Search Results (366)

This paper presents a digital twin-based lifecycle framework aimed at improving the safety and security of instrumentation and control (I&C) systems for nuclear power plants and small modular reactors. The approach formalizes DT components, functions, and stakeholder interactions across the entire lifecycle, enabling continuous V&V, accelerated commissioning, proactive fault detection, cyber-resilience, and faster and safer modification of I&C algorithms. The methodology is validated through case studies involving DT-supported V-model testing and Markov-based modeling of the intelligent diagnostic system of an NPP pump. The results show that the proposed DT-enabled lifecycle methodology increases test coverage, shortens verification time, and enhances proactive safety and security capabilities of I&C systems. The study outlines future research directions toward adaptive, explainable, and regulation-ready DTs for next-generation nuclear systems. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Background/Objectives: Home care plays a crucial role in contemporary healthcare systems, particularly in the long-term care of people with chronic and progressive illnesses. Family caregivers often experience substantial physical, emotional, and organizational burden. Telemedicine and digital health applications have the potential to support home care by improving health monitoring, communication, and care coordination. However, their use among family caregivers remains inconsistent, and little is known about how organizational support structures such as telemedicine centers influence acceptance and everyday use. This study aims to examine the benefits of telemedicine in home care and to evaluate the role of telemedicine centers as supportive infrastructures for family caregivers. Methods: A mixed-methods design was applied. Quantitative data were collected through an online survey of 58 family caregivers to assess the use of telemedicine and digital health applications, perceived benefits, barriers, and support needs. This was complemented by an in-depth qualitative case study exploring everyday caregiving experiences with telemedicine technologies and telemedicine center support. A systematic literature review informed the theoretical framework and the development of the empirical instruments. Results: Most respondents reported not using telemedicine or digital health applications in home care. Among users, telemedicine was associated with perceived improvements in quality of care, particularly through enhanced health monitoring, improved communication with healthcare professionals, and increased feelings of safety and control. Key barriers to adoption included technical complexity, data protection concerns, and limited digital literacy. Both quantitative findings and the qualitative case study highlighted the importance of structured support. Telemedicine centers were perceived as highly beneficial, providing technical assistance, training, coordination, and ongoing guidance that facilitated technology acceptance and sustained use. Conclusions: Telemedicine and digital health applications can meaningfully support home care and reduce caregiver burden when they are embedded in supportive socio-technical structures. Telemedicine centers can function as central points of contact that enhance usability, trust, and continuity of care. The findings suggest that successful implementation of telemedicine in home care requires not only technological solutions but also accessible organizational support and targeted training for family caregivers. Full article

This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabiliser bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS 2023 and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding an optimised geometry in which spindle-fillet stresses remain around 180–185 MPa under a severe loading case corresponding to the maximum admissible wheel load at the bearings, while the associated increase in mass is modest and compatible with practical design constraints. Full article

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction ${\mathrm{V}}_{\mathrm{H}\mathrm{S}}/{\mathrm{V}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

Mini-LC systems, including Cap-LC, Nano-LC and Chip-LC, offer a sustainable alternative to conventional LC methods thanks to their reduced solvent consumption, enhanced separation efficiency and environmentally friendly operation. Integrating micro-scale sample preparation techniques, such as µ-SPE, IT-SPME, LPME and QuEChERS, with Mini-LC significantly improving analytical sensitivity and selectivity. Mini-LC coupled with mass spectrometry has demonstrated excellent performance in the detection of trace levels of pesticides, pharmaceuticals, veterinary drug residues, perfluoroalkyl substances (PFASs), and mycotoxins. Despite current challenges relating to matrix effects, instrument stability and method standardization, Mini-LC represents a promising analytical platform for the cost-effective, high-sensitivity, green monitoring of contaminants in food safety and environmental analysis. This review summarizes recent advances in the application of Mini-LC techniques for analyzing emerging organic contaminants (EOCs) in food and environmental samples. This paper also provides a critical review of this topic, covering works published in the last four years (early 2022–mid 2025). Additionally, it discusses the use of these techniques in combination with mass spectrometry (e.g., low-resolution MS or high-resolution MS) for the detection of EOCs in food and environmental samples. Full article

Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant. Full article

Background/Objectives: Cardiac resynchronization therapy with defibrillator (CRT-D) is a well-established therapy for patients with heart failure (HF) and intraventricular conduction delays, but a non-negligible risk of infection and of lead functionality loss overtime is related to intravascular hardware. The novel DX system enables atrial sensing through a floating dipole integrated into the ICD lead, reducing the intravascular burden. In this prospective non-randomized study, we aimed to evaluate the safety and efficacy of a two-lead DX-based CRT system compared to a conventional three-lead (3L) CRT-D system. Methods: A total of 210 patients meeting CRT indications and no signs of sick sinus syndrome (SSS) (baseline HR ≥ 45 bpm, or at least 85 bpm at 6 min walking test) were enrolled. Patients were assigned to either the CRT-DX or conventional 3L CRT-D group. The primary endpoint was a composite clinical response, defined as the freedom from cardiovascular death, HF hospitalization, or new-onset atrial fibrillation (AF). Results: After a mean follow-up of 46.5 ± 1.9 months, both groups had comparable clinical and instrumental outcomes. CRT-DX patients exhibited higher atrial sensing amplitudes and no significant differences in loss of lead function. Conclusions: In conclusion, the CRT-DX system provides equivalent clinical and echocardiographic benefits compared to conventional CRT-D in patients without an indication for atrial pacing. This supports the use of the DX system as a safe and effective alternative in the majority of CRT recipients. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Inadequate force feedback and collision warnings in teleoperated surgical instruments elevate risks during intravascular cannulation. This study introduces an innovative master haptic interface that utilizes magnetorheological (MR) fluid to enhance surgeons’ operational perception during robot-assisted intervention surgery. The system delivers real-time haptic feedback to enhance surgical operational safety and automatically amplifies the feedback force when the contact force on the slave side surpasses the predefined threshold, enabling timely collision alerts. A series of preliminary experiments has been carried out to validate the efficacy of this particular type of haptic interface. The experimental results clearly indicate that the master haptic interface based on MR fluid and carefully designed can effectively enhance the operator’s haptic perception and provide collision alarms in a timely manner with haptic clues, improving the safety and operability of robot intravascular intervention. This research provides some insights into the functional improvements of safe and reliable robot-assisted catheter systems. Full article

Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly extract spatial and temporal degradation features from charge-cycle voltage and current measurements. Residual and inter-path connections enhance gradient flow and feature fusion, while a three-channel preprocessing strategy aligns cycle lengths and isolates padded regions, improving learning stability. Operating end-to-end, the model eliminates the need for handcrafted features and does not rely on discharge data or temperature measurements, enabling practical deployment in minimally instrumented environments. The model is evaluated on the NASA battery aging dataset under two scenarios: Same-Battery Evaluation and Leave-One-Battery-Out Cross-Battery Generalization. It achieves average RMSE values of 1.26% and 2.14%, converging within 816 and 395 epochs, respectively. An ablation study demonstrates that the dual-path design, ConvLSTM units, residual shortcuts, inter-path exchange, and preprocessing pipeline each contribute to accuracy, stability, and reduced training cost. With only 4913 parameters, the architecture remains robust to variations in initial capacity, cutoff voltage, and degradation behavior. Edge deployment on an NVIDIA Jetson AGX Orin confirms real-time feasibility, achieving 2.24 ms latency, 8.24 MB memory usage, and 12.9 W active power, supporting use in resource-constrained battery management systems. Full article

This paper presents an experimental study of the Automatic Emergency Braking (AEB) system, focusing on three essential testing phases: verifying the match between calculated and actual brake actuator operation time, validating the forecasted vs. real-time stabilized deceleration onset duration, and comparing the theoretically computed braking distance derived from mathematical models with its actual measurement. Standard instrumentation coupled with an original test procedure was utilized during the experiments. A full-scale experimental campaign was conducted on a specialized proving ground, thus substantiating the validity and robustness of the computational models used for assessing the AEB system parameters. The empirical outcomes confirmed that current-generation AEB systems offer dependable predictions regarding braking dynamics and exhibit prompt responsiveness to imminent collisions. However, it should be noted that variations in road conditions, driver behavior, and sensor precision may affect their performance. Consequently, additional efforts aimed at optimizing existing AEB solutions are required to minimize potential errors and enhance overall reliability. Finally, the significance of complying with design specifications and continuously upgrading AEB systems to meet evolving road safety standards is emphasized. Full article

The rapid advancement of Advanced Driver Assistance Systems (ADAS) is reshaping the future of mobility by offering potential improvements in safety, efficiency, and driving experience, yet consumer acceptance remains uneven across regions. This study addresses the gap in knowledge and trust by examining how Hungarian drivers, as part of the Central and Eastern European context, perceive and adopt ADAS technologies. To achieve this, we conducted two expert in-depth interviews to refine the research instrument, followed by an online survey of 179 drivers. Using k-means cluster analysis, we identified three distinct consumer segments: Conservative Controllers, who demonstrate low levels of trust and willingness to adopt ADAS; Cautious Adopters, who weigh costs and benefits carefully; and Pragmatic Innovators, who are open to experimentation and display the highest acceptance and willingness to pay. The results reveal that awareness and familiarity strongly influence acceptance, highlighting the role of consumer education and transparent communication in shaping adoption. The findings suggest that manufacturers, driving schools, and policymakers can accelerate the diffusion of ADAS by developing targeted strategies tailored to different consumer groups. Strengthening knowledge and trust in these systems will not only support their market success but also contribute to safer, more sustainable transportation. Full article

Background/Objective: Cesarean scar pregnancy (CSP) represents a challenging and potentially life-threatening form of ectopic pregnancy. This study aims to assess the feasibility, safety, and clinical efficacy of employing the hysteroscopic mechanical tissue removal system as a primary treatment modality for CSP. Methods: This retrospective cohort study included 53 patients diagnosed with CSP who underwent primary hysteroscopic resection at a tertiary care center. The surgical procedure was performed by prioritizing the “no-touch” vaginoscopic approach, which avoids instrumentation. Success rates, operation time, time to negative serum β-hCG, complications, and differences between the anatomical types of CSP (Type 1 vs. Type 2) were analyzed. Results: Primary hysteroscopic treatment was successful in 51 of 53 patients (96.2%). For the entire cohort, the median operative time was 7 min (range: 2–30), and the median interval to β-hCG negativization was 11 days (range: 6–45). The overall major complication rate was 3.8% (n = 2). One case was deemed unsuccessful due to conversion to laparotomy following uterine perforation during cervical dilation. Another patient, diagnosed with persistent trophoblastic disease requiring methotrexate (MTX) therapy, was also considered a treatment failure. Operative time was significantly longer in patients with Type II CSP compared with Type I (median 9 min vs. 5 min; p = 0.0004). Conclusions: Hysteroscopic mechanical tissue removal as a primary treatment for cesarean scar pregnancy represents an effective and safe “one-step” approach, characterized by a high success rate, rapid β-hCG resolution, and a low incidence of complications. This fertility-preserving, minimally invasive technique may be considered a primary treatment option for hemodynamically stable patients with CSP, provided that appropriate patient selection is undertaken and sufficient surgical expertise is available. Full article

This study investigates lightning activity and evaluates a near-real-time lightning warning system for the inner Hanoi area, using data collected during 2020–2024 from the Strike Guard (SG) and EFM-100C instruments located in Chuong My, Hanoi, Vietnam. Lightning detection data were incorporated with rainfall and lightning location information from the Vietnam Meteorological and Hydrological Administration (VNMHA) for quality checking. The SG data over the research area revealed clear diurnal and seasonal variations, with lightning most frequent in the late afternoon and two major peaks in June and September corresponding to the summer monsoon. A combined warning method using EFM-100C electric field measurements and SG alert states achieved an average lead time of 15 min, a Probability of Detection (POD) of 82.22%, a Critical Success Index (CSI) of 76.55%, an F1 score of 86.72%, and a False Alarm Ratio (FAR) of 8.26%. These results demonstrate that integrating electric field and optical–electromagnetic measurements can provide effective localized lightning warnings for the urban areas. The approach is cost-efficient, operationally feasible, and particularly valuable for protecting critical infrastructure regions, supporting enhanced lightning safety and disaster mitigation in northern Vietnam. Full article