Search Results (9,955)

The range-extended sprayer can effectively balance the requirements of economy and power performance, which represents the development and transformation trend of intelligent plant protection machinery in the future. To more intuitively and reliably explore the energy variation rules of the range-extended sprayer under different energy management strategies (EMSs) and achieve optimal fuel economy, a co-simulation platform for energy management of the range-extended sprayer under multi-condition cyclic operation was established based on AVL-CRUISE and MATLAB Simulink. Meanwhile, a fuzzy control-based EMS optimized by the particle swarm optimization (PSO) algorithm was proposed. Simulation results show that the comprehensive fuel consumption of the PSO-optimized fuzzy control EMS is 3.68 kg; compared with the conventional fuzzy control strategy, its fuel economy is improved by 4.90%, and by 8.23% compared with the multi-point power following strategy. Subsequently, an energy management test platform for the range-extended sprayer was built, and experimental verification was carried out. The platform test results indicate that the electricity difference between the platform test and the simulation test is 0.38%, and the fuel consumption difference is 1.6%, both within a reasonable range. This further verifies the reliability of the simulation platform for the improved energy management model and the feasibility of the proposed EMSs. The research content and results provide theoretical basis and technical support for the optimization of EMSs and the joint simulation method of energy management for range-extended sprayers. Full article

Modern manufacturing quality control demands intelligent, adaptive inspection systems capable of real-time contamination detection with minimal computational overhead. We present a five-agent vision framework for material-aware contamination detection in manufacturing environments. The system comprises: a Material Classification Agent that identifies contamination type (fiber, sand, or mixed), three Material-Specific Detection Agents, each employing patch-based CNNs optimized for their respective material with dynamic patch size selection (128 px, 256 px, 384 px), and an Adaptation Agent that monitors performance and eliminates consistently failing patch size configurations. This hierarchical architecture enables intelligent routing to specialized detectors and continuous refinement through performance-driven adaptation. The Material Classification Agent achieves 98% accuracy in contamination type identification. Material-specific agents demonstrate F1-scores of 0.968 (fiber), 0.977 (sand), and 0.977 (mixed) with real-time inference (2.40–11.11 ms per 512 × 512 image). The Adaptation Agent implements selective patch size elimination: configurations failing quality thresholds (F1 < 0.5) across multiple evaluation cycles are removed from the detection pipeline. On the synthetic test split used in this study, comparative evaluation against PatchCore, WinCLIP, and PaDiM shows 3–45× higher F1-scores with superior accuracy–latency trade-offs, validating the efficacy of specialized material-aware architectures for manufacturing contamination detection. Full article

Digitalization of railway traffic dispatching systems is a key step in the modernization of railway telecommunication infrastructure. This paper presents a case study of the migration from legacy analog technology to a software-centric dispatching platform that integrates digital signal processing, optical fiber transmission, and Internet Protocol (IP)-based network architectures, as implemented in the Serbian railway system. The modernization is performed through an iterative, incremental process: existing analog dispatcher equipment and established operating procedures are preserved, while digital dispatching centers, trackside communication nodes, and radio-dispatching services are introduced gradually. This staged evolution enables high-capacity, noise-resilient communication and seamless interconnection between the old and the new subsystems without disrupting railway operations. The adoption of software-based control and integrated digital signal processing provides modular scalability, real-time system supervision, automated diagnostics, and improved maintainability. One of critical services within the new architecture, the Centralized Call Record- and Message-Archiving System (CCRMAS), provides a centralized platform that captures, secures, and retrieves operational railway communication in real time for monitoring, post-incident analysis, and regulatory compliance. The resulting architecture, deployed within Serbian Railways, establishes a scalable and resilient foundation for future automation, interoperability, and integration within intelligent railway traffic-management environments. Thus, the paper extracts a generalizable hybrid migration architecture model and transferable design principles, supported by deployment artifacts and illustrated through migration scenarios, that can be applied to the modernization of other legacy-intensive railway networks. Full article

The high-speed Electromagnetic Suspension (EMS) maglev guidance system exhibits inherent characteristics of strong nonlinearity, parameter time-variation, and complex external disturbances. To further optimize and improve the control performance of the guidance system for high-speed maglev trains, a novel intelligent control strategy that integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with Super-Twisting Sliding Mode Control (STSMC) is proposed. Focusing on a single-ended guidance unit with differential control of dual electromagnets, an STSMC controller is first designed based on a cascaded control framework. To overcome the limitation of offline parameter tuning in dynamic operational conditions, a reinforcement learning optimization framework employing DDPG is introduced. A multi-objective hybrid reward function is formulated, incorporating error convergence, sliding mode stability, and chattering suppression, thereby realizing the online self-tuning of core STSMC parameters via real-time interaction between the agent and the environment. Numerical simulations under typical disturbance conditions verify that the proposed DDPG-STSMC controller significantly reduces the amplitude of guidance gap variation and accelerates dynamic recovery compared to conventional PID control. Its superior performance in disturbance rejection, control accuracy, and operational adaptability is validated. This study, conducted through high-fidelity numerical simulations based on actual system parameters, provides a robust theoretical foundation for subsequent hardware-in-the-loop (HIL) experimentation. Full article

The spotted lanternfly (SLF, Lycorma delicatula) is an invasive planthopper causing severe agricultural and environmental damage in 20 U.S. states. SLF control remains constrained by (1) overreliance on broad-spectrum pesticides that harm nearby ecosystems, (2) inefficiency and ecological risk of alternative methods, and (3) underutilization of SLF behavioral traits and artificial intelligence (AI) in IPM. This study introduces AI-LyD, an AI-driven IPM framework integrating behavioral ecology, predictive modeling, image-based detection, and low-cost physical controls. Incorporating SLF behavioral constraints, including cold-exposure requirements for egg hatching, into ecological models improved prediction accuracy (AUC = 0.821, Sensitivity = 0.888, Kappa = 0.642) and reconstructed SLF distributions consistent with current proliferation trends. A YOLO-based detection model leveraging SLF clustering behavior improved identification accuracy from 84% to 96% and reduced false positives from 42% to 8% in real-world drone-collected imagery. Exploiting SLF crawling, jumping, and hydrophobic behaviors, the novel Aquabex water-moat device with an optimized 60° opening trapped 85% of Stage I–IV nymphs and reduced adult invasions by 67%, at an estimated cost below USD $0.50 per unit. Field deployments across four locations in Hunterdon County, New Jersey, achieved a 91% population reduction (95% CI: 90.1–92.0%). Together, these results establish AI-LyD as the first operational, scalable SLF IPM system, and this paradigm can be applied to controlling other invasive species. Full article

The resilience of Intelligent Transportation Systems (ITSs) heavily relies on distributed Battery Energy Storage Systems (BESSs) deployed in harsh, unattended highway environments. Traditional maintenance strategies often fail to account for the dynamic feedback between battery aging, environmental stress, and maintenance response latency. This study proposes a system dynamics (SD) framework to evaluate and optimize the resilience of these critical power infrastructures. By modeling the nonlinear interactions among sensor data, controller logic, and remote discharge terminals, we simulate the system’s dynamic behavior over a 36-month lifecycle. The results reveal a critical “scalability threshold”: when battery pack quantity exceeds 40 units, the system’s self-healing time increases disproportionately, degrading resilience. Furthermore, the study identifies 384 V as the optimal “Resilience Topology Voltage”, offering the fastest recovery speed by balancing thermal stability with consistency management efficiency. These findings provide theoretical guidelines for configuring BESS capacity and optimizing remote maintenance protocols to ensure uninterrupted highway operations. Full article

Hospital wastewater (HWW) carries a high and variable burden of pathogenic microorganisms, along with a diverse spectrum of emerging contaminants, such as pharmaceutically active compounds (PhACs) and antimicrobial resistance (AMR) determinants, posing significant challenges to conventional municipal treatment systems. The COVID-19 pandemic intensified the global use of disinfection technologies for infection control, inadvertently leading to the generation and release of novel classes of disinfection by-products (DBPs) and transformation products (TPs). These emerging by-products, alongside the persistent release of pharmaceuticals and AMR elements, have exposed critical limitations in conventional and advanced disinfection processes when applied to such complex matrices. This review synthesizes recent literature on disinfection-oriented advanced treatment strategies and other contaminants of emerging concern in hospital effluents worldwide. The discussed technologies include chlorine-based disinfection (e.g., free chlorine and chlorine dioxide), ozonation, ultraviolet irradiation (UV), electrochemical disinfection (ECD), nanomaterial-enabled disinfection, and combined multi-barrier schemes. While real-time monitoring of key compounds in HWW is increasingly feasible, critical bottlenecks remain: culture-based indicators may underestimate viable but non-culturable populations, molecular assays quantify genes without directly reflecting infectivity or transfer potential, and complex matrices hinder methodological harmonization. Future efforts should prioritize risk-based multi-barrier design, activity-informed monitoring, and intelligent process control to achieve robust co-mitigation of pathogens, PhACs, and AMR while minimizing disinfection by-products (DBPs) and life-cycle energy consumption. Full article

Vertical farming requires precise environmental control, yet long-term multivariable analyses linking environmental dynamics and crop growth remain limited. This study analyzes a two-year operational dataset from a commercial vertical farm in South Korea to evaluate the suitability of advanced artificial intelligence models for harvest yield prediction. Conventional machine learning models and recent deep learning architectures were systematically benchmarked under identical conditions. Among them, the patch-based Transformer model achieved the highest predictive accuracy (R² = 0.942; RMSE = 5.81 g per plant). The variable-importance analysis revealed that daily light integral and CO₂ concentration were the dominant drivers of harvest yield variability, jointly accounting for more than 76% of total contribution. These findings demonstrate the effectiveness of Transformer-based architectures for long-term multivariate time series modeling and provide actionable insights for the data-driven optimization of vertical farming systems. Full article

In protected horticultural production, tomato late blight shows strong environmental inducibility, with a short latent period, rapid risk accumulation, and a limited control window, which challenges conventional post-event disease monitoring. To address this, a tomato late blight risk perception and predictive control approach for protected production is proposed, integrating deep temporal modeling of environmental factors, visual symptom perception, and risk-driven greenhouse control to enable prospective assessment and proactive intervention. Based on disease mechanisms and real greenhouse conditions, an artificial intelligence (AI) framework covering perception, prediction, and regulation is constructed, moving beyond reliance on visible symptoms alone. Long-term evolution of key variables, including temperature, air humidity, leaf wetness, and light intensity, is modeled using deep temporal networks, while early weak lesions and subtle texture changes are captured by visual models. Cross-modal fusion in a unified risk space generates continuous risk scores to drive greenhouse regulation. Experiments on a multimodal dataset from a real greenhouse in Bayannur, Inner Mongolia, show that the proposed method outperforms vision-based and environment-based baselines in recognition and risk prediction. It achieves about 0.95 accuracy, 0.94 F1-score, and over 0.97 area under the receiver operating characteristic curve (AUC), while providing more than 20 h of early warning before disease onset. In environmental modeling, the deep temporal model consistently surpasses threshold-based methods, logistic regression, and long short-term memory/gated recurrent unit (LSTM/GRU) baselines in risk lead time, false alert rate, and prediction stability. Full article

This paper presents the development and investigation of an intelligent control system for a high-frequency ozone generator integrated into an IoT-based and telecommunication environment. A cyber-physical nonlinear mathematical model combining the electrical, thermal, gas-dynamic, and chemical subsystems of the ozone generation process is proposed. The model was implemented in discrete-time form and experimentally validated using the corona–discharge-based high-frequency ozonator ETRO-02. The deviation between simulation and experimental results did not exceed 5.3% for settling time, 6.7% for overshoot, 1.6% for steady-state ozone concentration, and 0.9% for gas temperature, confirming the adequacy of the proposed model. Based on this model, a hierarchical two-level intelligent control architecture is synthesized, consisting of a fast local control loop with a cycle time of 1–5 ms and a supervisory monitoring layer. The proposed adaptive state-feedback control law with online gain adjustment ensures stable real-time operation under nonlinear dynamics, ±20% parameter variations, network delays of 1–10 ms, and packet loss probabilities of up to 5%. As a result, the settling time is reduced from 420 ms to 160 ms, the overshoot from 12.5% to 3.1%, and the steady-state error from 6.5% to 1.6%, while the specific energy consumption decreases from 11.8 to 6.2 Wh/m³. The obtained results demonstrate that the integration of a cyber-physical model with a millisecond-level intelligent control system significantly improves the dynamic performance, robustness, and energy efficiency of high-frequency ozone generators compared to classical control and monitoring-oriented IoT systems. Unlike cloud-centric IoT monitoring architectures that operate at second-level update cycles, the proposed system closes the control loop locally at the millisecond scale, enabling stabilization of fast nonlinear electro-plasma dynamics. The results demonstrate that edge-intelligent adaptive control significantly enhances both dynamic performance and energy efficiency, confirming the feasibility of millisecond-level cyber-physical regulation for industrial ozone generation systems. Full article

One of the most common and severe complications of diabetes mellitus is diabetic foot, making early detection a public health priority. Infrared thermography is a promising noninvasive technique for identifying abnormal thermal patterns associated with inflammation, neuropathy, angiopathy, and tissue damage. This technique involves acquiring infrared radiation emitted by the skin and processing it to generate thermal maps that reflect underlying physiological changes. However, the reliability of thermographic assessments depends on strict technical conditions, including sensor performance, environmental control, and reproducible measurements. Despite its advantages, the clinical adoption of thermography is limited by the absence of standardized acquisition protocols and the influence of external and physiological factors on temperature measurements. Addressing these challenges is essential to ensure the accurate interpretation and validation of results. Recent advances, such as the incorporation of artificial intelligence algorithms and the development of portable, low-cost devices, offer new opportunities to enhance thermography’s applicability in clinical settings and home monitoring. Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) affects over 600,000 individuals worldwide each year, and its incidence continues to rise. There is a growing need for novel therapeutic strategies that achieve high antitumor efficacy while minimizing functional impairment. We developed a novel approach to enhance intracellular delivery of immunotoxins (ITs) by combining a photosensitizer under illumination. This method, termed intelligent Targeted Anti-body Phototherapy (iTAP), utilizes light as a spatiotemporal trigger to promote the cytoplasmic release of toxins. In the present study, we investigated the in vivo therapeutic efficacy of iTAP using an EGFR-targeted IT composed of cetuximab conjugated to saporin (IT-Cmab), administered in combination with the clinically used photodynamic therapy (PDT) photosensitizer NPe6, in a xenograft mouse model. Methods: Sa3 cells were implanted subcutaneously into the right hind limb of nude mice. Mice were randomized into four groups (n = 5): (i) iTAP (IT-Cmab plus NPe6), (ii) IT-Cmab alone, (iii) NPe6 alone, and (iv) saline control. Treatment was initiated once tumors exceeded 40 mm³. Mice received intraperitoneal IT-Cmab (0.5 mg/kg), followed 72 h later by intravenous NPe6 (5 mg/kg). Tumors were irradiated 3–4 h later using a custom LED device (670 nm, 262 mW/cm², 30 J/cm²). Tumor volume and body weight were monitored over time, and antitumor effects were analyzed using a linear mixed-effects model. Results: iTAP treatment produced the earliest and most pronounced inhibition of tumor growth among the four groups. Significant suppression was observed from day 9 and persisted throughout the study. IT-Cmab alone showed a moderate but sustained antitumor effect with a later onset, whereas NPe6-mediated PDT exhibited only a delayed and weaker response. On the final day, median tumor volumes showed substantial reductions relative to the Control group (601%), with decreases of 41% in PDT (357%), 55% in IT-Cmab (271%), and 70% in iTAP (178%). Overall, iTAP demonstrated the strongest and most durable therapeutic efficacy in vivo. Conclusions: These findings indicate that iTAP represents a promising therapeutic strategy for HNSCC. Full article

Background/Objectives: The surgical treatment of basal cell carcinoma (BCC) remains challenging due to the time-consuming, expensive and invasive nature of Mohs micrographic surgery. The objective is to develop a standardized protocol for managing diagnosis, surgery, and margin control within a single patient visit. Methods: Several protocols were tested to establish a “BCC-One-Stop-Shop”, combining in vivo and ex vivo margin mapping of BCC, pre- and postoperatively using Line-field confocal optical coherence tomography (LC-OCT). We introduce an algorithm enabling real-time localization of LC-OCT acquisitions on a previously acquired dermoscopy image. Additionally, an artificial intelligence model provides a BCC probability score based on LC-OCT images. Together, the co-localization algorithm and AI BCC model generate a color-coded visualization of the tumor within the dermoscopy image, allowing precise pre-operative in vivo margin assessment. Results: We found our protocol, the implementation of the co-localization tool and the AI model, to be quick to apply, easy to learn and helpful regarding the initial determination of BCC tumor margins. Patients responded positively to the recognizable visualization of the disease. Conclusions: Pre- and postoperative margin mapping using LC-OCT imaging appears to be effective and feasible and could reduce time, costs, resources, excision sizes and patient burden by sparing additional excision steps in micrographic surgery. The integration of real-time co-localization and the AI-calculated probability score represent meaningful and practical enhancements for routine clinical use. To further evaluate the efficacy and safety of the BCC-One-Stop-Shop-Method and the newly introduced device features, larger-scale studies are warranted and are currently being conducted. Full article

Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR-based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Error (ATE) RMSE by 11.2–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments. Full article

Welding copper (Cu) and aluminum (Al) is highly demanded for lightweight and cost-effective manufacturing. However, it faces significant challenges. First, substantial differences in physical properties may lead to high residual stresses and distortion. Second, brittle intermetallic compounds (IMCs) readily form at the interface, severely compromising the joint’s mechanical properties and electrical conductivity. Third, the native oxide film on Al impedes effective wetting and bonding. Therefore, effective control over the interfacial microstructure of the welded joint is essential. This review provides a critical analysis and comparison of several typical welding techniques, including laser welding (LW), friction stir welding (FSW), ultrasonic welding (UW), brazing and soldering, and welding–brazing. These analyses focus on their process characteristics, joint microstructures, and corresponding formation mechanisms. Furthermore, this review synthesizes key strategies for enhancing joint quality, including process parameter optimization, introduction of functional interlayers, and external assistance, aimed at optimizing joint microstructure and minimizing defects. Based on the analysis, this work provides comparative insights into process selection and microstructure control, and highlights future directions: advancing novel methods such as magnetic pulse welding and transient liquid phase bonding; developing intelligent real-time process control to suppress brittle IMCs and associated defects; promoting sustainable practices and establishing standardized performance evaluation; and systematically investigating long-term reliability to support the industrial application of robust Cu/Al joints. Full article