bluuebunny/crossref_metadata_2025 huggingface dataset:
Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data
Authors: Li Yao , Zhang Zhongyuan (Jasper) , Saarela Olli , Sharma Divya , Xu Wei
Published: February 2025

Complex biological features such as the human microbiome and gene expressions play a crucial role in human health by mediating various biomedical processes that influence disease progression, such as immune responses and metabolic processes. Understanding these mediation roles is essential for gaining insights into disease pathogenesis and improving treatment outcomes. However, analyzing such high-dimensional mediation features presents challenges due to their inherent structural and correlations, such as the hierarchical taxonomic structures in microbial operational taxonomic units (OTUs), gene–pathway relationships, and the high dimensionality of the datasets, which complicates mediation analysis. We propose the Med-CNN model, an iterative approach using Convolutional Neural Networks (CNNs) to incorporate the complex biological network of the mediation features. The output values from network-specific CNN models are condensed into an integrative mediation metric (IMM), which captures essential biological information for estimating mediation effects. Our approach is designed to handle high-dimensional data and accommodate their unique structures and non-linear interactive mediation effects. Through comprehensive simulation studies, we evaluated the performance of our algorithm across different scenarios, including various mediation effects, effect sizes, and sample sizes, and we compared it to conventional methods. Our simulations demonstrated consistently lower biases in mediation effect estimates, with values ranging from 0.17 to 0.56, which were lower than other established methods ranging from 0.24 to 13.27. In a real data application, our method identified a mediation effect of 0.06 between ethnicity and vaginal pH levels.
A Computational Approach to Predictive Modeling Using Connection-Based Topological Descriptors: Applications in Coumarin Anti-Cancer Drug Properties
Authors: Hayat Sakander , Wazzan Suha
Published: February 2025

Cheminformatics bridges chemistry, computer science, and information technology to predict chemical behaviors using quantitative structure–property relationships (QSPRs). This study advances QSPR modeling by introducing novel connection-based graphical invariants, specifically designed to enhance the predictive accuracy for physicochemical properties (PCPs) of benzenoid hydrocarbons (BHs). Employing cutting-edge computational methods, we evaluate these invariants against established descriptors in modeling the normal boiling point and standard heat of formation. The findings reveal superior predictive performance by newly proposed invariants, such as the sum-connectivity connection index, outperforming traditional indices like the Zagreb connection indices. Furthermore, we extend these methods to model the physicochemical properties of coumarin-related anti-cancer drugs, demonstrating their potential in drug development. The statistical analysis suggests that the most appropriate structure–property models are nonlinear. This work not only proposes robust tools for PCP estimation but also advocates for rigorous testing of descriptors to ensure relevance in cheminformatics.
Understanding Social Engineering Victimisation on Social Networking Sites: A Comprehensive Review of Factors Influencing User Susceptibility to Cyber-Attacks
Authors: Alshammari Saad S. , Soh Ben , Li Alice
Published: February 2025

The widespread adoption of social networking sites (SNSs) has brought social-engineering victimisation (SEV) to the forefront as a significant concern in recent years. Common examples of social-engineering attacks include phishing websites, fake user accounts, fraudulent messages, impersonation of close friends, and malicious links shared through comments or posts on SNS platforms. The increasing number of SNS users is closely linked to a rise in SEV incidents. Consequently, it is essential to explore relevant theories, frameworks, and contributing factors to better understand this phenomenon. This study systematises and analyses 47 scholarly works on SEV in SNSs, examining theories, frameworks, and influencing factors. A total of 90 independent variables were identified and grouped into seven perspectives: socio-demographics, personality traits, socio-emotional factors, habitual factors, perceptual/cognitive factors, message characteristics, and sender characteristics; these were considered alongside mediating variables. The correlations between these variables and victimisation outcomes were evaluated, uncovering factors that increase vulnerability and highlighting contradictory findings in existing studies. This systematised analysis emphasises the limitations in current research and identifies future research directions in order to deepen the understanding of the factors influencing SEV. By addressing these gaps, this study aims to advance mitigation strategies and provide actionable insights to reduce SEV in SNS contexts.
Hindrances to the Utilisation of the Metaverse for Net-Zero Buildings in South Africa
Authors: Oguntona Olusegun Aanuoluwapo , Akinradewo Opeoluwa Israel
Published: February 2025

Net-zero buildings (NZBs) are a key component of sustainable development in the architecture, engineering, and construction (AEC) industry, offering a path to mitigating environmental impacts. The Metaverse, as an emerging digital technology, has the potential to enhance NZB optimisation by facilitating design simulation, stakeholder collaboration, and real-time performance assessment. However, its integration into the AEC sector faces several obstacles. This study investigates the hindrances to Metaverse adoption for NZBs within South Africa’s AEC sector, a region striving to meet sustainability goals amid technological and infrastructural constraints. A quantitative research design was employed, utilising a structured questionnaire to gather data from registered and practising built environment professionals. Out of 163 distributed questionnaires, 121 valid responses were analysed using descriptive statistics and exploratory factor analysis. The findings categorise the hindrances into five key clusters: (1) Metaverse adoption barriers, (2) integration challenges, (3) technological limitations, (4) acceptance-related concerns, and (5) human- and skill-based obstacles. This study concludes that despite the Metaverse’s transformative potential for advancing NZBs, overcoming these barriers requires strategic interventions from industry professionals and policymakers. This research provides actionable insights to facilitate the effective integration of Metaverse technologies into sustainable construction practices, ensuring a more seamless transition towards digitalised NZB implementation.
Risk Assessment of RNAi-Based Potential Pesticide dsNlAtg3 and Its Homologues for Nilaparvata lugens and Non-Target Organisms
Authors: Li Kai , Chen Tongtong , Li Yuliang , Sun Kai , Pang Kun , Yu Xiaoping , Hao Peiying
Published: February 2025

The brown planthopper (Nilaparvata lugens) is an insect pest of rice, which mainly feeds on the phloem sap of the leaf sheath. RNA interference (RNAi) has application prospects in pest control, but it is necessary to select target genes and design suitable dsRNA fragments for RNAi so that it can achieve effective pest control and avoid risks to non-target organisms. NlAtg3 is a key protein in the autophagy pathway of N. lugens. Three kinds of dsRNA fragments of the NlAtg3 gene (dsNlAtg3-474×1, dsNlAtg3-138×3 and dsNlAtg3-47×10) were designed to compare the RNAi efficiency and specificity against the target insect N. lugens and non-target organisms through microinjection. The results showed that the fragment dsNlAtg3-474×1 showed strong inhibitory effects on the survival of N. lugens, which resulted in the survival rate decreasing to zero on the fifth day, while the survival rate of a closely related species, Sogatella furcifera, dropped to 2.22%. In contrast, dsNlAtg3-47×10 specifically designed against N. lugens only showed slight or no inhibitory effects on S. furcifera and other non-target organisms such as Drosophila melanogaster, but still showed good lethal effects against N. lugens, with the survival rate dropping to 18.89% on the ninth day. In addition, after being fed N. lugens injected with dsNlAtg3-47×10 fragments, the survival rate of the natural enemies Dolomedes sulfureus and Tytthus chinensis did not show significant change, compared with those treated with the dsGFP control. Our results suggest that the NlAtg3 gene can serve as a potential target for controlling N. lugens. Moreover, by designing suitable RNAi fragments, it is possible to avoid harm to non-target organisms while effectively inhibiting the target insect N. lugens.
Medical–Legal Entomology in Action: Evaluation of Insect-Based Post-Mortem Interval Estimation in South Korean Death Investigations
Authors: Baek In-Seong , Oh Hyeon-Seok , Kim Yi-Re , Kang Min-Gyu , Jung Jae-Bong , Park Sang-Hyun
Published: February 2025

Estimating the minimum post-mortem interval (PMI-min) is crucial in criminal investigations for identifying victims and the circumstances surrounding their death. Traditional post-mortem indicators are reliable only within the first 48–72 h post-mortem. This study explored forensic entomology as an alternative method for PMI estimation, focusing on three cases in Busan, South Korea. Forensic cases involving insect specimens collected from bodies found in Busan from 2022 were examined. Personal and photographic data were documented, and insect specimens were collected, preserved, and identified based on both their morphological characteristics and DNA sequences. To enhance the estimation accuracy, corrected death scene temperatures were calculated using an electronic thermo-hygrometer and meteorological data, applying both quadratic regression and the Support Vector Machine (SVM) model. The PMI-min was estimated using growth models and developmental data from established studies. Lucilia sericata (Meigen) at different life-cycle stages were discovered in all of the cases, whereas Chrysomya megacephala (Fabricius) was found in only two out of the three cases. In each case, the estimated time of death based on the necrophagous flies differed from the deceased’s last known activity by approximately one–two days. These discrepancies may arise from the pre-colonization interval (PCI), a critical but often overlooked factor for accurate PMI-min estimation. Additional factors, including weather conditions, oviposition timing, mixed fly populations, and maggot-generated heat, further contribute to the uncertainty of PMI-min estimates. Future research should integrate these variables and employ advanced technologies such as machine learning to improve the accuracy of these estimates.
Benefits and Pitfalls of Uraemic Toxin Measurement in Peritoneal Dialysis
Authors: Malaweera Aruni , Huang Louis , McMahon Lawrence
Published: February 2025

Chronic kidney disease is a global health burden with a rising incidence and prevalence in developed and developing nations. Once established, it results in a progressive accumulation of a myriad of uraemic toxins. Peritoneal dialysis (PD) uses the body’s peritoneal membrane to remove these toxins across a semipermeable membrane to restore and maintain homeostasis. Traditionally, dialysis adequacy has been measured through clearance of urea and creatinine. However, numerous studies have shown marginal links comparing the clearance of urea and creatinine with clinical outcomes reflected in the recent changes to the ISPD guidelines on dialysis adequacy. Instead, attention has focused on protein-bound uraemic toxins (PBTs). Produced by gut bacteria, these molecules are highly protein-bound and poorly removed by either dialysis or absorptive agents. Elevated concentrations of molecules such as p-cresyl sulfate and indoxyl sulfate have been associated with abnormal cellular function and poor patient outcomes. However, widespread use of these measures to determine dialysis adequacy has been limited by the need for specialized techniques required for measurement. Altering the gut microbiome to reduce generation of PBTs through increased dietary fiber might be an alternate approach to better patient outcomes, with some initial positive reports. This report explores advantages and limitations of measuring uraemic toxins in PD, now and in the foreseeable future.
Oral Health Status and Factors Associated with Oral Health in Patients with Alzheimer’s Disease: A Matched Case-Control Observational Study
Authors: Aghasizadeh Sherbaf Reza , Kaposvári George Michael , Nagy Katalin , Pakáski Magdolna , Gajdács Márió , Matusovits Danica , Baráth Zoltán
Published: February 2025

Background: Alzheimer’s disease (AD) is a chronic neurodegenerative disease, ranking as the seventh leading cause of death in both sexes. There is increasing awareness of the role of chronic periodontal disease and severe tooth loss as a modifiable risk factor for developing AD. The aim of the present observational study was to assess AD patients with non-affected healthy controls in the context of their dental and periodontal health outcomes; additionally, the potential impact of anamnestic factors and lifestyle habits on oral health outcomes was also studied. Methods: A total of n = 41 AD patients receiving treatment at the Department of Psychiatry, University of Szeged, were compared with n = 41 age- and gender-matched controls from individuals seeking dental treatment and from retirement homes (mean age was 83.32 ± 7.82 years). Dental and periodontal status indices were assessed according to World Health Organization (WHO) criteria. Results: Overall, 51.2%, 68.3%, and 87.8% of AD patients received mood stabilizers, drugs for their non-cognitive symptoms and cognitive symptoms, respectively. Severe tooth loss was observed in 43.9% of AD patients and 56.1% of controls, respectively. There were no significant differences among AD patients and controls regarding the dental status indices studied (p > 0.05 for all indicators). AD patients had significantly higher plaque indices (%) (59.06 ± 15.45 vs. 41.35 ± 7.97; p < 0.001), bleeding on probing (BOP%) (62.65 ± 12.00 vs. 40.12 ± 10.86; p < 0.001), pocket depth [PD] (2.63 ± 0.56 vs. 2.29 ± 0.13; p = 0.002) and attachment loss [AL] (2.85 ± 0.79 vs. 2.39 ± 0.41; p = 0.026) values, compared to controls. Smoking (vs. non-smokers; 56.28 ± 12.36 vs. 51.40 ± 13.23, p = 0.038) and consumption of alcohol (vs. non-drinkers; 58.68 ± 9.86 vs. 54.78 ± 14.86, p = 0.040) were associated with higher plaque indices [%], while no similar effects were shown for dental status parameters (p > 0.05). In contrast, coffee intake and vitamin supplement use had no significant effect on dental or periodontal status parameters (p > 0.05 in all cases). Conclusions: The results of our study underscore the substantial treatment needs of AD patients, calling for heightened awareness among dental healthcare professionals.
Direct Distillation: A Novel Approach for Efficient Diffusion Model Inference
Authors: Li Zilai , Zhang Rongkai
Published: February 2025

Diffusion models are among the most common techniques used for image generation, having achieved state-of-the-art performance by implementing auto-regressive algorithms. However, multi-step inference processes are typically slow and require extensive computational resources. To address this issue, we propose the use of an information bottleneck to reschedule inference using a new sampling strategy, which employs a lightweight distilled neural network to map intermediate stages to the final output. This approach reduces the number of iterations and FLOPS required for inference while ensuring the diversity of generated images. A series of validation experiments were conducted involving the COCO dataset as well as the LAION dataset and two proposed distillation models, requiring 57.5 million and 13.5 million parameters, respectively. Results showed that these models were able to bypass 40–50% of the inference steps originally required by a stable U-Net diffusion model, which included 859 million parameters. In the original sampling process, each inference step required 67,749 million multiply–accumulate operations (MACs), while our two distillate models only required 3954 million MACs and 3922 million MACs per inference step. In addition, our distillation algorithm produced a Fréchet inception distance (FID) of 16.75 in eight steps, which was remarkably lower than those of the progressive distillation, adversarial distillation, and DDIM solver algorithms, which produced FID values of 21.0, 30.0, 22.3, and 24.0, respectively. Notably, this process did not require parameters from the original diffusion model to establish a new distillation model prior to training. Information theory was used to further analyze primary bottlenecks in the FID results of existing distillation algorithms, demonstrating that both GANs and typical distillation failed to achieve generative diversity while implicitly studying incorrect posterior probability distributions. Meanwhile, we use information theory to analyze the latest distillation models including LCM-SDXL, SDXL-Turbo, SDXL-Lightning, DMD, and MSD, which reveals the basic reason for the diversity problem confronted by them, and compare those distillation models with our algorithm in the FID and CLIP Score.
A Dynamic Trajectory Temporal Density Model for Analyzing Maritime Traffic Patterns
Authors: Jiang Dapeng , Shi Guoyou , Ma Lin , Li Weifeng , Wang Xinjian , Zhu Guibing
Published: February 2025

This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data.
Wave Attenuation by Australian Temperate Mangroves
Authors: Reef Ruth , Sayers Sabrina
Published: February 2025

Wave attenuation by natural coastal features is recognised as a soft engineering approach to shoreline protection from storm surges and destructive waves. The effectiveness of wave energy dissipation is determined, in part, by vegetation structure, extent, and distribution. Mangroves line ca. 15% of the world’s coastlines, primarily in tropical and subtropical regions but also extending into temperate climates, where mangroves are shorter and multi-stemmed. Using wave loggers deployed across mangrove and non-mangrove shorelines, we studied the wave attenuating capacity and the drag coefficient (CD) of temperate Avicennia marina mangrove forests of varying structure in Western Port, Australia. The structure of the vegetation obstructing the flow path was represented along each transect in a three-dimensional point cloud derived from overlapping uncrewed aerial vehicle (UAV) images and structure-from-motion (SfM) algorithms. The wave attenuation coefficient (b) calculated from a fitted exponential decay model at the vegetated sites was on average 0.011 m−1 relative to only 0.009 m−1 at the unvegetated site. We calculated a CD for this forest type that ranged between 2.7 and 4.9, which is within the range of other pencil-rooted species such as Sonneratia sp. but significantly lower than prop-rooted species such as Rhizophora spp. Wave attenuation efficiency significantly decreased with increasing water depth, highlighting the dominance of near-bed friction on attenuation in this forest type. The UAV-derived point cloud did not describe the vegetation (especially near-bed) in sufficient detail to accurately depict the obstacles. We found that a temperate mangrove greenbelt of just 100 m can decrease incoming wave heights by close to 70%, indicating that, similarly to tropical and subtropical forests, temperate mangroves significantly attenuate incoming wave energy under normal sea conditions.
Strength Model for Cement-Stabilized Marine Clay: SEM Image Analysis and Microstructural Insights
Authors: Xu Liyang , Wang Xipeng , Qi Yanzhi , Yuan Chang , Ding Zhi , Xu Riqing
Published: February 2025

This study investigates the strength development of cement-stabilized marine clay, which is influenced by a complex interplay of microstructural factors. To optimize its performance for coastal and offshore engineering, we explored the relationship between microstructure and unconfined compressive strength (UCS). Using Scanning Electron Microscopy (SEM) and the Pore/Crack Analysis System (PCAS), we analyzed samples with varying cement contents (10%, 15%, and 20%) and curing times (3, 7, 14, and 28 days). Key microstructural parameters, including porosity, particle shape, size, and arrangement, were quantified and correlated with UCS results. A novel comprehensive micro-parameter was introduced to encapsulate the combined effects of these factors, revealing an exponential relationship with strength development. The findings provide a quantitative framework for predicting the performance of cement-stabilized marine clay, contributing to more efficient solutions in geotechnical engineering.
‘A Part of Our Work Disappeared’: AI Automated Publishing in Social Media Journalism
Authors: Petruccio Petra , Neilson Tai , Stöcker Christian
Published: February 2025

This study explores the implementation of artificial intelligence (AI) in social media journalism. We apply a labour process approach to examine why German newspaper editors adopt AI publishing and how it influences journalistic work. Automated publishing services (APSs) are used in newsrooms to select, edit, and publish content on social media platforms. In-depth interviews with German news editors revealed that the reasons for implementing APSs include economic dependence on platforms, the centralisation of news roles, and the intensification of work. Furthermore, resistance to fully automated social media publishing in some newsrooms has resulted in semi- or hybrid-automated approaches. Resistance is primarily expressed through concerns over the loss of editorial control, content diversity, and the quality of user engagement.
A Methodological Framework for AI-Assisted Diagnosis of Ovarian Masses Using CT and MR Imaging
Authors: Adusumilli Pratik , Ravikumar Nishant , Hall Geoff , Scarsbrook Andrew F.
Published: February 2025

Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes.
Looking Without Knowing: Evidence for Language-Mediated Eye Movements to Masked Words in Hindi-English Bilinguals
Authors: Prasad Seema , Puri Shivam , Kapiley Keerthana , Rafeekh Riya , Mishra Ramesh
Published: February 2025

Cross-linguistic activation has been frequently demonstrated in bilinguals through eye movements using the visual world paradigm. In this study, we explored if such activations could operate below thresholds of awareness, at least in the visual modality. Participants listened to a spoken word in Hindi or English and viewed a display containing masked printed words. One of the printed words was a phonological cohort of the translation equivalent of the spoken word (TE cohort). Previous studies using this paradigm with clearly visible words on a similar sample have demonstrated robust activation of TE cohorts. We tracked eye movements to a blank screen where the masked written words had appeared accompanied by spoken words. Analyses of fixation proportions and dwell times revealed that participants looked more often and for longer duration at quadrants that contained the TE cohorts compared to distractors. This is one of the few studies to show that cross-linguistic activation occurs even with masked visual information. We discuss the implications for bilingual parallel activation and unconscious processing of habitual visual information.
Contribution of Active Surface of NiFe-Layered Double Hydroxide on the Removal of Methyl Orange
Authors: Zhao Yanping , Lv Fengzhu , Ou Yanwen , Lv Guocheng , Zhao Shifeng
Published: February 2025

Layered double hydroxides (LDHs) have potential applications for pollutant removal. Enhancing their pollutant removal ability by fully utilizing the synergistic effects of physical adsorption and chemical catalysis has received widespread attention. In this study, a high methyl orange (MO) removal capacity was achieved by utilizing the synergistic effects of physical adsorption and chemical catalysis of NiFe-LDH. wNiFe-LDH showed a significant removal amount of MO, up to 506.30 mg/g due to its reserving of the active surface to the largest extent. Experiment and molecular simulation clarified the high removal capacity derived from surface adsorption and the degradation ability of the active surface. The presence of more -OH groups on the surface enhanced the removal of MO, and the vacancies in the surface were beneficial for the formation of •O2− and contributed to the degradation of MO. As K2S2O8 was introduced, the removal rate of MO improved to 100% from 60.67%. However, a deeper study showed that the degradation was incomplete, as K2S2O8 inhibited the formation of •O2−, and the active species in the system changed to holes. The degradation path of MO was also altered. Thus, this study gives new insight into the reactivity of the active surface of NiFe-LDH and affords a new path to preserve the active surface.
Closed-Loop Transient Longitudinal Trajectory Tracking for Connected Vehicles
Authors: Hua Lingyun , Zhu Guoming
Published: February 2025

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In this paper, based on a linearized vehicle model with the help of feedback linearization, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws to minimize the tracking error of optimal speed or brake distance trajectories, respectively, and maintain brake safety. A unified Kalman filter is used to estimate system states based on noisy measurements. Both acceleration and deceleration LQIT controls are designed to handle the change of upperlevel optimal control strategies to varying traffic. Simulation and co-simulation studies validated the proposed LQIT control strategies in Simulink with the SUMO traffic model using a real-world map under manipulated driving conditions. The simulation results show that under changing traffic conditions, the LQIT acceleration control is able to reduce the static tracking error by 99.8%, compared with the vehicle controlled only by the high-level optimal acceleration control without a trajectory tracker, achieving less tracking error and overshoot than using a PI control. The LQIT deceleration control reduces the brake distance error by 48% over the optimal deceleration control alone and ensures a safer brake distance than a coupled PI control. The traffic model used in the SUMO co-simulation confirms the capability of handling varying traffic for the developed LQIT control strategies.
Development of a Bicycle-like Magnetic-Wheeled Climbing Robot with Adaptive Plane-Transition Capabilities
Authors: Bu Yongjian , Dun Lide , Deng Yongtao , Jiang Bingdong , Jiang Aihua , Zhu Haifei
Published: February 2025

Although robots are increasingly expected to perform inspection tasks in three-dimensional ferromagnetic structural environments, magnetic-wheeled climbing robots face significant challenges in overcoming obstacles and transiting between planes. In this paper, we propose a novel bicycle-like magnetic-wheeled climbing robot, named BiMagBot, featuring two magnetic wheels that allow the adaptive adjustment of magnetic adhesion without the need for active control. The front wheel incorporates an arc tentacle mechanism that rotates a ring magnet to adjust the magnetic adhesion, while the rear wheel uses an eccentric shaft-hole design to facilitate a smooth transition of magnetic adhesion between surfaces. The magnetic forces acting on both wheels during transitions through concave corners were analyzed and discussed via simulations to elucidate the underlying principles. A prototype of the robot was developed and tested experimentally. The results show that the front and rear wheels can adjust the magnetic adhesion during the transition of corners with angles ranging from 90° to 315°. The robot only weighs 1.6 kg, but it can carry a weight of 2 kg with a speed of 0.9 m/s to transit across concave corners, demonstrating comprehensive capabilities in plane transition, ease of control, and load capacity.
A Novel Improved Binary Optimization Algorithm and Its Application in FS Problems
Authors: Wu Boyuan , Luo Jia
Published: February 2025

With the rapid advancement of artificial intelligence (AI) technology, the demand for vast amounts of data for training AI algorithms to attain intelligence has become indispensable. However, in the realm of big data technology, the high feature dimensions of the data frequently give rise to overfitting issues during training, thereby diminishing model accuracy. To enhance model prediction accuracy, feature selection (FS) methods have arisen with the goal of eliminating redundant features within datasets. In this paper, a highly efficient FS method with advanced FS performance, called EMEPO, is proposed. It combines three learning strategies on the basis of the Parrot Optimizer (PO) to better ensure FS performance. Firstly, a novel exploitation strategy is introduced, which integrates randomness, optimality, and Levy flight to enhance the algorithm’s local exploitation capabilities, reduce execution time in solving FS problems, and enhance classification accuracy. Secondly, a multi-population evolutionary strategy is introduced, which takes into account the diversity of individuals based on fitness values to optimize the balance between exploration and exploitation stages of the algorithm, ultimately improving the algorithm’s capability to explore the FS solution space globally. Finally, a unique exploration strategy is introduced, focusing on individual diversity learning to boost population diversity in solving FS problems. This approach improves the algorithm’s capacity to avoid local suboptimal feature subsets. The EMEPO-based FS method is tested on 23 FS datasets spanning low-, medium-, and high-dimensional data. The results show exceptional performance in classification accuracy, feature reduction, execution efficiency, convergence speed, and stability. This indicates the high promise of the EMEPO-based FS method as an effective and efficient approach for feature selection.
Can Clinical, Psychological, and Cognitive Patient-Reported Outcome Measures (PROMs) Help to Discriminate Women with Fibromyalgia from Those with Other Localized/Regional Pain Conditions? A Diagnostic Accuracy Study
Authors: Cigarán-Mendez Margarita , Tejera-Alonso Ángela , Gómez-Calero Cristina , Fernández-de-las-Peñas César , López-Redondo Mónica , Valera-Calero Juan A. , Fernández-Palacios Francisco G. , Pacho-Hernández Juan C.
Published: February 2025

Background and Objectives: The heterogeneous clinical manifestations of fibromyalgia syndrome have led to the revision of diagnostic criteria in the last decade. The aim of this study was to determine the capability of clinical, psychological, and cognitive patient-related outcome measures (PROMs) to differentiate women with fibromyalgia syndrome (FMS) from women with localized or regional pain conditions. Materials and Methods: A diagnostic accuracy study was conducted. Clinical (pain intensity—NPRS; related disability—FIQ), psychological (anxiety/depressive levels—HADS-A/HADS-D), and cognitive (sleep quality—PSQI; pain hypervigilance—PVAQ-9) PROMs were collected in 129 women with FMS and 65 women with localized/regional chronic pain conditions. The area under the receiver operating characteristic (ROC) curve, cut-off point, sensitivity/specificity values, and positive and negative likelihood (LR) ratios of each variable were calculated. Results: Women with FMS showed higher levels of pain, related disability, and anxiety/depressive levels, worse sleep quality, and higher levels of hypervigilance (all, p < 0.001) than women without FMS. All PROMs showed excellent discriminatory power and good sensitivity (pain intensity: ROC 0.987, sensitivity 91.5%; related disability: ROC 0.980, sensitivity 93.8%; HADS-A: ROC 0.901, sensitivity 81.4%; HADS-D: ROC 0.906, sensitivity 85.3%; PSQI: ROC 0.909, sensitivity 79.1%; PVAQ-9: ROC 0.798, sensitivity 80.6%). Specificity was extremely small for all variables (<18%) except for pain hypervigilance (specificity: 34%). Conclusions: Women with FMS exhibited worse clinical, psychological, and cognitive variables than women with localized/regional chronic pain. Although all PROMs had good discriminatory power, related disability and pain hypervigilance were those showing the best models. These PROMs could be combined with the American College of Rheumatology (ACR) diagnostic criteria to better discriminate between women with and without FMS. Studies investigating the relevance of combining these PROMs with the ACR diagnostic criteria in clinical settings are needed.
Machine Learning–Based Prediction of Complex Combination Phases in High-Entropy Alloys
Authors: Thampiriyanon Jirapracha , Khumkoa Sakhob
Published: February 2025

High-entropy alloys (HEAs) are a novel class of materials that exhibit exceptional mechanical, thermal and corrosion-resistant properties, making them highly promising for various industrial applications. The complex and variable compositions of HEAs present significant challenges in accurately predicting their phase structures, which is crucial for tailoring their properties for specific applications. This study proposes a machine learning (ML)–based approach to predict HEA phase structures using experimentally validated datasets containing chemical composition–derived features. A Boolean vector technique was employed to encode multiphase classifications, allowing the model to accurately capture complex phase relationships. Four ML algorithms consisting of support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF) and neural network (NN) were utilized to develop predictive models. The models were evaluated using rigorous cross-validation (CV) and tested on unseen data samples. The results demonstrate that NN and KNN outperform other models, achieving an impressive test accuracy of 84.85%. Feature importance analysis reveals that valence election concentration and melting temperature are the most influential factors governing phase formation. This study highlights the effectiveness of ML in accelerating HEA design by significantly reducing reliance on experimental trial-and-error approaches. The predictive models can be applied in alloy design to rapidly screen compositions with desired phase structures, guiding experimental synthesis and optimizing material properties for advanced application. The findings underscore the potential of ML in computational materials science and open new avenues for integration artificial intelligence–driven approaches with conventional metallurgical techniques to enhance HEA development.
Green Tea with Rhubarb Root Reduces Plasma Lipids While Preserving Gut Microbial Stability in a Healthy Human Cohort
Authors: Lloyd Amanda J. , Martinez-Martin MJ Pilar , Warren-Walker Alina , Hitchings Matthew D. , Moron-Garcia Odin M. , Watson Alison , Villarreal-Ramos Bernardo , Lyons Laura , Wilson Thomas , Allison Gordon , Beckmann Manfred
Published: February 2025

Background/Objectives: Cardiovascular diseases remain a leading cause of mortality and morbidity, and dyslipidaemia is one of the major risk factors. The widespread use of herbs and medicinal plants in traditional medicine has garnered increasing recognition as a valuable resource for increasing wellness and reducing the onset of disease. Several epidemiologic and clinical studies have shown that altering blood lipid profiles and maintaining gut homeostasis may protect against cardiovascular diseases. Methods: A randomised, active-controlled parallel human clinical trial (n = 52) with three herbal tea infusions (green (Camellia sinensis) tea with rhubarb root, green tea with senna, and active control green tea) daily for 21 days in a free-living healthy adult cohort was conducted to assess the potential for health benefits in terms of plasma lipids and gut health. Paired plasma samples were analysed using Afinion lipid panels (total cholesterol, LDL (low-density lipoprotein) cholesterol, HDL (high-density lipoprotein) cholesterol, triglycerides, and non-HDL cholesterol) and paired stool samples were analysed using 16S rRNA amplicon sequencing to determine bacterial diversity within the gut microbiome. Results: Among participants providing fasting blood samples before and after the intervention (n = 47), consumption of herbal rhubarb root tea and green tea significantly lowered total cholesterol, LDL-cholesterol, and non-HDL cholesterol (p < 0.05) in plasma after 21 days of daily consumption when compared with concentrations before the intervention. No significant change was observed in the senna tea group. In participants providing stool samples (n = 48), no significant differences in overall microbial composition were observed between pre- and post-intervention, even at the genus level. While no significant changes in overall microbial composition were observed, specific bacterial genera, such as Dorea spp., showed correlations with LDL cholesterol concentrations, suggesting potential microbiota-mediated effects of tea consumption. Diet and BMI was maintained in each of the three groups before and after the trial. Conclusions: It was found that drinking a cup of rhubarb root herbal or green tea infusion for 21 days produced beneficial effects on lipid profiles and maintained gut eubiosis without observable adverse effects in a healthy human cohort. More studies are needed to fully understand the effects of rhubarb root and green tea in fatty acid metabolism and gut microbial composition.
Relation Between Major Climatic Indices and Subseasonal Precipitation in Rio Grande do Sul State, Brazil
Authors: Arruda Angela Maria de , Centeno Luana Nunes , Nunes André Becker
Published: February 2025

This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region.
Evaluation of Single and Multi-Strain Probiotics with Gentamicin Against E. coli O157:H7: Insights from In Vitro and In Vivo Studies
Authors: Fneish Fatima H. , Abd El Galil Khaled H. , Domiati Souraya A.
Published: February 2025

The emergence of antibiotic-resistant food-borne pathogens, especially Escherichia coli O157:H7, highlights the urgent need for innovative treatment strategies, particularly in light of rising resistances and the ongoing controversy surrounding antibiotic use in response to E. coli O157:H7 infections. To address this issue, we explored the potential of single- and multi-strain probiotics, both independently and in combination with gentamicin, through a series of in vitro and in vivo experiments. In vitro, gentamicin alone produced a mean inhibition zone of 12.9 ± 2.27 mm against E. coli O157:H7. The combination of gentamicin with single-strain probiotics (P1) increased the inhibition zone to 16.5 ± 2.24 mm (p < 0.05), while the combination with multi-strain probiotics (P2) resulted in the largest inhibition zone of 19 ± 2.8 mm (p < 0.05). In vivo, mice infected with E. coli O157:H7 and treated with P2, gentamicin (G), or their combination (G+P2), achieved 100% survival, no pathological symptoms, and full weight recovery within seven days. Conversely, mice treated with P1 or G+P1 exhibited lower survival rates (71.4% and 85%, respectively) and slower weight recovery. Hematological parameters improved across all groups, but kidney function analysis showed significantly higher serum creatinine levels in the P1, G, G+P1, and G+P2 groups compared to the P2 group (P1: 0.63 ± 0.15 mg/dL; G: 0.34 ± 0.09 mg/dL; G+P1: 0.53 ± 0.19 mg/dL; G+P2: 0.5 ± 0.23 mg/dL vs. P2: 0.24 ± 0.2 mg/dL). Histological analysis showed better intestinal and kidney tissue recovery in the P2 group, while the P1 and G+P1 groups exhibited abnormal ileal structures and severe cortical bleeding. These findings highlight the promise of multi-strain probiotics, alone or in conjunction with antibiotics, as a therapeutic strategy for E. coli O157:H7 infections. However, the nephrotoxicity associated with gentamicin co-administration remains a limitation, warranting further studies to optimize this approach.
A Systematic Review and Meta-Analysis of 16S rRNA and Cancer Microbiome Atlas Datasets to Characterize Microbiota Signatures in Normal Breast, Mastitis, and Breast Cancer
Authors: Rad Sima Kianpour , Yeo Kenny K. L. , Wu Fangmeinuo , Li Runhao , Nourmohammadi Saeed , Tomita Yoko , Price Timothy J. , Ingman Wendy V. , Townsend Amanda R. , Smith Eric
Published: February 2025

The breast tissue microbiome has been increasingly recognized as a potential contributor to breast cancer development and progression. However, inconsistencies in microbial composition across studies have hindered the identification of definitive microbial signatures. We conducted a systematic review and meta-analysis of 11 studies using 16S rRNA sequencing to characterize the bacterial microbiome in 1260 fresh breast tissue samples, including normal, mastitis-affected, benign, cancer-adjacent, and cancerous tissues. Studies published until 31 December 2023 were included if they analyzed human breast tissue using Illumina short-read 16S rRNA sequencing with sufficient metadata, while non-human samples, non-breast tissues, non-English articles, and those lacking metadata or using alternative sequencing methods were excluded. We also incorporated microbiome data from The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort to enhance our analyses. Our meta-analysis identified Proteobacteria, Firmicutes, Actinobacteriota, and Bacteroidota as the dominant phyla in breast tissue, with Staphylococcus and Corynebacterium frequently detected across studies. While microbial diversity was similar between cancer and cancer-adjacent tissues, they both exhibited a lower diversity compared to normal and mastitis-affected tissues. Variability in bacterial genera was observed across primer sets and studies, emphasizing the need for standardized methodologies in microbiome research. An analysis of TCGA-BRCA data confirmed the dominance of Staphylococcus and Corynebacterium, which was associated with breast cancer proliferation-related gene expression programs. Notably, high Staphylococcus abundance was associated with a 4.1-fold increased mortality risk. These findings underscore the potential clinical relevance of the breast microbiome in tumor progression and emphasize the importance of methodological consistency. Future studies to establish causal relationships, elucidate underlying mechanisms, and assess microbiome-targeted interventions are warranted.