bluuebunny/crossref_metadata_2025 huggingface dataset:
ALD Deposited Hydroxyl-Rich NiOx to Enhance SAM Anchoring for Stable and Efficient Perovskite Solar Cells
Authors: Guo Fengming , Yu Xuteng , Li Yuheng , Chen Yong , Li Chi , Liu Chunming , Gao Peng
Published: February 2025

The interface between nickel oxide (NiOx) and self-assembled monolayers (SAMs) in perovskite solar cells (PSCs) often suffers from limited adsorption strength, poor energy-level alignment, and inadequate defect passivation, which hinder device performance and stability. To address these issues, we introduce a hybrid hole selective layer (HSL) combining atomic layer deposition (ALD)-fabricated NiOx with full-aromatic SAM molecules, creating a highly stable and efficient interface. ALD NiOx, enriched with hydroxyl groups, provides robust adsorption sites for the SAM molecule MeO-PhPACz, ensuring a strong, stable interaction. This hybrid HSL enhances energy-level alignment, hole selectivity, and defect passivation at the NiOx/perovskite interface. Devices utilizing this approach demonstrate significant performance improvements, achieving a power conversion efficiency (PCE) of 21.74%, with reduced voltage losses and minimal hysteresis. Furthermore, operational stability tests reveal enhanced durability under elevated humidity and temperature conditions. These findings highlight the potential of ALD NiOx and SAM hybrid HSL to overcome existing barriers, advancing the commercial viability of PSC technologies.
RanDEL: Dynamic Feature-Based Ransomware Detection and Classification Using Advanced Ensemble Techniques
Authors: Altaf Mahroosha , Iqbal Zafar , Imran Azhar , Muhammad Zia , Shafi Qaisar
Published: February 2025

Ransomware attacks are sophisticated, frequent, and pose severe cybersecurity challenges. They are challenging to detect due to the emergence of more robust encrypting algorithms, polymorphic nature, and zero-day threat exploitation. Prior techniques use static analysis and signature matching techniques for detection, but the drawback is that they cannot detect new, zero-day, and polymorphic ransomware. This research aims to provide an accurate, cost-effective and efficient framework for Ransomware Detection using Ensemble Learning (RanDEL). The proposed model is based on two machine learning models Gaussian Naive Bayes and Multi-Layer Perceptron. The framework uses soft voting, stacking, and bagging ensemble techniques for ransomware classification into Goodware, Encryptor ransomware, and Locker ransomware. It uses a publicly available dataset 1 based on dynamic features and is capable of detecting and classifying zero-day, metamorphic, and polymorphic ransomware. The developed framework demonstrated maximum efficiency and achieved the highest accuracy of 99.25%. Using feature reduction, model tuning, and pruning strategies, we have significantly reduced resource use, notably processing time. This study reveals the effectiveness of ensemble models over standalone models.
Influence of the Use of ICT on University Students in Different Cultures After the COVID-19 Pandemic
Authors: Martin Herrero Jose Antonio , Torres García Ana Victoria , Vega-Hernández María Concepción , Iglesias Carrera Marcos , Kubo Masako
Published: February 2025

Background: Once the covid-19 pandemic ended, the abusive use of the internet and new information and communication technologies (ICT) was detected among university students. What has been the impact on the academic performance of university students? How did the pandemic affect students' relationship with ICT’s? The objective of this research was to explore the use of mobile phones and the internet in students from different cultures (Spanish and Japanese) after the pandemic.
Methodology: This is a descriptive and exploratory analysis study in university students (n=206) from Spanish and Japanese culture to understand the perception of academic performance after the covid-19 pandemic, the general use of ICT and the abusive use of both of the internet and the mobile phone. For this study, a semi-structured questionnaire was applied with the scales Internet Over-use Scale (IOS) and Cell-Phone Over (COS).
Results:Almost half of university students use the internet and mobile phones excessively. There are statistically significant differences between students of Spanish and Japanese culture regarding the consequences derived from excessive Internet use and by sex in terms of tolerance of ICT abuse. There are more university students of Japanese culture with excessive use of the Internet compared to those of Spanish culture. And a lower percentage of abusive use of both the Internet and the mobile phone in university men than in women.
Conclusions:Many of the university students use ICT excessively and show signs of pathological addiction. Extreme internet use is related to extreme mobile phone use in this vulnerable group. Abusive use has influenced the habits of university students after the covid-19 pandemic.
Advancements in Targeted Drug Delivery: Innovations in Liposomal, Nanoparticle, and Vesicular Systems
Authors: Sengar Ashutosh
Published: February 2025

Targeted drug delivery has become a miraculous tool in the new millennium of medicine to deliver the greatest therapeutic action, minimize systemic side effects, and attain site-specific drug activity. This review offers advances in liposomal, nanoparticle, and vesicular drug delivery systems with emphasis on their capacity for maximal bioavailability and regulation of drug release. There has been controversy over the use of some of these other carriers such as tablet formulations, proniosomes, and other age-new carriers. Introduction of nanotechnology with biodegradable items and use of artificial intelligence has added new dimensions to patient-specific medicine based on the patient's need.In spite of these developments, regulatory problems, biocompatibility, and scale-up manufacture are concerns that persist as barriers to universal clinical use. Some of the recent technologies such as 3D-printed pharmaceuticals, stimuli-responsive nanocarriers, and drug design using artificial intelligence have the potential to bypass these concerns. Future direction is also set here in the focus on the application of green drug delivery concepts, precision medicine, and combination regimens in setting the future of targeted drug delivery systems. Through ongoing research and technology development, these technologies are poised to revolutionize medicine by maximizing therapeutic effect, reducing side effects, and maximizing drug delivery.
A Simple Control Strategy for Planar 2R Underactuated Robot via DEA Optimization
Authors: Huang Zixin , Gong Xiangyu , Wan Xiao , Zhou Hongjian
Published: February 2025

In various fields, planar 2R underactuated robot have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. Utilizing the differential evolution algorithm (DEA) with the considering the underactuated constraint to enhance the PD controller parameters to get the best control effect, which make all links reach the goal state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows higher stability and convergence rate, highlighting its important contribution in the field of underactuated robots.
Why Does the Gut Have So Many Species? The Paradox of the Plankton in Human Microbial Ecology
Authors: Bhattacharya Riddhiman
Published: February 2025

The human gut nurtures an exceptionally diverse microbial community, with over a thousand species coexisting despite apparent competition for limited resources. This phenomenon challenges the classical principle of competitive exclusion, drawing parallels with Hutchinson’s ‘Paradox of the Plankton’. Through an ecological lens, this paper examines mechanisms that sustain such high species richness, categorising them into niche-based and neutral processes. Niche differentiation, environmental heterogeneity, evolutionary trade-offs, and microbial interactions create conditions for stable coexistence, while stochastic forces such as ecological drift, migration, and speciation also shape community structure. Furthermore, higher-level selection, influenced by host-microbiome interactions, may play a role in maintaining diversity. A deeper understanding of these processes is crucial, not only for theoretical ecology but also for advancing microbiome-based therapeutic interventions.
Coverage and Treatment Processes of Nordic Wastewater Treatment Plants from the Perspective of EU Urban Wastewater Treatment Directive revised in 2024
Authors: Tiwari Ananda , Valkama Kristiina , Krolicka Adriana , Miettinen Ilkka T. , Pitkänen Tarja
Published: February 2025

Understanding wastewater treatment plant (WWTP) coverage and treatment processes in Nordic countries (Finland, Sweden, Norway, Denmark, Iceland, and their autonomous regions) is important in efforts improving sustainable sanitation and in implementation of upcoming new legislative requirements for urban wastewater treatment. The recast of Urban Wastewater Treatment Directive came into force in beginning of 2025 and mandates setting up national systems for wastewater surveillance, and mandates monitoring of antimicrobial resistance (AMR), in wastewater influent (pre-treatment) and effluent (post-treatment) across European Union (EU) or the European Economic Area (EEA) countries. Monitoring influent and effluent informs operational efficiency, provides wastewater surveillance (WWS) to track population health, and assesses the risks of anthropogenic pollutants and pathogens in the receiving waters. This study investigates WWTP coverage, treatment methods, and operational challenges in the Nordics via analyzing the outcomes received from a Webropol survey of environmental authorities, wastewater experts, and policymakers. Survey results were fortified with systematic review of peer-reviewed publications and government documents. We found, ~85–90% of the Nordic population is connected to centralized WWTPs, highlighting the feasibility of WWS for public health monitoring. Treatment processes vary across the region, shaped by population density, their location either in coastal or inland, or the sensitivity of recipient water bodies. Survey revealed, secondary treatment is nearly universal in Sweden and Finland but covers only about 4% of WWTPs in Iceland. Finland, Sweden, and Denmark enforce strict effluent standards, while Norway and Iceland face challenges in adopting similar practices due to harsh terrain, cold climates, and the practicality of discharging wastewater effluents into the oligotrophic Atlantic Ocean.
Towards Sustainable Cryptography: A Comprehensive Assessment of Compute Efficiency and Scope 1–3 Emissions for Partially Homomorphic Encryption in the Cloud
Authors: Ozpinar Alper , Serengil Sefik Ilkin
Published: February 2025

Quantum computing was in its infancy while cloud adoption increased but secure data processing methods in distributed environments became more important. As cloud-based operations continue to expand, allowing computation to be performed directly on encrypted data without the need for exposing private keys will be an important use case of homomorphic encryption. Fully homomorphic encryption (FHE) encompasses both addition and multiplication, whereas partial homomorphic encryption (PHE) is limited to either type of operation, which can provide practical efficiency benefits for certain applications. In this research, LightPHE, a Python-based PHE framework is implemented along with the implementation performance and environmental sustainability evaluation. The evaluation scope extends beyond the typical fair assessment of cryptography to include energy consumption profile and carbon emissions. The framework combines proven PHE algorithms and stays true to modular design principles as the foundation for secure application development. Experimental evaluations were performed on several cloud platforms such as Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark. The performance evaluation has included key generations, encryption, decryption and homomorphic operation, also the energy consumption was based on computational resource utilization. The environmental impact was evaluated via thorough analysis of Scope 1-3 emissions, as well as standardized data center efficiency metrics based on regional carbon intensity data. The study’s results showed unique trends in the trade-offs between computational performance and energy efficiency. Rather than calculating emissions for every algorithm variant, the analysis focuses on low and mid impact cases such as 80-bit and 128-bit resource-intensive homomorphic operations—where environmental considerations are most critical with Colab L4 GPU. Since the comparison with all cpu and gpu types including all data centers that will lead to another research. The results suggested that in both high-performance configurations and distributed environments, different optimization characteristics emerged in the energy-energy view where a lower total energy consumption is observed even at the expense of higher instantaneous demand. LightPHE offered uniform security across configurations, but differing environmental impact. This work empirically demonstrates the environmental considerations inherent to cryptographic implementations and lays the groundwork for future work in quantifiably assessing both security and sustainability in cloud-based encryption systems. The findings of this research provide practical recommendations for organizations looking to deploy secure, efficient, and sustainable data processing systems in modern cloud settings.
History, Developments, and Political and Social Impacts of Journalism Industry in Ireland: An Integrative Review
Authors: Almakaty Safran
Published: February 2025

The history of journalism in Ireland is complicated and has changed over a long time. Starting with the first newspapers in the early 18th century to the rise of digital media in the 21st century, journalism in Ireland has been important in influencing public views and keeping leaders in check. Early newspapers played a key role in the political struggles of the time, including the fight for Irish independence. As the field developed, so did its influence, with major newspapers like The Irish Times and The Irish Independent becoming pillars of Irish journalism. In recent years, the growth of online news websites and social media has drastically changed how news is made and shared. These changes not only changed how information spreads but also created significant ethical and professional issues for reporters. Digital platforms have made news more accessible, but they have also led to challenges such as misinformation and sensationalism.The effects of these changes on Irish society are deep, affecting public discussions, political choices, and community involvement. It is clear from the history and changes in journalism in Ireland that the field still significantly impacts the country’s cultural and political scene, shaping how citizens understand and engage with the world around them.
In Search of an Encounter with the Sacred: The Taoist Hermits in Contemporary China
Authors: An Saiping
Published: February 2025

This study directs its focus towards contemporary Taoist hermits in the Zhongnan Mountains and Huashan, located in Shanxi Province, China. Initially, it delves into the motivations underlying the adoption of a hermitic lifestyle among these Taoists. Subsequently, it analyzes the diverse means by which these hermits sustain their livelihoods in the mountainous regions. Next, it conducts an examination of the various religious practices and extraordinary experiences of these Taoist hermits. Also, this study contends that some Taoist hermits do not live in complete isolation from secular society; rather, they sustain a tangible connection with the external world. This study unveils a distinct contemporary Chinese Taoist group, which has hitherto been overlooked by previous scholarship that predominantly concentrated on the scrutiny of urban or rural Taoist groups in contemporary China.
Looking into Raw Material Costs Through Machine Learning to Improve Efficiency
Authors: Bin Khairudin Adruce , Jia Fong Lee , Khongsuwan Wadthanak , Yu Xiang Teh , Thong Khai Junn Bryan , Ul Amin Noor
Published: February 2025

This document is meant to show how machine learning can analyze the raw material costs that are affecting the business operations of small-to-medium enterprises. Currently, SMEs face the challenge of rising raw material costs, cost inefficiencies and lack of predictive insights into cost management, which create problems for trying to operate a small business smoothly in Malaysia. Machine learning can help find solutions to those operational problems through using regression to analyze and forecast the costs of raw materials in general. To do that, three regression models are used which are Linear Regression (LR), Decision Tree Regression (DTR) and Random Forest Regression (RFR). By analyzing the dataset by raw material prices, it is shown that the DTR outperforms the other models in forecasting the prices of raw materials in general with an R2-score of 0.91, followed by the RFR at 0.87, and LR at 0.79. With these findings in mind, it could serve as how machine learning can help in enhancing in how SMEs can adapt to factors with the implementations of cost management strategies, helping decision-making to ensure competitiveness and profit sustainability, with the framework serving as a solution that is both practical and scalable to offer SMEs opportunities to respond to market shifts and mitigate risk.
Key Amniotic Fluid miRNAs as Promising Target Molecules for the Antenatal Prevention of Pulmonary Hypoplasia Associated with Congenital Diaphragmatic Hernia
Authors: Timofeeva Angelika V. , Fedorov Ivan S. , Naberezhnev Yuri I. , Tetruashvili Nana K. , Sukhikh Gennady T.
Published: February 2025

Congenital diaphragmatic hernia (CDH) remains associated with high morbidity and mortality, primarily due to pulmonary hypoplasia and hypertension. Current antenatal diagnostic methods, such as ultrasound and MRI, are unable to assess the severity of defects in lung and pulmonary vascular structures, which are critical determinants of the diverse phenotypes of CDH. Aberrant epigenetic regulation of lung development during gestation is believed to play a significant role in the pathogenesis of CDH. In this study, we aimed to identify miRNA patterns in amniotic fluid able to categorize CDH-fetuses for the personalized selection of effective treatment strategies at the antenatal and/or postnatal stages. Using deep sequencing and quantitative real-time PCR, we identified a set of miRNAs—miR-485-3p, miR-320b, miR-320a-3p, miR-221-3p, miR-200b-3p, miR-100-5p, miR-92a-3p, miR-30c-5p, miR-26a-5p, and let-7c-5p—whose reduced expression in amniotic fluid at 19–24 weeks of gestation allowed us to categorize fetuses with CDH into two distinct groups: one significantly different from the control group (non-CDH) and the other closely resembling it. Notably, no significant correlations were found between the content of these miRNAs in amniotic fluid and severity of lung hypoplasia assessed by ultrasound or MRI. However, the level of each of the miRNAs significantly positively correlated with that of miR-200b-3p, whose role in ensuring proper bronchopulmonary tissue structure during prenatal development—as well as its therapeutic potential for CDH-associated hypoplastic lungs—has been previously demonstrated.
Digital Pedagogical Competency Framework for Teacher (DPCFT) for 21st Century Teachers
Authors: Joshi Dirgha Raj , Upadhayaya Prashu Ram , Chapai Krishna Prasad Sharma , Singh Ammar Bahadur
Published: February 2025

The aim of the research was to develop the digital pedagogical competencies framework of teachers based on the existing literature or developed framework. The existing literature were reviewed by synthesis methods. The developed model covers eight key skills as (1) learning and sharing tools (2) internet surfing and communication tools, (3) course management and evaluation tools, (4) audio and visual document development tools (5) writing and presentation tools, (6) AI tools (7) subject related applications, and (8) data analysis and visualization tools including policy and ethical awareness of teachers with twenty-six sub skills. All skills were developed based on the necessary digital supports to the teachers of 21st century. This model provides a structured approach to integrating digital tools into education while emphasizing ethical and policy awareness. It highlights the importance of using technology to enhance teaching, learning, and research through collaboration, analysis, and content creation. By fostering responsible digital citizenship and adherence to ICT policies, educators can create more effective and inclusive learning environments. Ultimately, this framework ensures that digital resources are used efficiently, ethically, and in alignment with transformative pedagogical practices.
Echoes of Tradition: Analyzing the Cultural and Linguistic Diversity of Iligan City
Authors: Eslit Edgar
Published: February 2025

As cities around the world face the forces of globalization, their tradition, culture, and linguistic landscapes are continually shaped and reshaped. This study delves into the heart of Iligan City, a place where tradition and modernity converge, examining how its diverse cultural communities navigate the complexities of identity, language, and social cohesion in both traditional and digital spaces. Through a qualitative approach blending ethnography, digital phenomenology, and cultural analysis, this research uncovers how Iligan’s multicultural and multilingual heritage serves as a dynamic force in fostering resilience and adaptability amidst rapid global change. The study explores the pivotal role of language choices, code-switching, and cultural practices in the negotiation of identities, while also shedding light on the tensions between preserving heritage and embracing global influences. Drawing on theories of Cultural Relativism, Sociolinguistics, and Globalization, the findings emphasize the importance of academic language revitalization, cultural mapping, and digital archiving in safeguarding Iligan's unique identity. The research provides essential insights into the preservation of cultural vitality, offering practical recommendations for policy-making, educational frameworks, and community-led initiatives. This study calls for a deeper understanding of how globalization impacts language practices, opening avenues for future research on how internationalization and digital spaces influence cultural and linguistic dynamics.
Enhance the Emotional Dimension of Caring: The Emotional Labor Model in Pediatric Nursing
Authors: Diogo Paula
Published: February 2025

Background/Objectives: The health-disease processes experienced by children and their families are often accompanied by profound emotional distress, which can prove to be a significant challenge for the nurses entrusted with their care. The objective of this article is to introduce the Emotional Labor Model in Pediatric Nursing and demonstrate its applicability to improving care for children and families. Methods: A doctoral thesis (2010), with a qualitative approach and Grounded Theory methodolo-gy, led to the emergence of a medium-range theory, revealing how nurses use emotions as a therapeutic tool in pediatric hospitalization. Corbin and Strauss (2015) argue that from a medium-range theory emerges the formulation of a logical, systematic and ex-planatory model, that has been validated in different pediatric settings, through re-search, clinical internships and discussion with clinical nurses and expert academics. Results: This model provides guidance for operationalization emotional labor in the context of pediatric nursing and is characterized by “Promoting a safe and affectionate environment”, “Nurturing care with affection”, “Facilitating the management of the client's emotions”, “Building stability in the relationship” and “Regulating one's (nurses) emotional disposition to care”. The applicability of this model requires triple centrality: in the clients, in the nurses, and in the nurse-client relationship. Conclu-sions: Humanizing care is facilitated by an awareness of the emotional experiences of clients and the ability of nurses to manage emotionality. Research-based models of care are necessary to guide and empower pediatric nurses in this emotional dimension of nursing.
Self-Supervised Learning Principles Challenges and Emerging Directions
Authors: Nadine Jenifer
Published: February 2025

Self-supervised learning (SSL) has emerged as a transformative paradigm in machine learning, enabling models to learn meaningful representations from vast amounts of unlabeled data. By leveraging pretext tasks that generate supervisory signals intrinsically from data, SSL has significantly reduced the need for costly human annotations and has demonstrated remarkable performance across diverse domains, including computer vision, natural language processing, speech processing, robotics, and healthcare. This survey provides a comprehensive overview of self-supervised learning, covering its fundamental principles, major methodological approaches, and real-world applications. We categorize SSL into four primary paradigms: contrastive learning, clustering-based learning, generative modeling, and predictive learning. We discuss the theoretical underpinnings of these approaches, highlight their strengths and limitations, and analyze their impact on downstream tasks. Additionally, we explore the integration of SSL with deep learning architectures and its role in improving model generalization, robustness, and efficiency. Despite its successes, SSL faces several challenges, including the computational cost of large-scale training, sensitivity to domain shifts, difficulties in designing optimal pretext tasks, and a lack of theoretical understanding. We outline open research questions and promising future directions, such as multimodal SSL, efficient pretraining techniques, self-supervised reinforcement learning, and fairness-aware SSL. As self-supervised learning continues to evolve, it holds the potential to redefine machine learning by enabling more scalable, efficient, and generalizable models. This survey aims to provide researchers and practitioners with a comprehensive understanding of SSL, facilitating further advancements in this rapidly growing field.
A Solution Method for Partial Differential Equations by Fusing Fourier Operators and U-Net
Authors: Zhang Tao , Zhu Xinran
Published: February 2025

In scientific and engineering computations, the efficient solution of partial differential equations (PDEs) is of great significance. This paper proposes an innovative method based on the combination of the U-Net neural network and the Fourier neural operator, aiming to improve the accuracy and efficiency of solving PDEs. The unique encoder-decoder structure and skip connections of the U-Net neural network can effectively extract and integrate spatial domain features, accurately depicting the spatial structure of PDEs. The Fourier neural operator, by means of the Fourier transform, deeply explores and processes features in the frequency domain, capturing the frequency characteristics of the solutions of PDEs. By organically combining the two, this method not only preserves spatial details but also makes full use of frequency domain information. It significantly reduces the model's dependence on large scale data and enhances its generalization ability. Experimental results show that compared with traditional methods, this method performs outstandingly in various complex partial differential equation(PDE) solving tasks, achieving higher accuracy. It provides a new and highly promising solution for the solution of PDEs and is expected to be widely applied in related fields.
Unveiling the Systemic Impact of Congestion in Heart Failure: A Narrative Review of Multisystem Pathophysiology and Clinical Implications
Authors: Mocan Daniela , Rasinar Florin Claudiu , Lala Radu Ioan , Sabau Ronela , Groza Iulia , Sulea Bratu Damaris , Balta Diana Federica , Puschita Maria
Published: February 2025

Congestion is a key clinical feature of heart failure (HF), contributing to hospitalizations, disease progression, and poor outcomes. While traditionally considered a hemodynamic issue, congestion is now recognized as a systemic process affecting multiple organs. Renal dysfunction arises from impaired perfusion and sodium retention, leading to maladaptive left ventricular remodeling. Hepatic congestion contributes to cholestatic liver injury, while metabolic disturbances drive anemia, muscle wasting, and systemic inflammation. Additionally, congestion disrupts the intestinal barrier and immune function, exacerbating HF progression. Given its widespread impact, effective congestion management requires a shift from a cardiovascular-centered approach to a comprehensive, multidisciplinary strategy. Targeted decongestive therapy, metabolic and nutritional optimization, and immune modulation are crucial in mitigating congestion-related organ dysfunction. Early recognition and intervention are essential to slow disease progression, preserve functional capacity, and improve survival. Addressing HF congestion through personalized, evidence-based strategies is vital for optimizing long-term care and advancing treatment paradigms.
Erythrocytes Membrane Fingerprint in the Veterinary Field: The Importance of the Membrane Profiling and Its application in companion animals
Authors: Belà Benedetta , Gramenzi Alessandro , Prasinou Paraskevi , Ferreri Carla
Published: February 2025

The importance of lipid molecules present at the level of cell membranes is already well known. They can act as second messengers participating in signal transduction processes regulating various organ functions; furthermore, their nature significantly influences cellular properties and functions. Recent studies have seen how the lipid composition of cell membranes is connected to the animal lifespan and the onset of several pathological conditions. While in the human field numerous studies have been conducted aimed at characterizing the membrane lipidomic profile, in the animal field, especially in pets, the number of studies is very limited. In recent years, preliminary analyses have been conducted to provide an initial information on the composition of membrane fatty acids in healthy pets and those with chronic enteropathy. The results of these studies are very interesting as they highlight differences in fatty acid composition between the two groups of animals. Obviously, a greater number of works is needed to obtain more reliable results and to analyze how the membrane lipid composition can vary in different breeds and sizes of dogs and cats trying to understand the mechanisms underlying it. The present review is divided into three main parts: the first one examines the close influence of fatty acids on membrane properties/functions, the second one presents the main lipidomic analyses conducted so far on companion animals and the third and final part summarizes the latest works on the link between membrane lipid profile and animal lifespan, also focusing on dietary and non-dietary strategies able to influence it. Membrane lipidomics allows to obtain a concrete overview of animal metabolism and nutrition, furthermore lipid alterations could be used as biomarkers for the early diagnosis of pathologies; this represents an innovative tool in the veterinary field to monitor the metabolic/health status of animals.
Ecological Network Optimization and Ecological Security Pattern Construction for Kunming's Main Urban Area Based on the MSPA-MCR Model
Authors: Chen Wendi , Zhao Junsan , Chen Guoping , Lin Yilin , Yang Haibo , Chen Qiaoxiong
Published: February 2025

Rapid urbanization has greatly altered urban ecological spaces and habitat quality functions, threatening regional biodiversity and the sustainability of landscapes. Therefore, constructing a comprehensive ecological network and ecological safety patterns is crucial for ecosystem management and regional development. However, simple quantification of ecological networks fails to meet the construction needs of ecological safety patterns, and most studies focus solely on network quantification analysis, thus overlooking the importance of spatial analysis. This study proposes a method of ecological network quantification assessment combined with hotspot analysis and coupled with standard deviational ellipse spatial analysis, which not only satisfies quantitative analysis but also adds spatial analysis methods, facilitating a more comprehensive construction of safety patterns. Firstly, through Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity indices, ecological source areas in the main urban area of Kunming were identified, integrating various resistance factors and corrective factors to construct an ecological resistance surface. The Minimum Cumulative Resistance (MCR) model was used to identify potential ecological corridors, and their importance was evaluated using the gravity model, thus establishing an ecological network. Secondly, based on network structure indices, the ecological network was assessed and optimized. On this basis, combined with hotspot analysis coupled with standard deviational ellipse spatial analysis, an ecological safety pattern was constructed. The results show: the core area of the study region is 2402.28 km², accounting for 52.07% of the total area; there are 13 ecological source areas totaling 2102.89 km², accounting for 45.58% of the total area; there are 178 potential ecological corridors, including 15 level one ecological corridors and 19 level two ecological corridors; 103 ecological nodes, 70 'stepping stones', and 48 ecological breakpoints were identified. In terms of ecological network optimization, 6 new ecological source areas were added, covering an area of 16.22 km², and the potential ecological corridors increased to 324, including 11 new level two ecological corridors, 51 new ecological nodes, 15 'stepping stones', and 24 major ecological breakpoints. After optimization, the network closure index (α), network connectivity index (β), and network connectivity rate index (γ) improved by 15.16%, 24.56%, and 17.79%, respectively. Based on the network structure quantitative analysis and hotspot analysis coupled with the standard deviational ellipse's spatial analysis, an 'one axis, two belts, five zones' ecological safety pattern was constructed.
Stingers—A Review of Current Understanding and Management
Authors: Gebeyehu Teleale Fikru , Harrop James S , Dian Joshua A , Matsoukas Stavros , Vaccaro Alexander R
Published: February 2025

Stingers are as transient sensory and motor loss of one upper extremity due to stretch or compression injury to the brachial plexus or the exiting cervical nerve roots. All nerve roots that make the brachial plexus are prone to injury, but the upper roots of the brachial plexus (C5 and C6) are most frequently affected. Although the true incidence of stingers is hard to know, American Football and Rugby are the two sports with the highest incidence. Mostly, stingers have a promising prognosis. However, with advancing sports medicine and rehabilitation, so did the general understanding and management of stingers among the medical professionals. This review is a summary of the current understanding of the pathology, clinical features, investigative modalities, novel radiologic measurements, management, rehabilitation and return-to-play decision protocol for stingers.
Quantification and Analysis of Factors Influencing Territorial Spatial Conflicts in the Gully Region of the Loess Plateau: A Case Study of Qingyang City, Gansu Province, China
Authors: Zhang Meijuan , Tang Xianglong
Published: February 2025

The gully region of the Loess Plateau is among the most ecologically fragile areas in China, where rapid urbanization and unique geographical characteristics have intensified territorial spatial conflicts. Characterizing the spatial evolution and the factors driving these conflicts is crucial for promoting sustainable development in the region. This study utilizes land use remote sensing data from 1990, 2000, 2010, and 2020 to examine the evolution of territorial spatial conflicts in Qingyang City, Gansu Province. A transfer probability matrix and a comprehensive spatial conflict index model were employed, coupled with the optimal parameter geographical detector (OPGD) model, to identify key influencing factors. Our findings reveal significant regional differences in spatial patterns, with ecological and urban spaces expanding while agricultural land contracted. Over time, the intensity of spatial conflicts has increased, particularly in the central urban area of Xifeng District and the urban-rural fringe. The main driving factors identified include the normalized difference vegetation index (NDVI), land use intensity, and annual average precipitation, with land use intensity becoming increasingly influential in recent decades. These results provide valuable insights for future territorial spatial planning and management in Qingyang City.
Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures
Authors: Zaboski Brian A , Bednarek Lora
Published: February 2025

Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances in neuroimaging, EEG, and other multimodal datasets have created opportunities to identify biomarkers and predict outcomes, yet traditional statistical methods often fall short in analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges by leveraging architectures capable of classification, prediction, and data generation. This brief review provides an overview of five key DL architectures—feedforward neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers—and their applications in OCD research and clinical practice. We highlight how these models have been used to identify neural predictors of treatment response, diagnose and classify OCD, and advance precision psychiatry. We conclude by discussing the clinical implementation of DL, summarizing its advances and promises in OCD, and underscoring key challenges for the field.
Shifting Epidemiological Dynamics of Mosquito-Borne Diseases and Challenges for Their Elimination in India Under the Impact of Climate Change and Global Warming
Authors: Ahmad Ayashaa , Chester Karishma , Sathua Sandeep Kumar , Das Avijit , Tyagi Brij Kishore , Sarkar Manas
Published: February 2025

The propensity with which the arthropod vectors of human and animal diseases have intensified pathogen transmission under the impact of changing climate has portrayed the later as the biggest health threat in the 21st century, with vector-borne diseases affecting more than half of the world population. There are indications of climate change driving spatiotemporal shifts, particularly bolstered by anthropization and vector densification, in prevalence as well as exacerbation of the vector-borne diseases. Identification of such climate-sensitive infections is crucial for mitigating disease threat. Regional changes in the temperature, precipitation, humidity and wind patterns etc. con-tribute to the expansion of diseases like malaria, dengue etc., and emergence of new pathogens and their ability to adapt diverse breeding habitats. Classic case studies malaria forming new foci in the Thar Desert region while dengue spread to hitherto entirely virgin lands of the Kerala State in India. There are multiple governmental programmes to tackle health problems, especially associated with the vector-borne diseases, on a wider front and on a longer-term basis through the creation of permanent multifunctional healthcare delivery institutions. However, addressing these challenges require an interdisciplinary approach that combines entomology, epidemiology, ecology, and consumer public health interwoven within the gamut of Integrated Disease Management.
Stakeholder Perspectives on Irish Agri-Environmental Measures and HOLOS-IE Digital Platform Development
Authors: Siddique Mahjabin , Collier Rem , Barry Aideen , Khalil Mohammad Ibrahim
Published: February 2025

Agriculture is responsible for 37.8% of Ireland’s greenhouse gas (GHG) emissions, with a target to reduce overall emissions by 42% by 2030. For this, aligning with the Climate Action Plan and the European Green Deal, many agri-environmental schemes have been employed. Several models are also available for accounting and reporting but mostly fail to report on a mixed farm level. HOLOS-IE, an agricultural system-based digital platform enabling the simulation of the sectoral GHGs and net farm carbon footprint, is in development to combat this. To ensure the model's development, we engaged with various stakeholders through interviews, workshops, conferences, and online surveys. These engagements revealed several key points, including improving soil health, offering financial support for transitioning to sustainable practices and adopting biodiversity initiatives to improve farms’ environmental impact. When discussing the HOLOS-IE model, 84.13% of stakeholders thought it would be beneficial, 9.52% were neutral, and 6.35% did not believe it would be helpful. Stakeholders recommended enhancing accuracy, improving user-friendliness and accessibility, considering profitability, and ensuring data confidentiality to improve the model. These will guide the model’s refinement, focusing on developing a reliable, user-centred tool to assess farm carbon footprint, and report on and plan climate-resilient agricultural practices.
Comparative Analysis of Predictive Models for Individual Cancer Risk: Approaches and Applications
Authors: Westerlinck Philippe
Published: February 2025

This article provides a comprehensive analysis of predictive models for individual cancer risk, examining their development, application, and evaluation. The study covers various cancer types, highlighting the diversity and sophistication of models over time. Utilizing data from PubMed, Web of Science, and Scopus, the research includes models developed for 22 cancer types, with significant emphasis on breast and colorectal cancers due to their prevalence and early detection benefits. The analysis reveals an uneven distribution of models, often concentrated in the United States and the United Kingdom, with a notable gap in models for rarer cancers. Key methodologies such as logistic regression and Cox proportional hazards models dominate the field, reflecting a preference for established statistical techniques. The study underscores the importance of incorporating multiple risk factors, including genetic, environmental, lifestyle, and clinical data, to enhance predictive accuracy. Despite advancements, the article identifies a critical need for external validation and standardization in reporting practices to improve model reliability and generalizability. The findings emphasize the potential of these models in personalized cancer prevention and early detection, while also calling for continued research and methodological harmonization to address existing gaps and challenges.
The Influence of Cultural and Structural Dynamics on Supply Chain Resilience in Multinational Corporations
Authors: Johnson Harry
Published: February 2025

This research examines the cultural and structural elements influencing supply chain resilience in multinational organisations. The report offers a thorough analysis of how organisational culture, leadership, digital transformation, supplier relationships, decision-making frameworks, sustainability, and geopolitical threats affect resilience, elucidating how businesses manage disruptions. The study used a qualitative technique, including comprehensive interviews with 35 supply chain specialists from several international corporations. The results indicate that a culture of flexibility, proactive risk management, and cross-functional cooperation improves an organization's capacity to react successfully to supply chain disruptions. The commitment of leadership to resilience, staff involvement, and knowledge-sharing enhances resilience initiatives. The research underscores the crucial impact of digital transformation on enhancing supply chain visibility and mitigating risks. Organisations that use sophisticated technologies like artificial intelligence, predictive analytics, and blockchain experience improved forecasting and agility. The efficacy of digital adoption is contingent upon organisational preparedness and cultural receptivity to technological transformation. Supplier connections are essential, since trust-based alliances, diversity, and nearshoring techniques effectively mitigate supply chain risks. Decision-making frameworks influence resilience, since decentralised models provide swifter reactions and enhanced problem-solving ability. Sustainability and regulatory compliance have become essential structural elements, with firms that emphasise environmental and ethical aspects exhibiting enhanced long-term stability. Geopolitical and economic concerns persistently threaten multinational firms, requiring ongoing adaptation and strategic planning. The research indicates that supply chain resilience is a continuous process necessitating the amalgamation of cultural flexibility and structural readiness. Organisations that embrace a comprehensive strategy, integrating technology, leadership, sustainability, and agile decision-making, are optimally equipped to navigate challenges and retain long-term competitiveness in an increasingly volatile global business landscape.
Digital Distractions in the Classroom Among Students: A Cross-Sectional Study
Authors: Thapa Pramila , Sharma Prakash , Goundar Sam , Tsirkas Panagiotis , Valamontes Antonios , Adamopoulos Ioannis
Published: February 2025

Background: The integration of digital devices in education has transformed learning experiences, offering instant access to information and enhancing collaboration. However, their misuse for non-academic purposes, such as social media and gaming, has led to significant classroom distractions, negatively impacting student focus and academic performance. This study investigates the prevalence of digital distractions (DD) in classrooms and explores strategies to enhance student engagement while minimizing disruptions.Methods: A quantitative cross-sectional study was conducted using a Google Forms survey distributed to students in a selected academic institution. A convenience sampling method was employed, and data were analyzed using descriptive statistics.Results: The 51.2% of students reported using digital devices 1 to 3 times per class, primarily for non-academic activities such as texting (21.9%), social networking (14.1%), and checking the time (41.2%). The primary benefits of using digital devices were staying connected (29.2%), entertainment (19.8%), and alleviating boredom (20.4%). The most cited negative effects were reduced attention (68.6%), distraction of peers (38.5%), and missed instructions (30.3%). 66.3% of participants supported banning digital devices in classrooms, while 59.7% favored restricting non-academic use.Conclusion: Digital devices play a dual role in education—they enhance learning while also contributing to distractions. To address this challenge, clear policies, digital literacy programs, and self-regulation strategies are essential for promoting responsible device use and improving classroom focus. Future research should explore adaptive strategies to balance digital engagement with academic performance.