Search Results (8,877)

Survival-rule induction is commonly guided by log-rank separation, whereas some prognostic questions target conditional future lifetime. We propose a directional finite-horizon mean residual life (MRL) criterion for survival-rule induction under right censoring, combining normalized subgroup support with a survival-weighted restricted-MRL discrepancy. The framework includes favorable and adverse objectives, training-only horizon selection and tuning, overlapping-rule prediction, and finite-candidate plug-in consistency. Across nine simulation scenarios (200 replications each), MRL recovered the true subgroup partition more accurately under delayed benefit, crossing hazards, delayed benefit with 60% censoring, and small-sample crossing; log-rank was stronger under proportional hazards, early-only effects, rare subgroups, and the adverse stress test. In repeated nested analyses, Cox proportional hazards achieved the lowest mean integrated Brier score (IBS) in WHAS100 (0.1797) and malignant melanoma (0.1300); controlled log-rank also yielded lower IBS than MRL (0.1926 vs. 0.2038 and 0.1358 vs. 0.1404, respectively). MRL nevertheless identified different conditional-lifetime structures. These results position MRL-guided rules as an estimand-specific complement to hazard-oriented methods rather than a universally superior predictor. Full article

The lightweight design of an aircraft engine pylon requires an efficient structural layout capable of accommodating multiple load cases. An integrated lightweight design framework combining multi-load topology optimization and size optimization is developed. The three-field SIMP method with a weighted-compliance objective is employed to identify the dominant load-transfer paths under multiple representative load cases. Based on the resulting topology, a parametric model is constructed and optimized to reduce structural mass subject to strength and manufacturability constraints. The optimized member dimensions are subsequently used to reconstruct an engineering-manufacturable pylon configuration, whose structural performance is evaluated through finite element analysis. The results demonstrate that the multi-load topology optimization produces a stable primary load-bearing framework, while the subsequent size optimization reduces the structural mass from 238 kg to 156 kg, a reduction of 82 kg. The proposed framework provides a practical route for the lightweight design of aircraft engine pylons and can serve as a reference for other complex aerospace load-bearing structures. Full article

Remote fault-diagnosis services in dynamic edge–cloud environments depend on timely monitoring-data upload, remote inference, and result delivery, making their communication layer sensitive to variations in available bandwidth, link latency, and packet loss rate. This study addresses the network-state forecasting layer that supports such services rather than the fault-classification model itself. We propose Horizon-Aware Transformer–Ridge Fusion (HATR-Fusion), which combines a nonlinear Transformer expert with a low-variance ridge-regression expert for joint short- and long-horizon forecasting. Historical available bandwidth, link latency, packet loss rate, mobility, and offered load are used as inputs. Preprocessing statistics are estimated using the training set only, and validation-calibrated convex fusion weights are frozen before test inference. Experiments on controlled synthetic trajectories from eight links sampled at 1-min intervals, using 10 neural-network initialization seeds and ten baselines including DLinear and iTransformer, show that HATR-Fusion reduces mean absolute error (MAE) relative to the standalone Transformer by 6.94–8.31% over the 10-min horizon and by 3.02–4.40% over the 60-min horizon, with all six paired improvements remaining significant after Holm correction. Against iTransformer, HATR-Fusion is significantly more accurate for short-horizon bandwidth and latency, whereas iTransformer is significantly more accurate for long-horizon latency and packet loss; short-horizon packet loss and long-horizon bandwidth are not significantly different after Holm correction. The six-task mean normalized mean absolute error (NMAE) is 0.07106 for HATR-Fusion and 0.07047 for iTransformer, indicating comparable overall accuracy with task-dependent differences between the two methods. Ablation results show complementary short- and long-range contributions from ridge regression and Transformer, while input-quality sensitivity analysis identifies a limitation of the fixed fusion weights under corrupted or missing history. The conclusions are therefore restricted to scenarios with relatively stable input quality and distribution shifts comparable to those evaluated in this study. Full article

Solar irradiance forecasting accuracy depends on the prediction horizon because the use of recent observations, meteorological variables, and seasonal patterns changes with lead time. However, weak persistence baselines, temporally unreliable validation, and inconsistent test samples can overstate the advantage of complex models. This paper presents a leakage-safe, horizon-specific solar irradiance forecasting framework combining boosted trees, validation-weighted convex forecast combinations, and seasonal reference models. Direct forecasts are evaluated at 10-minute and 1-, 3-, 6-, 12-, and 24-hour horizons. Models are tuned using expanding-window validation; preprocessing is fitted only to the training data; and every method is evaluated on a predefined canonical test support. XGBoost achieves the lowest root mean square error (RMSE), 10.78 W/m${}^2$, at 10 min, whereas CatBoost achieves the lowest RMSE, 16.08 W/m${}^2$, at 1 h. At 3, 12, and 24 h, the lowest RMSE is obtained by seasonally guided forecast combinations. At 6 h, the XGBoost and two-day seasonal-mean combination ranks first by RMSE, although its gain over the two-day seasonal mean results in a larger mean absolute error (MAE). Daily-block significance tests show an improvement over the reference models at 10 min, while improvements over seasonal references at longer horizons are not statistically significant after adjustment. The framework provides a reproducible benchmark for horizon-dependent solar irradiance forecasting. Full article

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer and is one of the leading causes of cancer-related deaths globally. Despite current developments, reliable biomarkers for effective diagnosis, prognosis, and patient stratification are still lacking. Methods: We analyzed publicly available TCGA-LUAD and GEO datasets using integrative bioinformatics approaches, including differential gene expression, weighted gene co-expression network analysis (WGCNA), survival modeling, mutation profiling, immune cell infiltration scores, machine learning, and bulk-RNA and single-cell RNA sequencing. Results: A total of 5581 deregulated genes were identified, with the turquoise module (298 genes) showing strong correlation with LUAD (Corr = −0.79, p < 2.2 × 10⁻³⁰⁸). The integration of two analyses yielded 281 overlapping genes, out of which nine candidates (ANO2, CHIAP2, CPED1, DNASE1L3, GSTM5, HTR3C, PRKCE, SLC14A1, and WNT3A) were selected via LASSO Cox regression to build a prognostic risk model. High-risk patients have significantly worse survival (log-rank p = 0.0027). CPED1 exhibited the highest mutation frequency, with 41% of TCGA-LUAD samples harboring mutations. Among all CPED1 mutation events, missense mutations were the most common (47%). GSEA and KEGG analysis revealed significant enrichment of pathways such as nucleocytoplasmic transport, oxidative phosphorylation, protein processing in the endoplasmic reticulum, ribosome, and ribosome biogenesis in high-risk patients. Immune infiltration analysis indicated differences in immune cell infiltration scores between high- and low-immune-score groups, with M1 macrophages showing strong statistical correlation with aDC, monocytes, and CD4⁺ naïve T cells. Machine learning confirmed that the combined Enet+PLS model predicted CPED1 as a core predictor, and CPED1 was successfully validated in independent GEO datasets (GSE43458 and GSE31210), showing strong diagnostic accuracy (AUCs up to 0.98). Finally, single-cell RNA sequencing revealed that CPED1 was mostly expressed in fibroblasts and myeloid cells, with CPED1 significantly downregulated in LUAD compared with normal samples. Conclusions: This study integrates multi-omics and machine learning to highlight CPED1 as a promising candidate biomarker, with potential diagnostic and prognostic relevance in LUAD. The nine-gene risk signature stratified patients by survival outcomes in the TCGA cohort. Genomic and immune analyses revealed features associated with the high-immune-score group. As the study is entirely computational and the prognostic model lacks external survival validation, these findings should be regarded as preliminary and hypothesis-generating, requiring future independent validation and functional studies to confirm the biological significance and clinical utility of CPED1 and related genes. Full article

Investigation of the molecular mechanisms of postmortem processes to identify postmortem interval (PMI) markers is highly relevant in forensic medicine, given the insufficient accuracy and substantial limitations of current routine PMI estimation methods. This exploratory study aimed to describe changes in NADH fluorescence decay parameters in rat skeletal muscle during the early postmortem period and to assess their potential for PMI estimation. Using time-resolved fluorescence spectroscopy, the short- and long-lifetime components (τ₁ and τ₂), the amplitude-weighted mean fluorescence lifetime (τ_m), the relative contributions of the short- and long-lifetime components (α₁ and α₂, respectively) and fluorescence intensity were measured in 10 Wistar rats before death, immediately after death, every 30 min during the first 6 h, and at 24 h postmortem. Immediately after death, τ₁ decreased compared with the antemortem value (p = 0.02). τ₁, τ_m, α₁, and α₂ were dependent on the postmortem interval. For PMI prediction within the first 6 h after death, a partial least squares regression model combining α₁, τ_m, and the difference between rectal and room temperatures was constructed. The model demonstrated promising predictive performance, with a root mean square error (RMSE) of 0.89 h (95% CI: 0.63–0.99), a mean absolute error (MAE) of 0.68 h (95% CI: 0.49–0.82) and an R² of 0.762 (95% CI: 0.689–0.886); the prediction error did not exceed 1 h in 73.2% of cases. These data are preliminary and require confirmation in studies with larger samples under different ambient temperature conditions and subsequent validation on human cadaveric material. Full article

We propose a resolution-adaptive Bayesian wavelet-denoising method for noisy one-dimensional signals. The main contribution is a spike-and-slab prior whose continuous slab is a mixture of a compactly supported Wendland-type polynomial kernel and the semicircle density, with data-adaptive, resolution-specific mixture weights, produced by a low-dimensional empirical-Bayes trend. The Wendland component concentrates mass near zero and vanishes smoothly at the support boundary, whereas the semicircle component is more dispersed. This construction combines explicit sparsity and support control with an interpretable mechanism for adapting the shrinkage shape across resolutions. Under squared-error loss, we derive the posterior-mean estimator; establish key symmetry, boundedness, continuity, and limiting properties; define pointwise fixed-hyperparameter bias, variance, and risk; and develop an empirical-Bayes estimation procedure. The Wendland contribution has finite-sum expressions under a Laplace working likelihood, while the semicircle contribution is evaluated by stable one-dimensional integration. Simulations using the Bumps, Blocks, Doppler, and HeaviSine signals compare the proposed Gaussian- and Laplace-likelihood versions with universal thresholding, false-discovery-rate (FDR) thresholding, cross-validation (CV), Stein’s unbiased risk estimate (SURE), the Bayesian adaptive multiresolution shrinker (BAMS), and a nonlocal-prior (NLP)-based method. In the primary Gaussian-error simulation study, the Gaussian-likelihood version was the strongest non-NLP method in 24 of the 36 design cells, including 11 of the 12 low signal-to-noise ratio (SNR) cells, and had a substantially more favorable computational profile than the Laplace-likelihood version. Analysis of a seismic acceleration trace from the 2008 Chino Hills earthquake illustrates attenuation of rapid fluctuations and preservation of the dominant acceleration event under the chosen diagnostics. Using the processed channel-1 trace as surrogate truth, the corresponding semi-synthetic validation showed that WS–Gaussian improved on the noisy observation at lower and moderate SNRs but not at the highest SNR. Full article

Background/Objectives: The programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) immune checkpoint is a key therapeutic target in cancer immunotherapy, but small-molecule inhibition remains challenging due to its shallow and dynamic interaction interface. This study aimed to develop an artificial intelligence (AI)-guided workflow to identify novel small-molecule inhibitors targeting the PD-L1 dimer interface. Methods: A combined computational and experimental approach was established. A support vector regression-genetic algorithm (SVR-GA) model was trained on a dataset of 1385 known PD-L1 inhibitors to predict activity and guide molecular generation. From 470 AI-generated candidates, docking and molecular dynamics (MD) simulations were used for virtual screening. Selected compounds were synthesized and evaluated for PD-1/PD-L1 binding disruption using homogeneous time-resolved fluorescence (HTRF) assays. Cytotoxicity was tested in MDA-MB-231 and 4T1 cell monocultures, and in vivo efficacy was assessed in an immunocompetent 4T1 tumor model. Results: Two hits, PD-L1-Ser and PD-L1-Ser-OEt, were identified. Both disrupted PD-1/PD-L1 binding in HTRF assays, with PD-L1-Ser-OEt showing higher potency (IC₅₀ = 0.2068 μM). Both compounds exhibited limited direct cytotoxicity in cancer cell monocultures, suggesting an immune-mediated mechanism. In the 4T1 syngeneic mouse model, both inhibitors suppressed tumor growth without causing body weight loss. PD-L1-Ser-OEt demonstrated superior antitumor efficacy and elevated serum levels of IFN-γ and IL-4. Conclusions: This AI-guided workflow combining machine-learning-based molecular generation with structure validation is feasible for discovering PD-L1 dimer-interface inhibitors. PD-L1-Ser-OEt represents a promising lead compound for further development as an immune checkpoint inhibitor. Full article

Continuous radar surface velocity records require purpose-specific conditioning because the temporal information available for processing differs between real-time monitoring and retrospective analysis. Short-period fluctuations and spikes can obscure stage-related flow responses under both settings. We evaluated a stepwise quality control framework that separates these two processing roles using 10 min records from six monitoring sites in South Korea. Causal preprocessing combined Huber-weighted recursive least squares, fuzzy correction, and a trailing Hampel filter to generate a provisional series. Retrospective processing applied a centered Hampel filter followed by criterion-based zero-phase moving average smoothing. Causal preprocessing reduced the standard deviation of successive velocity increments by 14.1–53.4%, with a further 1.4–7.8% reduction observed after centered filtering. A three-point moving-average window was selected at all sites, retaining 98.4–99.9% of the peak velocity and a velocity sum ratio of 1.000. Three sites satisfied all the selection criteria, two satisfied the shape retention criteria, and one was retained under a flagged fallback because the increment variance and slope criteria were not met. Postprocessed index-velocity-method-derived hydrographs showed a lower RMSE and higher R² when compared to the operational stage–discharge benchmark at all sites, while signed biases varied by site. The proposed framework provides a traceable pathway from observation availability and data status to shape assessment and downstream discharge evaluation. Full article

Accurate estimation of SOC for lithium-ion batteries is a very important job in battery management systems, but under complex dynamic operating conditions, model misalignment often happens, and filtering algorithms usually do not make enough use of historical data, so the estimation accuracy is lowered. This paper puts forward a lithium-ion battery SOC estimation method that is based on weighted multi-innovation unscented Kalman filtering (WMIUKF); a hybrid parameter identification strategy that combines FFRLS and PSO is introduced to supply initial values for the global optimization of the model and to track dynamic drifts. To deal with the problems that the unscented Kalman Filter (UKF) does not make effective use of historical information and lacks an adaptive correction mechanism, multi-innovation theory and exponentially decaying weighting factors are incorporated into it; then, by fusing current and historical multi-step prediction residuals, a weighted freshness matrix can be constructed, and through this the method, we can improve the utilization efficiency of historical data and the system’s ability to resist interference. The performance of the proposed algorithm was validated through comparative experiments under various typical dynamic operating conditions, as well as at different temperatures (0 °C–45 °C) and discharge rates (0.5 C–2 C). The results indicate that the PSO-FFRLS hybrid parameter identification effectively improves model accuracy; compared to the UKF, MIUKF, and PSO-MIUKF algorithms, the WMIUKF achieved optimal SOC tracking under all types of dynamic operating conditions, with a root mean square error (RMSE) of no more than 0.58%. Even under extreme temperatures and high-rate discharge conditions, the error remained stable at a low level, demonstrating good environmental adaptability and robustness. Full article

Accurate calibration of Manning’s roughness coefficients is essential for reliable river hydraulic modelling, flood prediction, and water resources management, yet conventional calibration methods often struggle with high-dimensional parameter spaces and nonlinear hydraulic interactions. This study proposes and evaluates a hierarchical artificial intelligence framework for the inverse calibration of spatially distributed Manning’s roughness coefficients across three channel zones (left bank, main channel, and right bank), using a 48 km reach of the Tigris River in Baghdad as a case study. A one-dimensional HEC-RAS hydraulic model based on 30 measured cross-sections generated 18,360 simulations by systematically varying Manning’s roughness coefficients (0.02–0.045). Three calibration strategies were evaluated: (i) a simple Gradient Boosting Regression model based on a weighted composite roughness formula, (ii) conventional machine learning models (Random Forest, Gradient Boosting, and Multi-Layer Perceptron), and (iii) a deep learning framework combining a three-layer neural network (64 → 32 → 16 neurons), Differential Evolution optimisation, and cubic spline interpolation. Calibration accuracy increased with model complexity. The deep learning framework achieved the best performance, reducing the root mean square error by 96.6% (from 1.202 to 0.041 m), with R² = 0.992 and negligible bias (−0.004 m). Conventional machine learning models produced spatially variable Manning’s roughness distributions, with the calibrated main-channel roughness (mean n = 0.0512) being 34.0–57.5% higher than the corresponding bank values. The proposed framework provides an effective approach for calibrating spatially distributed roughness coefficients in one-dimensional hydraulic models, with strong potential to improve river hydraulic simulations and support future applications to flood modelling. Full article

Sustained uphill motion of two-wheeled self-balancing wheelchairs is challenging. Slope-induced gravity changes the equilibrium condition, driving-torque demand, and velocity response. This paper proposes a smoothly weighted hybrid control method. The method combines nonlinear model predictive control and a linear quadratic regulator for pitch stabilization and uphill velocity tracking. The control design accounts for the slope-dependent equilibrium condition and steady-state torque demand. NMPC handles large-deviation recovery, velocity regulation, and actuator constraints. LQR improves local stabilization near the equilibrium point. The two controller outputs are coordinated by a continuously varying weight, which provides a smooth transfer of control authority across the transition region. MATLAB/Simulink simulations compare the proposed method with standalone NMPC, LQR, and SMC under several slope angles. Disturbance-recovery tests are also conducted under external torque disturbances. The results show stable uphill motion under the tested slope conditions. After finite-duration disturbances, the controller recovers both pitch posture and uphill velocity. Under a sustained torque disturbance, pitch stability is retained, but velocity regulation degrades. The proposed method improves pitch stabilization and velocity maintenance under the tested conditions. The recorded wheel-end torque remains bounded without sustained saturation in the three hybrid-controller cases. These results demonstrate numerical feasibility under the specified nominal simulation conditions, while uncertainty-robust and real-time performance remain to be validated. Full article

This paper presents a systematic literature review of intelligent and advanced control methods for automotive wheel-slip regulation in anti-lock braking systems (ABS). Following a PRISMA-based workflow, records from IEEE Xplore, Scopus, and Engineering Village were searched for the 2014–2025 period. The search returned 5034 records; after removal of duplicates, patents, and out-of-range items, 1803 records were screened, 406 reports were assessed at full-text level, and 60 studies met the final eligibility criteria. The included studies were classified using a six-class taxonomy: fuzzy and neuro-fuzzy control, adaptive and self-tuning control, robust nonlinear control, predictive and optimization-based control, learning-assisted control, and hybrid or integrated control. The review shows that robust nonlinear and hybrid/integrated methods dominate the evidence base, while fuzzy and predictive methods remain important recurring families. Validation is still strongly simulation-weighted: many studies report braking-performance gains in slip tracking, stopping distance, chattering reduction, or road-friction robustness, but comparatively few provide hardware-in-the-loop, laboratory, or processor-level evidence. Only a very limited subset reports concrete embedded metrics such as execution time, sampling-period compliance, memory usage, or deadline margin. By combining systematic study selection, braking-specific control equations, primary-method classification, validation coding, embedded-implementation assessment, and comparative-scope analysis, this review identifies a persistent gap between algorithmic ABS performance and deployable real-time embedded feasibility. The findings motivate standardized benchmarking of intelligent braking controllers under common wheel-slip scenarios, common plant models, and common embedded timing metrics. Full article

Purpose: We aimed to compare the real-world effectiveness of four myopia-control spectacle lens interventions for childhood myopia control using a doubly robust causal inference framework. Design: This was a retrospective cohort study with multi-treatment augmented inverse probability weighting. Participants: A total of 2764 myopic children (right eyes) aged 3–18 years were included, screened from 13,071 electronic medical records at Ineye Hospital, Chengdu University of Traditional Chinese Medicine, between January 2023 and January 2026. Methods: Participants were allocated to four myopia-control spectacle lens groups and one single-vision spectacle lens group: Higher-Order Aberration Defocus (n = 912), Defocus Incorporated Multiple Segments (n = 800), Highly Aspherical Lenslets (n = 476), Diversified Segmental Defocus Optimization (n = 238), and single-vision lenses (n = 338). A doubly robust estimator combining multinomial propensity-score weighting with outcome regression was applied to adjust for confounding. Outcome Measures: The primary outcomes were one-year changes in axial length and spherical equivalent. The secondary outcomes were the rates of rapid progression. Treatment-effect heterogeneity was explored across prespecified baseline age, refractive error, and axial length subgroups. Results: After adjustment, all four myopia-control designs significantly outperformed single-vision correction (p < 0.05). The design based on Higher-Order Aberration Defocus demonstrated the greatest efficacy, with an axial length change of 0.107 mm and a spherical equivalent change of −0.146 D. Within this group, 25.5% of children exhibited rapid axial elongation (≥0.36 mm/year) and 8.3% exhibited rapid myopic progression (≥−0.50 D/year). The treatment effect was statistically significant in children with low myopia or an axial length no greater than 26 mm (p < 0.05), but not in those with high myopia or pathological axial elongation. Conclusions: All four myopia-control spectacle lens designs demonstrated significant advantages over single-vision correction in real-world clinical practice. Particular emphasis should be placed on the role of myopia-control spectacles during the early stages of myopia. As myopia progresses to high myopia or pathological axial elongation, the efficacy of myopia-control spectacle lenses as a standalone intervention diminishes, and combined approaches incorporating multiple myopia management strategies may be warranted. Full article

Background/Objectives: Operating room nurse scheduling is a complex healthcare optimization problem. Operating room settings are particularly challenging because permanent and subcontracted nurses operate under complex 16 h and 24 h shift structures, with continuous surgical coverage requirements and recovery-period requirements. To our knowledge, few existing models simultaneously integrate nurse preferences, recovery-period requirements, and heterogeneous shift structures within a unified goal programming framework. This study aims to develop and implement a goal programming model that incorporates nurses’ needs and preferences as flexible constraints to optimize shift scheduling in an operating room department. Methods: This single-center case study combined a qualitative component, an analysis of scheduling records, and mathematical optimization modeling. It was conducted at the operating room department of a public hospital in Türkiye employing 37 permanent and 7 subcontracted nurses in the shift rotation, together with a head nurse responsible for the roster. The hospital was selected purposively as a high-volume public center with a dual-tier staffing model and a fully manual scheduling process; semi-structured interviews were then conducted with all 37 permanent nurses and the head nurse (n = 38). The seven subcontracted nurses were not interviewed; the constraints applying to them were derived from national regulatory guidance and operational information provided by the head nurse. Scheduling requirements were formalized as five flexible constraints informed by nurses’ preferences and institutional requirements and incorporated into a goal programming model alongside obligatory coverage and staffing constraints. Penalty weights were calibrated through structured consultation with the head nurse. The model was solved to proven optimality using Python with the OR-Tools CP-SAT solver. Results: The optimized 28-day schedule eliminated direct night-to-day shift transitions, which, under the manual schedule, affected approximately three nurse assignments per week. Weekly night shifts were limited to a maximum of two per nurse in every planning block, a limit that individual nurses exceeded under the manual system. Monthly working days were standardized to a range of 18–20 days (mean = 19.84, SD = 0.49) from an irregular 14–26-day range (mean = 20.57, SD = 3.51), an 86% reduction in the standard deviation. All identified rest-period violations for subcontracted nurses on 16 h and 24 h duties were eliminated in the optimized schedule. Conclusions: A goal programming model integrating flexible constraints informed by nurses’ preferences and institutional requirements generated a schedule with greater equality in the distribution of monthly working days and improved compliance with the predefined scheduling objectives compared with the historical manual schedule. The model offers nurse managers a potentially adaptable decision-support tool that requires prospective validation in other settings. Future work should extend the model to incorporate dynamic patient demand, cost optimization, and multi-department scheduling scenarios. Full article