Search Results (21,469)

Precise temperature control is crucial for maintaining product quality and optimizing energy efficiency in multi-zone continuous crystallizers. However, such industrial processes typically exhibit complex nonlinear dynamics and strong coupling effects. More critically, physical constraints often prevent sensor installation, rendering temperatures in key regions unobservable and challenging traditional closed-loop control strategies. To address partial observability and model uncertainty, this paper proposes a Model-Based Reinforcement Learning (MBRL) framework utilizing solely offline historical data. The core innovation lies in developing a Recursive State Space Model (RSSM) that serves not only as a high-fidelity digital twin but, more critically, is deployed as a real-time “virtual sensor” to infer unobservable system states. This virtual sensing capability provides precise state estimates for downstream policy optimization. Additionally, a multi-objective reward function is designed to balance tracking error, stability, and control cost. Experimental results demonstrate that the proposed virtual sensor exhibits exceptional long-term stability, maintaining high fidelity and effectively suppressing error accumulation during long-term multi-step autoregressive predictions. Consequently, the trained agent outperforms traditional Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) controllers, achieving over 67% improvement in temperature tracking accuracy while reducing control action costs by more than 93%, indicating smoother system operation and enhanced energy efficiency. Full article

The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many existing studies remain fragmented and insufficiently address practical challenges such as heterogeneous data, sparse fault labels, and cross-site generalization. This review provides an engineering-oriented synthesis of AI-based methods for wind turbine fault detection and O&M, focusing on drivetrain diagnostics as a representative application. The literature is organized along an end-to-end O&M workflow, including SCADA-based condition monitoring, component-level fault diagnosis, health assessment and remaining useful life estimation, multi-modal blade inspection, and DT (Digital Twin) integration. Traditional ML (machine learning), ensemble methods, deep learning, physics-informed learning, and transfer learning are reviewed with respect to their data requirements, operational assumptions, and deployment constraints. Beyond algorithmic performance, this review discusses data governance, alarm design, model updating, and interpretability, and summarizes public datasets and emerging data resources. The aim is to bridge methodological advances and practical O&M requirements, supporting reliable and deployable AI applications in wind energy systems. Full article

Background/Objectives: Antimicrobial peptides (AMPs) represent a promising class of therapeutics with diverse biological functions, including antibacterial, anti-fungal, anti-parasitic and anti-tumoral activities. Previous works demonstrated the successful repurposing of the two synthetic AMPs 19-2.5 and 19-4LF for cutaneous leishmaniasis, when the compounds were administered in solution on skin lesions caused by Leishmania major in a BALB/c mouse model. In this research project, we assessed the activity of 19-4LF, 19-2.5, and their hybrid 19-2.5LF derivative when formulated as a cream for topical administration in the same animal model. Methods: The peptides were formulated in DAC cream and applied to the wound of BALB/C mice for 30 days. Lesion progression was monitored using a digital caliper. Parasite burden was measured by qPCR. Parasite viability was assessed using MTT and microscopy imaging assays. Results: The three peptides in cream formulation succeeded in reducing the skin lesion. Peptide 19-4LF was the most potent, followed by 19-2.5LF and then 19-2.5. In addition, 19-4LF was able to significantly reduce the parasite burden in the skin lesions of infected mice, as measured by quantifying L. major Lm18S ribosomal gene mRNA levels using qPCR. Moreover, when combined, the peptides exhibited synergistic effects on L. major promastigotes and significantly reduced the number of amastigotes in infected macrophages. Conclusions: These studies support the approach of repurposing these AMPs as antileishmanial drugs and identify 19-4LF as a lead candidate for further studies. While historical barriers to peptide therapeutics included high production costs, recent advancements in biological fermentation and synthesis strategies have significantly improved their economic viability. Furthermore, the use of nanotechnology delivery systems can reduce the required dosage, further making peptide therapy a sustainable option for neglected diseases, including leishmaniasis. Full article

This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article

Exercise-induced muscle damage (EIMD) has classically been attributed to localized mechanical disruption following eccentric contractions. Emerging evidence, however, indicates that EIMD represents a systems-level failure of stress integration within skeletal muscle rather than a purely mechanical lesion. Mechanical loading initiates disturbances in intracellular Ca²⁺ homeostasis, which interact with metabolic stress, redox imbalance, and immune activation to form self-reinforcing feedback loops. When compensatory capacity is exceeded, transient injury may shift toward maladaptive remodeling marked by mitochondrial dysfunction, ferroptosis, chronic inflammation, and impaired regeneration. Recent studies identify reactive oxygen species accumulation, iron-dependent lipid peroxidation, dysregulated energy sensing, and aberrant immune polarization as key molecular tipping points governing injury reversibility. Beyond their regenerative role, satellite cells act as integrators of metabolic history and epigenetic memory, linking repetitive injury to reduced muscle adaptability, age-related sarcopenia, and heightened metabolic disease risk. Here, we synthesize evidence from animal models, clinical studies, and multi-omics analyses to establish a systems biology framework for EIMD. We delineate the spatiotemporal interactions among mechanical, metabolic, oxidative, immune, and regenerative modules; identify regulatory nodes that determine adaptive repair versus pathological outcomes; and critically evaluate current nutritional, physical, pharmacological, and regenerative interventions from a mechanism-oriented perspective. Finally, we discuss how multi-omics, digital monitoring, and individualized rehabilitation may enable precision management of EIMD and advance understanding of muscle stress resilience and adaptive limits. Full article

This paper presents the design and evaluation of an interactive simulator for plaster turning in porcelain slip-casting. Whereas most virtual pottery systems model clay deformation, our tool simulates the subtractive shaping of rigid plaster blanks, an essential intermediate step in mould-master production. Co-designed with expert practitioners through a user-centred process, the simulator follows workshop practice from blank preparation to the geometric constraints of the turning wheel. We report five iterative prototypes and show how expert feedback replaced generic sculpting metaphors with task-faithful interactions, including correct hand positioning, rotation-dependent turning, and authentic preparatory routines. Our evaluation suggests that the system supports the acquisition of tacit procedural knowledge while also producing geometric data compatible with physically based rendering workflows. This research contributes to the digital preservation of intangible cultural heritage by making the material reasoning of porcelain manufacture accessible in a virtual environment. Full article

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to minimize downtime and energy consumption. However, the increasing use of AI and digital infrastructure has also led to an increase in demand for computing energy, raising concerns about data center efficiency and carbon footprint, leading to the division between Green AI and Red AI. Industry 5.0 expands this paradigm, focusing on human–machine collaboration and sustainable design, where AI supports personalization, circular economy practices, and the integration of renewable energy. Generative AI and digital twins (DTs) enable real-time energy modeling, helping companies simulate outcomes and choose the most sustainable paths. Automation also enables predictive maintenance, extending machine life and reducing material waste. At the same time, AI is contributing to the development of decentralized energy systems, such as smart grids and microgrids, which increase resilience and reduce emissions. A key challenge is balancing the energy efficiency benefits of automation with the sustainability of the AI infrastructure itself, which requires innovation in energy-efficient computing and green algorithms. From this perspective, AI-based automation represents both a solution and a challenge: it accelerates the achievement of sustainable development goals while requiring responsible technological management to ensure long-term ecological sustainability. Full article

The expansion of Internet-of-Things deployments at the network edge challenges service continuity, as single points of failure can interrupt critical data-processing pipelines. This paper introduces the Operational Digital Twin (ODT) —a live, state-synchronized standby system designed for node-level failover in resource-constrained edge environments. In contrast to Digital Twins designed for modeling and analysis, an ODT is designed for operational continuity, standing ready to assume control when the primary node fails. We instantiate this concept through a self-configuring, high-availability architecture that implements the ODT for node-level redundancy. To ground this new conceptual category empirically, we define and validate four measurable criteria for ODT fidelity—state fidelity, synchronization timeliness, behavioral mirroring, and failover validation—establishing a framework that extends beyond passive replication. The design adopts a primary–secondary model with automated node discovery, configuration mirroring, and Virtual IP-based failover. Fault-injection experiments demonstrate low failover latency, prompt service restoration, limited message loss during transitions, and minimal resource overhead. These findings demonstrate that the proposed Operational Digital Twin mechanism reduces single points of failure and provides a lightweight, cost-efficient approach to sustaining reliable data processing in distributed edge environments. Full article

Big data analytics (BDA) has emerged as a crucial strategic asset for organizations aiming to enhance their sustainable company performance; nevertheless, empirical information elucidating the correlation between analytics and sustainability results is scarce, especially in developing nations. This study examines the influence of big data analytics (BDA) on sustainable firm performance (SFP) within the Libyan telecommunications sector, focusing on the mediating roles of organizational learning (OL) and process-oriented dynamic capabilities (PODCs), utilizing dynamic capability and organizational learning theories. A quantitative, cross-sectional research design was utilized. A systematic questionnaire was used to collect data from personnel at five different managerial and functional levels in the Libyan telecoms sector. There were 354 valid replies from a group of 5400 professionals who worked in the managerial, technical, and strategic areas. We used Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0 to look at the proposed research model. We used measurement scales from previous investigations. The findings demonstrate that BDA exerts a positive and statistically significant influence on SFP. Nonetheless, this direct effect is quite minor when juxtaposed with the indirect effects conveyed by OL and PODCs. Both organizational learning and process-oriented dynamic capabilities significantly and partially mediate the relationship between big data analytics (BDA) and sustainable performance. This shows that analytics-driven sustainability outcomes depend heavily on a company’s ability to learn from data and change how it does things. This study enhances the Business and Management literature by elucidating the inadequacy of analytics investments in producing robust sustainability outcomes. It emphasizes the essential function of supplementary organizational capabilities in converting data-driven insights into enduring economic, environmental, and social value. From a practical standpoint, the findings indicate that managers and policymakers in developing economies ought to prioritize learning systems and adaptive process capabilities in conjunction with digital investments to fully harness the sustainability potential of big data analytics. Full article

Adolescents are heavy users of digital media but often lack critical skills, increasing their vulnerability to harmful online content. The integration of game elements into learning and training offers a promising strategy to support positive behavioural change and strengthen adolescents’ skills. This paper describes the development of a conceptual framework for Dress-DIGITARIAN, a serious game aimed at improving health literacy, coping skills, and self-esteem, grounded in Self-Determination Theory (SDT). The framework was constructed to generate higher-order understanding through a multi-level process: analyzing general theory (SDT), integrating mid-range models (the Octalysis framework), and incorporating empirical insights derived from two data collection phases with the target population. This integrative approach informed and guided the game’s design through participatory methods. Developed through collaboration between schools and research institutions, this approach bridges theory and practice by aligning game mechanics with adolescents’ psychological needs. It also underscores the value of involving adolescents in research, not only to enhance scientific rigour but also to empower them as agents of change capable of contributing to health promotion policies and educational innovation. This study does not report the results of a completed intervention or outcome evaluation, which will be conducted in the sixth phase at the end of the current school year. Future research is needed to assess the model’s effectiveness and scalability and to identify areas for further refinement. Full article

This paper introduces a cost-effective customised Printed Circuit Board (PCB) designed to establish an accurate Internet of Things (IoT) platform integrated with established Digital Twin (DT) models for advanced structural monitoring. The study focuses on developing a low-cost, precise PCB to synchronise real-time data between physical structures and their DT counterparts. The methodology includes a robust communication architecture utilising MQTT protocols, facilitating reliable data transmission and efficient integration with MATLAB for processing. Validation tests demonstrate high accuracy in data capture, with less than 1% deviation from conventional systems across multiple structural damage scenarios. This research highlights the potential of cost-effective PCB solutions for enhancing SHM and developing more resilient, proactive infrastructure management strategies. Full article

Two-dimensional phase unwrapping (PU) of interferometric synthetic aperture radar (InSAR) data remains difficult when steep deformation gradients and multi-source disturbances violate the Itoh condition. This study proposes FPUNet, a Fourier-enhanced encoder–decoder for joint denoising and 2-D PU, in which frequency-domain global context modeling is combined with complementary multi-scale spatial aggregation and attention-based feature refinement. Specifically, the bottleneck cascades a Fourier Mixed Residual Block (FMRB), atrous spatial pyramid pooling (ASPP), and a convolutional block attention module (CBAM) to suppress noise while preserving deformation-related fringe structures. FPUNet is trained end-to-end on realistically simulated Sentinel-1 interferograms generated from Shuttle Radar Topography Mission (SRTM) digital elevation models using a physics-informed composite loss that enforces data fidelity, gradient consistency, spectral regularization, and selective rewrapping consistency. On a synthetic benchmark of 1800 test interferograms, FPUNet achieves an RMSE of 0.79 rad, improving over a plain U-Net (1.61 rad) and producing fewer large fringe-number errors than least-squares, SNAPHU, PUNet, and DLPU. Experiments on real Sentinel-1 data over the Datong mining area and the 2022 Menyuan and Luding earthquakes further indicate improved phase closure and rewrapping consistency, particularly in high-gradient coseismic fringes, supporting FPUNet as a robust PU module for InSAR deformation monitoring. Full article

Digital behaviour in higher education must be approached not only as a psychological phenomenon but also as a pedagogical issue with direct implications for academic wellbeing and learning processes. The present study evaluates an integrated model that links frequency of online activity, problematic Internet use, nomophobia, and phubbing among university students. A quantitative-transversal methodology was applied. A structural equation model was specified using partial least squares (PLS-SEM) in order to analyse the combined direct and indirect effects between the constructs studied. The sample comprised 1922 Spanish university students. The instrument was made up of four scales designed to assess the frequency and type of Internet use, problematic Internet use, nomophobia, and phubbing. The results support the four hypotheses established via the model to explain the relationships between the variables. The explanatory power of the model around the construct of phubbing stood out, and nomophobia was determined to have a partial mediating role between problematic Internet use and phubbing. Guidance is discussed for the design of interventions to address the issues these phenomena cause. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

The paper presents a comprehensive methodological system for assessing the level of economic security of Russian regions, based on the synthesis of several complementary approaches and accounting for regional specifics. The central idea is a shift from static monitoring to dynamic analysis, which allows not only for capturing the current state but also for identifying the direction and stability of trends over time. The proposed methodology based on four stages: forming a set of indicators, normalizing their values, aggregating them into integral indices, and then visualizing them for operational decision-making. An important feature of sustainable development is the introduction of mechanisms to account for regional specifics through the clustering of regions and adjustment coefficients, which helps to mitigate the influence of geographical and structural differences on the results comparability. Together, they form an integrated system for diagnosing, planning, and monitoring the economic security of regions. The paper provides examples of threshold values for indicators such as the share of households with internet access, the length of the road network, birth rate, the volume of building commissioning, and innovation expenditures. A classification of regions into stability zones and recommendations for policy measures within each zone accompany the threshold analysis. In particular, for digitalization and transport infrastructure, measures are proposed to enhance monitoring, improve service accessibility, and invest in infrastructure; for the demographic component, measures are proposed to support families and improve quality of life. The practical significance of the research lies in creating a universal, yet flexible, toolkit for monitoring, ranking, and planning regional policy in the field of economic security. The proposed system was designed for application both at the federal level and for interregional analysis, including scenario planning and modeling the impact of management decisions. Thus, this study contributes to the literature by bridging the theory of economic security, the imperatives of sustainable regional development, and the practical potential of information technologies. It offers a concrete, scalable methodology for transforming regional economic security management into a data-driven, forward-looking, and context-sensitive process. In the future, the authors intend to further develop the methodology by considering the sectoral specialization of regions, integrating with medium- and long-term forecasting systems, and creating an automated monitoring platform. Full article