Search Results (7,607)

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

Assembly sequence planning for prefabricated modular cabin units must satisfy geometric, precedence, stability, direction, and tool constraints while remaining responsive to workshop disturbances. This study formulates the task as a constrained, scalarized multi-criteria optimization problem, and combines constraint-aware greedy screening, blockwise split-and-recombination operators, and a feasibility-guided population injection in an improved genetic algorithm (IGA). The fair computational study used identical population sizes, evaluation budgets, stopping rules, crossover probabilities, and mutation probabilities for GA and IGA, with 30 independent runs on a reconstructed, anonymized 15-component benchmark derived from the component attributes and sequences reported in the submitted manuscript. IGA reached the best-known fitness of 0.115741 in 30/30 runs and required a mean of 10.6 generations to reach that value, whereas GA reached it in 26/30 runs and required 124.8 generations among successful runs. Simulated annealing and ant colony optimization reached the same best-known value, so global optimality is not claimed for the 15-component benchmark; exhaustive enumeration only verifies the optimum of a reduced nine-component instance. Ablation, diversity, parameter sensitivity, and synthetic 30–60-component tests clarify the contributions and limits of each mechanism. The event-triggered dynamic planning layer was additionally evaluated for six disturbance types and completed replanning in 0.52–0.62 s, reducing normalized waiting times by 33.3–100% in the release-delay scenarios. The results support feasibility and rapid replanning for this computational benchmark, while validation on complete shipyard matrices and measured task-time data remains necessary. Full article

Urban regeneration interventions extend beyond the renewal of physical building stock and the improvement of living conditions, as they also reshape local microclimatic conditions. This study examines changes in Surface Urban Heat Island (SUHI) patterns within an urban regeneration area in Istanbul, Türkiye, using multi-temporal remote sensing data. Landsat imagery from 2019, 2020, 2022, and 2023 was used to derive Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and SUHI intensity. Pearson correlation analysis, Getis–Ord Gi* hot spot analysis, and zonal statistics were applied to evaluate statistical relationships, spatial clustering, and local thermal changes within the regeneration areas. The findings show that urban regeneration did not produce a uniform thermal response. Demolition reduced built-up intensity, whereas redevelopment of previously vegetated or open parcels increased impervious surface cover and limited vegetation recovery. Correlation results revealed strong negative associations between NDVI and both LST and SUHI, while NDBI was positively associated with these thermal variables, indicating that land-cover composition was strongly associated with local thermal conditions. Overall, the study demonstrates that renewing building stock alone may not be sufficient to ensure improvement in the urban thermal environment. Strengthening green infrastructure, expanding permeable surfaces, and embedding climate-responsive planning measures into regeneration strategies are therefore critical. The results also indicate that multi-temporal remote sensing, combined with spatial statistics and zonal assessment, provides an effective decision-support framework for monitoring SUHI dynamics and evaluating the environmental performance of urban regeneration initiatives. Full article

Selective catalytic reduction (SCR) systems in coal-fired power plants must maintain low NOx emissions during increasingly frequent load changes. Variations in flue gas temperature and flow complicate ammonia-injection control and can cause NOx overshoot or excessive NH₃ slip. This study evaluates an implicit generalized predictive self-tuning controller using a coupled dynamic model of a 660 MW ultra-supercritical coal-fired power plant and its SCR system. The controller combines recursive least-squares identification with generalized predictive control (GPC) and is compared with proportional–integral–derivative (PID) control between 50% and 75% turbine heat acceptance (THA), at load-cycling rates of 0.5–2.0% $P_{\mathrm{e}0}$ min⁻¹. GPC improves NOx set-point tracking and reduces NH₃ slip over the conditions examined. During loading-down, the maximum outlet NOx concentrations are 48.43 mg m⁻³ with GPC and 65.78 mg m⁻³ with PID. During loading-up at 1.0% and 2.0% $P_{\mathrm{e}0}$ min⁻¹, GPC reduces the cumulative NH₃-slip index by 46.52% and 75.56%, respectively. The identified model coefficients vary more strongly at higher ramp rates, while the loading-down response also depends on the transient SCR inlet temperature. These results indicate that online model adaptation can improve ammonia-injection control during load-cycling. Full article

Background: Pediatric urology has traditionally relied on anatomical classifications, standardized diagnostic pathways, and disease-specific treatment algorithms. However, children with the same anatomical diagnosis may have substantially different risks of disease progression, renal injury, complications, and need for intervention. Personalized and precision medicine aim to integrate clinical, imaging, functional, biological, genomic, and longitudinal information to support individualized risk assessment and decision-making. Methods: This narrative review was based on a targeted literature search of PubMed/MEDLINE, Scopus, Embase and Web of Science databases. Search terms included combinations of “personalized medicine,” “precision medicine,” “risk stratification,” “pediatric urology,” “biomarkers,” “genomics,” and “artificial intelligence,” together with terms related to major pediatric urological conditions. Additional relevant publications were identified from reference lists of key articles. The literature was narratively synthesized according to its relevance to individualized risk assessment and clinical decision-making. Results: Current evidence supports an evolving role for individualized risk assessment in vesicoureteral reflux, antenatal hydronephrosis and ureteropelvic junction obstruction, congenital anomalies of the kidney and urinary tract, hypospadias, undescended testes, and disorders of sex development. Biomarkers, genomic testing, advanced imaging, and artificial intelligence may provide additional information for phenotyping and outcome prediction. However, most predictive and AI-based models remain insufficiently validated, with limitations related to external validation, calibration, reproducibility, clinical utility, and generalizability. Longitudinal reassessment is particularly important because risk may change with growth, disease progression, and treatment response. Conclusions: Personalized pediatric urology should move beyond diagnosis-based algorithms toward dynamic, risk-adapted decision-making. The goal is not to increase the number of investigations or interventions, but to identify which child is most likely to benefit from additional testing, surveillance, or treatment. Future implementation will depend on robust validation of predictive models and demonstration that personalized approaches improve clinically meaningful outcomes. Full article

Maintaining stable dissolved oxygen (DO) and pH conditions is critical for reliable operation of bioreactor systems used in cell culture and bioprocess manufacturing. However, accurate regulation of DO and pH remains challenging due to nonlinear process dynamics and variations in operating conditions. In particular, temperature fluctuations can affect gas solubility, gas–liquid mass transfer, and CO₂ buffering equilibrium, resulting in deviations in DO and pH. Existing control methods often rely on predefined mechanistic models or reactor-specific parameter identification, which may limit adaptability under changing operating conditions. This paper proposes a disturbance-compensated data-driven adaptive predictive control framework for DO and pH stabilization in bioreactor systems under dynamic temperature disturbances. Based on dynamic linearization, the proposed framework establishes an online input–output representation using measured gas composition, temperature disturbance, and environmental responses. An adaptive gain adjustment mechanism and pseudo-partial-derivative estimation method are developed to update the control relationship online without requiring an explicit process model or iterative optimization. Furthermore, temperature variations are incorporated as measurable disturbances to achieve real-time compensation of their effects on DO and pH dynamics. The proposed framework was evaluated through simulations and experiments using a 3 L bioreactor platform. Compared with a PID controller with temperature feedforward and conventional model-free adaptive predictive control, the proposed method reduced DO and pH tracking errors and improved recovery performance under temperature disturbances. The results demonstrate that the proposed data-driven adaptive predictive control strategy provides an effective approach for DO and pH stabilization in bioreactor systems under temperature-varying conditions. Full article

Poly(vinyl alcohol) (PVA) is a promising biodegradable polymer; however, its reliance on petroleum and incompatibility with raw lignin additives hinder sustainable PVA composite development. In this study, mechanically robust PVA/functionalized acid-soluble lignin (fLGAS) composite films were developed by means of aqueous solution casting without toxic chemical crosslinkers. Commercial kraft lignin was enzymatically modified using laccase from Coriolopsis spp., improving its compatibility with the PVA matrix. Molecular characterization confirmed the formation of an intermolecular hydrogen-bonding network between PVA and fLGAS, which significantly enhanced the thermal stability of the composites. Additionally, this network improved moisture response, achieving complete surface wetting (water contact angle decreasing from 47.04° to 0°) and swelling ratios exceeding 250% after 2 h. Nanoindentation and dynamic mechanical analysis characterizations revealed that mechanical properties enhanced with fLGAS loading up to 5 wt%, while the overall properties were governed by the balance between hydrogen bonding and crystallinity. This work provides an eco-friendly framework for industrial lignin valorization and sustainable functional film design. Full article

Object detection in Wide Area Motion Imagery (WAMI) is crucial for large-scale intelligent surveillance and monitoring systems. However, detecting extremely small moving targets in low-frame-rate grayscale WAMI remains highly challenging. In particular, three key problems limit the performance of existing methods: weak motion responses caused by low target contrast can lead to missed detections; densely distributed targets often produce merged responses that are difficult to separate; and registration artifacts, parallax, and dynamic background clutter can generate numerous false alarms. To address these problems, we propose MDF-Det, a coarse-to-fine spatiotemporal framework for WAMI small moving target detection. The framework consists of three complementary components. First, a Motion and Appearance Feature Fusion (MAFF) strategy integrates dense optical-flow-derived motion saliency with grayscale frame differencing to enhance weak target responses and improve candidate preservation. Second, a Spatial Attention-Guided Target Decoupling (SA-TD) module employs fine-grained heatmap decoding and step-threshold degradation to separate merged responses in densely populated scenes. Finally, a Scene-Prior Guided Filtering (SPGF) mechanism learns complementary vehicle-accessibility and motion-activity priors from scene context to suppress contextually implausible false alarms caused by complex background interference. Extensive experiments on six evaluation AOIs of the WPAFB 2009 dataset demonstrate that MDF-Det achieves an average $F_1$ score of 0.878, corresponding to a relative improvement of 5.5% over the strongest baseline. Full article

The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to improving the efficiency and sustainability of modern transportation systems. The aim of this paper is to present and systematize the application of modern ITS technologies for improving road safety, reducing emissions, and shortening travel time. Based on an analysis of the relevant literature, the fundamental components and architecture of ITS are presented, including sensor systems, V2X communication, IoT, cloud and edge computing, as well as the application of artificial intelligence in traffic data processing and prediction. The analyzed studies demonstrate that ITS enables dynamic traffic flow management, route optimization, reduction in congestion and emissions, and more efficient responses to emergency situations. Particular attention is devoted to the possibility of simultaneously considering travel time, energy consumption, emissions, noise, and road safety. As a synthesis of the analyzed findings, an integrated algorithm for intelligent traffic management is proposed, operating as a closed feedback loop encompassing data collection, state assessment, prediction, optimization, and control. Future ITS development is expected to focus on the integration of AI, IoT, 6G, and edge computing technologies and their validation using real-world traffic data. Full article

Microsatellite instability-high (MSI-H) and deficient mismatch repair (dMMR) define a molecular subtype of colorectal cancer (CRC) and predict benefit from immune checkpoint inhibition. Clinically, MSI/MMR assessment may yield discordant results across assays, anatomic sites, or time points. A challenging scenario occurs when tissue analysis identifies microsatellite-stable (MSS) or mismatch repair-proficient (pMMR) CRC, whereas circulating tumor DNA (ctDNA) analysis indicates MSI-H. This review evaluates evidence for MSI/MMR heterogeneity and subclonality in CRC. We reviewed literature on spatial, temporal and subclonal MSI/MMR heterogeneity across human cancers, focusing on CRC. Explanations for tissue–plasma and tissue–tissue discordance, including assay limitations, sampling bias, lesions misattribution, biological evolution, and treatment-related selection, were assessed. Although discordance more commonly reflects assay limitations, sampling bias, or profiling of different lesions, increasing evidence supports genuine biological heterogeneity. Distinct tumor regions may show retained MMR protein expression in one area and regional loss with MSI in another. Noncanonical MMR defects, epigenetic heterogeneity, post-treatment evolution, adaptive mutator-state, and immune selection may also generate dynamic or subclonal instability. Therapeutic relevance may depend not simply on MSI detection, but on whether the unstable clone is sufficiently dominant to generate broadly shared neoantigens across the disease burden. MSI/MMR discordance requires careful interpretation and should not automatically be considered as true biological heterogeneity. Nevertheless, genuine subclonality occurs in CRC and other cancers and may affect responsiveness to immune checkpoint inhibition. Integrated tissue, plasma, spatial and longitudinal analyses may improve treatment decisions and biomarker development. Full article

Urban–rural transition zones (URTZs) represent critical spatial units where urban expansion interacts with ecosystems. In arid regions, however, the response of ecological environmental quality (EEQ) to rapid urban expansion and spatial restructuring remains insufficiently understood. Taking Xinjiang as a representative arid-region case, this study develops an analytical framework integrating dynamic URTZs identification, EEQ assessment, and the analysis of influencing factors and nonlinear responses to systematically investigate URTZs expansion and EEQ changes across 13 typical urban agglomerations from 2002 to 2022. URTZs were identified using K-means clustering by integrating population density, nighttime light intensity, and impervious surface information. An improved remote sensing ecological index (ARSEI) was then developed by incorporating the abundance index (AI) into the traditional RSEI framework. Finally, XGBoost and SHAP were employed to identify the key determinants of EEQ and reveal their nonlinear responses and interactions. The results showed that: (1) URTZs expanded rapidly and continuously from 2002 to 2022, with their total area increasing by more than threefold and exhibiting a spatial restructuring pattern characterized by expansion from central cities toward multiple nodes. (2) Despite the rapid expansion of URTZs, overall EEQ remained at a relatively high level; however, the grade structure exhibited a trend of “expansion at both ends and contraction in the middle,” intensifying the spatial differentiation of EEQ. (3) XGBoost and SHAP analyses identified precipitation (PRE), population density (POP), digital elevation model (DEM), and slope as major factors explaining the spatial variation in EEQ. Interaction analysis further revealed strong interactions between PRE × DEM and PRE × POP. High EEQ values were primarily distributed in areas characterized by favorable precipitation conditions, moderate elevations, and gentle terrain, indicating that the synergistic effects of hydrothermal conditions and topographic constraints play a significant role in shaping EEQ in URTZs. These findings demonstrate that rapid URTZs expansion in arid regions does not necessarily lead to an overall decline in EEQ but may intensify its spatial differentiation. Therefore, ecological governance of URTZs should shift from a singular focus on controlling urban expansion toward differentiated spatial management that jointly considers hydrothermal conditions, topographic constraints, population concentration, and ecological carrying capacity, thereby promoting coordinated urbanization and ecological conservation. Full article

To elucidate the long-term changes and driving mechanisms of soil moisture on the Loess Plateau under large-scale ecological restoration, this study analyzed 0–200 cm profile soil water storage from 2000 to 2024 together with precipitation, temperature, and NDVI data. Theil–Sen trend estimation, the Mann–Kendall test, partial correlation analysis, and residual trend analysis were employed to systematically investigate the spatiotemporal variations in soil water storage, its responses to climatic and vegetation factors, and the relative roles of climatic and non-climatic factors. The results showed that soil water storage on the Loess Plateau exhibited pronounced spatial heterogeneity and an overall increasing trend during 2000–2024. Areas with significant and non-significant increases accounted for 52.22% and 41.25% of the study area, respectively, whereas decreasing areas accounted for only 6.53%. Soil water storage was generally positively correlated with precipitation and NDVI, with significant positive correlations covering 80.4% and 90.6% of the study area, respectively. In contrast, soil water storage was predominantly negatively correlated with temperature, with significant negative correlations occurring across 92.3% of the study area. Areas with increasing residual trends accounted for 85% of the study area. The mean relative contributions of climatic and non-climatic factors to changes in soil water storage were 47% and 53%, respectively, indicating that soil water storage dynamics were jointly influenced by hydrothermal climatic conditions and non-climatic factors, including vegetation restoration and land-use change. Overall, soil moisture conditions within the 0–200 cm soil profile of the Loess Plateau improved over the past 25 years, although localized water limited areas remained at risk of soil water storage decline. Ecological restoration should therefore account for regional water resource carrying capacity and optimize vegetation types and planting density to promote coordinated development between ecosystem restoration and water security. Full article

Ground fissures pose a serious threat to the deformation stability and operational safety of high-speed railway embankments. In this study, a three-dimensional transient finite element model was developed to investigate the dynamic response of a double-track high-speed railway embankment crossing an active ground fissure. Natural and CFG pile–raft composite foundations were compared under different train speeds and single- and double-line operating conditions. The results show that the ground fissure causes abrupt changes in displacement, acceleration, and dynamic stress, accompanied by evident hanging-wall amplification and asymmetric deformation. Double-line operation intensifies wave interference and dynamic amplification near the fissure, while increasing train speed further aggravates these effects, particularly for acceleration. The CFG pile–raft composite foundation effectively reduces dynamic response amplitudes, limits downward disturbance propagation, and improves deformation compatibility across the fissure through raft bridging and pile–soil load transfer. The proposed evaluation indices further quantify response asymmetry, double-line interference, and speed-induced amplification. These findings provide a basis for the dynamic stability assessment and reinforcement design of high-speed railway embankments in ground-fissure regions. Full article

During drilling circulation, the wellbore temperature field changes in response to drilling-fluid rheology, borehole geometry, drillstring eccentricity, and cuttings concentration. Conventional wellbore temperature models use fixed heat-transfer parameters and therefore cannot accurately represent real-time heat-transfer conditions. To address this limitation, a transient temperature model was developed. Bottomhole annular-fluid temperatures generated by OLGA were used as synthetic observations for MHE calibration. An MHE-based calibration method was developed to estimate the equivalent heat-transfer correction factor. The temperature model was first benchmarked against the Kabir analytical model and the commercial simulator OLGA. The estimated correction factor was then used by the mechanistic model to update the wellbore temperature field. The results showed that, under different initial values of the correction factor and assumed measurement-noise standard deviations, the equivalent heat-transfer correction factor converged to a steady-state value of approximately 0.941. Additional sensitivity tests showed generally stable calibration performance under moderate parameter settings, whereas stronger observation noise reduced calibration stability and accuracy. Under varying operating conditions, the calibrated bottomhole temperature yielded a mean absolute error of 0.033–0.332 °C, representing a reduction of 45.97–98.73% relative to the corresponding uncalibrated results. When the observation delay was extended to 2400 s (d = 8), the MAE was still reduced by 26.50–68.20% across different operating stages. These numerical results show that the proposed method can use bottomhole temperature observations to dynamically compensate for equivalent heat-transfer model mismatch under the investigated simulation conditions. The method reduces model-prediction errors and improves bottomhole temperature prediction under the tested varying operating conditions. The proposed method has potential applications in wellbore temperature prediction and high-temperature risk assessment for deep-well drilling. Full article

Existing lightweight optimization studies on gantry crane girders merely adopt static strength and stiffness constraints, ignoring fatigue damage induced by dynamic loads and welding fabrication, which results in impractical optimal designs. To address this limitation, a novel multi-objective topology optimization method incorporating static, dynamic, fatigue and minimum weld thickness constraints is proposed in this work. With structural weight and compliance minimization and first-order natural frequency maximization as the optimization targets, the model is constrained by structural stress, displacement, vibration frequency and minimum weld thickness, and a modified genetic algorithm is utilized to acquire the Pareto optimal solution set. Finite element analysis is conducted to compare the static performance, modal characteristics and transient dynamic responses of the original and optimized girders under diverse working conditions. The results demonstrate that the optimized girder exhibits comprehensive performance improvements, with a 15.6% reduction in structural mass, 8.3% decrease in maximum equivalent stress, 10.9% reduction in mid-span deflection, 12.1% increase in first-order natural frequency, and 18.3% extension in fatigue life. The proposed method can effectively support the precise lightweight design of crane metal structures and provides a feasible technical solution for their high-efficiency lightweight optimization. Full article