Search Results (13,400)

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies. Full article

This study explores the readiness of Micro and Small Enterprises (MSEs) in Mexico, specifically grocery stores, to implement the Virtual Value Chain (VVC) through Information and Communication Technologies for Development (ICT4D). A mixed-methods approach was used, combining diagnostic tools, structured surveys, and interviews. Quantitative data were analyzed using descriptive statistics, correlation analysis, and machine learning to identify digital adoption patterns. The results indicate that limited technology adoption remains the main obstacle to VVC integration. Significant associations were found between digital engagement and the age and educational level of store managers. Key digital gaps persist in inventory control, supplier coordination, and demand forecasting. Although machine learning models did not significantly outperform baseline predictions on willingness to adopt technology, the findings emphasize the potential of targeted training and accessible mobile solutions. The study proposes a new diagnostic and predictive framework to assess VVC readiness in low-resource contexts. It shows that ICT, when strategically aligned with business operations and paired with adequate training, can enhance sustainability and livelihoods. Although the study is limited to one geographic area and one business sector, it offers a foundation for scaling similar initiatives. The findings support context-sensitive strategies and capacity-building efforts tailored to the realities of MSEs in emerging economies. Full article

Background/Objectives: The global threat of antimicrobial resistance is a significant public health challenge, leading to prolonged hospitalizations, increased costs, and elevated mortality. Romania faces one of Europe’s highest burdens of antimicrobial consumption and resistance. This study aimed to investigate the factors that influence antibiotic prescribing practices among physicians in pediatric care in Romania. Method: This quantitative, cross-sectional study collected data using a self-administered, structured questionnaire from 154 healthcare professionals (family physicians, pediatricians, and other specialists) providing pediatric care in Romania. Participants were recruited via non-probability convenience sampling. The 29-question survey gathered demographic data and explored perceptions and practices regarding antibiotic therapy in children using a 5-point Likert scale. Results: The majority of participants were family physicians (64.94%) with over 15 years of experience (53.90%), primarily practicing in urban settings (61.69%). Only 21.43% had attended an antibiotic stewardship course in the last three years. Physicians generally base their prescribing on clinical symptoms. While physicians strongly agreed they follow guidelines, personal experience also held significant weight. High parental demand for antibiotics was perceived, but physicians largely denied ceding to parental tone or insistence without a medical indication. A strong consensus existed on antibiotic overuse in Romanian children, and a high interest in continuous education on rational antibiotic use was noted. Pediatricians showed significantly higher guideline adherence and diagnostic test use than family physicians. Rural physicians reported lower guideline adherence and less frequent diagnostic testing. Stewardship course participation and access to rapid diagnostic tests were associated with more evidence-based practices. Conclusions: Romanian physicians exhibit a nuanced approach to antibiotic prescribing, balancing guidelines with personal experience and facing significant perceived parental pressure. Professional profile (specialty, experience, practice environment) and access to diagnostic resources significantly influence prescribing decisions. Full article

Background: Differentiating Parkinson’s disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with ¹²³I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT^® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed ¹²³I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT^® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT^® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT^® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of ¹²³I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT^® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging. Full article

Real estate plays a pivotal role in fostering national economic growth and ensuring social stability. In China, housing constitutes the largest fixed asset for the majority of households. Given the extensive network of upstream and downstream industries associated with real estate, the government places significant emphasis on its regulation and development, employing a variety of policy instruments to maintain market stability. This study adopts a quantitative approach to conduct a text analysis of China’s real estate policies through the lens of knowledge mapping and LDA topic modeling, while also comparing policy content across 21 different cities. The findings indicate that real estate policy in China transcends mere market regulation. It also encompasses governance within the construction industry as well as provisions for housing security. Furthermore, due to the diverse roles that real estate plays in economic development and urban construction, there is notable regional heterogeneity in policy priorities. By text analysis of real estate policies, this study provides a systematic overview of policy content, thereby laying a foundation for more nuanced and regionally differentiated research within the realm of real estate policy. Full article

Chaotic systems appear in a wide range of natural and engineering contexts, making the design of reliable and flexible control strategies a crucial challenge. This work proposes a robust control scheme based on the Fractional-Order Backstepping Control (FOBC) method for the stabilization of non-commensurate fractional-order chaotic systems subject to bounded uncertainties and external disturbances. The method is developed through a rigorous stability analysis grounded in the Mittag–Leffler function, enabling the step-by-step stabilization of each subsystem. By incorporating fractional-order derivatives into carefully selected Lyapunov candidate functions, the proposed controller ensures global system stability. The performance of the FOBC approach is validated on fractional-order versions of the Duffing–Holmes system and the Rayleigh oscillator, with the results compared against those of a fractional-order PID (FOPID) controller. Numerical evaluations demonstrate the superior performance of the proposed strategy: the error dynamics converge rapidly to zero, the system exhibits strong robustness by restoring state variables to equilibrium quickly after disturbances, and the method achieves low energy dissipation with a high error convergence speed. These quantitative indices confirm the efficiency of FOBC over existing methods. The integration of fractional-order dynamics within the backstepping framework offers a powerful, robust, and resilient approach to stabilizing complex chaotic systems in the presence of uncertainties and external perturbations. Full article

This study focuses on tourist-oriented urban historic districts. In recent years, many such districts have experienced commercial intensification and homogenization, placing pressure on sustainable development. The prior work is largely descriptive and offers limited mechanism-level guidance for governance. In response, this study employs Tianzifang as an empirical case and proposes an online-review-driven mechanism-identification framework. Drawing on 3005 online reviews, a quantitative–qualitative mixed approach was adopted: word-frequency and semantic-network analyses of the full corpus mapped topics and their relational structure; guided by these structures, grounded-theory coding was conducted on a negative-review subsample (n = 602); the results indicate a double-helix interaction between culture–commerce and expectation–reality, associated with lower perceived authenticity, affective disconnect, stronger negative word-of-mouth, and perceived declines in attractiveness. The main contributions are: a mechanism identification framework with a replicable quantitative–qualitative integration workflow; the construction of a double-helix mechanism coupling culture–commerce and expectation–reality; and, on this basis, a governance strategy framework to support fine-grained management and the sustainable renewal of urban historic districts. Full article

Large-scale water network projects are a crucial approach for the rational allocation of water resources and addressing water resource crises. Reliable sustainability evaluation is essential to ensure the sustainable operation of large-scale water network projects. This study develops an improved Fuzzy Comprehensive Evaluation (FCE) method based on Game Theory weight fusion (GWF) for the quantitative evaluation of the sustainability of water network projects. By combining the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and Game Theory approach, the study integrates the advantages of both subjective and objective weighting methods to achieve the allocation of indicator weights; the sustainability of the Jiaodong Water Network Project was quantitatively evaluated by employing the improved FCE method. The results indicate that the resource and management dimensions are the two most critical factors affecting the sustainability of large-scale water network projects. Indicators with high weight such as per capita water resources, the rationality of the management system, and level of management intelligence are the primary risk factors affecting the sustainable operation of large-scale water network projects. The sustainability evaluation value of the Jiaodong Water Network Project is 82.83 points, which is classified as “high” sustainability. This validates the reliability of the evaluation indicator system and the method used. Full article

Sustainability is a foundational principle in Chilean education, reflected in curricular objectives related to environmental care, economic development, and social well-being. This study analyzes the integration of sustainability concepts and dimensions into the curricular bases of the third year (11th grade) and fourth year (12th grade) in Chilean secondary education. Using a sequential explanatory mixed-methods design and content analysis, the quantitative phase identified six key sustainability-related terms and their presence across curricular components and subject areas. The qualitative phase examined the inclusion of the environmental, social, and economic dimensions within those areas. The results show that sustainability concepts appear in seven subject areas, with greater emphasis on learning objectives and educational purposes. However, the environmental dimension dominates, while the social and economic aspects are underrepresented. These findings reveal conceptual ambiguities and uneven integration, highlighting challenges for implementing a multidimensional sustainability approach in Chilean classrooms. Full article

Obesity is a major global health issue associated with an increased risk of early-onset metabolic disorders and chronic inflammation. Identifying the epigenetic mechanisms that contribute to obesity-related metabolic and inflammatory dysregulation is crucial for developing effective prevention and treatment strategies. This pilot study aimed to investigate the effects of obesity on the expression of microRNAs (miRNAs) related to metabolic disorders in human peripheral blood mononuclear cells from metabolically healthy obese subjects and non-obese controls. Differentially expressed miRNAs in TaqMan human miRNA arrays were quantified using quantitative PCR. To validate the robustness and generalizability of our findings, we performed cross-validation using the publicly available GSE155096 dataset. The expression of miR-145-5p was significantly increased (4.913-fold change) in obese individuals compared to the non-obese control group. Two miRNAs, miR-27b-3p and miR-17-5p, were downregulated 2.207- and 1.448-fold, respectively, approaching significance. A positive correlation was established between miR-145-5p and free triiodothyronine, eosinophils, and vitamin D. A cross-validation analysis confirmed the direction of change for these key miRNAs. The data suggest that miR-145-5p, miR-27b-3p, and miR-17-5p could be implicated in the progression of obesity in causing metabolic abnormalities, clarifying how molecular factors cause the metabolic deregulation associated with obesity. Full article

Biophilic design (BD) has become one of the most critical design approaches for improving the user experience and sustainability in hospitality settings. This paper examines how Biophilic Design Elements (BDEs) can be integrated into the interior architecture of rooftop restaurants and how the presence of BDEs can enhance guest satisfaction and restaurant operations. The study is based on the Nature Preferences Theory (NPT) and Dynamic Capabilities Theory (DCT), creating a framework that explores the relationship between biophilic principles and measurable user outcomes, as well as design innovation. A mixed-methods design was employed, where qualitative insights gathered from a Delphi panel of interior design and hospitality professionals were integrated with quantitative data collected through guest surveys. The study suggested that some high-performance BDEs, which are natural materials, utilise daylight, greenery, and water, as well as culturally embedded design motifs. The results indicate that users are positive about an environment with principles of well-being, authenticity, and sensory connection with nature. Researchers focused on context-sensitive, flexible, and low-cost strategies that are adaptable to rooftops in developing urban cities. The study is significant as it presents real-life biophilic design methods applicable in hospitality environments atop buildings and demonstrates how they may align with the Sustainable Development Goals (SDGs). The suggested framework applies to both academic studies and the industry, focusing future designs on nature, user experience, and operational sustainability. Full article

Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R² values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63–98.28% for silt, 0.26–36.16% for clay, and 0.64–96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. Full article

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines. Full article

This paper investigates the subjective value perceived by teachers, defined as their overall appreciation of EU-funded educational projects in terms of usefulness, relevance, and impact on education, regarding projects implemented in Romanian schools during the period 2014–2022. The main factors influencing the perceived value were identified through a quantitative approach using a questionnaire-based survey, administered to a sample of 1050 teachers from various regions of the country. The results reveal that improvements achieved in various aspects of the educational environment quality have a positive influence on the analyzed indicator. These improvements can be grouped into two categories of factors that act at the level of school, on the one hand, and at the level of students, on the other hand, both having a significant impact on increasing the perceived value of EU-funded educational projects. The differences between schools that benefited from such educational projects and other schools were also addressed, as well as the influence of the dominant socio-economic status of children studying in different schools on the improvement of the quality of the educational environment. The conclusions highlight the strategic role of European funding in reducing educational disparities and the need to target support to vulnerable schools. The practical and managerial implications include strengthening infrastructure, adapting methodologies, and developing staff competencies, alongside interventions aimed at improving student progress. Full article