bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Cross-Sectional Anatomy, Computed Tomography and Magnetic Resonance of the NormalSix-Banded Armadillo (Euphractus sexcintus) Head
Authors: Morales Bordon Daniel , Morales Manuel , Paz-Oliva Pablo , Encinoso Mario , Morales Inmaculada , Roldan-Medina Natalia , Conde Felipe Magnolia Maria , Ramirez Zarzosa Gregorio , Morales-Espino Alejandro , Ros Alvaro , Jaber José Raduan
Published: January 2025

In this research, we have studied the head of the six-banded armadillo, applying advanced imaging techniques such as CT and MRI. Furthermore, by combining the images obtained through these techniques with anatomical cross sections, we present an adequate description of the structures that constitute the head of this species. This anatomical information could provide a valuable diagnostic tool for the clinical evaluation of different desorders in six-banded armadillo including skull malformations, fractures, and neoplasia.
Similarity Principle for Ship’s Plate Structures
Authors: Yu Haicheng , Wu Songhao , Liu Wenlong , Yang Haiyi
Published: January 2025

The similarity principle serves as the theoretical foundation for experimental research on scaled ship structures. While extensive studies have been conducted on global longitudinal responses using scaled hydroelastic segmented ship models, there is a notable gap in experimental research on scaled local ship plate structures. This research introduces a novel composite similarity principle tailored for local ship plate structures, drawing upon classical beam and plate theories. We propose a design methodology to determine the appropriate scaled model for ship plate structures. To validate our proposed similarity principle and its associated design method, we present several numerical experiments. These methods are not only expected to inform future experimental design for local ship plate structures but also to enhance the current experimental framework, contributing to a more holistic approach to ship structural experiment research.
FissureNet: A Concrete Bridge Segmentation Method Based on High-Resolution Images from UAV
Authors: Wang Di , Song Fengquan , Yang Xianyi , Jiang Shixin , Lei Xuelin
Published: January 2025

Accurate semantic segmentation of concrete bridge cracks is crucial for road health monitoring. Currently, crack images are primarily obtained through manual field inspections. As an automated robotic technology, Unmanned aerial vehicles (UVAs) can provide high-resolution images, offering a new, efficient, and cost-effective method for rapid semantic segmentation of cracks. However, the fine local structures, elongated continuous global forms, and the imbalance between crack pixels and background pixels make it challenging for traditional convolutional networks to perform this task effectively. To address these challenges, we have introduced FissureNet, a network based on an enhanced DeepLabv3+ model. Initially, the network utilizes a lightweight architecture to replace the original backbone, thereby reducing model complexity. Subsequently, inspired by the biological movement of a snake swaying side to side, we introduced dynamic strip convolution (DPconv), where the convolution kernels can twist like a snake to better fit our target crack objects. Enriched with examples of bio-inspired applications of biomimetics in the computer field . All atrous convolution branches in the original atrous spatial pyramid pooling (ASPP) module have been replaced with our newly proposed DPconv. This convolution adaptively focuses on small local structures. Moreover, we have incorporenabling more precise extraction of crack features.ated a multi-perspective feature extraction strategy, introducing a vision transformer (VIT) to capture global features. This strategy is combined with traditional convolution to form a hybrid structure that intensifies focus on features from both global and local perspectives, enhancing the continuity of narrow crack segmentation. Lastly, an attention gata mechanism introduced in the decoding phase improves feature extraction near crack edges and significantly reduces the disparity between crack and background pixel counts. Empirical validation on the SDNET2018 dataset has demonstrated that FissureNet outperforms several classical methods in terms of accuracy and continuity in segmenting concrete bridge cracks.
AI-Generated Text and Plagiarism Detection: Pandora’s Tech-Box Unmasked
Authors: Eslit Edgar
Published: January 2025

The use of AI-text generators and plagiarism detectors have made a big challenge to academic community because of the growing occurrence of "false positives" in student’s academic output. These mistakes, where original student work is mistakenly flagged as AI-generated or plagiarized, can have damaging effects on students' academic performance and emotional well-being. This research article explores into the root causes of these “false positives”, examining the limitations of current AI-generated text and plagiarism detection tools with their impact on the educational context. Drawing on the educational theories like constructivism and cognitive load theory, the researcher promotes for a more nuanced approach that balances the potential benefits of AI with the crucial role of human judgment. Using qualitative content analysis and a critical examination of existing literature, the paper proposes a framework for reasonable and more effective AI-assisted or text plagiarism detection. This includes developing more sophisticated AI models that better understand context and language, fostering human intervention in the evaluation process, and updating policies based on the paper’s suggestions to ensure that AI will serve as a supportive tool rather than a retaliatory one. Overall, this article emphasizes the importance of a human-centered approach to AI in education, where technology enhances, rather than hampers, the learning and assessment process.
Leveraging AI Models for Detection and Classification of Alzheimer’s Disease Using MRI and Clinical Data
Authors: Oncu Emir
Published: January 2025

Background and Objective: Alzheimer’s disease (AD) is a challenging neurodegenerative disorder to diagnose, necessitating innovative solutions for early detection and classification. Traditional diagnostic methods often lack sensitivity or scalability, highlighting the need for advanced approaches. This study proposes a dual-model framework integrating an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN) to enhance diagnostic accuracy. Methods: The framework combines two AI models. The ANN was trained on clinical data from 1,200 patients, incorporating 31 demographic, symptomatic, and behavioral features, to assess Alzheimer’s risk. The CNN analyzed 4,876 Magnetic Resonance Imaging (MRI) images to confirm the diagnosis and classify the disease into four stages: mild demented, moderate demented, very mild demented, and non-demented. Grad-CAM visualizations enhanced interpretability, offering clinically relevant insights. Results: The ANN model achieved an accuracy of 87.08% in assessing Alzheimer’s risk, while the CNN model excelled with a 97% accuracy in disease staging. Grad-CAM visualizations highlighted critical regions in the MRI images, enhancing the transparency and reliability of the diagnostic process. The results demonstrate the complementary strengths of both models in providing a comprehensive diagnostic solution. Conclusion: The integrated ANN-CNN framework shows promise in revolutionizing Alzheimer’s diagnostics by combining clinical and imaging data for accurate detection. While limited by MRI availability and variability in clinical data, the framework underscores AI's potential in advancing neurodegenerative disease diagnosis. Future directions include integrating wearable technology and lightweight CNNs to improve scalability, accessibility, and early intervention.
Noncommutative Heisenberg-Robertson-Schrodinger Uncertainty Principles
Authors: Krishna K. Mahesh
Published: January 2025

Let $\mathcal{E}$ be a Hilbert C*-module over a unital C*-algebra $\mathcal{A}$. Let $A: \mathcal{D}(A) \subseteq \mathcal{E} \to \mathcal{E}$ and $B: \mathcal{D}(B)\subseteq \mathcal{E}\to \mathcal{E}$ be possibly unbounded self-adjoint morphisms. Then for all $x \in \mathcal{D}(AB)\cap \mathcal{D}(BA)$ with $\langle x, x \rangle =1$, we show that \begin{align*} (1) \quad \Delta _x(B)^2d_x(A)^2+\Delta _x(A)^2d_x(B)^2\geq \frac{(\langle \{A,B\}x, x \rangle -\{\langle Ax, x \rangle,\langle Bx, x \rangle\})^2-(\langle [A,B]x, x \rangle +[\langle Ax, x \rangle,\langle Bx, x \rangle])^2}{2} \end{align*} and \begin{align*} (2) \quad \Delta _x(A)\Delta _x(B)\geq \frac{\sqrt{\|(\langle \{A,B\}x, x \rangle -\{\langle Ax, x \rangle,\langle Bx, x \rangle\})^2-(\langle [A,B]x, x \rangle +[\langle Ax, x \rangle,\langle Bx, x \rangle])^2\|}}{2}, \end{align*} where \begin{align*} &\Delta _x(A)\coloneqq \|Ax-\langle Ax, x \rangle x \|, \quad d_x(A)\coloneqq \sqrt{\langle Ax, Ax \rangle -\langle Ax, x \rangle^2},\\ &[A,B] \coloneqq AB-BA, \quad \{A,B\}\coloneqq AB+BA, \\ & \{\langle Ax, x \rangle,\langle Bx, x \rangle\}\coloneqq \langle Ax, x \rangle\langle Bx, x \rangle +\langle Bx, x \rangle\langle Ax, x \rangle, \\ & [\langle Ax, x \rangle,\langle Bx, x \rangle]\coloneqq \langle Ax, x \rangle\langle Bx, x \rangle -\langle Bx, x \rangle\langle Ax, x \rangle. \end{align*} We call Inequalities (1) and (2) as noncommutative Heisenberg-Robertson-Schrodinger uncertainty principles. They reduce to the Heisenberg-Robertson-Schrodinger uncertainty principle (derived by Schrodinger in 1930) whenever $\mathcal{A}=\mathbb{C}$.
Sustainable Energy Storage Systems: Polypyrrole Filled Polyimide-Modified Carbon Nanotube Sheets with Remarkable Energy Density
Authors: Andezai Andekuba , Iroh Jude
Published: January 2025

Organic materials are emerging as promising candidate for electrode material for lithium-ion batteries and supercapacitors because of their unique properties and potential advantages over traditional inorganic materials. This paper is focused on polypyrrole doped surface modified carbon nanotuube sheets as electrode materials for energy storage devices such as lithium-ion batteries and supercapacitors. The advantages, challenges, and ongoing developments in this area are discussed and electrode materials properties such as energy density, cycling stability, and specific capacitance are systematically investigated.
This paper also addresses the limitations of organic electrode materials such as low energy density, cycling stability, poor discharge capability. It presents a systematic study and investigation of polypyrrole doped polyimide-modified carbon nanotube sheets. The effect of polyimide processing temperature and polypyrrole deposition time on the electrochemical properties of the electrode materials were investigated.
Electrochemical impedance spectroscopy, EIS showed that short deposition times of about 60s and low processing temperatures between 80 and 150˚C provided the best balance between electrode material structure, and electrochemical properties. This study provides a methodology for constructing electrode materials with a combination of high energy and power densities. This study also shows that the processing conditions that produced electrode materials that exhibited a combination of redox reactions and charge displacement mechanisms, typical of a pseudosupercapacitor, resulted in the highest specific capacitance and energy density.
A Novel Additive Manufacturing Approach: Directed Energy Deposition with Real-Time Continuously Variable Composition
Authors: Li Jinlong , Zhang Xiaowei , An Zhe , Li Biqiang , Wang Yizheng , Yang Yaoyuan , Tong Kexin , Zhu Yingze
Published: January 2025

A novel approach focused on continuous real-time variable composition is becoming more popular in additive manufacturing (AM). At present, there is a lack of knowledge on multi-material laser directed energy deposition (MML-DED) additive manufacturing of continuously variable composition. Here we show the novel approach to deposit the layer which combine titanium, nickel and niobium three elements with dynamic continuous variable composition. The experimental results reveal that the mass norm precent of tita-nium element gradually continuous increase along the laser beam traversing direction, precent of nickel element basically no change and precent of niobium element gradually decrease. This study will inspire the scholars and engineers seeking approach and under-standing of how to utilize AM method to innovate the alloy design and intricate geome-tries building engaging in the additive manufacturing and relevant fields to meet future needs in numerous applications.
Analyzing User Sentiments: A Python- Based Approach
Authors: T L Varshitha , S K Geethanjali
Published: January 2025

Sentiment Analysis (SA), often referred to as opinion mining, has emerged as a cornerstone in understanding and interpreting emotional tone embedded in textual data. This research leverages the power and flexibility of Python, a leading programming language in data analysis and natural language processing (NLP), to develop an advanced sentiment analysis system. By integrating machine learning techniques with lexicon-based methodologies, the proposed system delivers precise and real-time sentiment evaluation for diverse textual inputs. The study employs robust preprocessing pipelines, sophisticated algorithms, and Python’s extensive NLP libraries, such as NLTK and Scikit-learn, to handle large-scale datasets efficiently. Furthermore, the system’s adaptability across various contexts—ranging from customer feedback to social media analytics—demonstrates its versatility and practical value. By blending technical rigor with real-world applicability, this paper not only illustrates Python’s efficacy in building scalable sentiment analysis solutions but also sets a benchmark for future advancements in the field. The proposed framework lays the foundation for exploring emerging challenges, such as sarcasm detection and multilingual sentiment analysis, thereby paving the way for innovative applications in NLP and beyond. The integration of advanced visualization techniques enhances the interpretability of sentiment results, making the system user-friendly for both technical and non-technical audiences. By addressing existing limitations in traditional sentiment analysis approaches, this research contributes to the evolving landscape of NLP-driven data insights. The study also incorporates innovative preprocessing techniques, including lemmatization and stopword removal, to ensure data integrity and accuracy in sentiment predictions [1]. A comparative analysis of machine learning algorithms highlights the optimal models for diverse datasets, offering valuable insights for researchers and developers.
Cloud Computing Framework for Space Farming Data Analysis
Authors: Janairo Adrian Genevie , Concepcion II Ronnie , Fernando Arvin , Guillermo Marielet
Published: January 2025

The study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. The research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the ESP-NOW technology, communications between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through AWS IoT to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted to an mAP, precision, and recall of 99.5%, 99.9%, and 100.0% respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real-time through a web dashboard. With this use case, immediate actions can be done accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming.
Evaluation of Feature Selection Methods and Machine Learning Models for Identifying Collided Positions of Containers Equipped with an Accelerometer
Authors: Zhang Xin , Song Zihan , Park Do-Myung , Park Byung-Kwon
Published: January 2025

In the logistics and trade that are highly dependent on containers, efficient identification of col-lided positions is of great significance for enhancing cargo safety supervision and accident respon-sibility. Traditional methods that rely on visual inspections require a lot of manpower, are time-consuming and costly. This study proposes a machine learning-based system to identify col-lided positions using the data collected through accelerometers installed on container doors. This study also uses feature selection techniques to reduce the data dimensionality, thereby improving computational efficiency and reducing computational costs. The feature selection methods evalu-ated include: Pearson Correlation Coefficient, Mutual Information, Sequential Forward Selection, Sequential Backward Selection, and Extreme Tree. The machine learning models evaluated in-clude Decision Tree, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting. Experimental results show that feature selection effectively reduces data di-mensionality and computational cost while maintaining or improving classification accuracy. The best combination of classification model and feature selection method is the combination of K-Nearest Neighbor and Extreme Tree, which achieved the best balance in terms of accuracy 0.9712, execution time 0.0300 seconds and CPU utilization 0.10%. It has a considerably practical significance in the logistics field in terms of safer and more efficient cargo transportation, reliable accident responsibility identification with reduced computing costs.
Immune Microenvironment in Oral Potentially Malignant Disorders and Oral Cancer
Authors: Ali Aiman , Rigueira Molska Graziella , Yeo Huiling , Esfandiari Najmeh , Jeong Will , Huang Michelle , Magalhaes Marco
Published: January 2025

Multiple studies have investigated the impact of the tumor immune microenvironment (TIME) on oral squamous cell carcinoma (OSCC), with most focusing on three key cellular components: lymphocytes, macrophages, and neutrophils, as well as the molecular mechanisms underlying inflammation-mediated OSCC invasion. Although the specific roles of each cell type vary depending on their subtypes and the characteristics of OSCC, several consistent patterns have been identified. TIME plays a critical role at every stage of OSCC progression, from tumor initiation and growth to invasion and metastasis. Understanding the communication signals -the language- between tumor cells and the TIME, encoded through various proteins secreted by immune cells, is essential for controlling tumor progression and developing effective treatments for OSCC.
A Data Library of One and Two-Handed Manipulation of Large Objects from Everyday Living Combined with Locomotion: A Foundation for Roboticists and Virtual Reality and Gaming Engineers
Authors: Luttmer Nathaniel G , Baum Nathan I , Flores-Gonzalez Josue , Hollerbach John M , Minor Mark A
Published: January 2025

The purpose of this paper is to provide researchers with a description of a data library representing humans interaction with medium to large sized objects from everyday living. The library includes motion capture data characterizing human and object motion as well as data for characterizing haptic interaction with the object via force and torque measurements via a load cell and inertial measurement unit (IMU) readings of the object accelerations. Objects include a box, luggage, briefcase, walker, shopping cart, wheelbarrow, and door. Data collected includes multiple types of interactions with each object, such as manipulating the object and walking while interacting with the object (e.g., pulling, pushing, carrying, operating, etc.). Data processing techniques for synchronizing data, deriving human biomechanics, and segmenting trials are presented. The database will be available on AddBiomechanics, SimTK, and github.
Patent Analysis and Trends Related to 2D Nanomaterials for Active Food Packaging
Authors: Barbieri Massimo
Published: January 2025

Active food packaging technology encompasses systems that incorporate active substances into the polymeric matrix. The embedded components exhibit antimicrobial, antifungal, antioxidant properties and are able to absorb or reduce oxygen, carbon dioxide or ethylene, thereby enhancing the quality and safety of food products. The utilization of 2D nanomaterials, such as graphene, has facilitated the advent of novel avenues for the advancement of active packaging (AP). The integration of these materials with polymers has the potential to enhance the barrier, thermal, and mechanical properties of packaging. The objective of this paper is to provide a comprehensive overview of patented two-dimensional materials in the field of active packaging.
Design, Synthesis, and Tribological Behavior of an Eco-Friendly Methylbenzotriazole-Amide Derivative
Authors: Yang Fan , Li Zenghui , Yang Hongmei , Zhao Yanan , Sun Xiuli , Tang Yong
Published: January 2025

Benzotriazole and its derivatives show good tribological properties, anti-oxidation, anti-corrosion, rust prevention and dispersion capabilities as lubricating additives, becoming the common multifunctional additives. And how to avoid sulfur- or phosphorus-introduction to improve their functionality and the compatibility with hydrocarbons is the forefront research. In this study, methylbenzotriazole and oleic acid were applied to synthesize a new methylbenzotriazole-amide that with long alkyl-chain (E)-N-(2-(5-methyl-2H-benzodiazole-2-yl)ethyl)octadec-9-enamide (MeBz-2-C18), which was characterized by nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), FT-IR and thermogravimetric analysis (TGA). The thermal stability and tribological properties of MeBz-2-C18 were compared with the commercially available benzotriazole oleamide (T406). The results show that MeBz-2-C18 has better thermal stability and base oil compatibility than that of T406, and 0.5 wt.% addition of MeBz-2-C18 could decrease the average wear scar diameter (ave. WSD) by 21.6%. The wear surface analysis and DFT calculation show that the amide group in MeBz-2-C18 is preferentially broken during friction, which would reduce the interfacial shear force and easily react with the metal surface to form iron oxide film, thus demonstrating a better anti-wear and friction reducing performance, indicating its potential application as an environmental friendly multifunctional additive.
A Review of Polymer Electrolyte Membrane Fuel Cells (PEMFCs)
Authors: Orilade Mejehiemi , Iroh Jude
Published: January 2025

Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that can transform the chemical energy stored in fuel directly into electrical energy, Commercialization of PEM fuel cells has progressed significantly in recent years. One of the primary obstacles preventing polymer electrolyte membrane fuel cells (PEMFCs) from being a commercially viable product is their durability. To offset insufficient fuel cell durability, it is critical to understand their degradation phenomena and study the degradation mechanisms of its components. This will allow novel component materials to be produced and novel viable designs to be achieved. The various causes of degradation in important cell components, such as membranes, gas diffusion layers, and catalyst layers were reviewed, which included accelerated stress test (AST) analysis. Thermodynamic and kinetic factors linked to fuel cell operation are discussed along with a discussion of reactions that may be involved in catalyst deterioration. The use of water collectors in PEMFCs mitigates the challenge of cathodic flooding in PEMFCs. Optimization of PEMFCs efficiency can also be achieved by introducing platinum, iron, and nickel deposition as support catalyst as well as by reduction in fuel cell ripples. Zirconated-based proton conducting materials show prospects as an alternative for Nafion due to its stability at high temperatures.
Algebraic Gestalt-Archetypes of Probabilities in Genomic DNAs, Cyclic Gray Codes, Quantum Bioinformatics
Authors: Petoukhov Sergey
Published: January 2025

The article is devoted to the study of the regularities of the statistical organization of nucleotide sequences of single-stranded DNAs in genomes of higher and lower organisms, as well as their connections with cyclic Gray codes and the problem of holistic structures (gestalts) in physiology. The author presents stable statistical structures of an algebraic nature, which are found in many genomic DNAs and are called algebraic gestalt-archetypes of probabilities in genomic DNAs. They are discussed as a possible basis for several genetically inherited physiological and psychophysical properties. The numerical rules of these genomic archetypes realized in nature for a representative class of genomic DNAs, whose initial data were taken by the author from the publicly available genomic data bank “GenBank”, are formulated. The analysis of single-stranded genomic DNAs was carried out using the author's method of "hierarchies of multilayer statistics", representing the nucleotide sequence of DNA as a multilayer text structure, in which each n-th layer is a sequence of n-plets (that is, of monoplets, or duplets, or triplets, etc.). In each such layer, the percentages of each of the types of its n-plets are calculated, the values ​​​​of which are inserted into the so-called genetic (2n∙2n) Karnaugh matrices, whose columns and rows are numbered by n-bit Gray codes by analogy with Karnaugh maps from the Boolean algebra of logic. The data of the analysis of the nucleotide sequence of DNA of the human chromosome № 1, containing about 250 million nucleotides, are represented as an example. The obtained data are discussed in light of the problem of genetically inherited holistic structures in biology and the tasks of developing algebraic biology, genetic biomechanics, quantum bioinformatics, artificial intelligence, and genetic algorithms.
The Lake Chivero Contamination Crisis of 2024: A Wake-Up Call for Global Water Resource Management
Authors: Mbisva Shandirai
Published: January 2025

Water contamination results in dangerous risks to ecosystems and human populations, mostly in areas heavily reliant on vulnerable water sources. Lake Chivero is known as a primary water reservoir for the capital of Zimbabwe which is Harare. An unfortunate event occurred in Zimbabwe, which resulted in severe contamination in Lake Chivero. Several deaths of several wildlife species were reported, including rhinos, zebras, and wildebeests. This case study highlights the ecological and public health dangers posed by such contamination, focusing on cyanobacterial blooms and nutrient overloading. Focusing on recent reports and published literature, it underscores the impacts of eutrophication, microbial contamination, and toxin production, which jeopardize aquatic biodiversity, food safety, and human health. By synthesizing existing data and recent incidents, the aim is to inform the scientific community about the consequences of these events, emphasizing the urgent need for interdisciplinary approaches to mitigate water contamination risks.
Ventilation and Bedding Effects on Air Quality in Swine Confinement Systems
Authors: Choi Hong Lim , Febrisiantosa Andi , Renggaman Anriansyah , Sudiarto Sartika Indah Amalia , Yun Chan Nyeong , Suresh Arumuganainar
Published: January 2025

Reducing odor and particulate matter (PM) in confined swine houses is critical for environmental sustainability and animal welfare. This study evaluated the effects of different housing designs and bedding systems on aerial environmental conditions in swine confinement facilities. Three experimental swine house models (M1, M2, and M3) were constructed with distinct ventilation and bedding configurations. M1 employed circulating airflow with negative pressure and a recirculating ventilation system. M2 featured a plug flow air pattern with negative pressure and a perforated plastic bed, while M3 utilized a sawdust bedding system with recirculating ventilation. Each model housed nine fattening swine in a 12 m² area, and the study was conducted over 110 days (May 6–August 26, 2018). Measurements included odorous compounds, volatile organic acids (VOA), and PM concentrations. Ammonia (NH₃) levels were highest in M1 (9.07 ppm) and lowest in M3 (5.94 ppm). Hydrogen sulfide (H₂S) was undetectable in M3, while M1 and M2 recorded measurable levels. VOA concentrations were significantly lower in M3 (84.86 ppbv) compared to M1 (884.29 ppbv) and M2 (605.31 ppbv). PM10 levels exhibited notable variations, with concentrations of 115.25 μg/m³ in M1, 218.83 μg/m³ in M2, and 312.00 μg/m³ in M3. However, bacterial concentrations were highest in M3. The sawdust bedding system (M3) significantly reduced volatile fatty acids and odorous compounds compared to pit-based systems, while the ventilation system influenced odor control. Although M3 had higher PM levels, the findings emphasize the need for swine farming systems to balance environmental management with animal welfare considerations.
Gear Teeth of Ball Mills: Wear, Restoration, and Mathematical Modeling
Authors: Smailova Baglan , Buzauova Toty , Bartenev Igor
Published: January 2025

This study focuses on the restoration of large-module gear teeth for ball mills through electroslag surfacing, emphasizing practical results and mathematical modeling. The research explores wear mechanisms, restoration processes, and laboratory experiments to evaluate the hardness and surface roughness of restored teeth. Optimal surfacing modes were identified, including key parameters such as current strength, slag bath voltage, and cooling water flow rate. These parameters ensure the durability and operational efficiency of restored gears. Mathematical modeling and experimental analysis have provided equations that describe the dependencies between surfacing modes, wear resistance, and mechanical properties. The findings are highly relevant for industries engaged in gear restoration, enabling significant cost reductions and improved reliability.
The Costs-of-Living Side-by-Side with Monkeys- Economic Impacts on Commercial Farms and Property by Toque Macaques and Proposed Deterrent Strategies in a Rural Agriculture Area of Kurunegala District, Sri Lanka
Authors: Jayarathne Yeshanthika , Nahallage Charmalie , Huffman Michael
Published: January 2025

As the human population has grown and expanded, increasing pressure is being put on natural habitats in Sri Lanka. This situation has led to a noticeable increase in human-primate conflicts. To understand the situation, we studied the interactions between humans and macaques in three administrative divisions of Kurunegala District. Data was gathered from 875 informants through interviewer-administered questionnaires between 2020 and 2022. The monthly economic loss by commercial farmers due to macaque damage to fruits and vegetables doubled by 2022, amounting to approximately 5000 LKR. In non-fruiting seasons, losses from coconut damage increased, ranging from 3000 to 14,000 LKR/month, decreasing by over 50% during fruiting seasons. Property damage per household averaged between 850 ~4,000 LKR/month. A cost of approximately 1200~3000 LKR was borne per household/month to deter monkeys from the fields. Macaques were the primary culprits for crop damage in this area, and were also responsible for property damage, surpassing that of other animals. The consensus among the community is that either relocating macaques to other forested areas or sterilizing them to control their population could mitigate the issue to some extent. An integrated management plan involving relevant stakeholders is necessary to address the conflict arising from human-macaque crop utilization.
Investigation of Image Change Detection Based on Multiscale and Multiplex Technologies
Authors: Qi Yongjun , Jia Zhengzheng , Chen Jiahao , Song Yuanmeng , Yang Shaohua , Zhu Jie , Liu Xin , Zheng Hongxing
Published: January 2025

In order to provide better geographic information services on time, a detection technique on the variation of remote sensing image has been investigated in this paper. Based on multiscale feature multiplex, a consistent regularization method together with a semi-supervised strategy has been proposed, which can solve the problems of unclear change edges and the issue of missing detection of very small targets. By constructing a learning training model of detection network, the performances have been enhanced with limited labeled data and effectively handle change detection tasks in complex scenes. The network consists of two parts. One part is the supervised multiscale feature fusion and multiplex, which enhances feature expression capabilities using channel and spatial attention mechanisms. We extract variation features through complex multiscale feature fusion and reuse strategies to alleviate unclear change edges and the omission of very small targets. The other part is the consistency regularization method for semi-supervised learning model, which adopts an alternating training among models. The prediction differences have been minimized, then updated model parameters. This constructed model of detection network has been verified at different labeling ratios on the datasets such as Wuhan university, China, building change detection and google earth imagery Guangzhou, China, change detection, etc. The experimental results show that this proposed technique demonstrates the best overall performance in complex situations with small dataset labeling ratios among existing detection methods.
A Short-Term Fertilization of the Nitrogen-Fixing Bacterium (NFB) Kosakonia radicincitans GXGL-4A Agent Can Modify the Transcriptome Expression Profiling of Cucumber (Cucumis sativus L.) Roots to Enhance Resistance to Drought
Authors: Feng Baoyun , Wang Erxing , Zhang Yating , Xu Lurong , Xue Yanwen , Chen Yunpeng
Published: January 2025

The application of nitrogen-fixing bacterium (NFB) as biofertilizer can greatly reduce or even avoid the environmental pollution caused by the excessive use of chemical nitrogen fertilizer. In this study, four nitrate transporter (NRT) genes (the Csa1G047430, Csa2G416080, Csa3G027720 and Csa5G161290) subcloned from a Chinese cucumber (Cucumis sativus L.) cultivar “Xintaimici” by RT-PCR were subjected to a determination of the relative expression level using fluorescent quantitative RT-PCR (qRT-PCR). The results showed that the expression of Csa3G027720 gene was significantly up-regulated in the root samples treated with liquid soil extract, and similarly Csa1G047430 and Csa3G027720 genes showed a significant up-regulation when treated with the bacterial cells of GXGL-4A. Differential transcriptome analysis of cucumber roots were conducted between the GXGL-4A-treated group and the control group treated with an equal volume of sterile water. A total of 418 differentially expressed genes (DEGs) were detected and categorized into 20 functional groups. Only the KEGG pathway of phenylpropanoid biosynthesis reached a significant level (P-adjust=0.0279) among all the 17 enriched KEGG pathways. Hence, we speculate that the NFB bacterial strain GXGL-4A, as a biological stimulus, promote the phenylpropanoid biosynthesis in cucumber roots after colonization in cucumber rhizosphere. Under drought condition, the rhizosphere electrical conductivities (EC) of the NFB treatment groups were extremely significantly higher than that of the control group, suggesting that the application of NFB significantly enhanced cucumber's resistance to drought.
3D Magnetic Inversion with Mixed Lp Norm Regularization for Unveiling the Spatial Distribution of Mesozoic Volcanic Arcs in the Northern South China Sea
Authors: Chen Zhongwei , Guo Lianghui
Published: January 2025

The high magnetic anomaly belt trending in the NE direction in the northern slope of the South China Sea (SCS) has long been associated with an ancient volcanic arc zone. However, the exact spatial distribution of the ancient volcanic arc zone remain unclear, limiting the understanding of its formation and controls on the evolution of the SCS. In this paper, we assemble the high-resolution reduced-to-pole (RTP) aeromagnetic anomaly data around the northern SCS, and then conduct the 3-D magnetic inversion based on mixed Lp norm regularization upon the RTP aeromagnetic data to obtain the 3-D susceptibility model of the crust in the study area. Previous borehole and deep seismic reflection profiling data are involved in the inversion for constraints. Our result confirms that the high magnetic anomaly belt in the study area is caused by deep-seated magnetic bodies, which, supported by drilling and magnetization data, are identified as the Mesozoic volcanic arc zone induced by the ancient subduction. Our model demonstrates that the Mesozoic volcanic arc zone trends northeastward and extends at least from the Dongsha Rise to Taiwan Island, primarily distributed at depths of 20-28 km in the lower crust. Our model provides significant evidence for understanding the tectonic evolution of the northern SCS prior to its extensional phase.
Effect of a Gluten-Free Diet on the Intestinal Microbiota of Women with Celiac Disease
Authors: (MMM) Maria del Mar Morcillo , (AHJ) Javier Arranz Herrero , (GRD) Daniel González Reguero , (RMM) Marina Robas Mora , (DIR) Rocío de la Iglesia González , (UMN) Natalia Úbeda Martín , (AAE) Elena Alonso Aperte , (JGP) Pedro Antonio Jimenez Gómez , (RSP) Paloma Reche Sainz
Published: January 2025

Celiac disease (CD) is an autoimmune disorder characterized by small intestinal enteropathy triggered by gluten ingestion, often associated with gut dysbiosis. The most effective treatment is strict adherence to a gluten-free diet (GFD), which alleviates symptoms. To evaluate the long-term impact of GFD, this study analyzed the gut microbiota of 10 women with CD on a GFD for over a year compared to 10 healthy controls with unrestricted diets. Taxonomic diversity (16S rRNA gene sequencing and the analysis of α and β-diversity), metabolic functionality (Biolog EcoPlates®), and antibiotic resistance profiles (Cenoantibiogram) were assessed. Metagenomic analysis revealed no significant differences in taxonomic diversity but highlighted variations in the abundance of specific bacterial genera. Women with CD showed increased proportions of Bacteroides, Streptococcus, and Clostridium, associated with inflammation, but also elevated levels of beneficial genera such as Roseburia, Oxalobacter, and Paraprevotella. Despite no significant differences in metabolic diversity, higher minimum inhibitory concentrations (MICs) in women in the healthy control group suggest that dietary substrates in unrestricted diets may promote the growth of fast-growing bacteria capable of rapidly developing and disseminating antibiotic resistance mechanisms. These findings indicate that prolonged adherence to a GFD in CD supports remission of gut dysbiosis, enhances microbiota functionality, and may reduce the risk of antibiotic resistance, emphasizing the importance of dietary man agement in CD.
Computer-Aided/Internet of Medical Things (CAD/IoMT)-Powered Detection of Brain Tumor
Authors: Boko Misbahu Koramar , Ibrahim Abdullahi Umar , Al-Turjman Fadi , Pwavodi Pwadubashiyi Coston
Published: January 2025

Background/Objectives: Brain tumor continue to cause concern globally due to increasing number of cases and mortality. As the first line of action, accurate and early screening of brain tumor is critical for proper treatment and extending the life span of patients. Currently, medical expert relies heavily on the use of magnetic resonance imaging (MRI) for the detection of brain tumor. However, one of the limitation of MRI revolves around manual interpretation, which is time consuming and can be prone to errors especially when dealing with large number of cases. Thus, in order to address this issue, we proposed the development of CAD/IoMT-powered platform that enables real-time and fast detection of brain tumor. Methods: We proposed a framework known as I-BRAIN-DETECT, which is a CAD-based system, integrated with IoMT for fast and real-time detection of brain tumors and no tumor from MRIs. The overall methodology revolves around the use of 2 publicly accessible datasets, image pre-processing, feature extraction and classification using untrained customized CNN and 5 pre-trained CNNs which include ResNet-18, ResNet-50, DenseNet121, MobileNetV2 and EfficientNetB0. Results: Performance evaluation and comparative between implemented models has shown that pre-trained ResNet18 achieve the best result with 98.83% accuracy, 98.33 % recall, 99.33% precision, 99.33% specificity, 98.83% F1-score and 99.92 AUC for binary classification, while MobileNetV2 achieved the best result with 92.93% accuracy, 92.93 % recall, 93.37% precision, 97.67% specificity, 92.79% F1-score and 100 AUC for multiclass-classification. Conclusions: The developed platform can now be access by both patients and medical experts for real-time screening of brain tumor.
The Role of Blood Perfusion on the Thermal Interaction Between Magnetic Nanoparticles and Cancerous Tumor: A Computational Study
Authors: Maniotis Nikolaos , Mitropoulos Spyridon , Tsiantos Vassilios
Published: January 2025

In this study, the role of blood perfusion in modulating thermal response of tumors during magnetic nanoparticle hyperthermia was investigated through computational modeling. The thermal dissipation of 15 nm magnetite nanoparticles was estimated using micromagnetic simulations of their hysteresis loops under a magnetic field of 20 mT and frequency of 100 kHz. These calculations provided precise energy loss parameters, serving as inputs to simulate the temperature distribution in a tumor embedded within healthy tissue. Temperature-dependent blood perfusion rates, derived from experimental models, were integrated to differentiate the vascular dynamics in normal and cancerous tissues. The simulations were conducted using a bioheat transfer model on a 2D axisymmetric tumor geometry with magnetite nanoparticles dispersed uniformly in the tumor volume. Results showed that tumor tissues exhibit limited blood perfusion enhancement under hyperthermic conditions compared to healthy tissues, leading to localized heat retention favorable for therapeutic purposes. The computational framework validated these findings by achieving therapeutic tumor temperatures (41–45°C) without significant overheating of surrounding healthy tissues, highlighting the critical interplay between perfusion and energy dissipation. These results demonstrate the efficacy of combining nanoparticle modeling with temperature-dependent perfusion for optimizing magnetic nanoparticle-based hyperthermia protocols.
The Crosstalk Between Endothelial Cells, Smooth Muscle Cells and Macrophages in Atherosclerosis
Authors: Gong Sihe , Li Yanni , Yan Kaijie , Shi Zhonghong , Leng Jing , Bao Yimin , Ning Ke
Published: January 2025

Atherosclerosis (AS) is a chronic inflammatory vascular disease closely tied to cellular metabolism. Recent genome-wide association study data have suggested that the significant roles of endothelial cells, smooth muscle cells, and macrophages in the regression and exacerbation of AS. However, the impact of cellular crosstalk and cellular metabolic derangements on disease progression in AS is vaguely understood. In this review, we analyze the roles of the three cell types in AS. We also summarize the crosstalk between the two of them, and the associated molecules and consequences involved. In addition, we emphasize potential therapeutic targets and highlight the importance of the three-cell co-culture model and extracellular vesicles in AS-related research, providing ideas for future studies.
Personality Meets Technology: Quantile Regression Analysis of Openness to Experience in Digital Wallet Usage
Authors: Putri Fildzah Rudyah , Mahdiyah Mahdiyah , Madani Faisal , Dahlia Mutiara
Published: January 2025

Digital wallets have become an important part of financial transactions, especially among college students. This study aims to analyze the influence of the personality dimension of Openness to Experience on the use of digital wallets using quantile regression analysis. This approach allows exploration of variations in the influence of Openness to Experience at low, medium, and high levels of transaction intensity. Data were taken from 212 students of Jakarta State University measured through an online survey. The results showed that Openness to Experience had a positive effect on the use of digital wallets, with a stronger effect on the upper quantile. This suggests that individuals with high levels of openness tend to be more enthusiastic in adopting new technologies and exploring digital wallet features.
A felsőoktatás és a tudomány nemzetköziesedésének hatása a magyar anyanyelvű oktatásra és terminológiára
Authors: Fóris Ágota
Published: January 2025

A tanulmány célja annak a vizsgálata, hogy a nemzetköziesedés folyamatai milyen hatástgyakorolnak a magyar nyelv státusára és a magyar nyelvhasználatra a felsőoktatásban és atudományban. A vizsgálat módszere kvalitatív, tartalomelemzés: a releváns szakirodalom ésdokumentáció átvizsgálását tartalmazza. Megállapítható, hogy a tudományos publikációk és amagyarországi felsőoktatás nemzetköziesedése felgyorsult, és egyre több intézmény tér át azangol közvetítő nyelvű oktatásra és az angol nyelvű publikálásra. Az Akadémia nemzetstratégiaijelentőségű témának tartja a magyar nyelv és a magyar szaknyelv ügyét, de az angol nyelvűtudományosságot is, és a tudományos kétnyelvűség mellett foglal állást. A magyar tudományosnyelv támogatásának az alapja a diszciplináris terminológia kialakítása és elérhetőségének abiztosítása, de sem az Akadémiának, sem az egyetemeknek nincs explicit terminológiastratégiája,bár az Akadémia tervezi egy ilyen dokumentum létrehozását.
Mid-Tudor Queenship and Memory: The Making and Re-making of Lady Jane Grey and Mary I. Edited by Valerie Schutte and Jessica S. Hower. Palgrave Macmillan, Basingstoke, 2023. ISBN 978-3-031-35688-9 (ebook) xi + 273 pp. £109.99
Authors: Thomas Melita
Published: January 2025

Mid-Tudor Queenship and Memory: The Making and Re-making of Lady Jane Grey and Mary I. Edited by Valerie Schutte and Jessica S. Hower. Palgrave Macmillan, Basingstoke, 2023. ISBN 978-3-031-35688-9 (ebook) xi + 273 pp. £109.99
Systematic Literature Review: Interpersonal Communication Problems In Hearing Children From Deaf Parents
Authors: Setyaningsih Santi , Utari Prahastiwi , Satyawan Ignatius Agung
Published: January 2025

The main problem in families in which there are Deaf parents and Hearing children is the problem of communication, both verbally and in sign language. Hearing children with Deaf parents are able to communicate in two ways, either verbally with non-Deaf people or using sign language with Deaf people. Communication between children and Deaf parents is an important problem in their social and emotional development. This study aims to determine the communication problems that arise between children and Deaf parents. The main focus is to explore the obstacles experienced by children in understanding and conveying messages from their parents who have hearing impairments. This study uses  the Systematic Literature Review (SLR) method. Researchers found 127 Scopus articles in a search using Science Direct and with the help of Mendeley and VOSViewer which were  then filtered into 18 articles to be analyzed descriptively. The findings show that lack of communication should not be a reason why Deaf parents cannot have access or information about their children. And appreciate that hearing children or Children of Deaf Adults or CODA have different ways of communicating with Deaf parents, the Deaf community and the hearing or non-Deaf community.