Search Results (3,636)

Soybean (Glycine max) is a major economic and food crop whose yield is severely affected by frogeye leaf spot (FLS), caused by Cercospora sojina. Current knowledge of resistance genes remains insufficient for effective molecular breeding. In this study, a recombinant inbred line (RIL) population derived from a cross between the resistant parent, Henong 60 (H60), and the susceptible parent, Dongnong L13 (DN L13), was evaluated under field conditions in Acheng (AC) and Xiangyang (XY). Plants were artificially inoculated with physiological race 7 of C. sojina, and disease severity at the R3 growth stage was recorded. Genotyping using the SoySNP660K chip yielded 54,836 high-quality single-nucleotide polymorphism (SNP) markers. A genome-wide association study (GWAS) was performed using the 3VmrMLM model by integrating dual-environment phenotypic data, and four quantitative trait nucleotides (QTNs) significantly associated with resistance to FLS were identified on chromosomes 8 (1), 17 (1), and 20 (2). By the analysis of genomic annotation, functional enrichment, metabolic pathway analyses, haplotype–phenotype association and quantitative real-time PCR (qRT-PCR), Glyma.20G155700 and Glyma.17G070500 are intended to be candidate genes related to soybean resistance to race 7 of FLS. The findings of this study provide insights into the genetic mechanisms underlying resistance to FLS in soybean. The identified molecular markers and candidate genes may provide useful resources for marker-assisted breeding and the development of disease-resistant germplasm. Full article

Landfills represent a significant source of methane emissions, with important environmental, climatic and safety impacts due to the widespread and variable nature of these emissions. Traditional monitoring methods, such as flow chambers coupled with flame ionisation detectors (FIDs), provide high accuracy but are limited in terms of spatial representativeness, operational flexibility and cost, especially during large-scale or continuous monitoring campaigns. Within this context, the European ESCAPE project aims to develop a low-cost, portable and modular platform for the detection and quantification of low methane concentrations in ambient air at complex environmental sites. The system is based on commercial MOX and NDIR sensors integrated into portable toolboxes equipped with dedicated chambers, regulated suction systems and autonomous data acquisition units with real-time transmission. This work describes the development and testing of two identical toolboxes to assess system reproducibility and the transferability of predictive models between devices. Laboratory and field tests were carried out under controlled and real landfill conditions, with comparisons against portable FID measurements. Results showed good agreement between predicted methane concentrations and reference data, with correlation indexes up to 0.77. Moreover, transferring the machine learning model between toolboxes did not produce statistically significant performance reductions, demonstrating promising robustness and generalizability of the proposed calibration strategy. Full article

The primary objective of this document is to provide a structured reference for performance evaluation of the different sensor technologies that can be found in counter-drone systems. Standardised parametric models for the sensor(s), drone(s) and the external environment are proposed, with the aim of predicting sensor performance under real-world conditions in representative protective scenarios against hostile drones. In addition to defining these models, the document introduces a set of standardised reference parameter values to ensure consistency, comparability, and practical usability in cases where complete system data are unavailable. A set of clearly stated assumptions will therefore be formulated, dictating the boundary conditions for which the selected model can or cannot be used. The advantages of the parametric and standardised approach are numerous. For example, it makes it straightforward to compare the performance of different systems during a tender process, given that a set of common parameters must be known and provided (supplier data or standardised default values) for each system. This modelling approach also reduces the costs linked to practical field tests/deployment. Note that the focus is placed on developing simpler models to ensure ease of use and practical applicability, while maintaining an acceptable level of reliability and accuracy. The framework presented in this work is strictly oriented towards the defensive sensing function of C-sUAS systems and does not address effector design, kinetic engagement or offensive sUAS capabilities. Full article

Image segmentation is a critical step in computer vision systems for phytosanitary diagnosis in cacao production. However, the reliability of classical segmentation methods remains insufficiently assessed under real field conditions, where images captured under non-standardized conditions are often affected by variable illumination, complex backgrounds, partial occlusions, and chromatic similarity between cacao pods and surrounding vegetation. This study compares global thresholding, K-means clustering, and GrabCut using 343 cocoa pod images captured in uncontrolled agricultural environments with non-standardized mobile devices; low-resolution images were retained to preserve external validity. Robustness was assessed on the full dataset using unsupervised structural metrics, including the segmented area ratio (AS), the largest component ratio (LCR), and the catastrophic failure rate (FC), while accuracy was validated on 50 manually annotated images using Intersection over Union (IoU). Wilcoxon signed-rank tests indicated statistically significant differences among methods. GrabCut achieved the best performance (IoU = 0.814), high structural coherence (LCR = 0.985), and a low catastrophic failure rate (FC = 1.7%). In contrast, K-means showed severe fragmentation and instability, whereas global thresholding was highly sensitive to illumination variability and complex backgrounds. These results indicate that GrabCut provides a robust training-free baseline for cocoa pod segmentation under uncontrolled field conditions, particularly for offline phytosanitary analysis where annotated datasets, supervised training, or GPU-based deployment are limited. Full article

Industrial dust control is shifting from single-device removal toward integrated risk diagnosis, mechanism-guided collector selection, and system-level optimization under complex operating conditions. However, harsh field environments, variable dust properties, high-temperature and high-humidity operation, combustible metal dust, and long-term equipment degradation still limit the stable performance and safe application of existing technologies. This review systematically categorizes recent research into a unified analytical framework covering monitoring, removal mechanisms, dust collectors, and system optimization. First, traditional sampling, online sensing, and intelligent monitoring methods are compared in terms of real-time capability, accuracy, calibration demand, and field adaptability. Second, physical separation, interface modification, agglomeration enhancement, and special-condition safety mechanisms are synthesized to clarify how dust properties and operating environments affect removal behavior. Third, mainstream dust collectors and their optimization strategies are evaluated based on their efficiency, pressure drop, clogging, energy consumption, and safety risk. Finally, system-level layout, simulation control, safety protection, and lifecycle management are discussed. This review highlights that future industrial dust control should couple multi-source monitoring, mechanism-based equipment selection, adaptive operation, and safety-oriented system management rather than treating monitoring, collectors, and risk control as isolated tasks. Full article

Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for estimating digital mobility outcomes from wearables, originally implemented in a combination of MATLAB, R, and Python codes. To overcome the licencing, reproducibility, and accessibility limitations of this implementation, the pipeline has been re-implemented and re-validated, against gold standards, as the open-source mobgap Python package. Here, we describe the mobgap ecosystem, detail how algorithms can be integrated and benchmarked in a reproducible way and present a re-validation of the pipeline against reference data across six clinical cohorts under real-world conditions. Validation results showed that across all cohorts, walking speed was estimated with an absolute error of 0.10 m/s and an intraclass correlation coefficient (ICC) of 0.81, demonstrating comparable or superior performance to the original implementation. Mobgap (v1.2) is openly available and is intended to serve as a reproducible reference implementation and benchmarking platform for researchers developing or validating mobility analysis algorithms using wearable data. Full article

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U²-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies. Full article

Accurate three-dimensional representations of tree structure are essential for fire modeling, radiative transfer simulation, synthetic data generation, and digital twins of forests, yet detailed 3D structure is rarely available at required scales. Current approaches approximate crowns with smooth geometric primitives, discarding the clumping, gaps, and irregular branching present in real trees. We present TreeFlow, a conditional flow matching model that generates realistic 3D tree point clouds from species, acquisition platform, and height. The model uses a transformer trained on real laser scanning data from the FOR-species20K benchmark to learn a velocity field transporting samples from a Gaussian distribution to the source data distribution. We evaluate generation quality by comparing conditioning and distributional fidelity metrics to scans of real trees. Generated trees match or approach the intra-class baseline on five of six metrics, with a Chamfer distance of 0.581 m versus 0.559 m for real trees of the same genus and height class. Performance is strongest below 25 m and degrades with increasing height. TreeFlow generates individual-tree point clouds conditioned on scalar inventory attributes using a model trained entirely on real laser scanning data. Full article

This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure. Full article

Structural health monitoring (SHM) systems fundamentally rely on effective sensing technologies for reliable damage detection and structural condition assessment. Among the available sensing approaches, piezoelectric (PZT)-based transducers are widely used in civil engineering due to their dual actuation–sensing capability, high sensitivity, low cost, and suitability for real-time monitoring. However, SHM performance not only depends on the sensing hardware, but also on the signal processing techniques that extract meaningful damage-related information from measured responses. Recently, Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential to enhance automation and improve the performance of SHM systems. This paper provides a critical review of signal processing and data-driven learning approaches for PZT-based guided-wave (GW) SHM and nondestructive testing (NDT), with applications to metallic, composite, and concrete structures. The review covers developments from early ML-based GW SHM methods to recent advances in DL, hybrid frameworks, and physics-informed approaches. Although emphasis is placed on civil infrastructure, developments in other fields such as aerospace and energy engineering are also reviewed due to their role in validating GW-based SHM methodologies. The fundamental theory of PZT sensing and guided wave propagation is introduced to establish the required background for monitoring techniques. Classical signal processing methods are then reviewed, followed by AI-based SHM frameworks, with particular emphasis on hybrid approaches that integrate physics-based signal processing with data-driven models to improve robustness, accuracy, and generalization. Key challenges such as environmental variability, sensor degradation, limited labeled data, and model transferability are discussed, along with future research directions including physics-informed machine learning (PIML), transfer learning, explainable AI, and baseline-free SHM. The review highlights that hybrid and physics-informed frameworks offer strong potential for field deployment by improving robustness, reducing data dependency, and enhancing generalization capability. A key contribution of this work is the comparative synthesis of signal processing, ML, DL, and hybrid methodologies across different material systems and structural types, together with a structured discussion of the challenges and future research directions for real-world implementation. Full article

The recovery of low- and medium-temperature waste heat using Organic Rankine Cycles (ORCs) is increasingly important for improving the efficiency and sustainability of industrial and energy systems. In compact ORC turboexpanders, high specific power output and large pressure ratios often require single- or two-stage turbines operating in transonic or supersonic regimes. Under these conditions, stator blade design is complicated by strong compressible-flow effects and, for organic working fluids, by real-gas thermodynamic behavior. Conventional supersonic stator design methods, such as the method of characteristics, are mainly applicable to the diverging supersonic portion of the blade passage, while the converging region is typically defined using empirical or heuristic prescriptions. This paper presents a physics-informed neural-network-based design method for supersonic turbine stator blades. The proposed framework generates the complete inter-blade passage, including both the converging and diverging regions, starting from a prescribed mean-line geometry and Mach number distribution. The velocity field is obtained by solving the governing equations of steady, inviscid, adiabatic, irrotational compressible flow within a PINN formulation. A hard boundary-condition strategy is used to impose the specified mean-line velocity distribution exactly, while real-fluid thermodynamic effects are incorporated through lookup tables for the speed of sound and density. The blade contours are then reconstructed from stream-function isolines predicted from the computed velocity field. The method is demonstrated for two working fluids: air, treated as a perfect gas, and toluene undergoing transcritical expansion. The resulting blade passages are first validated using inviscid CFD simulations, which show close agreement between the prescribed and computed mean-line Mach number distributions. Turbulent CFD simulations of the final blade cascades confirm smooth acceleration through the inter-blade passage, with no strong internal shocks and only weak fishtail shocks downstream of the trailing edge. For both fluids, the post-expansion ratio is approximately unity and the exit flow angle remains close to the prescribed blade metal angle, indicating well-matched supersonic stator designs. The results demonstrate that the proposed PINN-based design method provides a physically consistent approach for generating supersonic stator blade profiles for both ideal-gas and real-gas turbine applications. Full article

In the field of optical metrology, wavelength-tunable interferometry is widely used to obtain the phase information of measured objects. Due to the modulo 2π operation, the extracted phase is inherently wrapped into the range of −π to π, which necessitates phase unwrapping to restore the actual phase profile. However, traditional phase-shifting methods suffer from low accuracy caused by phase shift miscalibration, coupling signals, atmospheric turbulence, and measurement noise. To address these issues, this paper proposes a deep learning-based phase-unwrapping method using a deep convolutional neural network, which formulates the unwrapping task as a multiclass classification problem. The proposed method employs an encoder–decoder residual network (ResNet) architecture that treats phase unwrapping as a pixel-wise semantic segmentation task, enabling end-to-end continuous phase reconstruction. It also adopts a 2N − 1 algorithm-based dataset generation strategy that inherently suppresses phase-shift miscalibration and harmonic coupling errors without relying on Zernike polynomial representations. Furthermore, a large-scale data augmentation pipeline (16-fold expansion to 20,992 training samples) endows the network with a strong generalization capability and noise immunity. The quantitative experimental results demonstrate that the proposed method achieves 100% phase-unwrapping accuracy under noise-free conditions and 99.03% accuracy under severe noise (standard deviation = 1.5), substantially outperforming the quality-guided method (QG, 69.87%) and the transport-of-intensity equation method (TIE, 77.53%) under identical conditions. On real interferometric data acquired using a wavelength-tuning interferometer, the proposed method successfully unwraps the phase even under heavy noise where conventional methods fail completely. These results confirm that the proposed method has favorable noise resistance and potential applicability in high-precision optical metrology. Full article

Accurate object detection in agricultural environments remains challenging due to illumination variation, background clutter, partial occlusion, and overlapping fruits. Conventional object detection methods mainly rely on deterministic data augmentation strategies or feature-level refinement, which often exhibit limited robustness under complex field conditions. To address this issue, this paper proposes a Diffusion-Regularized Object Detection (DROD) framework for robust pineapple target detection in agricultural imagery. The proposed framework introduces a mathematically grounded forward diffusion and diffusion-guided representation mechanism directly in the image domain, where stochastic perturbations are generated through forward diffusion and semantically meaningful image representations are learned via diffusion-guided representation. A unified optimization framework and theoretical analyses of perturbation propagation, Lipschitz stability, and training convergence are further established to provide mathematical support for the proposed method. Extensive experiments were conducted on a self-constructed dataset containing 1600 real-world pineapple images collected under practical agricultural conditions. Comparative evaluations involving YOLOv8-s, YOLOv8-l, traditional data augmentation, and the recent JTA:GAN method demonstrate that the proposed DROD framework consistently achieves the best detection performance in terms of Precision, Recall, mAP@0.5, and mAP@0.5:0.95 while maintaining computational complexity and inference speed comparable to the original YOLOv8 architecture. Furthermore, ablation studies, diffusion parameter sensitivity analysis, visualization analysis, and experimental validation under different perturbation levels consistently verify the effectiveness and robustness of the proposed diffusion mechanism. These results demonstrate that diffusion-based regularization provides an effective and computationally efficient solution for robust agricultural object detection and offers a practical framework for intelligent precision agriculture applications. Full article

In 2006, I argued that household energy research was dominated by survey-based, economic and technical analyses, while too often failing to combine measured energy use with qualitative accounts of how people use energy in their homes. Two decades later, in this Perspective, I revisit this argument and ask whether the gap between theory, method, and real-world residential energy use has been resolved. Drawing on both my own work over the last 20 years and recent comprehensive analyses of the literature in the field, I argue that residential energy research has expanded in scale, scope and technical capacity, with stronger metering, modelling and monitoring tools. However, much of the evidence base remains weighted towards short-term quantitative and technical evaluations, while research on routines, comfort, trust, social relations, housing conditions and aesthetics is still less consistently integrated. I conclude that integrated mixed-method designs are needed. These designs can build on the strengths of meters, models and monitoring by connecting them with how energy is used, managed and shifted in homes, so that residential energy research can better support energy transitions that are both effective and just in the face of climate change. Full article

The assessment of post-earthquake performance of structures is a crucial task in structural health monitoring. To address this challenge, a novel performance-based probabilistic post-earthquake assessment (PBPPA) method is proposed. This method involves a modeling stage and an application stage. First, twenty earthquake waves with fortification intensity and rare intensity are selected from the PEER (Pacific Earthquake Engineering Research Center) database, and the mean and standard deviation are consistent with the target values. Then, OpenSEES is used to establish a finite-element model for the monitored structure. Next, the damage state and performance measures of structural elements are calculated according to the standard for seismic resilience assessment of buildings. Finally, the conditional probability density function of the repair cost relative to the monitoring data is presented, along with a probabilistic rating method of three levels. Using the Old Hall of Nanjing Museum as an example, the proposed method is verified with numerical simulation and real-world monitoring data collected during earthquakes. It is found that the most likely occurring repair cost and deviation of the conditional probability density function are proportional to the intensity of dynamic responses, which is consistent with the actual situation. Therefore, the presented method can be used as the decision-making basis for the determination of the structural post-earthquake condition and provides a useful reference for the future development of post-earthquake assessment methods. Overall, the proposed method is a valuable contribution to the field of structural health monitoring and has the potential to enhance the safety and resilience of structures after earthquakes. Full article