Search Results (5,002)

Herbicides are chemical compounds that are toxic to weed plants. Modern agriculture relies heavily on herbicides for the control of weeds to maximize crop yields. Herbicide usage in the Australian grains industry is estimated to have increased by more than 65% from 2014 to 2024, which equates to more than AUD 2.50 billion dollars per year. The increased popularity of herbicides in farming systems has raised concerns about their negative impacts on the environment, human health and agricultural sustainability due to the rapid evolution of herbicide resistance, as well as their behaviour and fate in the soil. Due to excessive use of herbicides, soil and water pollution, reduced biodiversity and depression in soil heterotrophic bacteria (including denitrifying bacteria) and fungi are becoming increasingly common. Biological degradation governed by microorganisms serves as a major natural remediation process for a variety of pollutants including herbicides. This review provides a brief overview of the present status of herbicide residues in Australian farming systems, with a focus on the microbial degradation of herbicides in soil. It highlights key bacterial and fungal strains involved and the environmental factors influencing the biodegradation process. Recent advancements, including the application of omics technologies, are outlined to provide a comprehensive understanding of the biodegradation process. Full article

Background/Objectives: This study reports pilot-scale production of gluten-free rice malt extract powder from Thai Chainat 1 rice as a sustainable alternative to barley malt extract. Methods: The process combined controlled malting with sequential enzymatic hydrolysis, optimized through bench-scale validation and scaled up to a 1500 L pilot system. Results: The resulting powder was rich in fermentable sugars (maltose 43.9 g/100 g, glucose 14.3 g/100 g), protein (5.2 g/100 g), γ-aminobutyric acid (GABA, 245.2 mg/100 g), and thiamine (0.64 mg/100 g), while free of detectable gluten, aflatoxins, and heavy metals. Microbiological quality met international safety standards. Shelf-life studies under ambient and accelerated conditions demonstrated chemical stability and bioactive retention for up to three years in laminated and HDPE packaging. Application trials confirmed that the rice malt extract powder supported yeast, bacterial, and mold growth comparably to commercial malt extract in culture media, with optimized yeast–mold agar formulations enabling direct substitution without supplementary glucose. The powder was further applied to a gluten-free malt beverage, yielding a beer-like product with acceptable physicochemical and nutritional quality, though residual alcohol levels exceeded the non-alcoholic threshold and required process optimization. Conclusions: Rice malt extract powder represents a safe, functional ingredient suitable for food, beverage, and industrial microbiology applications, offering opportunities to reduce import dependency and advance gluten-free innovation in emerging markets. Full article

A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve alone. With widespread applications in domains such as robotics, smart grids, and communication networks, the coordination and control of networked systems have become a vital research focus—driven by the complexity of distributed interactions and decision-making processes. Graph-based reinforcement learning (GRL) has emerged as a powerful paradigm that combines reinforcement learning with graph signal processing and graph neural networks (GNNs) to develop policies that are relationally aware, scalable, and adaptable to diverse network topologies. This survey aims to advance research in this evolving area by providing a comprehensive overview of GRL in the context of networked coordination and control. It covers the fundamental principles of reinforcement learning and graph neural networks, examines state-of-the-art GRL models and algorithms, reviews training methodologies, discusses key challenges, and highlights real-world applications. By synthesizing theoretical foundations, empirical insights, and open research questions, this survey serves as a cohesive and structured resource for the study and advancement of GRL-enabled networked systems. Full article

Cyanuric acid (CYA) is widely used as a chlorine stabilizer in swimming pools, but concentrations above 75 mg L⁻¹ cause overstabilization and loss of disinfection capacity. This study evaluated CYA removal by advanced oxidation processes, including heterogeneous photocatalysis, photo-Fenton, photo-persulfate, and anodic oxidation (AO). AO with boron-doped diamond anodes proved most effective, achieving up to 90% total organic carbon removal in ultrapure water. When applied to real swimming pool samples (118 and 251 mg L⁻¹ CYA), the process achieved significant CYA abatement, demonstrating its potential as a practical strategy to control overstabilization without additional chemicals. Full article

Efficient composting is essential for sustainable organic waste management, yet conventional monitoring approaches are limited by single-parameter measurements and delayed response. This study presents an integrated sensor–AI framework designed to capture the interaction between thermal, chemical, and environmental factors governing composting. A distributed in-pile sensor network continuously measured temperature, moisture, and pH, while ambient parameters and gaseous emissions (O₂, CO₂, CH₄) were recorded to validate process dynamics. Statistical analyses, including correlation and regression modeling, were applied to quantify parameter interdependencies and the influence of external conditions. Results showed strong positive associations between temperature, moisture, and CO₂, and an inverse relationship with O₂, indicating active microbial respiration and accelerated decomposition. The validated sensors maintained high accuracy (±0.5 °C, ±3%, ±0.1 pH units) and supported real-time feedback control, leading to improved nutrient enrichment (notably N, P, and K) in the final compost. The framework demonstrates a transition from static measurement to intelligent, feedback-driven management, providing a scalable and reliable platform for optimizing compost quality and advancing sustainable waste-to-resource applications. Full article

Conventional electro-permanent magnet (EPM) lifting/holding systems, typically based on NdFeB magnets, face efficiency limitations because continuous current is required either for standby condition to avoid accidentally attracting the objects around or for gently approaching and separating from sensitive iron-based target objects during gripping and releasing processes. Low Coercive Force (LCF) magnets offer an alternative, as their magnetization can be tuned with short current pulses and maintained without continuous current. However, this approach demands fast and precise flux control to eliminate the issues mentioned above. This paper introduces a novel flux control method based on the Hill Climb Search (HCS) algorithm. Once the required flux is identified, the system rapidly adjusts the magnetization of LCF magnet by applying optimized pulse trains within a short time. Experimental evaluation confirms that the proposed method effectively establishes and sustains the target magnetization level without additional current input. This approach has significant potential to advance and expand the use of Low Coercivity EPM systems as an alternative to classical systems. Full article

As drones become increasingly integrated into civilian and industrial domains, the demand for natural and accessible control interfaces continues to grow. Conventional manual controllers require technical expertise and impose cognitive overhead, limiting their usability in dynamic and time-critical scenarios. To address these limitations, this paper presents a multilingual voice-driven control framework for quadrotor drones, enabling real-time operation in both English and Arabic. The proposed architecture combines offline Speech-to-Text (STT) processing with large language models (LLMs) to interpret spoken commands and translate them into executable control code. Specifically, Vosk is employed for bilingual STT, while Google Gemini provides semantic disambiguation, contextual inference, and code generation. The system is designed for continuous, low-latency operation within an edge–cloud hybrid configuration, offering an intuitive and robust human–drone interface. While speech recognition and safety validation are processed entirely offline, high-level reasoning and code generation currently rely on cloud-based LLM inference. Experimental evaluation demonstrates an average speech recognition accuracy of 95% and end-to-end command execution latency between 300 and 500 ms, validating the feasibility of reliable, multilingual, voice-based UAV control. This research advances multimodal human–robot interaction by showcasing the integration of offline speech recognition and LLMs for adaptive, safe, and scalable aerial autonomy. Full article

Deep-sea submersibles, often featuring a symmetrical design for hydrodynamic stability, operate as safety-critical systems in extreme environments, where the tight dynamic coupling between subsystems like hydraulics and propulsion creates complex failure modes that are challenging to diagnose. A localized fault in one system can propagate, inducing anomalous behavior in another and confounding conventional single-system monitoring approaches. This paper introduces a novel unsupervised anomaly detection framework, the Dual-Stream Coupled Autoencoder (DSC-AE), designed specifically to address this cross-system fault challenge. Our approach leverages a dual-encoder, single-decoder architecture that explicitly models the normal coupling relationship between the hydraulic and propulsion systems by forcing them into a shared latent representation. This architectural design establishes a holistic and accurate baseline of healthy, system-wide operation. Any deviation from this learned coupling manifold is robustly identified as an anomaly. We validate our model using real-world operational data from the deep-sea submersible, including curated test cases of intra-system and inter-system faults. Furthermore, we demonstrate that the proposed framework offers crucial diagnostic interpretability; by analyzing the model’s reconstruction error heatmaps, it is possible to trace fault origins and their subsequent propagation pathways, providing intuitive and actionable decision support for submersible operation and maintenance. This powerful diagnostic capability is substantiated by superior quantitative performance, where the DSC-AE significantly outperforms baseline methods in detecting propagated faults, achieving higher accuracy and recall, among other performance metrics. Full article

Phenol red is a widely used, low-cost, label-free colorimetric pH indicator that bridges traditional colorimetric assays with modern quantitative imaging and cell-based screening platforms. Its protonation-dependent absorbance shift (430–560 nm) allows for the real-time monitoring of extracellular acidification, which indirectly reflects cellular metabolism, growth, and respiration. Although phenol red lacks the molecular specificity of genetically encoded or fluorogenic biosensors, it remains useful in systems where pH changes are effective proxies for physiological processes. Existing tissue digestion protocols often overlook key parameters, especially pH control and enzyme cofactor use. This study presents a straightforward, spectrophotometric method to monitor and adjust the pH of low-volume (1 mL) buffered enzymatic dissociation media using phenol red and a plate reader. We titrated dissociation solutions to physiological pH (~7.4) using spectrophotometric pH measurements validated against conventional glass pH probe readings, confirming method reliability. Accurate pH assessment is critical for isolating viable primary cells for downstream applications such as tissue engineering, single-cell omics, and neurophysiological assays. We highlight that papain-based dissociation media supplemented with L-cysteine can be acidic (pH 6.6) if unadjusted, compromising cell viability. This accessible approach enhances reproducibility by promoting pH documentation concerning dissociation conditions that contribute to advancing consistency in biomedical, cellular, neuronal, and tissue engineering research. Full article

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Crack segmentation in images plays a pivotal role in the monitoring of structural surfaces, serving as a fundamental technique for assessing structural integrity. However, existing methods that rely solely on RGB images exhibit high sensitivity to light conditions, which significantly restricts their adaptability in complex environmental scenarios. To address this, we propose a structure-aware progressive multi-modal fusion network (SPMFNet) for RGB-thermal (RGB-T) crack segmentation. The main idea is to integrate complementary information from RGB and thermal images and incorporate structural priors (edge information) to achieve accurate segmentation. Here, to better fuse multi-layer features from different modalities, a progressive multi-modal fusion strategy is designed. In the shallow encoder layers, two gate control attention (GCA) modules are introduced to dynamically regulate the fusion process through a gating mechanism, allowing the network to adaptively integrate modality-specific structural details based on the input. In the deeper layers, two attention feature fusion (AFF) modules are employed to enhance semantic consistency by leveraging both local and global attention, thereby facilitating the effective interaction and complementarity of high-level multi-modal features. In addition, edge prior information is introduced to encourage the predicted crack regions to preserve structural integrity, which is constrained by a joint loss of edge-guided loss, multi-scale focal loss, and adaptive fusion loss. Experimental results on publicly available RGB-T crack detection datasets demonstrate that the proposed method outperforms both classical and advanced approaches, verifying the effectiveness of the progressive fusion strategy and the utilization of the structural prior. Full article

The growing demand from the automotive industry for lightweight, high-performance, and advanced manufacturing techniques for efficient and cost-effective production has accelerated the adoption of fiber-reinforced polymer composites. However, considering the manufacturing complexity of these materials, design remains challenging due to the intricate and interdependent relationships between the process conditions, the part geometry, and the resulting microstructure and quality. This research utilized the Autodesk Moldflow Insight software to design an injection molding process for the manufacturing of discontinuous glass fiber-reinforced polymer parts through computer modeling. A geometrically complex car fender was used as a case study. The effects of various process parameters, particularly gate locations, on the injection-molded parts’ properties (such as the fiber orientation, volumetric shrinkage, and shear rate) were investigated. Multiple injection molding process configurations were designed and simulated, including three, four, and five gates at varying locations. Based on the optimal performance (i.e., low shrinkage, a consistent fiber orientation, and a controllable shear rate), an optimal configuration with four gates at appropriate locations (corresponding to the second gate location set) was identified based on multicriteria decision-making analysis, i.e., volumetric shrinkage of ${8.5}_{-2.2}^{+1.4}\%$, a fiber orientation tensor of 0.927 ± 0.011, and a stable shear rate < 74,324 (1/s). A reduced strain closure model (modified Folgar–Tucker model) was used to predict the glass fiber orientation. A multicriteria decision-making technique, based on similarity ranking with an ideal solution, was employed to optimize the gate location. The simulation results clearly demonstrate that the gate placement is crucial for material behavior during molding and for reducing common defects. The simulation-based injection molding process design for the manufacturing of discontinuous fiber-reinforced polymer parts proposed in this paper can improve the production efficiency, reduce trial-and-error rates, and improve part quality. Full article

Background/Objectives: Artificial Intelligence (AI) is rapidly advancing in medicine, facilitating personalized care by leveraging complex clinical data, imaging, and patient monitoring. This study characterizes current practices in AI use within oncology clinical trials by analyzing completed U.S. trials within the Cancer Control Continuum (CCC), a framework that spans the stages of cancer etiology, prevention, detection, diagnosis, treatment, and survivorship. Methods: This cross-sectional study analyzed U.S.-based oncology trials registered on ClinicalTrials.gov between January 2015 and April 2025. Using AI-related MeSH terms, we identified trials addressing stages of the CCC. Results: Fifty completed oncology trials involving AI were identified; 66% were interventional and 34% observational. Machine Learning was the most common AI application, though specific algorithm details were often lacking. Other AI domains included Natural Language Processing, Computer Vision, and Integrated Systems. Most trials were single-center with limited participant enrollment. Few published results or reported outcomes, indicating notable reporting gaps. Conclusions: This analysis of ClinicalTrials.gov reveals a dynamic and innovative landscape of AI applications transforming oncology care, from cutting-edge Machine Learning models enhancing early cancer detection to intelligent chatbots supporting treatment adherence and personalized survivorship interventions. These trials highlight AI’s growing role in improving outcomes across the CCC in advancing personalized cancer care. Standardized reporting and enhanced data sharing will be important for facilitating the broader application of trial findings, accelerating the development and clinical integration of reliable AI tools to advance cancer care. Full article

Erosion-corrosion is a predominant failure mechanism in the petrochemical, energy, and offshore engineering sectors, causing substantial economic losses and posing significant threats to equipment safety and personnel well-being. To address this critical issue, the present study employs a systematic approach to examine the current status and estimate the future trends in erosion-corrosion research. By utilizing bibliometric techniques, the study constructs a comprehensive knowledge map to analyze the chronological progress, research institutions, journal distribution, collaborative networks, research hotspots and cutting-edge trends in this field. The bibliometric analysis reveals that research hotspots are primarily focused on the erosion-corrosion mechanism, equipment, materials, coating structure reinforcement, and new process of anticorrosion strategies. These findings suggest an interdisciplinary integration trend and the emergence of intelligent prevention and control methods. By elucidating the evolution and future direction of erosion-corrosion research, this study offers valuable insights for advancing academic progress and technological innovation in this area. Full article