Search Results (10,270)

Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics. Full article

The increasing availability of bounded lifetime observations in different disciplines has intensified the demand for flexible models capable of accommodating complex failure mechanisms. Motivated by this need, a comprehensive inferential framework is developed for the unit Bilal (UBilal) distribution using generalized progressive hybrid censoring, which guarantees a minimum number of observed failures while controlling experimental duration. Classical inference is established through maximum likelihood estimation, which is accompanied by asymptotic confidence intervals based on both normal and log-transformed approximations. Moreover, a Bayesian framework using a Metropolis–Hastings Markov chain Monte Carlo algorithm is presented. The proposed methodology further provides inference for important reliability characteristics, including the reliability and hazard rate functions, through both frequentist and Bayesian paradigms proposed. An extensive Monte Carlo investigation is conducted under diverse censoring schemes, sample sizes, and prior specifications to evaluate estimation accuracy, interval performance, and the influence of censoring severity. The simulation results show that Bayesian methods always provide better estimates and more reliable interval estimates, especially when prior information is used. Using two real datasets from thermodynamics and meteorology, the numerical results demonstrate that the UBilal model provides an excellent fit and yields reliable inference under bounded observations. Overall, the proposed methodology presents an efficient Bayesian inferential framework for bounded lifetime data collected through the generalized progressive hybrid censoring and expands the applicability of the UBilal model to reliability and related fields. Full article

Construction schedule compression requires balancing project-duration reduction against direct, indirect, and risk-related cost increases under uncertain activity performance. Many construction time–cost trade-off models still rely on deterministic durations or predefined execution modes, which limits their ability to represent practical activity-level crashing decisions under uncertainty. This study develops a stochastic risk-aware time–cost optimization framework for construction scheduling using bounded integer crash-day decision variables. Activity durations are represented using triangular distributions based on optimistic, most-likely, and pessimistic estimates, while Monte Carlo simulation is used to propagate uncertainty through the precedence network. Expected and Conditional Value-at-Risk-oriented indicators are integrated into risk-adjusted duration and cost measures, which are then combined through a nonlinear normalized objective function. A Hybrid Genetic Algorithm–Gray Wolf Optimizer is implemented to solve the resulting discrete stochastic optimization problem and is benchmarked against seven metaheuristic algorithms under identical evaluation conditions. The framework is demonstrated using a 30-activity construction project reconstructed from Microsoft Project data. The proposed Hybrid GA–GWO reduced the deterministic project duration from 895 to 699 working days and achieved the best descriptive objective performance across 30 independent runs. However, after Bonferroni correction, its differences from the Genetic Algorithm and MPGWO-DLL were not statistically significant, indicating that these methods remain competitive alternatives. Additional Monte Carlo convergence, tornado sensitivity, correlated-duration sensitivity, and computational-time analyses were added to evaluate the stability, parameter dependence, and practical applicability of the framework. The findings show that the proposed framework can support risk-aware construction schedule-crashing decisions by identifying activity-level acceleration plans while explicitly accounting for downside schedule and cost risk. Broader validation in larger and more diverse real-world projects remains necessary. Full article

The nonlinear operational stiffness characteristics of machine tool spindles directly influence machining precision and bearing service life. Traditional static stiffness tests rely on direct differential operations on raw experimental load–displacement data, which are highly susceptible to measurement noise and fundamentally fail to capture accurate nonlinear features. To address this limitation, this study proposes a physics-driven parameter identification methodology to accurately extract the static nonlinear axial stiffness characteristics of spindles under static non-rotating conditions. Specifically, the smoothness priors approach (SPA) is introduced as a robust preprocessing technique to mitigate high-frequency noise while preserving the underlying low-frequency displacement trends, thereby ensuring high-fidelity feature extraction. Subsequently, a physics-dependent spindle mechanics model is directly integrated with a two-stage hybrid optimization algorithm—combining Global Search and Pattern Search—to inversely reconstruct the actual nonlinear load–displacement relationships. Numerical simulation results demonstrate that the hybrid optimization algorithm exhibits high computational accuracy, with an identification error of only 0.67% under ideal conditions and bounded parameter identification errors within 4.68% under synthetic noise levels up to 5%. Furthermore, experimental validation conducted on a position-preloaded spindle setup under initial preloads of 507 N and 862 N yields corresponding verification errors of 12.3% and 11.6%, respectively, confirming that the proposed method can effectively extract the static nonlinear stiffness features of the spindle from noisy measurements. The results demonstrate that this approach successfully overcomes the bottlenecks of conventional techniques, providing a robust and practical tool for the precise characterization of the spindle’s static nonlinear baseline stiffness. While currently validated under static non-rotating conditions, the established framework provides a fundamental baseline for extending parameter identification to dynamic operational environments in future studies. Full article

Coronary artery calcification (CAC) is a hallmark of advanced atherosclerosis and a major determinant of procedural complexity during percutaneous coronary intervention (PCI). Once considered a passive consequence of vascular degeneration, CAC is now recognized as an active, highly regulated process driven by inflammation, oxidative stress, extracellular vesicle release, osteogenic differentiation of vascular smooth muscle cells, and biomechanical remodeling. These mechanisms generate a spectrum of calcific phenotypes, ranging from microcalcifications and sheet calcium to nodular calcium and calcified nodules. Calcified nodules represent an advanced fibrocalcific plaque phenotype characterized by fractured calcific plates, luminal calcium protrusion, surface disruption, and variable thrombus formation. They can be characterized using intravascular ultrasound (IVUS), optical coherence tomography (OCT), and hybrid near-infrared spectroscopy–IVUS imaging, and are associated with coronary thrombosis, stent underexpansion, restenosis, target lesion failure, and the need for advanced calcium-modification strategies. A structured literature search of PubMed/MEDLINE, Web of Science Core Collection, and Scopus identified 83 publications published between 2020 and 2026 for inclusion in the narrative synthesis. This narrative review summarizes the biological and biomechaniclam mechanisms of coronary calcification and calcified nodule formation, compares multimodality intravascular imaging criteria, and discusses contemporary imaging-guided PCI strategies, including balloon-based modification, rotational and orbital atherectomy, excimer laser coronary atherectomy, intravascular lithotripsy, and hybrid approaches. By integrating pathobiology, intravascular imaging criteria, and lesion-specific PCI strategies, this review provides a clinically oriented framework for the assessment and management of calcified nodules. Future directions include standardized imaging definitions, prospectively validated morphology-guided treatment algorithms, and computational and artificial intelligence-assisted plaque characterization. Full article

Epilepsy is a challenging brain disease that requires significant clinical findings. (1) Background: The aim of this study is to improve the success rate of epilepsy detection using a newly developed method by optimizing the high-dimensional dataset obtained from brain MRI images. Standard machine learning models fall short of achieving the desired success in high-dimensional datasets. To achieve this, we aimed to develop an optimized hybrid model by combining the local classification power of the k-Nearest Neighbor classifier and the anomaly detection success of the negative selection algorithm. (2) Methods: Cortical and subcortical brain regions were analyzed to examine volumetric differences. A dataset was created by identifying regions statistically significant for epilepsy. This dataset was then optimized using the Scatter Search Snake Optimization algorithm. The performances of six different machine learning models trained on this optimized dataset were compared. (3) Results: The standard and popular models, SVM (82.70%), kNN (78.70%), RF (69.30%), MLP (73.30%), and NSA (95.89%), demonstrated a detection success rate. In contrast, the proposed hybrid model, kNN-NSA (98.65%), demonstrated a detection success rate. (4) Conclusions: The optimized hybrid kNN-NSA approach, which considers local density in such high-dimensional datasets and tolerates outliers within the self-data, appears to outperform traditional methods. Furthermore, this study has demonstrated that volumetric differences in regions not previously reported in the literature, such as WM-hypointensities, ventral DC, and choroid plexus, may be effective in the decision-making process for diagnosing epilepsy, as they are also found to be significant. Full article

Water resource management increasingly requires allocation methods that address rising demand, climate variability, and operational inefficiency. This study proposes an elite-transfer hybrid Genetic Algorithm–Particle Swarm Optimization framework for constrained water allocation under a weighted-sum scalarization model. The model combines four normalized objectives: minimizing water shortage, an operational cost coefficient, and allocation imbalance while maximizing utilization efficiency through an equivalent minimization term. A bidirectional elite-transfer mechanism links GA-based global exploration with PSO-based local refinement. The framework was evaluated using two capacity-constrained surrogate scenarios anchored to hydrometeorological records from Türkiye: Melekbahçe station (E21A033) in the Upper Euphrates Basin and Beşdeğirmen station (E12A003) in the Sakarya Basin. Daily streamflow records supported scenario construction, while precipitation and air-temperature data characterized local conditions. The proposed method was compared with standalone GA and standalone PSO, Differential Evolution, Grey Wolf Optimizer, a scalarized NSGA-II, adapted Kao–Zahara and Garg GA–PSO hybrids, and a no-elite ablation. All methods used the same objective formulation, normalization bounds, constraint-repair procedure, equal objective weights, tuning protocol, paired random seeds, and a budget of 10,000 objective-function evaluations. Performance was assessed through 30 paired runs per scenario. The proposed framework achieved the lowest mean scalar fitness values—0.1670 for Melekbahçe and 0.1752 for Beşdeğirmen—and reached the predefined convergence region after averages of 4587 and 4780 evaluations, respectively. All fitness and convergence improvements remained significant after Holm correction. Paired rank-biserial correlations ranged from 0.957 to 1.000 against the seven general comparators and were 0.824 and 0.781 against the no-elite ablation. The findings support the framework under the tested surrogate scenarios but do not establish Pareto-front dominance or universal superiority. Future work should examine measured operational data, additional basins, dynamic scheduling, alternative weights, and Pareto-based extensions. Full article

This study re-assesses the root causes of inflation through an innovative hybrid analytical framework integrating deep neural networks, random forest algorithms, and causal inference (DoWhy) within the Quantity-Theoretical Inflation Theory, using monthly data from Turkey for the period 2003M05–2025M01. The findings reveal that inflation expectations and exchange rate fluctuations are the most influential drivers of inflation, overshadowing other variables such as money supply, real interest rates, and global oil prices. Although the exact ordering of predictors is model dependent, and the random forest assigns the leading role to the real interest rate, the signal shared across all three methodological pathways rests on expectations and the exchange rate. While money supply growth aligns with monetarist theory and remains a consistent source of upward price pressure, its impact is often mediated through expectations and currency depreciation. Causal estimates confirm that Turkey’s persistent inflation is fueled not just by macroeconomic imbalances but by the erosion of policy credibility and weak anchoring of expectations, particularly after 2017. The study contributes methodologically by introducing AI-powered modeling to inflation analysis in emerging markets, and empirically by validating the central role of expectations and exchange rate pass-through in a structurally fragile, import-dependent economy. The results suggest that sustainable price stability in Turkey hinges on reestablishing central bank independence, adopting orthodox inflation targeting, and regaining public trust through consistent and transparent communication. Full article

In this paper, we propose a correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday’s law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB R2021a and conduct comprehensive experiments, including simulation comparative experiments, open-loop speed identification experiments, and closed-loop sensorless control experiments. Comparative simulation experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions; open-loop experiments verify that the constructed platform meets variable-speed constant-frequency (VSCF) power generation requirements, and closed-loop experiments compare CWEKF with MRAS under different speeds and parameter variations. The results show that the CWEKF has a maximum rotor speed estimation error <5 r/min, the response time has been reduced by over 65% compared to the traditional EKF, and it outperforms EKF, AEKF, RWEKF, and MRAS in estimation accuracy and stability, exhibiting significantly improved robustness under parameter variations and strong noise conditions. Full article

To improve the accuracy and stability of new energy vehicle (NEV) sales forecasting in China, this study develops a hybrid forecasting framework integrating the Informer model, autoregressive integrated moving average (ARIMA), and the Boomerang Aerodynamic Ellipse Optimization (BAEO) algorithm. Monthly data from January 2016 to December 2023 covering 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan) were collected from authoritative statistical sources. A multidimensional feature system was established by incorporating factors related to charging infrastructure, transportation demand, market development, and environmental conditions. Data preprocessing techniques, including Min–Max normalization, lagged variables, rolling statistical features, and seasonal sine–cosine encoding, were applied to capture temporal dependencies and periodic patterns. The BAEO algorithm was employed to optimize the key hyperparameters of the Informer model, while the ARIMA model was introduced to correct linear patterns in forecasting residuals. The proposed BAEO–Informer–ARIMA framework was evaluated against seasonal autoregressive integrated moving average (SARIMA), Prophet, extreme gradient boosting (XGBoost), long short-term memory (LSTM), gated recurrent unit (GRU), Transformer, and Informer models under the same chronological evaluation strategy. Results show that the proposed framework achieved superior forecasting performance, with a coefficient of determination (R²) of 0.9544, mean absolute error (MAE) of 24,068, root mean square error (RMSE) of 26,822, and mean absolute percentage error (MAPE) of 3.39%. Furthermore, uncertainty analysis based on rolling-validation forecast errors was conducted to establish a 90% confidence interval for future projections. Forecast results for 2024–2030 reveal sustained NEV sales growth with gradually decreasing growth rates and persistent seasonal variations. This study provides quantitative insights for NEV market planning, charging infrastructure deployment, and low-carbon policy formulation. Full article

Demand for high-quality interactive and real-time rendering remains challenging, as it requires balancing image realism with computational resources. Static parameter tuning of traditional approaches cannot provide adaptive rendering according to the varying complexity of dynamic scenes. This limitation arises from two main deficiencies in existing rendering pipelines: reactive methods that only enhance images after rendering without optimizing the renderer itself, and proactive methods that still rely on manual parameter calibration for each scene. These shortcomings are solved by this paper with an innovative optimization method that is a combination of a genetic algorithm (GA) and artificial neural networks (ANNs). This method offers a closed-loop system that is not found in any other static pipeline. Specifically, in our approach, ANNs will be used to predict the renderer’s initial parameter values from scene descriptor data, such as the number of polygons, lighting, and materials. After predicting the parameters, GA will optimize them based on the fitness value, which is determined by maximizing one objective (perceptual quality, defined by the SSIM measure) and minimizing another (rendering time). Our approach can be easily implemented within standard pipeline frameworks (Autodesk Maya Arnold). Full article

The integration of renewable energy into smart grids is beneficial for a sustainable future and the environment. Still, it has challenges such as energy optimisation, environmental conditions and power quality degradation. Existing Maximum Power Point Tracking (MPPT) techniques often focus on tracking efficiency under steady-state conditions, ignoring the impact of real-time variation in environmental conditions and load. The predictive power flow control (PPFC) algorithm is available with one or more fixed MPPT algorithms. No studies have reported on how the choice of MPPT affects PPFC harmonic mitigation. This paper addresses both concerns through a systematic comparative analysis of MPPT techniques integrated with a PPFC method to mitigate harmonics in renewable-integrated smart grid systems. To address this research gap, a comprehensive comparative analysis of various MPPT techniques, such as Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and hybrid Machine Learning (ML) techniques, integrated with PPFC to achieve effective harmonic mitigation in a smart grid environment is conducted. A 3 MW solar farm integrated with a battery storage system is modelled in MTALB/Simulink 2025b under real-time varying conditions, such as environmental and load variations over time in Auckland, New Zealand. The study focuses on key performance parameters such as total harmonic distortion (THD), power loss, stability and efficiency. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller, integrated with forecast-based power flow control, achieved overall performance by providing higher efficiency (97.5%), effective harmonic mitigation, and enhanced system stability under the nonlinear behaviour of the photovoltaic system. The proposed ANFIS-based system ensured a stable and smooth power output under varying environmental conditions, outperforming conventional and other intelligent MPPT techniques. Full article

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures. Full article

The Internet of Medical Things (IoMT) is growing quickly, which has greatly increased the cybersecurity attacks on the healthcare systems. Security techniques used for preventing attacks can improve patient safety, which is most important. As most of the datasets generated by the IoMT have high dimensions, feature selection is needed for accurate identification of data, along with deployment in real-time with limited resources. Moreover, the most influential features need to be identified for intrusion detection. Thus, this paper proposes a novel explainable hybrid framework, DeepMedShield-XAI, using the particle swarm optimization (PSO) method for feature selection and classifying the selected features using deep learning algorithms. The highest performing model among the three deep learning models is the deep neural network (DNN) with 99.68% and 99.87% test accuracy on the CICIoMT2024 and IoMT_TrafficData datasets. The findings of explainable artificial intelligence (XAI) methods reveal that the CICIoMT2024 dataset relies on connection-level features like length, protocol type, and TCP flags, while the IoMT_TrafficData dataset uses flow-based attributes like flow length, byte counts, and packet speeds without a single feature dominating across attack types. The proposed DeepMedShield-XAI framework: the results indicate that cyberattacks and unauthorized access attempts can be detected early by DeepMedShield-XAI, which can substantially improve the security of IoMT devices. This drives research on lightweight, explainable, and real-time security frameworks to protect patient data and ensure healthcare system reliability. Full article

Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, this paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems. Full article