Search Results (20,252)

Defibrillators require high-performance power electronic converters capable of rapidly charging a high-voltage capacitor and delivering controlled therapeutic waveforms while ensuring patient safety. This paper presents a predictive control strategy for the flyback converter of a defibrillator, including both its charging and discharging stages, together with the design and verification workflow for its implementation. The proposed system integrates the FPGA implementation of the charging-stage control with a hardware-in-the-loop (HIL) emulation of the flyback converter and discharge stage within a unified model-based design framework. The charging stage consists of a flyback converter regulated by a Finite Control Set Model Predictive Control (FCS-MPC) strategy, while the discharge stage employs a full-bridge converter to generate truncated exponential biphasic (BTE) waveforms. To regulate the switching frequency without sacrificing the fast dynamic response of predictive control, the Period Control Approach (PCA) is incorporated into the FCS-MPC. The proposed solution is benchmarked against conventional FCS-MPC and a hysteresis controller, highlighting the advantages of PCA-based predictive control in terms of switching-frequency regulation while preserving accurate current tracking. The proposed control system and the corresponding defibrillator model are developed in MATLAB/Simulink and automatically translated into synthesizable VHDL using HDL Coder. This approach enables FPGA implementation of the control strategy and HIL emulation of the power converters without manual HDL programming. The proposed methodology covers the entire workflow, from simulation to real-time FPGA implementation and HIL emulation. Simulation results demonstrate accurate current tracking, proper BTE waveform generation, and improved switching-frequency regulation compared with both conventional FCS-MPC and hysteresis-based control. HIL experiments on a Xilinx Artix-7 FPGA confirm the real-time operation of the implemented predictive controller interacting with the emulated flyback converter. The experimental results are consistent with the simulation results. This work provides a solid foundation for the development and validation of digitally controlled defibrillators based on advanced predictive control techniques. The results demonstrate the feasibility of the proposed approach in both simulation and reconfigurable hardware. Full article

Increasing soil salinisation challenges food production, since most crops are highly sensitive to salinity. The identification of wild halophytes adapted to saline environments with nutritional value represents a promising strategy for food production on salt-affected farmlands. Assessing the nutritional potential of such species requires evaluating their proximate composition, mineral profile, and antioxidant properties. In parallel, citizen-science approaches complement and enhance scientific research by actively engaging potential final consumers in the research process, thereby improving the societal relevance, dissemination, and potential impact of scientific findings. In this study, we characterised the nutritional profile of the facultative halophyte Cakile maritima and found it to be comparable, and in some respects superior, to that of other conventional leafy vegetables, particularly regarding its mineral composition and bioactive compounds. Moreover, similar though not identical nutritional characteristics were observed in two C. maritima leaf morphotypes analysed, since differences in dry matter, ashes, total proteins and carbohydrates were identified. Interestingly, vitamin C accumulation was organ- and morphotype-dependent. Finally, the biochemical characterisation was complemented with an online survey that showed a clear predisposition of consumers towards the incorporation of wild edible plants into their diet, together with a sensory evaluation in which 32 participants assessed the acceptability of up to 11 dishes prepared using C. maritima. Sensory evaluation revealed a prominent bitter flavour amongst dishes containing C. maritima, with weighted scores for negative perceptions slightly exceeding those for positive ones, suggesting that its sensory profile may limit its acceptance by the general public while offering potential for specific consumer segments. Overall, our findings highlight the nutritional potential of C. maritima and support its valorisation as a sustainable species for saline agriculture. Full article

Hospital pathology laboratories represent challenging healthcare environments for Indoor Air Quality (IAQ) management due to the coexistence of hazardous chemical emissions, transient pollutant peaks, and stringent ventilation requirements. This study proposes an integrated data-driven framework for IAQ assessment and predictive ventilation management based on a high-resolution monitoring campaign conducted over 17 calendar days in a pathology grossing room (V = 113.90 m³) and an adjacent chemical storage room (V = 56.03 m³). The monitoring system generated 4356 synchronized 1-min observations, of which 4232 complete multivariate records were retained after data-quality screening. The proposed methodology combines three complementary analytical layers: (i) predictive modelling of pollution episodes using supervised machine learning architectures; (ii) multivariate anomaly detection to identify atypical environmental states; and (iii) temporal dependency analysis based on Granger causality and Bayesian networks to investigate predictive relationships between occupancy-related indicators, ventilation behaviour, and pollutant evolution. This integrated framework enables the transition from descriptive IAQ assessment toward predictive environmental management in healthcare facilities. Baseline statistical diagnostics demonstrated the limited capability of conventional linear approaches, with an Ordinary Least Squares (OLS) model explaining only 7.4% of TVOC variability (R² = 0.074). Ventilation assessment identified an approximately 38% deficit relative to the selected ASHRAE 170 ventilation requirement in the monitored grossing room. Among the evaluated predictive models, Random Forest achieved the highest test-set performance (R² = 0.78; MAE = 10.5 ppb), enabling short-term forecasting of TVOC evolution. Isolation Forest identified 212 atypical environmental states, corresponding to 5.01% of the valid analytical observations, with substantially higher TVOC concentrations than under normal operating conditions. The proposed framework establishes a transferable methodology for predictive IAQ assessment and ventilation management in chemically intensive healthcare facilities, providing decision-support information for risk-informed HVAC operation within existing regulatory and ventilation requirements. Full article

Background/Objectives: Inpatient falls are an important patient-safety concern in rehabilitation hospitals; however, factors determining whether a fall results in fracture remain poorly understood. This study investigated clinical factors associated with radiographically confirmed fracture after inpatient falls, evaluated the fracture-discrimination performance of the Morse Fall Scale and Hendrich II Fall Risk Model, and characterized the resulting orthopaedic burden. Methods: This single-center retrospective cohort study included adults who experienced a documented fall while hospitalized in a tertiary physical medicine and rehabilitation hospital. Patients with and without radiographically confirmed fractures were compared regarding demographic characteristics, comorbidity burden, bone health, continence, mobility, medication exposure, laboratory findings, and fall circumstances. Factors associated with fracture were evaluated using univariable analyses and a parsimonious Firth penalized logistic regression model. Discrimination of the Morse and Hendrich II scores was assessed using receiver operating characteristic analysis. Results: Among 91 adult inpatient fall events, 51 (56.0%) resulted in fracture. In unadjusted analyses, patients with fractures had lower lumbar spine T-scores and higher frequencies of urinary and fecal incontinence, whereas age, sex, femoral T-score, mobility status, fall location, and fall mechanism did not differ significantly between groups. In the adjusted Firth model comprising 87 complete cases and 49 fracture events, no variable reached independent statistical significance; lower lumbar T-score (adjusted odds ratio [aOR], 0.74; 95% confidence interval [CI], 0.52–1.05; p = 0.073) and urinary incontinence (aOR, 2.30; 95% CI, 0.87–6.12; p = 0.082) showed the strongest associations. The Morse and Hendrich II scores demonstrated poor fracture discrimination, with AUCs of 0.372 (95% CI, 0.255–0.489) and 0.411 (95% CI, 0.295–0.526), respectively, without a significant difference between them (p = 0.500). Among patients with fractures, 41.2% sustained major fractures, 27.5% underwent surgery, and 11.8% developed complications. Conclusions: Among documented inpatient falls in this rehabilitation cohort, fractures were common and imposed a clinically important orthopaedic burden. Conventional fall-risk scores did not distinguish falls resulting in fracture, suggesting that prediction of fall occurrence and prediction of orthopaedic injury represent distinct clinical objectives. Fracture-prevention strategies in rehabilitation settings should therefore complement general fall-risk assessment with targeted evaluation of skeletal and functional vulnerability. Full article

The increasing penetration of renewable generation and the rapid growth of data-center demand require coordinated use of heterogeneous flexibility while respecting network operating limits. This study proposes a three-objective electricity–computing–carbon dispatch method that minimizes physical system cost, direct carbon dioxide emissions, and unutilized renewable energy. Data centers are represented through task arrivals, deadlines, server capacity, and facility efficiency, whereas pumped-storage plants retain independent power limits, reservoir states, terminal conditions, and connection locations. A diversity-archive disturbance–multi-objective particle swarm optimizer (DAD-MOPSO) couples archive diversity and stagnation feedback with resource-group temporal-block disturbance. In 30 paired runs, DAD-MOPSO produced feasible final solutions in all runs and reduced mean spacing by 51.8% relative to conventional MOPSO, with a Holm-adjusted p-value of 0.0071. Repeated evaluation of 12 workload-resource cases showed that pumped storage provided the dominant improvement in renewable-energy utilization, while workload rescheduling provided a smaller marginal contribution. Factorial analysis indicated partially overlapping rather than universally superadditive flexibility. Fixed-total-capacity tests further revealed pronounced siting dependence: the same 1200 MW/7200 MWh storage capacity was feasible in 30/30 runs at Bus16 but only 16/30 runs at Bus27 because of undervoltage. The proposed framework therefore provides a unified basis for coordinating heterogeneous flexibility and distinguishing marginal, interaction, siting, and network-limited effects. Full article

This study addresses the challenges of slow attenuation and significant hazards associated with explosive shock waves confined within conventional underground tunnel walls. A novel serrated passive shock-attenuating tunnel design is proposed, grounded in the principle of viscous dissipation within the boundary layer. The investigation encompasses both experimental analyses and numerical simulations. The study found that shock waves in smooth tunnels primarily propagate as one-dimensional plane waves, resulting in concentrated energy and gradual attenuation. Conversely, the serrated tunnel geometry generated a continuous reflection, scattered and vortex formation due to abrupt geometric discontinuities, led to distortion and fragmentation of the shock front and the emergence of a three-dimensional discrete pressure field. Through turbulent dissipation mechanisms, energy is rapidly transformed into small-scale vortices, effectively reducing wave velocity and markedly diminishing the forward peak pressure. Under conditions of high-equivalent explosions, the serrated structure demonstrates enhanced efficacy in energy dissipation and peak pressure attenuation, significantly curtailed the effective propagation distance of high-pressure shock waves. Optimization of the serration spacing identified 30 cm as the optimal interval, minimizing stress peaks both centrally and at the tunnel entrance, thereby maximizing wave attenuation. Comparative analysis between simulation and experimental was resulted that corroborates the wave-attenuation performance of the serrated design, offering a critical foundation for the development of blast-resistant underground structures. Full article

Background/Objectives: Military personnel are frequently exposed to high-risk acoustic trauma from blast waves. While macroscopic injuries like tympanic membrane perforation (TMP) are easily identified, subclinical microscopic damage, such as hidden hearing loss (HHL), often goes undetected by standard audiometry. This study investigates the auditory impacts of an accidental ammunition depot explosion, focusing on the diagnostic value of extended high-frequency (EHF) audiometry and objective electrophysiological measures in detecting early-stage blast-induced damage. Methods: This retrospective case series included eight military personnel involved in a 120 mm howitzer shell explosion at an ammunition depot. Subjects were categorized into direct blast exposure (n = 2) and indirect impact (n = 6). Audiological evaluations included otoscopy, standard pure-tone audiometry (0.25–8 kHz), EHF audiometry (9–14 kHz), auditory brainstem response (ABR), and distortion product otoacoustic emissions (DPOAEs). Results were compared to reference and control groups. Two patients with direct trauma underwent type I tympanoplasty to repair TMP. Results: Directly exposed personnel sustained near-total TMPs and significant mixed hearing loss. In contrast, the six indirectly impacted personnel exhibited “clinically normal” standard audiograms; however, their EHF thresholds at 9, 10, 12.5, and 14 kHz were significantly elevated compared to controls (p < 0.05). DPOAE amplitudes at 1, 1.5, and 10 kHz were significantly reduced in the blast group, indicating outer hair cell dysfunction. ABR showed a prolonged wave I latency with statistical significance (p = 0.014), but a decreased wave I amplitude was nonsignificant between groups; these findings are suggestive of possible cochlear synaptopathy, consistent with, but not diagnostic of, HHL. Conclusions: Blast exposure may produce both overt and subclinical auditory injury. Even in individuals with normal conventional audiograms, our preliminary findings suggest that EHF audiometry and otoacoustic emission testing show promise in identifying early cochlear dysfunction consistent with HHL. While further validation in larger cohorts is required to establish definitive diagnostic utility, these hypothesis-generating observations highlight the potential value of advanced audiologic assessments for the detection and monitoring of blast-related auditory injury in military personnel. Full article

Building thermal performance has become a major concern as global warming intensifies and building energy demand is expected to rise by 30% by 2030. Bio-based construction materials offer a promising solution by improving thermal comfort and reducing operational energy use. This study presents a computational framework for optimising bio-based masonry block design through the integration of numerical simulation and data-driven modelling. A parametric thermal model was developed in COMSOL Multiphysics to simulate steady-state heat transfer through hollow masonry blocks with varying cavity geometries and material properties. A surrogate model-based approach was then used to generate a dataset of simulated block configurations, from which key thermal performance indicators, particularly heat flux, were extracted. These outputs were used to train a machine learning surrogate model capable of accurately predicting thermal performance across a wide design space without repeated finite-element simulations. The proposed workflow achieved an average heat-flux mismatch of 0.23%, reduced computational cost, and improved prediction accuracy by approximately 7%. In addition, the surrogate-assisted optimisation identified block geometries with substantially improved thermal performance compared with conventional reference configurations. The proposed methodology provides a scalable digital design framework for the development and evaluation of bio-based masonry materials. Future work will incorporate real-time sensor networks and Internet of Things (IoT) systems for the continuous monitoring and validation of masonry wall thermal performance. Full article

Reliable discrimination between power swings and short-circuit faults is essential for secure transmission line protection, since misclassification may lead to unnecessary tripping or delayed fault clearing. Conventional power swing blocking techniques, particularly those based on impedance trajectory analysis, often require system-dependent settings and may exhibit reduced reliability under dynamic operating conditions with increasing renewable generation penetration. This paper proposes a setting-independent method for power swing and fault discrimination based on the Second Central Moment (SCM) of normalized instantaneous voltage and current signals. In this context, setting-independent means that the method does not require line-specific protection settings or case-by-case threshold tuning, although nominal voltage, nominal current, system frequency, and sampling frequency are required for signal normalization and sliding-window implementation. The SCM provides a statistical measure of signal dispersion that enables classification into three operating states: steady-state operation, power swing conditions, and fault events. Common SCM decision boundaries are applied without adjustment across the evaluated transmission lines, operating conditions, fault characteristics, power-swing frequencies, and levels of inverter-based resource penetration. The proposed method is validated through time-domain simulations using the Kundur two-area benchmark system and the IEEE 14-bus network under a wide range of disturbance scenarios, including oscillatory conditions, symmetrical and asymmetrical faults, renewable integration, and swing–fault sequences. For benchmarking purposes, the SCM-based algorithm is compared with a commercial Swing Center Voltage (SCV)-based blocking scheme widely implemented in digital relays. The results show that the proposed method achieves reliable swing–fault discrimination with low computational complexity while providing earlier blocking activation under slow oscillatory conditions and inherent fault discrimination capability. These characteristics support its practical application in real-time transmission line protection. Full article

Accurate water level observations are essential for flood forecasting, hydrological analysis, and water resource management; however, sensor malfunctions and telemetry errors frequently introduce anomalous observations that compromise data quality. This study proposes a reconstruction-oriented Bidirectional Long Short-Term Memory (BiLSTM) Autoencoder for river water level anomaly correction and compares its performance with conventional first-, second-, and third-order polynomial and exponential regression models. The proposed framework incorporates a simplified encoder–decoder architecture, a dynamic block masking strategy to emulate contiguous sensor failures in highly autocorrelated water level series, and a threshold-based peak-oriented training scheme to improve reconstruction during high-flow events. Model hyperparameters were optimized using Gaussian process-based Bayesian optimization. The methodology was evaluated using hourly observed water level data from the Han River, Republic of Korea. Results showed that the proposed BiLSTM Autoencoder achieved reconstruction accuracy comparable to conventional regression models during calibration while exhibiting superior generalization to unseen validation datasets and better preserving the temporal continuity and dynamic characteristics of downstream hydrographs. Furthermore, a model calibrated using a relatively short but hydrologically representative period successfully reconstructed a substantially longer unseen record. Synthetic outlier injection experiments further demonstrated that reconstruction accuracy gradually deteriorated with increasing training data contamination, emphasizing the importance of high-quality training data for reliable sequence reconstruction. The proposed framework demonstrates potential as an effective sequence-reconstruction approach for offline river water level quality control. Full article

Background: Identification of ICU patients at increased risk of death may support clinical assessment and prioritization of care. However, many mortality prediction approaches incorporate extensive laboratory information, conventional severity scores, or computationally complex models that may limit straightforward interpretation at the bedside. This study investigated whether routinely recorded physiological measurements collected during the first 24 h of ICU care could support an interpretable machine learning approach to subsequent in-hospital mortality prediction. Methods: We performed a retrospective analysis of 51,598 adult first ICU admissions from MIMIC-IV using a strict 24 h landmark design. Patients with a missing ICU duration, a documented ICU stay shorter than 24 h, or a documented death occurring on or before the 24 h landmark were excluded. Physiological measurements recorded during the first 24 h were summarized to construct the predictor set. Five classification algorithms—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—were developed using a stratified 80:20 train–test split. Performance was evaluated using discrimination, precision–recall, calibration, threshold-dependent classification measures, and decision-curve analysis. Bootstrap resampling was used to estimate 95% confidence intervals and pairwise performance differences. Shapley Additive Explanations (SHAPs) were used to interpret the XGBoost model. Results: The revised cohort included 46,259 survivors and 5339 patients with an in-hospital mortality outcome. XGBoost achieved the numerically highest AUROC of 0.803 (95% CI: 0.790–0.815) and AUPRC of 0.341 (95% CI: 0.313–0.369), with a Brier score of 0.080 (95% CI: 0.076–0.084). Paired bootstrap comparisons showed that XGBoost had a significantly higher AUROC than Random Forest and LightGBM, but not CatBoost. Its AUPRC was significantly higher than that of Random Forest but did not differ significantly from LightGBM or CatBoost, while its Brier score was significantly lower than that of Random Forest but did not differ significantly from LightGBM or CatBoost. At the default probability threshold of 0.50, XGBoost achieved high specificity (0.990) but low recall (0.091), whereas lowering the threshold increased mortality detection at the cost of additional false-positive classifications. Conclusions: Routinely recorded physiological information accumulated during the first 24 h of ICU care supported an interpretable machine learning framework with useful discrimination and probability estimation for subsequent in-hospital mortality. XGBoost achieved the highest numerical performance, although differences among the gradient-boosting models were generally small and not significant for all evaluated metrics. Threshold-sensitivity analysis demonstrated that classification behavior depended strongly on the selected operating threshold. Independent multicenter validation and prospective clinical evaluation are required before routine deployment. Full article

Coal and gangue separation is crucial for clean and efficient coal utilization. However, practical sorting environments are highly unstructured, where irregular fragments exhibit random distribution, varying orientations, and occlusion. Conventional 2D vision methods cannot provide the 3D geometric information required for intelligent sorting, while model-based 6D pose estimation is unsuitable for coal gangue fragments due to their irregular morphology and lack of predefined models. This study proposes an RGB-D cascaded fusion framework for partial point cloud-based 3D geometric estimation. The framework follows a detection–segmentation–reconstruction pipeline, where object detection provides semantic priors, zero-shot segmentation generates pixel-level masks, and the masks guide depth extraction and local point cloud reconstruction. Based on the reconstructed point clouds, task-oriented geometric parameters, including centroid position, oriented bounding box dimensions, visible volume, and principal orientation, are estimated for intelligent separation. Experiments on a dedicated RGB-D coal and gangue dataset demonstrate that the proposed method achieves a centroid localization error of 14.22 mm, dimension error of 9.34%, and volume error of 11.3%. Compared with detection-box-based depth reconstruction, the proposed semantic-guided strategy significantly improves geometric estimation accuracy. The proposed framework provides reliable 3D perception for intelligent coal gangue separation and robotic sorting. Full article

The growing demand for sustainable protein sources has increased interest in edible insects and their potential role within circular bioeconomy strategies. This study evaluated the suitability of brewer’s spent grain (BSG), cocoa bean shell (CBS), and banana peel (BP) as alternative substrates for Tenebrio molitor rearing and assessed the valorization of the resulting biomass through pilot-scale supercritical CO₂ extraction (CO₂-SFE). Larvae reared on BSG-based and BP-based diets achieved growth performances comparable to those obtained with a conventional wheat bran diet, whereas CBS-based diet resulted in lower weight gain and feed intake. No significant differences in feed conversion ratio were observed among diets. Substrate composition also affected biomass nutritional quality, with larvae reared on the BSG-based diet exhibiting the highest protein content (60.4%), whereas larvae reared on CBS-based diet exhibited the highest lipid content (29.3%). CO₂-SFE generated protein-enriched meals and lipid-rich extracts, yielding 12.9–15.4% oil depending on the substrate. Following extraction, protein content increased to 68.5%, 50.3%, and 60.2% in BSG-, CBS-, and BP-derived meals, respectively. The extracted oils were characterized by high levels of unsaturated fatty acids, with oleic acid (42.4–47.6%) as the predominant component. Overall, the results demonstrate that agro-industrial by-products can be effectively converted into valuable insect-derived ingredients and highlight the potential of integrating substrate selection with green extraction technologies within insect biorefinery systems. Full article

Pulsed Field Ablation (PFA) is an emerging technique for the treatment of cardiac arrhythmias based on the irreversible electroporation of cardiomyocytes. It is rapidly replacing conventional thermal ablation owing to its tissue selectivity. However, its widespread clinical adoption has also highlighted several procedure-related complications. Although PFA is generally regarded as tissue-selective, increasing evidence indicates that exposing circulating blood to intense electric fields may induce red blood cell (RBC) damage and hemolysis. In this work, we develop a physics-based mathematical model to predict the extent of hemolysis during PFA procedures. The model combines a simplified representation of the electric field generated by a bipolar PFA catheter with experimentally derived hemolysis–response relationships, allowing the volume of damaged blood and free hemoglobin (fHb) release to be predicted as functions of pulse amplitude, pulse number, and device geometry. The proposed framework is consistent with both classical electroporation theory and recent clinical observations reporting a dose-dependent increase in hemolysis following PFA. The results provide a mechanistic interpretation of blood damage during PFA. Despite several simplifying assumptions, the proposed framework yields a quantitative tool for assessing hemolytic risk and may support the optimization of future PFA protocols. Full article

Recent advances have been achieved in intelligent fault diagnosis of rolling bearings. However, noise interference and data imbalance remain critical challenges for achieving accurate and reliable fault diagnosis in practical industrial applications. To address the limited robustness of conventional deep learning models under noisy conditions and their bias toward majority classes in imbalanced scenarios, this study proposes a robust bearing fault diagnosis method based on an adaptive weighted heterogeneous ensemble learning framework. The proposed method begins with continuous wavelet transform (CWT), which is employed to preprocess raw vibration signals and convert them into time–frequency images. Subsequently, a residual convolutional denoising autoencoder augmented by the convolutional block attention module is developed, namely CBAM-RCDAE. CBAM-RCDAE is capable of effectively reducing and eliminating noise interference in two-dimensional image data, thus enhancing fault diagnosis accuracy. Furthermore, a heterogeneous ensemble learning framework consisting of three base learners, including Swin Transformer, a multi-scale convolutional neural network, and BiLSTM, is developed to enhance generalization capability. An adaptive weight selection (AWS) strategy is introduced to adjust the weights and aggregate the outputs of the three base learners for final fault classification. The proposed method is extensively evaluated on the PU and CWRU bearing datasets. Experimental results demonstrate that, under the most challenging imbalanced conditions, the proposed method improves the G-mean metric by 5.89% and 4.95% compared with the state-of-the-art methods on the PU and CWRU datasets, respectively. In addition, the proposed method exhibits superior noise robustness, enabling reliable fault diagnosis performance across various noise levels. Full article