bluuebunny/crossref_metadata_2025 huggingface dataset:
Cyclin-Dependent Kinase Inhibition in Prostate Cancer: Past, Present, and Future
Authors: Siskin Matthew , Economides Minas P. , Wise David R.
Published: February 2025

Background: Despite significant progress, prostate cancer remains a leading cause of death. Cyclin-dependent kinase (CDK) 4/6 inhibitors, which are already approved for the treatment of hormone receptor-positive breast cancer, are undergoing extensive testing as monotherapy and in various combinations as a potentially valuable treatment modality in prostate cancer patients. Thus far, a limited number of these studies have published results, which have been largely disappointing. Areas Covered: In this review, we describe the biologic rationale for the use of CDK4/6 inhibitors in prostate cancer, the existing clinical data describing their use in prostate cancer, and ongoing clinical trials of CDK4/6 inhibitors as monotherapy and in combination for the treatment of prostate cancer. In particular, we focus on possible resistance mechanisms that may be particularly relevant in prostate cancer patients, leading to de novo and acquired resistance, and we highlight novel strategies that can overcome this resistance. Conclusions: Current clinical trials are actively working to (1) refine the role of CDK4/6 inhibitors in prostate cancer patients; (2) develop new inhibitors of other cell-cycle targets, such as CDK2 and CDK7; and (3) explore novel combination therapies with inhibitors of other relevant pathways, such as PI3K or MAPK. Further genomic subtyping of advanced prostate cancer will likely shed light on the subsets of patients most likely to benefit from cell-cycle-targeted agents.
Advanced Therapeutic Approaches for Metastatic Ovarian Cancer
Authors: Choe Soohyun , Jeon Minyeong , Yoon Hyunho
Published: February 2025

Ovarian cancer is the fifth leading cause of cancer-related death among women, which is one of the most common gynecological cancers worldwide. Although several cytoreductive surgeries and chemotherapies have been attempted to address ovarian cancer, the disease still shows poor prognosis and survival rates due to prevalent metastasis. Peritoneal metastasis is recognized as the primary route of metastatic progression in ovarian cancer. It causes severe symptoms in patients, but it is generally difficult to detect at an early stage. Current anti-cancer therapy is insufficient to completely treat metastatic ovarian cancer due to its high rates of recurrence and resistance. Therefore, developing strategies for treating metastatic ovarian cancer requires a deeper understanding of the tumor microenvironment (TME) and the identification of effective therapeutic targets through precision oncology. Given that various signaling pathways, including TGF-β, NF-κB, and PI3K/AKT/mTOR pathways, influence cancer progression, their activity and significance can vary depending on the cancer type. In ovarian cancer, these pathways are particularly important, as they not only drive tumor progression but also impact the TME, which contributes to the metastatic potential. The TME plays a critical role in driving metastatic features in ovarian cancer through altered immunologic interactions. Recent therapeutic advances have focused on targeting these distinct features to improve treatment outcomes. Deciphering the complex interaction between signaling pathways and immune populations contributing to metastatic ovarian cancer provides an opportunity to enhance anti-cancer efficacy. Hereby, this review highlights the mechanisms of signaling pathways in metastatic ovarian cancer and immunological interactions to understand improved immunotherapy against ovarian cancer.
Modification of Sepiolite for Its Catalytic Upgrading in the Hydrogenation of Furfural
Authors: Guerrero-Torres Antonio , Maderuelo-Solera Rocío , García-Sancho Cristina , Quirante-Sánchez José J. , Moreno-Tost Ramón , Maireles-Torres Pedro J. , Cecilia Juan A.
Published: February 2025

In this work, a new methodology for dispersing metal particles supported in clay has been described. For this purpose, a sepiolite has been modified by a microwave-assisted treatment to increase the surface area and pore volume due to the progressive leaching of the Mg2+-species located in the octahedral sheet. These materials have been used as support to incorporate Cu species on the surface by a precipitation-deposition process from the thermal decomposition of urea at 95 °C. Once calcined and reduced, the Cu-based catalysts showed a Cu0-particle size lower than 5 nm in the case of catalysts whose support is a sepiolite subjected to acid treatment. On the other hand, when raw sepiolite is used as a support, the Cu0-crystal size is much larger (15–20 nm). This difference in Cu0-crystal size showed a variable catalytic behavior for the hydrogenation reaction of furfural in gas-phase. Catalysts with larger particle size promote the hydrogenation reaction, obtaining a yield towards furfuryl alcohol close to 60% after 5 h at 190 °C. In contrast, catalysts with smaller particle size promote the hydrogenation reaction and subsequently the hydrogenolysis reaction, obtaining methylfuran as a product with a yield of 58% after 1 h of reaction at 190 °C; however, the sites where hydrogenolysis are involved are more prone to be deactivated.
Joint Angular Kinematics and Gross Motor Function in Typically Developing Healthy Children
Authors: Moses Monday Omoniyi , Onuegbu Ngozi Florence , Deku Prince De-Gualle , Nyarko Mary Abena , Owusu Lydia Boampong , Emikpe Abigael Omowumi , John Emmanuel Babatunde , Soangra Rahul , Yifieyeh Abiboye Cheduko , Titiloye Nicholas Akinwale
Published: February 2025

Objective: The aim of this study was to establish the interactions between joint angular kinematics and gross motor function in typically developing healthy Ghanaian children. Methods: A descriptive cross-sectional study design was employed. A total of 150 (69 (46.0%), 3.25 ± 0.08-year-old boys and 81 (54.0%), 3.25 ± 0.06-year-old girls) 2–4-year-old children were recruited. Joint angular kinematic variables [left hip flexion (LHF), left hip extension (LHE), right hip flexion (RHF), left knee flexion (LKF), right hip extension (RHE), left knee extension (LKE), right knee flexion (RKF), left ankle dorsi-flexion (LADF), right knee extension (RKE), right ankle plantar flexion (RAPF), left ankle plantar flexion (LAPF), and right ankle dorsi-flexion (RADF)] and gross motor function (lying and rolling, sitting, crawling and kneeling, standing, and walking, running, and jumping) were measured with standard scales. Results: The correlations between lying and rolling vs. RHE (r = 0.221; p-value < 0.01), LKE (r = −0.267; p-value < 0.01), LAPF (r = 0.264; p-value < 0.01), and RADF (r = 0.240; p-value < 0.01); crawling and kneeling vs. LKE (r = 0.196; p-value < 0.05) and RADF (r = 0.188; p-value < 0.05); and walking, running, and jumping vs. LKE (r = −0.214; p-value < 0.01) and RADF (r = −0.207; p-value < 0.05) were significant. Conclusions: There was a negative correlation between joint angular kinematics and total gross motor function in this sampled population. Typically, developing healthy children should be exposed to a range of motion, flexibility, and active transportation programs for optimal active lifestyles and improvements in gross motor skills.
Comparative Study on the Anti-Corrosion Performance of Epoxy Coatings Containing BIB and HFTC
Authors: Yao Yong , Huang Weitao , Rao Mumin , Cao Yang , Huang Congshu , Liang Yu , Tan Canxing , Mo Yeqiang
Published: February 2025

The main load-bearing structure of offshore wind power is mainly metal, and the corrosion of metal structures is particularly serious when exposed to corrosive environments, such as high salt and humidity for a long period of time and has attracted more and more attention from researchers at home and abroad. Epoxy resin is used as a matrix resin in both primer and middle coatings. In anti-corrosion coatings, when additives are added to the epoxy system, the affinity and hydrophobicity of the additives themselves affect the protective effect of the system. In this study, the effects of two additives, BIB (containing hydrophilic groups) and HFTC (containing both hydrophilic and hydrophobic groups), on the corrosion protection properties of epoxy adhesives were investigated. The impact of these additives on the contact angle, water absorption rate, salt spray resistance, and overall corrosion resistance was evaluated using various experimental methods. The results show that the BIB additive is not conducive to the enhancement of epoxy coatings’ anti-corrosive properties due to its good hydrophilicity. The addition of HFTC can effectively improve the protective performance of the coating, and when the addition of HFTC is 0.6%, the salt spray resistance of the composite coating is optimized. This study provides valuable insights into the optimization of epoxy systems for enhanced corrosion protection in marine environments.
Preparation and Photocatalytic Performance of Silver-Loaded Micro-Arc Oxidation TiO2 Coating
Authors: Fan Xingping , Xia Ying , Fan Wei , Li Yulong
Published: February 2025

Using a Ti6Al4V (TC4) titanium alloy plate as a substrate, micro-arc oxidation technology was applied at an oxidation time of 3.5 min and a voltage of 480 V, 495 V, and 510 V. A TiO2–containing ceramic layer was prepared on the surface of the TC4 alloy, and the TiO2–containing coating was doped with silver ions. The surface microstructure, phase structure, and photocatalytic performance of ceramic coatings before and after doping with silver ions were analyzed using instruments such as X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM). The results showed that as the oxidation voltage increased, the number of large pores first decreased and then increased. At a voltage of 495 V, the total area of various pores reached 45–50 μm2. After the voltage rose to 510 V, the maximum pore area decreased. TiO2 exists in the surface pores of the morphology in the form of rutile, and the loading of silver ions further enhances its photocatalytic performance. The degradation rate of methyl orange by undoped silver ion samples can reach 15.5%, and the degradation rate of methyl orange can reach about 31% when 4 g/L Ag2O is added to the electrolyte. Increasing the concentration of doped silver ions can enhance the degradation rate of methyl orange.
Thermal Expansion of Electrofused MgO-Based Spinel Systems Containing Fe2O3, Al2O3-Fe2O3, Al2O3-Cr2O3-Fe2O3 and Al2O3-NiO-Fe2O3
Authors: Zienert Tilo , Borges Otávio H. , Pandolfelli Victor C. , Aneziris Christos G.
Published: February 2025

Magnesia-spinel multicomponent materials have been used as refractories for a long time. In addition to a few binary systems, the influence of spinel phases on the thermal expansion (α) of MgO or the resulting compound has not been studied so far. As α is critical for refractories in application, this work investigates the thermal expansion of complex MgO-based spinel systems using X-ray diffraction (XRD) in combination with Rietveld refinement in the temperature range between 30 °C and 1200 °C. All studied periclase solid solutions, in contact with spinels of the systems Mg1.01(Al0.23Cr1.64Fe0.13)O4, Fe3O4–MgFe2O4, NiFe2O4–NiAl2O4, MgAl2O4–MgFe2O4, Fe3O4–FeAl2O4 and Fe3O4·NiFe2O4·2MgAl2O4 showed α trends below plain MgO, or even decreasing values above 1000 °C. Many spinels showed large negative thermal expansion coefficients. It was found that the structural change in spinels is constrained, leading to a common analytical expression to calculate the lattice parameter of spinels with temperature, which was used to study the nature of the investigated spinels in more detail. The work highlights that Cr-free MgO-spinel systems show similar or even better high-temperature behaviour than commonly used magnesia–chrome aggregates.
Activity Patterns of Native Carnivores in Central Chile: Are They Influenced by Landscape Type?
Authors: Ramírez-Alvarez Diego , Arenas-Rodríguez Kathia , Kaiser Melanie , Napolitano Constanza
Published: February 2025

Landscapes can be selectively used by different carnivore species, leading to habitat specialization, which acts as a limiting resource for maintaining healthy populations. Between 1 March 2021 and 31 March 2022, we set up 30 camera traps in three different landscapes of central Chile: (a) Mediterranean coastal sclerophyllous forest (SF), (b) Mediterranean coastal thorn forest (TF), and (c) exotic monoculture tree plantations (MP), with a total capture effort of 10,046 camera-days (3098 TF, 3446 MP, and 3502 SF). We described the daily activity patterns for each native carnivore species recorded in each landscape, based on the density of independent records per hour of the day. We assessed the overlap between the activity patterns of each carnivore species in the different macrohabitats based on their coefficient of overlapping (Δ). We identified 9120 carnivore records, corresponding to 3888 independent events: 3140 for Lycalopex fox species, 276 for guiña Leopardus guigna, 434 for skunk Conepatus chinga, and 38 for the lesser grison Galictis cuja. Our study revealed differences of activity patterns with high to medium overlap, among landscape types for C. chinga and Lycalopex spp.—for skunk, between native forests and exotic monoculture tree plantations, and for foxes, among all landscape types. The carnivore community of the highly anthropized central Chile is mostly composed of habitat generalists and habitat specialists with high adaptability to landscape fragmentation, which has been crucial for their long-term survival.
Diversity and Genetic Structure of Maize Landraces Cultivated in the Zoque Region from Chiapas, Mexico
Authors: Hernández Eduardo de la Cruz , Andueza-Noh Rubén H. , Latournerie-Moreno Luis , Ruiz-Sanchez Esau , Gordillo Ruiz Mercedes C. , Pérez Gilberto Rodríguez
Published: February 2025

In Mesoamerica, maize is one of the most important food crops, with México being the center of its origin, domestication, and diversity. The state of Chiapas in southern Mexico is one of the areas with the highest maize landrace diversity. However, information on its genetic diversity, conservation status, and the potential use of maize landraces throughout the entire Chiapas region is lacking. One region where local farmers use and preserve a wide diversity of maize landraces is the Zoque region. Until now, however, the genetic diversity of these maize landraces has not been studied. The aim of this study was to analyze the diversity and genetic structure of maize cultivated in the Zoque region, from Chiapas, Mexico, by using 17 landraces and 48 ISSR loci. The analysis revealed two genetic groups based on geographical origin. The genetic diversity level was moderate (Hbay = 0.29 and I = 0.36) and distributed mainly within landraces (70%). The maize landrace blanco belongs to the Tuxpeño race and Bacalito blanco belongs to the Olotillo race from the Miguel Hidalgo municipality have greater diversity values (Hbay = 0.36, I = 0.45 and Hbay = 0.35, I = 0.45, respectively). The results indicated that the maize landraces cultivated in the Zoque region, Chiapas, Mexico, constitute a valuable genetic resource that can be used for genetic improvement and in conservation programs.
Spatial Dataset of Climate Robust and High-Yield Agricultural Areas in Brandenburg: Results of a Classification Framework Using Bio-Economic Climate Simulations
Authors: von Czettritz Hannah Jona , Uthes Sandra , Schuler Johannes , Kersebaum Kurt-Christian , Zander Peter
Published: February 2025

Coherent spatial data are crucial for informed land use and regional planning decisions, particularly in the context of securing a crisis-proof food supply and adapting to climate change. This dataset provides spatial information on climate-robust and high-yield agricultural arable land in Brandenburg, Germany, based on the results of a classification using bio-economic climate simulations. The dataset is intended to support regional planning and policy makers in zoning decisions (e.g., photovoltaic power plants) by identifying climate-robust arable land with high current and stable future production potential that should be reserved for agricultural use. The classification method used to generate the dataset includes a wide range of indicators, including established approaches, such as a soil quality index, drought, water, and wind erosion risk, as well as a dynamic approach, using bio-economic simulations, which determine the production potential under future climate scenarios. The dataset is a valuable resource for spatial planning and climate change adaptation, contributing to long-term food security especially in dry areas such as the state of Brandenburg facing increased production risk under future climatic conditions, thereby serving globally as an example for land use planning challenges related to climate change.
Digital Age and Medicine: Visualization and Evaluation of Foot Anatomy with Artificial Intelligence
Authors: Başgün Ferda , Altunbey Tuba , Ay Sevinç , Söylemez Derya Öztürk , Emre Elif , Başgün Nurseda
Published: February 2025

Background/Objectives: Artificial intelligence (AI) has attracted great interest due to its applicability in many fields. The adoption of visual illustration techniques produced by AI in the field of graphic design has further expanded the field of use of this technology. This study focuses on foot anatomy illustrations generated by Adobe Firefly and Microsoft Designer Image Creator applications, evaluating them based on detail, clarity, anatomical realism, accuracy, and aesthetic appeal. Methods: The illustrations were created using text-based scripts, and five anatomists compared them to traditional illustrations from the Sobotta Atlas of Human Anatomy. Results: Fleiss’ Kappa statistic was used to analyze consistency among expert evaluations. For the four figures generated by both AI applications, Fleiss’ Kappa agreement was high. Adobe Firefly performed slightly better in illustrating phalanx and ankle bones, but its anatomical accuracy was lower for tarsal and metatarsal bones. Microsoft Designer Image Creator excelled in illustrating metatarsal bones, while its tarsal and phalanx illustrations were less anatomically accurate than Adobe Firefly and the atlas drawings. Both programs showed average realism in ankle structures, while the tarsal bones had low realism. Conclusions: Artificial intelligence applications within the scope of the study showed fast performance. Aesthetic appeal is dominant at first glance in the resulting drawings. In general, both applications have struggled to reflect anatomical reality.
Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System
Authors: Bozdag Ahmet , Yildirim Muhammed , Karaduman Mucahit , Mutlu Hursit Burak , Karaduman Gulsah , Aksoy Aziz
Published: February 2025

Background/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases.
Water Hammer Phenomenon in Coronary Arteries: Scientific Basis for Diagnostic and Predictive Modeling with Acoustic Action Mapping
Authors: Ngo Khiem D. , Nguyen Thach , Pham Huan Dat , Tran Hadrian , Ha Dat Q. , Dinh Truong S. , Mihas Imran , Kodenchery Mihas , Gibson C. Michael , Nguyen Hien Q. , Nguyen Thang , Loc Vu T. , Nguyen Chinh D. , Tien Hoang Anh , Talarico Ernest , Zuin Marco , Rigatelli Gianluca , Nanjundappa Aravinda , Nguyen Quynh T. N. , Nguyen The-Hung
Published: February 2025

Background: In the study of coronary artery disease, the mechanisms underlying atherosclerosis initiation and progression or regression remain incompletely understood. Our research conceptualized the cardiovascular system as an integrated network of pumps and pipes, advocating for a paradigm shift from static imaging of coronary stenosis to dynamic assessments of coronary flow. Further review of fluid mechanics highlighted the water hammer phenomenon as a compelling analog for processes in coronary arteries. Methods: In this review, the analytical methodology employed a comprehensive, multifaceted approach that incorporated a review of fluid mechanics principles, in vitro acoustic experimentation, frame-by-frame visual angiographic assessments of in vivo coronary flow, and an artificial intelligence (AI) protocol designed to analyze the water hammer phenomenon within an acoustic framework. In the analysis of coronary flow, the angiograms were selected from patients with unstable angina if they had previously undergone one or more coronary angiograms, allowing for a longitudinal comparison of dynamic flow and phenomena. Results: The acoustic investigations pinpointed pockets of contrast concentrations, which might correspond to compression and rarefaction zones. Compression antinodes were correlated to severe stenosis, due to rapid shifts from low-pressure diastolic flow to high-pressure systolic surges, resulting in intimal injury. Rarefaction antinodes were correlated with milder lesions, due to de-escalating transitions from high systolic pressure to lower diastolic pressure. The areas of nodes remained without lesions. Based on the locations of antinodes and nodes, a coronary acoustic action map was constructed, enabling the identification of existing lesions, forecasting the progression of current lesions, and predicting the development of future lesions. Conclusions: The results suggested that intimal injury was likely induced by acoustic retrograde pressure waves from the water hammer phenomenon and developed new lesions at specifically exact locations.
Podcasts Created by University Students: A Way to Improve Subject Understanding, Connection with Peers, and Academic Performance
Authors: Hernandez-Lopez Montserrat , Mendoza-Jimenez Javier
Published: February 2025

The integration of teacher-designed audiovisual tools in education has gained significant prominence, with podcasting emerging as a powerful pedagogical resource. While its benefits in enhancing learning flexibility and knowledge acquisition are well-documented, limited research examines the impact of student-generated podcasts. This study addresses that gap by investigating whether student-produced content enhances learning, improves academic performance, and enriches the classroom environment. A total of 190 students from the Tourism and Pedagogy degrees at the Universidad de La Laguna (Tenerife, Spain) participated by actively engaging in podcast creation as part of their coursework. They subsequently completed a survey assessing various aspects of the activity. Hypothesis testing revealed that student-generated podcasts not only improve academic outcomes but also cultivate a more dynamic and engaging learning experience. Findings suggest that beyond reinforcing subject mastery, podcasting yields intangible educational benefits, fostering deeper engagement and a more enriched classroom atmosphere. These results underscore the pedagogical value of student-driven content creation, positioning podcasting as an innovative instructional strategy that transcends conventional learning paradigms.
The Concept of an Innovative System for Monitoring Physiological Parameters Under Extreme Conditions
Authors: Sobotnicka Ewelina , Mocha Jan , Badura Grzegorz , Sobotnicki Aleksander , Dziuda Łukasz
Published: February 2025

This study presents a novel, non-invasive system designed to assess the fitness of flight personnel in extreme environments. Existing methods for evaluating pilot readiness lack objectivity, precision, and adaptability to dynamic flight conditions. Our system addresses these shortcomings by integrating mobile physiological measurement modules with an aircraft simulator, a Lower Body Negative Pressure (LBNP) system, and advanced analytical software. It continuously monitors critical physiological parameters, including heart rate, blood pressure, blood oxygen saturation, respiratory rate, and carotid blood flow, using innovative sensor placements that minimize motion artifacts. This approach offers a cost-effective and efficient alternative to traditional centrifuge-based assessments, which are costly and physically demanding. Preliminary tests demonstrate the system’s stability and ability to acquire consistent signals despite motion disturbances. Ongoing validation studies will compare the physiological data obtained in the LBNP simulator with centrifuge data, ensuring the system’s effectiveness in real-world aviation applications.
Multi-Scale Fusion MaxViT for Medical Image Classification with Hyperparameter Optimization Using Super Beluga Whale Optimization
Authors: Zhao Jiaqi , Liu Tiannuo , Sun Lin
Published: February 2025

This study presents an enhanced deep learning model, Multi-Scale Fusion MaxViT (MSF-MaxViT), designed for medical image classification. The aim is to improve both the accuracy and robustness of the image classification task. MSF-MaxViT incorporates a Parallel Attention mechanism for fusing local and global features, inspired by the MaxViT Block and Multihead Dynamic Attention, to improve feature representation. It also combines lightweight components such as the novel Multi-Scale Fusion Attention (MSFA) block, Feature Boosting (FB) Block, Coord Attention, and Edge Attention to enhance spatial and channel feature learning. To optimize the hyperparameters in the network model, the Super Beluga Whale Optimization (SBWO) algorithm is used, which combines bi-interpolation and adaptive parameter tuning, and experiments have shown that it has a relatively excellent convergence performance. The network model, combined with the improved SBWO algorithm, has an image classification accuracy of 92.87% on the HAM10000 dataset, which is 1.85% higher than that of MaxViT, proving the practicality and effectiveness of the model.
Optimization of CMOS Decoders Using Three-Transistor Logic
Authors: Balobas Dimitrios , Konofaos Nikos
Published: February 2025

Decoders are among the most fundamental components in digital circuit design. They are widely used in combinational logic to convert and route binary data, as well as in memory array logic, for decoding binary addresses that point to the memory locations to be accessed. Due to their extensive utilization, optimizing decoder cells can potentially yield perceivable improvements in a digital system. This paper introduces 3-Transistor Logic (3TL), a new design approach for the optimization of CMOS decoder circuits, which combines static CMOS, Transmission-Gate Logic, and Dual-Value Logic. A complete transistor-level design methodology is demonstrated for decoder sizes from 2×4 up to 8×256, using 15 nm FinFET technology. Furthermore, an extensive comparative analysis is conducted with transistor-level simulations, evaluating the new circuits against conventional static CMOS and other previously proposed designs. The results show that 3TL circuits offer the best overall performance in terms of active power consumption, standby power consumption, and delay, owing largely to the fact that they are designed with logic efficiency and the minimum possible number of transistors.
Leveraging Federated Learning for Malware Classification: A Heterogeneous Integration Approach
Authors: Chen Kongyang , Zhang Wangjun , Liu Zhangmao , Mi Bing
Published: February 2025

The increasing complexity and frequency of malware attacks pose significant challenges to cybersecurity, as traditional methods struggle to keep pace with the evolving threat landscape. Current malware classification techniques often fail to account for the heterogeneity of malware data and models across different clients, limiting their effectiveness. In this chapter, we propose a distributed model enhancement-based malware classification method that leverages federated learning to address these limitations. Our approach employs generative adversarial networks to generate synthetic malware data, transforming non-independent datasets into approximately independent ones to mitigate data heterogeneity. Additionally, we utilize knowledge distillation to facilitate the transfer of knowledge between client-specific models and a global classification model, promoting effective collaboration among diverse systems. Inspired by active defense theory, our method identifies suboptimal models during training and replaces them on a central server, ensuring all clients operate with optimal classification capabilities. We conducted extensive experimentation on the Malimg dataset and the Microsoft Malware Classification Challenge (MMCC) dataset. In scenarios characterized by both model heterogeneity and data heterogeneity, our proposed method demonstrated its effectiveness by improving the global malware classification model’s accuracy to 96.80%. Overall, our research presents a robust framework for improving malware classification while maintaining data privacy across distributed environments, highlighting its potential to strengthen cybersecurity defenses against increasingly sophisticated malware threats.
Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms
Authors: Ekinci Gökhan , Ozturk Harun Kemal
Published: February 2025

Wind energy is a crucial renewable resource for sustainable power generation; however, challenges such as high initial investment costs and difficulties in identifying efficient locations hinder its widespread adoption. Accurate wind energy forecasting is essential for energy planning, trading, and grid optimization. This study presents short-term, medium-term, and long-term –wind power forecasts for the Söke–Çatalbük Wind Power Plant in Aydın, Turkey, using meteorological data and production records from 2018 to 2022. Five machine learning algorithms were employed—Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors Regression (KNN), and Multi-Layer Perceptron (MLP ANN)—utilizing both MinMax and Standard Scaling methods. Prediction performance was evaluated using Mean Absolute Error (MAE), Coefficient of Determination (R2), and Root Mean Square Error (RMSE) metrics. The results indicate that Min-Max Scaling improved short-term predictions with KNN, while XGBoost and Random Forest provided more stable and accurate forecasts in medium- and long-term predictions. Additionally, Standard Scaling significantly enhanced MLP ANN’s performance in medium-term forecasting. These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. The results are particularly valuable for energy planners and policymakers seeking to maximize the efficiency of wind power plants and facilitate the integration of renewable energy sources into national grids more effectively.
Semantic-to-Instance Segmentation of Time-Invariant Offshore Wind Farms Using Sentinel-1 Time Series and Time-Shift Augmentation
Authors: de Carvalho Osmar Luiz Ferreira , de Carvalho Junior Osmar Abílio , de Albuquerque Anesmar Olino , Silva Daniel Guerreiro e
Published: February 2025

The rapid expansion of offshore wind energy requires effective monitoring to balance renewable energy development with environmental and marine spatial planning. This study proposes a novel offshore wind farm detection methodology integrating Sentinel-1 SAR time series, a time-shift augmentation strategy, and semantic-to-instance segmentation transformation. The methodology consists of (1) constructing a dataset with offshore wind farms labeled from Sentinel-1 SAR time series, (2) applying a time-shift augmentation strategy by randomizing image sequences during training (avoiding overfitting due to chronological ordering), (3) evaluating six deep learning architectures (U-Net, U-Net++, LinkNet, DeepLabv3+, FPN, and SegFormer) across time-series lengths of 1, 5, 10, and 15 images, and (4) converting the semantic segmentation results into instance-level detections using Geographic Information System tools. The results show that increasing the time-series length from 1 to 15 images significantly improves performance, with the Intersection over Union increasing from 63.29% to 81.65% and the F-score from 77.52% to 89.90%, using the best model (LinkNet). Also, models trained with time-shift augmentation achieved a 25% higher IoU and an 18% higher F-score than those trained without it. The semantic-to-instance transformation achieved 99.7% overall quality in per-object evaluation, highlighting the effectiveness of our approach.
Challenges in Applying Multimodal Imaging Technologies to Quantify In Vivo Glycogen and Intramuscular Fat in Livestock
Authors: Alvarenga Tharcilla I. R. C. , McGilchrist Peter , Keller Marianne D. , Pethick David W.
Published: February 2025

Predicting meat quality, especially dark, firm and dry meat, as well as muscle fat prior to slaughter, presents a challenge in practice. Medical as well as high-frequency ultrasound applications can be utilized to predict body composition and meat quality aspects. Ultrasounds are non-invasive, rapid-to-operate in vivo and show high correlations to the animal production traits being estimated. Farm animal ultrasounds are used to predict intramuscular fat content in the beef cattle industry. Challenges are identified in applying ultrasound technology to detect glycogen content in farm animals due to a wide range of fat, muscle and water composition. Other technologies and methods are reported in this literature review to overcome issues in the practicability and accuracy of ultrasound technology when estimating muscle glycogen levels in cattle. The discussion of other tools such as hyperspectral imaging, microwave sensor technology and digital infrared thermal imaging were addressed because of their superior accuracy in estimating moisture and fat components.
Beef- and Pork-Based Dishes from Catering Services: Composition and In Vitro Digestion Effects on Digestibility and Lipid Oxidation
Authors: Ariz-Hernandez Itziar , Schulz Patrick , Garayoa Roncesvalles , Ansorena Diana , Astiasaran Iciar
Published: February 2025

Twelve meat-based dishes (beef/pork) prepared using different cooking methods and ingredients were collected from two catering services. Their nutritional composition and lipid oxidation status was analyzed. Subsequently, the samples underwent an in vitro digestion process to evaluate their digestibility and the effect of digestion on lipid oxidation. The protein content of the dishes ranged from 17% to 34%, with no clear influence from the type of meat or cooking method. Lipid content showed considerable variability (2.5–15.1%), with all dishes exhibiting a high omega-6/omega-3 ratio. In vitro dry matter digestibility ranged from 58% to 86%, protein digestibility from 77% to 93%, and lipid digestibility from 7.3% to 46%. Among all dishes, “roasted pork loin” showed the highest digestibility values. Regarding lipid oxidation, grilled samples exhibited the lowest levels before digestion (less than 0.85 ppm MDA), whereas most of the roasted dishes exceeded 4 ppm MDA. After digestion, all samples—except “stewed veal—a”—suffered an increase in oxidation. Stewed dishes had the smallest increase (less than 60%) and “roasted pork meatballs” exhibited the highest increase (more than 600%). This study enhances the knowledge of the nutritional value of meat-based dishes and the impact of the digestion process.
Pore Structure Evolution of Coal After Supercritical CO2–Water–Rock Treatment: A Multifractal Analysis
Authors: Zheng Sijian , Liu Yanzhi , Huang Fansheng , Liu Shiqi , Sang Shuxun , Dai Xuguang , Wang Meng
Published: February 2025

The evolution of coal’s pore structure is crucial to the efficient capture of carbon dioxide (CO2) within coalbeds, as it provides both adsorption sites and seepage space for the adsorbed- and free-phase CO2, respectively. However, the conventional single fractal method for characterizing pore structure fails to depict the intricacies and variations in coal pores. This study innovatively applies the low-temperature N2/CO2 sorption measurement and multifractal theory to investigate the evolution of the microporous structure of coals (e.g., from the Huainan coalfield) during the supercritical CO2(ScCO2)–water–rock interaction process. Firstly, we observed that the ScCO2–water–rock interaction does not significantly alter the coal’s pore morphology. Notably, taking the ZJ-8# sample as an example, low-temperature N2 sorption testing displayed a stable pore volume following the reaction, accompanied by an increase in specific surface area. Within the CO2 sorption testing range, the ZJ-8# sample’s pore volume remained unchanged, while the specific surface and pore width performed displayed a slight decrease. Secondly, by introducing key parameters from multifractal theory (such as Dq, α(q), τ(q), and f(α)), we assessed the heterogeneity characteristics of the coal’s pore structure before and after the ScCO2–water–rock reaction. The N2 sorption analysis reveals an increase in pore heterogeneity for the ZJ-8# sample and a decrease for the GQ-13# sample within the sorption testing range. In the context of low-temperature CO2 sorption analysis, the pore distribution complexity and heterogeneity of the GQ-11# and GQ-13# samples’ pores were escalated after ScCO2–water–rock interaction. The experimental and analysis results elucidated the dual roles of precipitation and dissolution exerted by the ScCO2–water–rock interaction on the micropores of coal reservoirs, underscoring the heterogeneous nature of the reaction’s influence on pore structures. The application of fractal theory offers a novel perspective compared to traditional pore characterization methods, significantly improving the precision and comprehensiveness of pore structure change descriptions.
Taking a Cross-Faculty Stand Against Racism and Inequality: What Are Enabling and Inhibiting Factors Influencing the Placement Experiences of Black, Asian, Ethnic Minoritised Students in the Schools of Social Work, Education, and Health Sciences?
Authors: Cornish Carlene , Jong Stephanie T. , Albarran Isabella , Kale Swati , Brownsword Sarah , Playfair Cat , Vauzour Sophie , Odu Tina , Lusigi Godfrey , Shikuku Virginia
Published: February 2025

Many Black, Asian, and ethnic minoritised students on university courses leading to professional qualifications face racism on placement. Our own institutional review at one UK university highlighted differential placement outcomes for students in the Schools of Social Work, Education, and Health Sciences. To investigate, a qualitative study was conducted between April to October 2024, using focus groups and interviews with 20 students and 19 staff (lecturers and placement supervisors from the NHS, County Council, and schools). Researchers used NVivo to support the thematic analysis of the qualitative data. Key findings identified various enabling factors, including the significance of supervisory placement support and the quality of placement environment. However, our data further confirmed several inhibiting factors, including power dynamics, systemic failures, and broken trust, contributing to racialised and oppressive placement conditions. We recommend that the university and placement providers have a dedicated system for reporting race-specific incidents to a dedicated person/team who are trained and accountable for tackling and preventing racists incidents on placements.
An RB1CC1 Missense Variant in Nova Scotia Duck Tolling Retrievers with Degenerative Encephalopathy
Authors: Guo Juyuan , Bullock Garrett , O’Brien Dennis P. , Johnson Gary S. , Katz Martin L.
Published: February 2025

Background/Objectives: A slowly progressive hereditary neurological disorder classified as degenerative encephalopathy (DE) occurs in Nova Scotia Duck Tolling Retrievers. The disease is characterized by frequent episodes of pronounced involuntary movements during sleep, cognitive impairment, anxiety, heightened sensitivity to sensory stimuli, and compulsive behaviors. The clinical signs are accompanied by the degeneration of several brain regions. A study was undertaken to identify the molecular genetic basis of this disorder. Methods: Whole genome sequences (WGSs) from the DNA of affected and unaffected Nova Scotia Duck Tolling Retrievers were aligned to the Dog10K_Boxer_Tasha reference genome assembly and to the WGSs of 334 additional control dogs generated by this laboratory. Results: A missense C>T variant was identified in RB1CC1 exon 22 chromosome 29:4891014 that was uniquely homozygous in the affected dog. This variant predicts a p.G1503R change in the amino acid sequence of RB1CC1. Genotyping of 2950 Nova Scotia Duck Tolling Retrievers at the variant locus found complete concordance between the disease phenotype and RB1CC1 genotype. Conclusions: RBCC1 is an essential component of a protein complex that mediates the initiation of autophagosome formation. Therefore, it appears likely that the disease results, at least in part, from impaired autophagy. Consistent with this possibility, brain neurons of an affected dog were found to contain abnormal lysosomal storage body-like inclusions. This disorder could serve as a valuable model to elucidate the mechanisms underlying human diseases associated with impaired autophagy. Identification of the disease-causing DNA sequence variant will enable owners of Nova Scotia Duck Tolling Retrievers to screen their dogs for the RB1CC1 risk variant.
A New Function of the DRD1 Gene: GnRH Secretion Regulation in Sheep Hypothalamic Neurons
Authors: Zhai Manjun , Cao Shaoqi , Liang Huihui , Xie Yifan , Zhao Zongsheng
Published: February 2025

Background: Dopamine (DA) is an important neurotransmitter that is widely present in the central nervous system. DA plays a crucial regulatory role in mammalian emotion, endocrine function, and reproduction through the activation of dopamine receptors. We compared the transcriptomes of hypothalamic tissues from Kazakh sheep during the nonbreeding season of anoestrus and during the nutrient-induced nonbreeding season of oestrus. Our research findings suggest that the dopamine receptor D1 (DRD1) gene may be a candidate gene for the regulation of sheep oestrus. However, the underlying mechanism through which DRD1 regulates sheep oestrus is still poorly understood. Methods: In the present study, the expression of DRD1 mRNA in the hypothalamus of oestrous Kazakh sheep was significantly greater than that in the anoestrous phase. Immunohistochemical staining revealed that DRD1 was more widely expressed in hypothalamic tissue and was more highly expressed during oestrus than during anoestrus. Hypothalamic neuron experiments further indicated that DRD1 affects the expression of GnRH through dopamine synapses and calcium signalling pathways. Results: moreover, the overexpression of the DRD1 gene promoted the secretion of GnRH, while knocking down the DRD1 gene reduced the secretion of GnRH. Conclusions: The present study revealed that the DRD1 gene plays a crucial regulatory role in the secretion of the hormone GnRH in the hypothalamus of Kazakh sheep.
A Forensic Approach to Complex Identification Cases: The Collapse of an Italian Cemetery into the Sea
Authors: Tettamanti Camilla , Frigiolini Francesca , Franceschetti Lorenzo , Barranco Rosario , Lo Pinto Sara , Casarino Lucia , Verdiani Simonetta , Porcu Mattia , Cattaneo Cristina , De Angelis Danilo , Cummaudo Marco , De Stefano Francesco , Ventura Francesco
Published: February 2025

Background/Objectives: On 22 February 2021, a coastal landslide in Italy caused the collapse of an old cemetery, displacing approximately 370 coffins, with over 200 plunging into the sea. This disaster necessitated the recovery and identification of human remains under challenging conditions to provide closure to families and uphold the dignity of the deceased. Methods: Recovery operations involved firefighters and scuba divers, followed by forensic analysis conducted by the Medical Staff of Legal and Forensic Medicine. A post-mortem team utilized forms adapted from Interpol’s Disaster Victim Identification (DVI) standards to document remains, which included 140 decomposed bodies and 193 bags of commingled skeletal remains. DNA samples were collected from 147 bone fragments, primarily long bones and teeth, and compared with ante-mortem data gathered from relatives. Results: Of the 77 eligible relatives, 66 consented to DNA sample collection for genetic profiling, and 28 bodies were identified. Personal effects, clothing, medical devices, and a strong match between non-genetic AM and PM data led to an attribution of identity of other 19 individuals. Advanced post-mortem phenomena were observed in remains spanning from the late 19th century to 2017. An identification area at the cemetery facilitated streamlined operations, emphasizing environmental preservation and forensic accuracy. Conclusions: The cemetery collapse highlights the necessity for tailored forensic approaches in disaster scenarios. Accurate identification methods, combining genetic analysis and secondary means, are crucial for ensuring dignified burials and providing closure to affected families.