Search Results (1,222)

Despite the importance of financial literacy, particularly in sustaining and improving rural agriculture, it is documented in the literature that little is known about financial literacy, particularly in rural communities in developing countries. Responding to the calls for research to address this gap, the current study investigates how financial literacy relates to access to funding, innovative service adoption, and sustainable food production among agricultural food producers in Nigeria’s rural communities. A probability sampling technique was used to draw 460 samples from registered rural farmers in the Central Bank of Nigeria’s Anchored Borrower’s Programme for food production in Edo State, Nigeria. Quantitative data were collected using a structured questionnaire. The hypotheses were tested using regression analysis, while descriptive statistics were deployed to analyse the demographic data of the respondents. The outcomes suggest that financial literacy has significant links with access to funding, innovative service adoption and sustainable food production among agricultural food producers in Nigerian rural communities. Based on the outcomes, it is concluded that financial literacy significantly influences sustainable food production in Nigerian rural communities. As such, there is a need for the Nigerian government and financial authorities to embark on a financial literacy drive to increase financial literacy, particularly in light of ever-evolving disruptive financial technologies. Full article

Background: The rehabilitative treatment of muscle injuries is mostly conservative, but it does not always follow precise protocols. Appropriate physiotherapy, exercises, and training are essential components of the rehabilitation and reconditioning of injured muscles. The purpose of this review is to assess the good rehabilitative practices in the treatment of patients affected by muscle injuries. Methods: We performed research on Medline and Cochrane Database. Guidelines focusing on the rehabilitative treatment of muscle injuries were evaluated for inclusion. Statements about non-rehabilitative treatments were also reported only for the guidelines that mainly focused on rehabilitative treatments. Results: Eight guidelines meeting the inclusion criteria were included in the review. Results were framed into a narrative overview. Two of them mainly focused on hamstring rehabilitation, the others focused on several muscular districts. Conclusions: Conservative treatment of muscle injuries is currently the gold standard, with good results in terms of both rehabilitation times and post-injury sports performance. However, there is not a complete agreement on the type of exercises and the timing of rehabilitation when these should be performed. More research is needed to draw conclusions about the use of physical therapy instruments and other rehabilitation approaches and techniques. Full article

In this review, we explore the complexities of objectively assessing the response to immunotherapy in mycosis fungoides (MF), a prevalent form of cutaneous T-cell lymphoma. The core challenge lies in distinguishing between reactive and malignant lymphocytes amidst treatment, particularly given the absence of uniform pathological biomarkers for MF. We highlight the vital role of emerging histological technologies, such as multispectral imaging and spatial transcriptomics, in offering a more profound insight into the tumor microenvironment (TME) and its dynamic response to immunomodulatory therapies. Drawing on parallels with melanoma—another immunogenic skin cancer—our review suggests that methodologies and insights from melanoma could be instrumental in refining the approach to MF. We specifically focus on the prognostic implications of various TME cell types, including CD8+ tumor-infiltrating lymphocytes, natural killer (NK) cells, and histiocytes, in predicting therapy responses. The review culminates in a discussion about adapting and evolving treatment response quantification strategies from melanoma research to the distinct context of MF, advocating for the implementation of novel techniques like high-throughput T-cell receptor gene rearrangement analysis. This exploration underscores the urgent need for continued innovation and standardization in evaluating responses to immunotherapies in MF, a field rapidly evolving with new therapeutic strategies. Full article

Background: Sustainability in supply chain management (SCM) practices is becoming increasingly important as environmental responsibility and social concerns, as well as enterprises’ competitiveness in terms of innovation, risk, and economic performance, become increasingly urgent. This paper aims to identify and prioritize concepts for implementing sustainable supply chains, drawing on sustainable supply chain management (SSCM) and green supply chain management (GSCM) techniques. Corporate supply chain managers across various industries, markets, and supply chain segments brainstormed management practices to enhance the sustainability of their supply chains. Four industry sectors were surveyed across five different value chain segments. Methods: A group concept mapping (GCM) approach incorporating multi-dimensional scaling (MDS) and hierarchical cluster analysis (HCA) was used. A hierarchy of practices is proposed, and hypotheses are developed about achievability and impact. Results: A decision-making matrix prioritizes eight solution concepts based on two axes: impact (I) and ease of implementation (EoI). Conclusions: Eight concepts are prioritized based on the optimal effectiveness of implementing the solutions. Pattern matching reveals differences between emerging and developed markets, as well as supply chain segments, that decision-makers should be aware of. By analyzing supply chains from a multi-part perspective, this research goes beyond empirical studies based on a single industry, geographic region, or example case. Full article

To tackle the inefficiencies in 3D mine tunnel modeling and the tedious task of drawing centerlines, this study introduces a faster method for generating centerlines using CAD secondary development. Starting with the tunnel centerline, the research then dives into techniques for creating detailed 3D tunnel models. The team first broke down the steps and logic behind tunnel modeling, designing a 3D tunnel framework and its data structure—complete with key geometric components like traverse points, junctions, nodes, and centerlines. By refining older centerline drawing techniques, they built a CAD-powered tool that slashes time and effort. The study also harnessed advanced algorithms, such as surface fitting and curve lofting, to swiftly model tricky tunnel sections like curves and crossings. This method fixes common problems like warped or incomplete surfaces in linked tunnel models, delivering precise and lifelike 3D scenes for VR-based mining safety drills and simulations. Full article

In response to increasing sustainability demands, the packaging industry is shifting toward paper-based alternatives to replace polymer packaging. However, achieving complex, three-dimensional geometries comparable to plastics remains challenging due to the limited stretchability of paper. This study investigates advanced preconditioning techniques to enhance the formability of paper materials for deep-draw packaging applications. A custom-built test rig was developed at Syntegon Technology GmbH to systematically evaluate the effects of ultrasound-assisted moistening and segmented radiative heating. Under optimized conditions, 2.67 s moistening, 70.00 °C punch temperature, and 2999 W radiation power, maximum stretchability increased from 13.00% to 26.93%. The results confirm the effectiveness of ultrasound in accelerating moisture uptake and radiation heating in achieving uniform thermal distribution across the paper substrate. Although prototype constraints, such as the absence of inline conditioning and real-time measurement, limit process stability and scalability, the findings provide a strong foundation for developing industrial 3D paper forming processes that support sustainable packaging innovation. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Phishing attacks pose significant risks to security, drawing considerable attention from both security professionals and customers. Despite extensive research, the current phishing website detection mechanisms often fail to efficiently diagnose unknown attacks due to their poor performances in the feature selection stage. Many techniques suffer from overfitting when working with huge datasets. To address this issue, we propose a feature selection strategy based on a convolutional graph network, which utilizes a dataset containing both labels and features, along with hyperparameters for a Support Vector Machine (SVM) and a graph neural network (GNN). Our technique consists of three main stages: (1) preprocessing the data by dividing them into testing and training sets, (2) constructing a graph from pairwise feature distances using the Manhattan distance and adding self-loops to nodes, and (3) implementing a GraphSAGE model with node embeddings and training the GNN by updating the node embeddings through message passing from neighbors, calculating the hinge loss, applying the softmax function, and updating weights via backpropagation. Additionally, we compute the neighborhood random walk (NRW) distance using a random walk with restart to create an adjacency matrix that captures the node relationships. The node features are ranked based on gradient significance to select the top k features, and the SVM is trained using the selected features, with the hyperparameters tuned through cross-validation. We evaluated our model on a test set, calculating the performance metrics and validating the effectiveness of the PhishGNN dataset. Our model achieved a precision of 90.78%, an F1-score of 93.79%, a recall of 97%, and an accuracy of 93.53%, outperforming the existing techniques. Full article

Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global optimization challenges. A deep neural network (DNN) is fine-tuned using this hybrid optimization method to address the challenges of hyperparameter selection and overfitting, which are common in DL approaches for BRCA classification. The proposed IGWO–SOA model demonstrates optimal performance in identifying key attributes that contribute to accurate cancer detection using the CBIS-DDSM dataset. Its effectiveness is validated using performance metrics such as loss, F1-score, precision, accuracy, and recall. Notably, the model achieved an impressive accuracy of 99.4%, outperforming existing methods in the domain. By optimizing both the learning parameters and model structure, this research establishes an advanced deep learning framework built upon the IGWO–SOA approach, presenting a robust and reliable method for early BRCA detection with significant potential to improve diagnostic precision. Full article

Sampling with replacement occurs when drawing without removing individuals from finite populations. It is a common distribution technique used in physics, biology, and medicine. It is used in state analysis of qubits, the physics of particle interactions, studies of genetic variation and variability, and analyzing the treatment effects from clinical trial analyses. When applied, sample statistics should be accompanied by confidence intervals. The major difficulty in expressing the confidence intervals in sampling with replacement consists of discreetness regarding the probability distribution. As a result, no mathematical formula can handle an optimum solution. Using a simple algorithm is proposed in order to obtain confidence intervals for sampling with replacement variables (x from m trials with replacement) and their proportion ($x/m$). A question-based discussion is presented. Traditional confidence intervals often require large sample sizes. Confidence intervals, constructed in a deterministic way provided by the proposed algorithm for sampling with replacement, allow constructing intervals without constraints. Full article

This study aims to evaluate the sustainability performance of EU regions through a comprehensive and data-driven Environmental, Social, Governance (ESG) framework, addressing the increasing demand for regional-level analysis in sustainable finance and policy design. Leveraging Partial Least Squares (PLS) regression and cluster analysis, we construct composite ESG indicators that adjust for economic size using GDP normalization and LOESS smoothing. Drawing on panel data from 2010 to 2023 and over 170 indicators, we model the determinants of ESG performance at both the national and regional levels across the EU-27. Time-based ESG trajectories are assessed using Compound Annual Growth Rates (CAGR), capturing resilience to shocks such as the COVID-19 pandemic and geopolitical instability. Our findings reveal clear spatial disparities in ESG performance, highlighting the structural gaps in governance, environmental quality, and social cohesion. The model captures patterns of convergence and divergence across EU regions and identifies common drivers influencing sustainability outcomes. This paper introduces an integrated framework that combines PLS regression, clustering, and time-based trend analysis to assess ESG performance at the subnational level. The originality of this study lies in its multi-layered approach, offering a replicable and scalable model for evaluating sustainability with direct implications for green finance, policy prioritization, and regional development. This study contributes to the literature by applying advanced data-driven techniques to assess ESG dynamics in complex economic systems. Full article

In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, we address this challenge by leveraging both local texture cues and global frequency domain information in a complementary manner to enhance the robustness of face forgery detection. Specifically, we introduce a local texture mining and enhancement module. The input image is segmented into patches and a subset is strategically masked, then texture enhanced. This joint masking and enhancement strategy forces the model to focus on generalizable localized texture traces, mitigates overfitting to specific identity features and enabling the model to capture more meaningful subtle traces of forgery. Additionally, we extract multi-scale frequency domain features from the face image using wavelet transform, thereby preserving various frequency domain characteristics of the image. And we propose an innovative frequency-domain processing strategy to adjust the contributions of different frequency-domain components through frequency-domain selection and dynamic weighting. This Facilitates the model’s ability to uncover frequency-domain inconsistencies across various global frequency layers. Furthermore, we propose an integrated framework that combines these two feature modalities, enhanced with spatial attention and channel attention mechanisms, to foster a synergistic effect. Extensive experiments conducted on several benchmark datasets demonstrate that the proposed technique demonstrates superior performance and generalization capabilities compared to existing methods. Full article

Small Language Models (SLMs) have emerged as a critical area of study within natural language processing, attracting growing attention from both academia and industry. This systematic literature review provides a comprehensive and reproducible analysis of recent developments and advancements in SLMs post-2023. Drawing on 70 English-language studies published between January 2023 and January 2025, identified through Scopus, IEEE Xplore, Web of Science, and ACM Digital Library, and focusing primarily on SLMs (including those with up to 7 billion parameters), this review offers a structured overview of the current state of the art and potential future directions. Designed as a resource for researchers seeking an in-depth global synthesis, the review examines key dimensions such as publication trends, visual data representations, contributing institutions, and the availability of public datasets. It highlights prevailing research challenges and outlines proposed solutions, with a particular focus on widely adopted model architectures, as well as common compression and optimization techniques. This study also evaluates the criteria used to assess the effectiveness of SLMs and discusses emerging de facto standards for industry. The curated data and insights aim to support and inform ongoing and future research in this rapidly evolving field. Full article

In this paper, I question what the relationship between psychology and spirit would mean for mental well-being if the ideas of the human being and the notion of spirit are viewed from the perspective of Max Scheler’s philosophical anthropology. Scheler provides a view of the human being and of spirit that differs radically from the generally held views, and his philosophical anthropology provides intellectual nourishment. This approach means that I do not look at spirituality as a religious activity or technique, but rather as a dimension of what constitutes the human being, and I explore how this view of spirituality is related to mental health. This paper is therefore divided into two parts. In the first part, I provide a summary of Scheler’s view of five ideologies of the human being in the history of Western philosophy that he identified, pointing out what he saw as their shortcomings. Next, I examine Scheler’s own philosophical anthropology that views the human being as the meeting place of the interpenetrating movements of spirit and impulsion, and as ens amans—a loving being. After that, I explore Scheler’s notion of spirit and personalism, drawing attention to the crucial role of what he describes as the dimension of spirit in his anthropology. In the second part of this paper, I explore the basic theories of well-being—hedonism, desire theories, and objective list theories—and question what a reading of spirituality as the participation in the movement of love would mean to addressing mental health. I conclude that spirituality should not be viewed as just another source of practices and techniques that could enhance human mental health. Rather, spirituality should be understood as a human being’s execution of the act that constitutes the core of his or her being. Spirituality viewed as the execution of the spiritual act of love—spirituality as loving being. Full article

With the growing popularity of ice sports, indoor ice sports venues are drawing an increasing number of spectators. Maintaining comfort in spectator zones presents a significant challenge for the operational scheduling of climate control systems, which integrate ventilation, heating, and dehumidification functions. To explore economic cost potential while ensuring user comfort, this study proposes a demand response-integrated optimization model for climate control systems. To enhance the model’s practicality and decision-making efficiency, a two-stage optimization method combining multi-objective optimization algorithms with the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. In terms of algorithm comparison, the performance of three typical multi-objective optimization algorithms—NSGA-II, standard MOEA/D, and Multi-Objective Brown Bear Optimization (MOBBO)—is systematically evaluated. The results show that NSGA-II demonstrates the best overall performance based on evaluation metrics including runtime, HV, and IGD. Simulations conducted in China’s cold regions show that, under comparable comfort levels, schedules incorporating dynamic tariffs are significantly more economically efficient than those that do not. They reduce operating costs by 25.3%, 24.4%, and 18.7% on typical summer, transitional, and winter days, respectively. Compared to single-objective optimization approaches that focus solely on either comfort enhancement or cost reduction, the proposed multi-objective model achieves a better balance between user comfort and economic performance. This study not only provides an efficient and sustainable solution for climate control scheduling in energy-intensive buildings such as ice sports venues but also offers a valuable methodological reference for energy management and optimization in similar settings. Full article