bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring
Authors: Qin Ruiqian , Wang Yiming , Liu Xiaoyan , Yu Helong
Published: January 2025

Introduction

Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification. However, complex climatic conditions in real agricultural environments challenge model robustness, and current mainstream models struggle with accurate recognition of the same diseases across different plant species.

Methods

This paper proposes the SIS-YOLOv8 model, which enhances adaptability to complex agricultural climates by improving the YOLOv8 network structure. The research introduces three key modules: 1) a Fusion-Inception Conv module to improve feature extraction against complex backgrounds like rain and haze; 2) a C2f-SIS module incorporating Style Randomization to enhance generalization ability for different crop diseases and extract more detailed disease features; and 3) an SPPF-IS module to boost model robustness through feature fusion. To reduce the model’s parameter size, this study employs the Dep Graph pruning method, significantly decreasing parameter volume by 19.9% and computational load while maintaining accuracy.

Results

Experimental results show that the SIS-YOLOv8 model outperforms the original YOLOv8n model in disease detection tasks for potatoes and tomatoes, with improvements of 8.2% in accuracy, 4% in recall rate, 5.9% in mAP50, and 6.3% in mAP50-95.

Discussion

Through these network structure optimizations, the SIS-YOLOv8 model demonstrates enhanced adaptability to complex agricultural environments, offering an effective solution for automatic crop disease detection. By improving model efficiency and robustness, our approach not only advances agricultural disease monitoring but also contributes to the broader adoption of AI-driven solutions for sustainable crop management in diverse climates.
LiDAR point cloud denoising for individual tree extraction based on the Noise4Denoise
Authors: Lu Xiangfei , Ye Zongyu , Fu Liyong , Wang Huaiyi , Wang Kaiyu , Dou Yaquan , Xie Dongbo , Zhao Xiaodi
Published: January 2025

The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.DEN4 introduces a multilevel noise separation module that effectively distinguishes between signal and noise, thereby improving the signal-to-noise ratio (SNR) and reducing the error.The experimental results demonstrate that DEN4 significantly outperforms traditional denoising methods in several key metrics, including mean square error (MSE), SNR, Hausdorff distance, and structural similarity index (SSIM).In the 60 sample dataset, DEN4 achieved the best mean and standard deviation on all metrics: Specifically, the MSE mean was found to be 0.0094, with a standard deviation of 0.0008, the SNR mean was 149.1570, with a standard deviation of 0.5628, the Hausdorff mean was 0.8503, with a standard deviation of 0.0947, and the SSIM mean was 0.8399, with a standard deviation of 0.0054. For instance, in the S10 dataset, DEN4 attained a 70.2% diminution in MSE and a 37.8% augmentation in SNR in comparison with PTD.The findings demonstrate the efficacy of DEN4 in multiple forest datasets, its ability to maintain geometric integrity, and its enhanced stability without the necessity for pre-labelled data. The algorithm's superior performance and robustness in diverse forest environments underscores its potential application in single tree segmentation and forest resource management.
How to implement pairing assistance during fighting COVID-19 in China: collaborative governance between local governments under the authoritative regulation
Authors: Wei Changwei , Xu Jiaxi , Wang Zhixiang , Wu Huangyue , Wang Juan
Published: January 2025

Background

The pairing assistance policy represents a distinctive instrument utilized by the Chinese government to address major public crises. This study examines the development of a pairing assistance policy by the Chinese Government through its central authority to foster collaborative governance among local governments in areas affected by COVID-19.

Methods

The aim of the study was to gain a clear understanding of how the policy of pairing assistance in public health emergencies is successfully implemented through the top-down application of authority. A case study design was used as a methodology to present an explanatory framework for implementing pairing assistance policies during major public crises. We focus on the operational process of pairing assistance, using the assistance provided by Jiangsu Province to Huangshi City in Hubei Province as an illustration.

Results

This paper finds that responding to a crisis requires the guidance of a central authority and the cooperation of local governments. The process is driven by three key factors: the vertical intervention of the crisis, the inevitability of horizontal cooperation and the policy allocation and incentives of the bureaucracy. The three stages of co-operative governance based on authoritative regulation work together in a step-by-step manner to enhance the effectiveness of crisis response.

Conclusion

The results of the study indicate that collaborative governance under the authoritative regulation is the main reason why provincial counterpart support mechanism plays a great role in COVID-19. This study is the first to approach the study of pairing assistance from the perspective of government authority. It broadens the research horizon of local government cooperation and provides a model for future collaboration.
Governance of prepaid consumption in China's sports industry based on collaborative governance theory: theoretical framework, realistic dilemmas, and practical pathways
Authors: Su Jianuo , He Jinsheng
Published: January 2025

The rapid development of prepaid consumption in China's sports industry has been hindered by fundamental issues such as operators’ blind expansion, lack of transparency in business information, absence of standard contract texts, and inadequate consumer rights protection. These challenges have led to business closures, unfair contract terms, difficulties in card refunds and transfers, and the excessive issuance of long-term or lifetime prepaid cards. These problems severely damage the industry's integrity and impede its healthy and sustainable development. Currently, the governance of prepaid consumption in the sports industry faces several realistic dilemmas, including unclear collaborative regulatory and law enforcement responsibilities of the government, an urgent need to improve the legal framework, ineffective government regulatory tools, difficulties in ensuring the safety of prepaid funds, and limitations in the credit evaluation system. By introducing the concept of collaborative governance, new ideas and practical solutions are proposed to address these issues. The practical pathways for collaborative governance of prepaid consumption in the sports industry based on this concept include constructing a collaborative regulatory mechanism to solidify the foundation of collaborative governance, improving legal regulations to broaden collaborative relief channels, innovating government regulatory tools to build a collaborative regulatory platform; optimizing fund custody mechanisms to ensure the safety of prepaid funds; and enhancing the credit regulatory system to refine the credit reward and punishment mechanism.
In vitro ovicidal studies on egg-parasitic fungus Pochonia chlamydosporia and safety tests on mice
Authors: Ma Yuan , Lv Jinbao , Jiang Lili , Fan Zhaobin , Hao Luyao , Li Zhengyi , Ma Chengyu , Wang Rui , Luo Hongliang
Published: January 2025

Introduction

The control of parasites infections in livestock is an ongoing concern, with parasites developing resistance to commonly used antiparasitic drugs. The current study investigated in vitro the destructive effect of the fungus Pochonia chlamydosporia on the eggs and oocysts of several equine parasites, as well as assessing the safety of the fungus in mice.

Methods

S. equinus, P. equorum, Anoplocephala spp eggs and Eimeria spp. oocysts were treated with P. chlamydosporia. The prepared preparation was also administered to mice, and the physiological indexes and lesions of major tissues and organs, as well as pathological sections of tissue, were then observed.

Results

P. chlamydosporia exhibited varying degrees of efficacy in the control of S. equinus, P. equorum, Anoplocephala spp eggs and Eimeria spp. oocysts. The acute toxicity test demonstrated that there was no death or toxicity symptom observed in the mice, with no significant difference in clinical observations, such as respiration, mental state, appetite, or feces, between the control and treated mice after the feeding of the biological preparation of P. chlamydosporia.

Discussion

These findings suggested that administration of P. chlamydosporia would be safe to use in livestock and provided a rationale for its potential clinical application, pending further analyses.
Identification and characterization of multiple novel viruses in fecal samples of cormorants
Authors: Ji Li , Zhao Ran , Pei Yifei , Sun Yijie , Sun Xiaoyi , Ji Likai , Wang Xiaochun , Liu Yuwei , Shen Quan , Yang Shixing , Wang Yan , Zhang Wen
Published: January 2025

Introduction

Cormorants, as protected wild animals by the State Forestry Administration of China, have a broad distribution across China. Previous studies have shown that they can be infected with multiple viruses in the Flaviviridae, Orthomyxoviridae, Paramyxoviridae, and Polyomaviridae families. There is limited knowledge about the other viruses that cormorants may carry and infect.

Methods

In this study, we employed viral metagenomics to identify novel viruses in the fecal samples collected from cormorants in Xiamen City, Fujian Province, China.

Results

Two novel viruses were identified, including one novel picornavirus named Cormhepa01 and one novel avain hepevirus named CormhepaE. The genome of Cormhepa01 is 7,463 bp in length, which encodes a 2,260 aa polyprotien. Similar to other known picornaviruses, the conserved NTPase, proteinase, and polymerase motifs are presented in the 2C, 3C, and 3D proteins separately. Based on the phylogenetic analysis and amino acid sequence alignment, the CormhepaE may be assigned to a new picornavirus genus. The partial genome of CormhepaE is 6,546 bp in length. Compared with other avian hepatitis E virus strains, CormhepaE has multiple variable sites, which are distributed in motifs of the methyltransferase, helicase, and RdRp domains, separately. Based on the phylogenetic analysis, CormhepaE, together with another strain MG737712 isolated from sparrow, formed a new species of the Avihepevirus genus in the Hepeviridae family.

Conclusion

We identified and characterized two novel cormorant viruses in this study. The findings of this study increase our understanding of the diversity of viruses in cormorants and provide practical viral genome information for the prevention and treatment of potential viral diseases affecting this species.
Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
Authors: Lucena Mateus Martinez de , Ribeiro Josafat Leal , Wagner Matheus , Fröhlich Antônio Augusto
Published: January 2025

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.
A Polycentric Approach for Addressing Wicked Social Problems
Authors: Lofthouse Jordan K. , Kral Leah
Published: January 2025

Most social problems are “wicked”, meaning that they are highly complex, intractable, open-ended, and multi-dimensional. In wicked learning environments, information is ambiguous, feedback may be slow, or causes and effects are difficult to ascertain. Using the insights from the Bloomington school of political economy, this paper argues that a polycentric approach is the most effective way to address wicked social problems. Polycentric systems are characterized by multiple, overlapping decision-making centers that have varying degrees of independence and interdependence. When decision-makers in governments, markets, and civil society tackle complex social problems simultaneously, various forms of cooperation and contestation emerge. These interactions subsequently produce the relevant knowledge and incentives to address wicked social problems on a variety of margins. Centralized, one-size-fits-all approaches are less likely to succeed because they have weaker epistemic and incentive-related qualities. We use two examples to illustrate our argument, including post-disaster recovery and climate change mitigation.
Parameter Calibration Method for Discrete Element Simulation of Soil–Wheat Crop Residues in Saline–Alkali Coastal Land
Authors: Liu Jie , Lu Tong , Zheng Shuai , Tian Yu , Han Miaomiao , Tai Muhao , He Xiaoning , Li Hongxiu , Wang Dongwei , Zhao Zhuang
Published: January 2025

After wheat harvesting in coastal saline–alkali land, when the straw is returned to the field and the soil is rotary tilled, the lack of reliable discrete element simulation parameter models restricts the optimization and improvement of special tillage and land preparation equipment for saline–alkali land to some extent. In this study, the Hertz–Mindlin with JKR model was used to calibrate the discrete element simulation parameters. Taking the soil-wheat crop residue mixture’s angle of repose as the test index, four groups of parameters that significantly affect the angle of repose and their optimal value ranges were screened out through the Plackett–Burman test and the steepest ascent test. Then, the Box–Behnken test was conducted to obtain the quadratic regression model of the significant parameters and the angle of repose, and the optimal values of the significant parameters were obtained. The optimal parameter combination was used for simulation tests, and the relative errors between the measured values and the simulation test values of the angle of repose and the wheat residue coverage rate were 0.74% and 1.34%. The reliable parameters provide a theoretical basis for the optimization and improvement of the equipment for soil preparation in saline–alkali land.
Analysis of the Production and Economic Indicators of Broiler Chicken Rearing in 2020–2023: A Case Study of a Polish Farm
Authors: Adaszyńska-Skwirzyńska Michalina , Konieczka Paweł , Bucław Mateusz , Majewska Danuta , Pietruszka Arkadiusz , Zych Sławomir , Szczerbińska Danuta
Published: January 2025

The study aimed to analyze and evaluate the production and economic performance of a broiler chicken farm in Poland during the years 2020–2023. Production performance was assessed by comparing final body weight (BW), feed intake (FI), feed conversion ratio (FCR), and the European Performance Efficiency Factor (EPEF). Economic results were determined using the sales value, production costs, profit (direct surplus), and profitability index (PI). During the study years, the broiler rearing period lasted on average 40 days, with six production cycles each year. The average BW was found to be 2.51 kg with an average FCR of 1.54 kg/kg. For the four production years, the EPEF was calculated, reaching a high average value of 399. Broiler chicken production in Poland was profitable despite the continuously increasing production costs. In the years studied, the cost of feed accounted for 69.6–76.1% of the total production costs, chicks 15.0–19.7%, and labor costs 1.59–2.39%. In all of the analyzed production cycles, the PI of broiler chicken rearing was above 100, averaging 120.48%, and the average profit per bird was 0.46 EUR. During the study period, several negative events occurred, including the COVID-19 pandemic, the spread of avian influenza outbreaks, and consequent fluctuations in feed and poultry prices. Despite these challenges, namely the consistently rising production costs, broiler production in the study farm remained profitable.
A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model
Authors: Yang Kun , Sun Xiaohua , Li Ruofan , He Zhenxue , Wang Xinxin , Wang Chao , Wang Bin , Wang Fushun , Liu Hongquan
Published: January 2025

Quantifying planting layouts during the seedling stage of mung beans (Vigna radiata L.) is crucial for assessing cultivation conditions and providing support for precise management. Traditional information extraction methods are often hindered by engineering workloads, time consumption, and labor costs. Applying deep-learning technologies for information extraction reduces these burdens and yields precise and reliable results, enabling a visual analysis of seedling distribution. In this work, an unmanned aerial vehicle (UAV) was employed to capture visible light images of mung bean seedlings in a field across three height gradients of 2 m, 5 m, and 7 m following a time series approach. To improve detection accuracy, a small target detection layer (p2) was integrated into the YOLOv8-obb model, facilitating the identification of mung bean seedlings. Image detection performance and seedling information were analyzed considering various dates, heights, and resolutions, and the K-means algorithm was utilized to cluster feature points and extract row information. Linear fitting was performed via the least squares method to calculate planting layout parameters. The results indicated that on the 13th day post seeding, a 2640 × 1978 image captured at 7 m above ground level exhibited optimal detection performance. Compared with YOLOv8, YOLOv8-obb, YOLOv9, and YOLOv10, the YOLOv8-obb-p2 model improved precision by 1.6%, 0.1%, 0.3%, and 2%, respectively, and F1 scores improved by 2.8%, 0.5%, 0.5%, and 3%, respectively. This model extracts precise information, providing reliable data for quantifying planting layout parameters. These findings can be utilized for rapid and large-scale assessments of mung bean seedling growth and development, providing theoretical and technical support for seedling counting and planting layouts in hole-seeded crops.
AI-Driven Innovations in Tourism: Developing a Hybrid Framework for the Saudi Tourism Sector
Authors: Alzahrani Abdulkareem , Alshehri Abdullah , Alamri Maha , Alqithami Saad
Published: January 2025

In alignment with Saudi Vision 2030’s strategic objectives to diversify and enhance the tourism sector, this study explores the integration of Artificial Intelligence (AI) in the Al-Baha district, a prime tourist destination in Saudi Arabia. Our research introduces a hybrid AI-based framework that leverages sentiment analysis to assess and enhance tourist satisfaction, capitalizing on data extracted from social media platforms such as YouTube. This framework seeks to improve the quality of tourism experiences and augment the business value within the region. By analyzing sentiments expressed in user-generated content, the proposed AI system provides real-time insights into tourist preferences and experiences, enabling targeted interventions and improvements. The conducted experiments demonstrated the framework’s efficacy in identifying positive, neutral and negative sentiments, with the Multinomial Naive Bayes classifier showing superior performance in terms of precision and recall. These results indicate significant potential for AI to transform tourism practices in Al-Baha, offering enhanced experiences to visitors and driving the economic sustainability of the sector in line with the national vision. This study underscores the transformative potential of AI in refining operational strategies and aligning them with evolving tourist expectations, thereby supporting the broader goals of Saudi Vision 2030 for the tourism industry.
Investigating the Effect of Pipecolic Acid on Specialized Metabolites Involved in Tomato Plant Defense Mechanisms Against Ralstonia solanacearum Wilt Pathogens
Authors: Sabharwal Usha , Rai Piyush Kant , Choure Kamlesh , Subramanian R. B. , Joo Jeong Chan , Pandey Ashutosh
Published: January 2025

The role of pipecolic acid (Pip) in plant immune responses, particularly against bacterial wilt pathogens, is significant. This research aimed to understand the interaction between plant defense-responsive enzymes and Pip by analyzing methanolic extracts from different treatments of tolerant (GAT5) and susceptible (GT2) tomato cultivars. LC-MS analysis demonstrated that the foliar application of Pip significantly influenced tomato metabolites, especially in bacterial wilt-infected plants, with a more pronounced effect in tolerant varieties. Principal component analysis (PCA) revealed that Pip-treated plants of tolerant varieties exhibited better coordinated metabolome profiles than those of susceptible varieties. Notable variations were observed in the levels of specialized metabolites, such as salicylic acid (SA), N-hydroxy pipecolic acid (NHP), and Pip, which are essential for producing defense compounds. Molecular docking studies further explored Pip’s interactions with key plant enzymes involved in defense mechanisms and showed that Pip acts as an effective organic inducer of systemic acquired resistance (SAR). These findings highlight Pip’s potential as a natural agent for enhancing plant tolerance to pathogens, offering promising implications for agricultural practices and improving crop resilience against diseases. This study enhances our understanding of Pip’s role in plant defense and provides a foundation for developing Pip-based strategies for sustainable agriculture.
Analysis of Reproductive Strategies and Immunological Interactions in Batrachochytrium dendrobatidis-Resistant Japanese Tree Frogs
Authors: Lee Ji-Eun , Park Jun-Kyu , Do Yuno
Published: January 2025

The amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), has been implicated as an agent of acute declines in amphibian populations worldwide. East Asian amphibians have been coexisting with Bd for long periods and thus are considered resistant; among the many is the Japanese tree frog, Dryophytes japonicus. Our study focused Bd infection effects on reproductive behaviors and physiological parameters in D. japonicus as a function of better understanding the chronic effect of the disease on long-term population viability. During the peak breeding season, we captured 70 males and quantified the chorus size, calling behaviors, physiological states, innate immunity, and sperm quality of individuals. In a simple comparison, all parameters were not significantly different. However, in the NMDS analysis, we were able to confirm subtle trends in some items according to infection and correlations between several items. Importantly, sperm density and sperm vitality tend to increase with Bd infection load, suggesting increased reproductive effort following infection. Additionally, this analysis indicated that innate immunity was positively related to Bd infection intensities, indicating the activation of immunity upon infection. These findings indicate that Bd-resistant D. japonicus maintains reproductive capabilities and physio-logical stability despite Bd infection, likely due to a co-evolved immune system. The present work offers insight into how amphibian populations may have some endurance in the presence of Bd and points out the importance of studying resistant species as a means to understand long-term ecological effects. Our results suggest that resistance to Bd may not simply prevent cata-strophic declines but actively contribute to the dynamics of Bd prevalence in amphibian communities, and confer implications for conservation strategies.
Identification of Candidate Genes Associated with Meat Production of Aberdeen Angus Cattle
Authors: Kolpakov Vladimir , Bukareva Elena , Kosyan Dianna , Ruchay Alexey , Ryazanov Vitaly
Published: January 2025

The use of GWASs in agriculture allows associations between SNPs and quantitative or qualitative traits in cattle to be estimated. This study investigated the relationships among a number of economically useful carcass traits (slaughter weight, meat yield, and meat marbling) with a group of SNPs that can act as DNA markers. Blood samples from Aberdeen Angus bulls (n = 260) were used as material for SNP genotyping. Genetic architecture for the kill yield trait showed the presence of 31 SNPs. Three SNPs were found to be the most reliable: BTB-00197584 (p = 7.20 × 10−5), Hapmap46735-BTA-86653 (p = 5.05 × 10−5), and BTB-00676077 (p = 7.02 × 10−5). There were 10 SNPs associated with meat yield. For meat yield, 7 SNPs were identified, four of them exceeding the threshold of established validity: ARS-BFGL-NGS-30557 (p = 1.28 × 10−12), ARS-BFGL-NGS-68920, ARS-BFGL-NGS-30466 on chromosome 8 (p = 1.20 × 10−6 and 3.03 × 10−6, respectively) and ARS-BFGL-NGS-40640 (p = 7.10 × 10−6). For marbling, 11 SNPs were identified on 6 of 29 chromosomes. These SNPs can act as potential markers for productivity assessment in cattle.
Dietary Hydroxyproline + Vitamin C and Eucommia ulmoides Improved the Growth and Flesh Quality of Pacific White Shrimp (Litopenaeus vannamei)
Authors: Fei Xinyi , Li Xiaoqin , Huang Tianyu , Tan Beiping , Leng Xiangjun
Published: January 2025

Pacific white shrimp (Litopenaeus vannamei) is an important aquaculture shrimp in China and globally due to its high nutritional value and delicious flavors [...]
Natural Deep Eutectic Solvents as Green Alternatives for Extracting Bioactive Compounds from Sideritis Taxa with Potential Cosmetic Applications
Authors: Zissi Lamprini , Dimaki Virginia D. , Birba Vassiliki S. , Galani Vassiliki C. , Magafa Vassiliki , Hatziantoniou Sophia , Lamari Fotini N.
Published: January 2025

This study investigated the potential of natural deep eutectic solvents (NADESs) for extracting bioactive compounds from the aerial parts of two mountain tea taxa, Sideritis clandestina ssp. peloponnesiaca (Boiss. & Heldr.) Baden and Sideritis raeseri Boiss. & Heldr. ssp. raeseri. Five NADEs, composed of betaine, glycerol, glucose, urea, citric acid, and sucrose, were evaluated for their extraction efficiency compared to conventional solvents (water and 70% ethanol). The total phenolic content (TPC) and antioxidant activity (FRAP and DPPH assays) were determined. Results showed that water was not a good extraction solvent. Despite the great solvent-dependent differences, most NADEs, particularly the betaine–glycerol–glucose mixture (BGG4), exhibited comparable or even superior extraction efficiency and antioxidant activity compared to 70% ethanol. The secondary metabolites in the BGG4 and 70% ethanol extracts were determined with HPLC-MS. The BGG4 extracts of both Sideritis taxa had a rich phenolic profile, with the major ingredients being chlorogenic acid, verbascoside, and non-, mono- and di-acetylated allosyl hypolaetin glycosides. Although distinct quantitative differences in their composition compared to the respective 70% ethanol extracts, and between them were noted, overall, the content of secondary metabolites in both S. raeseri extracts was lower than that of the S. clandestina extracts. These findings suggest that NADEs, particularly BGG4, are promising green solvents for extracting bioactive compounds from Sideritis taxa, paving the way for potential applications in the development of natural and sustainable cosmetic products.
Unraveling the AMPK-SIRT1-FOXO Pathway: The In-Depth Analysis and Breakthrough Prospects of Oxidative Stress-Induced Diseases
Authors: Guan Guangqi , Chen Yaoxing , Dong Yulan
Published: January 2025

Oxidative stress (OS) refers to the production of a substantial amount of reactive oxygen species (ROS), leading to cellular and organ damage. This imbalance between oxidant and antioxidant activity contributes to various diseases, including cancer, cardiovascular disease, diabetes, and neurodegenerative conditions. The body’s antioxidant system, mediated by various signaling pathways, includes the AMPK-SIRT1-FOXO pathway. In oxidative stress conditions, AMPK, an energy sensor, activates SIRT1, which in turn stimulates the FOXO transcription factor. This cascade enhances mitochondrial function, reduces mitochondrial damage, and mitigates OS-induced cellular injury. This review provides a comprehensive analysis of the biological roles, regulatory mechanisms, and functions of the AMPK-SIRT1-FOXO pathway in diseases influenced by OS, offering new insights and methods for understanding OS pathogenesis and its therapeutic approaches.
Hovenia dulcis Thunb. Honey Exerts Antiviral Effect Against Influenza A Virus Infection Through Mitochondrial Stress-Mediated Enhancement of Innate Immunity
Authors: Kwon Eun-Bin , Kim Buyun , Kim Young-Eun , Na Sung-Joon , Han Sang Mi , Woo Soon Ok , Choi Hong Min , Moon Siwon , Kim Young Soo , Choi Jang-Gi
Published: January 2025

To combat influenza A virus (IAV) infection, it is vital to develop effective therapeutic strategies, including immunomodulators. In this study, we examined the antiviral effects of Hovenia dulcis Thunb. honey (HDH) against IAV using RAW 264.7 cells. HDH treatment significantly reduced IAV infection and viral protein expression. Moreover, it enhanced the production of interferon (IFN)-β, activated the innate immune response through the cyclic GMP–AMP synthase (cGAS)–stimulator of interferon genes (STING) pathway, and upregulated IFN signaling through signal transducer and activator of transcription (STAT)1/2 phosphorylation and interferon-stimulated gene (ISG) expression. In addition, HDH decreased IAV-induced intracellular and mitochondrial reactive oxygen species (ROS) production by upregulating the expression of antioxidant proteins, such as Sirt3 and SOD2. The results suggest that HDH is a potential therapeutic agent inhibiting viral replication and boosting host antiviral immunity.
Differential Effects of Alcoholic and Non-Alcoholic Beer Intake on Renin–Angiotensin System Modulation in Spontaneous Hypertensive Rats
Authors: Mayas María Dolores , Cueto-Ureña Cristina , Ramírez-Expósito María Jesús , Martínez-Martos José Manuel
Published: January 2025

This study investigates the effects of alcoholic (AB) and non-alcoholic beer (NAB) consumption on blood pressure and the activity of enzymes regulating the renin–angiotensin system (RAS) in Wistar–Kyoto (WKY) and spontaneously hypertensive rats (SHRs), with WKY rats used as normotensive controls for SHRs. The RAS is crucial for long-term blood pressure regulation, with angiotensin II (AngII) being a potent vasoconstrictor. The aim was to explore the biochemical mechanisms by which beer might influence cardiovascular health. WKY and SHRs were divided into groups receiving tap water (TW), non-alcoholic beer (NAB), alcoholic beer (AB), TW or NAB and TW or AB for 12 weeks. Systolic blood pressure (SBP), body weight, and biochemical parameters (electrolytes, glucose, renal and liver function, lipid profile) were monitored, and the RAS enzyme activity in serum and various tissues was analyzed. Beer consumption, regardless of alcohol content, did not significantly affect SBP or body weight. However, NAB and AB altered the serum electrolyte levels in both strains. AB consumption increased liver enzyme activity. Significant changes were observed in the RAS enzyme activity across tissues, varying by strain, beer type, and tissue. Moderate beer consumption did not elevate blood pressure in WKY or SHRs. Nevertheless, beer modulated RAS-regulating enzyme activities, indicating potential impacts on cardiovascular homeostasis.
Biodiesel and Biolubricant Production from Waste Cooking Oil: Transesterification Reactor Modeling
Authors: Álvarez Pedro M. , Collado Contreras Javier , Nogales-Delgado Sergio
Published: January 2025

Biodiesel and biolubricants play strategic roles in green technologies, as they can be produced in biorefineries. The design of industrial facilities is essential to assess the industrial implementation of these processes, with few studies about this subject in the literature. The aim of this work was to produce biodiesel and a biolubricant from waste cooking oil through double transesterification with methanol and trimethylolpropane, obtaining high conversion values (>97 and 98%, respectively). The biolubricant (characterized according to the UNE-14214 standard) had a density of 951 kg·m−3, a viscosity of 127 cSt (at 40 °C), an acidity value of 0.43 mgKOH·g−1, flash and combustion points of 225 and 232 °C, and an oxidation stability of 6 h through the Rancimat method. Also, a kinetic study was carried out (at temperatures ranging from 80 to 140 °C and with catalyst concentrations from 0.3 to 0.9% w/w and working pressures from 210 to 760 mmHg) to establish the main kinetic parameters, obtaining a second-order reaction and an activation energy of 17.8 kJ·mol−1. Finally, a conceptual design was included, considering the main components of the facility. Thus, the projected plant worked in a discontinuous regime (producing 2 cubic meters per day), pointing out the feasibility of this plant at an industrial scale.
Characterization of Soil Enhancement Mixtures at Low-Magnitude Currents
Authors: Aman Fazlul , Muhammad Usman , Mohamad Nor Normiza , Hamid Haziah
Published: January 2025

Regarding the installation of grounding systems, it is not common for the installation site to be re-located due to high soil resistivity. Enhancement materials have been widely used for their known advantages, including their ability to reduce the resistivity of soil. Although some studies have been conducted on the characterization of EMs under AC of various frequencies, limited studies can be found on their performance when mixed with various percentages of water content (WC) and soil. In this work, sand mixed with controlled percentages of enhancement materials (EM 1, EM 2 and EM 3) and controlled amounts of water content was placed on a parallel plate and tested under AC of various frequencies to obtain the resistivity and permittivity. It was found that dry sand–EM mixtures can be represented by an R-C parallel circuit, while wet sand–EM mixtures can be represented by a nonlinear R. These new test data providing the soil resistivity and permittivity of various sand–EM mixtures are important in selecting suitable EMs to be used in grounding systems.
ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
Authors: Kong Dewei , Yuan Guoshun , Yu Hongjiang , Wang Shuai , Zhang Bo
Published: January 2025

Anomalous Sound Detection (ASD) is crucial for ensuring industrial equipment safety and enhancing production efficiency. However, existing methods, while pursuing high detection accuracy, are often associated with high computational complexity, making them unsuitable for resource-constrained environments. This study proposes an efficient self-supervised ASD framework that integrates spectral features, lightweight neural networks, and various anomaly scoring methods. Unlike traditional Log-Mel features, spectral features retain richer frequency domain details, providing high-quality inputs that enhance detection accuracy. The framework includes two network architectures: the lightweight ASDNet, optimized for resource-limited scenarios, and SpecMFN, which combines SpecNet and MobileFaceNet for advanced feature extraction and classification. These architectures employ various anomaly scoring methods, enabling complex decision boundaries to effectively detect diverse anomalous patterns. Experimental results demonstrate that ASDNet achieves an average AUC of 94.42% and a pAUC of 87.18%, outperforming existing methods by 6.75% and 9.34%, respectively, while significantly reducing FLOPs (85.4 M, a 93.81% reduction) and parameters (0.51 M, a 41.38% reduction). SpecMFN achieves AUC and pAUC values of 94.36% and 88.60%, respectively, with FLOPs reduced by 86.6%. These results highlight the framework’s ability to balance performance and computational efficiency, making it a robust and practical solution for ASD tasks in industrial and resource-constrained environments.
A Survey on Hardware Accelerators for Large Language Models
Authors: Kachris Christoforos
Published: January 2025

Large language models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. As the demand for more sophisticated LLMs continues to grow, there is a pressing need to address the computational challenges associated with their scale and complexity. This paper presents a comprehensive survey of hardware accelerators designed to enhance the performance and energy efficiency of large language models. By examining a diverse range of accelerators, including GPUs, FPGAs, and custom-designed architectures, we explore the landscape of hardware solutions tailored to meet the unique computational demands of LLMs. The survey encompasses an in-depth analysis of architecture, performance metrics, and energy efficiency considerations, providing valuable insights for researchers, engineers, and decision-makers aiming to optimize the deployment of LLMs in real-world applications.
Generic Equations for Long Gravity Waves in Incompressible Fluid with Finite Amplitude
Authors: Kruglov Vladimir I.
Published: January 2025

We present the derivation of generic equations describing the long gravity waves in incompressible fluid with a decaying effect. We show that in this theory, the only restriction to the surface deviation is connected to the stability condition for the waves. Derivation of these generic equations is based on Euler equations for inviscid incompressible fluid and the definition of dynamic pressure which leads to a correct dispersion equation for gravity waves. These derived generic equations for the velocity of fluid and the surface deviation describe the propagation of long gravity waves in incompressible fluid with finite amplitude. We also find the necessary and sufficient conditions for generic equations with dissipation of energy or a decaying effect. The developed approach can significantly improve the accuracy of theory for long gravity waves in incompressible fluid. We also find the quasi-periodic and solitary wave solutions for generic equations with a decaying effect.
Impact of Architectural Styles on Acoustic Characteristics in Selected European Churches
Authors: Di Loreto Samantha , Pierantozzi Mariano , Lori Valter , Serpilli Fabio
Published: January 2025

This study explores the acoustic properties of European Churches, influenced by architectural design, historical context, and spatial configurations. A comparative analysis of 83 Churches from different regions and periods combines literature reviews and empirical data to understand the interplay between architecture and acoustics. Key geometric parameters—volume, surface area, length, height, and aisle count—were compared with acoustic metrics to provide a comprehensive view of these sacred spaces. The study identified the key factors influencing acoustic characteristics, uncovering significant variability within the same architectural style. Linear Discriminant Analysis (LDA) further highlighted distinct patterns and outliers, showing that Gothic, Neoclassical, and modern architectural styles possess unique acoustic signatures. These findings challenge the assumption of uniform acoustics within similar styles, revealing that even minor architectural differences can substantially impact sound behavior. Outliers were particularly informative, representing Churches with unique acoustic properties, which shed light on how specific design elements affect sound propagation. The study underscores the complexity of the relationship between architecture and acoustics in Churches and suggests that further research should consider both quantitative measures and subjective experiences to fully capture the acoustic environment of these historic spaces.
Development and Application of a Cost–Effective Analytical Method for Hydrofluorocarbons Using Preconcentrator–Gas Chromatograph–Mass Spectrometer
Authors: Ichikawa Yujiro , Ono Yukiko , Sasaka Kouki
Published: January 2025

The expansion of atmospheric observation networks for hydrofluorocarbons (HFCs), which are closely related to climate change and contribute to global warming, significantly impacts our society and daily life. Their emissions are estimated to increase in the future, which is a major challenge. Observations of HFCs globally were performed using expensive GHG–specific equipment installed at AGAGE and other sites, but many research institutions find it difficult to install such equipment. Therefore, we successfully developed a measurement method for six components of HFCs (HFC–23, HFC–32, HFC–125, HFC–134a, HFC–143a, and HFC–152a) with high atmospheric concentrations in various parts of the world by optimizing measurement parameters such as the sample transfer volume and rate, module cooling temperatures, the injection time, the GC oven temperature program, and the monitored ions of a commercially available preconcentrator–GC–MS. Because this developed measurement method is cost–effective and simpler to operate than those of GHG–specific equipment, it is expected to provide an opportunity for many research institutes to measure HFCs. Furthermore, in addition to HFCs, we confirmed that simultaneous measurements can be performed for 97 volatile organic compounds (VOCs), including hazardous components. This research can contribute to the observation of HFCs in countries and regions where the actual status of emissions is unclear or where no or few atmospheric observations were conducted. The results of those observations can be used to formulate more detailed global warming countermeasures.
Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI
Authors: Le Dang Bich Thuy , Narayanan Ram , Sadinski Meredith , Nacev Aleksandar , Yan Yuling , Venkataraman Srirama S.
Published: January 2025

This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason score 4+3 or higher) were included. Before transperineal biopsy guided by low-field (58–74mT) MRI, a radiologist annotated suspicious regions of interest (ROIs) on high-field (3T) MRI. Rigid image registration was performed to align corresponding regions on both high- and low-field images, ensuring an accurate propagation of annotations to the co-registered low-field images for texture feature calculations. For each cancerous ROI, a matching ROI of identical size was drawn in a non-suspicious region presumed to be normal tissue. Four Haralick texture features (Energy, Correlation, Contrast, and Homogeneity) were extracted and compared between cancerous and non-suspicious ROIs. Two extraction methods were used: the direct computation of texture measures within the ROIs and a sliding window technique generating texture maps across the prostate from which average values were derived. The results demonstrated statistically significant differences in texture features between cancerous and non-suspicious regions. Specifically, Energy and Homogeneity were elevated (p-values: <0.00001–0.004), while Contrast and Correlation were reduced (p-values: <0.00001–0.03) in cancerous ROIs. These findings suggest that Haralick texture features are both feasible and informative for differentiating abnormalities, offering promise in assisting prostate cancer detection on low-field MRI.
Biomechanical and Biological Multidisciplinary Strategies in the Orthodontic Treatment of Patients with Periodontal Diseases: A Review of the Literature
Authors: Viglianisi Gaia , Polizzi Alessandro , Lombardi Teresa , Amato Mariacristina , Grippaudo Cristina , Isola Gaetano
Published: January 2025

Orthodontic treatment aims to correct malocclusions and ensure the overall health and stability of the periodontium. The relationship between orthodontic therapy and periodontal health is intricate and multifaceted, and a comprehensive approach is often required to achieve optimal outcomes. Firstly, this article delves into the impact of orthodontic mechanics on periodontal tissues, emphasizing the importance of minimizing iatrogenic effects such as root resorption and gingival recession. Understanding the biomechanical principles allows for the development of treatment plans that mitigate these risks while achieving the desired tooth movement. Effective communication and coordinated treatment protocols are essential for managing periodontal issues before, during, and after orthodontic intervention. To optimize outcomes, periodontal considerations such as gingival biotype, attachment levels, and bone support must be integrated into treatment planning. Additionally, adjunctive periodontal therapies such as selective alveolar decortication and regenerative procedures are explored as valuable tools to enhance periodontal support and optimize treatment outcomes. This narrative review explores strategies to attain periodontal goals in orthodontic patients, thereby facilitating successful treatment. Furthermore, the review examines the role of interdisciplinary collaborations between orthodontists and periodontists. In conclusion, achieving periodontal goals in orthodontic patients requires a comprehensive approach that addresses biomechanical principles, interdisciplinary collaboration, patient education, and adjunctive periodontal therapies. By integrating periodontal considerations into orthodontic treatment planning and execution, clinicians can ensure straight teeth and a healthy and stable periodontium, ultimately leading to successful treatment outcomes and long-term oral health.