bluuebunny/crossref_metadata_2025 huggingface dataset:
Parenting in the Digital Age: A Scoping Review of Digital Early Childhood Parenting Interventions in Low- and Middle-Income Countries (LMIC)
Authors: Jäggi Lena , Hartinger Stella M. , Fink Günther , McCoy Dana C. , Alvarado Llatance Milagros , Hinckley Kristen , Ramirez-Varela Lucero , Aguilar Leonel , Castellanos Andreana , Mäusezahl Daniel
Published: January 2025

Objectives

This scoping review examines the evidence and knowledge gaps regarding the effectiveness of digital early childhood parenting interventions in Low- and Middle-Income Countries (LMICs).

Methods

Using PRISMA-ScR and PICOS frameworks, we systematically reviewed studies published since 2010 from four databases, focusing on the impact of digital parenting interventions on Early Childhood Development and parent-level outcomes.

Results

Of 1,399 studies identified, 13 met inclusion criteria, evaluating digital interventions for parents of children aged 0–5 years. These interventions included digital-only and hybrid approaches, leveraging technologies for tasks such as sharing health and ECD information, reminders, group chats, or screening. Among ECD studies, three of four with parent-reported outcomes found positive effects, but none of three using direct assessments did. Parent-level outcomes, such as mental health and parenting behaviors, showed consistent positive impacts.

Conclusion

Digital parenting interventions are feasible in LMICs but face challenges in implementation and reaching vulnerable families. Most studies are small-scale with variable designs and outcomes. Rigorous, high-quality studies are needed to establish effectiveness and optimize implementation strategies before these programs are deployed at scale.
Development and Evaluation of Transformer-Based Basic Fighter Maneuver Decision-Support Scheme for Piloting During Within-Visual-Range Air Combat
Authors: Dong Yiqun , He Shanshan , Zhao Yunmei , Ai Jianliang , Wang Can
Published: January 2025

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. Air combat engagement database (ACED) is a dedicated database for researching WVR air combat. Utilizing the data in ACED, a Transformer-based BFM decision support scheme is developed to enhance the pilot’s BFM decision making in WVR air combat. The proposed Transformer-based model significantly outperforms the baseline long short-term memory (LSTM)-based model in accuracy. To augment the interpretability of this approach, Shapley Additive Explanation (SHAP) analysis is employed, exhibiting the rationality of the Transformer-based model’s decisions. Furthermore, this study establishes a comprehensive framework for evaluating air combat performance, validated through the utilization of data from ACED. The application of the framework in WVR air combat experiments shows that the Transformer-based model increases the winning rate in combat from 30% to 70%, the average percentage of tactical advantage time from 4.81% to 14.73%, and the average situational advantage time share from 17.83% to 25.19%, which substantially improves air combat performance, thereby validating its effectiveness and applicability in WVR air combat scenarios.
An Experimental Study on the Flow Boiling Heat Transfer Characteristics of Deionized Water Under Rotational Hypergravity
Authors: Li Gen , Zhao Yiling , Zhang Xiaojie , Xie Jianxun , Fang Xiande
Published: January 2025

Flow boiling heat transfer in micro-channels under hypergravity conditions is a crucial research area for developing efficient cooling systems in aerospace applications. This experimental study investigated the flow boiling heat transfer characteristics of deionized water in a tube with a 2 mm diameter under various gravitational conditions ranging from normal gravity (1 g) to hypergravity (up to 5.1 g) by employing a centrifugal rotating platform. The study systematically analyzes the effects of gravity level, vapor quality, mass flux, and heat flux on the flow boiling heat transfer coefficient (HTC) of deionized water. Experimental results reveal that hypergravity significantly influenced the HTC at a higher vapor quality with up to a 40% deviation and was less pronounced at lower values with an approximately 10% deviation. This deterioration is attributed to the complex interplay of centrifugal force, the Coriolis force, buoyancy, and the specific properties of water, leading to vapor–liquid stratification and hindering effective heat transfer. Meanwhile, critical heat flux was found to increase with increasing gravity acceleration at high vapor qualities. This enhancement is attributed to improved buoyancy effects, liquid replenishment, and altered flow patterns under hypergravity conditions. Furthermore, five existing correlations for predicting flow boiling HTCs were evaluated against the experimental data, and all the correlations showed overestimated results. The consistent over-prediction by these correlations highlights the need for modifications to better capture heat transfer mechanisms under hypergravity conditions.
The Role of the Norway Rat, Rattus norvegicus, as a Reservoir of Zoonotic Helminth Species in the City of Barcelona (Spain)
Authors: Fuentes Màrius Vicent , Puchades-Colera Pablo , Gosálvez Carla , Sáez-Durán Sandra , Cholvi-Simó Maria , Ruvira Santiago , Sanxis-Furió Joan , Pascual Jordi , Bueno-Marí Rubén , Franco Sandra , Peracho Víctor , Montalvo Tomás , Trelis María , Debenedetti Ángela L. , Galán-Puchades María Teresa
Published: January 2025

Synanthropic rodents are involved in the transmission of several parasitic zoonoses to humans, such as helminth parasites. As part of a multidisciplinary study concerning the rodent population in Barcelona (Spain), 300 specimens of the Norway rat, Rattus norvegicus, were analyzed to elucidate their helminth community, mainly species with a zoonotic potential. The sample included 263 specimens from the sewage system and 37 from public gardens. A total of 253 (84.3%) rats were found to be parasitized, and 206 (68.7%) harbored zoonotic species. Adult rats were found to be more heavily parasitized with zoonotic helminths than juveniles, but the sex and site of capture had no influence. Six zoonotic helminths were identified: Hydatigera taeniaeformis larvae (1.7%), Rodentolepis nana (8.0%), Hymenolepis diminuta (21.3%), Calodium hepaticum (46.3%), Gongylonema neoplasticum (36.7%), and Moniliformis moniliformis (2.3%). Human zoonotic helminth infections often go unreported, so that the role of R. norvegicus in their transmission is unknown. According to our results, it is advisable to monitor and control rodent populations in deprived settlements close to cities and in cities themselves, and to promote good hygienic and sanitary practices, especially among vulnerable populations and workers with high exposure such as sewage workers.
Discovery of Effective Inhibitors Against Phosphodiesterase 9, a Potential Therapeutic Target of Alzheimer’s Disease with Antioxidant Capacities
Authors: Zhou Qian , Wu Xu-Nian , Luo Wei-Hao , Huang Qing-Hua , Feng Ling-Ling , Wu Yinuo , Zhang Chen
Published: January 2025

Alzheimer’s disease (AD) is a widely recognized type of dementia that leads to progressive cognitive decline and memory loss, affecting a significant number of people and their families worldwide. Given the multifactorial nature of AD, multitarget-directed ligands (MTDLs) hold promise in developing effective drugs for AD. Phosphodiesterase-9 (PDE9) is emerging as a promising target for AD therapy. In this study, by combining a PDE9 inhibitor C33 with the antioxidant melatonin, we designed and discovered a series of pyrazolopyrimidinone derivatives that simultaneously inhibit PDE9 and possess antioxidant activities. Molecular docking, together with dynamics simulations, were applied to accelerate compound design and reduce synthetic work. Four out of the 14 compounds were validated as effective PDE9 inhibitors with comparable antioxidant activity. Notably, compounds 17b and 17d demonstrated IC50 values of 91 and 89 nM against PDE9, respectively, with good antioxidant activities (ORAC (Trolox) of 2.00 and 2.60). This work provides a new approach for designing MTDLs for the treatment of AD and offers insights for further structural modifications of PDE9 inhibitors with antioxidant capacities.
Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models
Authors: Qiu Ruinan , Yin Yongfeng , Su Qingran , Guan Tianyi
Published: January 2025

In the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta-learning strategies, taking advantage of the diversity of heterogeneous classifiers. However, the fixed weight distribution strategy in traditional bagging methods often has limitations when handling complex or imbalanced datasets. This paper combines the concept of heterogeneous classifier integration in stacking with the weighted averaging strategy of bagging, proposing a new adaptive weight distribution approach to enhance bagging’s performance in heterogeneous ensemble settings. Specifically, we propose three weight generation functions with “high at both ends, low in the middle” curve shapes and demonstrate the superiority of this strategy over fixed weight methods on two datasets. Additionally, we design a specialized neural network, and by training it adequately, validate the rationality of the proposed adaptive weight distribution strategy, further improving the model’s robustness. The above methods are collectively called func-bagging. Experimental results show that func-bagging has an average 1.810% improvement in extreme performance compared to the base classifier, and is superior to stacking and bagging methods. It also has better dataset adaptability and interpretability than stacking and bagging. Therefore, func-bagging is particularly effective in scenarios with class imbalance and is applicable to classification tasks with imbalanced classes, such as anomaly detection.
MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
Authors: Xu Guangyuan , Guo Ruifeng , Yin Zhenyu , Zhang Feiqing
Published: January 2025

Bearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer learning. Firstly, this paper designs a feature extraction network, the Multi-scale Convolution-Convolutional Reconstruction Network (MCRCNet), which incorporates a multi-scale feature extraction module to extract bearing fault features at multiple scales, thereby enhancing the extraction ability of key information. Secondly, this paper designs an improved convolutional reconstruction module AcConv (Adaptive Convolution reconstruction), which highlights key feature information and reduces redundant features by reconstructing the feature map. Furthermore, this paper also modifies the loss function to improve the performance in the case of data imbalance, and introduces the Wasserstein distance to optimize the adversarial training process. The proposed method is experimentally verified on Case Western Reserve University, Jiangnan University, and laboratory datasets. The experimental results show that the method has good performance in most tasks and has good generalization ability, which provides a feasible solution for the research of bearing fault diagnosis.
Multi-Scale Feature Fusion and Context-Enhanced Spatial Sparse Convolution Single-Shot Detector for Unmanned Aerial Vehicle Image Object Detection
Authors: Qi Guimei , Yu Zhihong , Song Jian
Published: January 2025

Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonstrating its effectiveness in achieving an optimal balance between accuracy and efficiency. Nevertheless, the sparse convolution method encounters challenges related to the inadequate incorporation of global contextual information and exhibits network inflexibility attributable to its fixed mask ratios. To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue of information loss resulting from the convolution process applied exclusively to the masked region. Subsequently, a dynamic masking strategy is designed to determine the optimal mask ratios, thereby ensuring compact foreground coverage that enhances both accuracy and efficiency. Experiments on two datasets (i.e., VisDrone and ARH2000; the latter dataset was created by the researchers) demonstrate that the MFFCESSC-SSD remarkably outperforms the performance of the SSD and numerous conventional object detection algorithms in terms of accuracy and efficiency.
A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
Authors: Yatkin Muhammed Adil , Kõrgesaar Mihkel , Işlak Ümit
Published: January 2025

The analysis of continuous events for any application involves the discretization of an event into sequences with potential historical dependencies. These sequences represent time stamps or samplings of a continuous process collectively forming a time series dataset utilized for training recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for pattern prediction. The challenge is to ensure that the estimates from the trained models are consistent in the same input domain for different discretizations of the same or similar continuous history-dependent events. In other words, if different time stamps are used during the prediction phase after training, the model is still expected to give consistent predictions based on the knowledge it has learned. To address this, we present a novel RNN transition formula intended to produce consistent estimates in a wide range of engineering applications. The approach was validated with synthetically generated datasets in 1D, 2D, and 3D spaces, intentionally designed to exhibit high non-linearity and complexity. Furthermore, we have verified our results with real-world datasets to ensure practical applicability and robustness. These assessments show the ability of the proposed method, which involves restructuring the mathematical structure and extending conventional RNN architectures, to provide reliable and consistent estimates for complex time series data.
Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy
Authors: Suh Jungho , Chang Sungbok , Park Hyunjun
Published: January 2025

Eye Movement Desensitization and Reprocessing (EMDR) was recognized by the World Health Organization in 2013 as an evidence-based therapy for post-traumatic stress disorder (PTSD) and found to be effective for depression. Since then, EMDR has evolved into a personalized treatment focusing on stabilizing the physiological and psychological processes to alleviate symptoms of depression and stress. However, optimized parameters for video stimuli, such as speed (ssp), distance (d), and size (ssz), are not yet well defined in EMDR protocols. This study addresses this gap by employing an artificial neural network (ANN) methodology based on Francine Shapiro’s Adaptive Information Processing (AIP) model. The ANN was used to determine ideal values for video stimuli parameters, developing an integrated model to enhance EMDR outcomes. Of the 2860 ANN-modeled combinations, stimulus settings of 1.8 Hz speed, 70-pixel size, and 1440-pixel distance achieved the highest Predicted Effectiveness Score (PES) of 98.7%. An EMDR field test with electroencephalography (EEG) was conducted to assess the optimized video stimuli’s efficacy. Further, 16 participants, selected from a sample of 56 meeting CES-D depression criteria, were evaluated, and the top 50 PES values were selected for further analysis. EEG results indicated a 12.31% increase in effectiveness, showing a reduction in right frontal lobe beta waves. These findings highlight the technical advancements and therapeutic potential of the proposed ANN-optimized EMDR stimuli, demonstrating statistically significant improvements over traditional methods.
A Multi-Scale Convolutional Residual Time-Frequency Calibration Method for Low-Accuracy Air Pollution Data
Authors: Liu Jiahao , Shi Fei , Jia Zhenhong , Qin Jiwei
Published: January 2025

Air pollution concerns have led to the widespread deployment of air quality monitoring stations. While high-cost government stations provide accurate data, their deployment is limited, whereas low-cost sensors offer widespread coverage but with lower accuracy. To enhance the accuracy of measurement data from low-cost air monitoring sensors, this study proposes a Multi-Scale Convolutional Residual Time-Frequency Calibration Method (MCRTF-CM), focusing on the PM2.5 sensor as an example. This method leverages multi-scale convolution in the feature extractor to capture diverse features at various scales using parallel convolutional kernels. Residual connections merge the original and multi-scale features, preserving the initial input for enhanced stability. The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. Experimental results demonstrate that MCRTF-CM outperforms optimal Long Short-Term Memory (LSTM) networks, reducing RMSE, MAE, MSE, and MAPE by 13.59%, 14.04%, 25.33%, and 8.22%, respectively, indicating its better performance.
Evaluation of Frequency Effects on Fatigue Life at High Test Frequencies for SAE 1045 Steel Based on Thermography and Electrical Resistance Measurements
Authors: Ziman Jonas Anton , Weber Fabian , Koziol Janina , Otto Johannes Leon , Sauer Lukas Maximilian , Walther Frank , Starke Peter
Published: January 2025

This research provides a method for a reliable fatigue life estimation at high testing frequencies. The investigations are based on the lifetime prediction method StressLifeHCF considering test frequencies of 80 and 260 Hz for normalized SAE 1045 (C45E, 1.1191) steel. Therefore, load increase tests and constant amplitude tests were carried out using a resonant testing rig. To ensure a mechanism-oriented lifetime prediction, the material response to dynamic loading is monitored via temperature and electrical resistance measurements. Due to the higher energy input per time unit, when the test frequency is increased, the heat dissipation also increases. For this reason, a precise differentiation between frequency- and temperature-related effects for adequate fatigue assessment is challenging. To evaluate the temperature’s influence on electrical resistance, an electrical resistance-temperature hysteresis is measured, and the frequency influence is analyzed by considering cyclic deformation curves. In addition to an extension of the fatigue life due to an increased test frequency, the lifetime prediction method was validated for high frequencies. The generated S-N curves show a reliable agreement with the data points from conventional constant amplitude tests. In this context, the temperature correction of the electrical resistance proved to be an important input variable for a reliable lifetime prediction.
Key Stakeholders’ Perspectives on the Sports Science and Medicine Resources and Practices in English Non-League Male Football
Authors: Jackson Daniel T. , Blagrove Richard C. , Thain Peter K. , Weldon Anthony , Kelly Adam L.
Published: January 2025

Background: Sports science and medicine (SSM) is integral to professional football clubs. The level below professional football in England, ‘non-league football’ (NLF), consists of full-time and part-time clubs. The existing literature has exclusively focused on SSM in professional football, with the resources and practices in NLF currently unknown. Therefore, this study explored the SSM resources and practices within NLF by investigating the perspectives of key stakeholders working within NLF coaching and SSM disciplines. Methods: Fifty participants (coaching practitioners [n = 25] and SSM practitioners [n = 25]) from NLF clubs completed an anonymous online survey comprising 31 multiple-choice and Likert-scale questions, alongside optional open-ended comments. Results: Support was mixed for SSM evidence-based practices across clubs in Tiers 5–10 within the National League System. The most common SSM resources were the training ground (n = 39), resistance training equipment (n = 15), and rehabilitation area (n = 13). Fitness testing was frequent (86%) pre-season but rare end-of-season (8%). Workload monitoring primarily consisted of the session duration (80%) and time–motion data (36%). Performance analysis of competitive matches commonly used video (74%) or post-match technical analysis (40%). Injury monitoring generally occurred ‘always’ (44%) or ‘sometimes’ (28%). Nutritional support on match days was mostly fluids (80%), with ‘no support’ reported most outside match days (54%). Conclusions: The SSM resources and practices vary considerably within NLF, influenced by individual club constraints and barriers, including financial support, access to facilities, and equipment availability. These findings may inform future SSM provisions in NLF to enhance team performances and player availability.
Co-Hydrothermal Carbonization of Sawdust and Sewage Sludge: Assessing the Potential of the Hydrochar as an Adsorbent and the Ecotoxicity of the Process Water
Authors: Cavali Matheus , Hennig Thuanne Braúlio , Libardi Junior Nelson , Kim Boram , Garnier Vincent , Benbelkacem Hassen , Bayard Rémy , Woiciechowski Adenise Lorenci , Matias William Gerson , de Castilhos Junior Armando Borges
Published: January 2025

Hydrothermal carbonization (HTC) is a promising thermochemical process to convert residues into hydrochar. While conventional HTC utilizes one type of residue as raw material only, Co-HTC generally combines two. By mixing dry and wet wastes, Co-HTC can advantageously avoid water addition. Therefore, this work investigated the potential of hydrochar derived from the Co-HTC of sawdust and non-dewatered sewage sludge as a dye (methylene blue) adsorbent and evaluated the toxicity of the resulting Co-HTC process water (PW) on Daphnia magna. Three hydrochars were produced by Co-HTC at 180, 215, and 250 °C and named H-180, H-215, and H-250, respectively. For methylene blue adsorption, H-180 and H-215 had a better performance than H-250. Both H-180 and H-215 presented a maximum adsorption capacity of approximately 70 mg·g−1, which was superior compared with the adsorption of methylene blue by other hydrochars in the literature. Moreover, the removal percentage obtained with H-180 remained satisfactory even after five cycles. Regarding the toxicological assays of the PWs, raising the Co-HTC temperature increased the variety of substances in the PW composition, resulting in higher toxicity to D. magna. The EC50 values of PW-180, PW-215, and PW-250 were 1.13%, 0.97%, and 0.51%, respectively. This highlights the importance of searching for the treatment and valorization of the PW. Instead of viewing this by-product as an effluent to be treated and disposed of, it is imperative to assess the potential of PWs for obtaining other higher added-value products.
Explainable Artificial Intelligence with Integrated Gradients for the Detection of Adversarial Attacks on Text Classifiers
Authors: Moraliyage Harsha , Kulawardana Geemini , De Silva Daswin , Issadeen Zafar , Manic Milos , Katsura Seiichiro
Published: January 2025

Text classifiers are Artificial Intelligence (AI) models used to classify new documents or text vectors into predefined classes. They are typically built using supervised learning algorithms and labelled datasets. Text classifiers produce a predefined class as an output, which also makes them susceptible to adversarial attacks. Text classifiers with high accuracy that are trained using complex deep learning algorithms are equally susceptible to adversarial examples, due to subtle differences that are indiscernible to human experts. Recent work in this space is mostly focused on improving adversarial robustness and adversarial example detection, instead of detecting adversarial attacks. In this paper, we propose a novel approach, explainable AI with integrated gradients (IGs) for the detection of adversarial attacks on text classifiers. This approach uses IGs to unpack model behavior and identify terms that positively and negatively influence the target prediction. Instead of random substitution of words in the input, we select the top p% words with the greatest positive and negative influence as substitute candidates using attribution scores obtained from IGs to generate k samples of transformed inputs by replacing them with synonyms. This approach does not require changes to the model architecture or the training algorithm. The approach was empirically evaluated on three benchmark datasets, IMDB, SST-2, and AG News. Our approach outperforms baseline models on word substitution rate, detection accuracy, and F1 scores while maintaining equivalent detection performance against adversarial attacks.
Unraveling Elastic Fiber-Derived Signaling in Arterial Aging and Related Arterial Diseases
Authors: Wang Mingyi , McGraw Kimberly R. , Monticone Robert E. , Pintus Gianfranco
Published: January 2025

Arterial stiffening is a significant risk factor for the development of cardiovascular diseases, including hypertension, atherosclerosis, and arteriopathy. The destruction of elastic fibers, accompanied by vascular inflammatory remodeling, is a key process in the progression of arterial stiffening and related pathologies. In young, healthy arteries, intact elastic fibers create a resilient microenvironment that maintains the quiescence of arterial cells. However, with advancing age, these elastic fibers undergo post-translational modifications, such as oxidation, glycosylation, and calcification, leading to their eventual degeneration. This degeneration results in the release of degraded peptides and the formation of an inflammatory, stiffened niche. Elastic fiber degeneration profoundly impacts the proinflammatory phenotypes and behaviors of various arterial cells, including endothelial cells, smooth muscle cells, macrophages, fibroblasts, and mast cells. Notably, the degraded elastic fibers release elastin-derived peptides (EDPs), which act as potent inflammatory molecules. EDPs activate various arterial cellular processes, including inflammatory secretion, cell migration, proliferation, and calcification, by interacting with the elastin receptor complex (ERC). These elastin-related cellular events are commonly observed with aging and in diseased arteries. These findings suggest that the degeneration of the elastic fiber meshwork is a primary event driving arterial inflammation, stiffening, and adverse remodeling with advancing age. Therefore, preserving elastic fibers and blocking the EDP/ERC signaling pathways may offer promising therapeutic strategies for mitigating age-related arterial remodeling and related arterial diseases.
The Versatility of Serine Proteases from Brazilian Bothrops Venom: Their Roles in Snakebites and Drug Discovery
Authors: Romanazzi Marcela , Filardi Eloise T. M. , Pires Geovanna M. M. , Cerveja Marcos F. , Melo-dos-Santos Guilherme , Oliveira Isadora S. , Ferreira Isabela G. , Cerni Felipe A. , Santos-Filho Norival Alves , Monteiro Wuelton M. , Almeida José R. , Vaiyapuri Sakthivel , Pucca Manuela B.
Published: January 2025

Serine proteases are multifunctional and versatile venom components found in viper snakes, including the Bothrops species, a widely distributed genus notorious for causing the highest number of snakebites across Latin America. These enzymes, representing a significant fraction of Bothrops venom proteomes, exhibit a wide range of biological activities that influence blood coagulation, fibrinolysis, and inflammation. This review provides a comprehensive overview of serine proteases, with a particular focus on those found in the venom of Brazilian Bothrops snakes. The discussion begins with a summary of snake species found in Brazil and their medical relevance. Specifically addressing the Bothrops genus, this review explores the distribution of these species across Brazilian territory and their associated medical importance. Subsequently, the article investigates the biochemistry of Bothrops venoms and the clinical manifestations induced by envenomation. Finally, it offers an in-depth discussion on the serine proteases, highlighting their biochemical properties, mechanisms of action, and potential therapeutic applications. Furthermore, this review provides an in-depth exploration of the diverse serine proteases found in Bothrops venoms and their functional significance, from thrombin-like effects to potent fibrinogenolytic actions, which determine the clinical manifestations of envenomation. This review delves into the evolutionary adaptations and biochemical diversity of serine proteases in Bothrops venoms, emphasizing their critical roles in venom functionality and the resulting pathophysiological effects. Additionally, it opens new avenues for utilizing these enzymes in biomedical applications, underscoring their potential beyond toxinology.
Pig and Cow Blood During Cold Storage in CPDA-1 Solution: Hematology and Fluid Behavior
Authors: Windberger Ursula , Sparer Andreas
Published: January 2025

Nature equipped red blood cells (RBCs) with diverse mechanical properties, which makes it possible to examine blood with different RBC properties (size, shape, aggregability, deformability). We investigated whether the shelf life of cow blood (stiff RBCs, low aggregability) is longer compared with pig blood (deformability/aggregability comparable to human) due to a delay in RBC clustering and decomposition. Blood was drawn from conscious pigs and cows in their familiar environment to reduce stress and stored 30 days at +7 °C. RBCs remained intact in cow samples whereas pig samples became hemolytic after day 20. White blood cells and platelets decreased with similar percentages in both species. Hematocrit (HCT) decreased due to RBC shrinking in bovine samples and due to RBC decay in porcine samples. Blood viscosity increased in both species although HCT decreased. In porcine samples, shear thinning decreased progressively, indicating a gradual loss of sample cohesion with storage. Yield stress and storage modulus decreased with hemolysis. In HCT-native cow samples, shear thinning, yield stress, and storage modulus showed high intraindividual variability, but the mean values did not change over the time course. In HCT-adjusted (38%) cow samples, solidification occurred after day 7, followed by a reduction in cohesion and shear thinning until the end of storage.
Vigorous Exercise Enhances Verbal Fluency Performance in Healthy Young Adults
Authors: Khanna Maya M. , Guenther Corey L. , Eckerson Joan M. , Talamante Dion , Yeh Mary Elizabeth , Forby Megan , Hopkins Krystal , Munger Emmali , Rauh Grace , Chelluri Shringala , Schmidt Courtney , Walocha Isabel , Sacco Matthew
Published: January 2025

Background/Objectives: We examined the effects of cardiovascular exercise on verbal fluency using a between-groups design. Methods: Within our experimental (i.e., exercise) group, participants performed phonemic and semantic verbal fluency tasks (VFTs) before, during, and after a vigorous 30 min bout of cycling. Participants within our control group also completed these VFTs before, during, and after a non-physical activity. We compared the VFT performance of the experimental (exercise) and control (no-exercise) groups of participants in terms of the characteristics of the words that they produced within the VFTs. In addition, we examined these aspects of VFT performance for each participant group across time within the experiment session. Conclusions: From these comparisons, we see that exercise influenced VFT performance. Most notably, participants engaged in exercise changed their VFT performance over time, while control group participants did not. Exercising participants produced more words over the course of their exercise session that contained fewer letters over time and were lower in frequency during and after exercise as compared to before exercise. Additionally, topic switches in the VFTs increased after exercise as compared to before exercise. Participants in the control group did not change their VFT performance over time according to any of these measures. These findings indicate that exercise impacted participants’ lexical access and that these VFT performance changes were not due to practice effects.
Fear of Missing out and Online Social Anxiety in University Students: Mediation by Irrational Procrastination and Media Multitasking
Authors: Wu Weimiao , Zhang Jie , Jo Namjeong
Published: January 2025

With the rapid growth of internet mobile technology, recent research has increasingly focused on the mental health challenges faced by young people, particularly in relation to social media use. One significant concern is the impact of the fear of missing out (FoMO) and online social anxiety, yet the underlying mechanisms that link these factors remain largely unexplored. This study aims to address this gap by investigating the role of FoMO in predicting online social anxiety among university students, with a particular focus on understanding how irrational procrastination and media multitasking may mediate this relationship. In total, 451 university students completed a survey on demographics, FoMO, online social anxiety, irrational procrastination, and media multitasking questionnaires. After controlling for demographic variables, the findings revealed that (a) FoMO showed a significant positive correlation with online social anxiety; (b) the connection between FoMO and online social anxiety in university students was partially mediated by irrational procrastination; and (c) the connection between FoMO and online social anxiety in university students was partially mediated by media multitasking. This research contributes to the understanding of the psychological mechanisms that link FoMO to online social anxiety, offering insights that can inform interventions aimed at improving university students’ mental health in the digital age.
Effect of Grouting, Concrete Cover, and Combined Reinforcement on Masonry Retaining Walls
Authors: Cheng Wei , Zhu Cong , Shi Gongzuo , Liu Ze , Liu Cheng , Du Yinguang , Chen Yu , Zhuang Changchun , Gu Hongqiang
Published: January 2025

Masonry retaining walls used in civil engineering projects, such as highway embankments and slope protections, easily crack due to complex internal pore structures and exposure to harsh environmental conditions. To address these problems, practical reinforcement methods, including grouting reinforcement, concrete cover reinforcement, and combined reinforcement, were proposed to maintain retaining walls in this study. Nine cases of different reinforcement schemes were adopted to investigate the effects of grouting volumes, grouting hole numbers, and reinforcement methods. The results showed that as the grouting volume and grouting hole numbers increased, the cracks occurred at a lower height, showing a higher moment resistance capacity. In addition, the cracking moment was enhanced with a thicker concrete cover. Furthermore, combined grouting and concrete cover reinforcement improved the structural integrity and showed the best performance, in which the failure mode shifted from brittle to ductile. However, concrete cover reinforcement is associated with a higher price and longer construction cycle. Thus, decisions should be made depending on the engineering requirement.
Exploring the Influence of Environmental Characteristics on Emotional Perceptions in Metro Station Spaces
Authors: Shi Hedi , Chen Jianfei , Feng Zuhan , Liu Tong , Sun Donghui , Zhou Xiaolu
Published: January 2025

With the rapid expansion of urban metro networks, metro stations, as critical public spaces, not only influence transit efficiency and spatial layout optimization but also play a vital role in shaping users’ emotional experiences. This study examines metro station spaces as its primary research subject, exploring the correlation between physical environmental features and emotional perception within the framework of environmental psychology theory. This study adopts an innovative approach by integrating deep learning-based affective computing methods with semantic segmentation techniques in computer vision to systematically evaluate the impact of various physical environmental features and functional spaces on users’ emotional perceptions across multiple dimensions. The study provides empirical evidence for assessing and interpreting the relationship between environmental features and emotional perception, thereby enhancing the reliability of the research. The findings, quantified through deep learning methods, identify key factors influencing various emotional perception scores in metro stations. These insights will assist practitioners in gaining a deeper understanding of how metro station spaces impact users’ emotional experiences and can be applied to early-stage design and later-stage optimization of metro station spaces.
Sustainable Architecture and Human Health: A Case for Effective Circadian Daylighting Metrics
Authors: Mukherjee Bhaswati , Boubekri Mohamed
Published: January 2025

The development of the fluorescent lamp and the air-conditioning system resulted in buildings being lit inexpensively without having to rely on daylighting to save energy, as was the case during the incandescent lamp era. Consequently, architects were able to design buildings with deep floor plates for maximum occupancy, placing workstations far away from windows since daylighting was no longer a necessity. Floor-to-ceiling heights became lower to minimize the inhabitable volumes that needed to be cooled or heated. With the rising costs of land in some major American cities such as New York City and Chicago at the beginning of the twentieth century, developers sought to optimize their investments by erecting tall structures, giving rise to densely inhabited city centers with massive street canyons that limit sunlight access in the streets. Today, there is growing awareness in terms of the impact of the built environment on people’s health especially in terms of the health benefits of natural light. The fact that buildings, through their shapes and envelope, filter a large amount of daylight, which may impact building occupants’ health and well-being, should cause architects and building developers to take this issue seriously. The amount and quality of light we receive daily impacts many of our bodily functions and consequently several aspects of our health and well-being. The human circadian rhythm is entrained by intrinsically photosensitive retinal ganglion cells (ipRGCs) in our eyes that are responsible for non-visual responses due to the presence of a short-wavelength sensitive pigment called melanopsin. The entrainment of the circadian rhythm depends on several factors such as the intensity, wavelength, timing, and duration of light exposure. Recently, this field of research has gained popularity, and several researchers have tried to create metrics to quantify photopic light, which is the standard way of measuring visual light, into a measure of circadian effective lighting. This paper discusses the relationship between different parameters of daylighting and their non-visual effects on the human body. It also summarizes the existing metrics of daylighting, especially those focusing on its effects on the human circadian rhythm and its shortcomings. Finally, it discusses areas of future research that can address these shortcomings and potentially pave the way for a universally acceptable standardized metric.
Mapping Human Uterine Disorders Through Single-Cell Transcriptomics
Authors: Boldu-Fernández Sandra , Lliberos Carolina , Simon Carlos , Mas Aymara
Published: January 2025

Disruptions in uterine tissue function contribute to disorders such as endometriosis, adenomyosis, endometrial cancer, and fibroids, which all significantly impact health and fertility. Advances in transcriptomics, particularly single-cell RNA sequencing, have revolutionized uterine biological research by revealing the cellular heterogeneity and molecular mechanisms underlying disease states. Single-cell RNA sequencing and spatial transcriptomics have mapped endometrial and myometrial cellular landscapes, which helped to identify critical cell types, signaling pathways, and phase-specific dynamics. Said transcriptomic technologies also identified stromal and immune cell dysfunctions, such as fibroblast-to-myofibroblast transitions and impaired macrophage activity, which drive fibrosis, chronic inflammation, and lesion persistence in endometriosis. For endometrial cancer, scRNA-seq uncovered tumor microenvironmental complexities, identifying cancer-associated fibroblast subtypes and immune cell profiles contributing to progression and therapeutic resistance. Similarly, studies on adenomyosis highlighted disrupted signaling pathways, including Wnt and VEGF, and novel progenitor cell populations linked to tissue invasion and neuroinflammation, while single-cell approaches characterized smooth muscle and fibroblast subpopulations in uterine fibroids, elucidating their roles in extracellular matrix remodeling and signaling pathways like ERK and mTOR. Despite challenges such as scalability and reproducibility, single-cell transcriptomic approaches may have potential applications in biomarker discovery, therapeutic target identification, and personalized medicine in gynecological disorders.
Exploring Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard
Authors: Dauda Kazeem A. , Adeniyi Ebenezer J. , Lamidi Rasheed K. , Wahab Olalekan T.
Published: January 2025

High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. Moreover, in microarray research, it is common to identify groupings of genes involved in the same biological pathways. These gene groupings frequently collaborate and operate as a unified entity. Therefore, this study is motivated to adopt the idea of a penalized semi-parametric Bayesian Cox (PSBC) model through elastic-net and group lasso penalty functions (PSBC-EN and PSBC-GL) to incorporate the grouping structure of the covariates (genes) and optimally perform variable selection. The proposed methods assign a beta process prior to the cumulative baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma process prior used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and simulation scenarios were considered to compare and validate the efficiency of the modified methods with existing techniques, using Bayesian information criteria (BIC). The results of the simulated studies provided empirical evidence that the proposed methods performed better than the existing methods across a wide range of data scenarios. Similarly, the results of the real-life study showed that the proposed methods revealed a substantial improvement over the existing techniques in terms of feature selection and grouping behavior.
An Interfacial Study of Sucrose Ester-Stabilized Water-Free Foams
Authors: Kaade Wael , Drouet Lucille , Dousset Capucine , Daniellou Richard , Huc-Mathis Delphine
Published: January 2025

Sucrose esters are biodegradable surfactants widely used in the food and cosmetic industries due to their vast range of hydrophilic to lipophilic balance (HLB) values. However, their application has mostly been reported in aqueous media. With the rising demand for water-free products, a gap in the literature exists with regards to water-free colloids stabilized by sucrose esters. In particular, only two articles exist on sucrose ester-stabilized water-free foams, or oleofoams. This article explores for the first time the effects of sucrose ester HLB value on the physical properties of foams. The effects of oil triglyceride content on foamability were studied. The results showed that higher temperatures (90–100 °C) were needed to solubilize more hydrophilic sucrose esters, and these rendered the most encapsulated air (up to 62%) due to their high monoester content (>50%). Surface tension results also showed that the more hydrophilic sucrose esters reduced the oil/air surface tension the most. Regarding the oil triglyceride profile, results showed that with longer fatty acid chains, more air was incorporated into the foam. Sucrose esters have important untapped potential for use in the formation and stabilization of water-free foams.
Keeping the Ductus Arteriosus Patent: Current Strategy and Perspectives
Authors: Chatziantoniou Anastasios , Rorris Filippos-Paschalis , Samanidis George , Kanakis Meletios
Published: January 2025

Patent ductus arteriosus (PDA) continues to be a significant finding among infants, as well as adults. What is widely considered to be a problem though, in a group of patients with congenital heart disease, it is the only lifeline. We will initially study the anatomical and biochemical mechanisms affecting the PDA. The main focus of this review is on ductal-dependent congenital heart disease and biochemical and pharmacological approaches to maintaining ductus arteriosus patency, as well as surgical and interventional options for maintaining circulation. The present review aims to highlight gaps in the knowledge regarding the multifunctional role of ductus arteriosus endothelium and possibly propose a new approach to pharmacological maintenance of ductus arteriosus patency.
Local Equilibrium in Transient Heat Conduction
Authors: Glavatskiy Kirill
Published: January 2025

Extended irreversible thermodynamics (EIT) has been widely used to overcome the deficiencies of classical irreversible thermodynamics in describing fast transport phenomena. By employing fluxes as additional independent variables and rejecting local equilibrium hypothesis, EIT may provide a thermodynamically consistent framework for high-frequency and non-local processes. Here, we propose an alternative approach to EIT that shares the same objective but does not reject local equilibrium hypothesis. Using the rates of change of the energy density as the additional independent variable, we illustrate this approach for two typical problems of transient heat conduction: the Cattaneo-type flux model with thermodynamic inertia and the two-temperature model of energy transfer in a phonon–electron system.