Search Results (1,071)

Digital and green energy transitions are driving an unprecedented demand for Strategic and Critical Raw Materials (S-CRMs), necessitating the identification of alternative sources such as secondary raw materials from exploration and mining residues. This study investigates an integrated, multi-scale approach to map and recover S-CRMs from an abandoned exploration stockpile in Zlatá Baňa, Slovak Republic. A key aspect of the methodology is comprehensive chemical and mineralogical characterization (XRF, PXRD, FTIR, LIBS, and SEM-EDS), which provided scientific validation for the diagnostic absorption features observed in laboratory reflectance spectra. These laboratory-acquired signatures were then used as endmembers to classify Sentinel-2 imagery via the Spectral Angle Mapper (SAM) algorithm. This integration enabled the identification of three distinct residue classes, with classA (jarosite-rich residues) emerging as the most reactive facies. Subsequent bioleaching experiments using Acidithiobacillus ferrooxidans demonstrated that microbial activity more than doubled Zn mobilization compared to abiotic controls. This cross-disciplinary strategy confirms that the synergy between advanced analytical characterization and remote sensing provides a robust, cost-effective pathway for the sustainable recovery of S-CRMs in regions affected by historical and mining activities. Full article

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles. Full article

The study explores how consumer buying behavior is influenced by artificial intelligence (AI) personalization, with a specific focus on responsible and sustainability-aligned digital marketing. Using an explanatory sequential mixed-methods design, the study analyzes a quantitative survey and qualitative interviews. Results show that purchase behavior is strongly affected by exposure to AI messages—especially when recommendations are relevant, timely, and emotionally appealing—and by trust in AI, while perceived lack of trust inhibits purchasing. Qualitative findings underscore affective responses alongside ethical concerns, perceived transparency, and perceived control over data. Overall, the study shows that effective personalization depends not only on algorithmic sophistication but also on users’ sense of relevance and autonomy and on ethical data governance. The conclusions highlight sustainability-consistent implications for marketers: increase data transparency, segment customers by privacy sensitivity, and adopt accountable, consent-based personalization to build durable trust and loyalty. Future research should examine longitudinal effects and cultural differences, acknowledging limits of small purposive qualitative samples for generalization and exploring how consumer trust, ethical perceptions, and responses to AI personalization evolve over time. Full article

The performance of convolutional neural networks (CNNs) depends not only on model architecture but also on the structure and quality of the training data. While most artificial network interpretability methods focus on explaining trained models, less attention has been given to understanding how dataset composition itself shapes learning outcomes. This work introduces a novel framework that uses programmatically generated synthetic datasets to isolate and control visual features, enabling systematic evaluation of their contribution to CNN performance. Guided by principles from set theory, Shapley values, and the Apriori algorithm, we formalize an equivalence between CNN kernel weights and pattern frequency counts, showing that feature overlap across datasets predicts model generalization. Methods include the construction of four synthetic digit datasets with controlled object and background features, training lightweight CNNs under K-fold validation, and statistical evaluation of cross-dataset performance. The results show that internal object patterns significantly improve accuracy and F1 scores compared to non-object background features, and that a dataset similarity prediction algorithm achieves near-perfect correlation ($\rho =0.97$) between the predicted and observed performance. The conclusions highlight that dataset feature composition can be treated as a measurable proxy for model behavior, offering a new path for dataset evaluation, pruning, and design optimization. This approach provides a principled framework for predicting CNN performance without requiring full-scale model training. Full article

For the design, implementation, performance optimization, and predictive maintenance of high-speed real-time control systems with sub-millisecond control periods, the capability to acquire large volumes of high-rate control data in real time is required without interfering with normal control operation that is repeatedly executed in each extremely short control cycle. In this study, we propose a control-data acquisition method for high-speed real-time control systems with sub-millisecond control periods, in which control data are transferred to an external host device via Ethernet in real time. To enable the transmission of high-rate control data without disturbing the real-time control operation, a multicore microcontroller unit (MCU) is adopted, where the control task and the data transmission task are executed on separately assigned central processing unit (CPU) cores. Furthermore, by applying a double-buffering algorithm, continuous Ethernet communication without intermediate waiting time is achieved, resulting in a substantial improvement in transmission throughput. Using a control card based on TI’s multicore MCU TMS320F28388D, which consists of dual digital signal processor cores and one connectivity manager (CM) core, the proposed control-data acquisition method is implemented and an actual experimental environment is constructed. Experimental results show that the double-buffering transmission achieves a maximum throughput of 94.2 Mbps on a 100 Mbps Fast Ethernet link, providing a 38.5% improvement over the single-buffering case and verifying the high performance and efficiency of the proposed data acquisition method. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

Citrus, as a globally important economic crop, requires accurate assessment of premium fruit production potential for precise orchard management and enhanced economic benefits. This study develops a method for assessing the production potential of premium citrus using UAV-based multispectral imagery and ground data. Taking citrus orchards in Wuming District, Guangxi, China, as the experimental area, this study investigates techniques for assessing the production potential of premium fruit at the canopy scale of citrus trees in southern hilly regions, aiming to rapidly predict the quality production potential of citrus before fruit ripening. The methodology involved the following: (1) Segmenting the study area using a Digital Surface Model (DSM) and extracting individual tree canopies by integrating NDVI with a marker-controlled watershed algorithm. Canopy fruit boundaries were identified using the NPCI index. (2) Selecting key assessment indicators—NDVI, TCAVI, REOSAVI, canopy area, and canopy fruit area—through correlation analysis with nutritional quality metrics. (3) Establishing threshold levels for these indicators and constructing a production potential assessment model. Experimental results demonstrated an individual tree identification accuracy (precision) of 98.75%, a recall of 98.47%, and an F-score of 98.61%. Canopy area extraction achieved a coefficient of determination (R²) of 0.869 and a root mean square error (RMSE) of 0.489 m². The overall accuracy for production potential assessment reached 85.11%. This study provides a new approach for using UAV multispectral technology to non-destructively assess the production potential of premium citrus in the hilly regions of southern China, offering technical support for precise orchard management. Full article

Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

Background: Approximately half of U.S. adults have ≥1 cardiovascular disease (CVD) risk factors. Exercise is universally recommended as a first-line lifestyle therapy to prevent and treat CVD. Objective: We will conduct a feasibility and pilot efficacy randomized controlled trial to test the usability and user satisfaction of an evidence-based digital health tool we developed for physicians—the Prioritizes Personalizes Prescribes EXercise algorithm (P3-EX)—to treat patients with CVD risk factors (ClinicalTrials.gov: NCT07238556). Methods: We will recruit 24 physicians who do not prescribe written exercise prescriptions (ExRx) from two local CT hospitals. Physicians will recruit two patients each (N = 48); both patients must have CVD risk factors. Each physician will deliver a P3-EX ExRx to one patient (n = 24) and the Physical Activity Vital Sign ExRx to the other patient (n = 24) in a random sequence crossover design. Physicians and patients will rate the feasibility and acceptability of each ExRx method using validated questionnaires. Patients will perform their ExRx for 12 weeks and complete an exercise diary to monitor exercise adherence with weekly virtual oversight by Research Assistants. Before and after the exercise intervention, we will measure patient CVD risk factors and physical activity levels via accelerometry. Results: This trial has received Institutional Review Board approval (E-HHC-2025-0198) and will begin in January 2026, with findings published in 2027. Conclusions: This protocol provides the scientific rationale and methodology to test P3-EX within a real-world clinical setting, to inform the feasibility of using P3-EX as a digital health support tool by physicians, and preliminary efficacy of P3-EX to improve patient cardiovascular health and physical activity levels. Full article

Chronic low-grade inflammation, or “inflammaging,” represents a central mechanism linking dietary patterns, gut microbiome composition, and biological aging. Evidence from Blue Zone populations and Mediterranean diet studies demonstrates that specific nutritional interventions are associated with up to 23% lower all-cause mortality, with analyses suggesting that part of this association may be mediated by measurable improvements in inflammatory biomarkers. This paper synthesizes published evidence from Mediterranean diet trials, centenarian microbiome studies, and digital health platforms to propose a comprehensive digital health framework that integrates quarterly inflammation and microbiome monitoring with continuous lifestyle tracking to deliver personalized longevity interventions. This paper introduces the Longevity-Inflammation Index (L-II), a composite score combining high-sensitivity C-reactive protein, interleukin-6, tumor necrosis factor-alpha, and microbiome-derived markers, with scoring algorithms derived from centenarian population studies. The proposed platform leverages artificial intelligence to generate evidence-based recommendations adapted from centenarian and Mediterranean dietary patterns. Published evidence from multiple randomized controlled trials demonstrates that Mediterranean dietary interventions reduce hs-CRP by 18–32%, increase microbiome diversity by 6–28%, and improve metabolic markers including HOMA-IR and TG/HDL ratios. Digital health platforms demonstrate sustained engagement rates of 58–84% at 12 months, with dietary logging frequencies of 4–6 days per week. Cost-effectiveness analyses of dietary interventions show incremental cost-effectiveness ratios of USD 2100–4800 per quality-adjusted life year gained. This inflammation-centered digital health framework offers a scalable approach for translating longevity research into practical interventions for healthy aging, with validation studies needed to confirm the integrated platform’s efficacy and real-world implementation feasibility. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article