bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Airflow-Assisted Parameters on Droplet Deposition on Soybean Leaves at the V7 Growth Stage
Authors: Guo Yuefu , Wang Hao , Sun Wenfeng , Sun Yongli , Xing Rui , Zhang Kaige , Fang Xiaocheng , Sui Bin , Xu Jiehao
Published: January 2025

In agricultural production, the underside of crop leaves and the middle-lower canopy are key areas where pests and diseases typically develop at early stages. Increasing droplet deposition in these critical regions is essential for improving pesticide efficacy and crop yield. This study aims to optimize airflow-assisted parameters to enhance spray operation quality. By extracting the physical characteristics of soybean leaves at the V7 growth stage and conducting theoretical analysis, the study explored the factors influencing leaf orientation and droplet deposition, as well as the coupling relationship between these two aspects. A one-way fluid–structure coupling model was established using COMSOL software 6.1 to simulate the interaction between airflow and soybean leaves. The simulation results showed that airflow caused 71.1% of upper leaves, 66.7% of middle leaves, and 43.3% of lower leaves to have a flipping angle greater than 10°, with most flipped leaves (61.9%) concentrated on the windward side. Using droplet deposition on the middle-lower canopy and the underside of leaves as evaluation indices, a numerical simulation orthogonal experiment was conducted. The results indicated that the optimal operational parameters were an initial airflow speed of 20 m/s, an outlet-to-canopy distance of 0.45 m, and a forward airflow deflection angle of 32°. This optimal parameter combination improved droplet deposition. Field experiments confirmed these results, showing that compared to the spraying without optimization, droplet deposition on the lower and middle canopy and the underside of the leaves increased by 2.1 times and 2.3 times, respectively.
Validation of a Portable Ionized Calcium Detection Device and Changes in the Ionized-to-Total-Calcium Ratio in the Blood of Postpartum Holstein Cows
Authors: Cao Zhongming , Zhao Yan , Zhang Bo , Kastelic John P. , Hu Mengjie , Cheng Jia , Liu Mingchao , Gao Jian
Published: January 2025

The calcium requirements of dairy cows increase dramatically soon after calving, and many cows have subclinical hypocalcemia, adversely affecting health and performance. Traditional laboratory tests for calcium are complex and not easily adapted to rapid point-of-care applications. The objectives were to evaluate a portable iCa testing device, Horiba LAQUAtwin Ca-11C, for measuring ionized calcium (iCa) in the whole blood of dairy cows and to investigate the iCa-to-total-calcium (tCa) ratio in blood collected from dairy cows within 9 days after calving. This study was conducted on two large dairies in northern China. First, blood samples were collected from 246 cows within 3 days after calving, and whole-blood iCa concentrations were measured with a Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1. In addition, 885 blood samples were collected from 102 dairy cows between calving and 9 days postpartum, with iCa concentrations measured using a Horiba LAQUAtwin Ca-11C and tCa concentrations determined by a fully automated biochemical analyzer. The Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1 had significant consistency in the measurement of ionized calcium concentration in whole blood (the frequency of differences between the measured values was within ±20% of the average, reaching 95.53%), and the measurement results of the two instruments were correlated (Deming regression analysis R2 = 0.87). This implied the potential application of the Horiba LAQUAtwin Ca-11C as a simplified device for measuring iCa in dairy farms for on-site testing. Within the first 9 days after calving, the ratio of ionized calcium to total calcium fluctuated but overall remained within the range of 44.2 to 47.22%. Furthermore, significant variations in the ratio among individual cows indicated that individual differences and physiological states of cows affected calcium metabolism. This study identified the potential to use the Horiba LAQUAtwin Ca-11C for point-of-care testing on dairy farms. Nevertheless, the impacts of factors such as health status and individual cow differences on the ratio of ionized calcium to total calcium still require further investigation.
Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network
Authors: Zhang Chen , Chen Qiunan , Zhou Wenbing , Huang Xiaocheng
Published: January 2025

Accurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Grey Wolf Optimization (IGWO), integrated with a BP neural network (IGWO-BP). Key enhancements such as cubic chaotic mapping, refraction backward learning, nonlinear convergence factors, and updated position formulas were applied to improve the algorithm’s search efficiency. By optimizing the neural network’s weights and biases, a precise relationship between rock mechanics and displacement was established. The method was validated through a case study of the Lianhua Tunnel (YK37 + 330 section), utilizing field data of crown settlement and peripheral displacement. The approach accurately predicted mechanical parameters, with relative errors below 5.02% for crown settlement and 4.15% for peripheral displacement. These results demonstrate the reliability and practical applicability of the proposed technique for tunnel engineering.
On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data
Authors: Hachaj Tomasz , Piekarczyk Marcin
Published: January 2025

In this paper, we address the issues of the explainability of reinforcement learning-based machine learning agents trained with Proximal Policy Optimization (PPO) that utilizes visual sensor data. We propose an algorithm that allows an effective and intuitive approximation of the PPO-trained neural network (NN). We conduct several experiments to confirm our method’s effectiveness. Our proposed method works well for scenarios where semantic clustering of the scene is possible. Our approach is based on the solid theoretical foundation of Gradient-weighted Class Activation Mapping (GradCAM) and Classification and Regression Tree with additional proxy geometry heuristics. It excels in the explanation process in a virtual simulation system based on a video system with relatively low resolution. Depending on the convolutional feature extractor of the PPO-trained neural network, our method obtains 0.945 to 0.968 accuracy of approximation of the black-box model. The proposed method has important application aspects. Through its use, it is possible to estimate the causes of specific decisions made by the neural network due to the current state of the observed environment. This estimation makes it possible to determine whether the network makes decisions as expected (decision-making is related to the model’s observation of objects belonging to different semantic classes in the environment) and to detect unexpected, seemingly chaotic behavior that might be, for example, the result of data bias, bad design of the reward function or insufficient generalization abilities of the model. We publish all source codes so our experiments can be reproduced.
Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
Authors: Lv Bin , Zhang Shuo , Gong Haixia , Zhang Hongbo , Dong Bin , Wang Jianzhu , Du Cong , Wu Jianqing
Published: January 2025

Balancing detection performance and computational efficiency is critical for sustainable pavement disease detection in energy-constrained scenarios. However, existing visual methods often struggle to adapt to structural transformations and capture critical features of pavement diseases in complex environments, while their computational demands can be resource-intensive. To address these challenges, this paper proposes a structure perception and feature attention network (SPFAN). The network includes a structure perception module that employs the updated deformable convolution technique. This technique enables the model to dynamically adjust and focus on the actual pavement disease regions, improving the accuracy of feature extraction, especially for diseases with irregular shapes and sizes. Additionally, the convolutional block attention module (CBAM) is integrated to optimize feature map attention across channel and spatial dimensions, enhancing the model focus on critical disease features without significantly increasing complexity. To further improve robustness, the generalized intersection over union (GIoU) loss function is adopted, ensuring better stability across targets of varying shapes and sizes. Experimental results on real-world pavement disease images show that the mAP@0.5 of the proposed SPFAN increases from 66.2% to 71.2%, an improvement of 7.55%, while the F1-score also increases by 9.03%, compared to the baseline YOLOv8n model. Furthermore, while achieving significant accuracy improvements, the proposed method maintains a similar parameter count as the baseline, preserving its low computational demands and high efficiency, making it suitable for real-time pavement damage detection in energy-constrained environments.
Optimization Tools for the Design of Meta-Covers for Linear Antenna with Beam- and Null-Steering Capabilities
Authors: Longhi Michela , Vellucci Stefano , Barbuto Mirko , Monti Alessio , Bilotti Filiberto , Toscano Alessandro
Published: January 2025

This paper investigates the optimization of cylindrical metasurface meta-covers designed to enable beamforming capabilities in single linear antennas. This study focuses on the development and application of advanced optimization tools to tailor the electromagnetic response of these metasurfaces, enabling precise control over the radiation patterns of the antenna. In particular, we develop a genetic algorithm-based optimization tool, which achieves precise manipulation of the main beam direction and null placement in the radiation pattern. The results further expand the applications of metasurface-based meta-covers in enhancing the functionality of dipole antennas in various communication and sensing systems.
Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis
Authors: Zhao Haili , Wang Jun , Wu Minghui
Published: January 2025

As one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing the exposure factors and studying the environmental background that affects the incidence can provide the basis for accurate prevention and control and promote the healthy and stable development of society. Based on the county scale, this study determined the high-incidence areas through hot spot analysis and selected nine districts and counties covering meteorological stations and air monitoring stations. The explanatory power of each factor to the incidence of pulmonary tuberculosis was analyzed by geographical detector, and the main influencing factors were explored. The results show that the following: (1) The number and incidence of pulmonary tuberculosis in Gansu Province declined from 2020 to 2022. (2) The influence of meteorological conditions such as temperature, precipitation and air pressure on pulmonary tuberculosis in different regions shows significant regional differences. Although the meteorological influence in adjacent regions shows certain convergence, the change in wind speed has no significant influence on the risk of pulmonary tuberculosis. (3) PM10, altitude, temperature, population density and GDP per capita have strong explanatory power to the incidence of tuberculosis, and the interaction between any two factors exceeds the effect of a single factor in explanatory power, showing the characteristics of two-factor enhancement and nonlinear enhancement.
Why the Real Atmosphere Has More Energy than Climate Models: Implications for Ground-Based Telescopes
Authors: Tuck Adrian F.
Published: January 2025

The calculation of Gibbs free energy via the statistical multifractal analysis of airborne observations indicates that the atmosphere is not at local thermodynamic equilibrium. Both climate models and meteorological analyses assume that it is. Satellite retrievals use spectroscopic data taken at equilibrium in laboratories, leading to apparent consistency that is to some degree faulty. Line shapes of radiatively active species, the rotational energy of molecular nitrogen and oxygen, and the translational energy of all molecules are involved, resulting in less energy in models than exists in the real atmosphere. The resulting formulation of turbulence is from the smallest scales up and has implications for astronomical observation by adaptive optics. Kolmogorov (isotropy) is not evident. The effect of temperature on the overhead water vapour column at ground-based telescopes is also open to the effects of climate change. The degree to which the dynamic operational temperature differs from that obtained by the use of local thermodynamic equilibrium assumptions needs to be established by observational measurements. Some of the considerations will apply to the atmospheres of exoplanets with regard to photochemistry and signatures of life.
Investigation of the Influence of Atmospheric Scattering on Photolysis Rates Using the Cloud-J Module
Authors: Imanova Anastasia , Rozanov Eugene , Smyshlyaev Sergei , Zubov Vladimir , Egorova Tatiana
Published: January 2025

This study analyses the wide-band algorithm, Cloud-J v.8.0, from the point of view of the validity of the choice of wide spectral intervals to accelerate the calculations of photolysis rates in the lower and middle atmosphere, considering the features of solar radiation propagation, and to assess the influence of the processes of reflection and scattering on molecules, aerosols, and clouds. The results show that the calculations performed using Cloud-J v.8.0 are in agreement with the data obtained using the high-resolution LibRadtran model. The study also considers the factors influencing the propagation of the solar flux through the atmosphere in Cloud-J v.8.0, which occurs following theoretical concepts. It is shown that the presence of cloud layers can increase photolysis rates by up to 40% in the above-cloud layer and decrease them by up to 20% below the cloud layer. The presence of volcanic aerosol can increase the photolysis rates in the upper part of the layer and above it by up to 75% and decrease them by up to 75% in the underlying atmosphere. Rayleigh scattering can both enhance photolysis rates in the troposphere and reduce them at large zenith angles. Thus, Cloud-J offers a robust method for modelling atmospheric photodissociation processes with high computational efficiency.
Integrated Analysis of microRNA Targets Reveals New Insights into Transcriptional–Post-Transcriptional Regulatory Cross-Talk
Authors: Panni Simona , Pizzolotto Roberto
Published: January 2025

It is becoming increasingly clear that microRNAs are key players in gene regulatory networks, modulating gene expression at post-transcriptional level. Their involvement in almost all cellular processes predicts their role in diseases, and several microRNA-based therapeutics are currently undergoing clinical testing. Despite their undeniable relevance and the substantial body of literature demonstrating their role in cancer and other pathologies, the identification of functional interactions is still challenging. To address this issue, several resources have been developed to collect information from the literature, according to different criteria and reliability scores. In the present study, we have constructed a network of verified microRNA-mRNA interactions by integrating strong-evidence couples from different resources. Our analysis of the resulting network reveals that only one-fifth of the human genes exhibits experimental validated regulation by microRNAs. A very small subset of them is controlled by more than 20 microRNAs, and these hubs are highly enriched of pivotal transcription factors and regulatory proteins, strongly suggesting a complex interplay and a combinatorial effect between transcriptional and post-transcriptional gene control. Data analysis also reveals that several microRNAs control multiple targets involved in the same pathway or biological process, likely contributing to the coordinated control of the protein levels.
Community Composition of Epibiont Hydroids of the Naturalized Alien Macroalga Acanthophora spicifera in Pichilingue, Mexico
Authors: Licona Angeles Jessica , Castañeda Rivero Francisco Rubén , Mendoza-Becerril María A.
Published: January 2025

This study provides a detailed analysis of the community of hydrozoan epibionts of Acanthophora spicifera in La Paz Bay, Baja California Sur. The investigation was carried out in two season periods, corresponding to the warm and cold seasons on the pier of the Autonomous University of Baja California Sur (UABCS) Pichilingue research station. The percentage of epibiont coverage was calculated and analyzed. The average length of the thalli was 7.8 ± 4.6 cm. Eleven taxa of hydrozoans were recorded; the hydrozoan Obelia cf. dichotoma had the highest coverage percentage, and in summer, there was a higher taxa richness. It is recommended to monitor this macroalga and its epibionts for its wide distribution in the bay, although in UABCS, Pichilingue Pier has not seen epibionts that threaten biological diversity.
PRDM16 Enhances Osteoblastogenic RUNX2 via Canonical WNT10b/β-CATENIN Pathway in Testosterone-Treated Hypogonadal Men
Authors: Bathina Siresha , Prado Mia , Fuenmayor Lopez Virginia , Colleluori Georgia , Aguirre Lina , Chen Rui , Villareal Dennis T. , Armamento-Villareal Reina
Published: January 2025

We previously reported that PRDM16 mediated the improvement in body composition in testosterone (T)-treated hypogonadal men by shifting adipogenesis to myogenesis. Previous preclinical studies suggest that Prdm16 regulates Runx2, an important osteoblastic transcription factor, expression and activity. However, the changes in PRDM16, and other genes/proteins involved in osteoblastogenesis with T therapy in hypogonadal men are unexplored. We investigated the role of PRDM16 in RUNX2 activation by measuring changes in gene expression in peripheral blood monocytes (PBMCs) and proteins in the serum of hypogonadal men after T therapy for 6 months. Likewise, we evaluated changes in the WNT10b—β-CATENIN signaling pathway by gene expression and protein analyses. We found significant increases in PRDM16 and RUNX2 expression in PBMCs together with significant increases in serum proteins at 6 months when compared to baseline. There were also increases in gene and protein expressions of WNT10b, and β-CATENIN at 6 months. Furthermore, we found a significant positive correlation between % changes in PRDM16 and WNT10b. Our results suggest that T therapy activates PRDM16, leading to enhanced signaling in the canonical WNT10b—β-CATENIN-RUNX2 pathway, the pathway involved in osteoblastogenesis. The above findings may account for the improvement in bone density and quality in hypogonadal men treated with T.
Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
Authors: Ha Jihwan
Published: January 2025

Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the underlying pathogenesis of human diseases. However, identifying miRNA–disease associations using biological experiments is inefficient in terms of cost and time. Methods: Here, we discuss a novel machine-learning model that effectively predicts disease-related miRNAs using a graph convolutional neural network with neural collaborative filtering (GCNCF). By applying the graph convolutional neural network, we could effectively capture important miRNAs and disease feature vectors present in the network while preserving the network structure. By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. Results: Extensive experimental results based on area under the curve (AUC) scores (0.9216 and 0.9018) demonstrated the superiority of our model over previous models. Conclusions: We anticipate that our model could not only serve as an effective tool for predicting disease-related miRNAs but could be employed as a universal computational framework for inferring relationships across biological entities.
Growth Propagation of Liquid Spawn on Non-Woven Hemp Mats to Inform Digital Biofabrication of Mycelium-Based Composites
Authors: Biront Andreas , Sillen Mart , Van Dijck Patrick , Wurm Jan
Published: January 2025

Mycelium-based composites (MBCs) are highly valued for their ability to transform low-value organic materials into sustainable building materials, offering significant potential for decarbonizing the construction sector. The properties of MBCs are influenced by factors such as the mycelium species, substrate materials, fabrication growth parameters, and post-processing. Traditional fabrication methods involve combining grain spawn with loose substrates in a mold to achieve specific single functional properties, such as strength, acoustic absorption, or thermal insulation. However, recent advancements have focused on digital biofabrication to optimize MBC properties and expand their application scope. Despite these developments, existing research predominantly explores the use of grain spawn inoculation, with little focus on liquid spawn. Liquid spawn, however, holds significant potential, particularly in digital biofabrication, due to its ease of deposition and greater precision compared with grains. This paper, part of a digital biofabrication framework, investigates the growth kinetics of Ganoderma lucidum and Pleurotus ostreatus on hemp non-woven mats, offering flexibility and mold-free fabrication using liquid inoculation. By integrating mycelium growth kinetics into digital biofabricated materials, researchers can develop more sustainable, efficient, and specialized solutions using fewer resources, enhancing the adaptability and functionality of MBCs. The experiment involved pre-cultivating P. ostreatus and G. lucidum in yeast peptone dextrose (YPD) and complete yeast media (CYM) under static (ST) and shaking (SH) conditions. Four dilutions (1:10, 1:2, 1:1, and 2:1) were prepared and analyzed through imagery to assess growth kinetics. Results showed that lower dilutions promoted faster growth with full coverage, while higher dilutions offered slower growth with partial coverage. SH conditions resulted in slightly higher coverage and faster growth. To optimize the control of material properties within the digital biofabrication system, it is recommended to use CYM ST for P. ostreatus and YPD SH for G. lucidum, as their growth curves show clear separation between dilutions, reflecting distinct growth efficiencies and speeds that can be selected for desired outcomes.
Literature Review on Mitigation Measures for Bird Electrocutions Occurring Due to Streamers on Transmission Power Lines
Authors: Durgapersad Kaajial , Beutel Andreas , Mahatho Nishal
Published: January 2025

Bird streamer faults occur when the streamer, i.e., bird excretion, bridges the gap between two energized components or an energized and an earthed component of a tower structure. This results in a short circuit, which may be accompanied by an outage in the electricity supply. Due to the impact of these faults on electrical infrastructure and bird mortality, a detailed literature review to identify effective mitigation measures for sub-transmission lines (44–132 kV) and transmission lines (132–765 kV) was conducted. The findings show that there are several measures used internationally to reduce streamer-related faults, e.g., bird perch deterrents, shields, changing tower design configurations, changing insulator types and properties, and bird runways. Bird perch deterrents are typically most effectively used by many utilities; however, it is imperative that the perch deterrents are placed and installed correctly. Placement should be above potential problematic areas—to prevent the bridging of the gaps—at various points on different tower structures. Moreover, bird guards need to be monitored and maintained to ensure effectiveness over time. The involvement of a range of stakeholders when making environmental management decisions, such as researching and implementing the best mitigation measures, is also critical to ensure continued success.
Relationship Between Participative Decision-Making Within an Organization and Employees’ Cognitive Flexibility, Creativity, and Voice Behavior
Authors: Kwon Sun-Hee , Kim Jeong-Sik
Published: January 2025

The existing literature predominantly examines the direct effects of participative decision-making, often overlooking the mechanisms and processes that mediate or moderate its outcomes. This study addresses this gap by investigating the impact of participative decision-making on employees’ cognitive flexibility, creativity, and voice behavior. Specific contradictions and gaps in prior research are highlighted, particularly the limited understanding of how these variables interact. This study is grounded in self-determination theory and incorporates a research model that examines these relationships through the mediating role of cognitive flexibility. Data were collected from 310 employees in South Korean firms and analyzed using structural equation modeling, providing robust empirical evidence. Key findings demonstrate that participative decision-making significantly enhances cognitive flexibility, which, in turn, mediates its impact on creativity and voice behavior. Direct effects were more pronounced for creativity, while cognitive flexibility played a stronger mediating role in fostering voice behavior. These results underscore the theoretical and practical importance of participative decision-making in fostering innovation and adaptability within organizations. Practical implications for managers and policymakers include fostering a participative culture to enhance employee creativity and voice behavior. Finally, this study discusses limitations, such as reliance on self-reported data, and provides directions for future research.
Does Caregiver Engagement Predict Outcomes of Adolescent Wilderness Therapy?
Authors: Bettmann Joanna E. , Gutierrez Naomi Martinez , Jolley Annelise , Mills Laura
Published: January 2025

Existing research shows some links between wilderness therapy outcomes and familial functioning. However, wilderness therapy programs do not agree on what kind of caregiver involvement is required to improve adolescent program outcomes, nor has research examined different types of family engagement and their impact on adolescent treatment outcomes. Thus, the present study explored the research question: Does caregiver engagement in adolescent wilderness therapy foster improved outcomes? The study sample consisted of 4067 adolescent wilderness therapy clients from 12 different wilderness therapy programs. Using standardized measures and multilevel structural equation modeling, the authors found that caregiver program participation significantly predicted adolescent mental health outcomes of the program, suggesting that the more caregivers were involved in family interventions during the program, the more likely their adolescent child was to improve in the program. The study also found that greater caregiver effort predicted greater mean change in adolescent mental health outcomes of wilderness therapy. This study suggests the importance of enhancing familial interventions in adolescents’ wilderness therapy programs in order to improve adolescent outcomes. Given findings from this study, wilderness therapy programs should consider expanding the ways that they involve families in treatment in order to optimize adolescent outcomes.
Effects of a Body-Based Mindfulness Program on Alexithymia, Dispositional Mindfulness, and Distress Symptoms: A Pilot Clinical Trial
Authors: Quinto Rossella Mattea , Russo Francesca , Scafuto Francesca , Innamorati Marco , Montecucco Federico Nitamo , Ghiroldi Silvia
Published: January 2025

Mind–body practices have shown to be effective in reducing psychological distress and improving well-being and quality of life in clinical samples and healthy populations. We tested the effects of a body-based mindfulness intervention based on alexithymia, distress symptoms, and dispositional mindfulness among healthy adults. A total of seventy-three participants (mean age 40.1) were assigned to a body-based mindfulness program (i.e., experimental group) and the control group (i.e., waiting list). Distress symptoms were assessed with the Symptoms Questionnaire; alexithymia was measured using the Toronto Alexithymia Scale; and dispositional mindfulness was assessed with the Five Facets Mindfulness Questionnaire. Measures were completed at baseline and at one-week post-test. Even when controlling for baseline score, the body-based mindfulness program has shown to be effective in reducing distress symptoms (i.e., anxiety, depression, and somatic symptoms) and alexithymia, as well as improving dispositional mindfulness (i.e., observing, acting with awareness, and non-reacting). The findings from this study provide evidence that the body-based mindfulness program may help individuals to integrate bodily expressions, thoughts, and emotions, keeping an integrated sense of self.
Recent Advances in Anticancer Strategies
Authors: Hu Zhiwei , Bousbaa Hassan
Published: January 2025

Due to the intricate nature of cancer development and progression, various types of cancer are increasingly prevalent worldwide [...]
Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study
Authors: Chang Shu-Yu , Chang Wen-Shin , Shih Hou-Yu , Chang Chao-Hsiang , Wu Hsi-Chin , Tsai Chia-Wen , Wang Yun-Chi , Gu Jian , Bau Da-Tian
Published: January 2025

Background: This study aimed to investigate the polymorphic genotypes of MDM2 rs937282, rs937283, rs2279744, and rs769412, as well as the combined effects of MDM2 genotypes and environmental factors on RCC susceptibility. Methods: A total of 135 RCC patients and 590 controls were recruited for MDM2 genotyping using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Quantitative PCR was performed to assess MDM2 mRNA levels among 30 healthy individuals and 22 RCC patients. Results: MDM2 rs2279744, but not other polymorphisms, was significantly associated with an increased RCC risk (p = 0.0133). The MDM2 rs2279744 G allele was identified as a risk factor for RCC (odds ratio [OR] = 1.49, 95% confidence interval [CI] = 1.14–1.96, p = 0.0047). Among smokers (p = 0.0070), alcohol drinkers (p = 0.0233), individuals with hypertension (p = 0.0041), diabetes (p = 0.0225), and those with a family history of cancer (p = 0.0020), the MDM2 rs2279744 GT and GG genotypes exhibited increased RCC risks. However, this risk effect was not observed in non-smokers, non-drinkers, or individuals without hypertension, diabetes, or a family cancer history (all p > 0.05). Moreover, MDM2 mRNA levels were significantly higher in RCC patients compared to controls and varied among the rs2279744 genotypes, with GG genotype exhibiting the highest expression levels among both RCC patients and controls. Conclusions: This study highlights the association between MDM2 rs2279744 genotypes and RCC risk, suggesting that genotype-associated MDM2 mRNA levels could contribute to early RCC detection. Further studies are warranted to elucidate the detailed mechanisms underlying the role of MDM2 in RCC development.
Electrodeposition of Bismuth Dendrites on Oxide-Derived Copper Foam Enhancing Electrochemical CO2 Reduction to Formate
Authors: Xu Jialin , Lv Li , Wang Chunlai , Liang Yun
Published: January 2025

The electrochemical CO2 reduction reaction (CO2RR) to formate offers a promising pathway to mitigate the energy crisis and realize carbon neutrality. Bismuth (Bi), as a metal catalyst for the CO2RR, is considered to have great potential in producing formate, yet hindered in low current density and selectivity. Herein, we constructed an oxide-derived copper foam substrate (OD-Cu) to improve the electrocatalytic properties of Bi dendrites loaded on its surface. Bi electrodeposited on the OD-Cu (Bi/OD-Cu) grows as pinecone-like dendrites, exhibiting a high formate faradaic efficiency (FEformate) of 97.2% and a formate partial current density of ~24 mA·cm−2 at −0.97 V vs. RHE (reversible hydrogen electrode) in an H-cell. Notably, the Bi/OD-Cu electrode demonstrates an FEformate of 95.8% at −0.97 V vs. RHE and a total current density close to 90 mA·cm−2 at −1.17 V vs. RHE in a neutral flow cell. The experimental studies reveal that the remarkable CO2RR performance of the Bi/OD-Cu results from the electron transfer from Cu to Bi, which optimizes adsorption of the CO2•− and boosts reaction kinetics. This study emphasizes the crucial role of substrate engineering strategies in enhancing catalytic activity and shows the possibility for a porous metal electrode in advancing the industrialization of formate production.
Non-Linear Support Vector Machine Prediction of the Mechanical Properties of Asphalt Binders Subjected to Varying Temperatures and Frequencies Based on SARA
Authors: Song Shanglin , Ma Yiqian , Jiang Xiaoqiang , Li Dengzhou , Ma Xiaoyan , Qiu Shidong
Published: January 2025

This study investigates the effects of chemical fractions on the mechanical properties of asphalt binders and predicts the mechanical properties of asphalt binders based on the chemical fractions. Initially, four fractions—saturate, aromatic, resin, and asphaltene (SARA)—were isolated from 36 asphalt binders using a thin-layer chromatography with flame ionization detection (TLC-FID) analyzer. Subsequently, the complex modulus and phase angle of the asphalt binders were determined for a range of frequencies and temperatures. The relationships between SARA content, heavy components, colloidal instability index, and the complex modulus and phase angle were analyzed. Advanced models, including quadratic polynomial and non-linear support vector machine (SVM) with sigmoid and RBF (Gaussian) kernels, were employed to predict the complex modulus and phase angle of asphalt binders based on the SARA data, and the reliability of these prediction models was critically assessed. The findings indicate that the contents of asphaltenes, resins, aromatics, and saturates significantly influence the rheological properties at different frequencies, though a clear correlation between SARA contents and both the complex modulus and phase angle was not established. Alternative methods should be considered for studying the mechanical properties of asphalt derived from SARA. The RBF kernel demonstrated superior performance compared to the quadratic polynomial and non-linear SVM with the Sigmoid kernel. While the non-linear SVM with the RBF kernel accurately predicts the complex modulus, it fails to predict the phase angle at low frequencies. The validation of this model confirmed its efficacy in capturing the relationship between input (SARA) and output (complex modulus and phase angle) vectors for each asphalt binder. The predicted complex modulus master curves closely match the experimental results, yet the model only approximates the trend of phase angle variation with frequency.
Shell-like ZnO–Graphene/Epoxy Coating with Outstanding Anticorrosion Performance and Weather Resistance
Authors: Wang Yu , Ma Lei , Niu Yanan , Ma Huachao , Lv Yuguang , Lv Kuilin
Published: January 2025

Throughout millions of years of biological evolution, shell structures have developed a highly complex layered organic–inorganic structure that makes them effective against a wide range of external impacts, including mechanical stress and chemical corrosion. Therefore, shell-like biomimetic materials are considered to possess high strength and toughness. Nevertheless, although shell structures have exhibited superior performance across multiple domains, understanding of their structural complexities and corrosion protection mechanisms remains relatively limited within the scope of human knowledge. In this study, alternating ZnO–graphene/epoxy coatings featuring shell-like structures were synthesized, and their anticorrosion properties were evaluated through the incorporation of ZnO to enhance the dispersion of graphene. Electrochemical impedance spectroscopy (EIS) tests showed that with an increased number of ZnO–graphene layers, the coating resistance of the bionic composite coating also increased: from 8.21 × 107 Ω·cm2 of the pure epoxy coating to 7.64 × 108 Ω cm2. The composite coating, comprising three alternating layers of zinc oxide and four layers of epoxy resin, exhibited an electrochemical impedance two orders of magnitude greater than that of pure epoxy resin following immersion in a 3.5% sodium chloride solution, demonstrating excellent corrosion resistance. The results showed that with increased ZnO–graphene layers, ZnO–graphene disperses more uniformly in water and has greater rigidity.
Visualizing Ambiguity: Analyzing Linguistic Ambiguity Resolution in Text-to-Image Models
Authors: Elsharif Wala , Alzubaidi Mahmood , She James , Agus Marco
Published: January 2025

Text-to-image models have demonstrated remarkable progress in generating visual content from textual descriptions. However, the presence of linguistic ambiguity in the text prompts poses a potential challenge to these models, possibly leading to undesired or inaccurate outputs. This work conducts a preliminary study and provides insights into how text-to-image diffusion models resolve linguistic ambiguity through a series of experiments. We investigate a set of prompts that exhibit different types of linguistic ambiguities with different models and the images they generate, focusing on how the models’ interpretations of linguistic ambiguity compare to those of humans. In addition, we present a curated dataset of ambiguous prompts and their corresponding images known as the Visual Linguistic Ambiguity Benchmark (V-LAB) dataset. Furthermore, we report a number of limitations and failure modes caused by linguistic ambiguity in text-to-image models and propose prompt engineering guidelines to minimize the impact of ambiguity. The findings of this exploratory study contribute to the ongoing improvement of text-to-image models and provide valuable insights for future advancements in the field.
Reputation-Based Leader Selection Consensus Algorithm with Rewards for Blockchain Technology
Authors: Hussain Munir , Mehmood Amjad , Khan Muhammad Altaf , Khan Rabia , Lloret Jaime
Published: January 2025

Blockchain technology is an emerging decentralized and distributed technology that can maintain data security. It has the potential to transform many sectors completely. The core component of blockchain networks is the consensus algorithm because its efficiency, security, and scalability depend on it. A consensus problem is a difficult and significant task that must be considered carefully in a blockchain network. It has several practical applications such as distributed computing, load balancing, and blockchain transaction validation. Even though a lot of consensus algorithms have been proposed, the majority of them require many computational and communication resources. Similarly, they also suffer from high latency and low throughput. In this work, we proposed a new consensus algorithm for consortium blockchain for a leader selection using the reputation value of nodes and the voting process to ensure high performance. A security analysis is conducted to demonstrate the security of the proposed algorithm. The outcomes show that the proposed algorithm provides a strong defense against the network nodes’ abnormal behavior. The performance analysis is performed by using Hyperledger Fabric v2.1 and the results show that it performs better in terms of throughput, latency, CPU utilization, and communications costs than its rivals Trust-Varying Algo, FP-BFT, and Scalable and Trust-based algorithms.
Engaging Nurses in Effective Cost of Care Conversations to Address Cancer-Related Financial Toxicity: Results from an Exploratory Survey
Authors: Edward Jean S. , Wiggins Amanda Thaxton , Baser Louis G. , Fariduddin Haafsah , Doran Joanna F. , Bryant Monica F. , D’Orazio John A. , Northrip Kimberly D.
Published: January 2025

Few evidence-based trainings exist on how to equip healthcare providers, particularly nurses, with the skills to engage in cost of care conversations with patients/caregivers to mitigate the impact of cancer-related financial toxicity. This study evaluated a pilot training developed in collaboration with Triage Cancer® to prepare oncology nurses to identify and assist patients/caregivers facing financial and/or legal barriers to care. Ten pediatric oncology nurses completed the training and pre/post-surveys on behaviors related to financial and legal need screening, frequency and comfort level of answering questions, knowledge, and behavior changes, along with training evaluation questions. At baseline, six nurses reported never screening for financial needs and nine for legal needs. Following the training, seven nurses stated they were likely to screen for financial/legal needs. At six months post-training, nurses had referred 85 patients/caregivers to financial/legal navigation services. Comfort levels in answering financial/legal questions increased by 6.5 points and knowledge scores increased by 1.7 points post-training. Most nurses recommended this training to other healthcare providers who work with patients with cancer and their caregivers. This study highlights the importance of providing oncology nurses with resources to engage in cost of care conversations and oncology financial legal navigation programs to mitigate the impact of cancer-related financial toxicity.
How to Deal with Pulpitis: An Overview of New Approaches
Authors: Fiegler-Rudol Jakub , Niemczyk Wojciech , Janik Katarzyna , Zawilska Anna , Kępa Małgorzata , Tanasiewicz Marta
Published: January 2025

Background: Traditional root canal therapy (RCT) effectively removes diseased or necrotic pulp tissue and replaces it with inorganic materials. Regenerative endodontics is an alternative to conventional RCT by using biologically based approaches to restore the pulp–dentin complex. This review explores emerging techniques, including autogenic and allogenic pulp transplantation, platelet-rich fibrin, human amniotic membrane scaffolds, specialized pro-resolving mediators, nanofibrous and bioceramic scaffolds, injectable hydrogels, dentin matrix proteins, and cell-homing strategies. These methods utilize stem cells, growth factors, and biomaterials to regenerate vascularized, functional pulp tissue. Methods: A narrative review was conducted using PubMed, Scopus, and Embase to identify studies published between 2010 and 2023. In vitro, animal, and clinical studies focusing on innovative regenerative endodontic techniques were analyzed. Conclusions: Although regenerative endodontics demonstrates great potential, challenges remain in standardizing protocols, addressing biological variability, and achieving consistent clinical outcomes. Future research must focus on refining these techniques to ensure their safety, efficacy, and accessibility in routine practice. By addressing current limitations, regenerative endodontics could redefine the management of pulpitis, offering biologically based treatments that enhance tooth vitality, structural integrity, and long-term prognosis.
Optimal Time Series Forecasting Through the GARMA Model
Authors: Gadhi Adel Hassan A. , Peiris Shelton , Allen David E. , Hunt Richard
Published: January 2025

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using popular datasets such as sunspot data and Australian beer production data. This approach provides a robust framework for accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other implemented models, such as GARMA, TBATS, ARIMA, and ANN, highlighting its ability to address both long-memory and non-linear dynamics. Finally, we discuss the representativeness of the datasets selected and the adaptability of the proposed hybrid model to various time series scenarios.
Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments
Authors: Terrazas-Arellanes Fatima Elvira , Strycker Lisa , Alvez Giani Gabriel , Miller Bailey , Vargas Kathryn
Published: January 2025

As schools increasingly adopt multidimensional, phenomenon-based, digital-technology-enhanced science instruction, a concurrent shift is occurring in student performance assessment. Assessment instruments capable of measuring multiple dimensions must incorporate constructed responses to probe students’ ability to explain scientific phenomena and solve problems. Such assessments, unlike traditional multiple-choice tests, are time-consuming and labor-intensive for teachers to score. This study investigates the potential of an artificial intelligence machine learning system (AI-MLS) to address two critical questions: (1) How accurately can the AI-MLS replicate human scoring of multidimensional science assessments? and (2) How can the implementation of AI-MLS promote educational equity and reduce teacher workload? The present paper describes the development of the AI-MLS to rapidly and accurately score third- to fifth-grade students’ constructed responses on multidimensional science assessments. It summarizes key findings from the study, discusses findings in the broader context of fostering agency through digital technology, and offers insights into how artificial intelligence technology can be harnessed to support independent action and decision-making by teachers and students.
Multiselfing in Music Education
Authors: Cremata Radio
Published: January 2025

Multiselfing is a form of musicianship where one person digitally clones themself into several single selves, creating layers and a musical collective that would otherwise be impossible without the mediation of technology. There are various kinds of multiselfers. This article categorizes them as the following: singers, instrumentalists, loopers, live performers, and hybrids. While these five categories are presented distinctly here, they may often overlap. This article explores the notion of multiselfing and its implicit potential when situated in music education to develop comprehensive music skills. Comprehensive musicianship is important because it enables students to grow in broad musical knowledge and skills at all levels of instruction by synthesizing the musical materials they are working with and by making conceptual connections through performance, analysis, and composition. In addition to including many examples, this article also includes lists of resources and applications to help schoolteachers better understand how to integrate multiselfing into their pedagogic practices.