Search Results (5,280)

The development of fractional-order control has been derived from the mathematical generalization of classical calculus and has become an important tool in the modeling and control of dynamical systems with memory and hereditary effects. In spite of the rapid development of this area of control theory and applications, the overall scientific development, structure, and engineering relevance of fractional-order control remain insufficiently understood. In this paper, we address this problem by combining large-scale bibliometric analysis with representative controller performance studies. A total of 6482 publications indexed in the Web of Science database during the period 2010–2026 are analyzed. The bibliometric results indicate that fractional-order control is an increasingly connected global research field with strong roots in fractional calculus, advanced control theory, and growing interdisciplinary links with applied mathematics, automation, and computer science. To further illustrate controller level behavior, representative simulations are performed on a fractional-order time-delay process and an uncertain nonlinear system. For the fractional-order time-delay process, a well-tuned PID controller is compared with a realizable FOPID controller implemented through Oustaloup recursive approximation. The results show that the FOPID controller improves several performance measures, including overshoot, settling time, control energy, total variation, and sensitivity peak, while the comparison is interpreted as a performance trade-off rather than universal superiority. For the uncertain nonlinear system, fractional-order sliding mode control produces smoother control action and substantially reduces chattering. By combining bibliometric mapping with representative performance evaluation, this paper provides a comprehensive overview of fractional-order control as a globally active and practically relevant discipline in control engineering. Full article

Early detection of infectious disease outbreaks is critical in densely populated, resource-limited settings. This study aimed to describe the community-based surveillance (CBS) system and its application of the Data–Information–Knowledge–Wisdom (DIKW) framework in Bangladesh. CBS was implemented in 12 urban wards across Dhaka South, Rajshahi, and Sylhet, where trained community volunteers conducted routine household visits to identify five priority syndromes. Data were collected through a mobile application integrated with an automated pipeline for cleaning, geocoding, cluster detection, and alert generation. Between January and June 2025, 38,489 households were visited, enrolling 128,626 individuals. The system generated 10,191 alerts and 577 clusters, predominantly for suspected dengue (58.7%), followed by acute watery diarrhea (24.1%) and influenza-like illness (10.7%). Rajshahi contributed the majority of alerts and clusters. Spatiotemporal analysis identified ward-level outbreak signals, including localized dengue peaks across all three cities. Over 98% of records were synchronized within 24 h, and more than 99% of data entry errors were automatically corrected, ensuring timely and high-quality analytics. These findings demonstrate that digital CBS can effectively transform community-level data into actionable public health intelligence, supporting early outbreak detection and response. This translation enabled timely public health actions, including targeted outbreak investigations and localized vector control measures in identified hotspots. Integration with national surveillance platforms may further strengthen health system responsiveness and epidemic preparedness. Full article

The paper presents an automated, remotely controlled installation for the continuous-flow disinfection of treated wastewater. The proposed solution ensures the inactivation of microorganisms without heating the fluid and without the use of chemical disinfectants, thus reducing the environmental impact and resource consumption associated with conventional disinfection methods. The destruction of microorganisms is achieved by applying high-intensity electrical pulses, which cause irreversible permeabilization of cell membranes through the phenomenon of electroporation. The installation is fully automated and based on a closed-loop control system, in which a programmable logic controller (PLC) acquires data from specialized sensors and automatically regulates the process variables according to the measured operating conditions. The system implements a closed-loop control strategy, optimizing the amplitude, duration and frequency of the electrical pulses depending on the characteristics of the treated fluid and the working flow rate. By eliminating chemical reagents and limiting thermal effects, the proposed technology contributes to reducing energy consumption and increasing the sustainability of the disinfection process. The integration of electroporation with modern automation and monitoring solutions supports the implementation of circular economy principles and the development of sustainable strategies for the management and reuse of treated wastewater. The proposed PLC-SCADA architecture enables adaptive real-time control of the disinfection process by continuously adjusting pulse amplitude, duration, and repetition frequency according to wastewater characteristics and flow conditions. Compared with conventional chemical disinfection methods, the system eliminates the need for chemical reagents and minimizes the formation of secondary pollutants. In addition, the continuous-flow configuration facilitates integration into existing wastewater treatment infrastructures while supporting sustainable and energy-efficient operation. Full article

High-dimensional financial Extract–Transform–Load (ETL) pipelines often contain heterogeneous variables whose statistical properties may change between recurring data deliveries, affecting feature reliability before downstream machine learning models are trained. This study extends a previously proposed Canberra-based data drift monitoring framework by introducing a hypothesis-informed feature stability component for automated feature assessment and prioritization. Unlike the prior descriptive framework, which relied on univariate and bivariate exploratory metrics, the proposed extension adds an inferential layer and evaluates how this layer changes feature ranking relative to the original score and alternative marginal drift measures. The method combines univariate deviations in summary statistics, bivariate deviations in dependency-related metrics, and hypothesis-based evidence from Anderson–Darling, Mann–Whitney U, and Levene tests. The resulting p-values are aggregated using a Landau-calibrated harmonic mean p-value formulation and transformed into a bounded hypothesis score, which is integrated into a composite variable-level stability ranking. The framework operates on precomputed exploratory data analysis (EDA) outputs, enabling scalable comparison between a validated reference dataset and a current ETL delivery. The proposed extension provides an interpretable and computationally efficient mechanism for identifying unstable features and supporting feature review, exclusion, or prioritization in automated machine learning pipelines. Full article

The rise of artificial intelligence (AI) and classroom automation demands rethinking visual and arts education. To prevent learning standardization, it is imperative to cultivate a critical teacher profile capable of leading new digital ecologies. In this context, measuring the creative self-perception of future educators constitutes a fundamental pedagogical need. This instrumental study analyses the factor structure and internal consistency of the Creative Personality Scale (CPS), adapting it to contemporary technological challenges. It was administered to 90 pre-service teachers from the Early Childhood and Primary Education programmes at the University of Almería. Through an Exploratory Factor Analysis (EFA) using Principal Axis Factoring (PAF) with Oblimin rotation, the scale was refined to 17 items, confirming a robust three-dimensional structure: Imaginative Creativity, Behavioural Originality, and a Positive Attitude towards Challenges (explaining 44.17% of the variance; α = 0.862). While not a direct measure of pedagogical performance, these dimensions capture the psychological dispositions hypothesized as necessary for educators to critically navigate AI integration and mitigate algorithmic standardization. In conclusion, the adapted scale provides an initial exploratory validation of a diagnostic framework. Its application provides a foundational metric for teacher education programmes, aiming to foster learning environments where technology integration is deliberately guided by human judgment and sensitivity. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Background: The treatment of rectal cancer frequently involves a multimodal approach, including neoadjuvant therapy prior to surgery in patients with locally advanced disease. However, the response to such treatment is variable. Robust biomarkers to predict neoadjuvant therapy response represent an unmet clinical need; they could help to stratify patients for organ preservation strategies or treatment intensification. The tumour–stroma ratio (TSR) is an established prognostic marker that has recently gained attention for its potential predictive value when assessed in pre-treatment biopsies. Objective: This narrative review critically evaluates the existing evidence regarding TSR as a predictive biomarker for neoadjuvant therapy response in rectal cancer. Results: Emerging evidence from retrospective studies of large cohorts suggests that stroma-high tumours often demonstrate resistance to standard neoadjuvant chemoradiotherapy, resulting in lower major pathological response rates. Conversely, some smaller studies report no significant association between biopsy TSR and treatment efficacy. This conflicting evidence could be attributable to methodological heterogeneity, including inconsistent definitions, varying measurement techniques (manual versus automated), and mixed patient cohorts. The predictive value of TSR appears to be neoadjuvant regimen-specific, with stroma-high phenotypes interacting differently with treatments like short-course radiotherapy or intensified chemotherapy. Conclusions: TSR is a simple, biologically plausible, and readily assessable promising biomarker with apparently predictive as well as prognostic potential. It is likely to represent a regimen-specific predictor rather than a universal marker of resistance to neoadjuvant therapy in rectal cancer. Future clinical translation will require standardised, AI-driven quantification and robust prospective clinical validation. Full article

Background: Long-term care facility (LTCF) residents are highly susceptible to healthcare-associated infections, and prevention is challenging given frailty, dementia, communal living, and resource constraints. Environmental surface and air contamination contribute to transmission. Novel no-touch automated disinfection technologies have been studied in hospitals, but evidence specific to LTCFs is scarce. This scoping review summarizes recent LTCF-focused interventions, their effectiveness, and implementation considerations. Methods: This scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. We searched PubMed, Medline, Embase, CINAHL, and Scopus for observational or experimental studies evaluating environmental disinfection in LTCFs/nursing homes, excluding body decolonization, non-LTCF settings, and reviews/protocols. Two reviewers independently screened and extracted data via Covidence. This review has been registered on OSF (Open Science Framework). Results: Of 1491 records, 7 studies met the inclusion criteria (6 from the USA, 1 from Australia): one cluster randomized trial, one interrupted time series studies, three prospective observational studies, and two pre–post designs. Interventions included physical methods (HVAC-integrated UV/UVGI, continuous UVGI) and chemical approaches (dry hydrogen peroxide, room fogging plus chlorine dioxide wipes, hydrogen peroxide wipes). Outcomes were heterogeneous (surface SARS-CoV-2 RNA, COVID-19 attack/case rates, airborne/surface microbial loads, and one clinical endpoint—acute respiratory illness). Several studies reported reductions in environmental or airborne bioburden; however, UV-based studies did not demonstrate statistically significant reductions in clinical infections. Certainty was limited by small numbers, non-randomized designs, and diverse outcome measures. Conclusions: No-touch automated disinfection methods appear promising as supplements to standard infection prevention control bundles for reducing environmental contamination in LTCFs. Nevertheless, consistent clinical benefits are unproven. Rigorous, LTCF-tailored, adequately powered trials with standardized clinical and environmental outcomes, plus implementation and cost-effectiveness evaluations, are needed. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

Elastic backscatter lidar and ceilometer systems provide continuous observations of aerosol and cloud vertical structure, but the interpretation of conventional attenuated backscatter products is often limited by the dominance of signal amplitude, strong event-to-event variability, and the reduced visibility of subtle internal features. In this study, we present a refinement framework designed to extract additional structural information from elastic lidar measurements through multiscale local diagnostics applied directly to the native backscatter field. The methodology combines standardized residual fields, local gradients, variance-based metrics, space–time decorrelation scales and structure functions to highlight atmospheric boundaries, internal layering, mixing zones, and coherent structures that are not always evident in conventional representations. The approach is evaluated through three contrasting atmospheric case studies observed in 2024. Two spring events are associated with mineral dust intrusions characterized by different vertical coupling with the planetary boundary layer, while a summer case represents a non-dust regime dominated by diurnal boundary-layer evolution. The refined diagnostics consistently reveal features hidden or only weakly visible in the raw backscatter field, including sharp interfaces, embedded stratification, wave-like perturbations and transitions between decoupled and mixed atmospheric states. Results show that the proposed metrics enable a more objective description of aerosol-layer dynamics and boundary–layer interactions without requiring complex inversion procedures or auxiliary measurements. Because the method relies only on standard elastic lidar observations, it is in principle applicable to ceilometer and lidar monitoring networks. However, the present evaluation is based on three contrasting case studies and should therefore be regarded as a proof-of-concept demonstration. The framework offers a candidate pathway for enhanced atmospheric feature detection and improved interpretation of routine profiling observations, with automated regime classification as a longer-term goal requiring validation on larger and more diverse datasets. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

The choice of a symmetric encryption algorithm in practice is rarely as straightforward as it may appear from theoretical comparisons alone. In addition to security considerations, real-world selection often depends on execution time, reliability, entropy-related behavior, resource efficiency, and suitability for different types of data. This paper presents an experimental software platform for benchmarking and multi-criteria recommendation of symmetric encryption algorithms. The platform combines automated encryption and decryption tests, metric collection, comparative analysis, and result visualization within a unified evaluation workflow. It also incorporates a multi-criteria model that transforms raw experimental measurements into an overall ranking and supports context-aware recommendation according to the requirements of a given usage scenario. The experimental study includes repeated tests on different input categories in order to examine algorithm behavior under varied operating conditions. The obtained results show that algorithm performance and overall suitability are strongly dependent on the evaluation perspective and the application context, which suggests that no single symmetric method should be regarded as universally optimal. The proposed platform offers a practical basis for comparative cryptographic analysis and may be useful both for research purposes and for informed decision-making in security-oriented software environments. Full article

The rapid evolution of data-driven enterprises demands scalable and intelligent systems capable of managing substantial volumes of heterogeneous data in real time. However, traditional systems lack a holistic approach to managing distributed data engineering, real-time analytics, and intelligent decision-making. To address these limitations, this paper proposes a Cognitive Lakehouse Framework that integrates distributed data processing, transformer-based deep learning, real-time analytics, and autonomous decision intelligence. Data are gathered from high-velocity, heterogeneous streams using Apache Kafka. Subsequently, data are processed using the hybrid batch/streaming paradigm, implemented via Apache Spark and Apache Flink, providing low latency and scalability. For data storage, a unified lakehouse layer is created using Delta Lake and Apache Iceberg, both of which support ACID transactions and schema evolution. In addition, transformer-based Deep Learning (DL) algorithms are utilized to capture temporal dependencies for predictive analytics, anomaly detection, and adaptive learning. Model lifecycle management is handled by MLflow, while ClickHouse and Apache Druid are used for real-time analytics. The architecture uses microservices and an event-driven approach on Kubernetes, and the workflow is automated with Apache Airflow. The performance assessment is conducted using TPC-H, TPC-DS, and real-time stream data to measure latency, throughput, and accuracy. Data quality, security, and compliance are provided by governance layers consisting of Apache Ranger and Apache Atlas. Experimental results show that significant gains can be made in terms of performance, with an accuracy of 98.5%, a query response time of 120 ms, a peak throughput of 85,000 records/s, and an end-to-end latency of 95 ms. Full article

Background/Objectives: Accurate post-mortem interval (PMI) estimation remains challenging in forensic pathology. Although potassium (K⁺) is the most well-validated single biomarker in vitreous humor (VH), multivariate approaches may enhance precision by capturing the complex cascade of post-mortem biochemical changes. This study aimed to develop and validate a multivariate PMI estimation model incorporating three biochemical markers—potassium, ammonium (NH₄⁺), and albumin (ALB)—in vitreous humor using automated clinical chemistry platforms for practical forensic application. Methods: Vitreous humor samples from 38 autopsy cases with documented PMIs (39.5–285 h; mean, 105.5 h) were analyzed for K⁺ (Cobas C8000), NH₄⁺ (Cobas C8000), and ALB (Immage 800 nephelometry). Univariate and multivariate regression analyses were performed, with the residual standard error (RSE) as the primary measure of accuracy. Model validation was conducted by back-calculating PMI in four samples completely distinct from the training cohort. Results: All three analytes demonstrated strong individual correlations with PMI (R²: K⁺ = 0.88, ALB = 0.78, NH₄⁺ = 0.69; all p < 0.001). The multivariate regression model [PMI = 40.25[Alb] + 0.01573[NH₄⁺] + 5.339[K⁺] − 53.032] yielded an RMSE of ±15.5 h (MSE = 240.25 h²), outperforming potassium-only models (RMSE = ±22.6 h). Although NH₄⁺ showed limited statistical significance in the multivariate model (p = 0.128), its inclusion improved overall predictive accuracy. External validation in an independent cohort of four subjects (distinct from the 38 subjects in the training set) demonstrated a mean absolute error (MAE) of 20.4 h. Conclusions: The multivariate approach combining K⁺, NH₄⁺, and ALB in VH improves PMI estimation accuracy compared with single-marker methods. The use of automated clinical chemistry platforms enhances reproducibility and facilitates practical implementation in forensic laboratories. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article