bluuebunny/crossref_metadata_2025 huggingface dataset:
The quality of Tibetan sheep meat from pastures was synergistically regulated by the rumen microbiota and related genes at different phenological stages
Authors: Chen Xiaowei , Sha Yuzhu , Liu Xiu , He Yanyu , Li Wenhao , Yao Liangwei , Wang Jiqing , Yang Wenxin , Chen Qianling , Gao Min , Huang Wei , Ma Bin
Published: January 2025

Meat quality is a key indicator of meat performance in ruminants, and its mechanism and regulation are also key to ruminant research. Studies have shown that animal meat quality is related to the gut microbiota. In this study, RT-qPCR and 16S omics were employed to assess meat quality and intestinal microbiota. The objective was to investigate the influence of seasonal variations on the meat quality of Tibetan sheep ewes by examining the rumen microflora, meat quality attributes, and associated gene expression profiles over three distinct months: May, August, and December.The results indicate that muscle tenderness was significantly greater (p < 0.001) in the grass period than in the regrowth and dry grass periods and was highest in the longest dorsal muscle. The cooking rate of the foreleg muscle was significantly greater (p < 0.05) than that during the regrowth and dry grass periods, and the pH24h significantly differed (p < 0.05) across the different seasonal periods. The crude protein content of the longest back muscle and the foreleg muscle was significantly greater (p < 0.001) than that of the wither and grass stages during the regrowth period and slightly decreased during the grass stage. The crude fat and crude ash contents of the three groups differed significantly, and the fat content during the grass stage was significantly (p < 0.05) greater than that during the regrowth stage and the wither stage. Expression analysis of genes related to meat quality revealed that the expression of the ADSL gene was significantly greater (p < 0.05) in the anterior and posterior leg muscles during the grass period than during the regrowth and wilting periods, whereas the expression of the FABP3 gene was lower than that during these two periods. Correlation analysis revealed that Rikenellaceae_RC9_gut_group was significantly positively correlated (p < 0.05) with shear forceand cooked meat percentage and significantly negatively correlated (p < 0.05). Ruminococcus and Butyrivibrio were significantly positively correlated (p < 0.05) with CAST and highly significantly positively correlated (p < 0.05). In conclusion, meat quality during different seasons is regulated by the rumen microbiota and their associated genes.
Case report: Kinetics of viral load, immunoglobulins, and cytokines in a fatal case of Crimean-Congo hemorrhagic fever virus
Authors: Mhamadi Moufid , Sene Ousseynou , Diamanka Arfang , Mhamadi Moundhir , Mendy Marie Pedapa , Diouf Andy Mahine , Ndiaye Mignane , Barry Mamadou Aliou , Niang Sagne Samba , Traoré Mahfouz Cissé , Sall Amadou Alpha , Fall Gamou , Dia Ndongo , Faye Oumar
Published: January 2025

A fatal case of Crimean-Congo hemorrhagic fever virus (CCHFV) was detected in Dakar, Senegal, West Africa, in April 2023. To elucidate the mechanisms involved in this outcome, the kinetics of viral load, immunoglobulins, and cytokines were assessed using RT-qPCR and ELISA. A high IL-6 titer, low IL-10 titer, and delayed IgG response were associated with disease severity. These biomarkers may serve as predictors of disease severity, potentially aiding in early intervention and treatment strategies.
Kårebolssätern and other shielings - the emergence and development of Swedish transhumant pastoralism
Authors: Svensson Eva , Pettersson Susanne , Johansson Annie
Published: January 2025

Transhumant pastoralism, characterized by the seasonal movement of livestock, has been a traditional practice in various forms globally for millennia. The Scandinavian shieling system—specifically, the Kårebolssätern site—exemplifies this practice, where designated areas provide grazing and dairy processing facilities. However, many of these systems, including Kårebolssätern, have faced abandonment or decline, primarily due to modernization and industrial forces. An interdisciplinary research project compared Kårebolssätern’s history to other shielings in Sweden. The study utilized various methodologies, including pollen analysis, archaeological mapping, and historical documentation, to trace human impact and land use, indicating shifts between grazing and cereal cultivation. Findings demonstrated that Kårebolssätern had significant early cultivation of barley and hemp, and by the 13th century, its use intensified amidst changes in local agricultural conditions. Four chronological phases regarding the emergence and development of Swedish shielings over two millennia were detected. The results offer insights into the adaptability of shielings amidst socio-economic transformations. As modern pressures threaten the remaining shielings, preserving their historical significance and adaptive strategies may provide pathways for their future survival. The study ultimately highlights the resilience of pastoral practices within evolving landscapes.
Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
Authors: Lee Ju-Hwan , Vu Dang Thanh , Lee Nam-Kyung , Shin Il-Hong , Kim Jin-Young
Published: January 2025

This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs’ inherent interpretability through our novel approach to knowledge distillation. We introduce visual concept knowledge distillation (VICO-KD), which transfers both explicit and implicit visual concepts from the teacher to the student model while preserving the local interpretability of the CBM, enabling accurate classification and clear visualization of evidence. VICO-KD demonstrates superior performance on benchmark datasets compared to Vanilla-KD, ensuring the student model learns visual concepts while maintaining the local interpretation capabilities of the teacher CBM. Our methodology shows competitive performance against existing concept models, and the student model, trained via VICO-KD, exhibits enhanced performance compared to the teacher during interventions. This study highlights the effectiveness of combining a CBM with KD to improve both interpretability and explainability in compressed models while maintaining locality properties.
AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks
Authors: Göktepe Körpeoğlu Seda , Filiz Ahsen , Göktepe Yıldız Sevda
Published: January 2025

Considering that creative thinkers are individuals who can think outside of the box, exhibit original thoughts, and demonstrate problem-solving skills, it is likely that there is a relationship between mathematical problem-solving beliefs (MPSBs) and creative thinking dispositions (CTDs). This study aimed to predict teachers’ MPSBs with their CTDs and some demographic features. Three different artificial intelligence models (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)) were developed, and artificial data were obtained. The inputs of the research were determined as CTD, gender, age, educational level, school level, and teaching experiences, and the output was determined as MPSBs. Afterward, whether there was a relationship between real and artificial results was examined with statistical analysis. The research results show that there is a statistically significant, positive, and moderate relationship between artificial ANN MPSB scores and real MPSB scores (r = 0.422; p < 0.05), as well as artificial ANFIS MPSB scores and real MPSB scores (r = 0.564; p < 0.05). These results are important sources of evidence indicating that artificial intelligence methods accurately predict teachers’ MPSB scores.
Analysis of the Effect of Frequency Weighting According to the Sperling Index Method on the Dynamic Response of the Railway Vehicle Carbody to Vertical Vibrations
Authors: Apostol Ioana Izabela , Dumitriu Mădălina , Mazilu Traian
Published: January 2025

The topic of the paper is part of the research area in which the methods of ride comfort evaluation are investigated, to identify their advantages and limitations, the correlation of the comfort indices obtained by different methods, and even the improvement of these methods as an application methodology or as an interpretation. In this paper, the Sperling index method is analyzed from a new perspective, specifically, the effect that the frequency weighting functions specific to this method have on the dynamic response of the railway vehicle carbody, as evaluated by the power spectral density (PSD) of the acceleration. The analysis is based on numerical simulation results for three different cases: respectively, the unweighted PSD of the acceleration, the frequency-weighted PSD of the acceleration with the function for the evaluation of ride quality (RQ)and the weighting function for the evaluation of ride comfort (RC). The applications for the numerical simulation are based on an original vehicle–track system model with 15 degrees of freedom, which includes a rigid–flexible coupled model of the vehicle and the equivalent model of the track. This model considers important elements that can influence the vertical vibration behavior of the carbody, namely, the carbody structural flexibility, which is represented by an equivalent Euler–Bernoulli beam, the system through which the longitudinal forces are transmitted from the bogies to the carbody, and the elasticity of the wheel–rail contact. Based on the analyses developed in the paper, the relevant conclusions regarding the effect of the frequency weighting functions specific to the Sperling index method on the dominant vertical vibration modes of the carbody are synthesized. These conclusions are correlated with the results of the Wz index for the RQ evaluation and the RC evaluation.
Performance of Microconcretes with Different Percentages of Recycled Tire Rubber Granulate
Authors: Villanueva Purificación Bautiste , Barrio María Isabel Prieto , Escamilla Alfonso Cobo
Published: January 2025

This paper investigates the short-term behavior of microconcretes with recycled rubber (RmCs) for extensive use as structural and non-structural materials. The physical and mechanical properties of a typical microconcrete composition have been experimentally evaluated by replacing the fine aggregate with rubber granules in volumetric percentages of 10%, 20%, and 30%. The results obtained are compared with the data provided by other authors for crumb rubber concretes (CRCs). Material investment costs have also been estimated to determine the economic impact of using rubber as a fine aggregate in these products. It is observed that the use of small percentages of recycled rubber (up to 20%) produces significant increases in slump as well as important drops in compressive strength, although it substantially improves its post-critical behavior. These trends tend to stabilize with higher percentages of rubber (30%). It is also noted that the experimental results and predictive models developed for concretes are not applicable to microconcretes, so more specific research is desirable for this type of product. Regarding the economic profitability of the investment in RmCs, it is found that it is necessary to make recycled rubber cheaper and to ensure its technological performance in order to guarantee the quality of the final product.
Emergence of Self-Identity in Artificial Intelligence: A Mathematical Framework and Empirical Study with Generative Large Language Models
Authors: Lee Minhyeok
Published: January 2025

This paper introduces a mathematical framework for defining and quantifying self-identity in artificial intelligence (AI) systems, addressing a critical gap in the theoretical foundations of artificial consciousness. While existing approaches to artificial self-awareness often rely on heuristic implementations or philosophical abstractions, we present a formal framework grounded in metric space theory, measure theory, and functional analysis. Our framework posits that self-identity emerges from two mathematically quantifiable conditions: the existence of a connected continuum of memories C⊆M in a metric space (M,dM), and a continuous mapping I:M→S that maintains consistent self-recognition across this continuum, where (S,dS) represents the metric space of possible self-identities. To validate this theoretical framework, we conducted empirical experiments using the Llama 3.2 1B model, employing low-rank adaptation (LoRA) for efficient fine-tuning. The model was trained on a synthetic dataset containing temporally structured memories, designed to capture the complexity of coherent self-identity formation. Our evaluation metrics included quantitative measures of self-awareness, response consistency, and linguistic precision. The experimental results demonstrate substantial improvements in measurable self-awareness metrics, with the primary self-awareness score increasing from 0.276 to 0.801 (190.2% improvement) after fine-tuning. In contrast to earlier methods that view self-identity as an emergent trait, our framework introduces tangible metrics to assess and measure artificial self-awareness. This enables the structured creation of AI systems with validated self-identity features. The implications of our study are immediately relevant to the fields of humanoid robotics and autonomous systems. Additionally, it opens up new prospects for controlled adjustments of self-identity in contexts that demand different levels of personal involvement. Moreover, the mathematical underpinning of our framework serves as the basis for forthcoming investigations into AI, linking theoretical models to real-world applications in current AI technologies.
Genome-Wide Scans for Selection Signatures in Haimen Goats Reveal Candidate Genes Associated with Growth Traits
Authors: Zhang Zhen , Lu Jiafeng , Wang Yifei , Liu Zhipeng , Li Dongxu , Deng Kaiping , Zhang Guomin , Zhao Bingru , You Peihua , Fan Yixuan , Wang Feng , Wang Ziyu
Published: January 2025

Understanding the genetic characteristics of indigenous goat breeds is vital for their conservation and breeding. Haimen goats, native to China’s Yangtze River Delta, possess distinctive traits such as white hair, moderate growth rate, high-quality meat, and small body size. However, knowledge regarding the genetic structure and germplasm characteristics of Haimen goats remains limited. In this study, we performed 20× whole-genome resequencing of 90 goats (60 Haimen goats and 30 Boer goats) to identify single-nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) associated with growth traits. Here, we analyzed population genetic structure and genome-wide selection signatures between the Haimen and Boer goats based on whole-genome resequencing data. The principal component analysis (PCA) and neighbor-joining (N-J) tree results demonstrated significant genetic differentiation between the Haimen and Boer goats. The nucleotide diversity (Pi) and linkage disequilibrium (LD) decay results indicated higher genomic diversity in the Haimen goat population. Furthermore, selective sweep analysis identified candidate genes associated with growth traits. These genes exhibited strong selection signatures and were related to body size (DONSON, BMPR1B, and EPHA5), muscle development (GART, VGLL3, MYH15), and fat metabolism (ADAMTS5, LRP6, XDH, CPT1A, and GPD1). We also identified growth-related candidate genes (NCOR1, DPP6, NOTCH2, and FGGY) specific to Haimen goats. Among these genes, pancreatic lipase-related protein 1 (PNLIPRP1) emerged as the primary candidate gene influencing growth phenotypes. Further analysis revealed that a 26 bp Indel in PNLIPRP1 increased its gene expression, suggesting that this Indel could serve as a molecular marker for early marker-assisted selection, potentially enhancing early growth in goats. These findings provide valuable molecular markers and candidate genes for improving growth traits in Haimen goat breeding.
TMPRSS2 as a Key Player in Viral Pathogenesis: Influenza and Coronaviruses
Authors: Barros de Lima Gilmara , Nencioni Everton , Thimoteo Fábio , Perea Camila , Pinto Rafaela Fuzaro Alves , Sasaki Sergio Daishi
Published: January 2025

TMPRSS2, a human transmembrane protease enzyme, plays a crucial role in the spread of certain viruses, including influenza and coronaviruses. This enzyme promotes viral infection by cleaving viral glycoproteins, which helps viruses like SARS-CoV-2 and influenza A enter cells more effectively. Genetic differences in TMPRSS2 may affect people’s susceptibility to COVID-19, underscoring the need for studies that consider diverse populations. Beyond infectious diseases, TMPRSS2 has also been linked to some cancers, suggesting it could be a valuable target for drug development. This review provides a summary of TMPRSS2 inhibitors currently under study, with some already in clinical trials to test their effectiveness against viral infections. As we uncover more about TMPRSS2’s role in pathogenesis, it could open new doors for therapies to combat future outbreaks.
Recent Advances in the Mechanisms of Postoperative Neurocognitive Dysfunction: A Narrative Review
Authors: Wang Tingting , Huang Xin , Sun Shujun , Wang Yafeng , Han Linlin , Zhang Tao , Zhang Tianhao , Chen Xiangdong
Published: January 2025

Postoperative neurocognitive dysfunction (PND) is a prevalent and debilitating complication in elderly surgical patients, characterized by persistent cognitive decline that negatively affects recovery and quality of life. As the aging population grows, the rising number of elderly surgical patients has made PND an urgent clinical challenge. Despite increasing research efforts, the pathophysiological mechanisms underlying PND remain inadequately characterized, underscoring the need for a more integrated framework to guide targeted interventions. To better understand the molecular mechanisms and therapeutic targets of PND, this narrative review synthesized evidence from peer-reviewed studies, identified through comprehensive searches of PubMed, Embase, Cochrane Library, and Web of Science. Key findings highlight neuroinflammation, oxidative stress, mitochondrial dysfunction, neurotransmitter imbalances, microvascular changes, and white matter lesions as central to PND pathophysiology, with particular parallels to encephalocele- and sepsis-associated cognitive impairments. Among these, neuroinflammation, mediated by pathways such as the NLRP3 inflammasome and blood–brain barrier disruption, emerges as a pivotal driver, triggering cascades that exacerbate neuronal injury. Oxidative stress and mitochondrial dysfunction synergistically amplify these effects, while neurotransmitter imbalances and microvascular alterations, including white matter lesions, contribute to synaptic dysfunction and cognitive decline. Anesthetic agents modulate these interconnected pathways, exhibiting both protective and detrimental effects. Propofol and dexmedetomidine demonstrate neuroprotective properties by suppressing neuroinflammation and microglial activation, whereas inhalational anesthetics like sevoflurane intensify oxidative stress and inflammatory responses. Ketamine, with its anti-inflammatory potential, offers promise but requires further evaluation to determine its long-term safety and efficacy. By bridging molecular insights with clinical practice, this review highlights the critical role of personalized anesthetic strategies in mitigating PND and improving cognitive recovery in elderly surgical patients. It aims to inform future research and clinical decision-making to address this multifaceted challenge.
Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models
Authors: Noh Mi Jin , Kim Yang Sok
Published: January 2025

Background/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes. Therefore, we predicted diabetes using an AI model and quantitatively examined causal relationships using a causal discovery and inference model. Methods: Kaggle’s dataset from the National Institute of Diabetes and Digestive and Kidney Diseases was analyzed using logistic regression, deep learning, gradient boosting, and decision trees. Causal discovery techniques, such as LiNGAM, were employed to infer relationships between variables. Results: The study achieved high accuracy across models using logistic regression (84.84%) and deep learning (84.83%). The causal model highlighted factors such as physical activity, difficulty in walking, and heavy drinking as direct contributors to diabetes. Conclusions: By combining AI with causal inference, this study provides both predictive performance and insight into the factors affecting diabetes, paving the way for tailored interventions.
Experimental Study on the Seismic Performance of Buckling-Restrained Braces with Different Lengths
Authors: Wu Kechuan , Wei Guanglan , Lin Chi , Zhang Longfei , Yu Wenzheng , Lan Xiang
Published: January 2025

To investigate the differences in seismic performance of buckling-restrained braces (BRBs) with significantly different lengths and to explore the influence of length on the energy dissipation efficiency of BRBs within the same structure, this study designed and fabricated two BRBs with lengths of 8.5 m and 3 m based on an actual engineering project. Low-cycle reciprocating load tests were conducted to compare the performance of the two BRBs in terms of hysteretic energy dissipation capacity, tension–compression bearing capacity imbalance coefficient, cumulative plastic deformation capacity, and low-cycle fatigue life. Additionally, the energy dissipation and damping efficiency of BRBs of different lengths within the same structure was analyzed. The results indicate that under cyclic loading based on design displacement, the 8.5 m BRB exhibits a greater equivalent viscous damping ratio, cumulative hysteretic energy dissipation, and cumulative plastic deformation, leading to more efficient energy dissipation and damping effects. The length of the brace is a significant factor affecting the imbalance coefficient of tension–compression bearing capacity, with longer braces resulting in a larger imbalance coefficient. The 3 m BRB shows less deviation from the mean values of various fatigue parameters, indicating more stable low-cycle fatigue performance. Within the same structure, shorter BRBs with larger design displacements achieve higher energy dissipation efficiency, allowing for more effective utilization of their energy dissipation capacity. This study’s conclusions provide valuable references for designers in the rational selection of BRBs of different lengths in actual engineering projects and offer preliminary insights into the energy dissipation efficiency of BRBs of varying lengths within a structure.
TRIM25: A Global Player of Cell Death Pathways and Promising Target of Tumor-Sensitizing Therapies
Authors: Eberhardt Wolfgang , Nasrullah Usman , Pfeilschifter Josef
Published: January 2025

Therapy resistance still constitutes a common hurdle in the treatment of many human cancers and is a major reason for treatment failure and patient relapse, concomitantly with a dismal prognosis. In addition to “intrinsic resistance”, e.g., acquired by random mutations, cancer cells typically escape from certain treatments (“acquired resistance”) by a large variety of means, including suppression of apoptosis and other cell death pathways via upregulation of anti-apoptotic factors or through inhibition of tumor-suppressive proteins. Therefore, ideally, the tumor-cell-restricted induction of apoptosis is still considered a promising avenue for the development of novel, tumor (re)sensitizing therapies. A growing body of evidence has highlighted the multifaceted role of tripartite motif 25 (TRIM25) in controlling different aspects of tumorigenesis, including chemotherapeutic drug resistance. Accordingly, overexpression of TRIM25 is observed in many tumors and frequently correlates with a poor patient survival. In addition to its originally described function in antiviral innate immune response, TRIM25 can play critical yet context-dependent roles in apoptotic- and non-apoptotic-regulated cell death pathways, including pyroposis, necroptosis, ferroptosis, and autophagy. The review summarizes current knowledge of molecular mechanisms by which TRIM25 can interfere with different cell death modalities and thereby affect the success of currently used chemotherapeutics. A better understanding of the complex repertoire of cell death modulatory effects by TRIM25 is an essential prerequisite for validating TRIM25 as a potential target for future anticancer therapy to surmount the high failure rate of currently used chemotherapies.
Effects of Base Materials (α-Alumina and/or γ-Alumina) on Volatile Organic Compounds (VOCs)-Sensing Properties of Adsorption/Combustion-Type Microsensors
Authors: Hyodo Takeo , Matsuura Yuma , Inao Genki , Sasahara Takahiko , Shimizu Yasuhiro , Ueda Taro
Published: January 2025

The sensing properties of adsorption/combustion-type microsensors using 5 wt% Pt-loaded aluminas, which consist of two kinds of alumina (α-Al2O3 and γ-Al2O3), as sensing (catalytic) materials for ethanol and toluene, were investigated in air, and the mixing effects of α-Al2O3 with γ-Al2O3 on the dynamic and static responses of the sensors were discussed in this study. The mixing of 50 wt% α-Al2O3 with γ-Al2O3 was the most effective in enhancing the dynamic responses to ethanol, which originated from the flash combustion behavior of ethanol and/or their partially decomposed products adsorbed on the sensing films from 150 °C to 450 °C, while further mixing of α-Al2O3 with γ-Al2O3 tended to increase the dynamic responses to toluene. On the other hand, the static responses to both ethanol and toluene, which arise from their catalytic combustion at elevated temperatures (450 °C), mainly increased with an increase in the addition of α-Al2O3 in the 5 wt% Pt-loaded aluminas. These results indicate that the synergistic effects of the catalytic activity and the thermal conductivity of the 5 wt% Pt-loaded aluminas are the most important for the sensing properties of these sensors to ethanol and toluene.
Solid Lipid Nanoparticles for Skin Delivery of Trans-Resveratrol: Impact of Preparation Methods on Formulation Stability
Authors: Cruz Andressa Tardim , Di Filippo Leonardo Delello , Duarte Jonatas Lobato , Guillot Antonio José , Pérez-García Alberto , Melero Ana , Chorilli Marlus
Published: January 2025

Trans-resveratrol (RES) is a natural polyphenol known for its antioxidant, anti-inflammatory, and anti-aging properties, making it highly valuable in cosmetic applications. Solid lipid nanoparticles (SLNs) offer a promising solution to enhance RES’s stability and cutaneous availability. This study aimed to develop and characterize SLNs encapsulating RES for enhanced skin delivery. Multiple methodologies were evaluated to determine the impact of preparation methods on formulation stability. SLNs were formulated using stearic acid, soy phosphatidylcholine, polysorbate 80, cetyltrimethylammonium bromide, and poloxamer 407, with variations in heating temperatures and homogenization techniques. Stability assessments were conducted over 90 days, examining organoleptic properties of the hydrodynamic diameter, polydispersity index, and zeta potential. Encapsulation efficiency and skin permeation studies were performed to investigate the efficacy of SLNs in delivering RES. Results demonstrated that formulations prepared with Ultra Turrax at 24,000 rpm and heating at higher temperatures exhibited enhanced stability and smaller particle sizes. The selected formulations, F1 (prepared at 80 °C) and F2 (prepared at 70 °C) presented encapsulation efficiencies of 70% and 72%, respectively. Skin permeation studies confirmed the ability of SLNs to facilitate RES delivery through the skin. The study concludes that SLNs are suitable carriers for RES skin delivery, offering improved stability and sustained release, thus representing a promising approach for topical applications to leverage RES’s cutaneous therapeutic benefits.
Bridelia ferruginea Tea Consumption Improves Antioxidant Status in Individuals Living with Type 2 Diabetes
Authors: Appiah Collins Afriyie , Ngounda Jennifer , Boakye-Yiadom Mavis , Mills-Robertson Felix Charles , Nel Mariette , Johnson Rabia , Walsh Corinna
Published: January 2025

Background: It is well-known that persistent hyperglycaemia predisposes individuals with diabetes to oxidative stress. Bridelia ferruginea Benth., a tropical African plant, is known for its antioxidant activity. Methods: This comparative cross-sectional study assessed the oxidative status and associated parameters in 70 individuals living with type 2 diabetes (ILWT2D) who were receiving standard diabetes treatment and consistently drank Bridelia tea (Bridelia group) compared to 92 ILWT2D receiving standard diabetes treatment only (comparator group). Lipid peroxidation assessed using thiobarbituric acid reactive substances (TBARS) served as an indicator of oxidative stress. In addition, the total antioxidant capacity (TAC), glycated haemoglobin (HbA1c), and dietary intake of antioxidant-rich foods were assessed. Results: The comparator group had significantly better glycaemic control [median HbA1c—7.7% (IQR 6.7–9.4)] than the Bridelia group [9.2% (7.6–11.4)], p = 0.001. The comparator group had been on metformin treatment for a significantly longer period than the Bridelia group (p < 0.0001). Participants in the comparator group consumed antioxidant-rich fruits more frequently (monthly basis) than those in the Bridelia group who ate fruits seldomly (p < 0.0001). There was no significant difference (p = 0.11) observed in oxidative stress levels between the Bridelia group and the comparator group [TBARS: 323.0 ng/L (287.5–374.0) and 317.0 ng/L (272.5–342.0), respectively]. Nonetheless, the Bridelia group had significantly higher antioxidant capacity (p = 0.001) compared to the comparator group [TAC: 1.01 mmol/L (0.93–1.10) versus 0.92 mmol/L (0.84–1.03), respectively]. Participants in the comparator group, who did not drink Bridelia tea, had been on longer metformin treatment with better glycaemic control. However, those who drank the Bridelia tea showed comparable levels of oxidative stress and exhibited elevated antioxidant levels compared to those who did not. Conclusions: Bridelia tea consumption may serve as a sustainable source of antioxidants; however, its effect on mitigating oxidative stress in ILWT2D requires further investigation, particularly given that no significant improvement in TBARS was observed. Future studies are needed to clarify the potential role of Bridelia tea in oxidative stress management in resource-limited settings like Ghana.
Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments
Authors: Duan Pengfei , Chen Dawei
Published: January 2025

Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats.
Does Investor Sentiment Influence South African ETF Flows During Different Market Conditions?
Authors: Shenjere Paidamoyo Aurleen , Ferreira-Schenk Sune , Moodley Fabian
Published: January 2025

The exponential growth in popularity of ETFs over the last three decades has solidified ETFs as an essential component of many investors’ portfolios. Investor sentiment is one of the factors that influence market returns of ETFs during times of market volatility. This article highlights the gap in the literature by examining the role sentiment plays in ETF volatility and providing a more comprehensive understanding of how sentiment interacts with market conditions to affect ETF pricing in the South African context. This article aims to determine the effect of investor sentiment on JSE-listed ETF returns under changing market conditions. The study followed a quantitative methodology using monthly closing prices of seven JSE ETFs and an investor sentiment index. A sample period from October 2008 to December 2023 was used. For a more complex understanding of how sentiment evolved and influenced market regimes, the Markov regime-switching model was integrated with Principal Component Analysis. The results found that investor sentiment had a significant impact on most of the ETFs in both the bull and bear regimes. The bull market was more dominant than the bear market across the ETF returns. Therefore, investor sentiment affected the returns of JSE ETFs. Identifying the effect of investor sentiment on ETFs results in ETF portfolios being less affected by changing market conditions by using risk management techniques and diversifying across asset classes and investing methods.
Hayekian Hurdles: Challenges to Cryptocurrency as a Viable Basis for a New Monetary Order
Authors: Freitas Luís Pedro , Cerdeira Jorge , Lourenço Diogo
Published: January 2025

The rise of cryptocurrencies over the past decade has promised to challenge the dominance of fiat money systems and reshape monetary policy. However, recent developments, including market volatility and the collapse of key exchanges like FTX, have eroded public trust, raising skepticism of a feasible transition to a crypto-based monetary system. This paper explores why cryptocurrencies have not met the expectations of their proponents, particularly those who saw them as a step towards Friedrich Hayek’s vision for competitive currency issuance. While cryptocurrencies reflect some aspects of Hayek’s model, their instability—especially in Bitcoin-like assets—undermines their role as a reliable alternative to fiat money. The paper also considers how central bank independence and regulatory gaps further hinder the development of a robust cryptocurrency framework. Despite the continued relevance of Hayek’s ideas in today’s monetary landscape, the entrenched structures of modern central banks and the rise of Central Bank Digital Currencies suggest that a decentralised currency order remains unlikely in the near future.
Fine Tuning of an Advanced Planner for Cognitive Training of Older Adults
Authors: Gaspari Mauro , Mioni Giovanna , Signorello Dario , Stablum Franca , Zuppiroli Sara
Published: January 2025

Developing effective cognitive training tools for older adults, specifically addressing executive functions such as planning, is a challenging task. It is of paramount importance to ensure the implementation of engaging activities that must be tailored to the specific needs and expectations of older adults. Furthermore, it is essential to provide the appropriate level of complexity for the planning task. A human-centred approach was used to address the issues identified in the design of the tool. Two pilot studies were conducted with older adults to fine-tune the training task and optimize its suitability for them. This also led to an enhancement of the underlying planning engine, transitioning from a simple fast-forward planner (PDDL4J) to an advanced heuristic search planner (ENHSP). The results show that user studies enabled the development of a cognitive training system that gradually increased the proposed difficulty levels of the planning task while maintaining usability and satisfaction among older adults. This highlights the importance of conducting user studies when implementing cognitive training tools for older adults.
Deep Fashion Designer: Generative Adversarial Networks for Fashion Item Generation Based on Many-to-One Image Translation
Authors: Jung Jaewon , Kim Hyeji , Park Jongyoul
Published: January 2025

Generative adversarial networks (GANs) have demonstrated remarkable performance in various fashion-related applications, including virtual try-ons, compatible clothing recommendations, fashion editing, and the generation of fashion items. Despite this progress, limited research has addressed the specific challenge of generating a compatible fashion item with an ensemble consisting of distinct categories, such as tops, bottoms, and shoes. In response to this gap, we propose a novel GANs framework, termed Deep Fashion Designer Generative Adversarial Networks (DFDGAN), designed to address this challenge. Our model accepts a series of source images representing different fashion categories as inputs and generates a compatible fashion item, potentially from a different category. The architecture of our model comprises several key components: an encoder, a mapping network, a generator, and a discriminator. Through rigorous experimentation, we benchmark our model against existing baselines, validating the effectiveness of each architectural choice. Furthermore, qualitative results indicate that our framework successfully generates fashion items compatible with the input items, thereby advancing the field of fashion item generation.
Smart Wireless Sensor Networks with Virtual Sensors for Forest Fire Evolution Prediction Using Machine Learning
Authors: Haque Ahshanul , Soliman Hamdy
Published: January 2025

Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to emulate forest fire dynamics and predict fire scenarios using machine learning. Building on this foundation, this study explores the integration of virtual sensors to enhance the prediction capabilities of the WSN. Virtual sensors were generated using polynomial regression models and incorporated into a supervector framework, effectively augmenting the data from physical sensors. The enhanced dataset was used to train a multi-layer perceptron neural network (MLP NN) to classify multiple fire scenarios, covering both early warning and advanced fire states. Our experimental results demonstrate that the addition of virtual sensors significantly improves the accuracy of fire scenario predictions, especially in complex situations like “Fire with Thundering” and “Fire with Thundering and Lightning”. The extended model’s ability to predict early warning scenarios such as lightning and smoke is particularly promising for proactive fire management strategies. This paper highlights the potential of combining physical and virtual sensors in WSNs to achieve superior prediction accuracy and scalability of the field without any extra cost. Such findings pave the way for deploying scalable (cost-effective), intelligent monitoring systems capable of addressing the growing challenges of forest fire prevention and management. We obtained significant results in specific scenarios based on the number of virtual sensors added, while in some scenarios, the results were less promising compared to using only physical sensors. However, the integration of virtual sensors enables coverage of much larger areas, making it a highly promising approach despite these variations. Future work includes further optimization of the virtual sensor generation process and expanding the system’s capability to handle large-scale forest environments. Moreover, utilizing virtual sensors will alleviate many challenges associated with the huge number of deployed physical sensors.
Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
Authors: Zeng Shuang , Liu Chang , Zhang Heng , Zhang Baoqun , Zhao Yutong
Published: January 2025

To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an acquisition function aimed at expected improvement, this framework optimizes hyperparameter settings to enhance model adaptability and precision. Through a regional case study, this method demonstrated improved predictive accuracy and operational efficiency, highlighting its advantages in both runtime and performance.
Development of a Gluten Standard from Relevant Sources of Wheat and Investigation into Gluten Content of Supplemental Enzymes Generated During Fermentation
Authors: Kim Pyeongsug , Leeuwendaal Natasha Kim , Charari Jonathon Niño , Colom Joan , Deaton John , Rea Kieran
Published: January 2025

During fermentation, bacterial and fungal species synthesize substrate-specific enzymes to obtain nutrients. During this process, potential allergenic products, including immunologically important gluten peptides, can be created. Current protocols for assessing the levels of these peptides often overlook the specific gluten source. In this study, wheat sources provided by commercial enzyme suppliers underwent gluten extraction before being pooled into a Complete Gluten Mix, which then underwent variations of hydrolysis utilizing the digestive enzymes, pepsin and trypsin complexes. The resulting gluten peptide profiles were examined using the Wes automated Western blot system to confirm the presence of small, immunologically relevant gluten peptides. These hydrolysates were further tested for suitability as a relevant calibrant against commercially available ELISA standards. The PT3 calibrant, a hydrolyzed version of the Complete Gluten Mix, was found to be the most suitable, as it contained <50 kDa gluten peptides and gave similar absorbance readings to the majority of ELISA kit standards tested, and overlaid the GlutenTox® Competitive G12 antibody calibration curve, which was designed against the 33-mer immunogenic peptide from wheat. Additionally, no gluten bands were observed on the Wes for the enzymes of interest, which was confirmed through ELISA analysis.
Impact of Microwave Time on the Structure and Functional Properties of Glycosylated Soy 7S Globulins
Authors: Zhang Jixin , Cui Tao , Zhang Lan , Xu Huiqing , Xu Jingguo , Wang Jun
Published: January 2025

This study examined the effects of varying microwave treatment durations (0–120 s) on the structural and functional properties of glycosylated soybean 7S protein. The results indicated that microwaving for 60 s significantly altered the structure of 7S, resulting in a more ordered protein configuration. The treated protein exhibited the largest particle size (152.3 nm), lowest polydispersity index (0.248), highest α-helix content (47.86%), and lowest β-sheet, β-turn, and random coil contents (12.33%, 16.07%, and 22.41%, respectively). It also showed the lowest endogenous fluorescence and surface hydrophobicity, and the highest thermal denaturation temperature (76.8 °C). Additionally, microwaving for ≤90 s led to increased peptide modifications, with carbamylation and deamidation being the most prevalent. A microwave treatment time of 60 s also notably enhanced the functional properties of glycosylated soybean 7S protein, optimizing water-holding capacity (6.060 g/g), emulsification activity, and stability (45.191 m2/g and 33.63 min). The foaming capacity was second only to the 120 s treatment (32% at 60 s versus 34% at 120 s), though the oil-holding capacity (22.73 g/g) and foaming stability (33.42%) were significantly lower than those of the controls. Microwave treatment durations exceeding or below 60 s led to the structural disintegration of the protein, diminishing most of its functional properties. This study explores the mechanism of how microwave processing time affects the structure and functional properties of glycosylated soybean 7S protein and identifies 60 s as the optimal microwave processing time. It meets the demands for healthy and delicious food in home cooking, providing scientific evidence for using microwave processing technology to enhance the nutritional value and quality of food.
Biological Functions and Health Benefits of Flavonoids in Fruits and Vegetables: A Contemporary Review
Authors: Zheng Xiaoyuan , Zhang Xuejiao , Zeng Fankui
Published: January 2025

Flavonoids, being prevalent in fruits and vegetables, are essential to the diverse stages of plant growth, development, and storage. Furthermore, flavonoids have been shown to exert substantial beneficial effects on human health, prompting heightened scientific interest in their potential advantages. This review elucidates the functions of flavonoids in fruits and vegetables, confirming their position as natural sources of these compounds, despite the differences in type and concentration among various species. This review elucidates the significance of flavonoids in the growth and development of fruits and vegetables, highlighting their roles in enhancing pigmentation and providing protection against both biotic and abiotic stresses. In relation to human health, flavonoids are recognized for their ability to combat aging, mitigate inflammation, safeguard the nervous system, and promote overall well-being. Additionally, this review proposes avenues for future research in the domain of flavonoids, underscoring the necessity for ongoing exploration of their potential applications and benefits.