Search Results (21,112)

The developmental cycle of Pholiota nameko can be divided into four stages: the mycelial stage (JS), the primordium stage (FH), the growth stage (SZ), and the maturity stage (CS). In this study, transcriptome sequencing was performed on P. nameko at these four stages, followed by the screening of differentially expressed genes and functional annotation via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. By integrating the annotation results of all differentially expressed genes (DEGs), we screened for candidate genes potentially correlated with the growth and development of P. nameko. The expression levels of these candidate genes were then compared using real-time quantitative PCR (RT-qPCR) to identify those with the highest expression. The results showed that in the FH vs CS and SZ vs CS comparisons, DEGs were mainly enriched in pathways related to protein processing, fatty acid metabolism, and linoleic acid metabolism, suggesting that alterations in these specific metabolic pathways may be closely associated with the growth and development of P. nameko. Through analysis based on upregulation and log₂ fold change (log₂FC) values, further screening identified 13 candidate genes, and the gene Cluster-7415.13 with the highest expression level was preliminarily screened out through RT-qPCR analysis. This gene may be potentially involved in processes such as rapid cell expansion, cell wall synthesis, nutrient absorption, and active growth-stage metabolism. Full article

Gait symmetry analysis has emerged as an important tool in rehabilitation engineering and neurological disorder assessment, as it provides clinically relevant indicators of mobility impairment and gait abnormalities. The proposed framework integrates gait symmetry variability, statistical gait features and Dynamic Time Warping (DTW)-based temporal alignment to enhance robustness against gait variations and irregular walking patterns. A hybrid feature vector comprising DTW similarity scores, the hybrid gait symmetry index (GSI), and statistical gait descriptors was employed to classify gait patterns into five categories: normal, slow, medium, fast, and abnormal. The system was implemented as a wearable edge-computing platform using an NI myRIO device equipped with a tri-axial Inertial Measurement Unit (IMU) mounted on the subject’s body. The onboard FPGA performs real-time signal preprocessing, GSI computation, feature extraction, constrained DTW matching, and gait classification using fixed-point streaming architectures and BRAM-based buffering. Meanwhile, the embedded ARM processor manages TCP/IP communication and transmits real-time gait information to a remote monitoring workstation via a WiFi interface for visualization and analysis. Operating at a clock frequency of 100 MHz, the complete architecture achieves an end-to-end processing latency of approximately 4 ms. The proposed FPGA-based implementation provides low-latency, energy-efficient, and real-time gait analysis, making it well suited for wearable rehabilitation systems, assistive healthcare devices, and continuous mobility monitoring applications. Full article

In scrubbing tower (ST) systems, outlet vapor temperature regulation is subject to strict thermal constraints, requiring the bottom temperature to remain below a critical safety threshold. Transient overshoots can rapidly trigger pyrolytic coking, foul mass-transfer packed beds and spray nozzles, and ultimately force complete production shutdowns. Due to feedstock compositional drifts, high thermal inertia, and significant transport delays, high-fidelity predictive identification is essential for proactive early warning and for overcoming the limitations of reactive feedback control. To address these bottlenecks, this paper introduces an offset-free, hard-constrained, data-driven adaptive optimal control paradigm, designated as the improved GRU-coupled conjugate gradient linear quadratic regulator (IGRUCG-LQR). First, by constructing an augmented state space embedded with integral error, the proposed paradigm eliminates permanent tracking offsets induced by long-term nonstationary drifts. Second, automatic differentiation is used to extract the time-varying Jacobian matrix of a gated recurrent unit (GRU) online, thereby tracking the nonlinear evolution of the underlying thermodynamic baseline with high fidelity. To manage the critical trade-off between strict actuator saturation and short real-time sampling intervals, the conjugate gradient (CG) method is fused with a hard-boundary projection operator, enforcing physical constraints with high computational efficiency and without complex matrix inversions. Experimental validation on a real-world industrial dataset demonstrates that the proposed paradigm secures the safety baseline while achieving high-resolution transient tracking. Furthermore, it significantly suppresses high-frequency valve chattering to mitigate mechanical fatigue, establishing a solid theoretical and engineering foundation for the prolonged stable operation of safety-critical processes. The proposed framework achieves an Integral Absolute Error (IAE) of 86.66 and an Integral Time Absolute Error (ITAE) of 4064.49. Full article

Controlled institutional environments such as prisons and psychiatric hospitals shape human behaviour through surveillance, discipline, and spatial regulation. Although these environments have been extensively examined from architectural and sociological perspectives, comparatively few studies have systematically investigated their psychological implications using an environmental psychology framework. This study examines how controlled institutional spaces influence environmental perception, privacy, belonging, proxemic relationships, crowding, environmental stress, and spatial discipline through a comparative analysis of One Flew Over the Cuckoo’s Nest (1975) and The Shawshank Redemption (1994). A qualitative comparative film analysis was conducted using a structured analytical framework derived from environmental psychology and supported by theories of surveillance, discipline, total institutions, and hegemony. Twenty-four candidate scenes were initially identified and independently coded by two researchers according to predefined observable spatial and behavioural indicators. Following the coding process, four scenes exhibiting overlapping analytical characteristics were excluded, resulting in a final dataset of twenty scenes. The findings demonstrate that architectural elements such as surveillance visibility, hierarchical spatial organization, controlled circulation, restricted privacy, and shared institutional environments shape psychological experiences including alienation, environmental stress, adaptation, resistance, and place attachment. At the same time, the comparative analysis shows that similar spatial conditions may generate different psychological responses depending on individuals’ interactions with institutional environments. The study contributes methodologically to qualitative film analysis through a systematic scene selection and coding procedure while providing a comprehensive framework for examining environmental psychology concepts through the cinematic representations of controlled institutional environments. The findings should be interpreted as analyses of cinematic representations rather than empirical evidence of real prison or psychiatric hospital environments. Full article

Evaluating wearable assistive robots in real-world caregiving is challenging because temporally aligned and repeatable A/B comparisons are rarely available. Conventional evaluations assume that assist-on and assist-off trials are comparable in task content, posture, and movement timing. However, caregivers adjust their posture and timing across human–human interactions and task sequences. This study proposes a causally inspired diagnostic framework based on DBN/SCM-inspired time-series modeling and movement-fixed counterfactual estimation. We represented the multimodal observations using intervention, robot state, movement context, EMG, and context variables. Node-specific relationships were approximated using Attention-based Sparse Variational Gaussian Process regressors. We evaluated the framework at three levels of environmental complexity. These levels comprised controlled trunk flexion, partially controlled bed-to-wheelchair transfer, and real-world caregiving. The proposed framework is intended as a diagnostic counterfactual evaluation tool rather than as a method for strict causal identification. Across experiments, one-step EMG prediction accuracy alone was insufficient to identify intervention-sensitive models. In controlled validation, the selected movement-decoupled robot-only model reproduced an EMG-reducing response consistent with the controlled A/B reference. When fitted to the partially controlled transfer data, the selected structural specification identified an EMG-increasing response in supported contexts. In the real-world caregiving case study, the global assist-mediated response (AMR) was near zero despite a positive pooled A/B difference. However, the stratified analysis identified localized supported responses. The near-zero AMR indicates that the pooled difference was not reproduced through the modeled assist intervention–robot state–EMG pathway under fixed movement context. This result should not be interpreted as evidence of overall device ineffectiveness. These findings suggest that context-fixed counterfactual diagnosis can help interpret assistive responses under increasing environmental complexity. Full article

The development of ferrous metallurgy, as with most industrial sectors, is progressing toward the adoption of IIoT technologies and the development of digital automatic control systems for electrotechnical and mechatronic complexes. This direction is implemented within the paradigm of digital twins (DTs), which enable the use of advanced design methods, virtual commissioning, and maintenance. The concept of relatively simple object-oriented DTs created using available software and applicable at individual stages of the equipment lifecycle has been substantiated. The relationship between the object-oriented approach and M. Grieves’ classification system has been determined. The contribution of this paper lies in the fact that this problem is addressed for the first time using the example of electrotechnical systems of rolling mills. Definitions of DTs are provided, along with a brief overview of digital platforms developed by leading manufacturers of metallurgical equipment. The development of object-oriented DTs based on Simulink Real-Time modules and domains of the Simscape library is substantiated. A methodology for their virtual tuning using Hardware-in-the-Loop (HIL) simulation is proposed. The results of developing an aggregated DT of interconnected electric drives of the upper and lower rolls (UMD and LMD) of the horizontal stand of the 5000 plate rolling mill are presented. An example of DT implementation in a programmable logic controller (PLC) based on a multicore processor using CODESYS 3.5 software is provided. The advantages and prospects of this approach are discussed. Validation of the results is performed by comparing processes during virtual tuning with oscillograms obtained from the actual mill. Satisfactory accuracy is confirmed, and recommendations for the broader application of the developed object-oriented digital twins are given. Full article

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article

This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers. Full article

Autonomous UAVs operating in forest environments require accurate branch perception to enable reliable environmental understanding for downstream tasks such as navigation, environmental inspection, and obstacle avoidance. However, existing semantic segmentation methods primarily focus on pixel-wise classification and often fail to preserve the structural topology of branches, making it difficult to distinguish reliable branch segments from complex regions such as junctions and overlapping structures. In addition, achieving robust real-time performance on resource-constrained onboard platforms remains challenging due to the slender and irregular characteristics of natural branches. To address these challenges, this paper proposes a topology-aware branch perception framework that integrates real-time efficient semantic segmentation with structural topology analysis. For branch segmentation, a Strip-Swift Pyramid Pooling Module (SSPPM) is introduced to enhance elongated structure representation through progressive pooling and strip-based directional context aggregation. A reparameterized Golden Cudgel Block (GCBlock) and a Boundary Optimization Module (BOM) are further incorporated to improve deployment efficiency and boundary quality. Building upon the predicted segmentation masks, a topology-aware structural analysis module is developed to identify and remove branch junctions and multi-branch regions through skeleton-based connectivity analysis, preserving only structurally stable branch segments for subsequent perception and analysis. This strategy improves the robustness and consistency of branch representation in cluttered natural environments. Experimental results demonstrate that the proposed method achieves 89.94% mIoU on the Drone-Branch dataset. When deployed on the NVIDIA Jetson Orin Nano with TensorRT acceleration, the proposed segmentation network achieves a stable inference latency of 13.7 ms. The topology-aware structural analysis is subsequently applied as a post-processing stage to improve the reliability of extracted branch structures. These results demonstrate the effectiveness of the proposed framework for branch perception on resource-constrained UAV platforms. Full article

Parameter optimization of injection and production is an important method used to enhance recovery rate and reduce water cut, mainly aiming to determine the injection and production strategies during the development process to maximize the economic benefits throughout the reservoir development process. However, current optimization approaches for injection and production face challenges such as complex and inefficient optimization models and high-dimensional discrete variables, making it difficult to improve the global optimization ability of the algorithm (avoiding local optimal solutions during the optimization process) and the controllability of the time for completing the optimization of injection and production parameters under real and limited numerical simulations. This paper proposes a high-dimensional multi-discrete injection and production parameter non-gradient optimization method (MNOM), which combines the upper confidence bound (UCB) algorithm and effectively explores better injection and production systems and small-layer water-allocation schemes, achieving the maximization of net present value (NPV) over the entire development period. Specifically, this method models the injection and production parameter optimization problem as a Monte Carlo tree search process (Monte Carlo tree search, MCTS), and implements the optimization of injection and production cycles and small-layer water allocation through a genetic algorithm (CLGA) proxy optimized by a convolutional long short-term memory network (ConvLSTM). This method effectively overcomes the spatial and temporal limitations of the search process, maps production dynamics to the random strategies of injection and production parameters, and estimates the expected return of each policy. The CLGA proxy rapidly identifies suitable well-control schedules and water-allocation schemes in real time based on the production status at different development stages, thereby improving overall production performance. The proposed method has two innovative points. Firstly, MCTS can explore the large-scale discrete space of injection and production parameter optimization variables through tree decomposition, combined with the UCB incentive mechanism, to improve the global optimization ability. Secondly, the model training process is completely based on existing physical laws, with good temporal evolution, and the trained strategy can quickly adapt to the production status of the target layer without the need for a complete re-training from the beginning, enabling offline application and having good real-time controllability. In order to verify the effectiveness of the method proposed in the article, tests were conducted on a 3D reservoir actual model. Compared with gradient-based approaches, classical evolutionary algorithms, and proximal policy optimization (PPO), MNOM not only achieves stronger global search performance and requires 55–78% fewer iterations, but also improves the effective sweep volume by 1.1% to 5.5% compared to other optimization methods; furthermore, when compared with the PPO method, it is found that in offline optimization, if the production regime changes, the training strategy has better real-time controllability. The MNOM method can still maintain the original optimization effect compared to the PPO method when the production regime changes, demonstrating better engineering adaptability. The research results show that the proposed multi-strategy fusion high-dimensional multi-discrete injection and production parameter non-gradient optimization method can effectively improve recovery rate, expand effective sweep volume, and balance global optimization ability, optimization efficiency, and real-time controllability under the constraint of limited numerical simulations, and it has good engineering adaptability and application prospects. Full article

Facility Layout Planning (FLP) remains a complex task for manufacturers seeking to improve productivity, reduce daily operating costs, and stay competitive in fast-changing markets. Traditional methods such as Systematic Layout Planning (SLP) offer useful guidelines for designing department layouts but still rely heavily on judgment and experience. At the same time, modern optimization and simulation techniques provide valuable quantitative insights. These techniques are often used separately rather than as part of an integrated process. In this work, a hybrid layout-planning approach that combines these techniques is developed and validated through an industrial case study, providing a practical decision-support process for facility layout planning. The process starts with SLP, which develops an initial layout using activity relationship charts, material-flow analysis, and handling-cost estimates. A simulation model then evaluates throughput, machine utilization, and work-in-progress, providing early indications of the layout’s real-world performance. A Genetic Algorithm (GA) is used to find improved configurations that reduce distances and costs. The optimized layouts are further tested through simulation. To demonstrate practical use, the framework was applied at a transformer manufacturing plant. It resulted in an approximately 35% reduction in material-handling costs. The results show that the optimized layout reduced material-handling costs from 7062.5 to approximately 4560 L.E. per transformer while increasing monthly throughput by 2.46% (approximately 11 transformers per month). Additionally, a what-if analysis was performed to identify opportunities for improvement, such as increasing production by using an automatic laser-cutting machine. The findings support data-driven decisions in facility layout design and long-term operational planning. Full article

This study investigates the degradation mechanisms of 2835-packaged white LEDs commonly used in residential lighting scenarios under various environmental conditions and explores how standard ageing tests compare to those that better reflect real-world use cases. The samples consist of commercially available LED strips, both cool and warm white. The luminous output of each LED on a strip is spectrally measured individually before ageing, then aged under particular conditions for a certain time and measured individually again. The measurements for a given LED are then analysed from multiple aspects, including material degradation processes during ageing, for example by following changes in phosphor efficiency. Full article

Gearbox fault diagnosis under noisy operating conditions remains a critical yet unsolved challenge for industrial condition monitoring. The primary challenge originates from a fundamental conflict: fault signatures are present within certain frequency ranges, yet standard deep learning models process raw vibration signals without recognizing which frequencies are relevant. Moreover, noise affects the entire spectrum uniformly. Existing transformer-based methods for vibration analysis treat time steps as tokens and therefore fail to capture cross-sensor dependencies, while conventional denoising approaches apply fixed filters that cannot adapt to the varying spectral characteristics of different fault types and noise levels. We propose the Adaptive Frequency-Aware Inverted Transformer (AF-iTransformer), a lightweight transformer framework that lets the model learn which frequencies to attend to on a per-sample basis. In particular, we propose a learnable spectral filter that transforms the input signal to the frequency domain via FFT. Then it predicts a soft frequency mask conditioned on signal statistics and applies it before reconstructing the filtered signal through iFFT, allowing the model to suppress noise bands while preserving fault-relevant spectral content dynamically. After adaptive filtering, the architecture employs channel-level tokenization to uniformly represent heterogeneous channels as input tokens, relying on cross-channel attention to automatically learn their distinct contributions to fault diagnosis. Feature-wise linear modulation is introduced to inject signal-level statistics at every encoder layer. Furthermore, the framework utilizes residual attention propagation to stabilize deep training, and an auxiliary spectrum prediction head provides spectral regularization during training. On the UConn Gearbox dataset with nine fault categories, AF-iTransformer achieves 99.63% accuracy on clean data and maintains 95.1% at 0 dB signal-to-noise ratio, substantially outperforming all baselines under noisy conditions. On the SEU gearbox dataset with five fault categories, AF-iTransformer achieves 99.74% clean accuracy. On 2 GB edge GPUs, AF-iTransformer achieves a per-window inference latency of 7.3–13 ms with peak memory below 17 MB, and 1.4–4.7 ms on modern CPUs, confirming its viability for real-time industrial deployment. Full article

Thailand’s ceramic manufacturing tradition possesses a long and distinguished history, reflecting the nation’s rich cultural heritage, artistic excellence, and capacity for technological adaptation. Traditional Thai ceramics extend beyond their functional purposes, serving as important expressions of indigenous knowledge, craftsmanship, social values, and religious beliefs that have been transmitted across generations. While preserving their distinctive Thai characteristics, these ceramic traditions have continuously evolved through cultural exchanges with neighboring civilizations, particularly China and India, as well as later influences from the West. Among the various decorative techniques employed in Thai ceramics, stamping and hand-painted ornamentation are recognized as two of the most significant methods, contributing to the aesthetic and cultural value of ceramic works. These techniques enable ceramic products to embody both artistic expression and practical functionality by harmoniously integrating aesthetic design with reliable craftsmanship. In contemporary manufacturing environments, traditional stamping and painting methods are increasingly integrated with semi-automated processes and advanced ceramic machinery to enhance production efficiency while preserving cultural authenticity. This study proposes an Internet of Things (IoT)-based control system for ceramic stamping and painting machines, designed to support remote operation, real-time monitoring, and performance evaluation, with particular attention given to response time and error characteristics. By incorporating sensors, controllers, and networked communication technologies into ceramic manufacturing equipment, the proposed system establishes a meaningful connection between intelligent automation and traditional artistic practices. Full article

Network Intrusion Detection Systems (NIDS) face a trade-off between detection accuracy and computing efficiency, particularly in the edge environment with strict real-time requirements and limited resources. Current approaches rely on complicated models, which are computationally demanding, or simple ones, which sacrifice detection performance. To deal with this issue, we propose a lightweight cloud-edge cooperative two-stage NIDS architecture, which separates the real-time detection and detailed classification. At the edge, a decision tree based on feature selection is used for rapid binary classification by using only the top 10 most informative features, thus efficiently screening out abnormal traffic with minimum processing cost. Meanwhile, the cloud server identifies attack classification accurately by using a hybrid CNN-BiLSTM-Attention model to capture the spatial structures, temporal relationships, and semantic relevance. This hierarchical design effectively balances detection performance and system efficiency. Experiments conducted on UNSW-NB15, NSL-KDD, and CIC-IDS2017 datasets indicate that our suggested scheme can obtain competitive performance both at the edge and in the cloud. The edge model obtains binary classification accuracy of 86.04%, 95.27%, and 99.01%, respectively, with very low processing cost (less than 100 FLOPs per sample). The cloud model achieves multi-class accuracy of 92.23%, 97.18%, and 98.66%, respectively, with AUC values higher than 0.98. The hierarchical cloud–edge collaborative design provides an efficient and accurate solution for intrusion detection under resource-restricted situations. Full article