Search Results (865)

High-temperature stress has become an important factor that has restricted the aquaculture industry. Huso dauricus is a high-economic-value fish that has faced the threat of thermal stress. Based on this point, our investigation aimed to explore the detailed mechanism of the negative impacts of heat stress on the liver metabolism functions in Huso dauricus. In this study, we set one control group (19 °C) and four high-temperature treatment groups (22 °C, 25 °C, 28 °C, 31 °C) with 40 fish in each group for continuous 53-day heat exposure. Histological analysis, biochemical detection, and transcriptome technology were used to explore the effects of heat stress on the liver structure and functions of juvenile Huso dauricus. It suggested heat-stress-induced obvious liver injury and reactive oxygen species accumulation in Huso dauricus with a time/temperature-dependent manner. Serum total protein, transaminase, and alkaline phosphatase activities showed significant changes under heat stress (p < 0.05). In addition, 6433 differentially expressed genes (DEGs) were identified based on the RNA-seq project. Gene Ontology enrichment analysis showed that various DEGs could be mapped to the lipid-metabolism-related terms. KEGG enrichment and immunohistochemistry analysis showed that ferroptosis and FoxO signaling pathways were significantly enriched (p < 0.05). These results demonstrated that thermal stress induced oxidative stress damage in the liver of juvenile Huso dauricus, which triggered lipid metabolism disorder and hepatocyte ferroptosis to disrupt normal liver functions. In conclusion, chronic thermal stress can cause antioxidant capacity imbalance in the liver of Huso dauricus to mediate the ferroptosis process, which would finally disturb the lipid metabolism homeostasis. In further research, it will be necessary to verify the detailed cellular signaling pathways that are involved in the heat-stress-induced liver function disorder response based on the in vitro experiment, while the multi-organ crosswalk mode under the thermal stress status is also essential for understanding the comprehensive mechanism of heat-stress-mediated negative effects on fish species. Full article

Coal gangue, a major by-product of coal mining, poses significant environmental challenges due to its large-scale accumulation, land occupation, and potential for air and water pollution. This manuscript presents a comprehensive overview of continuous chamber gangue storage technology as a sustainable mining solution for coal mines. The principles of this approach emphasize minimizing disturbance to overlying strata, enabling uninterrupted mining operations, and reducing both production costs and environmental risks. By storing the surface or underground gangue in continuous chambers, the proposed method ensures the roof stability, maximizes the waste storage, and prevents the interaction between mining and waste management processes. Detailed storage sequences and excavation methods are discussed, including continuous and jump-back excavation strategies tailored to varying roof conditions. The process flows for both underground and ground-based chamber storage are described, highlighting the integration of gangue crushing, paste preparation, and pipeline transport for efficient underground storage. In a case study with annual storage of 500,000 t gangue, the annual economic benefit reached CNY 1,111,425,000. This technology not only addresses the urgent need for sustainable coal gangue management, but also aligns with the goals of resource conservation, ecological protection, and the advancement of green mining practices in the coal industry. Full article

Gantry cranes play a pivotal role in industrial production. Gantry cranes exhibit clear double-swing characteristics in actual working conditions, complicating anti-swing control. Most existing anti-swing control methods are based on a simplified single-pendulum model. The present paper puts forward a double-pendulum model for gantry cranes and proposes an adaptive hierarchical sliding mode control based on a neural network according to the actual working conditions. The use of a neural network and adaptive layered sliding mode control can effectively inhibit chattering, thus improving control performance and stability and achieving the goal of anti-shaking control, thus effectively inhibiting residual oscillation. This method has been demonstrated to be effective in achieving the objective of anti-shudder control, thereby effectively suppressing residual oscillation. Compared with hierarchical sliding mode control, the proposed method reduces the maximum residual oscillation angle of the hook and payload by approximately 80%. In comparison with the conventional sliding mode control, the maximum residual oscillation angle is reduced by approximately 84%. Furthermore, the control force amplitude is reduced to 5.23 N, representing decreases of 30.2% and 37.4%, respectively. These comparative results demonstrate the superior oscillation suppression. The system also shows a reliable performance against potential disturbances. Full article

The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R² = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness against disturbance in wireless environments. To be applied to embedded microprocessors, the continuous-time chaotic system is discretized using the Grunwald–Letnikov approximation. To avoid the loss of generality of chaotic behavior, Lyapunov exponents are computed to validate the preservation of chaos in the discrete-time domain. The FBELNN controller is then developed to synchronize two non-identical chaotic systems under different initial conditions, enabling secure data encryption and decryption. Additionally, the DOB is introduced to estimate and mitigate the effects of bounded uncertainties and external disturbances, enhancing the system’s resilience to stealthy attacks. The proposed control structure is experimentally implemented on a wireless communication system utilizing ESP32 microcontrollers (Espressif Systems, Shanghai, China) based on the ESP-NOW protocol. Both control and feedback signals of the electric drive system are encrypted using chaotic states, and real-time decryption at the receiver confirms system integrity. Experimental results verify the effectiveness of the proposed method in achieving robust synchronization, accurate signal recovery, and a reliable wireless control system. The combination of FBELNN and DOB demonstrates significant potential for real-time, low-cost, and secure applications in smart electric drive systems and industrial automation. Full article

The cement industry, a foundation of global infrastructure development, significantly contributes to environmental pollution. Key sources of pollution include dust emissions; greenhouse gases, particularly carbon dioxide; and the release of toxic substances such as heavy metals and particulate matter. These pollutants contribute to air, water, and soil degradation and are linked to severe health conditions in nearby populations, including respiratory disorders, cardiovascular diseases, and increased mortality rates. Noise pollution is also a significant issue, inducing auditory diseases that affect most workers in cement plants, and disturbing the population living in the neighborhoods and fauna behavior. This review explores the pollution paths and the multifaceted impacts of cement production on the environment. It also highlights the social challenges faced by communities, underscoring the urgent need for stricter environmental policies and the adoption of greener technologies to mitigate the adverse effects of cement production on both the environment and human health. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

To address the growing demand for industrial software in the digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector, and to ensure the stable integration and operation of multi-source heterogeneous industrial software under complex conditions—such as heterogeneous compatibility, component dependencies, and uncertainty disturbances—this study established a comprehensive evaluation index system for service composition and optimal selection (SCOS). The system incorporated key criteria including service time, service cost, service reputation, service delivery quality, and availability. Based on this, a bi-objective SCOS model was established with the goal of maximizing both quality of service (QoS) and availability. To efficiently solve the proposed model, a hybrid enhanced multi-objective Gray Wolf Optimizer (HEMOGWO) was developed. This algorithm integrated Tent chaotic mapping and a Levy flight-enhanced differential evolution (DE) strategy. Extensive experiments were conducted, including performance evaluation on 17 benchmark functions and case studies involving nine industrial software integration scenarios of varying scales. Comparative results against state-of-the-art, multi-objective, optimization algorithms—such as MOGWO, MOEA/D_DE, MOPSO, and NSGA-III—demonstrate the effectiveness and feasibility of the proposed approach. Full article

Reliable and accurate docking remains a fundamental challenge for autonomous mobile robots (AMRs) operating in complex industrial environments with dynamic lighting, motion blur, and occlusion. This study proposes a novel multi-sensor fusion-based docking framework that significantly enhances robustness and precision by integrating YOLOv8-based AprilTag detection, depth-aided 3D localization, and LiDAR-based orientation correction. A key contribution of this work is the construction of a custom AprilTag dataset featuring real-world visual disturbances, enabling the YOLOv8 model to achieve high-accuracy detection and ID classification under challenging conditions. To ensure precise spatial localization, 2D visual tag coordinates are fused with depth data to compute 3D positions in the robot’s frame. A LiDAR group-symmetry mechanism estimates heading deviation, which is combined with visual feedback in a hybrid PID controller to correct angular errors. A finite-state machine governs the docking sequence, including detection, approach, yaw alignment, and final engagement. Simulation and experimental results demonstrate that the proposed system achieves higher docking success rates and improved pose accuracy under various challenging conditions compared to traditional vision- or LiDAR-only approaches. Full article

Tainan’s Cigu, located on Taiwan’s southwestern coast, is a prominent aquaculture hub known for its extensive ponds, tidal flats, and lagoons. This study explored the novel integration of kayaking within aquavoltaic (APV) aquaculture ponds, creating a unique hybrid tourism landscape that merges industrial land use (aquaculture and energy production) with nature-based recreation. We investigated the relationships among facility maintenance and safety professionalism (FM), the perceived value of kayaking training (PV), and green energy and sustainable development recognition (GS) within these APV systems in Cigu, Taiwan. While integrating recreation with renewable energy and aquaculture is an emerging approach to multifunctional land use, the mechanisms influencing visitors’ sustainability perceptions remain underexplored. Using data from 613 kayaking participants and structural equation modeling, we tested a theoretical framework encompassing direct, mediated, and moderated relationships. Our findings reveal that FM significantly influences both PV (β = 0.68, p < 0.001) and GS (β = 0.29, p < 0.001). Furthermore, PV strongly affects GS (β = 0.56, p < 0.001). Importantly, PV partially mediates the relationship between FM and GS, with the indirect effect (0.38) accounting for 57% of the total effect. We also identified significant moderating effects of APV coverage, guide expertise, and operational visibility. Complementary observational data obtained with underwater cameras confirm that non-motorized kayaking causes minimal ecological disturbance to cultured species, exhibiting significantly lower behavioral impacts than motorized alternatives. These findings advance the theoretical understanding of experiential learning in novel technological landscapes and provide evidence-based guidelines for optimizing recreational integration within production environments. Full article

Tea disease detection is of great significance to the tea industry. In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. Finally, the map values of 89.7% and 68.5% were obtained on a self-made tea data set and a public tea disease data set, which were improved by 3.9% and 4.3%, respectively, compared with the original benchmark model, and the reasoning speed of the model was 164.3 fps. Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes. Full article

Ensuring cybersecurity and health management is a fundamental requirement in modern chemical industry plants operating under the Industry 4.0 framework. Traditionally, these two concerns have been addressed independently, despite sharing multiple underlying elements which suggest the viability of a unified detection and localization solution. This study introduces a computational intelligence framework based on fuzzy techniques, which allows for the early identification and precise localization of both faults and cyberattacks, along with the capability to recognize previously unseen events during runtime. Once new events are identified and classified, the training database is updated, creating a mechanism for continuous learning. This integrated approach simplifies the computational complexity of supervisory systems and enhances collaboration between the Operational Technology and Information Technology teams within chemical plants. The proposed methodology demonstrates strong robustness and reliability, even in complex conditions characterized by noisy measurements and disturbances, achieving outstanding performance due to its excellent discrimination capabilities. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article