bluuebunny/crossref_metadata_2025 huggingface dataset:
Fermented Cassava Residue Meal Improves Meat Quality by Regulating Muscle Fiber and Enhancing Lipid Metabolism in Huanjiang Mini-Pigs
Authors: Jiang Huijiao , Azad Md. Abul Kalam , Zhu Qian , Ni Hengjia , Kong Xiangfeng
Published: January 2025

This research investigated the effects of cassava residue meal (CRM) and fermented CRM (FCRM) on the growth performance, serum lipid indicators, carcass traits, and meat quality of Huanjiang mini-pigs. One hundred twenty Huanjiang mini-pigs with similar BW (body weight, 8.85 ± 0.64 kg) were divided into three dietary treatment groups: the CON group with a basal diet; the CRM group with a diet containing 5% CRM; and the FCRM group with a diet containing 5% FCRM. The feeding trial was conducted for 30 days. The findings revealed that dietary CRM and FCRM did not influence the growth performance and diarrhea rate of Huanjiang mini-pigs (p > 0.05). Dietary FCRM supplementation increased serum total cholesterol level compared to the CON group while decreasing serum cholinesterase level compared to the CON and CRM groups (p < 0.05). The level of serum low-density lipoprotein-cholesterol was higher (p < 0.05) in the CRM and FCRM groups in regard to the CON group. In addition, CRM and FCRM supplementation reduced (p < 0.05) the backfat thickness and carcass yield of Huanjiang mini-pigs. The meat quality analysis showed that dietary CRM and FCRM improved the meat quality by increasing the intramuscular fat content and redness (a*) value and decreasing the lightness (L*) value. Moreover, the MyHC-IIx expression in the longissimus thoracis (LT) muscle was upregulated (p < 0.05) in the CRM group, while MYHC-IIb expression displayed an increasing trend (p = 0.076) in the FCRM group relative to the CON group. The fatty acid composition in the LT muscle also revealed that the C20:0 level was lower in the CRM and FCRM groups, while the ∑SFA and ∑SFA/∑UFA were reduced (p < 0.05) in the FCRM group relative to the CON group. In summary, dietary CRM and FCRM supplementation improved the meat quality of Huanjiang mini-pigs without affecting growth performance and diarrhea rate. Notably, FCRM exhibited better effects on meat quality than CRM.
Occurrence and Multi-Locus Genotyping of Giardia duodenalis in Black Goats from Fujian Province, China
Authors: Huang Shou-Xiao , Hu Kai , Fu Peng-Fei , Li Si-Ang , Liu Yang , Niu Zhipeng , Zhou Dong-Hui
Published: January 2025

Giardia duodenalis is a zoonotic parasite that causes gastrointestinal diseases in both humans and animals. To evaluate the prevalence and genetic diversity of G. duodenalis in black goats, we collected 539 fecal samples from nine districts in Fujian Province, China. The presence of G. duodenalis was confirmed through nested PCR targeting the SSU rRNA gene, and genotyping was performed at the beta-giardin, glutamate dehydrogenase, and triosephosphate isomerase loci. Among the samples, 115 tested positive, yielding an overall infection rate of 21.34%. Assemblages A and E were identified, with assemblage E being predominant. Statistical analysis revealed significant regional differences in infection rates (p < 0.01), with Zhangzhou exhibiting the highest infection rate (39%) and Fuzhou the lowest (3.13%). No significant differences in infection rates were observed based on age: 24.56% (56/228) for goats <1 year, 14.92% (27/181) for goats 1–2 years, 26.8% (26/97) for goats 2–3 years, and 18.18% (6/33) for goats ≥ 3 years. Similarly, no significant differences were found between sexes: 24.84% (40/161) for males and 19.84% (75/378) for females. Notably, assemblage A, a zoonotic genotype, was detected, indicating a potential risk of cross-species transmission. This study contributes to a deeper understanding of G. duodenalis in black goats and provides critical data for the development of targeted control strategies in Fujian Province.
Study of Interface Adhesion Between Polyurethane and Aggregate Based on Surface Free Energy Theory and Molecular Dynamics Simulation
Authors: Zhang Zengping , Ji Jiajun , Li Xue , Shi Zhongbao , Wang Junjie
Published: January 2025

In order to eliminate the negative effects caused by traditional pavements, permeable pavements are gradually being used in road construction. In recent years, polyurethane (PU) has been used as a new binder in permeable pavement mixtures. However, compared to traditional pavement mixtures, the adhesion properties between PU and aggregate have not been systematically analyzed. In addition, no clear standards have been established for the performance testing of PU mixtures, posing significant challenges for the selection of materials and the optimization of formulations for PU mixtures. Therefore, this paper proposes new methods for evaluating the performance of PU mixtures from a microscopic point of view, aiming at evaluating the adhesion properties between PU and aggregates. In this study, a PU binder was synthesized. The adhesion properties of this PU binder with aggregate were evaluated by surface free energy measurement and molecular dynamics (MD) simulation. Finally, the effects of different environmental conditions and aggregate types on the PU–aggregate adhesion properties were investigated. The results showed that the adhesion between PU and basalt is consistently better than that with limestone, although the adhesion between PU and aggregate decreased under acidic conditions. It implies that the PU–basalt mixture has better water resistance than the PU–limestone mixture. Furthermore, the results of the surface free energy measurements and MD simulations for the evaluation of adhesion at the PU–aggregate interface showed good correlation with the macroscopic performance experiments, which can be extended to the study of the adhesion properties of other materials.
Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering
Authors: Fei Yunqiao , Fan Jingchao , Zhou Guomin
Published: January 2025

In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field.
PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
Authors: Jia Huidi , Wang Qiang , Fu Bo , Zheng Zhimin , Tang Yandong
Published: January 2025

Light scattering and attenuation in water degrade underwater images with low visibility and color distortion, which often interfere with the high-level visual tasks of underwater autonomous robots. Most existing deep learning methods for underwater image enhancement only supervise the final output of network and ignore the promotion effect of the intermediate results on the final feature representation. These supervision methods affect the feature representation ability, network efficiency, and ability. In this paper, we present a novel idea of multiple-stage supervision to guide the network to learn useful features correctly and progressively. With this idea, we propose a pixel-wise Progressive Guided Network (PGN) for underwater image enhancement to take advantage of the network’s intermediate results and promote the final enhancement effect. The Pixel-Wise Attention Module is designed by introducing supervision in each stage to progressively promote the representation ability of the features and the recovered image quality. The experimental results on several datasets demonstrate that our method outperforms recent state-of-the-art underwater image enhancement methods.
Dynamic Stiffness Measurements of Road Pavements by Means of Impact Hammer in a Non-Resonant Configuration
Authors: Bolognese Matteo , Greco Erica , Bianco Francesco , Licitra Gaetano
Published: January 2025

The different sources of noise in a vehicle have long been known, and they include noise from the engine and other mechanical parts, aerodynamic noise, and rolling noise. More specifically, the latter concerns the interaction between the tire and the road surface, and so it is also known as Tire–Road Noise (TRN). One of the parameters influencing TRN is pavement stiffness. The empirical measurement of pavement stiffness, and in particular, its frequency spectrum (dynamic stiffness), is not easy to determine, and only in the last decade have studies emerged about this subject. In these works, two different instrumental chains are employed as follows: the impact hammer one and the dynamic exciter (shaker) one, which has established itself over time as a reference. The objective of this work is to develop a system for the dynamic stiffness measurements of road pavements using the impact hammer capable of producing a similar performance to the shaker while minimizing costs. During the work, a measurement aid device named Test Automation Device (TAD) was designed and implemented to increase the quality of the measurements. In line with the practical execution of the measurement, the analysis and the representation of the results were optimized to obtain results that adhere to the stiffness model proposed in the literature. In the present paper, the TAD, the measurement optimization work, the data analysis performed, and the proposed representation method will be described. Finally, we will present the results obtained and possible future perspectives.
Damage Evolution and Failure Precursor of Rock-like Material Under Uniaxial Compression Based on Strain Rate Field Statistics
Authors: Jin Jin , Cao Ping , Zhang Jun , Wang Yanchao , Miao Chenxi , Li Jie , Bai Xiaohong
Published: January 2025

In rock engineering, it is crucial to collect and analyze precursor information of rock failure. This paper has attempted to study the strain rate field of rock-like material to obtain the precursor information of its failure. Based on the available laboratory experiments, the intact BPM (bonded-particle model) and other BPMs with a single open prefabricated flaw were simulated by PFC (Particle Flow Code). The volume strain rate field data before the peak stress have been obtained from two hundred measurement circles across each model. The strain rate field data have been firstly statistically analyzed to explore the failure precursor based on the intact model and 45° flaw model and then compared to find the influence of the pre-existing flaw on the damage evolution and precursor signal. The results indicate that (1) all types of statistical data are positively correlated with the increment of microcracks; (2) corresponding to the fluctuation patterns of statistical data, the damage evolution of BPMs in the pre-peak stage can be divided into three parts; (3) the pre-existing flaw would accelerate the damage evolution; (4) the location and evolution rate of damage could be determined by comprehensively analyzing the average deviation curve, the coefficient of variation, and the contour maps of the strain rate field. These analyses of the particle displacement field can be used to distinguish the impacts of the flaw angle and provide some assistance for the failure forecast.
A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
Authors: Seo Seung-Won , Choi Gyumin , Jung Ho-Jin , Choi Mi-Jin , Oh Young-Dae , Jang Hyun-Seok , Lim Han-Kyu , Jo Seongil
Published: January 2025

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.
Quantifying the Impact and Policy Implications of Transitioning to Zonal and Nodal Pricing in the Electricity Market: A South Korean Case Study
Authors: Kwag Kyuhyeong , Shin Hansol , Oh Hyobin , Yun Hyeongseok , Yoon Hyojeong , Kim Wook
Published: January 2025

Electricity markets are transitioning to zonal and nodal pricing to maximize social welfare, improve price signals, and enhance congestion management. South Korea, traditionally reliant on uniform pricing, is evaluating this transition but lacks a detailed impact analysis. This study assessed the impacts of various zonal and nodal pricing schemes on power systems and provided policy implications for this transition. We (1) modeled the power system at various levels of granularity, obtaining a detailed 4579-node representation; (2) constructed a set of zonal and nodal pricing schemes reflecting changes in market-clearing models and pricing mechanisms; and (3) performed quantitative analyses through simulations for each scheme. Under the current system marginal price (SMP)-based uniform pricing, the schemes with the least market impact are SMP-based zonal pricing with two bidding zones and extended locational marginal pricing. These results can guide the development of an appropriate pricing transition pathway, although a market price reduction of 4.8–7.0% appears inevitable. Within the Korean electricity market, wherein a Transco is a monopoly retailer, we identified potential conflicts of interest for the Transco in zonal and nodal pricing. By focusing on South Korea, this study offers valuable insights into any electricity market considering the transition to zonal and nodal pricing.
Explainable AI for DeepFake Detection
Authors: Mansoor Nazneen , Iliev Alexander I.
Published: January 2025

The surge in technological advancements has resulted in concerns over its misuse in politics and entertainment, making reliable detection methods essential. This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand the models’ internal decision-making processes. The CNNs’ performance is evaluated through F1-scores ranging from 0.8 to 0.9, demonstrating their effectiveness. By analyzing the facial features learned by the models, this study provides explainable results for classifying images as real or fake. This interpretability is crucial in understanding how deepfake detection models operate. Although numerous detection models exist, they often lack transparency in their decision-making processes. This research fills that gap by offering insights into how these models distinguish real from manipulated images. The findings highlight the importance of interpretability in deep neural networks, providing a better understanding of their hierarchical structures and decision processes.
On the Frequency of Internal Gravity Waves in the Atmosphere: Comparing Theory with Observations
Authors: Zakinyan Robert G. , Kamil Alaa H. , Svetlichny Vladislav A. , Zakinyan Arthur R.
Published: January 2025

This paper is devoted to the dynamics of the propagation of non-planetary scale internal gravity waves (IGWs) in the stratified atmosphere. We consider the system of equations describing internal gravity waves in three approximations: (1) the incompressible fluid approximation, (2) the anelastic gas (compressible fluid) approximation, and (3) a new approximation called the non-Boussinesq gas approximation. For each approximation, a different dispersion relation is given, from which it follows that the oscillation frequency of internal gravity waves depends on the direction of propagation, the horizontal and vertical components of the wave vector, the vertical gradient of the background temperature, and the background wind shear. In each of the three cases, the maximum frequency of internal gravity waves is different. Moreover, in the anelastic gas approximation, the maximum frequency is equal to the Brunt–Väisälä buoyancy frequency, and in the incompressible fluid approximation, it is larger than the Brunt–Väisälä frequency by a factor of 7≅2.6. In the model proposed in this paper, the value of the maximum frequency of internal gravity waves occupies an intermediate position between the above limits. The question arises: which of the above fluid representations adequately describe the dynamics of internal gravity waves? This paper compares the above theories with observational data and experiments.
Comparative Study on Environmental Impact of Electric Vehicle Batteries from a Regional and Energy Perspective
Authors: Feng Ruiqi , Guo Wei , Zhang Chenjie , Nie Yuxuan , Li Jiajing
Published: January 2025

Against the backdrop of the global goal of “carbon neutrality”, the advancement of electric vehicles (EVs) holds substantial importance for diminishing the reliance on fossil fuels, mitigating vehicular emissions, and fostering the transition of the automotive sector towards a sustainable, low-carbon paradigm. The wide application of electric vehicles not only reduces the dependence on non-renewable resources such as oil, but also concurrently effectuates a substantial reduction in carbon emissions within the transportation sector. In the realm of electric vehicles, ternary lithium batteries (NCM) and lithium iron phosphate batteries (LFP) are two widely used batteries. This study examines the resource utilization and environmental repercussions associated with the production of 1 kW ternary lithium batteries and lithium iron phosphate batteries, employing a life cycle assessment (LCA) framework. The importance of clean energy in reducing environmental pollution and global warming potential is revealed by introducing five different power generation types and the regional power generation structure in China into the power battery production process. The findings of the investigation indicate that lithium iron phosphate batteries exhibit pronounced superiority in terms of environmental sustainability, while ternary lithium batteries are more advantageous in terms of performance. The mitigation of environmental pollution associated with battery production can be significantly achieved by the holistic integration of clean energy sources and the systematic optimization of manufacturing processes. Specific interventions encompass enhancing the energy efficiency of the production process, incorporating renewable energy sources for power generation, and minimizing the utilization of hazardous materials. By implementing these strategies, the battery sector can advance towards a more environmentally benign and sustainable trajectory.
GRAMMAR-Lambda Delivers Efficient Understanding of the Genetic Basis for Head Size in Catfish
Authors: Zhao Yunfeng , Gao Jin , Feng Hong , Jiang Li
Published: January 2025

The shape of the skull plays a crucial role in the evolution and adaptation of species to their environments. In the case of aquaculture fish, the size of the head is also an important economic trait, as it is linked to fillet yield and ornamental value. This study applies our GRAMMAR-Lambda method to perform a genome-wide association study analysis on loci related to head size in catfish. Compared with traditional GWAS methods, the GRAMMAR-Lambda method offers higher computational efficiency, statistical power, and stability, especially in complex population structures. This research identifies many candidate genes closely related to cranial morphology in terms of head length, width, and depth in catfish, including bmpr1bb, fgfrl1b, nipbl, foxp2, and pax5, etc. Based on the results of gene–gene interaction analysis, we speculate that there may be frequent genetic interactions between chromosome 19 and chromosome 29 in bone development. Additionally, many candidate genes, gene families, and mechanisms (such as SOCE mechanisms) affecting skeletal development and morphology have been identified. These findings contribute to our understanding of the genetic architecture of head size and will support marker-assisted breeding in aquaculture, also reflecting the potential application of the GRAMMAR-Lambda method in genetic studies of complex traits.
Anti-Inflammatory and Anti-Migratory Effects of Morin on Non-Small-Cell Lung Cancer Metastasis via Inhibition of NLRP3/MAPK Signaling Pathway
Authors: Arjsri Punnida , Srisawad Kamonwan , Umsumarng Sonthaya , Thippraphan Pilaiporn , Anuchapreeda Songyot , Dejkriengkraikul Pornngarm
Published: January 2025

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths globally, with a persistently low five-year survival rate of only 14–17%. High rates of metastasis contribute significantly to the poor prognosis of NSCLC, in which inflammation plays an important role by enhancing tumor growth, angiogenesis, and metastasis. Targeting inflammatory pathways within cancer cells may thus represent a promising strategy for inhibiting NSCLC metastasis. This study evaluated the anti-inflammatory and anti-metastatic properties of morin, a bioactive compound derived from a Thai medicinal herb, focusing on its effects on NLRP3 inflammasome-mediated pathways in an in vitro NSCLC model. The A549 and H1299 cell lines were stimulated with lipopolysaccharide (LPS) and adenosine triphosphate (ATP) to activate the NLRP3 pathway. The inhibition effects exhibited by morin in reducing pro-inflammatory secretion in LPS- and ATP-stimulated NSCLC cells were assessed by ELISA, while wound healing and trans-well invasion assays evaluated its impact on cell migration and invasion. RT-qPCR measurement quantified the expression of inflammatory genes, and zymography and Western blotting were used to examine changes in invasive protein levels, epithelial-to-mesenchymal transition (EMT) markers, and underlying molecular mechanisms. Our findings demonstrated the significant ability of morin to decrease the production of IL-1β, IL-18, and IL-6 in a dose-dependent manner (p < 0.05), as well as suppress NSCLC cell migration and invasion. Morin downregulated invasive proteins (MMP-2, MMP-9, u-PAR, u-PA, MT1-MMP) and EMT markers (fibronectin, N-cadherin, vimentin) (p < 0.01) while also reducing the mRNA levels of NLRP3, IL-1β, IL-18, and IL-6. Mechanistic investigations revealed that morin suppressed NLRP3 inflammasome activity and inactivated MAPK pathways. Specifically, it decreased the expression of NLRP3 and ASC proteins and reduced caspase-1 activity, while reducing the phosphorylation of ERK, JNK, and p38 proteins. Collectively, these findings suggest that morin’s inactivation of the NLRP3 inflammasome pathway could offer a novel therapeutic strategy for counteracting pro-tumorigenic inflammation and metastatic progression in NSCLC.
Anxiolytic, Antidepressant, and Anticholinesterase Effects of Essential Oil from Myrcia sylvatica (G.Mey.) DC
Authors: Silva Júnior Antônio Quaresma da , Garcia Mariana Maciel , Farias Wanderson da Silva , Sousa Deise Juliane dos Anjos de , Barroso Adenilson de Sousa , Figueiredo Pablo Luis Baia , Santos Gabriela B. dos , de Oliveira Ricardo Bezerra , Mourão Rosa Helena Veras
Published: January 2025

Aromatic plants are rich sources of essential oils (EOs), recognized for their therapeutic properties due to their diversity of phytochemicals. This study investigated the anxiolytic and antidepressant effects of Myrcia sylvatica essential oil (MsEO) through inhalation in an animal model and its in vitro anticholinesterase (AChE) activity. The EO was obtained by hydrodistillation, and its volatile constituents were analyzed by GC-MS. Swiss mice were exposed to doses of 0.1%, 1%, and 2% of the EO via an inhalation apparatus. The anxiolytic activity was assessed using the elevated plus maze and light–dark box tests, while antidepressant activity was evaluated using the tail suspension and forced swimming tests. To examine potential side effects, the animals were subjected to rotarod, Y-maze, and Morris water maze tests to assess motor coordination, memory, and learning. Anticholinesterase activity was determined by direct bioautography and colorimetry based on the Ellman method. The results demonstrated that inhalation of MsEO at doses of 0.1% and 1% significantly reduced anxiety and depressive-like behaviors without impairing memory, learning, or motor coordination in the animals. Moreover, MsEO inhibited acetylcholinesterase with an IC50 of 0.47 μg/mL. These findings suggest that MsEO has potential therapeutic applications for anxiety and depression disorders, with additional anticholinesterase activity warranting further investigation in cognitive-related conditions.
Identification of Cognitive Training for Individuals with Parkinson’s Disease: A Systematic Review
Authors: Gattoni Marina Francesca , Gobbo Silvia , Feroldi Sarah , Salvatore Anna , Navarro Jorge , Sorbi Sandro , Saibene Francesca Lea
Published: January 2025

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder, characterised by cardinal motor features and a multitude of non-motor manifestations. Among them, cognitive impairment in PD has been recognised as a defined clinical entity, and it might lead to an increased risk of developing dementia. Consequently, the present review aimed to ascertain the available interventions for the training of cognitive abilities in persons with PD (PwPD). Methods: PRISMA guidelines were followed to select studies in the following databases: PubMed, PsycINFO, and Web of Science. Two independent reviewers conducted the different phases of the review, and a third expert was called in to address any doubts/conflicts. Randomised controlled trials and randomised clinical trials concerning cognitive training with cognitive outcomes in PwPD were selected. Results: A total of 28 articles were included. The considered studies applied various experimental interventions for the training of cognitive functions in PwPD: computer-based platforms, exergames, paper-and-pencil programmes, dual-task or treadmill training with action observation therapy, motor imagery, and virtual reality components, interventions targeting precise cognitive domain, tele-rehabilitation, transcranial direct current stimulation, structured cognitive training, and multimodal treatments. Cognitive functions were assessed employing neuropsychological tests, self-report questionnaires, and computerised batteries. Conclusions: Overall, the review reported better performances in the experimental groups compared to the control groups, in several cognitive domains. Structured cognitive training emerged as the most effective strategy to enhance cognitive functioning in PwPD. However, further studies are necessary to determine the most appropriate and useful training and to develop interventions that also consider patients’ quality of life.
CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer
Authors: Liu Kai , Ren Tao , Lan Zhangli , Yang Yang , Liu Rong , Xu Yuantong
Published: January 2025

Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks.
Cyclic Behavior of Seismically Non-Conforming Interior Reinforced Concrete Beam–Column Joints
Authors: Sabbahfar Peyman , Melo José , Costa Aníbal , Varum Humberto , Rodrigues Hugo
Published: January 2025

The cyclic behavior of reinforced concrete (RC) beam–column joints (BCJs) is still one of the critical issues in structural engineering. In this context, for the purpose of gaining a more in-depth insight about the sophisticated behavior of BCJs under cyclic loading scenarios, the current work aims to investigate the cyclic behavior of reinforced concrete beam–column joints. The cyclic behavior of four interior reinforced concrete beam–column joints, with plain and deformed bars, which are representative of the seismically non-conforming structures from the 1970s, were experimentally investigated. The corresponding results of the specimens were compared with each other to better understand and highlight the differences between the force–drift curves and envelopes and damage patterns of each of the specimens. Furthermore, a numerical validation of the laboratory testing results was established with the DIANA FEA code for both monotonic and cyclic loading scenarios, and the force–displacement plots were compared with the associated laboratory results for validation purposes. The crack propagation and final damage states of the numerical models of beam–column joints are presented and discussed in detail. The results showed good agreement between the numerical and experimental behavior, and a graphical representation of the critical regions affected by damages was also shown, which could ultimately contribute to future retrofitting solutions for strengthening the BCJ region in existing RC structures.
A Multi-Faceted Analysis of Enablers and Barriers of Industrialised Building: Global Insights for the Australian Context
Authors: Soltani Sahar , Abbasnejad Behzad , Gu Ning , Yu Rongrong , Maxwell Duncan
Published: January 2025

This study examines the renewed interest in Industrialised Building (IB) adoption in Australia amid the housing crisis, addressing the gap between potential and implementation. Drawing on a systematic review of 171 peer-reviewed articles (1998–2024), we examine how the interplay between micro-level decision-making, meso-level organisational routines, and macro-level institutional arrangements shapes global IB adoption patterns, with implications for the Australian context where limited research exists. Our analysis highlights that successful IB adoption depends on coordinated alignment across systemic levels, with government policies and sustainability initiatives emerging as key global drivers. However, adoption barriers differ by market maturity; Australia faces unique challenges, such as economic constraints, limited stakeholder collaboration, and misaligned institutional frameworks, despite advancements in technology and innovation. The findings advance construction innovation literature by presenting a theoretically grounded framework to address IB adoption barriers and enablers. In the Australian context, realising IB’s potential requires co-evolution across micro, meso, and macro levels, driven by workforce upskilling, stakeholder collaboration, and adaptive regulations to transform construction practices.
Genome-Wide DNA Methylation Confirms Oral Squamous Cell Carcinomas in Proliferative Verrucous Leukoplakia as a Distinct Oral Cancer Subtype: A Case–Control Study
Authors: Proaño Alex , Sarrion-Perez Gracia , Bagan Leticia , Bagan Jose
Published: January 2025

Background/Objectives: Oral cancers in patients with proliferative verrucous leukoplakia (PVL-OSCC) exhibit different clinical and prognostic outcomes from those seen in conventional oral squamous cell carcinomas (cOSSCs). The aim of the present study is to compare the genome-wide DNA methylation signatures in fresh frozen tissues between oral squamous cell carcinomas in patients with PVL and cOSCC using the Illumina Infinium MethylationEPIC BeadChip. Methods: This case–control study was carried out at the Stomatology and Maxillofacial Surgery Department of the General University Hospital of Valencia. For the epigenomic study, unsupervised exploratory bioinformatic analyses were performed using principal component and heatmap analysis. Supervised differential methylation analyses were conducted using a rank-based regression model and a penalized logistic regression model to identify potential prognostic biomarkers. Results: The unsupervised analyses of the global methylation profiles did not allow us to differentiate between the distinct oral cancer groups. However, the two supervised analyses confirmed the existence of two oral carcinoma phenotypes. We identified 21 differentially methylated CpGs corresponding to 14 genes. Among them, three CpGs had not been previously assigned to any known gene, and the remaining were associated with genes unrelated to oral cancer. The AGL, WRB, and ARL15 genes were identified as potential prognostic biomarkers. Conclusions: This study emphasizes the significant role of epigenetic dysregulation in OSCC, particularly in cases preceded by PVL. We have provided data on differential methylation genes that could be involved in the molecular carcinogenesis of PVL-OSCC.
Dual MOF and CuInS2-Constructed Dual Z-Scheme Heterojunctions for Enhanced Photocatalytic Hydrogen Production and Methylene Blue Degradation
Authors: Liang Yuning , Wang Baohui , Cheng Tao , Bi Mingchun , Chi Weimeng , Liu Yuxi , Zhang Wenjing , Liu Yuxuan
Published: January 2025

A novel dual Z-scheme heterojunction photocatalyst was constructed by introducing the narrow-bandgap semiconductor CuInS2 (CIS) into the dual metal-organic framework (MOF) system of UiO-66(Zr) and NH2-MIL-101(Fe). This structure effectively overcomes the limitations of conventional photocatalysts in terms of light absorption range and the separation efficiency of photogenerated charge carriers. The prepared ternary catalyst, (UiO-66(Zr))-(NH2-MIL-101(Fe))/CuInS2, exhibited excellent photocatalytic performance under visible light irradiation, achieving a hydrogen production rate of 888 μmol g−1 h−1 and a methylene blue (MB) degradation efficiency of up to 95.03%. The significant enhancement in performance is attributed to the material’s porous structure, extended light absorption range, and optimized electron transfer pathways. Additionally, the construction of the dual Z-scheme heterojunction further promotes the separation and migration of photogenerated charge carriers, suppressing electron–hole recombination. This study demonstrates the great potential of dual Z-scheme heterojunctions in improving photocatalytic efficiency and provides an important theoretical foundation and design strategy for the development of efficient photocatalysts.
Cannabidiol Modulates Neuroinflammatory and Estrogen-Related Pathways in a Sex-Specific Manner in a Chronic Stress Model of Depression
Authors: Bright Uri , Akirav Irit
Published: January 2025

Evidence indicates a bidirectional link between depressive symptoms and neuroinflammation. This study evaluated chronic cannabidiol (CBD) treatment effects in male and female rats subjected to the unpredictable chronic mild stress (UCMS) model of depression. We analyzed the gene expression related to neuroinflammation, cannabinoid signaling, estrogen receptors, and specific microRNAs in the ventromedial prefrontal cortex (vmPFC), CA1, and ventral subiculum (VS). UCMS influenced immobility in a sex-specific manner, increasing it in males and decreasing it in females, effects that were reversed by CBD. CBD also normalized the UCMS-induced upregulation of tumor necrosis factor α (TNF-α) in the CA1 and VS in males. In both sexes, UCMS induced the upregulation of the nuclear factor kappa B subunit 1 (NF-κB1) gene in the VS, which was unaffected by CBD. Additionally, CBD reversed CB1 downregulation in the VS of males but not in the vmPFC of either sex. In males, CBD restored the UCMS-induced downregulation of VS estrogen receptor genes ERα and ERβ. UCMS also altered miR-146a-5p expression, downregulating it in females (VS) and upregulating it in males (CA1), with no CBD effect. These findings highlight the sex-specific mechanisms of CBD’s antidepressant effect, with hippocampal neuroinflammatory and estrogenic pathways playing a key role in males.
A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures
Authors: Godarzi Neda , Hejazi Farzad
Published: January 2025

Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices.
Microstructure and Mechanical Behavior of Magnetron Co-Sputtering MoTaN Coatings
Authors: Hsu Jia-Yi , Wu Fan-Bean
Published: January 2025

In recent years, there have been important developments in the refractory metal nitride coatings used for versatile applications, such as MoN, TaN, NbN, etc. Engineered approaches, including the deposition method, microstructure control, structural design, and the addition of functional elements, are put into practice for the promotion of coating characteristics. This study focuses on the microstructure and mechanical properties of ternary molybdenum tantalum nitride, MoTaN, coatings. MoTaN was deposited using a reactive radio frequency (r.f.) magnetron co-sputtering system with Mo/Ta target input power modulation control. The effects of composition and microstructure variations on its mechanical properties, including its hardness, elastic modulus, and wear behavior, were investigated. In general, the MoTaN coatings exhibited a columnar polycrystalline microstructure with MoN(111), Mo2N(111), Mo2N(200), TaN(200), and TaN(220) phases and orientations based on X-ray diffraction analysis. The addition of Ta triggered the transition of the primary orientation of Mo2N(111) into Mo2N(200). Transmission electron microscopy was utilized to analyze the transformation of the multiphase structure and changes in the grain size in terms of the Ta addition. According to nanoindentation and wear resistance analyses, superior hardness, elastic modulus, H/E, H3/E2, and wear-resistance values were identified for the MoTaN coatings with 6.8 to 10.4 at.% Ta, and a maximum hardness of 18.0 GPa was found for the MoTaN coating deposited at an input power of Mo/Ta = 150/100 W/W. An optimized hardness of 18.0 GPa and an elastic modulus of 220.7 GPa were obtained. The adjustment of the input power during deposition played a critical role in determining the overall performance of the MoTaN co-sputtering coatings. The MoTaN coating with optimized mechanical properties is attributed to its multiphase microstructure and fine columnar grain size of less than 30 nm.
The Richness, Similarity, and Conservation Status of the Herpetofauna of the Balsas Basin Biogeographic Province of Mexico
Authors: Lemos-Espinal Julio A. , Smith Geoffrey R.
Published: January 2025

The Balsas Basin (BB) is a biogeographic province in south-central Mexico that straddles the Mexican Transition Zone and the Neotropical region. We provide a list of the amphibian and reptile species of the BB based on a detailed review and update of recent species lists of its constituent states. The BB is home to 51 native amphibian and 155 native reptile species, which represent 14.7% of the herpetofauna of Mexico. No amphibian and six reptile species are endemic to the BB. Six species of amphibians and seven of reptiles are categorized as being of conservation concern status (vulnerable, endangered, or critically endangered) on the International Union for Conservation of Nature’s (IUCN) Red List. The main threat these species face is habitat loss due to urbanization, agriculture, and pollution. The herpetofauna of the BB shows significant overlap with neighboring provinces. The composition of amphibian species in the BB is closest to that of the Pacific Lowlands, whereas the composition of reptile species is closest to the Sierra Madre del Sur and the Transvolcanic Belt. These findings suggest that while the BB supports significant amphibian and reptile diversity, its conservation importance may be limited due to the low levels of endemism and the relatively small proportion of species at risk.
Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer’s Disease MRI Data
Authors: Akgüller Ömer , Balcı Mehmet Ali , Cioca Gabriela
Published: January 2025

Background: Alzheimer’s disease is a progressive neurological condition marked by a decline in cognitive abilities. Early diagnosis is crucial but challenging due to overlapping symptoms among impairment stages, necessitating non-invasive, reliable diagnostic tools. Methods: We applied information geometry and manifold learning to analyze grayscale MRI scans classified into No Impairment, Very Mild, Mild, and Moderate Impairment. Preprocessed images were reduced via Principal Component Analysis (retaining 95% variance) and converted into statistical manifolds using estimated mean vectors and covariance matrices. Geodesic distances, computed with the Fisher Information metric, quantified class differences. Graph Neural Networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, were utilized to categorize impairment levels using graph-based representations of the MRI data. Results: Significant differences in covariance structures were observed, with increased variability and stronger feature correlations at higher impairment levels. Geodesic distances between No Impairment and Mild Impairment (58.68, p<0.001) and between Mild and Moderate Impairment (58.28, p<0.001) are statistically significant. GCN and GraphSAGE achieve perfect classification accuracy (precision, recall, F1-Score: 1.0), correctly identifying all instances across classes. GAT attains an overall accuracy of 59.61%, with variable performance across classes. Conclusions: Integrating information geometry, manifold learning, and GNNs effectively differentiates AD impairment stages from MRI data. The strong performance of GCN and GraphSAGE indicates their potential to assist clinicians in the early identification and tracking of Alzheimer’s disease progression.