Search Results (3,313)

The optimal indoor lighting comfort can enhance physical and mental health and improve work efficiency. The traditional methods for evaluating lighting comfort have problems such as limited data analysis and poor subjectivity. To establish objective criteria, this study proposes a novel method combining deep learning and evoked potentials. This study collected visual evoked potentials across diverse indoor lighting conditions and employed Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) recurrent neural networks to classify temporal evoked electroencephalography data. The experimental results show that both LSTM and GRU achieve higher accuracy than the Feedforward Neural Network. Among them, LSTM performs best, reaching an accuracy of 80.16% while maintaining computational efficiency comparable to GRU. Such effective objective evaluation methods provide a scientific basis for optimizing indoor environments. Full article

Background: Cancer poses a significant health risk to humans, with prostate cancer (PCa) being the second most common and deadly form among men, following lung cancer. Each year, it affects over a million individuals and presents substantial diagnostic challenges due to variations in tissue appearance and imaging quality. In recent decades, various techniques utilizing Magnetic Resonance Imaging (MRI) have been developed for identifying and classifying PCa. Accurate classification in MRI typically requires the integration of complementary feature types, such as deep semantic representations from Convolutional Neural Networks (CNNs) and handcrafted descriptors like Histogram of Oriented Gradients (HOG). Therefore, a more robust and discriminative feature integration strategy is crucial for enhancing computer-aided diagnosis performance. Objectives: This study aims to develop a multi-stage hybrid learning model that combines deep and handcrafted features, investigates various feature reduction and classification techniques, and improves diagnostic accuracy for prostate cancer using magnetic resonance imaging. Methods: The proposed framework integrates deep features extracted from convolutional architectures with handcrafted texture descriptors to capture both semantic and structural information. Multiple dimensionality reduction methods, including singular value decomposition (SVD), were evaluated to optimize the fused feature space. Several machine learning (ML) classifiers were benchmarked to identify the most effective diagnostic configuration. The overall framework was validated using k-fold cross-validation to ensure reliability and minimize evaluation bias. Results: Experimental results on the Transverse Plane Prostate (TPP) dataset for binary classification tasks showed that the hybrid model significantly outperformed individual deep or handcrafted approaches, achieving superior accuracy of 99.74%, specificity of 99.87%, precision of 99.87%, sensitivity of 99.61%, and F1-score of 99.74%. Conclusions: By combining complementary feature extraction, dimensionality reduction, and optimized classification, the proposed model offers a reliable and generalizable solution for prostate cancer diagnosis and demonstrates strong potential for integration into intelligent clinical decision-support systems. Full article

To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural Fuzzy Inference System (ANFIS). Flow characteristic data of the solenoid valve were collected under four pressure conditions (0.2–0.5 MPa), drive frequencies (5–20 Hz), and duty cycles (10–90%) using an indoor test system. An ANFIS controller architecture was constructed with target flow rate and actual travel speed as input variables and PWM frequency-duty cycle combinations as output variables. This controller enhances the traditional single-output mode of ANFIS by achieving multi-output collaborative optimization through shared premise parameters, thereby strengthening the system’s nonlinear modeling and control capabilities. To validate the system’s practical performance, a field simulation test platform based on a spraying robot was constructed. By analyzing preset prescription map information, the system achieved precise variable-rate spraying operations during movement. Test results demonstrate that the steady-state error remains within 5.03% under various speed-varying conditions. This research provides a high-precision intelligent control solution for variable-rate spraying systems, holding significant implications for reducing pesticide application rates and advancing precision agriculture. Full article

Ester-based lubricants have been widely used owing to their excellent overall performance. In this study, the quantitative structure–property relationship (QSPR) approach was combined with molecular descriptors, a genetic algorithm (GA), and an artificial neural network (ANN) to systematically predict the key properties—kinematic viscosity at 40 °C and 100 °C, viscosity index, pour point, and flash point—of 64 diester-based lubricants. Quantum chemical calculations were first performed to obtain the equilibrium geometries and electronic information of the molecules. Geometry optimizations and frequency analyses were carried out using the Gaussian 16 software at the B3LYP/6-31G (d, p) level, providing a reliable foundation for molecular descriptor computation. Subsequently, topological, geometrical, and electronic descriptors were calculated using the RDKit toolkit, and the optimal feature subsets were selected by GA and used as ANN inputs for property prediction. The results showed that the ANN models exhibited good performance in predicting viscosity and flash point, with R² values of 0.9455 and 0.8835, respectively, indicating that the ANN effectively captured the nonlinear relationships between molecular structure and physicochemical properties. In contrast, the prediction accuracy for pour point was relatively lower (R² = 0.6155), suggesting that it is influenced by complex molecular packing and crystallization behaviors at low temperatures. Overall, the study demonstrates the feasibility of integrating quantum chemical calculations with the QSPR–ANN framework for lubricant property prediction, providing a theoretical basis and data-driven tool for molecular design and performance optimization of ester-based lubricants. Full article

This study develops a weather-driven bio-economic optimization framework for agricultural planning in Guyana by integrating weather simulation, crop modeling, and multi-objective optimization. Precipitation was modeled using a first-order Markov chain with fitted distribution, while temperature and relative humidity were simulated using stochastic differential equations. Reference evapotranspiration was estimated using an artificial neural network. These simulated weather variables were then used as inputs to AquaCrop to estimate rice, maize, and soybean yields across multiple planting intervals. A multi-objective optimization model was then applied to optimize gross profit, economic water productivity, and land use efficiency. Validation at the Rose Hall Estate showed strong accuracy for rice and maize (MAPE < 10%) and moderate accuracy for soybeans. Scenario analyses for the 2024–2025 season, assuming 25% and 50% export targets, revealed that rice–maize double cropping produced the highest profitability, while soybean–maize combinations were less favorable. The framework replaces static yield assumptions with dynamic, simulation-driven models that incorporate price forecasts and allow substitution of alternative forecasting or crop simulators to enhance precision. The scenario-based design provides a flexible decision-support platform for optimizing crop selection, planting intervals, and resource allocation under climate variability and market uncertainty. Moreover, the framework is scalable and well-suited for evidence-based agricultural planning. Full article

Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model of train operation, this paper proposes a train-speed trajectory-optimization method combining data-driven energy consumption estimation and deep reinforcement learning. First of all, using real subway operation data, the key unit basic resistance coefficient in train operation is analyzed by regression. Then, based on the identified model, the energy consumption experiment data of train operation is generated, into which Gaussian noise is introduced to simulate real-world sensor measurement errors and environmental uncertainties. The energy consumption estimation model based on a Backpropagation (BP) neural network is constructed and trained. Finally, the energy consumption estimation model serves as a component within the Deep Deterministic Policy Gradient (DDPG) algorithm environment, and the action adjustment mechanism and reward are designed by integrating the expert experience to complete the optimization training of the strategy network. Experimental results demonstrate that the proposed method reduces energy consumption by approximately 4.4% compared to actual manual operation data. Furthermore, it achieves a solution deviation of less than 0.3% compared to the theoretical optimal baseline (Dynamic Programming), proving its ability to approximate global optimality. In addition, the proposed algorithm can adapt to the changes in train mass, initial set running time, and halfway running time while ensuring convergence performance and trajectory energy saving during online use. Full article

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management. Full article

Because Internet of Things (IoT) networks are widely deployed, they have become attractive targets for botnet and distributed denial of service (DDoS) attacks, which require effective intrusion detection. Convolutional neural networks (CNNs) can achieve strong detection performance, but their many hyperparameters are usually tuned manually, which is costly and time-consuming. This paper proposes a new hybrid metaheuristic optimizer, FW-CNN, that combines Grey Wolf Optimization and Red Fox Optimization to automatically tune the key hyperparameters of a one-dimensional CNN for IoT intrusion detection. The Red Fox component enhances exploration and helps the search escape local optima, while the Grey Wolf component strengthens exploitation and guides convergence toward high-quality solutions. The proposed model is evaluated using the N-BaIoT dataset and compared with a feedforward neural network as well as a metaheuristic-optimized model based on the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-CNN. It achieves a final accuracy of 95.56%, improving on the feedforward network by 12.56 percentage points and outperforming the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-based CNN model by 1.02 percentage points. It also yields higher average precision, Kappa coefficient, and Jaccard similarity, and significantly reduces Hamming loss. These results indicate that the proposed hybrid optimizer is stable and effective for multi-class IoT intrusion detection in real environments. Full article

The high-precision establishment of drive motor models and “pump-motor” system models is crucial for the development of the agricultural machinery powertrain. The research of this paper studied the series combination of electric drive continuously variable transmission devices, hydraulic continuously variable transmission devices, and power shift transmission devices to form a dual continuously variable transmission system. A drive motor efficiency characteristics modeling method combining the improved sine cosine optimization algorithm and BP neural network (ISCA-BPNN) and a hydrostatic transmission device efficiency characteristics modeling method combining the partial least squares method and the idea of sampling without replacement (PLS-SWOR) were proposed. Various binary control strategies for agricultural tractors were designed and compared. The results show that the two proposed modeling methods can effectively establish the efficiency characteristics models of the motor and hydrostatic transmission device. For agricultural machinery equipped with a dual continuously variable transmission system, it is advisable to apply the comprehensive binary control strategy under medium and high loads, and the pure economic binary control strategy under medium and low loads. This study is expected to provide support for the high-level design and intelligent strategy development of continuously variable transmission agricultural machinery in the future. Full article

Lithofacies, as an integrated representation of key reservoir attributes including mineral composition and organic matter enrichment, provides crucial geological and engineering guidance for identifying “dual sweet spots” and designing fracturing strategies in deep shale gas reservoirs. However, reliable lithofacies characterization remains particularly challenging owing to significant reservoir heterogeneity, scarce core data, and imbalanced facies distribution. Conventional manual log interpretation tends to be cost prohibitive and inaccurate, while existing intelligent algorithms suffer from inadequate robustness and suboptimal efficiency, failing to meet demands for both precision and practicality in such complex reservoirs. To address these limitations, this study developed a super-integrated lithofacies identification model termed SRLCL, leveraging well-logging data and lithofacies classifications. The proposed framework synergistically combines multiple modeling advantages while maintaining a balance between data characteristics and optimization effectiveness. Specifically, SRLCL incorporates three key components: Newton-Weighted Oversampling (NWO) to mitigate data scarcity and class imbalance, the Polar Light Optimizer (PLO) to accelerate convergence and enhance optimization performance, and a Stacking ensemble architecture that integrates five heterogeneous algorithms—Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—to overcome the representational limitations of single-model or homogeneous ensemble approaches. Experimental results indicated that the NWO-PLO-SRLCL model achieved an overall accuracy of 93% in lithofacies identification, exceeding conventional methods by more than 6% while demonstrating remarkable generalization capability and stability. Furthermore, production simulations of fractured horizontal wells based on the lithofacies-controlled geological model showed only a 6.18% deviation from actual cumulative gas production, underscoring how accurate lithofacies identification facilitates development strategy optimization and provides a reliable foundation for efficient deep shale gas development. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

Peach maturity at harvest is a critical factor influencing fruit quality and postharvest life. Traditional destructive methods for maturity assessment, although effective, compromise fruit integrity and are unsuitable for practical implementation in modern production. This study presents a machine learning approach for non-destructive peach maturity prediction using tabular data collected from 701 ‘Redhaven’ peaches. Three neural network models suitable for small tabular datasets (TabNet, SAINT, and NODE) were applied and evaluated using classification metrics, including accuracy, F1-score, and AUC. The models demonstrated consistently strong performance across several feature configurations, with TabNet achieving the highest accuracy when all non-destructive measurements were available, while TabNet provided the most robust and practical performance on the comprehensive non-destructive subset and in optimized minimal-feature settings. These findings indicate that non-destructive sensing methods, particularly when combined with modern neural architectures, can reliably predict maturity and offer potential for real-time, automated fruit selection after harvest. The integration of such models into autonomous harvesting systems, for instance, through drone-based platforms equipped with appropriate sensors, could significantly improve efficiency and fruit quality management in horticultural peach production. Full article

This study introduces a hybrid framework combining an Artificial Neural Network (ANN) with the Jaya optimization algorithm to predict the minimum Carbon Fiber Reinforced Polymer (CFRP) area required for flexural strengthening of reinforced concrete (RC) cantilever walls. A multilayer perceptron (MLP) network was trained on 500 Jaya-optimized design scenarios incorporating twelve design variables, including geometry, loads, and material properties. The ANN achieved high predictive accuracy, with R-values near 1.0 across training, validation, and testing phases. Five independent test cases yielded an average error of 3.69%, and 10-fold cross-validation confirmed model robustness (R = 0.9996). A global perturbation-based sensitivity analysis was also conducted to quantify the influence of each input parameter, highlighting wall length, moment demand, and concrete strength as the most significant features. This integrated ANN–Jaya model enables rapid, code-compliant CFRP design in accordance with ACI 318 and ACI 440.2R-17, minimizing material usage and ensuring economic and sustainable retrofitting. The proposed approach offers a practical, data-driven alternative to traditional iterative methods, suitable for application in modern performance-based structural engineering. Full article

Financial time series are heterogeneous, nonstationary, and dispersed across institutions that cannot share raw data. While federated learning enables collaborative modeling under privacy constraints, fixed architectures struggle to accommodate cross-market drift and device-resource diversity; conversely, existing neural architecture search techniques presume centralized data and typically ignore communication, latency, and privacy budgets. This paper introduces FedRegNAS, a regime-aware federated NAS framework that jointly optimizes forecasting accuracy, communication cost, and on-device latency under user-level $(\epsilon ,\delta )$-differential privacy. FedRegNAS trains a shared temporal supernet composed of candidate operators (dilated temporal convolutions, gated recurrent units, and attention blocks) with regime-conditioned gating and lightweight market-aware personalization. Clients perform differentiable architecture updates locally via Gumbel-Softmax and mirror descent; the server aggregates architecture distributions through Dirichlet barycenters with participation-weighted trust, while model weights are combined by adaptive, staleness-robust federated averaging. A risk-sensitive objective emphasizes downside errors and integrates transaction-cost-aware profit terms. We further inject calibrated noise into architecture gradients to decouple privacy leakage from weight updates and schedule search-to-train phases to reduce communication. Across three real-world equity datasets, FedRegNAS improves directional accuracy by 3–7 percentage points and Sharpe ratio by 18–32%. Ablations highlight the importance of regime gating and barycentric aggregation, and analyses outline convergence of the architecture mirror-descent under standard smoothness assumptions. FedRegNAS yields adaptive, privacy-aware architectures that translate into materially better trading-relevant forecasts without centralizing data. Full article

Industrial environments still rely heavily on analog instruments for process supervision, as their robustness and low cost make them suitable for harsh conditions. However, these devices require manual readings, which limit automation and digital integration within Industry 4.0 frameworks. To address this gap, this study proposes an intelligent and cost-effective system for non-invasive acquisition of measurement data from analog industrial instruments, leveraging machine vision and Artificial Neural Networks (ANNs). The proposed framework exploits the geometric symmetry inherent in circular and linear scales to interpret pointer positions under varying lighting and perspective conditions. A dedicated image-processing pipeline is combined with lightweight ANN architectures optimized for embedded platforms, ensuring real-time inference without the need for high-end hardware. The processed data are wirelessly transmitted to a Human–Machine Interface (HMI) and web-based dashboard for real-time visualization. Experimental validation on pressure and flow instruments demonstrated an average Mean Absolute Error (MAE) of 0.589 PSI and 0.085 GPM, Root Mean Square Error (RMSE) values of 0.731 PSI and 0.097 GPM, and coefficients of determination (R²) of 0.985 and 0.978, respectively. The system achieved an average processing time of 3.74 ms per cycle on a Raspberry Pi 3 platform, outperforming Optical Character Recognition (OCR) and Convolutional Neural Network (CNN)-based methods in terms of computational efficiency and latency. The results confirm the feasibility of a symmetry-driven vision framework for real-time industrial supervision, providing a practical pathway to digitalize legacy analog instruments and promote low-cost, intelligent Industry 4.0 implementations. Full article