández Rosa , Rubio-Aparicio María
Published: January 2025

This study examines the association between problematic internet use, or internet addiction, and adolescent mental health, focusing on key psychological variables, assessing the strength of these associations, and identifying potential moderating factors. Methods: A search of the Web of Science databases over the past five years identified 830 articles. Of these, 33 met the inclusion criteria, involving 303,243 participants (average age 14.57; 49.44% female). The selection process was verified by two researchers. Results: Nine psychological variables were analyzed: depression, anxiety, stress, suicidal behaviour, psychological well-being, self-esteem, externalizing problems, aggressiveness, and impulsiveness. Internet addiction showed positive correlations with aggressiveness (r+ = 0.391), depression (r+ = 0.318), anxiety (r+ = 0.252), and suicidal behaviour (r+ = 0.264). Negative correlations were observed with psychological well-being (r+ = −0.312) and self-esteem (r+ = −0.306). No significant associations were found for externalizing problems, impulsiveness, or stress. None of the moderators showed a significant correlation with internet addiction and depression. Conclusions: Although limited by small sample sizes for some variables and the cross-sectional design of most studies, the findings confirm that there is a negative relationship between internet addiction and adolescent mental health. It is related to poorer self-perceived health, greater psychological distress, and greater aggression.
A Novel Simplified Analysis Model to Predict Behaviors of Single Piles Subjected to Reverse Faulting
Authors: Guo Deping , Liu Yulin , Tang Jincai , Zhang Zeng , Yao Chaofan , Li Yang , Wu Wang
Published: January 2025

Pile foundations are vulnerable to fault deformations. However, both the physical and numerical modeling of pile foundations under fault deformations are complicated and time-consuming. A simplified model is required for design and engineering practices. This study proposed a novel simplified analysis model to predict the behaviors of single piles subjected to reverse faulting. A two-dimensional beam–spring model is applied. The calculations of the stiffnesses of soil springs, skin friction, ultimate soil resistances, and Young’s modulus of sand are presented and discussed. The numerical results show a good agreement with the results of previous centrifuge tests. The parametric studies using the novel model show that ultimate horizontal soil resistance, skin friction, Young’s modulus of soil, pile stiffness, and sand density exhibit apparent effects on the responses of a single pile. The ultimate soil resistance controls the maximum inner forces, while Young’s modulus affects the increment of inner forces. The bending moment increases with pile stiffness initially and then remains relatively stable. Larger sand density leads to larger inner forces of the pile, owing to greater ultimate soil resistance and stiffness of the soil spring.
Development and Challenges of Low-Carbon Cities in China: Current Situation, Influencing Factors, and Sustainability Strategies
Authors: Li Yangluxi , Chen Huishu , Yu Peijun , Yang Li
Published: January 2025

As global climate change becomes increasingly severe, low-carbon city construction has emerged as a critical strategy to address this challenge. This study explores the concept, current development status, and challenges of low-carbon cities, focusing on the progress and issues in China’s low-carbon city construction. The research covers defining low-carbon cities, their background, policy impacts, and analysis of practical cases. Specifically, the research focuses on identifying the challenges faced in the development of low-carbon cities in China and proposing strategies to effectively address these obstacles. Findings suggest that difficulties, such as regional disparities, inconsistencies in policy implementation, and technological barriers, hinder progress. By synthesizing insights from previous studies, this paper proposes actionable strategies, including strengthening policy frameworks for consistent application, leveraging smart technologies for efficient energy and resource management, and fostering public engagement through targeted education. These recommendations provide a guideline for future research and practical actions, contributing to sustainable urban development and offering insights for policymakers and urban planners.
Surgical Management of Gastroenteropancreatic Neuroendocrine Tumors
Authors: Kenney Lisa M. , Hughes Marybeth
Published: January 2025

Background/Objectives: Neuroendocrine tumors (NETs) are heterogeneous malignancies arising from enterochromaffin cells that can arise from the gastrointestinal (GI) tract and pancreas. Surgical management is the cornerstone of treatment, with the optimal approach tailored by tumor grade, size, location, and presence of metastasis. This review discusses the current strategies for the surgical management of NETs of the gastroenteropancreatic tract. Methods: A review of the available literature was conducted to evaluate surgical approaches to NETs. Consensus guidelines were incorporated to synthesize evidence-based recommendations. Results: For gastric NETs, surgical approach depends on Rindi Classification, WHO grade, and tumor size, with endoscopic approaches favored for smaller and low-grade lesions. Small bowel NETs can be multifocal and thus often require a surgical approach with careful evaluation of the entire intestine. Pancreatic NETs are categorized as functional or non-functional, with enucleation or formal resection strategies based on size, location, functional status, and risk of malignancy. Colorectal NETs are primarily treated with transanal localized or formal surgical resection, depending on lesion size and depth of invasion or presence of lymph node involvement. Appendiceal NETs are either treated with appendectomy or right hemicolectomy, depending on the size, location, and invasiveness of the lesions. For metastatic NETs, cytoreduction, liver transplantation, and targeted therapies offer symptom relief and possible survival benefits. Conclusions: Surgical resection provides curative potential for localized NETs and symptom control in metastatic cases. Future research is essential to refine guidelines for intermediate-risk lesions and multifocal tumors, ensuring optimal outcomes for patients with gastroenteropancreatic NETs.
Bilirubin Triggers Calcium Elevations and Dysregulates Giant Depolarizing Potentials During Rat Hippocampus Maturation
Authors: Cellot Giada , Di Mauro Giuseppe , Ricci Chiara , Tiribelli Claudio , Bellarosa Cristina , Ballerini Laura
Published: January 2025

Neonatal hyperbilirubinemia may result in long-lasting motor, auditory and learning impairments. The mechanisms responsible for the localization of unconjugated bilirubin (UCB) to specific brain areas as well as those involved in potentially permanent central nervous system (CNS) dysfunctions are far from being clear. One area of investigation includes exploring how hyperbilirubinemia determines neuronal alterations predisposing to neurodevelopmental disorders. We focused on the hippocampus and pyramidal cell dysregulation of calcium homeostasis and synaptic activity, with a particular focus on early forms of correlated network activity, i.e., giant depolarizing potentials (GDPs), crucially involved in shaping mature synaptic networks. We performed live calcium imaging and patch clamp recordings from acute hippocampal slices isolated from wild-type rats exposed to exogenous high bilirubin concentration. We then explored the impact of endogenous bilirubin accumulation in hippocampal slices isolated from a genetic model of hyperbilirubinemia, i.e., Gunn rats. Our data show in both models an age-dependent dysregulation of calcium dynamics accompanied by severe alterations in GDPs, which were strongly reduced in hippocampal slices of hyperbilirubinemic rats, where the expression of GABAergic neurotransmission markers was also altered. We propose that hyperbilirubinemia damages neurons and affects the refinement of GABAergic synaptic circuitry during a critical period of hippocampal development.
Comprehensive Gait Analysis and Kinetic Intervention for Overweight and Obese Children
Authors: Popescu Cristina , Matei Daniela , Amzolini Anca Maria , Trăistaru Magdalena Rodica
Published: January 2025

Background/Objectives: Childhood obesity is a critical public health issue associated with biomechanical and functional impairments that influence gait and physical performance. This study aimed to evaluate the impact of a six-month personalized kinetic program on gait parameters and pelvic kinematics in overweight and obese children. Methods: The prospective observational study included 100 children aged 8 to 15, divided into a study group (SG, n = 50) and a control group (CG, n = 50). The SG participated in a tailored kinetic program focusing on flexibility, strength, and coordination exercises, while the CG maintained their usual activities. The program consisted of 60 min sessions conducted three times per week over a six-month period. Gait parameters and pelvic symmetry indices were assessed using the BTS G-WALK system. Ethical approval was granted by the Ethics Committee of the University of Medicine and Pharmacy, Craiova, under approval no. 38/1 March 2022. Results: Significant improvements were observed in the SG, with increases in cadence (steps/min), walking speed (m/s), and pelvic symmetry indices across all planes (sagittal, frontal, and transverse) (p < 0.0001). In contrast, no significant changes were observed in pelvic symmetry indices in the CG (p > 0.01). The Spearman correlation matrix and heatmaps highlighted a strong correlation between improved gait parameters and participation in the kinetic program (correlation coefficient over 0.45). Conclusions: The findings demonstrate that a targeted kinetic program can significantly improve gait mechanics and pelvic kinematics in overweight and obese children. These results emphasize the importance of personalized exercise interventions in managing obesity-related gait abnormalities and improving functional mobility.
C-Reactive Protein, International Normalized Ratio, and Fibrinogen in Diagnostic Scale of Complicated Acute Appendicitis
Authors: Hernández-González Leticia Lorena , Serrano-Guzmán Said José , Guzmán-Ortiz Jesús David , Pérez-Ceballos Hermelo Esteban , Cano-Pérez José Luis , Cruz-Hernández Víctor , Bernardino-Hernández Héctor Ulises , Martínez-Martínez Lucía Lourdes , Aguilar-Ruiz Sergio Roberto
Published: January 2025

Background/Objectives: Differentiating complicated acute appendicitis (CA) and uncomplicated acute appendicitis (UC) is essential to guide clinical management. While CA requires urgent surgical management, UC can be treated with antibiotic therapy in selected cases. However, accurate identification of CA remains a clinical challenge. This study aimed to identify factors associated with CA and to develop a diagnostic severity scale. Methods: In this retrospective study, we included 132 adult patients (>16 years) with a confirmed postsurgical diagnosis of appendicitis, of whom 52 had CA and 80 had UA. Signs, symptoms, comorbidities, laboratory values, and ultrasonographic findings were evaluated to determine predictive factors and construct a diagnostic scale. Results: The factors most significantly associated with CA were elevated plasma concentrations of C-reactive protein (>7.150 mg/dL), fibrinogen (481.5 mg/dL), International Normalized Ratio (INR) (>1.150), and the presence of free fluid periappendicular. The combination of these factors within one scale showed an area under the curve (AUC) of 0.84, with a sensitivity of 78.75% and a specificity of 82.69%. Conclusions: Serum C-reactive protein concentration, fibrinogen, and INR can be employed individually or as part of a scale as important indicators in diagnosing CA.
Study of Al7075 Localized Corrosion Inhibition by a SiO2 Superhydrophobic Coating Employing an Electrochemical Noise Technique
Authors: Vázquez Nuñez Luis Eduardo , Cornejo-Monroy Delfino , Sánchez-Santamaria Betania , Arias-Cerón José Saúl , Villanueva-Montellano Alfredo , López-Ibarra Aurora Abigail , Jáquez-Muñoz Jesús Manuel
Published: January 2025

Aluminum presents localized corrosion when exposed to marine and industrial environments. In this research, we study the pitting susceptibility of Al7075 coated by a SiO2 sol–gel coating as a corrosion inhibitor employing the electrochemical noise technique. The analysis methods were power spectral densities (PSDs) in noise impedance (Zn), complaints by wavelet transform, recurrence plots, and Hilbert–Huang study due to the chaotic behavior of the EN signal. To perform the EN, the parameters employed were 2048 data, 1 datum per second in NaCl, and H2SO4 electrolytes at 3.5 wt.% simulating marine and industrial environments. EN tests were performed following ASTM G199. The results showed an increase in the localized corrosion process when SiO2 coating was applied. This is due to the cracking morphology of the coating.
Abrasive Wear Characteristics of High-Cr Multicomponent White Cast Irons at Elevated Temperatures
Authors: Huq Mohammad Jobayer , Shimizu Kazumichi , Kusumoto Kenta
Published: January 2025

The abrasive wear phenomenon at elevated temperatures is very common in industries where operations are performed under extreme conditions. The occurrence of abrasive wear at high temperatures is typically far more severe than that under room-temperature conditions. Industrial machine parts are much more prone to wear at extreme temperatures. Wear due to high-temperature abrasion leads to higher costs. Due to the risk of damaging machine parts and increased costs, it is significant to investigate materials that reverse this loss. It has been proven in previous studies that high-chromium white cast irons with multiple components, including vanadium, molybdenum, tungsten, and cobalt, called MWCIs, are among the most useful materials that can be selected as wear-resistant materials at high temperatures because of their dominant behavior against wear. In this study, three series of high-chromium multicomponent white cast irons (18Cr, 27Cr, and 35Cr MWCI) were used to test their abrasive wear resistance capability. A higher percentage of Cr leads to the precipitation of hard M7C3 carbides, which results in a higher carbide volume percentage (CVF) and hence higher hardness. However, the addition of excessive Cr and less C results in carbide refinement and a drop in hardness. The microstructure is primarily austenite. This study shows that, at an operating temperature of 1073 K, the 27CrMWCI performs the best as an abrasive wear-resistant material compared to 18CrMWCI and 35CrMWCI due to its (27CrMWCI’s) higher CVF and hardness.
Noncovalent Interactions in Coordination Chemistry of Cyclic Trinuclear Copper(I) and Silver(I) Pyrazolates
Authors: Olbrykh Arina , Yakovlev Gleb , Titov Aleksei , Shubina Elena
Published: January 2025

Group 11 metals form with pyrazolate ligand complexes with a general formula of [MPz]n. The value of “n” varies depending on the type of substituent in the ligand and the metal atom. Copper(I) and silver(I) ions mainly form cyclic di-, tri-, and tetra-nuclear complexes or polymeric structures. Cyclic trinuclear d10 metal pyrazolates [MPzm]3 (M = Cu(I) and Ag(I); Pz = substituted pyrazolate ligand) are of particular interest because their planar structure allows them to form supramolecular aggregates via noncovalent metal–metal, metal–π, and metal–electron donor interactions. Designing complexes based on these interactions has been a focus of research for the last two decades. The ability of cyclic trinuclear copper(I) and silver(I) pyrazolates to form coordination and supramolecular structures determines their properties and potential applications in catalysis, gas sensing, molecular recognition, and photoluminescence. In this review, we discuss noncovalent interactions between cyclic trinuclear silver(I) and copper(I) complexes with various types of ligands.
The Association Between Childhood Experience of Living with a Heavy Drinker and Self-Rated Mental Health in the Adult General Population
Authors: Romac Danica , Gaić Đogaš Varja , Muslić Ljiljana , Krašić Sandro , Kušan Jukić Marija , Musić Milanović Sanja
Published: January 2025

Background and Objectives: Considering the link between childhood experiences with adult health and well-being, this study examined how living with a heavy drinker (HD) during childhood affected self-rated mental health (SRMH) in adulthood, while identifying risk and protective factors and assessing the prevalence within a regional context. Materials and Methods: Data (N = 11,113) were obtained from a cross-sectional DEEP SEAS survey (2021) of the general population, aged 18–64 years, in six countries (Croatia, Bosnia and Herzegovina, Slovenia, Austria, Hungary, and Italy). Results: A statistically significant difference in SRMH was found, related to the childhood experience of living with an HD (MD = −0.221, 95% CI −0.250–0.172, N = 10,886) and being negatively affected (MD = −0.216, 95% CI −0.311–0.122, N = 2978). The correlation analysis revealed that individuals who lived with an HD during childhood perceived poorer SRMH in adulthood, consistently across all observed countries. Subsequent logistic regression identified different predictors for SRMH between those who were negatively affected and those who were not. The relationship satisfaction (RAS) was the strongest predictor, significantly contributing to better SRMH, especially in the subgroup that was not negatively affected (OR 28.724, 95% CI 3.450–239.173). A high prevalence of individuals negatively affected was found, especially in Hungary (34.4%) and Croatia (26.5%). Conclusions: Growing up with someone who was a heavy drinker may have lasting negative consequences on SRMH, with a negative subjective evaluation of mental health in adulthood. Targeted public health and preventive measures are needed to protect those living with heavy drinkers.
Dynamic Event-Triggered-Based Finite-Time Distributed Tracking Control of Networked Multi-UAV Systems
Authors: Ren Ruichi , Luo Zhenbing , Lin Boxian , Li Meng , Shi Mengji , Qin Kaiyu
Published: January 2025

The distributed tracking of multiple unmanned aerial vehicles (UAVs) is a hotspot due to its broad applications in various fields, while continuous communication among UAVs is often impractical, especially in time-sensitive tasks or environments with limited bandwidth. With this in mind, this paper presents a finite-time leader-following distributed tracking control scheme for general multi-agent systems, with a particular emphasis on its application for networked UAVs. Theoretically, a dynamic event-triggered mechanism is proposed, which features a novel finite-time stable dynamic variable within its triggering rule, ensuring that neither controller updates nor trigger detection requires continuous communication. This event-triggered finite-time controller facilitates efficient network resource management and timely mission response in UAV cooperation, enhancing adaptability to onboard wireless communication networks and time-sensitive tasks. The method allows for the customization of parameters in the internal dynamic variables to adjust the convergence rate and event-triggering frequency of the system, while also preventing Zeno behavior. Moreover, a Lyapunov-based analysis is conducted to theoretically verify the finite-time stability of the closed-loop system and its applicability in directed communication networks. Finally, some numerical simulations are performed to validate the effectiveness of the proposed distributed control scheme for networked multi-UAV systems.
Subspace Learning for Dual High-Order Graph Learning Based on Boolean Weight
Authors: Wei Yilong , Ma Jinlin , Ma Ziping , Huang Yulei
Published: January 2025

Subspace learning has achieved promising performance as a key technique for unsupervised feature selection. The strength of subspace learning lies in its ability to identify a representative subspace encompassing a cluster of features that are capable of effectively approximating the space of the original features. Nonetheless, most existing unsupervised feature selection methods based on subspace learning are constrained by two primary challenges. (1) Many methods only predominantly focus on the relationships between samples in the data space but ignore the correlated information between features in the feature space, which is unreliable for exploiting the intrinsic spatial structure. (2) Graph-based methods typically only take account of one-order neighborhood structures, neglecting high-order neighborhood structures inherent in original data, thereby failing to accurately preserve local geometric characteristics of the data. To pursue filling this gap in research, taking dual high-order graph learning into account, we propose a framework called subspace learning for dual high-order graph learning based on Boolean weight (DHBWSL). Firstly, a framework for unsupervised feature selection based on subspace learning is proposed, which is extended by dual-graph regularization to fully investigate geometric structure information on dual spaces. Secondly, the dual high-order graph is designed by embedding Boolean weights to learn a more extensive node from the original space such that the appropriate high-order adjacency matrix can be selected adaptively and flexibly. Experimental results on 12 public datasets demonstrate that the proposed DHBWSL outperforms the nine recent state-of-the-art algorithms.
Time Series Data Generation Method with High Reliability Based on ACGAN
Authors: Liu Fang , Li Yuxin , Zheng Yuanfang
Published: January 2025

In the process of big data processing, especially in fields like industrial fault diagnosis, there is often the issue of small sample sizes. The data generation method based on Generative Adversarial Networks(GANs) is an effective way to solve this problem. Most of the existing data generation methods do not consider temporal characteristics in order to reduce complexity. This can lead to insufficient feature extraction capability. At the same time, there is a high degree of overlap between the generated data due to the low category differentiation of the real data. This leads to a lower level of category differentiation and reliability of the generated data. To address these issues, a time series data generation method with High Reliability based on the ACGAN (HR-ACGAN) is proposed, applied to the field of industrial fault diagnosis. First, a Bi-directional Long Short-Term Memory (Bi-LSTM) network layer is introduced into the discriminator.It can fully learn the temporal characteristics of the time series data and avoid the insufficient feature extraction capability. Further, an improved training objective function is designed in the generator to avoid high overlap of generated data and enhance the reliability of generated data. Finally, two representative datasets from the industrial fault domain were selected to conduct a simulation analysis of the proposed method. The experimental results show that the proposed method can generate data with high similarity. The dataset expanded with the generated data achieves high classification accuracy, effectively mitigating the issue of dataset imbalance. The proposed HR-ACGAN method can provide effective technical support for practical applications such as fault diagnosis.
Understanding and Designing Variation in Interdisciplinary Problem-Based Projects in Engineering Education
Authors: Kolmos Anette , Holgaard Jette Egelund , Routhe Henrik Worm
Published: January 2025

In various problem- and project-based traditions, interdisciplinarity is part of the definition, and there have been a few conceptualizations of interdisciplinary projects. However, with the increasing application of interdisciplinary projects, it is necessary to develop a more varied understanding. A recent study in a PBL context defines four interdisciplinary project types, and this article aims to develop a conceptual understanding that can inform the design of project types. To design interdisciplinary project collaboration, it is necessary to understand the characteristics of the disciplines involved. In the literature, we identified three major dimensions: knowledge, culture, and approach to learning, all of which play a role in the understanding of problems and collaboration. Furthermore, it is important to understand the move from the disciplinary to the more interdisciplinary project types. In this regard, problem design and team collaboration are chosen areas to exemplify the complexity of this move. As an outcome of this paper, a conceptual framework is developed combining the three dimensions of disciplinary understanding with the interdisciplinary project types. This creates an understanding of what to take into consideration in a design process moving from disciplinary to interdisciplinary projects, especially in the design of problems and interdisciplinary project collaboration.
Task Offloading Strategy for UAV-Assisted Mobile Edge Computing with Covert Transmission
Authors: Hu Zhijuan , Zhou Dongsheng , Shen Chao , Wang Tingting , Liu Liqiang
Published: January 2025

Task offloading strategies for unmanned aerial vehicle (UAV) -assisted mobile edge computing (MEC) systems have emerged as a promising solution for computationally intensive applications. However, the broadcast and open nature of radio transmissions makes such systems vulnerable to eavesdropping threats. Therefore, developing strategies that can perform task offloading in a secure communication environment is critical for both ensuring the security and optimizing the performance of MEC systems. In this paper, we first design an architecture that utilizes covert communication techniques to guarantee that a UAV-assisted MEC system can securely offload highly confidential tasks from the relevant user equipment (UE) and calculations. Then, utilizing the Markov Decision Process (MDP) as a framework and incorporating the Prioritized Experience Replay (PER) mechanism into the Deep Deterministic Policy Gradient (DDPG) algorithm, a PER-DDPG algorithm is proposed, aiming to minimize the maximum processing delay of the system and the correct detection rate of the warden by jointly optimizing resource allocation, the movement of the UAV base station (UAV-BS), and the transmit power of the jammer. Simulation results demonstrate the convergence and effectiveness of the proposed approach. Compared to baseline algorithms such as Deep Q-Network (DQN) and DDPG, the PER-DDPG algorithm achieves significant performance improvements, with an average reward increase of over 16% compared to DDPG and over 53% compared to DQN. Furthermore, PER-DDPG exhibits the fastest convergence speed among the three algorithms, highlighting its efficiency in optimizing task offloading and communication security.
Multi-Scale Feature Extraction to Improve P300 Detection in Brain–Computer Interfaces
Authors: Usman Muhammad , Lin Chun-Ling , Chen Yao-Tien
Published: January 2025

P300 detection is a difficult task in brain–computer interface (BCI) systems due to the low signal-to-noise ratio (SNR). In BCI systems, P300 waves are generated in electroencephalogram (EEG) signals using various oddball paradigms. Convolutional neural networks (CNNs) have previously shown excellent results for P300 detection compared to different machine learning models. However, current CNN architectures limit P300 detection accuracy because these models usually only extract single-scale features. Aiming to enhance P300 detection accuracy, an inception module-based CNN architecture, namely Inception-CNN, is introduced. Inception-CNN effectively learns discriminative features from both spatial and temporal information to reduce overfitting and computational complexity. Furthermore, it can extract multi-scale features, which effectively improves P300 detection accuracy and increases character spelling accuracy. To analyze the effect of the inception layer, two additional models are proposed: Inception-CNN-S, which uses the inception layer with a spatial convolution layer, and Inception-CNN-T, which uses the inception layer with a temporal convolution layer. The proposed model was evaluated on dataset II of BCI Competition III and dataset IIb of BCI Competition II. The experimental results show that Inception-CNN provides a promising solution for improving the accuracy of P300 detection, with F1 scores of 47.14%, 55.28%, and 78.94% for dataset II of BCI Competition III (Subject A and Subject B) and dataset IIb of BCI Competition II, respectively.
Recommendation Method Based on Glycemic Index for Intake Order of Foods Detected by Deep Learning
Authors: Lee Jae-young , Kwon Soon-kak
Published: January 2025

In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection network. The GIs for the detected foods are found by matching their names or categories with the information stored in the database. If the detected food name or category is not found in the database, the food information is found from a public API. The food is classified into one of the food categories based on nutrients, and the median GI of the corresponding category is assigned to the food. The food intake order is recommended from the lowest to the highest GI. We implemented a web service that visualizes the food analysis results and the recommended food intake order. In experimental results, the average inference time and accuracy were 57.1 ms and 98.99% for Mask R-CNN, respectively, and 24.4 ms and 91.72% for YOLOv11, respectively.
Research on Short-Term Load Forecasting of LSTM Regional Power Grid Based on Multi-Source Parameter Coupling
Authors: Li Bo , Liao Yaohua , Liu Siyang , Liu Chao , Wu Zhensheng
Published: January 2025

Regional power grid load has strong periodicity and randomness, and its load characteristics are affected by many factors. Traditional short-term power load-forecasting methods have certain limitations in accuracy and stability, especially when dealing with complex weather and voltage changes. To improve the prediction accuracy, this paper proposes a short-term power load-forecasting model of a regional power grid based on multi-source parameter coupling with a long short-term memory neural network (LSTM) and adopts an improved particle swarm optimization (IPSO) algorithm to optimize the LSTM network. Firstly, load characteristics under different time dimensions were analyzed, combined with meteorological factors such as daily maximum temperature, minimum temperature, rainfall, air humidity, and historical load data, multi-source data were coupled, and date types were quantified by independent thermal coding technology to ensure a reasonable model input data set. Different from traditional methods, this paper introduces real-time coupling data of intensive sensing, which makes the model more sensitive to capture the subtle characteristics of load changes. In order to further optimize the performance of the LSTM model, the IPSO algorithm, and linear difference decreasing inertia weight are introduced to improve the global optimization ability and convergence speed of the PSO algorithm and reduce the risk of local optimal solutions. Finally, the accuracy of the model is verified by the measured data of dense sensing in a regional power grid in northern China. The calculation results show that the prediction model based on multi-source parameter coupling has higher accuracy and stability in short-term load forecasting. Compared with traditional forecasting methods, the mean relative error (MAPE), the root mean square error (RMSE), and the mean absolute error (MAE) are reduced by 1.8149%, 154.0884, and 130.6769, respectively. In the typical day prediction of different seasons, the model can keep the relative error of more than 90% sampling points below 2%. The average relative error is basically less than 1%. The model proposed in this paper shows higher accuracy and stronger practical application potential compared with traditional forecasting methods, especially in voltage monitoring and power grid dispatching.
Innovative Capacitive Wireless Power System for Machines and Devices
Authors: Skóra Marcin , Rogala-Rojek Joanna , Jendrysik Sebastian , Stankiewicz Krzysztof , Polnik Bartosz , Kaczmarczyk Zbigniew , Kasprzak Marcin , Lasek Paweł , Przybyła Krzysztof
Published: January 2025

This article deals with the design, development, and analysis of a wireless power transfer system prototype based on capacitive coupling. The system is intended for the continuous charging of a suspended mine drivetrain. It consists of a resonant inverter, primary and secondary matching circuits, a suitable capacitive coupler, and a rectifier with a load, ultimately serving as a battery charger for a mobile energy storage device. The system successfully achieved the target output voltage of 320 V and a charger output power of 2 kW at an operating frequency of 300 kHz. Additionally, the total system efficiency was at the level of 60%, ensuring that the RMS voltages on passive components remained below 3 kV.
Unleashing Energy Potential: Insights of Energy Audit Practices
Authors: Liberova Veronika , Bremane Inguna , Lauka Dace , Laktuka Krista , Bezrucko Tereza , Zvirbule Karina , Bezrucko Alise Egija , Blumberga Dagnija
Published: January 2025

This article examines energy audit procedures as a crucial instrument for raising building and business energy efficiency in European Union (EU) Member States. Energy audits, which include technical, economic, and environmental aspects, are essential to reaching the EU’s climate targets, which include increasing energy independence and cutting emissions by 55% by 2030. The study highlights how crucial energy efficiency initiatives are to combating climate change, cutting energy use, and advancing sustainable development. A thorough examination of methods, financing sources, and legislative frameworks reveals differences in how Member States carry out directives such as the Energy Performance of Buildings Directive and the Energy Efficiency Directive. A case study on Latvia is included in the article, highlighting the country’s energy audit regulations, implementation difficulties, and successes. This study applied a bibliographic methodology or review of the scientific and other relevant literature, analyzing sources identified through targeted keyword searches in academic databases and a variety of online sources, including official legal websites, handbooks, reports, plans, and other publicly available digital resources. The integration of energy efficiency principles and country performances are compared using data from the Odyssee and Mure databases. The results highlight how important it is to standardize energy auditing practices, promote information sharing across national borders, and move past obstacles like public resistance and budgetary limitations. Policy recommendations to improve energy efficiency and aid the EU’s transition to climate neutrality by 2050 are included in the research’s conclusion.
Refinement of Finite Element Method Analysis Model of Pressurized Water Reactor Nuclear Fuel Spacer Grid Based on Experimental Data
Authors: Kim Minhee , Namgung Ihn
Published: January 2025

A Finite Element Method (FEM) analysis of the nuclear fuel spacer grid was conducted to assess the strength of components for the safety of nuclear power plants. The fuel assembly consists of fuel rods, upper end-fitting, lower end-fitting, guide tubes, and spacer grids. Spacer grids play a critical role in maintaining the proper spacing between fuel rods within a fuel assembly and ensuring smooth coolant flow. This role becomes particularly crucial during unforeseen emergencies, such as seismic loads, where minimizing deformation caused by external forces is essential. Therefore, this study proposes FEM models of spacer grids, mesh refinement of models, and analysis of the stiffness of the spacer grids concerning the presence or absence of pellet and clad. The results revealed significantly lower shear stiffness compared to normal stiffness, indicating potential vulnerability of the fuel assembly to large loads such as those experienced during seismic events.
Remote Sensing Detection of Forest Changes in the South Ridge Corridor and an Attribution Analysis
Authors: Wu Nan , Huang Linghui , Zhang Meng , Dou Yaqing , Mo Kehan , Liu Junang
Published: January 2025

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km2, with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the “dual carbon” goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km2, accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km2. From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km2 in 2008, with a low value of 37.71 km2 in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fire factors were spatially distributed in the center of the mountains, and their distribution was loose. Building or road factors were mostly distributed in clusters or lines. These research results are expected to provide technical and data support for the study of the large-scale spatiotemporal evolution of forests and its driving mechanisms.
Hydration Tracking via Saliva Osmolarity in Recruit Firefighters Throughout a 12-Week Fire School
Authors: Holland-Winkler A. Maleah , Moore Andrew R. , Parish Steven L. , Oberther Tiffany J.
Published: January 2025

Background: The purpose of this study was to assess hydration status via saliva osmolarity throughout a 12-week Fire School in recruit firefighters. Methods: At the beginning (AM) and end (PM) of the workday for 13 weeks (a 12-week Fire School and an additional transition week), saliva osmolarity was measured, recorded, and relayed to each participant in the sample, which included 23 recruit firefighters. The average weekly osmolarity scores were computed for AM and PM. Separate linear mixed-effects models were used to assess the changes in osmolarity at each time of day over the course of the training. Bonferroni-adjusted post hoc tests were used to identify significant differences between weeks while maintaining test-wise error (α = 0.05). Results: AM was significantly lower in week 12 than in weeks 4 and 6, and lower in week 13 than in weeks 2, 4, 6, and 7 (p ≤ 0.035 for all). PM was significantly lower in week 12 than in weeks 3 and 7, and was lower in week 13 than in weeks 1, 3, and 7 (p ≤ 0.019 for all). Conclusion: This study demonstrated that hydration status via saliva osmolarity improved during the last half of Fire School despite those weeks being more physically and thermally challenging.