Search Results (980)

Background/Objectives: Instrument fracture remains a significant complication in endodontics. This study compared the resistance to cyclic fatigue failure between rotary and reciprocating nickel–titanium file systems, as well as differences related to file size and taper. Methods: Nineteen rotary and reciprocating file types (n = 10 per group) were evaluated in three independent test series, harmonized according to file size and system. Cyclic fatigue testing was conducted using a static model with a stainless-steel artificial canal, with an internal diameter of 0.9 mm, a 75° curvature angle, and a fixed radius for each series. Files were operated using preset programs on the X-Smart Plus, Rooter X3000, and Sendoline Endo torque-controlled motors. Time to fracture was recorded digitally, and the total number of full rotations to failure was calculated. The fractured fragments were examined with scanning electron microscopy and fractographic analysis. The data were analyzed using linear models in Stata version 19, with significance set at p ≤ 0.05. Results: Reciprocating file systems demonstrated greater time-to-fracture fatigue resistance than rotary systems. However, these differences were diminished or, in some cases, eliminated when normalized to the number of complete rotations. Fractographic analysis indicated that fractures predominantly resulted from tensile stress rather than shear forces. Conclusions: Reciprocating kinematics generally enhanced fatigue resistance compared with continuous rotation. The results suggest that fatigue resistance in machine-driven nickel–titanium instruments cannot be predicted by motion type or file design alone but reflects a complex interaction between alloy composition, heat treatment, and cross-sectional geometry. Full article

Background: Major Depressive Disorder (MDD) is increasingly recognized as involving psychomotor slowing and impaired cortical timing. Objective vibrotactile assessments can quantify sensory and cognitive integration, potentially identifying mechanistic biomarkers of depression. Objective: To determine whether tactile performance metrics from the Brain Gauge system differentiate individuals with depression from healthy controls and to identify the most predictive domains using cross-validated modeling. Methods: Eighty-two adults (43 with depression, 39 controls) completed the Brain Gauge battery assessing reaction time (RT), RT variability, amplitude and duration discrimination, temporal order judgment, accuracy, and cortical plasticity. Results: After FDR correction, participants with depression showed significantly slower and more variable tactile responses (FDR-adjusted p < 0.05). Speed and RT variability remained independent predictors (OR = 4.14; OR = 0.015), yielding an AUC = 0.86 (sensitivity = 0.87; specificity = 0.77). These findings suggest reduced cortical stability and efficiency in depression. Conclusions: Tactile timing measures—particularly Speed and RT variability—objectively capture psychomotor and temporal instability in MDD. Cross-validated logistic modeling supports their potential as non-invasive digital biomarkers for depression phenotyping and monitoring. These findings suggest tactile timing instability as a clinically relevant neurofunctional dimension of major depressive disorder, with potential applications in psychiatric phenotyping, objective symptom monitoring, and future precision-guided treatment strategies. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

Background/Objectives: Digital health ecosystems increasingly integrate content, behavioral interventions, and commercial offerings across multiple platforms. While design consistency is established as critical for trust in commercial contexts, its associations with health behavior change and objective health outcomes remain underexplored. This study examined how cross-platform design consistency and aesthetic experience are associated with behavioral adoption through psychological pathways and investigated relationships between design-driven adoption and objective health outcomes. Methods: A convergent mixed-method design comprised five integrated studies: systematic content analysis of short-form videos (N = 200), expert evaluation and user testing (N = 33), a cross-sectional survey (N = 186), semi-structured interviews (N = 15), and a 3-month longitudinal health outcome analysis (N = 143). Structural equation modeling tested pathways from design features through psychological mediators and COM-B components (capability, opportunity, motivation) to behavioral adoption and health outcomes. Results: Design consistency was significantly associated with trust (β = 0.52), perceived value (β = 0.68), and reduced perceived risk (β = −0.41; all p < 0.001). Aesthetic experience predicted emotional resonance (β = 0.71, p < 0.001) and moderated design–trust associations. COM-B components mediated 75% of the intention-to-adoption pathway (total indirect effect = 0.51, p < 0.001). High-adoption users showed clinically meaningful improvements in weight (−2.8 kg, d = 0.89), HbA1c (−0.7%, d = 0.65), fasting glucose (−0.9 mmol/L, d = 0.72), and LDL-C (−0.4 mmol/L, d = 0.51) over three months. Conclusions: Within a single, influencer-centered Chinese digital health ecosystem, design consistency and aesthetic experience were significantly associated with trust, psychological readiness, and behavioral adoption. These findings are observational; randomized controlled trials and multi-site replication are required to establish causal mechanisms and assess generalizability. Full article

Within the Industry 5.0 paradigm, the management of critical assets requires advanced digital architectures capable of ensuring resilience and operational sustainability. The present systematic review analyzes the state of the art in predictive maintenance (PdM) technologies for turbines and turbomachinery, providing a technical examination of anomaly and fault detection frameworks, extended to remaining useful life (RUL) estimation and root cause analysis (RCA). The work addresses inherent sectoral challenges, ranging from the processing of high-dimensional multivariate time series (MTS) from Supervisory Control and Data Acquisition (SCADA) systems to labeled data scarcity and signal non-stationarity in real-world environments. Both purely data-driven frameworks and hybrid physics-informed models, such as Physics-Informed Neural Networks (PINNs), are critically evaluated against performance indicators. A significant contribution of this study lies in the classification of methodologies based on their readiness for real-time inference, emphasizing the role of Explainable AI (XAI) in providing transparent insights to domain experts, who remain central to decision-making processes. The primary objective of this review is to offer an analytical overview of progress to date against current technological gaps, tracing a clear trajectory for future developments. In this regard, the adoption of Generative AI and Large Language Models (LLMs) is identified as a fundamental step toward evolving into interactive, human-centric decision support systems. Full article

The natural period is a key parameter in seismic design, but current empirical code formulas act as lower bounds for design safety, making them overly conservative for the precise performance assessment of existing buildings. To derive an optimal best estimate of the actual dynamic behavior, this study proposes a novel methodology based on 283 measured data points worldwide. Overcoming the limitations of conventional single-variable models, this study introduces story height as a physical proxy variable alongside data clustering techniques. Story height extends beyond simple geometry, indirectly representing mass distribution and structural stiffness design levels, thereby effectively controlling the dispersion of heterogeneous global data on physical grounds. Consequently, the proposed piecewise bivariate non-linear regression model achieved a significantly lower RMSE across all structural systems compared to existing design codes and single-variable models, substantially improving prediction accuracy. Unlike traditional fixed-constant approaches, this continuously upgradable framework can serve as a robust foundational model for large-scale seismic screening in smart cities and digital twin-based maintenance systems. Full article

LoRaWAN’s role in global maritime logistics has allowed for efficient monitoring of ships and cargo, but it also comes with critical cybersecurity vulnerabilities. Experimental validation of three attack vectors—replay attacks, narrowband jamming and metadata inference—is conducted using a reproducible digital-twin LoRaWAN dataset reflecting Rotterdam port-like operational patterns (N = 20,000 baseline transmissions). Using controlled simulations and Kolmogorov–Smirnov statistical analysis, we show that: (1) replay attacks are feasible under Activation by Personalization (ABP) configurations lacking enforced frame-counter validation and exhibit no univariate separation from legitimate traffic under Kolmogorov–Smirnov analysis (p > 0.46 for all evaluated radio features); (2) narrowband jamming leads to significant SNR degradation (p = 2.36 × 10⁻⁵) on targeted channels without inducing broad distributional anomalies across other radio features; and (3) metadata-only analysis supports elevated metadata-based re-identification susceptibility (median $R_d=0.834$), indicating high predictability under passive observation which can reveal operationally relevant signals even when AES-128 is employed. Our proposed layered mitigation framework consists of mandatory Over-the-Air Activation (OTAA), cryptographic key rotation, channel diversity incorporating Adaptive Data Rate (ADR), gateway hardening, and protocol-level enforcement considerations, customized for maritime LPWAN scenarios. We provide experiment-backed evidence and actionable recommendations to connect academic LPWAN security research to that of industrial maritime practice. Full article

Background and Objectives: Immunohistochemistry (IHC) is a keystone in gastric cancer (GC) management, allowing treatment customization, including for advanced or metastatic diseases. This review aims to evaluate the critical role of IHC markers, analyzing their efficiency in molecular subclassification and prediction of response to gastric cancer-targeted therapies, while also describing state-of-the-art IHC techniques and perspectives. Results: The major challenges for the GC management were structured in two main sections, as follows: (i) the current paradigm of gastric neoplasia diagnosis, which includes subsections related to the methodological and morphological foundations, the epidemiological dynamics, and risk factors, as well as differential diagnosis of poorly differentiated tumors; and (ii) the progress in 3,3′-diaminobenzidine (DAB) application and advanced reagents in gastric cancer immunohistochemistry. Discussion: Considering the role of IHC and DAB, the following topics were successively addressed in seven sections: GC key biomarkers, such as human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1), and DNA replication mismatch repair (MMR) system, allow direct correlation between tissue morphology and protein expression; intestinal and gastrointestinal differentiation markers; emerging and aggressive histological subtypes; epithelial–mesenchymal transition, E-cadherin, and the process of tumor budding; implementation of innovative procedures in gastric cancer immunohistochemistry; and automation, quality control, and sustainability in the pathology laboratory. Perspectives: The main directions were focused on the integration of artificial intelligence (AI) algorithms for digital quantification of the IHC signal and also on the expansion of panels to new targets, such as Claudin 18.2 (CLDN 18.2), which redefines treatment approaches in advanced stages. Conclusions: Although faced with technical and biological limitations, immunohistochemistry remains indispensable in modern gastric oncology. The evolution towards digital pathology and the refinement of scoring criteria will transform IHC from a complementary test into a visual tool that is essential for personalizing oncological treatment. Full article

Post-combustion amine scrubbing using monoethanolamine (MEA) remains a leading carbon capture technology, yet its deployment is constrained by high regeneration energy requirements and the computational expense of rigorous process simulation. This study presents an integrated framework coupling high-fidelity rate-based process simulation with explainable machine learning to systematically characterize a ten-dimensional operating space for MEA-based CO₂ absorption. Latin hypercube sampling generated 10,000 steady-state cases, and five regression architectures were benchmarked under identical protocols. A neural network achieved the highest accuracy (R² = 0.9729, RMSE = 1.43%), while XGBoost was selected as the operational surrogate due to its robust computational efficiency (1.5 ms inference latency) and native compatibility with exact Shapley value decomposition. SHAP analysis identified liquid-to-gas ratio as the dominant efficiency determinant, contributing 46.6% of total predictive importance, followed by inlet temperature and MEA concentration, with these three parameters collectively explaining 85% of efficiency variation and establishing a compact control hierarchy suitable for reduced-order control architectures. Bivariate interaction analysis located a high-efficiency operating region, while sensitivity analysis confirmed the strong influence of inlet temperature across the operating envelope. Pareto optimization via NSGA-II generated tiered operational guidelines spanning the 85% to 98% capture efficiency range, quantifying a 39% specific regeneration duty penalty (3.1 to 4.3 MJ/kg CO₂) for pursuing maximum versus baseline capture targets. The framework demonstrates how explainable machine learning converts opaque process simulations into actionable engineering knowledge, providing a transparent and computationally efficient basis for design optimization and digital twin deployment in post-combustion carbon capture systems. Full article

The development of modern transport energy systems is driven by increasing demands for energy efficiency, environmental sustainability, and operational reliability of vehicles. One of the most critical challenges in internal combustion engine operation is the cold-start condition, which results in increased fuel consumption, intensified component wear, and elevated emissions. Under these conditions, the development of intelligent thermal conditioning systems capable of accelerating engine warm-up and maintaining optimal thermal regimes becomes essential. This study proposes an intelligent engine and vehicle thermal conditioning system based on the integration of digital twin technology and phase-change thermal (PCM) energy storage. A digital twin architecture of the engine thermal conditioning system is developed to enable the integration of monitoring, simulation and predictive control of engine thermal processes. A mathematical model of the thermal conditioning system describing the dynamic temperature behavior of the engine, coolant, engine oil and PCM-based thermal energy storage units is formulated. A model predictive control strategy is implemented within the digital twin environment to support decision-making and optimization of engine thermal conditioning processes. Simulation and experimental results demonstrate that the proposed system can reduce engine warm-up time by 17.8–68.4%, decrease fuel consumption during the cold start phase by approximately 19.5–56.25%, and reduce harmful emissions. These findings confirm the potential of integrating digital twin technologies, predictive control and phase change thermal energy storage for improving the energy efficiency and environmental performance of modern transport power systems. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

Friction stir welding (FSW) is a critical solid-state joining process for lightweight and high-performance metallic structures, particularly in aerospace and automotive manufacturing, yet conventional implementations remain largely dependent on offline parameter optimization and open-loop control. The purpose of this review is to examine how Industry 4.0 technologies enable the transition of FSW from a parameter-driven process into an intelligent, adaptive, and increasingly autonomous manufacturing capability. A structured review methodology was employed, including systematic literature selection and synthesis of recent research on smart sensing, industrial internet of things (IIoT), data analytics, machine learning, digital twins, automation, robotics, and human–machine interaction in FSW. The review reveals that Industry 4.0 integration enables real-time process monitoring, predictive quality assurance, closed-loop control, and virtual process optimization, resulting in improved weld quality, reliability, productivity, and scalability. Significant benefits are observed for safety-critical aerospace components and high-throughput automotive production, where adaptability and consistency are essential. However, persistent challenges remain in data standardization, model generalization, real-time digital twin integration, interoperability, cybersecurity, and workforce readiness. This review concludes that addressing these challenges through interdisciplinary research, standardization efforts, and human-centered system design is essential for enabling adaptive and data-driven FSW systems. The findings position intelligent FSW as a foundational technology for smart, resilient, and sustainable metal manufacturing in the Industry 4.0 era. Full article

Digital twin (DT) technologies are increasingly applied in manufacturing to support monitoring, optimization, and predictive maintenance; however, most implementations remain operationally focused and disconnected from system-level decision-making and lifecycle engineering. This limitation is particularly critical in manufacturing environments that exhibit System-of-Systems (SoS) characteristics, where performance emerges from the interactions among autonomous, interdependent subsystems. This study proposes an integrated systems engineering framework in which the digital twin functions as a system-level integrator rather than a standalone simulation tool. The framework embeds Quality Function Deployment (QFD), Analytic Hierarchy Process (AHP), Reliability and Safety analysis (RAMST), and Statistical Process Control (SPC) within a unified digital twin architecture, enabling explicit traceability from stakeholder requirements to design decisions, operational control, and lifecycle performance. The framework is demonstrated through a robotic fastening system operating under high variability, multi-vendor integration, and reliability constraints. A high-fidelity digital twin was developed in MATLAB Simscape and synchronized with operational data via virtual sensors and SPC-based monitoring. Results from a 35-month simulation study (n = 1050 operations) show a 30% reduction in system downtime and a 15% improvement in fastening quality (torque and angle compliance), supported by 95% confidence intervals, alongside enhanced fault detection and preventive maintenance capabilities. The findings demonstrate that integrating decision-making, monitoring, and learning within a single DT environment supports resilient, adaptive manufacturing systems aligned with Industry 4.0–5.0 objectives. Full article

Deep shale gas reservoirs in the southern Sichuan Basin (Weiyuan area) exhibit strong heterogeneity and complex pore-fracture networks. Traditional reservoir evaluation methods struggle to accurately capture their microscale pore characteristics and fracability, thereby restricting efficient development and precise sweet spot prediction. Therefore, integrating digital core technology with geological analysis is essential to systematically quantify key reservoir parameters, including microscale pore structure, mineral composition, and brittleness characteristics. To clarify the controlling factors of high-quality deep shale gas reservoirs in the Weiyuan area and assess their exploration and development potential, we performed digital core analysis at micron to nanometer scales. Three-dimensional digital core models of representative deep shale gas wells were constructed. Integrating mineral composition, geochemical characteristics, and pore space features, we discuss the geological conditions for deep shale gas accumulation and the fracability of horizontal wells, and we delineate favorable shale reservoir zones. The results show that digital core technology enables quantitative and visual characterization of each sublayer of the Longmaxi Formation shale reservoir, including mineral types, laminae types, pore-throat structures, and organic matter distribution. From the Long 11-1 sublayer to the Long 11-4 sublayer, the pore-throat radius, total pore volume, total throat volume, connected pore-throat percentage, and coordination number all gradually decrease. In the eastern Weiyuan area, the siliceous components in deep shale gas reservoirs at the base of the Longmaxi Formation are primarily of both biogenic and terrigenous origin. Due to local variations in the sedimentary environment, terrigenous input contributes significantly to the total siliceous content in this region. Although the Long 11-1 sublayer of the Longmaxi Formation is lithologically classified as mud shale, its particle size and mineral composition more closely resemble those of clayey siltstone or argillaceous sandstone, suggesting considerable potential for reservoir space development. Typical wells in the eastern Weiyuan area exhibit distinct lithological characteristics, including coarser grain sizes, stronger hydrodynamic conditions during deposition, and abundant terrigenous clastic supply. The rigid framework formed by silt- to sand-sized particles effectively mitigates compaction, thereby facilitating the preservation of intergranular pores and microfractures. High organic matter abundance, appropriate thermal maturity, and a considerable thickness of high-quality shale ensured sufficient hydrocarbon supply. The main types of natural fractures are intergranular and grain-edge fractures formed by differences in sedimentary grain size, and bedding-parallel fractures generated by hydrocarbon generation overpressure. Based on reservoir mineral composition, pore characteristics, areal porosity, and pore size distribution identified via digital core analysis, the bottom 0–3 m of the Long 11-1 sublayer is determined to be the optimal target interval. By delineating the microscopic characteristics of the shale reservoir and predicting rock mechanical parameters, a fracability evaluation index was established from digital core simulations. This guides the selection of target layers in deep shale gas reservoirs and optimizes hydraulic fracturing design. Full article