Search Results (4,562)

The tasks and objectives of grassland management have changed significantly in recent decades. One of the key elements of adapting to climatic and economic challenges is the optimal use and future sustainability of grasslands. Ferenc Balázs’s plant stand assessment method is a fast, efficient and widely applicable method for evaluating the quantitative and qualitative characteristics of forage in grasslands, as well as the economic value of pastures. This study is based on a three-dimensional coenological survey which is low-cost, does not require technical infrastructure, and empirically considers the species’ preference by livestock. As a result of our extended criteria approach, we assigned modified forage value (k-value) categories to 2310 vascular plant species. Based on our investigations in the presented case study, the Balázs method was proven to be well suited for estimating the yield of grasslands and determining the relative forage value of grasslands with a high degree of confidence in practice. As this method is non-destructive and involves little trampling, it is particularly suitable for monitoring grassland habitats with a high density of protected plant and animal species. Full article

The following study will look at the issue of the dealignment of the trajectory when drilling vertically (a fact), where measurement and process errors are still the primary source of error that can easily lead to the inclination angle having overshot the desired bounds. The current methods, such as the Extended Kalman Filters (EKFs), can incorrectly estimate non-Gaussian noises, unlike the classical particle filters (PFs), which are unable to handle significant measurement errors appropriately. We will solve these problems by creating a new deviation correction mechanism using a dung beetle optimizer particle filter (DBOPF) with a superior Model Predictive Controller (MPC). The DBOPF makes use of the prior knowledge and optimization process to enhance the precision of state estimation and is superior in noise reduction to traditional filters. The improved MPC introduces flexible constraints and weight adjustments in the form of a sigmoid function that enables solutions when the inclination angle exceeds the threshold, and priorities are given to control objectives dynamically. The simulation outcomes indicate that the approach is more effective in the correction of the trajectory and control of inclination angle than the conventional MPC and other optimization-based filters, such as the PSO and SSA, in the presence of the noisy drilling environment. Full article

This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance. Full article

The appearance of a negative mass term in the classical, non-relativistic Klein–Gordon equation deduced from mechanical interactions describes a repulsive interaction. In the case of a traveling wave, this results in an increase in amplitude and a decrease in the wave propagation velocity. Since this leads to dissipation, it is a symmetry-breaking phenomenon. After the repulsive interaction is eliminated, the system evolves towards the original state. Given that the interactions within the system are conservative, it would be assumed that even the original state is restored. The analysis to be presented shows that a wave with a lower angular frequency than the original one is transformed back to a slightly larger amplitude. This description is a suitable model of the axion effect, during which an electromagnetic wave interacts with a repulsive field and becomes of a continuously lower frequency. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Accurate and reliable vehicle localization is essential for autonomous driving in complex outdoor environments. Traditional feature-based visual–inertial odometry (VIO) suffers from sparse features and sensitivity to illumination, limiting robustness in outdoor scenes. Deep learning-based optical flow offers dense and illumination-robust motion cues. However, existing methods rely on simple bidirectional consistency checks that yield unreliable flow in low-texture or ambiguous regions. Global navigation satellite system (GNSS) measurements can complement VIO, but often degrade in urban areas due to multipath interference. This paper proposes a multi-sensor fusion system that integrates monocular VIO with GNSS measurements to achieve robust and drift-free localization. The proposed approach employs a hybrid VIO framework that utilizes a deep learning-based optical flow network, with an enhanced consistency constraint that incorporates local structure and motion coherence to extract robust flow measurements. The extracted optical flow serves as visual measurements, which are then fused with inertial measurements to improve localization accuracy. GNSS updates further enhance global localization stability by mitigating long-term drift. The proposed method is evaluated on the publicly available KITTI dataset. Extensive experiments demonstrate its superior localization performance compared to previous similar methods. The results show that the filter-based multi-sensor fusion framework with optical flow refined by the enhanced consistency constraint ensures accurate and reliable localization in large-scale outdoor environments. Full article

Background: Key performance indicators (KPIs) are widely used to evaluate embryologist performance in IVF laboratories, yet they are sensitive to patient demographics, treatment indications, and case allocation. Artificial intelligence (AI) offers opportunities to benchmark personal KPIs against context-aware expectations. This study evaluated whether personal CPR-based KPIs are measurable or distorted when compared with AI-derived predictions. Methods: We retrospectively analyzed 474 ICSI-only cycles performed by a single senior embryologist between 2022 and 2024. A Random Forest trained on 1294 institutional cycles generated AI-predicted clinical pregnancy rates (CPRs). Observed and predicted CPRs were compared across age groups, BMI categories, and physicians using cycle-level paired comparisons and a grouped calibration statistic. Results: Overall CPRs were similar between observed and predicted outcomes (0.31 vs. 0.33, p = 0.412). Age-stratified analysis showed significant discrepancy in the >40 group (0.11 vs. 0.18, p = 0.003), whereas CPR in the 35–40 group exceeded predictions (0.39 vs. 0.33, p = 0.018). BMI groups showed no miscalibration (p = 0.458). Physician-level comparisons suggested variability (p = 0.021), while grouped calibration was not statistically significant (p = 0.073). Conclusions: Personal embryologist KPIs are measurable but influenced by patient and physician factors. AI benchmarking may improve fairness by adjusting for case mix, yet systematic bias can persist in high-risk subgroups. Multi-operator, multi-center validation is needed to confirm generalizability. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

Background: Colorectal cancer is the third most diagnosed cancer and a leading cause of cancer-related deaths worldwide. Early detection significantly improves patient outcomes, yet many cases are identified only at late stages. The high molecular and genetic heterogeneity of colorectal cancer presents major challenges in accurate diagnosis, prognosis, and therapeutic stratification. Recent advances in gene expression profiling offer new opportunities to discover genes that play a role in colorectal cancer carcinogenesis and may contribute to early diagnosis, prognosis prediction, and the identification of novel therapeutic targets. Methods: This study involved 142 samples: 84 primary tumor samples, 27 liver metastases, and 31 adjacent non-tumor tissues serving as controls. RNA sequencing was performed on a subset of tissues (12 liver metastases and 3 adjacent non-tumor tissues) using a targeted RNA panel covering 395 cancer-related genes. Data processing and differential gene expression analysis were carried out using the DRAGEN RNA and DRAGEN Differential Expression tools. The expression of six genes involved in hypoxia and epithelial-to-mesenchymal transition (EMT) pathways (SLC16A3, ANXA2, P4HA1, SPP1, KRT19, and LGALS3) identified as significantly differentially expressed was validated across the whole cohort via quantitative real-time PCR. The relative expression levels were determined using the ΔΔct method and log2FC, and compared between different groups based on the sample type; clinical parameters; and mutational status of the genes KRAS, PIK3CA, APC, SMAD4, and TP53. Results: Our results suggest that the expression of all the validated genes is significantly altered in metastases compared to non-tumor control samples (p < 0.05). The most pronounced change occurred for the genes P4HA1 and SPP1, whose expression was significantly increased in metastases compared to non-tumor and primary tumor samples, as well as between clinical stages of CRC (p < 0.001). Furthermore, all genes, except for LGALS3, exhibited significantly altered expression between non-tumor samples and samples in stage I of the disease, suggesting that they play a role in the early stages of carcinogenesis (p < 0.05). Additionally, the results suggest the mutational status of the KRAS gene did not significantly affect the expression of any of the validated genes, indicating that these genes are not involved in the carcinogenesis of KRAS-mutated CRC. Conclusions: Based on our results, the genes P4HA1 and SPP1 appear to play a role in the progression and metastasis of colorectal cancer and are candidate genes for further investigation as potential biomarkers in CRC. Full article

This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies. The task involves classifying cars, pedestrians, and cyclists using radar-derived features. The grid search–optimized model employs a compact architecture with two hidden layers and 10 neurons per layer, leveraging kinematic correlations and motion descriptors to achieve mean accuracies of 90.06% (validation) and 90.00% (test). In contrast, the GA-optimized model adopts a deeper architecture with nine hidden layers and 30 neurons per layer, integrating an expanded feature set that includes object dimensions, signal-to-noise ratio (SNR), radar cross-section (RCS), and Kalman filter–based motion descriptors, resulting in substantially higher performance at approximately 97.40% mean accuracy on both validation and test datasets. Principal Component Analysis (PCA) and SHapley Additive exPlanations (SHAP) highlight the enhanced discriminative power of the new set of features, while parallelized GA execution enables efficient exploration of a broader hyperparameter space. Although currently optimized for urban traffic scenarios, the proposed approach can be extended to highway and extra-urban environments through targeted dataset expansion and developing additional features that are less sensitive to object kinematics, thereby improving robustness across diverse motion patterns and operational contexts. Full article

In this paper, a UAV target tracking method with 6G integrated sensing and communication (ISAC) is proposed to address the surveillance requirements for unmanned aerial vehicle (UAV) targets in the context of the rapid development of low-altitude economy. Firstly, a target tracking system model for UAVs is established based on the ISAC base station transceiver architecture. Then, an unscented Kalman filter (UKF) target tracking framework is designed to tackle the occlusion effect during UAV navigation. Specifically, the measurement position information of the UAV is obtained through a spatial rotation-based parameter estimation method. Subsequently, occlusion is detected by analyzing the Line-of-Sight (LoS) visibility between the UAV and the base station. On this basis, the problem of short-term and long-term trajectory loss under occlusion is solved by integrating cubic interpolation with a constant velocity (CV) model, which enables real-time UAV trajectory tracking. Finally, simulation results demonstrate that: (1) under no occlusion, the average estimation errors of the X/Y/Z axes are 0.82 m, 0.79 m, and 0.68 m, respectively; (2) under short-term occlusion, the average errors of the X/Y/Z axes are 1.25 m, 2.18 m, and 1.05 m, with a convergence time of 1 s after LoS recovery; (3) under long-term occlusion, the average errors of the X/Y/Z axes are 2.87 m, 3.79 m, and 1.85 m, with a convergence time of 5 s after LoS recovery; (4) the velocity estimation error can quickly converge to within 0.2 m/s after re-acquiring observations. The proposed method exhibits small trajectory and velocity estimation errors in different occlusion scenarios, effectively meeting the requirements for UAV target tracking. Full article