Search Results (1,183)

In directional coring drilling, toolface adjustment is performed during drilling interruption by rotating the drill string through the top drive. Because the bottom-hole toolface angle cannot be transmitted to the surface in real time, a dynamic prediction model is required to guide toolface control. Existing flexible drill string models, however, generally neglect the nonlinear wellbore friction caused by stick–slip motion, reducing prediction accuracy. To address this issue, a distributed-parameter torsional dynamic model is established and discretized into a multi-degree-of-freedom system. A friction-state-based prediction–correction iterative algorithm is proposed to resolve the strong coupling between wellbore friction and system dynamics. At each time step, the sticking or slipping state is identified from the predicted motion, and the wellbore friction torque is iteratively updated until the friction state and dynamic equilibrium simultaneously converge, enabling accurate prediction of the drill bit toolface angle. Simulation results show that the proposed model captures the key dynamic characteristics of toolface adjustment. Under typical operating conditions, the drill bit start-up delay is 3.53 s, the peak angular velocity reaches 1.73°/s, and the peak transmitted torque is 2.28 kN·m. After the top drive stops, the drill bit continues rotating because of inertia, resulting in a 2.12° toolface overshoot and an angular lag rate of 21.2%. In addition, the effects of weight on bit, top-drive speed, and equivalent damping on toolface adjustment are quantified, providing guidance for parameter optimization. The proposed method provides a theoretical basis for toolface prediction and control in intelligent directional coring drilling. Full article

In signalized urban road networks, periodic signal phase switching is a key factor influencing traffic-flow stability and operational efficiency. With the rapid development of Connected and Automated Vehicle (CAV) technologies, exploiting their enhanced perception, communication, and cooperative control capabilities has become an important research topic. To characterize the acceleration, deceleration, queueing, and discharge disturbances induced by signal phase transitions, this study proposes a Signal-Constrained Optimal Velocity Model (SC-OVM). By introducing a continuous signal decision function, the proposed model dynamically couples traffic signal states with vehicle-following behavior, including preceding-vehicle following and stop-line tracking within a unified optimal-velocity framework. Furthermore, linear stability analysis, boundary critical condition analysis, and disturbance probability modeling are integrated to reveal the instability mechanism caused by abrupt signal phase transitions, with extensions to stochastic prediction errors and adaptive Signal Phase and Timing (SPaT) inputs. Numerical simulations show that SC-OVM-controlled CAVs can smooth vehicle trajectories, reduce average delay, improve end-of-green passing performance, and achieve a balanced performance in efficiency, stability, and safety compared with the Full Velocity Difference Model (FVDM), Virtual Leading Vehicle model (VLV), and Intelligent Driver Model (IDM). The findings provide theoretical support and practical insights for stability modeling and cooperative control of mixed-traffic flow at signalized intersections. Full article

In this paper, a route- and environment-aware rule-based energy management system (EMS) strategy for EVs equipped with HESSs consisting of a lithium-ion battery and a supercapacitor is proposed. The proposed rule-based EMS integrates driving mode classification, traffic conditions, road gradient, wind resistance, ambient temperature, and the supercapacitor (SOC) to determine the optimal power-sharing strategy between the battery and the supercapacitor under various driving conditions, including city, highway, and stop-and-go traffic. A mathematical model of the EV powertrain, battery, and supercapacitor is developed. The performance of the proposed rule-based EMS strategy is validated in MATLAB/SIMULINK under three representative driving cycles: the Urban Dynamometer Driving Schedule (UDDS), Artemis Motorway 130, and a Central Business District (CBD) driving cycle. It is shown that the proposed rule-based EMS strategy significantly reduces the battery current stress while increasing supercapacitor usage. Full article

This paper investigates an infinite-horizon discounted stochastic maintenance optimization problem within the framework of dynamic programming. The system degradation is modeled by a discrete-time stochastic process affected by maintenance actions and random disturbances, while the objective is to minimize the expected discounted maintenance and degradation costs. The analysis is carried out on the Banach space of continuous functions equipped with the supremum norm. It is proved that the associated Bellman operator is well defined, maps the function space into itself, and is a contraction with contraction modulus equal to the discount factor. Consequently, the existence and uniqueness of the optimal value function follow from the Banach Fixed Point Theorem, and the convergence of value iteration is established. In addition, rigorous a priori and a posteriori error estimates are derived, providing theoretical stopping criteria for numerical computation. The theoretical results are complemented by numerical experiments illustrating the optimal stationary maintenance policy, the stability of the computed solution under grid refinement, and the influence of the discount factor on both the optimal policy and the convergence rate of value iteration. Full article

Based on the decoupled n-dodecane skeletal mechanism and the computational fluid dynamics (CFD) numerical framework, a multilayer feedforward neural network surrogate model was developed to predict ignition delay in a constant-volume combustion vessel. The Levenberg–Marquardt optimizer with adaptive damping coefficients was used for model training, with mean squared error as the loss function and an inherent early stopping mechanism to prevent overfitting without additional weight decay regularization. To eliminate random interference from initial parameter settings, the surrogate model underwent 1000 repeated training trials, each with random weight re-initialization. The effects of hidden neurons, data partition strategy, normalization scheme, and sample size on predictive performance were systematically examined. The optimal configuration—three hidden neurons, a 70:15:15 data split, and a 105-sample training set—showed low sensitivity to data normalization. The resulting surrogate model is concise and sample-efficient, maintaining satisfactory prediction accuracy at 800 K and 1100 K while substantially reducing computational overhead. It provides a practical and reliable tool for subsequent combustion prediction and uncertainty quantification of hydrocarbon fuels. The feedforward neural network surrogate model substantially cuts the computational overhead for fuel combustion prediction to merely 15–20 min for every batch of 60 samples. Full article

In this technological era, electrical energy is the bloodstream for the economic and social development of any country. It is the need of the time that developing countries like Pakistan have strategic planning for efficient generation and utilisation of electricity and have as much cheap electricity as possible at their disposal while respecting environmental constraints. The overloaded and ageing infrastructure of an electrical power network can impact system reliability and the sustainability of power generation, transmission and distribution mechanisms. The initiation of the planning process depends upon accurate load estimation to optimally fulfil consumers’ power needs. This paper compares statistical, hybrid and deep learning (DL) mechanisms, including SARIMAX, SARIMA with gradient boosting (SARIMA-GB), long short-term memory (LSTM) network, STL decomposition with LSTM, and CWT-LeNet-5-LSTM, for the prediction of residential electricity demand in the LESCO region of central Pakistan. The study uses 7670 daily feeder observations recorded between 1 January 2002 and 31 December 2022. The series is modelled at its native daily resolution and partitioned chronologically into a fitting span of 5216 days, a validation span of 920 days and a test span of 1534 days beginning 20 October 2018. All models receive the same block of 14 exogenous calendar variables, four annual Fourier harmonic pairs, day-of-week and month sine and cosine terms, a weekend indicator and a linear trend, and all neural models are trained with a validation split, early stopping and restoration of the best weights rather than for a fixed number of epochs. Accuracy is assessed with MAE, RMSE, MAPE and peak normalized RMSE under one recursive protocol at forecast leads of 1, 7, 14 and 30 days, because the ranking of the frameworks depends on the lead. Averaged over three random initialisations, the proposed CWT-LeNet-5-LSTM attains the lowest error at every multi-step lead, reaching an MAPE of 2.35 ± 0.28% at lead 30 against 3.32% for the multivariate LSTM, 3.89% for SARIMA-GB and 4.47% for SARIMAX. At lead 1, SARIMA-GB is the more accurate model (0.76% against 1.20 ± 0.17%) because the previous day’s observed load dominates one-step prediction for a series whose lag-one autocorrelation is 0.978. An architecture ablation isolates the contribution of the wavelet stage, the convolutional stage, the anisotropic pooling and the calendar fusion. A stratified analysis across seasons, weekdays, weekends and high-, medium- and low-load days shows where the advantage is concentrated. Additionally, Diebold–Mariano tests identify the statistical significance of the differences. Full article

Financial fraud keeps rising these days. Organizations attempt to stop this trend by using various methods, such as distributing guides on how to avoid scams and frauds and automatically generating alerts when suspicious activities occur. However, this passive approach does not mitigate the problem, as the trend is worsening, and it is usually too late when victims realize they have been scammed. Therefore, active approaches must be employed before scams reach the victims. A wide variety of preventive methods, such as neural networks and data mining, have been used to detect financial fraud data, but none have proven entirely effective in combating scams. Each method has its pros and cons. This research takes advantage of multiple machine learning techniques, such as k-nearest neighbors (kNN) and decision trees, by utilizing data fusion to detect financial fraud accurately. The data fusion function used here is self-adjusting through learning. During the training phase, the system is repeatedly applied to the dataset until an optimal detection rate is achieved. Experimental results from credit card transactions show that the proposed method outperforms each individual method. Parameter or threshold values for the data fusion are set heuristically. Future research will focus on developing reconfigurable data fusion by automatically adjusting the values. Full article

Broodiness is a natural behavior whereby female birds stop laying eggs to sit on and hatch them. This behavior is regulated through genetic, hormonal, and environmental factors. The Taihe Black-Boned Silky Fowl (TBSF), a Chinese traditional domestic breed, exhibits a strong brooding tendency; however, the molecular mechanisms underlying this trait remain unclear. In this study, we performed an integrated multi-omics analysis to characterize differences between two groups of TBSF hens: 8 individuals undergoing 30 days of active brooding (BR30) and 8 individuals in the normal laying egg stage (NB), selected from a total group of 230 hens. We combined 16S rRNA sequencing, untargeted metabolomics, and hepatic transcriptome sequencing, with statistical analyses including QIIME 1.9.1, OPLS-DA (VIP > 1), Student’s t-test (p ≤ 0.05), and DESeq2 (|log₂FC| ≥ 1, FDR < 0.05) for differentially expressed genes (DEGs), respectively, and Pearson’s correlation analysis for multi-omics integration. Phenotypically, brooder hens showed significantly reduced feed intake, body weight, and main digestive tissue indices, alongside altered liver and blood biochemical parameters. Hepatic transcriptome analysis identified 1582 DEGs between groups, enriched in pathways related to fatty acid oxidation and the amino acid degradation pathway. In addition, 16S rRNA sequencing revealed distinct gut microbial community structures: the NB group was enriched in Bacilliota and Pseudomonadota, while the BR30 group was enriched in Spirochaetota and Synergistota. Metabolomic profiling identified a total of 143 differential metabolites, which were enriched in lipid and amino acid metabolites, including alpha-linolenic acid and pyruvate metabolites. Multi-omics correlation analysis revealed tight associations between gut microbial taxa, circulating metabolites, and hepatic gene expression. Specifically, beneficial lipid metabolites, including phospholipids, lysophosphatidylcholines, and sphingomyelins, were positively correlated with Synergistes and the Christensenellaceae R-7, as well as with key hepatic lipid metabolism genes FABP1, LPL, and FADS2. In summary, this study reveals that the gut–liver axis plays a critical part in the modulation of energy metabolism during broodiness, and further highlights new insights into metabolic targets that could optimize reproductive behavior and enhance poultry production. Full article

This paper presents an efficient framework for surrogate modeling and rapid optimization of a dual-band circular patch antenna with a C-shaped slot (DB-CPAC) using multilayer perceptron (MLP) neural networks. Although highly accurate, traditional full-wave electromagnetic simulations are computationally expensive for geometric optimization due to complex slot-induced surface current perturbations. To address this limitation, a hybrid optimization framework based on Latin Hypercube Sampling (LHS) is proposed, combining the developed MLP model with a Method-of-Moments (MoM) simulator. The surrogate model uses an advanced modular architecture consisting of an ensemble of MLP neural networks for regressing center frequencies and classification MLP modules with a softmax output layer to estimate the probabilities of achieving bandwidth and gain targets. All networks are trained using the Levenberg–Marquardt algorithm with early stopping on data generated by a dedicated DB-CPAC_MoM_Sim software package. The proposed LHS-based optimizer employs the surrogate model for rapid global search and targeted local optimization before final MoM verification. Results show that this hybrid approach achieves an order-of-magnitude acceleration of the optimization process compared to conventional MoM methods while maintaining high accuracy. Full article

Symbolic regression and symbolic classification generate explicit mathematical expressions that combine predictive modelling with direct model interpretability. However, symbolic learning based on genetic programming can be computationally expensive because large populations of candidate expressions must be repeatedly evaluated over multiple generations. This paper presents FastSymbolicGP, a lightweight Python library for symbolic regression, binary classification, and multiclass classification through a compact, scikit-learn-compatible interface. The library implements tree-based genetic programming, protected mathematical operators, tournament selection, subtree crossover, subtree, hoist, and point mutation, elitism, validation-aware model selection, adaptive parsimony, expression complexity analysis, and Numba-compiled postfix evaluation. FastSymbolicGP was evaluated through 940 successful benchmark runs covering real-world scientific regression, binary and multiclass classification, physical law recovery, dynamical system identification, parameter sensitivity, ablation, and scalability. Across four real-world scientific regression datasets, FastSymbolicGP achieved the highest mean test $R^2$ on every dataset and obtained significantly better pooled paired results than gplearn and PySR under the evaluated configurations. These results are specific to the selected hyperparameters, primitive sets, stopping criteria, and computational budgets, and should not be interpreted as evidence of universal superiority. In binary classification, it achieved a mean balanced accuracy of 0.8507, compared with 0.7458 for gplearn, while validation-based threshold optimization and class weighting further improved performance under severe class imbalance. FastSymbolicGP also achieved competitive multiclass and dynamical system results while generally producing substantially simpler models than gplearn. In the scalability experiment with 50,000 samples, FastSymbolicGP was approximately 6.20 times faster than PySR and 1.63 times faster than gplearn, while obtaining predictive performance nearly identical to PySR. physical law experiments showed that nondimensionalization increased the dimensional validity rate of recovered FastSymbolicGP expressions from 24% to 96%, while reducing runtime and expression complexity. Analysis of the stored equation pools further showed that near-optimal model selection reduced symbolic complexity by an average of 28.6% when a simpler candidate was available, with negligible predictive degradation. These results indicate that FastSymbolicGP provides a practical balance of predictive performance, computational efficiency, task coverage, and symbolic interpretability for reproducible scientific and machine learning applications. Full article

We present an analogue of the classical Gittins index for a one-armed decision problem with a continuous spectrum of payoffs. The model assumes that the decision-maker observes independent realizations of a random variable and, at each step, decides whether to accept the current opportunity or continue observing. We show that the optimal strategy takes the form of a threshold rule, while the corresponding reservation index is determined by a one-dimensional fixed-point equation with a direct decision-theoretic interpretation. The model is illustrated with an example of bookmaker betting related to horse racing and the Kelly criterion. This perspective allows the proposed index to be viewed as a threshold of informational advantage. This, in turn, points to potential applications in optimal stopping problems and decision-making under uncertainty. Full article

Urban intelligent transportation systems increasingly rely on vehicle-to-infrastructure (V2I) communication for green-light optimal speed advisory (GLOSA) services. However, conventional GLOSA systems are vulnerable to roadside unit (RSU) coverage gaps and communication instability in mixed-traffic environments. In this paper, we propose a heterogeneous communication network cooperation framework that integrates decentralized multi-hop vehicle-to-vehicle (V2V) relaying with conventional V2I communication to extend signal phase and timing (SPaT) dissemination beyond direct RSU coverage. A lightweight gradient-based speed synchronization mechanism supports real-time trajectory adaptation with low computational overhead. The framework is evaluated through microscopic SUMO simulations with explicit communication impairment modeling across varied traffic densities and connected autonomous vehicle (CAV) penetration levels (10–70%). The results demonstrate reductions in travel time reductions of up to 22%, stop frequency of up to 95%, and CO₂ emission exceeding 18% relative to V2I-only GLOSA under 70% CAV penetration. At the lower bound of 10% CAV penetration, the framework still achieves measurable improvements of approximately 4–6% in travel time and 15–20% in stop frequency, confirming practical benefit even under minimal connected-vehicle adoption. The proposed framework maintains advisory continuity through distributed relay dissemination, offering a scalable and communication-resilient enhancement to intelligent transportation coordination in heterogeneous environments. Full article

Predicting the mechanical properties of bolted connection nodes in prefabricated Concrete-Filled Steel Tube (CFST) column–steel beam assemblies remains challenging due to complex nonlinear relationships, high degrees of parameter coupling, and limited generalization capabilities of traditional empirical formulas. This study proposes a data-driven prediction model integrating an Improved Particle Swarm Optimization (IPSO) algorithm with a Deep Neural Network (DNN). Drawing upon 196 sets of experimental data on CFST column–steel beam nodes with Extended Hollo-Bolt (EHB) connections from the published literature, the model employs bolt diameter, steel tube wall thickness, concrete compressive strength, beam–column cross-sectional parameters, and connection configuration parameters as input variables, while designating ultimate moment capacity, initial stiffness, and joint ductility coefficient as prediction targets. A multi-layer DNN is constructed to capture the highly nonlinear mapping between structural parameters and mechanical responses. The IPSO algorithm, enhanced with adaptive inertia weight and Lévy flight perturbation, performs global optimization of the network weights and hyperparameters to improve convergence speed and prediction stability. Five-fold cross-validation is embedded within the IPSO fitness evaluation loop to guide hyperparameter selection, while dropout regularization and early stopping are applied during final training to mitigate overfitting; prediction performance is ultimately verified on an independent hold-out test set. Experimental results demonstrate that the proposed IPSO-DNN model outperforms a tuned shallow neural network (SNN), Support Vector Regression (SVR), and Random Forest (RF) models across the coefficient of determination ($R^2$), root mean square error (RMSE), and mean absolute error (MAE), effectively capturing the nonlinear mechanical characteristics of CFST nodes under complex loading conditions. Full article

Relying solely on NEMA phases for urban green-wave control often restricts the feasible regions of network optimization, resulting in narrow bandwidths or unsolvable models. To address this limitation, this paper proposes a mixed-integer linear programming model for regional signal coordination based on a mixed-phase release strategy that integrates NEMA dual-ring phase and split phase. By utilizing shared lanes under split phasing, the model maximizes lane resource efficiency and extends coordination benefits to left-turn traffic. Introducing 0–1 decision variables establishes a unified formulation for internal phase offsets, enabling flexible, intersection-specific release selection. To balance network efficiency and fairness, the optimization objective minimizes the weighted sum of the red-wave bandwidth-to-cycle ratio, subject to spatiotemporal and clockwise closed-loop constraints. A real-world case study in Suzhou, solved via the branch-and-bound method, demonstrates that the optimal design deploys split phase at seven intersections and NEMA phases at two. VISSIM simulations confirm that compared to the NEMA-only approach, the proposed mixed model reduces red-wave bandwidth by 49.52%, average delays by 26.36%, and stops by 17.5%. The proposed model provides a system-level signal coordination framework for improving the adaptability and reliability of urban traffic control systems. Full article

Current thermal validation in food engineering commonly depends on rigid, temperature-only endpoints that generate thermal stress and the associated energy cost in the case of high-caliber matrices. To overcome this, this study provides a proof of concept for post-heating cooling inertia as the main kinetic driver of cumulative integrated lethality in mass-transfer-limited geometries (p value framework), followed by separate enzymatic and not microbial (or, e.g., oxidative) degradation following product heat transfer. Time-series and multi-spatial thermodynamic profiling was performed for the commercial validation runs of three pasteurization processes of large-diameter (90 mm) finely minced cooked chicken emulsions packed in polyamide casing. When active heating was stopped at a lower core limit of 71 °C, the thermal cooling inertia led total integrated lethality to increase by more than 216%, leading to an ultimate core p value of 76.87–89.56 min. It systematically exceeded the mandatory food safety limit (p ≥ 40 min) in all spatial coordinates in the forced convection chamber, confirming absolute thermal homogeneity. In a subsequent cold-chain stability challenge at around 70 days (0–4 °C), foodborne pathogens were not detected in the product, and saprophytic microflora was limited below critical spoilage bounds (total viable counts (TVC) and dominant lactic acid bacteria (LAB) progressively increased to 3.7 and 3.5 log10 CFU/g on day 70). The autoxidation pathways for primary and secondary lipids remained low (peroxide at 0.00 mmol/kg and malondialdehyde at less than 0.15 mg MDA/kg). On the other hand, a linear progression of free fatty acids (R² = 0.9673) determined that the single enzymatic lipid hydrolysis is responsible for the final biochemical limiting effect of the sensory lifespan. Shelf life evaluation via rigorous quantitative descriptive analysis showed that all organoleptic attributes remained well above market acceptance criteria until day 70. At the same time, this study supports a scalable, prediction-based thermodynamic paradigm that ensures biological safety and fundamentally optimizes industrial energy use. Full article