Search Results (867)

Aortic valve stenosis (AS) is assessed by echocardiography in clinical practice. Conventionally, the aortic valve area, peak transaortic valve velocity/gradient and the mean transvalvular gradient determine if the AS is categorized as mild, moderate or severe. Recently, the entity of paradoxical low-flow, low-gradient AS despite normal left ventricular ejection fraction (LVEF) was described and flow (as determined by stroke volume indexed to body surface area) was used to further categorize AS. The new European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) guidelines in 2025 recommended a new phenotype-based classification, which improved the prognostication of AS. There are now five phenotypes: (1) concordant high-gradient AS; (2) low-flow, low-gradient AS with reduced LVEF; (3) low-flow, low-gradient AS with preserved LVEF; (4) normal-flow, low-gradient AS with preserved LVEF; and (5) discordant high-gradient AS. These appear to have different underlying pathophysiology, and hence prognostication and therapy. In addition, categories of AS in the setting of reduced LVEF are further divided based on their responses to dobutamine or exercise stress, which may result in different therapeutic strategies. In the transaortic valvular replacement (TAVR) versus the surgical aortic valve replacement (SAVR) era, the classification of these AS groups may have differing implications on the appropriate interventions. Furthermore, there are investigations on the effect of AS on the left ventricle and other chambers and stages of AS based on the extent of cardiac damage, which may have important prognostic value post-AVR. On the other spectrum, there are new developments in imaging analysis, such as using artificial intelligence. This state-of-the-art paper will comprehensively review the important updates in AS and its clinical implications. Full article

Background: Tildrakizumab, an anti-IL-23p19 monoclonal antibody, has demonstrated efficacy in clinical trials, but real-world evidence remains crucial for confirming its profile in diverse populations. Methods: We have conducted a multicenter, retrospective observational study within the Spanish Psoriasis Group (GPS). This study updates previous findings with a larger sample size (n = 372) and longer follow-up. We assessed absolute Psoriasis Area and Severity Index (PASI), Body Surface Area (BSA), and the Dermatology Life Quality Index (DLQI) improvements, as well as safety, in patients with moderate-to-severe plaque psoriasis. Results: The cohort included a large population of patients with a high prevalence of comorbidities and prior biologic exposure. Effectiveness was high, with a significant proportion of patients achieving PASI < 1. Compared to recent real-world data, our cohort demonstrates superior complete clearance rates (PASI < 1) and includes a comprehensive DLQI assessment. Notably, 79 patients were aged ≥65 years, confirming the drug’s utility in the elderly. Safety was consistent with previous reports, with no new signals detected. Conclusions: Tildrakizumab shows robust effectiveness and safety in a complex, bio-experienced real-world population. The lack of clinical predictors of response suggests a need for future pharmacogenetic exploration. Full article

Physics-guided computational imaging typically aggregates data fidelity, geometric reconstruction, and sensor consistency into a single scalar loss. In low signal-to-noise ratio (low-SNR) non-line-of-sight imaging, this centralized approach creates asymmetric gradient conflicts where the dominant constraints suppress physically meaningful updates. We propose treating multi-constraint training as a gradient coordination problem rather than scalar balancing. Our framework coordinates heterogeneous objectives through branch-wise gradient routing: soft conflict projection (PCGrad), hard physical constraint enforcement (PhysGuard), learnable sensor calibration, and a staged training protocol that decouples representation learning from nuisance parameter estimation. On held-out test scenes, the fully staged model improved the peak signal-to-noise ratio (PSNR) from 19.09 dB to 20.49 dB and the structural similarity index (SSIM) from 0.67 to 0.71 over the baseline, with consistent gains across the 48, 28, and 25 dB SNR levels. Qualitative evaluation on seven real-world scenes indicates sharper structure recovery and fewer artifacts. In this NLOS setting, gradient-level coordination is more reliable than scalar aggregation under heterogeneous constraints. Full article

This paper introduces a rigorous class of two-dimensional Markov-switching autoregressive moving-average (2D MS-ARMA) models for spatial lattice data exhibiting regime-dependent dynamics. The switching mechanism is governed by a latent causal Markov random field that drives spatial transitions between regime-specific autoregressive and moving-average structures. We provide sufficient conditions for the existence of a strictly stationary solution through the top Lyapunov exponent associated with a sequence of random matrices obtained from a state-space representation constructed along the lexicographic order. For the first-order bidirectional specification, we derive explicit spectral conditions linking stationarity to the regime-dependent spectral radii. Sufficient conditions ensuring the existence of finite second-order moments are also provided. Parameter estimation is carried out using a variational expectation–maximization (VEM) algorithm based on a mean-field approximation of the posterior distribution of the hidden regimes. The E-step yields closed-form coordinate ascent updates, while the M-step relies on gradient-based numerical optimization with derivatives computed via recursive differentiation. Under increasing-domain asymptotics, we discuss the consistency and asymptotic behavior of the variational estimator. The proposed framework fills a methodological gap between classical one-dimensional Markov-switching ARMA models and spatial autoregressive structures by extending regime-switching theory to multi-indexed processes with rigorous probabilistic foundations. It provides a comprehensive basis for statistical inference, model diagnostics, and prediction in spatially heterogeneous environments. Full article

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict–optimize–update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity–mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity–mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R²) of 0.89 for duration prediction, as well as an MAE of 7.4 × 10⁴ CNY and an R² of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule–cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects. Full article

Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact on patient recovery, healthcare costs, and long-term oncological outcomes. While surgical expertise and perioperative care have matured, reported AL rates remain persistently high. This necessitates a shift in focus from purely technical modifications toward integrated, data-driven preventive strategies. Purpose: Five years after our initial review, this update synthesizes the rapid evolution in AL prevention. We evaluate the transition from empirical surgical pragmatism to evidence-based protocols, integrating recent breakthroughs in real-time perfusion monitoring, prophylactic endoluminal technologies, and multidisciplinary patient optimization. This work provides a contemporary “roadmap” for navigating the complexities of esophageal reconstruction. Conclusions: The prevention of AL has evolved into a multimodal “bundle” that begins well before the index operation. This review highlights the critical shift toward quantitative perfusion assessment via indocyanine green fluorescence angiography, which is increasingly replacing subjective visual inspection as the standard for anastomotic site selection. We discuss the emerging role of gastric ischemic preconditioning as a biological strategy to enhance conduit vascularity, alongside the paradigm of proactive management using preemptive endoluminal vacuum therapy to mitigate septic sequelae in high-risk cases. Furthermore, we examine technical refinements in conduit construction and conditioning—focusing on the ‘tension-perfusion’ relationship—and the essential role of structured prehabilitation within enhanced recovery after surgery frameworks. While the quality of evidence remains heterogeneous, the move toward standardized reporting and objective monitoring marks a new era of precision in esophageal surgery. Full article

This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains. Full article

Background: In end-stage liver disease (ESLD), cardiovascular changes are frequent and relate to the presence of hyperdynamic circulation. In 2019, diagnostic criteria for cirrhotic cardiomyopathy (CCM) were updated to include tissue Doppler and speckle tracking imaging in defining left ventricle (LV) systolic and diastolic dysfunction. Evaluation of diastolic function remains challenging, with frequent indeterminate cases and emerging evidence of worse prognosis. The aim of the present study was to evaluate the prevalence of LV systolic and diastolic dysfunction in cirrhosis, in correlation with liver disease severity and potential prognostic implications. Methods: We performed an observational, retrospective, non-randomized, single-center study that included 99 cirrhotic patients evaluated for liver transplant (LT) in a tertiary center. Liver disease severity and complications were analyzed with survival and echocardiography data to determine potential correlations with prognosis. For statistical analysis, IBM^® SPSS^® Statistics version 20 (Chicago, IL, USA) was utilized. A two-sided p-value < 0.05 was considered statistically significant. Results: Left atrial (LA) volume index (r = 0.230, p = 0.022), LA reservoir strain (r = 0.291, p = 0.003), and LA contraction strain absolute value (r = 0.223, p = 0.027) positively correlated with the severity of liver disease expressed by MELD Na score. LA dilation (≥34 mL/m²) was the most common echocardiographic finding. It was present in 69.7% of patients, with one third having severe LA dilation (>45 mL/m²), which was associated with worse survival (log rank p = 0.019). LA contraction strain with an absolute value higher than 16% was also associated with worse survival (log rank p = 0.024). In multivariable Cox analysis, only MELD-Na and LA volume index remained independently associated with mortality. Diastolic dysfunction appeared more prevalent among the non-surviving patients irrespective of the diagnostic criteria used (p = 0.023 for American Society of Echocardiography 2016 criteria; p = 0.032 for CCM 2019 criteria). On binomial logistic regression, the presence of significant diastolic dysfunction (>grade 1) was associated with an increased probability of composite end-point of death or LT in the presence of liver disease severity confounders. The use of the LA stiffness index in discerning diastolic function in patients with standard inconclusive evaluation may warrant further investigation. Conclusions: Echocardiographic alterations, particularly LA enlargement, are associated with liver disease severity and clinical outcomes in ESLD. These findings are hypothesis-generating and suggest a potential role for echocardiography in risk stratification, warranting validation in larger prospective studies. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively. Full article

Non-organic (muscle) weakness (NOw) is proposed as a distinct pathological entity characterized by maladaptive neuroplasticity (learning) affecting motor control. Functional deficits are most directly revealed through the manual muscle testing (MMT) break test, which uniquely exposes a muscle’s ability to adapt to increasing external load, potentially serving as an index of motor control integrity. We advance the “muscle-motor-movement PTSD” (mPTSD) model in which learning during pain or stress (trauma) yields chronic avoidance (inhibition) of the associated muscles. In a second stage, compensatory synergies develop, overriding attempts at hypertrophy-oriented training. This non-systematic, integrative review synthesizes clinical reports, learning theories, motor control and pain literature, and objective tests of force and movement over time during MMT. Predictive processing and reinforcement learning offer complementary accounts of how hyper-precise priors and passive avoidance may maintain NOw beyond functional recovery. Unexplained muscle weakness is found in non-specific musculoskeletal disorders and functional motor disorder (functional weakness), but may also contribute to other conditions, such as kinesiophobia. Effective alternative treatments for NOw may act by updating or erasing maladaptive motor learning by disrupting memory reconsolidation, allowing immediate restoration of function. Analogous to psychoneuroimmunology’s role in immune function, we propose “psychoneurokinesiology”, the study of how maladaptive learning affects movement. Full article

Generator faults are among the most expensive events in utility-scale wind turbines, and the remaining useful life (RUL) of a generator is strongly influenced by long-term thermal loading on windings and bearings. Although wind farms continuously log multi-point generator temperatures and operating variables via SCADA, these data are rarely converted into an actionable, quantitative RUL trajectory that can be used directly for maintenance planning. This study proposes a field-oriented RUL estimation framework that transforms multi-year SCADA records into degradation-focused indicators and converts them into a physically plausible, decision-ready RUL curve. First, SCADA data are cleaned and filtered by operating conditions, and temperature rises relative to ambient are extracted. Next, abnormal operation is detected and summarised using an abnormal operation index (AOI), and thermal severity indicators are aggregated into a health index (HI) that reflects both proximity to engineering limits and signal variability. The HI is then mapped to lifetime consumption to update an effective age relative to the generator’s designed lifetime, followed by smoothing and monotonicity enforcement to ensure a stable, non-increasing RUL trajectory. Field validation shows a highly smooth RUL profile (98.2%) and a near-linear long-term decreasing trend ($R^2=0.985$). The results demonstrate that SCADA temperature–operation data can support reliable online generator RUL prognostic monitoring without the need for additional sensors. Full article

An update to the ductility potential of Al-Si and all other cast aluminum alloys is provided. Through analysis of extreme data in the literature, it is demonstrated that the highest elongation values in cast aluminum alloys are quite similar to those in wrought aluminum alloys. Through meticulous attention to all details of liquid metal quality throughout the entire production system, castings with exceptional quality can be produced. Full article

Retrieval-augmented generation (RAG) has shown strong potential for knowledge-intensive tasks, yet its performance degrades sharply when applied to structured long-context documents in power systems, where dense entity–relation dependencies, cross-document references, and strict traceability requirements exist. To address this Structured Long-Context RAG (SLCRAG) challenge, this paper proposes a hierarchical graph-enhanced RAG (HG-RAG) framework tailored for power system question answering. HG-RAG constructs a globally consistent knowledge graph via sliding-window entity–relation extraction to mitigate semantic fragmentation, and employs multi-granularity structured indexing for precise entity/relation retrieval. A hierarchical structured retrieval mechanism with multi-hop expansion and semantic distillation maximizes recall while minimizing redundancy. Furthermore, a regex-enhanced retrieval module records authoritative file_path provenance and constrains downstream retrieval to the same source documents, effectively eliminating cross-document interference—especially in cases where different documents contain similar entities and relations. Combined with version control and deduplication-merging, HG-RAG supports incremental knowledge updates with minimal forgetting and negligible token overhead. Experimental results on a domain-authentic power system QA dataset demonstrate that HG-RAG outperforms LightRAG and GraphRAG, achieving up to 85.47% accuracy in short-answer tasks with significantly lower token consumption. Ablation studies confirm that semantic distillation primarily improves precision and efficiency, while regex-enhanced retrieval safeguards recall in edge cases. Full article