bluuebunny/crossref_metadata_2025 huggingface dataset:
Differences in Formation of Prepuce and Urethral Groove During Penile Development Between Guinea Pigs and Mice Are Controlled by Differential Expression of Shh, Fgf10, and Fgfr2
Authors: Wang Shanshan , Zheng Zhengui
Published: February 2025

The penile tubular urethra forms by canalization of the urethral plate without forming an obvious urethral groove in mice, while the urethral epithelium forms a fully open urethral groove before urethra closure through the distal-opening-proximal-closing process in humans and guinea pigs. Our knowledge of the mechanism of penile development is mainly based on studies in mice. To reveal how the fully opened urethral groove forms in humans and guinea pigs, we compared the expression patterns and levels of key developmental genes using in situ hybridization and quantitative PCR during glans and preputial development between guinea pigs and mice. Our results revealed that, compared with mouse preputial development, which started before sexual differentiation, preputial development in guinea pigs was delayed and initiated at the same time that sexual differentiation began. Fgf10 was mainly expressed in the urethral epithelium in developing genital tubercle (GT) of guinea pigs. The relative expression of Shh, Fgf8, Fgf10, Fgfr2, and Hoxd13 was reduced more than 4-fold in the GT of guinea pigs compared to that of mice. Hedgehog and Fgf inhibitors induced urethral groove formation and restrained preputial development in cultured mouse GT, while Shh and Fgf10 proteins induced preputial development in cultured guinea pig GT. Our discovery suggests that the differential expression of Shh and Fgf10/Fgfr2 may be the main reason a fully opened urethral groove forms in guinea pigs, and it may be similar in humans as well.
Shorter Infantile Amnesia in Females: Important Implications for the Next Generation
Authors: Yang Yuheng , Sakimoto Yuya , Goshima Makoto , Mitsushima Dai
Published: February 2025

The sex-specific development of hippocampal learning in juveniles remains unclear. Using an inhibitory avoidance task, we assessed contextual learning in both sexes of juvenile rats. While sex hormone levels and activating effects are low in juveniles, females showed superior performance to males, suggesting that females have a shorter period of infantile amnesia than males. It was already known that when infants are cared for by mothers with high parenting behavior, they are likely to become high parenting mothers themselves. In addition, neonatal testosterone is known to masculinize the brain, causing behavioral, neural, and hormonal sex differences. Here, we reviewed the purposeful significance of sex-specific development for learning, along with the interaction of developmental changes in the hormonal environment.
Interleukin 24 Promotes Mitochondrial Dysfunction, Glucose Regulation, and Apoptosis by Inactivating Glycogen Synthase Kinase 3 Beta in Human Prostate Cancer Cells
Authors: Kim Anastassiya , Lopez Sual , Smith Simira , Sony Alphons , Abreu Jennifer , de la Parra Columba , Sauane Moira
Published: February 2025

Interleukin 24 (IL-24) is a tumor-suppressing protein currently in clinical trials. We previously demonstrated that IL-24 leads to apoptosis in cancer cells through protein kinase A (PKA) activation in human breast cancer cells. To better understand the mechanism by which IL-24 induces apoptosis, we analyzed the role of glycogen synthase kinase-3 beta (GSK3β), a highly conserved serine/threonine kinase in cancer cells and a downstream target of PKA. Our studies show for the first time that GSK3β is inhibited following IL-24 treatment in human prostate cancer cells. We showed that the inhibition of GSK3β is mediated through PKA activation triggered by IL-24. IL-24 decreases the phosphorylation of glycogen synthase, substantially activating glycogen synthase and decreasing intracellular glucose levels. Notably, the expression of a constitutively active form of GSK3β abolishes the effect of IL-24. These results demonstrate a previously unrecognized role of IL-24 in apoptosis mediated through GSK3β regulation and its possible implications for metabolic stress, mitochondria dysfunction, and apoptosis. Future studies should precisely delineate the most effective combinations of IL-24 as a GSK3β inhibitor with cytotoxic agents for prostate and other cancers. GSK3β inhibition disrupts average glucose utilization in cancer cells, potentially creating metabolic stress that could be exploited therapeutically.
The Evolutionary Young Actin Nucleator Cobl Is Important for Proper Amelogenesis
Authors: Janitzek Hannes , González Delgado Jule , Haag Natja , Seemann Eric , Nietzsche Sandor , Sigusch Bernd , Qualmann Britta , Kessels Michael Manfred
Published: February 2025

The actin cytoskeleton plays an important role in morphological changes of ameloblasts during the formation of enamel, which is indispensable for teeth to withstand wear, fracture and caries progression. This study reveals that the actin nucleator Cobl is expressed in ameloblasts of mandibular molars during amelogenesis. Cobl expression was particularly pronounced during the secretory phase of the enamel-forming cells. Cobl colocalized with actin filaments at the cell cortex. Importantly, our analyses show an influence of Cobl on both ameloblast morphology and cytoskeletal organization as well as on enamel composition. At P0, Cobl knock-out causes an increased height of ameloblasts and an increased F-actin content at the apical membrane. During the maturation phase, the F-actin density at the apical membrane was instead significantly reduced when compared to WT mice. At the same time, Cobl-deficient mice showed an increased carbon content of the enamel and an increased enamel surface of mandibular molars. These findings demonstrate a decisive influence of the actin nucleator Cobl on the actin cytoskeleton and the morphology of ameloblasts during amelogenesis. Our work thus expands the understanding of the regulation of the actin cytoskeleton during amelogenesis and helps to further elucidate the complex processes of enamel formation during tooth development.
Pectin Extraction Process from Cocoa Pod Husk (Theobroma Cacao L.) and Characterization by Fourier Transform Infrared Spectroscopy
Authors: Santiago-Gómez Ismael , Carrera-Lanestosa Areli , González-Alejo Fanny Adabel , Guerra-Que Zenaida , García-Alamilla Ricardo , Rivera-Armenta José Luis , García-Alamilla Pedro
Published: February 2025

The Cocoa Pod Husk (CPH) accounts for 67–76% of the total cocoa fruit weight, making it its main agro-industrial waste of cocoa production. A valorization of this waste is possible through the extraction of pectin. In this study, pectin was extracted from CPH powder by acid hydrolysis using citric acid and sulfuric acid. Fourier transform infrared spectroscopy (FT-IR) was employed as a qualitative and quantitative characterization technique. The FT-IR of the pectin samples showed the bands visible at 1732 and 1626 cm−1 corresponding to the esterified and free carboxylic groups, respectively. These bands can be differentiated according to their degree of methyl esterification (DE) by analyzing the area under the curve. The extracted pectin showed no significant difference in yields (p ≤ 0.05) between the two acids; however, significant differences (p ≤ 0.05) were observed in DE and methoxylation percentage (MeO). According to the FT-IR results, pectin extracted with citric acid presented a lower DE (7.43%) and MeO (1.12%) compared to pectin extracted with sulfuric acid, which showed a DE of 18.15% and a 2.96% MeO. Pectin with a DE below 50% is classified as low-methylated, making it unsuitable for the food industry. However, these create a raw material that has a potential use in the pharmaceutical and bioenergy industries.
Correction: Sánchez-Tena et al. Effectiveness of a Spectacle Lens with a Specific Asymmetric Myopic Peripheral Defocus: 12-Month Results in a Spanish Population. Children 2024, 11, 177
Authors: Sánchez-Tena Miguel Ángel , Cleva Jose Miguel , Villa-Collar Cesar , Álvarez Marta , Ruiz-Pomeda Alicia , Martinez-Perez Clara , Andreu-Vazquez Cristina , Chamorro Eva , Alvarez-Peregrina Cristina
Published: February 2025

There was an error in the original publication for the abstract [...]
Predictive Value of Maternal HbA1c Levels for Fetal Hypertrophic Cardiomyopathy in Pregestational Diabetic Pregnancies
Authors: Chimenea Angel , Calderón Ana María , Antiñolo Guillermo , Moreno-Reina Eduardo , García-Díaz Lutgardo
Published: February 2025

(1) Background: This study investigated the utility of first-visit HbA1c levels as a predictor of fetal hypertrophic cardiomyopathy (FHCM) in women with pregestational diabetes mellitus (PGDM). (2) Methods: A retrospective observational cohort study was conducted among all pregnant women with PGDM between 2012 and 2019. (3) Results: Of 329 participants, 5.8% had fetuses diagnosed with FHCM. These women exhibited significantly higher pregestational HbA1c (8.2% vs. 7.3%, p = 0.003) and higher first-visit HbA1c (7.6% vs. 6.9%, p = 0.001) and were less likely to have planned their pregnancies (p = 0.035). Fetuses with FHCM demonstrated a higher incidence of macrosomia (63.2% vs. 17.7%, p < 0.001; OR 9.20, 95% CI 3.31–25.58). Receiver-operating characteristic (ROC) analysis indicated an adequate predictive capacity for FHCM using first-visit HbA1c (AUC 0.75). An HbA1c threshold of 7.15% provided the best discriminative power, encompassing 38.9% of the cohort. (4) Conclusions: These findings underscore the value of assessing first-visit HbA1c levels for predicting FHCM in women with PGDM. The significant association between glycemic status and FHCM highlights the importance of optimizing glycemic control before and during pregnancy. Establishing optimal HbA1c cutoffs enables effective risk stratification and supports targeted clinical interventions.
Exploring the Link Between Special Educational Needs and Mental Health of Schoolchildren and Their Parents
Authors: Núñez Juan Manuel , Pérez-Marín Marián , Soto-Rubio Ana
Published: February 2025

Background/Objectives: The relationship between executive functions, special educational needs (SEN), and psychopathology in school-aged children is critical to the design of effective educational and therapeutic interventions. This study examines the connection between executive functions, SEN, schoolchildren’s psychopathology, and parental mental health. The objectives were to describe the psychopathological profiles of schoolchildren, to analyze the psychopathological differences between students with and without SEN, and to know the mental health status of parents of children with SEN. Methods: A total of 123 schoolchildren with and without SEN, together with their parents, participated in the study. Validated instruments were used to assess executive functions and child psychopathology, and an ad hoc register was used to assess parental mental health. In addition, sociodemographic and clinical data were collected. Results: The results revealed that children with SEN showed a significantly more affected psychopathological profile compared to their peers without SEN in all areas assessed. In addition, parents of children with SEN reported higher levels of emotional overburden (r = 0.39, p < 0.01). Deficits in executive functions, such as inhibition (r = 0.41, p < 0.01), working memory (r = 0.37, p < 0.01), and cognitive flexibility (r = 0.33, p < 0.05), were also found to be related to greater psychopathological problems in children. Conclusions: These findings underscore the importance of considering both executive functions and psychopathological profiles in designing educational and therapeutic interventions for children with SEN. It is recommended that intervention programs should comprehensively address the educational and emotional needs of children, as well as the well-being of their parents, with a specific focus on improving executive functions and reducing psychopathological disorders.
Linoleic Fatty Acid from Rwandan Propolis: A Potential Antimicrobial Agent Against Cutibacterium acnes
Authors: Rouvier Florent , Abou Lydia , Wafo Emmanuel , Brunel Jean Michel
Published: February 2025

Acne is a very common skin condition that causes pimples in 80% of adolescents despite the many effective treatments developed. Various compounds have been employed in the treatment of acne, including erythromycin ointments and antiseptics, yielding mixed results. The rise in erythromycin-resistant C. acnes strains has driven the pursuit of new antimicrobial agents, especially those obtained from natural sources. Propolis that was collected in Rwanda was extracted, fractioned, and analyzed for its activity against C. acnes growth in accordance with NCLSI guidelines. Our work revealed that linoleic acid has a significant effect on C. acnes growth at a low concentration (16 µg/mL). A comparison of the antimicrobial activities of a broad panel of well-known fatty acids revealed a specific mode of action for linoleic acid, characterized by a significant membranotropic effect on Bacillus cereus established by measuring extracellular ATP levels as an indicator of membrane permeability. Our data suggest that linoleic acid is effective against C. acnes and could be a promising candidate for developing a propolis-based ointment for acne treatment.
Integrating Land Use/Land Cover and Climate Change Projections to Assess Future Hydrological Responses: A CMIP6-Based Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia
Authors: Lukas Paulos , Melesse Assefa M. , Kenea Tadesse Tujuba
Published: February 2025

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin.
Isolated Rem Sleep Behavior Disorder: A Model to Assess the Overnight Habituation of Emotional Reactivity
Authors: Leitner Caterina , Greco Viviana , Casoni Francesca , Lewis Penelope A. , Ferini-Strambi Luigi , Galbiati Andrea
Published: February 2025

(1) Background: Phasic events in rapid eye movement (REM) sleep are a core feature of isolated REM behavior disorder (iRBD), which is often associated with emotion dysregulation. This study explores the relationship between sleep and the overnight habituation of emotional reactivity in healthy controls (HCs) and iRBD patients, focusing on the role of REM phasic events and a specific non-REM waveform, namely sleep spindles. (2) Methods: Participants underwent polysomnography and completed arousal rating tasks and mood scales before and after sleep. In total, eight HCs (4 M, mean age 60.62 ± 6.8) and eight iRBD patients (7 M, mean age 68.25 ± 5.12) were included in the analyses. (3) Results: In HCs, longer REM sleep duration correlated positively with overnight habituation. In the whole sample, overnight habituation negatively correlated with REM sleep latency and wake-after-sleep onset, and positively with N2 sleep. Higher overnight habituation was associated with fewer REM arousals and awakenings in the whole sample, and with greater N2 sleep spindle density in HCs. (4) Conclusions: Our preliminary results suggest that REM sleep and spindles in N2 play critical roles in emotional processing. The study confirms the relationship between emotion dysregulation and REM phasic events, enhancing our understanding of how sleep impacts emotional reactivity and also in the prodromal phase of neurodegenerative disease.
Synthesis and Characterization of ONO Pincer Ligand Precursors and Metal Complexes with Ethyl, Isopropyl and Tert-Butyl Wingtip Groups
Authors: Miecznikowski John R. , Nicaise Olivier J. C. , Mercado Brandon Q. , Araujo Abigail J. , Bertolotti Natalia R. , Erickson Samantha L. , Trucchio Joseph P. , Corbett Michael J. , Padover Connor J. , Coulombe Stephanie L. , Wheeler Audrey J. , Ouellette Isaac P.
Published: February 2025

We have synthesized novel cobalt(II) and nickel(II) pincer ligand complexes containing novel tridentate ligand precursors that coordinate via oxygen, nitrogen, and oxygen donor atoms. The novel tridentate ONO ligands, which are neutral, incorporate a carbonyl-substituted imidazole functionality and contain R groups of ethyl, isopropyl, or tert-butyl. The ligand precursors were thoroughly characterized using NMR spectroscopy, ESI-MS, and IR spectroscopy. The metal complexes were thoroughly characterized using single crystal X-ray diffraction, elemental analysis, ESI-MS, and cyclic voltammetry. The nickel(II) and cobalt(II) complexes with ethyl, isopropyl, and t-butyl wingtip groups had a pseudo-octahedral geometry about the metal center. The nickel(II) complex with R = isopropyl had a monoclinic lattice with C121 space group (a = 21.7639(8); b = 11.0649(5); c = 10.9225(4); alpha = 90.0 degrees; beta = 90.609(3) degrees; gamma = 90.0 degrees). The cobalt(II) complex with R = ethyl had a monoclinic lattice with P21/n space group (a = 17.7907(7); b = 21.5278(6); c = 21.8597(7); alpha = 90.0 degrees; beta = 95.063(3) degrees; gamma = 90.0 degrees). The cobalt(II) complexes were paramagnetic with μeff = 1.59 BM (R = ethyl) and 6.67 BM (R = t-butyl). The nickel(II) complex was paramagnetic with μeff = 2.59 BM. The ligand precursors and metal complexes are redox-active.
The Mn-Doped UiO-66 Nano-Catalysts for Low-Temperature CO-SCR
Authors: Ma Ying , Li Ren , Gao Lingyu , He Hanbing
Published: February 2025

Metal–organic frameworks (MOFs) are demonstrated to exhibit high catalytic activity for their unique porous structure and dispersed active centers. In this paper, a Mn-doped UiO-66 catalyst was successfully synthesized using a one-step solvothermal method and was applied for the first time to the low-temperature selective catalytic reduction of NO by CO (CO-SCR). The addition of Mn increased the average particle size and specific surface area of UiO-66. Compared with pure UiO-66, the NO conversion rate of Mn1/6-UiO-66 in a sulfur-free environment increased from 89% to 98% at 150 °C due to the higher number of active sites and the synergistic catalytic effect of the doped metals. In a sulfur-containing environment, the NO conversion rate at 250 °C remained above 96% in 8 h.
PLD Growth of Ferrimagnetic Tm3Fe5O12 Thin Film with Perpendicular Magnetic Anisotropy on GGG
Authors: Li Zezhong , Wang Xin , Xiao Yinan , Zou Yuxiao , Wang Donghui , Yang Huaiwen , Zhang Hui , Li Yunliang , Liu Ying
Published: February 2025

Thulium Iron Garnet (TIG), as an emerging hotspot in rare-earth iron garnet systems, possesses a large magnetostriction constant (λ111) and a low damping coefficient. Therefore, it is possible to induce perpendicular magnetic anisotropy (PMA) through stress, which makes it more desirable for interfacial magnetic proximity or spin–orbit torque effects than Yttrium Iron Garnet (YIG). For achieving a high-quality TIG thin film and regulating its properties accordingly, understanding the effect of growth parameters on the film properties is essential. Using the Pulsed Laser Deposition (PLD) technique, we prepared TIG film on a Gadolinium Gallium Garnet (GGG) substrate. The correlations of its structural properties to the growth conditions are systematically studied, including the oxygen pressure and laser energy. With the annealing, a ferrimagnetic TIG thin film with PMA is successfully obtained. Our work provides a platform for achieving high-quality TIG thin films by experimentally regulating the growth factors.
Review of Molten Salt Corrosion in Stainless Steels and Superalloys
Authors: Wei Ying , La Peiqing , Zheng Yuehong , Zhan Faqi , Yu Haicun , Yang Penghui , Zhu Min , Bai Zemin , Gao Yunteng
Published: February 2025

In the context of the global energy structure transformation, concentrated solar power (CSP) technology has gained significant attention. Its future trajectory is oriented towards the construction of ultra-high temperature (700–1000 °C) power plants, aiming to enhance thermoelectric conversion efficiency and economic competitiveness. Chloride molten salts, serving as a crucial heat transfer and storage medium in the third-generation CSP system, offer numerous advantages. However, they are highly corrosive to metal materials. This paper provides a comprehensive review of the corrosion behaviors of stainless steels and high-temperature alloys in molten salts. It analyzes the impacts of factors such as temperature and oxygen, and it summarizes various corrosion types, including intergranular corrosion and hot corrosion, along with their underlying mechanisms. Simultaneously, it presents an overview of the types, characteristics, impurity effects, and purification methods of molten salts used for high-temperature heat storage and heat transfer. Moreover, it explores novel technologies such as alternative molten salts, solid particles, gases, liquid metals, and the carbon dioxide Brayton cycle, as well as research directions for improving material performance, like the application of nanoparticles and surface coatings. At present, the corrosion of metal materials in high-temperature molten salts poses a significant bottleneck in the development of CSP. Future research should prioritize the development of commercial alloy materials resistant to chloride molten salt corrosion and conduct in-depth investigations into related influencing factors. This will provide essential support for the advancement of CSP technology.
Drug Repurposing for the Discovery of Potential Inhibitors Targeting DJ-1 (PARK7) Against Parkinson’s Disease
Authors: Aldakhil Taibah , Altharawi Ali
Published: February 2025

Parkinson’s disease (PD) is a neurodegenerative disease characterized by increased movement dysfunction and cognitive loss. DJ-1 (PARK7) is an antioxidant that protects cells from oxidative stress, a major contributor to cellular damage and neurodegeneration in PD. Mutations in the DJ-1 gene reduce its neuroprotective ability contributing to PD onset and progression. The neuroprotective and antioxidant properties of DJ-1 make it a viable therapeutic target for developing novel PD therapeutics. A drug repurposing approach was applied to identify promising inhibitors for DJ-1. Three drugs—droxicam, pteroylglutamic acid, and niraparib—were identified based on their binding affinities and interactions. Further molecular dynamics simulations revealed that niraparib and pteroylglutamic acid were the most stable among the three complexes. Moreover, the binding strength of the complexes was confirmed by MMPBSA binding free energy analysis, with Niraparib (−13.50 kcal/mol) and pteroylglutamic Acid (−11.41 kcal/mol) as the most promising candidates. These results suggest that pteroylglutamic acid and niraparib may serve as useful DJ-1 inhibitors for PD-associated protein DJ-1. Further experimental validation and in vivo assessments are required to confirm the efficacy and safety of these drugs against PD.
Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model
Authors: Rafiq Adnan , Jaffar Arfan , Latif Ghazanfar , Masood Sohail , Abdelhamid Sherif E.
Published: February 2025

Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification of breast cancer from the histology photographs is very important for the diagnosis and effective treatment planning. Methods: In this article, we propose a DenseNet121-based deep learning model for breast cancer detection and multi-class classification. The experiments were performed using whole-slide histopathology images collected from the BreakHis dataset. Results: The proposed method attained state-of-the-art performance with a 98.50% accuracy and an AUC of 0.98 for the binary classification. In multi-class classification, it obtained competitive results with 92.50% accuracy and an AUC of 0.94. Conclusions: The proposed model outperforms state-of-the-art methods in distinguishing between benign and malignant tumors as well as in classifying specific malignancy subtypes. This study highlights the potential of deep learning in breast cancer diagnosis and establishes the foundation for developing advanced diagnostic tools.
A Composite Barrier Function Sliding Mode Control Method Based on an Extended State Observer for the Path Tracking of Unmanned Articulated Vehicles
Authors: Zhang Kanghua , Gu Xiaochao , Wang Nan , Cao Jialu , Wang Jixin , Zhang Shaokai , Li Xiang
Published: February 2025

Unmanned articulated vehicles play a crucial role in the intelligent mine system and have been extensively investigated and implemented in the fields of mine transportation, agriculture and forestry construction. However, the working environment of articulated wheeled vehicles is harsh and the working conditions are changeable. These conditions are often accompanied by load changes, road interference excitation caused by an unstructured environment and the dynamic nonlinear characteristics of articulated wheeled vehicles. The current research on path tracking control methods suitable for traditional wheeled vehicles does not meet the intelligent operation requirements of articulated wheeled vehicles, and it is necessary to combine the specific working environment and its own specific structural model characteristics. In this paper, a composite barrier function sliding mode control method based on an extended state observer is proposed to solve the problem of modeling uncertainty and unknown external disturbance in the path tracking control of unmanned articulated vehicles. Firstly, the mathematical model of the articulated wheeled working vehicle is built to derive the expected heading angle in the prediction horizon. Then, the strong nonlinear lumped disturbance in articulated dynamics is dynamically estimated by combining the composite nonlinear extended state observer. Afterward, based on the error compensation theory, a composite barrier function sliding mode controller suitable for articulated vehicle path tracking is derived. Finally, through simulation analysis and experimental verification, this method can estimate the strong nonlinear lumped disturbance caused by the structural characteristics of the articulated vehicle, and then compensate for the disturbance of the control quantity to achieve stable, robust and accurate path tracking control.
AlphaRouter: Bridging the Gap Between Reinforcement Learning and Optimization for Vehicle Routing with Monte Carlo Tree Searches
Authors: Kim Won-Jun , Jeong Junho , Kim Taeyeong , Lee Kichun
Published: February 2025

Deep reinforcement learning (DRL) as a routing problem solver has shown promising results in recent studies. However, an inherent gap exists between computationally driven DRL and optimization-based heuristics. While a DRL algorithm for a certain problem is able to solve several similar problem instances, traditional optimization algorithms focus on optimizing solutions to one specific problem instance. In this paper, we propose an approach, AlphaRouter, which solves routing problems while bridging the gap between reinforcement learning and optimization. Fitting to routing problems, our approach first proposes attention-enabled policy and value networks consisting of a policy network that produces a probability distribution over all possible nodes and a value network that produces the expected distance from any given state. We modify a Monte Carlo tree search (MCTS) for the routing problems, selectively combining it with the routing problems. Our experiments demonstrate that the combined approach is promising and yields better solutions compared to original reinforcement learning (RL) approaches without MCTS, with good performance comparable to classical heuristics.
Slot Allocation Protocol for UAV Swarm Ad Hoc Networks: A Distributed Coalition Formation Game Approach
Authors: Song Liubin , Guo Daoxing
Published: February 2025

With the rapid development of unmanned aerial vehicle (UAV) manufacturing technology, large-scale UAV swarm ad hoc networks are becoming widely used in military and civilian spheres. UAV swarms equipped with ad hoc networks and satellite networks are being developed for 6G heterogeneous networks, especially in offshore and remote areas. A key operational aspect in large-scale UAV swarm networks is slot allocation for large capacity and a low probability of conflict. Traditional methods typically form coalitions among UAVs that are in close spatial proximity to reduce internal network interference, thereby achieving greater throughput. However, significant internal interference still persists. Given that UAV networks are required to transmit a substantial amount of safety-related control information, any packet loss due to internal interference can easily pose potential risks. In this paper, we propose a distributed time coalition formation game algorithm that ensures the absence of internal interference and collisions while sharing time slot resources, thereby enhancing the network’s throughput performance. Instead of forming a coalition from UAVs within a contiguous block area as used in prior studies, UAV nodes with no interference from each other form a coalition that can be called a time coalition. UAVs belonging to one coalition share their transmitting slots with each other, and thus, every UAV node achieves the whole transmitting slots of coalition members. They can transmit data packets simultaneously with no interference. In addition, a distributed coalition formation game-based TDMA (DCFG-TDMA) protocol based on the distributed time coalition formation algorithm is designed for UAV swarm ad hoc networks. Our simulation results verify that the proposed algorithm can significantly improve the UAV throughput compared with that of the conventional TDMA protocol.
Digital Footprints of Academic Success: An Empirical Analysis of Moodle Logs and Traditional Factors for Student Performance
Authors: Shafiq Dalia , Marjani Mohsen , Habeeb Riyaz , Asirvatham David
Published: February 2025

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs.
A Review of Neutral-Point Voltage Balancing and Common-Mode Voltage Suppression Methods in Three-Level Converters
Authors: Tuluhong Ayiguzhali , Song Tengfei , Chang Qingpu , Xu Zhisen
Published: February 2025

With the continuous proliferation and development of renewable energy technologies, three-level converters have gained widespread application due to their lower output harmonics and higher output voltage. However, in practical applications, multilevel converters inevitably encounter issues such as common-mode voltage (CMV) and neutral-point voltage (NPV) imbalance, which significantly affect system stability and operational lifespan. This paper investigates the root causes of neutral-point voltage fluctuations and common-mode voltage generation. It provides an analysis and summary of solutions for CMV suppression and NPV balancing, focusing on space vector modulation (SVM) strategies, carrier-based modulation techniques, modulation waveform approaches, and intelligent control methods. Furthermore, the paper explores future development trends, aiming to provide valuable insights for subsequent theoretical research and practical applications.
A Millimeter-Wave CMOS Down-Conversion Mixer with Transformer-Based Harmonic Suppression
Authors: Yoo In-Cheol , Byeon Chul-Woo
Published: February 2025

In this paper, we present a millimeter-wave CMOS down-conversion mixer designed for 5G cellular communications. The proposed mixer integrates a local oscillator buffer, an RF transconductance (Gm) stage, and a switching stage. A transformer-based harmonic suppression technique and separate RF Gm stage and switching stage are employed to achieve a low noise figure (NF), high conversion gain (CG), and effective harmonic suppression. Intermodulation and gain characteristics are analyzed, demonstrating enhanced harmonic suppression, high gain, and low NF. Implemented in 65 nm CMOS technology, the proposed mixer occupies a core chip area of 0.51 mm2 and consumes a dc power of 7 mW. The implemented design achieves a CG of 6.4 dB, an NF of 6.1 dB, and an output third-order intercept point of 9.0 dBm at an RF frequency of 38.2 GHz. Additionally, harmonic suppression exceeds −26 dBc, highlighting the performance advantages of the proposed architecture.
An Approach to Control Multilevel Flying-Capacitor Converters Using Optimal Dynamic Programming Benchmark
Authors: Tebaldi Davide
Published: February 2025

The problem of balancing capacitor voltages is of utmost importance in multilevel converter topologies involving flying capacitors. In this study, a new minimum angular distance (MAD) algorithm is proposed to control the turning on and off of the switches, ensuring fast convergence of the capacitor voltages balancing problem in multilevel flying-capacitor converters. This algorithm was developed based on a preliminary analytical analysis of the capacitor voltage trajectories using the power-oriented model of the converter. Compared to other approaches, the proposed algorithm involves only simple and well-defined calculations, requires no training, and does not require any prediction of future values that the output current assumes. The proposed algorithm, implemented in the MATLAB/Simulink environment, is proven to give very good performance, verified against an optimal benchmark given by dynamic programming, in terms of capacitor voltages convergence time, efficiency, power loss, and total harmonic distortion.
Fetal Hypoxia Classification from Cardiotocography Signals Using Instantaneous Frequency and Common Spatial Pattern
Authors: Alqahtani Rawad A. , Alotibi Gaseb N. , Alotaiby Turky N.
Published: February 2025

Fetal hypoxia is a condition that is caused by insufficient oxygen supply to the fetus and poses serious risks, including abnormalities, birth defects, and potential mortality. Cardiotocography (CTG) monitoring is commonly used to identify fetal distress, including hypoxia, by categorizing cases as normal or hypoxia. However, traditional CTG interpretation, usually performed visually by experts, can be subjective and error-prone, resulting in observer variability and inconsistent outcomes. It highlights the need for an automated and objective diagnostic system to assist clinicians in interpreting CTG data more accurately and consistently. In this research, a fetal hypoxia diagnosis system is proposed based on CTG signals. The CTG dataset is first transformed into the time-frequency domain using instantaneous frequency and using common spatial pattern (CSP) for feature extraction. Finally, the extracted features are then used to train and evaluate four machine learning models for classification with a cross-validation 5-fold methodology. Objective criteria (pH values, BDecf, Apgar 1, and Apgar 5) and expert voting as a subjective criterion were used to classify the fetus as normal or hypoxia. The SVM model outperformed other models in detecting fetal hypoxia, achieving high accuracy across pH, BDecf, Apgar1, Apgar5, and expert voting in all steps. It achieved over 98% accuracy across all objective criteria and steps.
Optimizing Highway Electric Vehicle Scheduling and Battery Swapping Station Management for Enhanced Renewable Energy Utilization
Authors: Wang Dawei , Xu Hongke , Guo Jingwei , Dai Liang , Zhang Lina
Published: February 2025

The separation of battery charging and swapping processes enables highway operators to more flexibly manage the recharging of depleted batteries at battery swapping stations (BSSs), while also leveraging renewable energy (RE) resources to lower electricity costs. However, previous studies have not addressed the connection between BSS recharging strategies and the demand for fully charged batteries, which is determined by EV swapping schemes. To address this gap, the paper proposes a joint optimization approach for scheduling EVs and managing BSSs, considering RE generation along the highway. Specifically, a spatial-temporal network model is developed to represent the transportation-energy characteristics of EVs, incorporating BSS selection and battery swapping processes. Additionally, a BSS management model is formulated to plan the recharging of depleted batteries and ensure the availability of fully charged batteries to meet the swapping demands of EVs. The Lagrange relaxation algorithm is employed to handle the interaction between EV scheduling and BSS operations. A case study shows that this method effectively coordinates EV swapping schemes with BSS recharging strategies, resulting in a 17.3% improvement in RE generation utilization and a reduction in grid electricity consumption by RMB 1753.8.
A Quad-Core Dual-Mode Colpitts Voltage-Controlled Oscillator with Octave Tuning Range and Low Phase Noise
Authors: Qi Shihao , Xu Shang , Deng Ruxin , Wu Guoan , Zhan Lamin
Published: February 2025

In this paper, a novel Colpitts voltage-controlled oscillator (VCO) with low phase noise and an octave frequency tuning range is presented. To achieve low phase noise and a wide tuning range concurrently, the designed VCO employs quad-core-coupled structures, series resonators, dual-mode-coupled inductors, and push–push structures. The quad-core-coupled structures are used for phase noise improvement. The presented series resonators effectively expand the tuning range while reducing phase noise deterioration from amplitude-to-phase modulation (AM/PM) conversion. The dual-mode operation based on coupled inductors and quad-core structures further expands the tuning range. In addition, the adopted push–push structure increases the output frequency. Designed in a 180 nm SiGe BiCMOS process, the proposed Colpitts VCO operates from 7.2 to 14.5 GHz with an octave tuning range of 67.3%. The phase noise ranges from −131.4 to −121.8 dBc/Hz with a peak figure-of-merit (FoM) of 183.0 dBc/Hz and figure-of-merit-tuning (FoMT) of 199.5 dBc/Hz at a 1 MHz offset. The proposed VCO exhibits superior performance in phase noise and tuning range and achieves an octave tuning range for the first time in Colpitts VCOs.
A Discontinuous Entity Recognition Model Based on Global Feature Interaction Mechanism
Authors: Yu Hongli , Cui Yachao , Cao Han , Wang Haihang
Published: February 2025

So far, discontinuous Named Entity Recognition (NER) has become a focus of attention for many researchers and has spawned numerous methodologies. Among them, the grid tagging model has particularly stood out due to its flexibility and adaptability in discontinuous NER tasks. The core idea of the model is to convert text into a two-dimensional grid format and capture entity segments as well as their relationships within the text by tagging elements in the grid. However, while grid tagging models typically emphasize learning tag embeddings to represent entity segment information and relationships between word pairs within the same contextual space, they often neglect the extraction of global features. We believe that designing two distinct feature generators to capture two different types of information, namely entity segment tag features and relationship tag features, during the learning process, is beneficial for improving the model’s performance. In this work, we propose novel tag feature generators, specifically designing two different generators–a relationship tag feature generator and an entity segment tag feature generator. These generators are designed to assist each other in more effectively generating feature information during the representation learning process. Experimental results demonstrate that our model achieves significant performance improvements on multiple discontinuous NER datasets, exhibiting higher accuracy and efficiency compared to other state-of-the-art (SOTA) methods. Furthermore, we conducted detailed analyses and discussions on the different components of the model, verifying the effectiveness of each component and its contribution to the overall model performance.
SkinSavvy2: Augmented Skin Lesion Diagnosis and Personalized Medical Consultation System
Authors: Kim Hyungjoon , Kim Yunju , Song Wonho
Published: February 2025

The shortage of medical personnel and the busy lives of modern people have increased the desire for the self-diagnosis of diseases, and the latest large-scale language models and image recognition technologies have the potential to meet this demand. In particular, skin-related diseases are one of the areas where symptoms are visually distinguishable, making self-diagnosis and care possible. In this paper, we propose a system that classifies diseases through images of skin diseases and combines them with individual conditions such as age, skin type, and gender for self-diagnosis. First, we design the latest deep learning model-based skin disease classifier that can classify six types of skin diseases using the HAM10000 dataset and generate prompts by combining the personal information input. By utilizing the Generative Pre-trained Transformer (GPT) model, the system generates personalized care methods based on these prompts. We measured the accuracy of the classification model of the proposed system and validated the effectiveness of the proposed method through user evaluations.
Enhanced Receptive Field and Multi-Branch Feature Extraction in YOLO for Bridge Surface Defect Detection
Authors: Zhu Wenyuan , Yang Tao , Zhang Ruexue
Published: February 2025

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for bridge inspections and play a crucial role in detecting defects. Nevertheless, accurately identifying defects at various scales in complex contexts remains a significant challenge. To address this issue, we propose RDS-YOLO, an advanced algorithm based on YOLOv8n, designed to enhance small-scale defect detection through the integration of shallow, high-resolution features. The introduction of the RFW (Receptive Field Weighting) module dynamically expands the receptive field and balances multi-scale detection accuracy. Additionally, the DSF-Bottneck (Dilated Separable Fusion) module further optimizes feature extraction, emphasizing the representation of small defects against complex backgrounds. The SA-Head (Shuffle Attentio) module, with shared parameters, precisely localizes defect zones while reducing computational costs. Furthermore, the EigenCAM technique improves the interpretability of the model’s output, offering valuable insights for maintenance and monitoring tasks. The experimental results demonstrate that RDS-YOLO outperforms YOLOv8n, achieving a 3.7% increase in average detection precision and a 6.7% improvement in small defect detection accuracy.
Evaluation of Operational Parameters in a Pulse Combustor for Industrial Applications
Authors: Marin Raul Payri , Falla Hoyos Sergio , Soriano Elena Silvestre , Lekuona Aritz
Published: February 2025

The scientific community is showing growing interest in pulsed combustion technology due to its superior efficiency, environmental advantages, and diverse applications. Nevertheless, despite its promise, this technology remains underexplored because of its intricate nature and the numerous physicochemical mechanisms involved. This article introduces a pulse combustor, analyzing various power levels as a fundamental component. Each adjustment to operating parameters is shown to profoundly influence overall system behavior. This study advances pulse combustion technologies by addressing challenges, uncovering insights, and shaping future approaches to combustor design, with special attention to the critical role of resonance in the process. Key findings highlight the relationship between pressure fluctuations and temperature distributions, providing valuable insights for optimizing thermal management and enhancing combustor performance. Notably, the study reveals that the average pressure in the combustion chamber peaks at equivalence ratios close to the lower combustion limit (ϕ = 0.5). These results point to the potential of advanced sensor systems and control mechanisms to dynamically optimize combustor operations, boosting efficiency and reliability in industrial contexts. The innovative strategies presented not only enhance fuel efficiency but also lay the groundwork for more adaptable combustor designs, meeting the critical demand for precise control in challenging environments.