bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Pediatric Bipolar Disorder Through the Investigation of Clinical, Neuroanatomic, Neurophysiological and Neurocognitive Dimensions: A Pilot Study
Authors: Simonetti Alessio , Bernardi Evelina , Kurian Sherin , Restaino Antonio , Calderoni Claudia , De Chiara Emanuela , Bardi Francesca , Sani Gabriele , Soares Jair C. , Saxena Kirti
Published: February 2025

Background: Pathophysiological models of pediatric bipolar disorder (PBD) are lacking. Multimodal approaches may provide a comprehensive description of the complex relationship between the brain and behavior. Aim: To assess behavioral, neuropsychological, neurophysiological, and neuroanatomical alterations in youth with PBD. Methods: Subjects with PBD (n = 23) and healthy controls (HCs, n = 23) underwent (a) clinical assessments encompassing the severity of psychiatric symptoms, (b) neuropsychological evaluation, (c) analyses of event-related potentials (related to the passive viewing of fearful, neutral, and happy faces during electroencephalography recording, and (d) cortical thickness and deep gray matter volume measurement using magnetic resonance imaging. Canonical correlation analyses were used to assess the relationships between these dimensions. Results: Youth with PBD had higher levels of anxiety (p < 0.001) and borderline personality features (p < 0.001), greater commission errors for negative stimuli (p = 0.003), delayed deliberation time (p < 0.001), and smaller risk adjustment scores (p = 0.002) than HCs. Furthermore, they showed cortical thinning in the frontal, parietal, and occipital areas (all p < 0.001) and greater P300 for happy faces (p = 0.29). In youth with PBD, cortical thickening and P300 amplitude positively correlated with more commission errors for negative stimuli, longer deliberation times, reduced risk adjustment, higher levels of panic and separation anxiety, and greater levels of negative relationships, whereas they negatively correlated with levels of depression (overall loadings > or <0.3). Limitations: Small sample size, cross-sectional design, and limited variables investigated. Conclusions: This preliminary work showed that multimodal assessment might be a viable tool for providing a pathophysiological model that unifies brain and behavioral alterations in youth with PBD.
The Impact of Trauma and Substance Use on Emotion Regulation and Intimate Partner Violence Perpetration: Implications for Perpetrator Programs
Authors: Expósito-Álvarez Cristina , Roldán-Pardo Manuel , Vargas Viviana , Maeda Mina , Lila Marisol
Published: February 2025

(1) Background: Alcohol and/or other drug use problems (ADUPs) and trauma are key risk factors for intimate partner violence (IPV) that should be addressed in perpetrator programs. Participants with ADUPs and trauma histories may display greater difficulties in emotion regulation, which may increase the likelihood of IPV recidivism. The study aimed to examine differences among participants with trauma, ADUPs, ADUPs and trauma, and without such factors in dropout, IPV, and variables related to emotion regulation at pre- and post-intervention; (2) Methods: A sample of 312 men court-mandated to attend a perpetrator program (Contexto Program) was used. Variables related to emotion regulation difficulties included alexithymia, depressive symptomatology, and clinical syndromes. IPV variables included self-reported physical and psychological IPV and IPV recidivism risk assessed by facilitators. Comparisons between groups were made using one-way ANOVA, chi-square tests, and two-way repeated measures ANOVAs; (3) Results: Participants with ADUPs and trauma presented greater difficulties on variables related to emotion regulation, higher risk of IPV at pre-intervention, and higher dropout rates. At post-intervention, all participants improved their emotion regulation skills and reduced IPV recidivism risk, with participants with ADUPs and trauma maintaining a higher risk of IPV; (4) Conclusions: IPV perpetrators with ADUPs and trauma are high-risk participants. Interventions should target trauma and ADUPs to improve their effectiveness.
A Multidimensional Exploration Based on Hofstede’s Cultural Theory: An Empirical Study on Chinese Audience Acceptance of American Animated Films
Authors: Yu Tao , Yang Wei , Wu Ronghui , Xu Junping , Yang Jianhua
Published: February 2025

In the context of globalization, cross-cultural research is essential for understanding behaviors and values across different cultural backgrounds. The way audiences from diverse cultures interpret and accept film content significantly impacts the international dissemination and market performance of films. This study, grounded in Hofstede’s six cultural dimensions—power distance index (PDI), individualism vs. collectivism (IDV), uncertainty avoidance (UAI), masculinity vs. femininity (MAS), long-term vs. short-term orientation (LTO), and indulgence vs. restraint (IVR)—incorporates additional variables such as visual aesthetic appeal (VAA), narrative complexity (NCI), viewing motivation (VM), behavioral intentions (BIs), and brand loyalty (BL) to construct a multidimensional research framework. This framework aims to comprehensively examine the acceptance of American animated films among Chinese audiences and the cultural differences influencing such acceptance. Using structural equation modeling (SEM), this study analyzed the interrelationships between variables based on a sample of 507 participants with prior viewing experience. The findings reveal that different cultural dimensions significantly impact VM. PDI, UAI, and IDV exert significant negative influences on VM, with PDI being the most influential. Conversely, LTO and IVR do not demonstrate significant negative effects. In contrast, MAS, VAA, and NCI exhibit significant positive impacts on VM. Additionally, VM strongly influences audience acceptance, which, in turn, promotes the formation of BIs and repeated VM. This study extends the application of Hofstede’s cultural dimensions to the domain of cross-cultural media consumption, enriching the theoretical framework with additional dimensions and offering a novel perspective for cross-cultural research. Furthermore, the study uncovers the intricate interactions between cultural context and film content, proposing strategies to enhance the acceptance of cross-cultural films. These findings not only provide valuable insights for the production and marketing of animated films but also offer strategic guidance for filmmakers in diverse markets.
The Passive Control of Nonlinear Dynamic Response Using the Force Analogy Method in a Chimney Located in a Wind- and Seismic-Prone Region in Mexico
Authors: Hernández Barrios Hugo , Patlán Manjarrez Carlos Mauricio , Gómez Martínez Roberto
Published: February 2025

This study investigates the control of seismic and wind-induced responses in reinforced concrete chimneys, incorporating material nonlinearity and soil–structure interaction. Using the Force Analogy Method (FAM), an innovative approach for nonlinear dynamic analysis, the performance of tuned mass dampers (TMDs) is evaluated under diverse loading scenarios, including seismic sequences with mainshock–aftershock events and wind-induced loads, due to synoptic and hurricane winds, modeled as stochastic processes. The FAM approach enables computationally efficient analysis, effectively capturing the complex interactions between seismic and wind actions across varying soil foundation conditions. The results demonstrate the effectiveness of TMDs in reducing structural responses across various loading and foundation scenarios, emphasizing their efficiency in enhancing the safety and resilience of slender structures. This research provides valuable insights into the application of vibration control strategies for safeguarding critical infrastructure against extreme environmental loads.
Direct Load Control Strategy of Centralized Chiller Plants for Emergency Demand Response: A Field Experiment
Authors: Zhu Jie , Tian Zhe , Niu Jide , Lu Yakai , Zhou Haizhu , Li Yitong
Published: February 2025

As the penetration rate of renewable energy in the power grid increases, the imbalance between power supply and demand has become one of the key issues. Buildings and their heating, ventilation, and air conditioning (HVAC) systems are considered excellent flexible demand response (DR) resources that can reduce peak loads to alleviate operational pressures on the power grid. Centralized chiller plants are regarded as flexible resources with large capacity and rapid adjustability. The direct load control of chiller plants can respond to the power grid within minutes, making them highly suitable for participation in emergency DR. However, existing studies are generally based on simulations and lack experimental research in actual large-scale buildings to demonstrate the effectiveness of this method and provide related lessons learned. This study conducted field experiments on a centralized chiller plant within an industrial building in Guangdong, China. The results indicate that the strategy of shutting down chiller plants is an effective DR measure. It can complete the load reduction process within 15 min, rapidly decreasing the system power by 380~459 kW, with a maximum duration of up to 50 min, without significantly affecting the thermal comfort of indoor occupants. Additionally, the impact of existing control logic on the participation of chiller plants in the DR process is also discussed.
Numerical Simulation on an Ultra-Large Seven-Ring Internal Support System Considering the Effects of Soil–Structure Interaction and Temperature
Authors: Hu Hexiang , Tian Yu , Zheng Neimeng , Du Xiuli , Guo Haishan , Xu Zhonghua
Published: February 2025

The foundation pit area of Kunming International Comprehensive Transportation Hub is 56,800 m2, and the excavation depth ranges from 18 m to 25 m. Because the surrounding environment is very complex, the foundation pit is supported by an underground continuous wall and three layers of internal support system with seven rings. The force of this internal support system is coupled integrally, and the number of rings is the highest in the world at present. In this work, a finite element model considering the interaction between soil and the retaining structure is established. The Hardening Soil model with small strain stiffness is used to simulate and analyze the whole excavation process of the foundation pit. Considering the ultra-large plane size of the foundation pit, we cannot ignore the temperature effect, so the deformation of the underground continuous wall and the force of the internal support system under seasonal temperature variation are investigated. By comparing numerical simulation results with field measurements, the deformation of the ultra-large seven-ring internal support system, the deformation of the surrounding soil, and the axial force of the supports are analyzed. The results show that the finite element simulation agrees well with the measured data. This work provides a reliable method for analyzing ultra-large deep foundation pits.
Simulation-Based Optimization of Crane Lifting Position and Capacity Using a Construction Digital Twin for Prefabricated Bridge Deck Assembly
Authors: Jang Dae-Ho , Roh Gi-Tae , Jeon Chi-Ho , Shim Chang-Su
Published: February 2025

The growing adoption of off-site construction methods has increased the critical role of mobile cranes within the construction sector. This study develops a Construction Digital Twin (CDT) framework to optimize crane lifting positions and capacities for the installation of prefabricated bridge decks. By integrating 3D site modeling, Building Information Modeling (BIM), and crane simulations within the Unity game engine, the CDT overcomes the limitations of conventional 2D-based planning by providing a three-dimensional representation of site conditions. An exhaustive search method identifies optimal crane configurations, enhancing precision and efficiency. Simulation calibration using video analysis of real bridge deck installations aligns crane speed and cycle times with actual operations, improving reliability. Case studies demonstrate the CDT’s ability to reduce crane operation costs by 27% when employing a smaller capacity crane while maintaining operational efficiency. Additional DFA-focused simulations with varying deck dimensions revealed a potential 10% cost reduction by optimizing crane operations and deck design strategies. The CDT framework supports early-stage planning, reduces operational risks, and contributes to cost-effective and safer construction practices, offering a scalable solution adaptable to various construction scenarios.
Sulfur Dioxide-Tolerant Core@shell Ru@Pt Catalysts Toward Oxygen Electro-Reduction
Authors: Liu Yuxin , Bao Changyuan , Xu Guodong , Du Lei , Huang Bing
Published: February 2025

Proton exchange membrane fuel cells (PEMFCs) have achieved milestones in performance improvements and commercial launches. In the typical commercialized PEMFCs, the compressed air to cathode is usually supplied from ambient air, assuming that no costly pre-purification system is applied. Therefore, the working PEMFCs may suffer from the negative effects of the air impurities. In this regard, SO2, as the most poisonous species, may be fed along with air at the cathode and strongly adsorbed on the Pt surface, leading to Pt site deactivation. To address this challenge, we published a series of works in terms of poisoning mechanisms, regeneration protocols, and advanced poisoning-tolerant catalysts. Herein, we are aiming at developing a SO2-tolerant electrocatalyst toward a cathodic oxygen reduction reaction (ORR). We reasonably incorporate the Ru, synthesize Ru@Pt core@shell catalysts and investigate the relationships among Ru incorporation, ORR activity and SO2 tolerance. Impressively, the Ru@Pt/C exhibits higher initial ORR activity (0.288 A mg−1Pt), better SO2 poisoning resistance (33% loss in initial activity) than that of commercial Pt/C catalysts (0.252 A mg−1Pt; 62% loss). The engineered affinity between Pt and SO2 in the presence of Ru is uncovered to account for the improvement.
Enhanced Expression of TRIM46 in Ovarian Cancer Cells Induced by Tumor-Associated Macrophages Promotes Invasion via the Wnt/β-Catenin Pathway
Authors: Wang Yi-Yue , Choi Min-Jun , Kim Jin-Hyung , Choi Jung-Hye
Published: February 2025

Metastasis presents significant challenges in ovarian cancer treatment. Tumor-associated macrophages (TAMs) within the tumor microenvironment (TME) facilitate metastasis through epithelial-mesenchymal transition, yet the molecular underlying mechanisms are not fully understood. Here, we identified that tripartite motif-containing 46 (TRIM46) is significantly upregulated in ovarian cancer cells treated with a conditioned medium derived from macrophages stimulated by ovarian cancer cells (OC-MQs). Furthermore, TRIM46 was highly expressed in late-stage ovarian cancer patients and was associated with poor prognosis. Silencing of TRIM46 suppressed cancer cell invasion stimulated by OC-MQ and mesenchymal marker expression without affecting cell viability. Gene set enrichment analysis showed that the Wnt/β-catenin pathway is enriched in the high-TRIM46 expression group. Importantly, the inhibition of TRIM46-mediated β-catenin nuclear translocation and ovarian cancer cell invasion was reversed by CHIR99021, a Wnt/β-catenin activator. Additionally, C-X-C motif chemokine ligand 8 (CXCL8) was identified as being highly expressed in peritoneal MQs from the ascites of ovarian cancer patients and was positively correlated with C-X-C chemokine receptor 1/2 (CXCR1/2) expression in tumor cells. Notably, pre-treatment with reparixin, a CXCR1/2 inhibitor, blocked OC-MQ-induced TRIM46 expression and cell invasion. These results suggest that CXCL8 derived from TAMs promotes human ovarian cancer cell invasion via the Wnt/β-catenin pathway by upregulating TRIM46.
Comprehensive Search for Genes Involved in Thalidomide Teratogenicity Using Early Differentiation Models of Human Induced Pluripotent Stem Cells: Potential Applications in Reproductive and Developmental Toxicity Testing
Authors: Kato Yu , Inaba Takeshi , Shinke Koudai , Hiramatsu Noriko , Horie Tetsuhiro , Sakamoto Takuya , Hata Yuko , Sugihara Eiji , Takimoto Tetsuya , Nagai Noriaki , Ishigaki Yasuhito , Kojima Hajime , Nagano Osamu , Yamamoto Naoki , Saya Hideyuki
Published: February 2025

Developmental toxicity testing is essential to identify substances that may harm embryonic development. This study aimed to establish a protocol for evaluating developmental toxicity using human induced pluripotent stem cells (iPSCs) by analyzing cellular activity and gene expression changes. Two ICH S5(R3) positive substances, valproic acid (VPA), which is a substance previously detected as positive by other test methods, and thalidomide (Thalido), were examined during early trichoderm differentiation without fetal bovine serum. RNA-seq analysis identified seven candidate genes, including TP63, associated with altered expression following exposure to VPA or Thalido. These genes were implicated in pathways related to tissue development, cell growth, and molecular interactions. While the assay effectively detected VPA and Thalido, its limitations include testing only soluble substances and focusing on early differentiation stages. Nevertheless, the protocol demonstrates potential for the classification and evaluation of emerging modality drugs based on physical properties such as solubility, polarity, and pH. Integration with AI analysis may enhance its capacity to uncover genetic variations and evaluate previously uncharacterized substances. This study provides a foundation for alternative developmental toxicity testing methods, with further refinements in the culture method expected to improve accuracy and applicability in regulatory toxicology.
Protocol for the Determination of Total Iodine in Iodized Table Salts Using Ultra-High-Performance Liquid Chromatography
Authors: Jamilan Mohd Azerulazree , Abd Rashed Aswir , Md Noh Mohd Fairulnizal
Published: February 2025

Potassium iodate and potassium iodide are commonly fortified in iodized table salt, which must be continuously monitored to maintain quality. Our study reported an optimized detection method for total iodine in iodized table salt using 0.5 M sodium bisulfite as the reducing agent. The iodized table salt (0.5 g) was dissolved in 0.5 M sodium bisulfite solution prior to injection in ultra-high-performance liquid chromatography (UHPLC) coupled with a diode array detector using a weak anion-exchange column (2.1 mm × 150 mm, 5 μm). Iodide was eluted at 9.92 ± 0.06 min (λ = 223 nm) when an isocratic mobile phase of 1:1 (v/v) methanol/120 mM phosphate buffer mixed with tetrasodium pyrophosphate (pH 3.0) was running at 0.20 mL/min (15 min). Iodide was detected as total iodine from 10.0 to 50.0 mg/kg with a limit of detection (LOD) of 1.2 mg/kg and a limit of quantification (LOQ) of 3.7 mg/kg. The method was validated with relative standard deviations (RSDs) of 4.2%, 0.4%, 1.6%, and 0.8% for accuracy, repeatability, intermediate precision, and robustness, respectively. The determination of total iodine was successful on six (6) samples (n = 3), which recovered 87.2–106.9% of iodate and iodide spike. Thus, this study provides a validated protocol for the determination of total iodine in iodized table salt using 0.5 M sodium bisulfite.
Assessing Green Strategies for Urban Cooling in the Development of Nusantara Capital City, Indonesia
Authors: Pradana Radyan Putra , Bhanage Vinayak , Fajary Faiz Rohman , Hussainzada Wahidullah , Badriana Mochamad Riam , Lee Han Soo , Kubota Tetsu , Nimiya Hideyo , Putra I Dewa Gede Arya
Published: February 2025

The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and Forecasting model integrated with the urban canopy model (WRF-UCM). Numerical experiments at a 1 km spatial resolution were used to evaluate the impacts of green and mitigation strategies on the proposed master plan. In this process, five scenarios were analyzed, incorporating varying proportions of blue–green spaces and modifications to building walls and roof albedos. Among them, scenario 5, with 65% blue–green spaces, exhibited the highest cooling potential, reducing average urban surface temperatures by approximately 2 °C. In contrast, scenario 4, which allocated equal shares of built-up areas and mixed forests (50% each), achieved a more modest reduction of approximately 1 °C. The adoption of nature-based solutions and sustainable urban planning in Nusantara underscores the feasibility of climate-resilient urban development. This framework could inspire other cities worldwide, showcasing how urban growth can align with environmental sustainability.
Study of Grain Boundary: From Crystallization Engineering to Machine Learning
Authors: He Zhengran , Bi Sheng , Asare-Yeboah Kyeiwaa
Published: February 2025

Grain boundaries play a vital role in determining the structural, functional, mechanical, and electrical properties of semiconductor materials. Recent studies have yielded great advances in understanding and modulating the grain boundaries via semiconductor crystallization engineering and machine learning. In this article, we first provide a review of the miscellaneous methods and approaches that effectively control the nucleation formation, semiconductor crystallization, and grain boundary of organic semiconductors. Using the benchmark small molecular semiconductor 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS pentacene) as a representative example, the crystallization engineering methods include polymer additive mixing, solvent annealing, gas injection, and substrate temperature control. By studying the grain-width-dependent charge transport, we propose a grain boundary model as a fundamental basis to theoretically understand the intrinsic relation between grain boundary engineering and charge carrier mobility. Furthermore, we discuss the various machine learning algorithms and models used to analyze grain boundaries for the various important traits and properties, such as grain boundary crystallography, energy, mobility, and dislocation density. This work highlights the unique advantages of both crystallization engineering and machine learning methods, demonstrates new insights into discovering the presence of grain boundaries and understanding new properties of materials, and sheds light on the great potential of material application in various fields, such as organic electronics.
Research on Laser Cladding Inconel 625 Coating Process for TRT Blades
Authors: Yao Jian-Tao , Zhang Guo-Dong , Chen Ze-Lin , Qu Yu-Hu , Wang Li-Shuang , Dong Hui
Published: February 2025

In this study, Inconel 625 coatings were deposited onto the surface of 2Cr13 stainless steel via laser cladding technology to ensure their corrosion resistance and mechanical properties. The microstructure and characteristics of coatings were adjusted by varying laser power (1200, 1500, and 1800 W), scanning speed (10, 15, and 25 mm/s), and overlap rate (40%, 50%, and 70%). The results showed that the impact resistance of blades was improved by 23% to 30% compared to the substrate, whereas the self-corrosion current density was reduced by 94%–98%, which indicated the outstanding resistance of specimens to damage and corrosion. At the same time, the appropriate processing parameters enabled the surface hardness of the 2Cr13 substrate to be improved. This study provides practical technical guidance for the repair of 2Cr13 blades and a comprehensive enhancement of their corrosion resistance and mechanical properties through parameter optimization.
Commodities: The Year 2024 in Retrospect
Authors: Chevallier Julien
Published: February 2025

The year 2024 was marked by significant unpredictability and volatility in global commodity markets, characterized by notable price fluctuations, evolving policy frameworks, and unexpected disruptions [...]
A Novel Zeroing Neural Network for the Effective Solution of Supply Chain Inventory Balance Problems
Authors: Cao Xinwei , Li Penglei , Khan Ameer Tamoor
Published: February 2025

The issue of inventory balance in supply chain management represents a classic problem within the realms of management and logistics. It can be modeled using a mixture of equality and inequality constraints, encompassing specific considerations such as production, transportation, and inventory limitations. A Zeroing Neural Network (ZNN) model for time-varying linear equations and inequality systems is presented in this manuscript. In order to convert these systems into a mixed nonlinear framework, the method entails adding a non-negative slack variable. The ZNN model uses an exponential decay formula to obtain the desired solution and is built on the specification of an indefinite error function. The suggested ZNN model’s convergence is shown by the theoretical results. The results of the simulation confirm how well the ZNN handles inventory balance issues in limited circumstances.
A Frugal Approach Toward Modeling of Defects in Metal 3D Printing Through Statistical Methods in Finite Element Analysis
Authors: Raya Antonio Martínez , Braun Matías , Carrasco-Garrido Cristina , González-Albuixech Vicente F.
Published: February 2025

Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study.
The Effect of Different Laser Powers on the Properties of Ni65A Cladding Reinforced by WC
Authors: Huang Mengqiong , Yu Jincheng , Wang Jinyi , Xu Guilin , Jin Xin
Published: February 2025

In this paper, the effects of different laser powers on the microstructure, microhardness, and wear resistance of Ni65A/WC composite coatings were investigated by using laser cladding technology. The morphology, phase structure, elemental distribution, wear behaviour, and property changes of the fused coatings were systematically characterised and analysed. The mechanism of power parameters on coating properties was summarised. The results show that different laser powers significantly affect the microstructure of the coating and the distribution of the enhanced phase WC. Under the 800 W power condition, the WC particles were not sufficiently dissolved and the organisation was not dense. The hardness and abrasion resistance were low. Under 1200 W power conditions, the enhanced phases were uniformly dispersed. The best microstructure densities and homogeneity were observed. The generated hard phase and matrix toughness achieved a good balance. The hardness of the coating reached 375 HV while also showing optimum wear resistance and stable friction behaviour. Under 1600 W power conditions, although the hard phase was completely dissolved and re-precipitated, some areas of tissue coarsening made the wear resistance slightly inferior to that at 1200 W. The 2000 W power condition resulted in a significant deterioration in the coating properties due to the increase in cracks and pores caused by the overheating of the melt pool. For this reason, 1200 W power conditions proved to be the ideal parameter range for optimising the microstructure and mechanical properties of Ni65A/WC composite coatings. The study in this paper can provide an important reference for the design of high-performance wear-resistant coatings.
Woodland Birds from Neolithic Settlements in Bulgaria
Authors: Boev Zlatozar N.
Published: February 2025

In this study, a first attempt was made to present a general composition of birds (with an emphasis on forest birds) found in Neolithic settlements in Bulgaria. Based on data from 29 settlements, 82 bird taxa have been identified. Of these, 72 species have been identified to the species level. They constitute 17.3% (almost one-sixth) of the 417 recorded bird species in the country. About one-third (23 species) are inhabitants of forest ecosystems. Two species (2.7%) no longer nest in Bulgaria (Lyrurus tetrix and Phasianus colchicus), and another rare winter migrant (Buteo lagopus) has been found in two Neolithic settlements in Southern Bulgaria. Today, the two locations of Tetrao urogallus are far outside the limits of the species’ current shortened range. A very high number of the uncovered Neolithic birds (33 species, 45.8%) are included in the national Red Data Book of Bulgaria. Summer migrants significantly outnumber winter migrants.
The Critical Role of Penicillin in Syphilis Treatment and Emerging Resistance Challenges
Authors: Jaiswal Arun Kumar , Rodrigues Gomes Lucas Gabriel , Ferreira Maciel de Oliveira Aline , de Castro Soares Siomar , Azevedo Vasco
Published: February 2025

Syphilis, a global healthcare burden, is a sexually transmitted infection caused by the spirochete Treponema pallidum, a spiral-shaped, Gram-negative obligate human pathogen. Despite its easy identification and treatability, the disease affects over 50 million people worldwide, with 8 million new cases in the 15–49 age group annually, as per the WHO 2024 report. If left untreated, syphilis progresses through its primary, secondary, latent, and tertiary stages, causing severe complications like neurosyphilis, congenital syphilis, and organ damage. The first-line treatment, penicillin, faces challenges, including logistical issues, shortages, allergic reactions, and patient non-compliance. Secondary treatment options are sparse, and there are reported cases of T. pallidum strains resistant to those antibiotics. The absence of an effective vaccine for syphilis has led to efforts to control its spread through sexual education, condom usage, and post-exposure prophylaxis with doxycycline, which raises concerns about antimicrobial resistance (AMR). The continued reliance on penicillin and the increasing rates of doxycycline post-exposure prophylaxis (DoxyPEP) use have both contributed to concerns about AMR development. Recent works pointing to emerging antibiotic resistance and treatment failures highlight the urgent need for new antibiotics to manage syphilis effectively and reduce dependency on penicillin. This review has focused on the shortcomings and limitations of penicillin treatment, recently identified antimicrobial-resistant strains of T. pallidum, and case studies where its application failed to treat the disease adequately.
UAV Communication in Space–Air–Ground Integrated Networks (SAGINs): Technologies, Applications, and Challenges
Authors: Zhang Peiying , Chen Shengpeng , Zheng Xiangguo , Li Peiyan , Wang Guilong , Wang Ruixin , Wang Jian , Tan Lizhuang
Published: February 2025

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions.
Smart Glove: A Cost-Effective and Intuitive Interface for Advanced Drone Control
Authors: Randieri Cristian , Pollina Andrea , Puglisi Adriano , Napoli Christian
Published: February 2025

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse.
Optimization of Bandwidth Allocation and UAV Placement in Active RIS-Assisted UAV Communication Networks with Wireless Backhaul
Authors: Tran Thi-Thuy-Minh , Vu Binh-Minh , Shin Oh-Soon
Published: February 2025

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes.
Dynamics of the Interaction Between Two Coherent States in a Cavity with Finite Temperature Decay
Authors: Hernández-Sánchez Leonardi , Ramos-Prieto Irán , Soto-Eguibar Francisco , Moya-Cessa Héctor M.
Published: February 2025

In this study, we present an exact solution to the Lindblad master equation describing the interaction of two quantized electromagnetic fields in a decaying cavity coupled to a thermal reservoir at a finite temperature. The solution is obtained using the superoperator technique, leveraging commutation relations to factorize the exponential of the Lindblad superoperators into a product of exponentials. To demonstrate the applicability of this approach, we analyze the dynamics of the system both analytically and numerically for two initial conditions: nonentangled and entangled coherent states, exploring their temporal evolution. Additionally, we employ entropy and quantum discord analysis to characterize quantum correlations and analyze the behavior of entanglement (or lack thereof) during the evolution. This comprehensive analysis provides valuable insights into the behavior of open quantum systems and their interaction with the environment.
Machine Learning Predictors for Min-Entropy Estimation
Authors: Blanco-Romero Javier , Lorenzo Vicente , Almenares Mendoza Florina , Díaz-Sánchez Daniel
Published: February 2025

This study investigates the application of machine learning predictors for the estimation of min-entropy in random number generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number of target bits being predicted. Using data from generalized binary autoregressive models, a subset of Markov processes, we demonstrate that machine learning models (including a hybrid of convolutional and recurrent long short-term memory layers and the transformer-based GPT-2 model) outperform traditional NIST SP 800-90B predictors in certain scenarios. Our findings underscore the importance of considering the number of target bits in min-entropy assessment for RNGs and highlight the potential of machine learning approaches in enhancing entropy estimation techniques for improved cryptographic security.
Fiducial Inference in Linear Mixed-Effects Models
Authors: Yang Jie , Li Xinmin , Gao Hongwei , Zou Chenchen
Published: February 2025

We develop a novel framework for fiducial inference in linear mixed-effects (LME) models, with the standard deviation of random effects reformulated as coefficients. The exact fiducial density is derived as the equilibrium measure of a reversible Markov chain over the parameter space. The density is equivalent in form to a Bayesian LME with noninformative prior, while the underlying fiducial structure adds new benefits to unify the inference of random effects and all other parameters in a neat and simultaneous way. Our fiducial LME needs no additional tests or statistics for zero variance and is more suitable for small sample sizes. In simulation and empirical analysis, our confidence intervals (CIs) are comparable to those based on Bayesian and likelihood profiling methods. And our inference for the variance of random effects has competitive power with the likelihood ratio test.
PRG4CNN: A Probabilistic Model Checking-Driven Robustness Guarantee Framework for CNNs
Authors: Liu Yang , Fang Aohui
Published: February 2025

As an important kind of DNN (deep neural network), CNN (convolutional neural network) has made remarkable progress and been widely used in the vision and decision-making of autonomous robots. Nonetheless, in many scenarios, even a minor perturbation in input for CNNs may lead to serious errors, which means CNNs lack robustness. Formal verification is an effective method to guarantee the robustness of CNNs. Existing works predominantly concentrate on local robustness verification, which requires considerable time and space. Probabilistic robustness quantifies the robustness of CNNs, which is a practical mode of potential measurement. The state-of-the-art of probabilistic robustness verification is a test-driven approach, which is used to manually decide whether a DNN satisfies the probabilistic robustness and does not involve robustness repair. Robustness repair can improve the robustness of CNNs further. To address this issue, we propose a probabilistic model checking-driven robustness guarantee framework for CNNs, i.e., PRG4CNN. This is the first automated and complete framework for guaranteeing the probabilistic robustness of CNNs. It comprises four steps, as follows: (1) modeling a CNN as an MDP (Markov decision processes) by model learning, (2) specifying the probabilistic robustness of the CNN via the PCTL (Probabilistic Computational Tree Logic) formula, (3) verifying the probabilistic robustness with a probabilistic model checker, and (4) probabilistic robustness repair by counterexample-guided sensitivity analysis, if probabilistic robustness does not hold on the CNN. We here conduct experiments on various scales of CNNs trained on the handwriting dataset MNIST, and demonstrate the effectiveness of PRG4CNN.
Rapoport’s Rule, the Ecotone Concept, and Salinity Gradient Predict the Distribution of Benthic Foraminifera in a Southeastern Pacific Estuary
Authors: Fernández Leonardo D. , Marchant Margarita
Published: February 2025

This study explores the biogeographic processes shaping the distribution of benthic foraminifera along a salinity gradient in the Contaco Estuary, southeastern Pacific, Chile. The primary aim was to evaluate the applicability of key ecological paradigms—Rapoport’s rule, the mid-domain effect, ecotones, and source–sink dynamics—to unicellular eukaryotes in estuarine environments. A 1550 m longitudinal transect, sampled at 50 m intervals, revealed a pronounced salinity-driven pattern in species richness and diversity, with calcareous taxa dominating euhaline zones and agglutinated taxa thriving in brackish and freshwater areas. Source–sink dynamics were not supported, as beta diversity analyses identified turnover as the dominant driver, highlighting species replacement along the salinity gradient. Evidence of a longitudinal Rapoport effect was observed, with broader distribution ranges in low-salinity environments, reflecting adaptations to suboptimal conditions. Contrary to predictions, the mid-domain effect was not supported, as foraminiferal richness showed a monotonic decline. These findings extend macroecological principles to microbial communities, emphasizing deterministic processes in shaping estuarine diversity. This research provides a robust framework for understanding biodiversity patterns in dynamic ecosystems, offering valuable insights for conservation and ecological monitoring.
Designing Spiking Neural Network-Based Reinforcement Learning for 3D Robotic Arm Applications
Authors: Park Yuntae , Lee Jiwoon , Sim Donggyu , Cho Youngho , Park Cheolsoo
Published: February 2025

This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in 3D robotic manipulation, demonstrating its extension from traditional 2D tasks to complex 3D target-reaching scenarios with improved energy efficiency and stability. Additionally, with the inertial measurement unit data the system successfully mimics human arm movements, achieving a success rate of 0.95 among 50 trials and enabling an intuitive and accurate human–robot interaction system. This pioneering attempt highlights the feasibility of combining the biologically inspired spiking neural networks with the reinforcement learning algorithm to address the real-time challenges in high-dimensional robotic environments and advance the field of human–robot interaction systems.
Security, Privacy, Confidentiality, and Trust in the Blockchain: From Theory to Applications
Authors: Karpinski Mikolaj , Kuznetsov Oleksandr , Oliynykov Roman
Published: February 2025

From the financial and medical sectors to various supply chains, most industries have seen a sea change due to the blockchain [...]
Multithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equations
Authors: Silva Bruno , Lopes Luiz Guerreiro , Mendonça Fábio
Published: February 2025

This paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based parallel version of the PSO algorithm uses the inherent parallelism of modern hardware architectures. Its performance is compared against both sequential and multithreaded Central Processing Unit (CPU) implementations. The primary objective is to evaluate the efficiency and scalability of PSO across different hardware platforms with a focus on solving large-scale SNEs involving thousands of equations and variables. The GPU-parallelized and multithreaded versions of the algorithm were implemented in the Julia programming language. Performance analyses were conducted on an NVIDIA A100 GPU and an AMD EPYC 7643 CPU. The tests utilized a set of challenging, scalable SNEs with dimensions ranging from 1000 to 5000. Results demonstrate that the GPU accelerated modified PSO substantially outperforms its CPU counterparts, achieving substantial speedups and consistently surpassing the highly optimized multithreaded CPU implementation in terms of computation time and scalability as the problem size increases. Therefore, this work evaluates the trade-offs between different hardware platforms and underscores the potential of GPU-based parallelism for accelerating SNE solvers.
AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine
Authors: Eyselein Simon , Tismer Alexander , Raj Rohit , Rentschler Tobias , Riedelbauch Stefan
Published: February 2025

The growing number of Renewable Energy Sources has increased the demand for innovative and high-performing turbine designs. Due to the increase in computing resources over recent years, numerical optimization using Evolutionary Algorithms (EAs) has become established. Nevertheless, EAs require many expensive Computational Fluid Dynamics (CFD) simulations, and more computational resources are needed with an increasing number of design parameters. In this work, an adapted optimization algorithm is introduced. By employing an Artificial Intelligence (AI)-based design assistant, turbines with a similar flow field are clustered into groups and provide a dataset to train AI models. These AI models can predict the flow field’s clustering before a CFD simulation is performed. The turbine’s efficiency and cavitation volume are predicted by analyzing the turbine’s properties inside the predicted clustering group. Turbines with properties below a certain threshold are not CFD-simulated but estimated by the design assistant. By this procedure, currently, more than 30% of the CFD simulations are avoided, significantly reducing computational costs and accelerating the overall optimization workflow.