bluuebunny/crossref_metadata_2025 huggingface dataset:
Field Monitoring and Analysis of Factors Influencing Existing Tunnels Laterally Adjacent to Foundation Pit Excavations
Authors: Chen Huan , Wang Zhanming , Zhang Jian , Wang Zuocai , Peng Peng , Feng Tugen
Published: January 2025

To investigate the factors influencing existing tunnels adjacent to foundation pit excavations, this paper focuses on a case of excavation of MTR station foundation pits near an existing tunnel. Monitoring data are collected and analyzed, and a two-dimensional numerical model of the pit–tunnel system is established using ABAQUS (2022) software. This study examines the effect of excavation factors on the tunnel, including excavation depth, horizontal spacing between pit and tunnel, support pile wall dimensions, support structure insertion ratio, horizontal support stiffness, and geological conditions. The research results indicate that for tunnels in silty clay formations, the depth of pit excavation should be limited to 20 m. When the horizontal spacing between a foundation pit and tunnel in a silty clay formation is within 1.5 times the depth of excavation, the tunnel is more significantly affected by tunnel excavation works in the adjacent pits. Increasing the horizontal support stiffness has the greatest effect on controlling deformation and stress in the tunnel structure. The degree of influence on deformation and stress in the tunnel due to laterally adjacent excavation of a foundation pit varies with geological conditions. Among them, tunnels in muddy-silty clay and silty soils are the most affected, and it is recommended that reinforcement measures be optimized and monitoring be strengthened. The conclusions of the present study can provide a reference point and guidance for the optimal design of similar pit projects.
Research on Energy-Saving Optimization Method and Intelligent Control of Refrigeration Station Equipment Based on Fuzzy Neural Network
Authors: Lu Wansu , Liang Jiajia , Su Hao
Published: January 2025

Under the background of dual carbon, the retrofitting of the equipment operation system of a refrigeration station and the optimization combination of its control system are significant for its efficient operation and energy saving. The single-direction variable flow technology is often used in the chilled water system in refrigeration stations nowadays. However, the single-direction variable flow technology cannot achieve both thermal balance and flow balance for the chiller system, which is unfavorable for improving energy efficiency and reliability. To improve the reliability and energy efficiency of the refrigeration station equipment, the bidirectional variable flow technology of primary and secondary chilled water pumps was presented. Meanwhile, the feasibility of fuzzy neural networks in bidirectional variable flow systems and their energy-saving effect were studied. Before the energy saving retrofit, the refrigeration station used traditional PID (proportional-integral-derivative) controllers, and the chilled water system used single-direction variable flow technology; After the energy-saving retrofit, the refrigeration station adopted a fuzzy neural network control algorithm to optimize the PID controller parameters, and at the same time, the chilled water system used bidirectional variable flow technology. Through a large number of trial calculations of the established neural network model, it was found that 2 hidden layers and 25 hidden layer nodes can achieve higher accuracy. Specifically, the controller of the central refrigeration station consists of a training neural network and a predictive neural network working in parallel. The task of training neural networks is to learn the relationship between different input parameters and the whole energy consumption. Then it serves as the excitation function of the prediction network. The function of the predictive neural network is to find the control parameters that minimize energy consumption. The application results showed that before and after the retrofit annual power consumption and energy-saving effects were very Significant. After the energy-saving retrofit of the refrigeration station, the energy saving is 422,775 KWh every year, the energy-saving rate is 11.67%, and the annual saving cost is about 0.3382 million yuan. The results demonstrated that bidirectional variable flow technology and its control methods were feasible, reasonable, and worthy of promotion.
High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
Authors: Pan Zhiqiang , Kuang Yingchun , Lan Jianmei , Zhang Lizhuo
Published: January 2025

Existing diffusion models outperform generative models like Generative Adversarial Networks in image synthesis and editing. However, they struggle with high-precision image editing while preserving image details and the accuracy of editing instructions. To address these challenges, we propose a dual attention control method to achieve high-precision image editing. Our approach includes two key attention control modules: (1) cross-attention control module, which combines the cross-attention maps of the original and edited images through weighted parameters, ensures that the synthesized edited image retains the structure of the input image. (2) Self-attention control module, which varies based on the editing task, applied at “coarse” and “fine” layers, since the coarse layers help maintain input image details and the fine layers are better suited for style transformations. Experimental evaluations have demonstrated that our approach achieves excellent results in detail preservation, content consistency, visual realism, and semantic understanding, making it especially suitable for tasks requiring high-precision editing. Specifically, compared to the editing outcomes under no control conditions, the introduction of dual visual attention control has led to an increase of 6.19% in CLIP scores, a reduction of 29.3% in LPIPS, and a decrease of 24.7% in FID. These significant improvements not only validate the effectiveness of the dual attention control but also attest to the method’s substantial flexibility and adaptability across different scenarios. Notably, our approach is a zero-shot solution, requiring no user optimization or fine-tuning, facilitating real-world applications.
Isothermal Pyrolysis of Bamboo and Pinewood Biomass: Product Characterization and Comparative Study in a Fluidized Bed Reactor
Authors: Shezi Manqoba , Kiambi Sammy Lewis
Published: January 2025

Fast pyrolysis of biomass is crucial for sustainable biofuel production, necessitating thorough characterization of feedstocks to optimize thermal conversion technologies. This study investigated the isothermal pyrolysis of bamboo and pinewood biomass in a sand-fluidized bed reactor, aiming to assess biomass suitability for commercial bio-oil production. The pyrolysis products and biomass species were characterized through proximate and ultimate analyses, along with GCMS, FTIR, SEM/EDX, and structural analysis to assess their chemical and physical properties. Results indicated that pine bio-oil possesses superior energy density, with a higher calorific value (20.38 MJ/kg) compared to bamboo (18.70 MJ/kg). Pine biomass yielded greater organic phase bio-oil (BOP) at 13 wt%, while bamboo produced 9 wt%. Energy yields were also notable, with pine exhibiting an energy yield of 15% for bio-oil organic phase (EBOP), compared to 11% for bamboo. The fibrous nature of bamboo biomass resulted in less-reacted biomass at constant reaction time due to flow resistance during pyrolysis. Pine bio-oil organic phase (P-BOP) demonstrated a higher heating value (23.90 MJ/kg) than bamboo (B-BOP). The findings suggest that while both biomass types are viable renewable energy sources, pine biomass is more favorable for commercialization due to its superior energy properties and efficiency in pyrolysis.
Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume
Authors: Sheng Ting-Wen , Onthoni Djeane Debora , Gupta Pushpanjali , Lee Tsong-Hai , Sahoo Prasan Kumar
Published: January 2025

Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of 95% for automatic localization, a mean Intersection over Union (mIoU) of 92% for segmentation, and a mean R2 score of 97% for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images.
The Dose-Dependent Effects of Fluorocitrate on the Metabolism and Activity of Astrocytes and Neurons
Authors: Zhuang Huiling , Yuan Deliang , Shi Fuxiu , Wu Xujun , Luo Zhen , Gan Wenbiao
Published: January 2025

Background: Fluorocitrate (FC) ranging from 5 μM to 5 mM is often used as a specific metabolic inhibitor of the astrocytes to study astrocytic functions. Whether FC at such concentrations may affect neuronal metabolism and function in vivo remains unclear. Methods: We examined the effects of FC on the ATP levels and Ca2+ activity of the astrocytes and neurons in the motor cortices of living mice using two-photon microscopy. Results: We found that 25 μM and 250 μM of FC decreased the intracellular ATP levels and Ca2+ activity in the astrocytes in the motor cortex. Equally, 250 μM of FC, but not 25 μM of FC, reduced the intracellular ATP levels in the dendritic processes of the layer 5 pyramidal neurons. However, 25 μM of FC increased the neuronal Ca2+ activity, whereas ≥250 μM of FC decreased it. To test whether the differential effects of FC on neuronal Ca2+ activity reflect the direct effect of FC on the neurons or its indirect effect on the astrocytes, we used the CNO-hM3Dq chemogenetic approach to block astrocytic Ca2+ activity and examined the effect of FC. In the absence of astrocytic Ca2+ activity, 25 μM of FC still increased and ≥250 μM of FC reduced the dendritic Ca2+ activity of the neurons, respectively, suggesting a direct effect of 250 μM of FC on inhibiting neuronal Ca2+ activity. Further, 250 μM, but not 25 μM, of FC increased the size of the dendritic spines over 2 h. Conclusions: Our findings suggest that FC at high concentrations (≥250 μM) is not a specific inhibitor of astrocytic functions, as it directly affects neuronal metabolism and synaptic plasticity in vivo.
It Is a Catch-22 Situation! A Decade of Research Trends on Gay Wellbeing in China: A Bibliometric Analysis
Authors: Gong Jiankun , Miao Weishan
Published: January 2025

The wellbeing of gay men in China is shaped by a combination of cultural, social, and political factors, yet research on this topic remains fragmented despite growing global interest in LGBTQ+ health. This study provides a comprehensive bibliometric analysis of research trends on wellbeing of gays (MSM) in China, using the PRISMA 2020 guidelines for a systematic search strategy and VOSviewer for bibliometric mapping. Data from Scopus were analyzed to examine publication output, top journals, and authors, along with a co-occurrence analysis of keywords and co-authorship networks across countries and authors. Results show a steady rise in publications over the past decade, particularly after 2015, revealing strong domestic collaboration and emerging international partnerships. The study identifies key contributing journals and themes, while revealing that most research remains narrowly focused on HIV/AIDS, leaving gaps in understanding gay wellbeing from broader societal and cultural perspectives. Surprisingly, very few studies specifically examine or measure overall wellbeing, whether using quantitative or qualitative approaches, and there is limited exploration of how cultural factors influence the experiences of gay men in China. These findings underscore the need for more holistic research approaches that move beyond public health and HIV prevention to address the psychological, social, and cultural dimensions of wellbeing.
Voices of Loyal Members: Dual Role of Organizational Identification in the Process of Employee Voice
Authors: Jung Yongsuhk , Choi Yongjun
Published: January 2025

Based on Hirschman’s theory of loyalty and Packer’s normative conflict model, the present study examined the roles of organizational identification in the voice emergence and reaction processes, wherein individuals provide voice and receive evaluations for their voice behavior, respectively. Using a survey method, data were collected from 455 cadets and their supervisors at a military educational institute in South Korea, who live and work together under an honor-based organizational system that encourages voice behavior through formal and informal channels. Structural equation modeling (SEM) was used for hypothesis testing. Our findings from multi-source data demonstrated that, when controlling for two social exchange variables (i.e., leader–member exchange and perceived organizational support), organizational identification not only increases voice behavior but also strengthens the positive relationship between voice behavior and supervisor performance evaluations. Specifically, voice behavior has a positive relationship with performance appraisal only when organizational identification is high. Theoretical and practical implications of the findings and directions for future research are discussed.
The Roles of Psychological Inflexibility and Mindful Awareness on Distress in a Convenience Sample of Black American Adults in the United States
Authors: Masuda Akihiko , Goodnight Bradley L. , Caporino Nicole E. , Rapadas Cerila C. , Tully Erin C.
Published: January 2025

Background: In recent years, the conceptual framework of psychological flexibility/inflexibility has been of global interest in the field of behavioral health. Nevertheless, studies and evidence of psychological flexibility/inflexibility remain limited for underrepresented groups of individuals, including people of color in the United States (U.S.). Among these groups of individuals are Black Americans in the U.S. In response to this empirical gap, the present cross-sectional study investigated whether psychological inflexibility and mindful awareness were uniquely related to general psychological distress, somatization, depression, and anxiety in Black American adults in the United States. Methods: A convenience sample of 359 Black American college students completed self-report measures of interest online. Results: As predicted, correlational analyses showed that psychological inflexibility was positively associated with general psychological distress, somatization, depression, and anxiety, and that mindful awareness was negatively associated with these four distress variables. A path analysis model revealed that psychological inflexibility, but not mindful awareness, was uniquely associated with these distress variables. Conclusions: The present study extended previous findings with a convenience sample of Black American college students, suggesting that psychological inflexibility may be a useful construct for understanding psychological distress, more so than mindful awareness, among Black American adults in the U.S.
Seismic Failure Assessment Using Energy Outputs of Finite Element Analysis: A Strategy for Complex Heritage Masonry Structures Modeled with Concrete Damaged Plasticity Material
Authors: Remus Anna , Tezcan Selman , Sun Jiacheng , Milani Gabriele , Perucchio Renato
Published: January 2025

The structural assessment of masonry construction often requires the use of nonlinear 2D and 3D finite element analysis. This work describes a strategy for using energy outputs from such analyses to accurately assess failure conditions precipitated by increasing lateral load. The methodology relies on the analogy between plastic strains and fracture that is inherent to the concrete damaged plasticity (CDP) macro-model used to represent the quasi-brittle behavior of masonry material. At critical conditions, energy imparted to a structure by loading can no longer be completely stored as elastic strain energy and must be dissipated. This occurs with fractures in masonry, which are represented with plastic strains when using CDP material. The development of plastic dissipation energy can therefore be used as a measure for understanding the progressive collapse of a structure, as we illustrate with the following three case studies analyzed using Abaqus/CAE Explicit: the massive earthen pyramid at Huaca de la Luna (Trujillo, Peru), the Roman pozzolanic concrete vault of Diocletian’s Frigidarium (Rome, Italy), and the mixed-material triumphal arch of the San Pedro Apóstol Church of Andahuaylillas (Peru). The method is verified by other measures of failure and has particular applicability for seismic analysis of complex masonry and earthen structures.
Enhancing Engineering and Architectural Design Through Virtual Reality and Machine Learning Integration
Authors: Shehadeh Ali , Alshboul Odey
Published: January 2025

This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. Traditional clash detection methods in Building Information Modeling (BIM) systems are predominantly reactive, identifying discrepancies only after their occurrence, leading to costly and time-consuming design revisions. By integrating ML algorithms with VR-driven BIM, our approach proactively identifies and resolves clashes, as demonstrated across 28 diverse engineering projects. The results indicate a reduction in design clashes by 16% and iterative revisions by 15%, culminating in a 12% decrease in overall project timelines. This research underscores the transformative impact of combining VR and ML on additive manufacturing (AM) workflows, significantly improving efficiency and reducing the iterative nature of traditional methods. The findings highlight the framework’s scalability and adaptability, promising substantial advancements in engineering and architecture practices.
Head and Neck Cancer in Fanconi Anemia: Report of 11 Cases and a Systematic Review
Authors: de Pablo García-Cuenca Alba , Rofín-Fontanet Pau , Lorente-Guerrero Juan , Balmaña-Gelpí Judith , Carrasco-López Estela , Temprana-Salvador Jordi , Granado-Carrasco Raquel , Braña-García Irene , Vaquero-Martínez Pablo , Pujol-Pina Rosa , Bescós-Atín Coro
Published: January 2025

Background/Objectives: Head and neck cancer is one of the most common malignancies in patients with Fanconi anemia (FA), with a greater than 500-fold incidence of head and neck squamous cell carcinoma. Treatment of HNSCC in these patients is particularly challenging because of poor tolerance to radiation therapy and to some chemotherapeutic agents such as cisplatin. For these reasons, new research is needed to determine the best treatment course for these patients. With this goal in mind, we assessed the characteristics of head and neck squamous cell carcinoma (SCC) in Fanconi anemia (FA). Subjects and Methods: Data for 11 patients (mean age 31 years) with head and neck SCC and FA attended to between 2005 and 2021 were analyzed. Results: The primary tumor site was the oral cavity in eight patients, and five had advanced stages. All patients underwent primary tumor resection. Four patients received adjuvant radiotherapy (mean 57.2 Gy), but three developed toxicity. The mean follow-up was 48.4 months. Six patients experienced 19 episodes of primary tumor recurrence and five developed secondary head and neck tumors. The 3-year disease-free survival was 47%, and the 5-year cause-specific survival was 48%. These findings are similar to the data for 72 patients collected from the literature. Conclusions: The prognosis of head and neck cancer in patients with Fanconi anemia is poor, with an overall survival lower than 50% at 5 years.
Molecular Mechanisms and Clinical Aspects of Colitis-Associated Cancer in Ulcerative Colitis
Authors: Yamamoto-Furusho Jesus K. , Gutierrez-Herrera Fausto D.
Published: January 2025

Inflammatory bowel diseases have long been recognized as entities with a higher risk of colorectal cancer. An increasing amount of information has been published regarding ulcerative colitis-associated colorectal cancer and its unique mechanisms in recent decades, as ulcerative colitis constitutes a chronic process characterized by cycles of activity and remission of unpredictable durations and intensities; cumulative genomic alterations occur during active disease and mucosal healing, resulting in a special sequence of events different to the events associated with sporadic colorectal cancer. The recognition of the core differences between sporadic colorectal cancer and colitis-associated cancer is of great importance to understand and guide the directions in which new research could be performed, and how it could be applied to current clinical scenarios. A DSS/AOM murine model has allowed for a better understanding of the pathogenic mechanisms in colitis-associated cancer, as it is currently the closest model to this unique scenario. In this review, we provide a summary of the main molecular mechanisms and the clinical aspects of colitis-associated cancer in ulcerative colitis.
Polycarbonate–Acrylonitrile Butadiene Styrene Three Dimensional Printing Material Exhibits Biocompatibility and Enhances Osteogenesis and Gingival Tissue Formation with Human Cells
Authors: Xiao Li , Shimamura Naohiro , Kamio Takashi , Ide Ryoji , Mochizuki Mai , Nakahara Taka
Published: January 2025

Three dimensional (3D) printing materials are widely used in dental applications, but their biocompatibility and interactions with human cells require evaluation. This study aimed to identify materials meeting biocompatibility, mechanical strength, and tissue-forming requirements for safe dental applications. We assessed the cytotoxicity of resins and thermoplastic filaments in human HaCaT keratinocytes, gingival fibroblasts (hGFs), and stem cells from human exfoliated deciduous teeth (SHED) using PrestoBlue assays. Three resins, including two types of surgical guide resins, exhibited strong cytotoxicity after 4–72 h, while 2 h exposure to an FDA-approved surgical guide resin did not affect SHED cell viability. In contrast, six thermoplastic filaments showed no significant cytotoxicity even after 72 h. Among these, polycarbonate–acrylonitrile butadiene styrene (PC-ABS) demonstrated excellent toughness, heat resistance, and surface quality at a low cost. SHED cells cultured on PC-ABS dishes and micro bone structures showed strong proliferation and osteogenic potential. Culture inserts made of PC-ABS also supported the growth of HaCaT keratinocytes and the hGFs formed gingival tissue, which was superior to that formed on commercially available PET inserts. In conclusion, PC-ABS is a promising 3D printing material for dental applications due to its biocompatibility, ability to promote osteogenesis, and support for gingival tissue formation, with no observed cytotoxicity.
Bio-Based Interpolyelectrolyte Complexes for the Stabilization of Pickering-like Emulsions
Authors: Guerrero-Vasquez Francisco Joel , Ortega Francisco , Rubio Ramón G. , Guzmán Eduardo
Published: January 2025

This work studies the stabilization of Pickering-like emulsions using dispersions of interpolyelectrolyte complexes (IPECs) formed by chitosan (CS) and sodium alginate (ALG), two polymers from natural resources, as the aqueous phase and soybean oil as the oil phase. The ability of these bio-based IPECs to form stable emulsions was evaluated by varying the compositional ratio of CS to ALG (Z-ratio) and the oil volume fraction (ϕo). Turbidity, zeta potential, and dynamic light scattering measurements revealed the dependence of IPEC properties on the Z-ratio, with phase separation observed near stoichiometric ratios. Phase diagram analysis showed that stable oil-in-water (O/W) and water-in-oil (W/O) emulsions could be obtained under certain combinations of the Z-ratio and ϕo. Emulsion stability increased with higher Z-ratios due to increased interfacial activity of the complexes and reduced coalescence. Emulsions with high ϕo exhibited transitions from discrete droplets to bicontinuous interfacially jammed emulsion gels (bijels), suggesting tunable morphologies. These results highlight the potential of CHI-ALG IPECs as eco-friendly and efficient stabilizers of Pickering-like emulsions for applications in food, cosmetics and pharmaceuticals.
Exosomes in Dermatological Research: Unveiling Their Multifaceted Role in Cellular Communication, Healing, and Disease Modulation
Authors: Pinto Daniela , Giuliani Giammaria , Mondadori Giorgia , Rinaldi Fabio
Published: January 2025

Exosomes, a subtype of extracellular vehicles (EVs), play a pivotal role in cellular communication and have gained considerable attention in dermatological research. Formed through the inward budding of the endosomal membrane, exosomes facilitate the transfer of proteins, lipids, and nucleic acids, including microRNAs (miRNAs), thereby influencing the behavior and function of recipient cells. These vesicles are secreted by various cell types, including keratinocytes, and are crucial for maintaining skin homeostasis, regulating immune responses, and promoting wound healing. Exosomes have demonstrated therapeutic potential in addressing dermatological conditions such as hair disorders, skin cancers and photoaging through enhanced regeneration and reduced oxidative stress. However, they are also implicated in disease progression, with pathogens utilizing exosome release to evade host immune responses. Recent studies highlight the diverse origins and functions of exosomes, suggesting their promise as innovative therapeutic agents in dermatology. As research continues to elucidate their multifaceted roles, exosomes represent a frontier in understanding intercellular communication and developing novel treatments for skin-related diseases, underscoring their potential impact on both health and clinical applications. This review synthesizes the existing literature on exosome biology and isolation with a focus on their implications in dermatological contexts.
Electronic Structure and Optical Properties of GaAs Doped with Rare-Earth Elements (Sc, Y, La, Ce, and Pr)
Authors: Deng Yongrong , Zhang Chunhong , Qin Xinmao , Yan Wanjun
Published: January 2025

Herein, the electronic structure and optical properties of GaAs doped with rare-earth elements (Sc, Y, La, Ce, and Pr) were evaluated using the first-principles method. Results showed that the lattice constants and cell volume of GaAs increased after doping. Band structure calculations indicated that the lowest conduction band and highest valence band were evident at the G-point, demonstrating that rare-earth-element doping did not alter the material type of GaAs, which remained a direct-bandgap semiconductor. The bandgap of Sc-doped GaAs increased, whereas those of Y-, La-, Ce-, and Pr-doped GaAs decreased. Moreover, the density of energy levels increased. Doping with Ce and Pr introduced impurity levels, and the Fermi level shifted into the conduction band. Investigation of the optical properties revealed that the static dielectric constant increased with La doping but decreased with Y, La, Ce, and Pr doping. The variation trends of the extinction coefficients of the doped samples were consistent with that of undoped GaAs: the extinction coefficient shifted to a low-energy region. In addition, a slight redshift occurred in the absorption spectrum. The absorption peak also diminished owing to rare-earth-element doping. We concluded that Ce- and Pr-doped GaAs can form metal alloys with different compositions. Such doping may provide a new class of materials for use in optoelectronic devices.
Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning
Authors: Zhang Wei-Jun , Chen Wei-Teing , Liu Chien-Hung , Chen Shiuan-Wen , Lai Yu-Hua , You Shingchern D.
Published: January 2025

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. This study aims to evaluate the feasibility of training a deep neural network for the segmentation and detection of metastatic brain tumors in MRI using a very small dataset of 33 cases, by leveraging large public datasets of primary tumors; Methods: This study explores various methods, including supervised learning, two transfer learning approaches, and self-supervised learning, utilizing U-net and Swin UNETR models; Results: The self-supervised learning approach utilizing the Swin UNETR model yielded the best performance. The Dice score for small brain tumors was approximately 0.19. Sensitivity reached 100%, while specificity was 54.5%. When excluding subjects with hyperintensities, the specificity improved to 80.0%; Conclusions: It is feasible to train a model using self-supervised learning and a small dataset for the segmentation and detection of small brain tumors.
Longitudinal Perching Trajectory Planning for a Fixed-Wing Unmanned Aerial Vehicle at High Angle of Attack Based on the Estimation of Region of Attraction
Authors: Li Rui , Chen Gong , Lu Yanhui , Qin Kaiyu , Zhou Teng , Wang Wenzheng
Published: January 2025

In this paper, a method for planning the perching trajectory of a fixed-wing unmanned aerial vehicle (UAV) based on the estimation of the region of attraction (ROA) is proposed, to expand the feasible domain of a UAV in the presence of aerodynamic performance degradation and landing-limited conditions with high angle of attack (AOA). According to the aerodynamic characteristics of the system, the perching process is first decomposed into flight and landing segments, and the corresponding flight dynamic model and structural dynamic model are established, based on the Lagrange function, while the continuity of the two models is proved. Then, the structural dynamic model is analyzed for asymptotic stability based on the ROA estimation results from the Lyapunov function. On this basis, a fixed-wing UAV perching trajectory planning strategy is proposed. This strategy enables the UAV to achieve stable perching with a reasonable flight trajectory, as it fully considers the flight dynamic constraints of the UAV and the structural dynamic constraints of the landing gear. Our simulation results show that flight trajectory planning considering the ROA can significantly increase the number of available trajectories for fixed-wing UAVs during high AOA perching, which also greatly enhances its flexibility in trajectory selection.
Quantum Entanglement Asymmetry and the Cosmic Matter–Antimatter Imbalance: A Theoretical and Observational Analysis
Authors: Neukart Florian
Published: January 2025

We propose a distinct mechanism to explain the matter–antimatter imbalance observed in the universe, rooted in quantum entanglement asymmetry (QEA). Our concept of QEA differs from its usage in the recent literature, where it typically measures how much symmetry is broken within a subsystem of a larger quantum system. Here, we define QEA as an intrinsic asymmetry in the entanglement properties of particle–antiparticle pairs in the early universe, leading to a preferential survival of matter over antimatter. We develop a theoretical framework incorporating QEA into the standard cosmological model, providing clear justification for the asymmetry in entangled states and corresponding modifications to the Hamiltonian. Numerical simulations using lattice Quantum Chromodynamics (QCD) demonstrate that QEA can produce a net baryon asymmetry consistent with observations. We also predict specific signatures in Cosmic Microwave Background (CMB) anisotropies and large-scale structure formation, offering potential avenues for empirical verification. This work aims to deepen the understanding of cosmological asymmetries and highlight the significance of quantum entanglement in the universe’s evolution.
Understanding Imbalanced Transmission from R&D Inputs into Innovation Outputs and Impacts: Evidence from Kazakhstan
Authors: Slavova Stefka , Rubalcaba Luis , Franco-Riquelme José Nicanor
Published: January 2025

Innovation ecosystems use R&D inputs to generate innovation outputs first and innovation impacts later. But some countries show a relatively low transmission, such as in the case of Kazakhstan, the largest economy in Central Asia. This article analyzes the transmission from R&D into innovation outputs and impacts through a framework for which different factors matter, such as the company size, education and skills, competition, exports, and foreign ownership. Transmission is conceptually understood in two steps: from R&D into innovation outputs, and from innovation output into innovation impacts. The main hypothesis is that the high endowments of these company factors should lead to the better transmission of results and improved performance in terms of outputs and impacts. We test this using new evidence from Kazakhstan and the ECA region (Europe and Central as defined by the World Bank) as benchmarking, and data are from the Global Innovation Index (descriptive section) and the World Bank Enterprise Surveys (analytical section). The econometrics are a Crépon–Duguet–Mairesse (CDM) model in three steps: factors for propensity to invest in R&D, then to innovate, and, finally, innovation impacts on productivity. Results confirm the positive roles of factors, such as exports and education, in positive transmissions and uneven or insignificant results on productivity impacts from characteristics, such as age, size, and foreign ownership. The specifics for Kazakhstan suggest a potential for business innovation growth in the country. The paper concludes by suggesting key policy measures to unlock the potential for business innovation at a country level.
DGA-Based Fault Diagnosis Using Self-Organizing Neural Networks with Incremental Learning
Authors: Liu Siqi , Xie Zhiyuan , Hu Zhengwei
Published: January 2025

Power transformers are vital components of electrical power systems, ensuring reliable and efficient energy transfer between high-voltage transmission and low-voltage distribution networks. However, they are prone to various faults, such as insulation breakdowns, winding deformations, partial discharges, and short circuits, which can disrupt electrical service, incur significant economic losses, and pose safety risks. Traditional fault diagnosis methods, including visual inspection, dissolved gas analysis (DGA), and thermal imaging, face challenges such as subjectivity, intermittent data collection, and reliance on expert interpretation. To address these limitations, this paper proposes a novel distributed approach for multi-fault diagnosis of power transformers based on a self-organizing neural network combined with data augmentation and incremental learning techniques. The proposed framework addresses critical challenges, including data quality issues, computational complexity, and the need for real-time adaptability. Data cleaning and preprocessing techniques improve the reliability of input data, while data augmentation generates synthetic samples to mitigate data imbalance and enhance the recognition of rare fault patterns. A two-stage classification model integrates unsupervised and supervised learning, with k-means clustering applied in the first stage for initial fault categorization, followed by a self-organizing neural network in the second stage for refined fault diagnosis. The self-organizing neural network dynamically suppresses inactive nodes and optimizes its training parameter set, reducing computational complexity without sacrificing accuracy. Additionally, incremental learning enables the model to continuously adapt to new fault scenarios without modifying its architecture, ensuring real-time performance and adaptability across diverse operational conditions. Experimental validation demonstrates the effectiveness of the proposed method in achieving accurate, efficient, and adaptive fault diagnosis for power transformers, outperforming traditional and conventional machine learning approaches. This work provides a robust framework for integrating advanced machine learning techniques into power system monitoring, paving the way for automated, real-time, and reliable transformer fault diagnosis systems.
Design and Performance Analysis of a Novel Group of Translational Parallel Robots for a Three-Axis Grinding Machine
Authors: Zou Qi , Shi Yuancheng , Zhang Shuo , Zhang Haiqiang , Li Lijian , Huang Guanyu , Zhang Dan
Published: January 2025

There are limited parallel robots applicable to three-axis grinding machines due to the restricted reachable workspace originating from multiple kinematic chains with spatial kinematic joints. The parallel robot will gain significant potential in the industry if a larger workspace can be achieved. This research introduces a special relationship between upper triangular matrix and parallel robot structures for the purpose of designing a group of novel parallel robots without spatial kinematic joints. The detailed inverse kinematic solution of the selected parallel manipulator is derived in accordance with its straightforward architecture. Several singularity configurations are found on the basis of the first-order kinematic relation. The translational reachable workspace is close to a triangular prism. The dexterity and stiffness performances based on the Jacobian matrix are explored for the chosen parallel manipulator. Both indices display a downward trend as the mobile platform rises higher.
Progress of Deep Exploration Artificial-Source Electromagnetic Instruments in China: A Review
Authors: Zhang Yangyong , Ye Xuan , Chen Lei , Zhou Yue , Qu Daiming
Published: January 2025

As a type of widely used geophysical exploration equipment, artificial-source electromagnetic instruments are effectively used for resource and energy exploration due to their simple operation, deep-detecting depth, and higher resolution in the field, compared to natural-source electromagnetic instruments. However, after years of development, the current work is in a significant period. Due to the exploration requirement gradually shifting from shallow-surface to deep, traditional electromagnetic instruments are facing severe challenges. Over the course of the elaborate study by geophysicists in China, electromagnetic instruments have made innovative breakthroughs during recent years. The application in the deep exploration of artificial-source electromagnetic instruments has been further extended. By taking the time–frequency electromagnetic instrument, the low-temperature transient electromagnetic instrument, the surface electromagnetic prospecting instrument, and the wide-field electromagnetic instrument as examples, the purpose of this paper is to give a systematic introduction to the technical indicators and field applications. Then, an overview of the situation and progress of the instrument is given to help the readers understand the research directions. Finally, we offer the developing trends of the deep exploration artificial-source electromagnetic instrument.
CO2 Concentration Control in Cleanrooms Using an Improved Crested Porcupine Algorithm
Authors: Cai Tongtong , Wang Wei , Yang Yongjie
Published: January 2025

Cleanrooms are widely used in various industries, where the precise control of parameters such as CO2 concentration is crucial for optimal production. Most cleanrooms utilize variable air volume (VAV) air conditioning systems, but existing proportional–integral–derivative (PID) controllers in VAV systems often suffer from long response delays, excessive overshoot, and difficulties in handling dynamic changes in occupant conditions. This study introduces an Improved Crested Porcupine Optimizer (ICPO) to optimize PID controller parameters, aiming to enhance the control of VAV air supply. Additionally, a CO2 concentration control system for cleanrooms was designed based on an STM32 microcontroller. The results demonstrate that the Improved Crested Porcupine Optimizer PID (ICPO-PID) controller outperforms traditional PID, Fuzzy-PID, and Crested Porcupine Optimizer PID (CPO-PID) controllers in control accuracy, response speed, and robustness. In simulation, ICPO-PID achieves a settling time of just 59 s and an overshoot of only 5.14%. In real-world performance evaluations, ICPO-PID outperforms other controllers in terms of the Integral Absolute Error (IAE) and Integral Squared Error (ISE). Furthermore, ICPO-PID reduces energy consumption by approximately 40% during air volume adjustment compared to traditional PID. These results indicate that ICPO-PID is an efficient and reliable solution for maintaining cleanroom environments with precise CO2 concentration control.
Rapidly Exploring Random Trees Reinforcement Learning (RRT-RL): A New Era in Training Sample Diversity
Authors: Péter István , Kővári Bálint , Bécsi Tamás
Published: January 2025

Sample efficiency is a crucial problem in Reinforcement Learning, especially when tackling environments with sparse reward signals that make convergence and learning cumbersome. In this work, a novel method is developed that combines Rapidly Exploring Random Trees with Reinforcement Learning to mitigate the inefficiency of the trial-and-error-based experience-gathering concept through the systematic exploration of the state space. The combined approach eliminates the redundancy in irrelevant training samples. Consequently, the pivotal training signals, despite their sparsity, can be further exposed to support the learning process. Experiments are made on several OpenAI gym environments to demonstrate that the proposed method does not have any context-dependent components, and the results show that it can outperform the classic trial-and-error-based training approach.
Design and Performance Analysis of a Platform-Based Multi-Phase Interleaved Synchronous Buck Converter
Authors: Trape Mario A. , Hellany Ali , Rizk Jamal , Nagrial Mahmood
Published: January 2025

This paper proposes a design for a platform-based Multi-phase Interleaved Synchronous Buck Converter (MISBC). A custom platform was developed to compare the theoretical performance of a MISBC circuit simulated with Multisim to a prototype that was built at Western Sydney University. The work disclosed in this manuscript describes some steps adopted during the selection of each component and technical considerations taken during the design of the Printed Circuit Board (PCB). The platform designed has a maximum power output of 260 Watts, with a buck reduction of the nominal voltage from 97 Volts to 24 Volts at a maximum switching frequency of 50 kHz. This switching frequency is achieved with an open-loop circuit configuration coupled with synchronized signal generators, used to validate the dead band required between the activation of each set of transistors implemented in a half-bridge configuration. A summary of the results based on the duty cycle required to achieve the buck voltage desired highlights the advantages of each operating mode of the MISBC circuit. Here the theoretical performance is compared against the data acquired during functional evaluations of the prototype, making possible future interpretations of the ideal control algorithm required to maximize the performance output of MISBC circuits.
Do Stands Self-Thin Through a Common Point? An Additional Concept for the Self-Thinning Rule
Authors: West P. W. , Ratkowsky D. A.
Published: January 2025

Once the steady and ongoing mortality of individuals in a plant population has started, the widely accepted ‘self-thinning rule’ holds; this maintains that the set of progressive average sizes of the plants and their residual stocking densities bear a constant relationship to each other. This work aimed to quantify the self-thinning processes of monospecific Eucalyptus obliqua L’Hér. forest using data from each of a set of plots in Tasmania, Australia. Unexpectedly, it was found that, with or without extrapolation, the self-thinning lines of all the plots tended to intersect near a common plant average size and stocking density. This finding was extended using previously published data for monospecific forests of seven other tree species spread widely across the world, five hardwood and two softwood. All five showed the same phenomenon. Whilst this adds a new concept to the existing self-thinning rule, no immediate explanation could be offered as to why it occurs. It was concluded that it will require studying the various mechanisms that determine tree growth behavior, the competitive interactions between individuals for the resources each requires from the site (light, water, and mineral nutrients), and how these lead to the mortality of smaller, less competitively successful individuals.
Fractal Evolution Characteristics of Pore Structure in Coal-Acidified Stimulation
Authors: Zhou Dan , Lv Zhiqiang , Cao Yunxing , Liu Gaofeng , Zhang Xinsheng , Shi Bin , Zhang Junsheng , Liu Shimin
Published: January 2025

The pore structure and connectivity of coal are the primary factors influencing the permeability of coal reservoirs. However, clay and carbonate minerals are commonly found filling the pores and fractures within coal. To address the impact of these minerals on fracturing effectiveness, acidic fracturing technology has been introduced. This technique has proven to be an effective measure for enhancing the extraction rate of low-permeability coal seams with high mineral content. In this study, coal samples were treated with a 3% HCl solution, and the changes in the pore structure of the coal before and after acidification were analyzed through low-temperature nitrogen adsorption and X-ray diffraction (XRD) testing. The results were as follows: After acidification, the specific surface area, total pore volume, pore volume in different stages, and average pore size of the coal samples all significantly increased. Specifically, the BET specific surface area grew by an average of 4.8 times and the total pore volume expanded by an average of 7.7 times, with the pore volumes in the pore size ranges of <10 nm and 10–60 nm increasing by an average of 10.1 times and 7.7 times. The smoothness of the pore surface and connectivity of the pore structure in the coal samples improved, as indicated by decreased fractal dimensions D1 (reflecting pore surface roughness) and D2 (representing pore size distribution uniformity). The acidification mechanism was mainly attributed to the dissolution of carbonate minerals in the coal, which led to the removal of obstructive minerals such as ankerite and calcite that had accumulated in the coal pores. This resulted in the formation of new micropores and microfractures, achieving pore volume enhancement and pore expansion.