Search Results (7,616)

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. Full article

Background: Oral semaglutide is the first oral GLP-1 receptor agonist approved for treating patients with type 2 diabetes mellitus (T2DM). This real-world retrospective study evaluated its effectiveness and tolerability in patients requiring a third-line antidiabetic agent due to poor glucoregulation. Methods: Adult patients with T2DM who were taking oral semaglutide and were monitored at tertiary diabetes centers in Croatia were identified through electronic medical records between October 2022 and December 2024. Patients’ data were included in the analysis if they had been on oral semaglutide for at least six months. Results: A total of 163 patients (72 females and 91 males) were recruited, with 96.9% classified as overweight or obese. Among them, 145 had a BMI greater than 30 (mean BMI: 34.18 ± 4.60). The addition of oral semaglutide to their treatment regimen resulted in significant reductions in BMI, HbA1c, and both postprandial and fasting blood glucose levels, as well as in AST and ALT levels (all p < 0.05). There was also an increase in HDL levels (p = 0.007). The side effects observed were consistent with those previously recognized. Conclusions: This study demonstrates that oral semaglutide is safe and effective for glycemic and extraglycemic management in a real-world setting when used as a third-line agent. The best outcomes in terms of weight and HbA1c reduction can be expected when it is introduced early, ideally within the first five years of diabetes duration, and particularly in patients who are insulin naive. Full article

Background and Objectives: In early-stage HER2-positive breast cancer, ado-trastuzumab emtansine (T-DM1) has been adopted as the preferred adjuvant approach for patients left with residual invasive disease despite neoadjuvant therapy. The influence of different neoadjuvant regimens on subsequent outcomes in real-world settings remains uncertain. Materials and Methods: From 2019 to 2025, 102 patients treated with adjuvant T-DM1 following surgery after neoadjuvant chemotherapy were retrospectively assessed. Neoadjuvant regimens included doxorubicin plus cyclophosphamide followed by trastuzumab-paclitaxel, doxorubicin plus cyclophosphamide with pertuzumab–trastuzumab–docetaxel, or docetaxel–carboplatin–trastuzumab–pertuzumab. Clinical features, treatment response, survival, and toxicity were evaluated. Results: The mean age of the cohort was 49.7 years, and the majority of patients (80.4%) were aged 40 years or older. Hormone receptor positivity was 82.0%, and invasive ductal carcinoma accounted for 97.1% of cases. Regional responses included 39.2% with axillary pCR despite residual breast lesions, and 5.9% with breast pCR accompanied by axillary disease. Kaplan–Meier analysis demonstrated disease-free survival rates of 100%, 95.2%, and 92.2% at 1, 3, and 5 years, respectively. Adverse events were predominantly grade 1–2, while grade 3–4 toxicities occurred in under 5% of the cohort. Baseline characteristics varied across regimens, reflecting real-world treatment preferences, but survival outcomes remained comparable. Conclusions: Adjuvant T-DM1 was associated with high survival rates and manageable toxicity across different neoadjuvant regimens, underscoring its consistent benefit in routine clinical practice. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

Rice, a dietary staple for over half of the global population, is highly susceptible to bacterial and fungal diseases such as bacterial blight, brown spot, and leaf smut, which can severely reduce yields. Traditional manual detection is labor-intensive and often results in delayed intervention and excessive chemical use. Although deep learning models like convolutional neural networks (CNNs) achieve high accuracy, their computational demands hinder deployment in resource-limited agricultural settings. We propose MCP-X, an ultra-compact CNN with only 0.21 million parameters for real-time, on-device rice disease classification. MCP-X integrates a shallow encoder, multi-branch expert routing, a bi-level recurrent simulation encoder–decoder (BRSE), an efficient channel attention (ECA) module, and a lightweight classifier. Trained from scratch, MCP-X achieves 98.93% accuracy on PlantVillage and 96.59% on the Rice Disease Detection Dataset, without external pretraining. Mechanistically, expert routing diversifies feature branches, ECA enhances channel-wise signal relevance, and BRSE captures lesion-scale and texture cues—yielding complementary, stage-wise gains confirmed through ablation studies. Despite slightly higher FLOPs than MobileNetV2, MCP-X prioritizes a minimal memory footprint (~1.01 MB) and deployability over raw speed, running at 53.83 FPS (2.42 GFLOPs) on an RTX A5000. It achieves 16.7×, 287×, 420×, and 659× fewer parameters than MobileNetV2, ResNet152V2, ViT-Base, and VGG-16, respectively. When integrated into a multi-resolution ensemble, MCP-X attains 99.85% accuracy, demonstrating exceptional robustness across controlled and field datasets while maintaining efficiency for real-world agricultural applications. Full article

Few-shot knowledge graph (KG) completion is challenged by the dynamic and long-tail nature of real-world KGs, where only a handful of relation-specific triples are available for each new relation. Existing methods often over-rely on neighbor information and use sequential LSTM aggregators that impose an inappropriate order bias on inherently unordered triples. To address these limitations, we propose a lightweight yet principled framework that (1) enhances entity representations by explicitly integrating intrinsic (self) features with attention-aggregated neighbor context, and (2) introduces a permutation-invariant max-pooling aggregator to replace the LSTM-based reference set encoder. This design faithfully respects the set-based nature of triples while preserving critical entity semantics. Extensive experiments on the standard few-shot KG completion benchmarks NELL-One and Wiki-One demonstrate that our method consistently outperforms strong baselines, including non-LSTM models such as MetaR, and delivers robust gains across multiple evaluation metrics. These results show that carefully tailored, task-aligned refinements can achieve significant improvements without increasing model complexity. Full article

Unmanned aerial vehicles have become essential tools in a wide range of applications. As drone operations grow more complex, the accurate prediction of battery runtime and aerodynamic flight safety risks, particularly those caused by wind, becomes increasingly important. This study employs the Wind Resistance Risk Index (WRRI), to quantify the impact of wind conditions on UAV performance. While several predictive models have been introduced to address these issues, many have not been thoroughly validated under real operational conditions. This study focuses on the post-validation of a previously developed fuzzy-based predictive model, using telemetry data collected from four UAV missions. Key flights and battery parameters were analyzed. The results demonstrate that real-world flight data provide valuable insight into model reliability and highlight discrepancies that can guide future model refinement. This work contributes to enhancing UAV safety by bridging the gap between theoretical predictions and empirical evaluations, specifically under varying wind conditions. Full article

Miniaturised sampling and extraction are redefining therapeutic drug monitoring (TDM) by enabling low-volume sampling, simplifying collection, and improving patient acceptability, while also promoting decentralised workflows and more sustainable laboratory practices. This review critically appraises the current landscape, with emphasis on analytical performance, matrix compatibility, and readiness for clinical implementation. It examines validation requirements, the extent of alignment and existing gaps across major regulatory guidelines, and recurrent challenges such as haematocrit bias, real-world stability and transport, incurred sample reanalysis, device variability, commutability with conventional matrices, and inter-laboratory reproducibility. To make the evidence actionable, operational recommendations are distilled into a practical ten-point checklist designed to support validation and translation of miniaturised approaches into routine laboratory practice. Looking ahead, priorities include automation and portable platforms, advanced functional materials, and integration with digital tools and biosensors, alongside the development of harmonised frameworks tailored to miniaturised methods and prospective clinical studies that demonstrate impact on dosing decisions, adherence, and clinical outcomes. Overall, this review aims to equip researchers, laboratory professionals, and regulators with the knowledge to implement miniaturised bioanalysis and advance personalised medicine through TDM. Full article

Background and Clinical Significance: Barrett’s esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC). Accurately predicting which patients with BE are at the highest risk of progressing to EAC is a significant clinical challenge. This article discusses how the tissue systems pathology test (TSP-9, TissueCypher) can help guide risk-aligned care for patients with BE. TSP-9 is an AI-driven prognostic test that stratifies patients with BE for risk of progression to high-grade dysplasia (HGD)/EAC. Case Report Presentation: Three clinically low-risk patients had esophageal biopsies tested by TSP-9. The real-world utility of TSP-9 is demonstrated through a brief discussion of how the test was utilized to assess each patient’s personalized risk of BE progression to HGD/EAC and inform risk-aligned care. Conclusions: The use of validated AI-powered tools such as TSP-9 is poised to become standard practice in gastroenterology clinical settings and will help improve health outcomes for patients with BE to prevent EAC-related mortality. Full article

In the context of global efforts toward energy conservation and emission reduction, accurate short-term electric load forecasting plays a crucial role in improving energy efficiency, enabling low-carbon dispatching, and supporting sustainable power system operations. To address the growing demand for accuracy and stability in this domain, this paper proposes a novel prediction model tailored for power systems. The proposed method combines Spearman correlation analysis with modal decomposition techniques to compress redundant features while preserving key information, resulting in more informative and cleaner input representations. In terms of model architecture, this study integrates Bidirectional Gated Recurrent Units (BiGRUs) with dilated convolution. This design improves the model’s capacity to capture long-range dependencies and complex relationships. For parameter optimization, an Improved Beluga Whale Optimization (IBWO) algorithm is introduced, incorporating dynamic population initialization, adaptive Lévy flight mechanisms, and refined convergence procedures to enhance search efficiency and robustness. Experiments on real-world datasets demonstrate that the proposed model achieves excellent forecasting performance (RMSE = 26.1706, MAE = 18.5462, R² = 0.9812), combining high predictive accuracy with strong generalization. These advancements contribute to more efficient energy scheduling and reduced environmental impact, making the model well-suited for intelligent and sustainable load forecasting applications in environmentally conscious power systems. Full article

Three-piece alloy wheels are widely used across the automotive industry, favoured due to their lightweight construction and ease of customisation. Vehicle wheels must withstand forces generated during acceleration, braking, cornering, and impacts, ensuring safety and durability under real-world conditions. Finite element analysis (FEA) plays a crucial role in simulating these loading conditions, thoroughly assessing structural performance prior to manufacturing. This study develops and validates a digital FEA testing framework tailored to low-volume wheel manufacturers, demonstrating that FEA can replace traditional physical wheel fatigue tests where such facilities are unavailable. This research was conducted in collaboration with a UK company specialising in the design and manufacture of bespoke, limited-production three-piece alloy wheels. However, the absence of dedicated structural testing procedures caused many of their existing designs to be overengineered, resulting in excessive material usage, increased weight, and high production costs. In some cases, lack of testing also contributed to wheel failures. This work selected three of the company’s existing wheel designs and subjected them to comprehensive analysis. Using FEA, each wheel was evaluated under industry-standard radial, cornering, biaxial, and impact tests. To verify the simulations, a known case of wheel failure was analysed and compared to real-world values. Once verified, any design issues were addressed. The redesigned wheels achieved substantial weight reduction (up to 25%), while still meeting or exceeding the relevant safety standards and allowing for manufacturability. Ultimately, this work demonstrated that applying digital simulation techniques can significantly improve the performance and safety of custom three-piece alloy wheels. Full article

Power line sag monitoring is critical for ensuring transmission system reliability and optimizing grid capacity utilization. Traditional sag detection methods rely on hyperbolic cosine models that assume ideal catenary behavior under uniform loading conditions. However, these models impose restrictive assumptions about weight distribution and suspension conditions that limit accuracy under real-world scenarios involving wind loading, ice accumulation, and non-uniform environmental forces. This study introduces a novel Bézier curve-based mathematical framework for transmission line sag detection and monitoring. Unlike traditional hyperbolic cosine approaches, the proposed methodology eliminates idealized assumptions and provides enhanced flexibility for modeling actual conductor behavior under variable environmental conditions. The Bézier curve approach offers enhanced precision and computational efficiency through intuitive control point manipulation, making it well suited for Dynamic Line Rating (DLR) applications. Experimental validation was performed using a controlled laboratory setup with a 1:100 scaled transmission line model. Results demonstrate improvement in sag measurement accuracy, achieving an average error of 1.1% compared to 6.15% with traditional hyperbolic cosine methods—representing an 82% improvement in measurement precision. Statistical analysis over 30 independent experiments confirms measurement consistency with a 95% confidence interval of [0.93%, 1.27%]. The framework also demonstrates a 1.5 to 2 times increase in computational efficiency improvement over conventional template matching approaches. This mathematical framework establishes a robust foundation for advanced transmission line monitoring systems, with demonstrated advantages for power grid applications where traditional catenary models fail due to non-ideal environmental conditions. The enhanced accuracy and efficiency support improved Dynamic Line Rating implementations and grid modernization efforts. Full article

Heat treatment is a thermal-processing method involving controlled heating and cooling cycles designed to achieve the desired properties of materials. Among these steps, the cooling rate in heat treatment plays a crucial role, as it significantly influences the resulting material properties. In this paper, we investigated the feasibility of random forests in estimating the cooling-rate parameters for the steel-part heat treatment process. Random forests are particularly appealing in modeling an ensemble of expressive decision trees from which cooling can be modeled and estimated from the interaction of metal features. Our computational experiments using real-world data from industrial-scale operations demonstrated the advantageous properties of random forest regression models, particularly when combined with a random oversampling scheme. We also found that the chemical composition—specifically carbon and chromium content—as well as the weight of the steel parts, are key features that predict the cooling rate of steel parts. Furthermore, our validation using real-world cooling scenarios aligned closely with the practical insights of seasoned operators who routinely recommend cooling parameters for the metal-normalizing process. Our results highlight the effectiveness of the ensemble approach of random forest for practical applicability in industrial-scale heat treatment. Full article

Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments. Its core goal is to enable the model to continuously learn new scenarios and tasks after deployment, without forgetting existing knowledge, and finally achieving stable decision-making in the different scenarios over a long period. This paper proposes a continual learning method that combines feature-generation-replay with Mixture-of-Experts and Low-Rank Adaptation (MoE-LoRA). This method retains the key features of historical tasks by feature repla and realizes the adaptive selection of old and new knowledge by the Mixture-of-Experts (MoE), which alleviates the conflict between knowledge while ensuring learning efficiency. In the comparison experiments, we compared the proposed method with the representative continual learning methods, and the experimental results show that our method outperforms the representative continual learning methods, and the ablation experiments further demonstrate the role of each component. This work provides technical support for the long-term maintenance and new task expansion of data-driven autonomous guidance systems, laying a foundation for their stable operation in complex, variable real-world scenarios. Full article

We propose MUSE++, an advanced and lightweight speech enhancement (SE) framework that builds upon the original MUSE architecture by introducing three key improvements: a Mamba-based state space model, dynamic SNR-driven data augmentation, and an augmented multi-objective loss function. First, we replace the original multi-path enhanced Taylor (MET) transformer block with the Mamba architecture, enabling substantial reductions in model complexity and parameter count while maintaining robust enhancement capability. Second, we adopt a dynamic training strategy that varies the signal-to-noise ratios (SNRs) across diverse speech samples, promoting improved generalization to real-world acoustic scenarios. Third, we expand the model’s loss framework with additional objective measures, allowing the model to be empirically tuned towards both perceptual and objective SE metrics. Comprehensive experiments conducted on the VoiceBank-DEMAND dataset demonstrate that MUSE++ delivers consistently superior performance across standard evaluation metrics, including PESQ, CSIG, CBAK, COVL, SSNR, and STOI, while reducing the number of model parameters by over 65% compared to the baseline. These results highlight MUSE++ as a highly efficient and effective solution for speech enhancement, particularly in resource-constrained and real-time deployment scenarios. Full article