Search Results (67)

Air-assist atomization exploits high-velocity gas streams to shear liquid phases, achieving fine droplets at supply pressures significantly lower than those required by conventional pressure atomization. This technology is extensively deployed across diverse sectors, including pesticide spraying, food spray drying, sanitization/disinfection, and combustion atomization. Although specific operational fields dictate vastly contrasting droplet size distributions, velocities, and deposition uniformities—with target diameters spanning from <50 μm to >200 μm—their foundational atomization mechanisms remain inherently unified, governed primarily by toroidal vortex-induced primary breakup and Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) or Taylor Analogy Breakup (TAB) secondary breakup. This review systematically parses the atomization mechanisms, critical performance metrics, numerical simulation frameworks, and experimental characterization methodologies of air-assist nozzles. Crucially, from a novel “cross-disciplinary custom design” perspective, we contrast the optimal droplet parameter windows across the agricultural, food, sanitization, and combustion sectors (e.g., electrostatic plant protection yields a 203–1350% increase in abaxial leaf deposition; mine wind-assisted misting achieves a >90% collection efficiency for PM₁₀ dust; and SCR air-assisted injectors reduce the Sauter Mean Diameter (SMD) to 25 μm). Synthesized insights reveal that segmented VOF-to-DPM transition frameworks, corner-vortex-induced breakup theories, and closed-loop adaptive control architectures possess substantial cross-domain migration value. Current bottleneck challenges are highlighted, including the mesh dependency of droplet collision–coalescence models in dense spray regimes, the absence of robust atomization constitutive formulations for non-Newtonian fluids, and the ongoing paradigm shift from open-loop presetting to intelligent closed-loop regulation. This work establishes a comprehensive theoretical foundation and technical roadmap for cross-disciplinary integration and next-generation smart nozzle design in air-assist atomization. Full article

Rapid detection and localization of liquid fuel spills is critical for first responders assessing fire and health hazards, yet current methods require ground-based sampling or specialized instrumentation, limiting their practicality for wide-area emergency response. We present a drone-based passive colorimetric sensor system using test strips impregnated with Nile red, similar to colored confetti. Nile red is a solvatochromic dye that undergoes distinct visible color transitions upon exposure to different liquids. The dye is embedded within a polymer matrix that minimizes leaching while providing high optical contrast between dry, water-exposed, and fuel-exposed states. The sensor strips exhibit solvent-specific colorimetric responses within one minute of exposure, readily detectable by standard RGB cameras mounted on unmanned aerial vehicles (UAVs) at altitudes up to 50 m. Automated classification was validated at 20 m altitude, enabling remote surveillance of contaminated surfaces without specialized equipment. Color-corrected image analysis using Calibrite ColorChecker calibration ensures reliable interpretation under variable field illumination (625–77,000 lux). Systematic laboratory evaluation of twelve fossil and bio-derived fuels revealed characteristic hue shifts that clearly discriminate ethanol-containing gasoline blends from diesel-range fuels. Rather than identifying specific molecules, the method functionally categorizes contamination into gasoline/ethanol blends versus diesel-type fuels, reflecting bulk polarity rather than molecular composition. Field validation confirmed localization and classification of fuel-exposed sensors, achieving F1 scores of 0.94 for gasoline and 0.98 for diesel detection with no false positives in the tested scenarios. This cost-effective and scalable approach provides actionable information on both contamination location and fuel type, crucial for rapid hazard assessment in emergency response scenarios. Full article

This study proposes and implements a hierarchical communication system for inspection robots operating in practical caged broiler houses, where confined aisles, multi-tier cages, dust, humidity, and fluctuating wireless coverage impose challenges on stable remote inspection. The system uses Jetson Orin NX as the robot-side main controller, with industrial Ethernet and RS-485 supporting onboard device access, and Robot Operating System 2 (ROS 2) used for device coordination, data processing, and task scheduling. At the robot-cloud interaction level, Message Queuing Telemetry Transport (MQTT) is used for task delivery, status feedback, alarm reporting, and environmental data upload, while a real-time preview channel and sampled key-frame transmission support video monitoring and inspection-process retention. The proposed architecture organizes the control link and data link within an end-to-end closed loop and incorporates acknowledgment, buffering, reconnection, and abnormal-state handling mechanisms to improve communication availability under weak-network field conditions. Long-term field tests in a commercial caged broiler house demonstrated that the system could maintain reliable remote command execution, continuous status feedback, stable visual data transmission, and stable environmental telemetry upload during routine inspection tasks. The results indicate that the proposed communication architecture can provide practical support for remote inspection robots in smart poultry farming scenarios. Full article

In order to predict the wind speed of the excavation roadway, control the frequency conversion operation of the local fan in real-time, and realize the real-time monitoring, collaborative prediction, and frequency conversion control of the ventilation state of the excavation face, the frequency conversion ventilation control system of the excavation face is designed. Based on the theory of frequency conversion control, the genetic-neural network wind speed prediction optimization model was established, and the frequency conversion ventilation control system of the excavation face was designed by using S7-200 SMART PLC. The system test results show that the genetic-neural network optimization model can collaboratively predict wind speed according to the environmental parameters (dust concentration, methane concentration, temperature, and humidity, etc.) of different working conditions. The frequency conversion ventilation control system realizes the real-time monitoring of the environmental parameters of the excavation surface, and also provides two control modes: automatic and manual. Compared with the traditional constant power frequency control fan wind speed, the PID wind speed closed-loop control technology can control the fan wind speed by frequency conversion, so that the actual wind speed of the roadway continues to approach the predicted value stably. The variable frequency ventilation control system can be widely used in different types of mines to realize the adaptive control response of ventilation equipment. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions. Full article

The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on the Python PyQt5 library was developed for Maping Phosphate Mine to improve ventilation efficiency, lower dust concentration at the working face, and enhance safety by addressing uneven air volume distribution. The implementation of an integrated system, comprising a 3D ventilation network model, remote control capabilities, and smart algorithms, has successfully realized zonal planning and on-demand ventilation in the mine’s underground workings. To adapt to the fluctuating air demand at the tunneling face, a remote intelligent control scheme for louvered dampers was implemented. This dynamic demand-based strategy achieves precise distribution of air volume throughout the ventilation network. The research results demonstrate that the system effectively addresses the uneven distribution of air volume, thereby improving the overall ventilation environment and reducing the risk of ventilation-related accidents. The system serves dual purposes: it provides an intelligent ventilation control mechanism and integrates seamlessly with the key subsystems for underground safety production. This synergy is instrumental in advancing the mine’s digitalization and intelligent transformation initiatives. Field test results indicate that the system achieved a 30% reduction in energy consumption and a 70% decrease in dust concentration at the working face, respectively. Full article

Residues in the grain tank of seed-production wheat harvesters often cause varietal admixture, challenging seed purity maintenance above 99%. To address this, an intelligent cleaning system was developed for automatic residue recognition and removal. The system utilizes an RGB-D camera and an embedded AI unit paired with an improved lightweight object detection model. This model, enhanced for feature extraction and compressed via LAMP, was successfully deployed on a Jetson Nano, achieving 92.5% detection accuracy and 13.37 FPS for real-time 3D localization of impurities. A D–H kinematic model was established for the 4-DOF cleaning manipulator. By integrating the PSO and FWA models, the motion trajectory was optimized for time-optimality, reducing movement time from 9 s to 5.96 s. Furthermore, a gas–solid coupled simulation verified the separation capability of the cyclone-type dust extraction unit, which prevents motor damage and centralizes residue collection. Field tests confirmed the system’s comprehensive functionality, achieving an average cleaning rate of 92.6%. The proposed system successfully enables autonomous residue cleanup, effectively minimizing the risk of variety mixing and significantly improving the harvest purity and operational reliability of seed-production wheat. It presents a novel technological path for efficient seed production under the paradigm of smart agriculture. Full article

In parallel with achieving its 2040 Vision toward establishing smart cities, this study aims to pinpoint promising locations for future urban development in Oman, which reflect the unique physical attributes of the country, its renewable energy resources, and socio-economic conditions. To meet this goal at the national scale, the research relied on the following key factors: topography, diurnal temperature range, relative humidity, dust concentrations, wind speed, solar radiation, and access to electricity. These inputs were derived from remote sensing sources. A multi-layer spatial analysis was carried out within a Geographical Information System (GIS) environment to identify high-priority locations for future and sustainable urban growth. All parameters were assigned equal weights, particularly when applying a standard approach to produce a baseline suitability model at the national scale and to avoid subjective bias in the overall suitability assessment. Results showed that 2.1% of Oman’s land shows strong potential for sustainable urban development. Specifically, three locations stand out with the highest occurring along the southern section of the Arabian Sea between Al Jazir and Ad-Duqum. The other two locations occur at Salalah in the south and Sohar in the north. The promising locations occur proximate to major harbors and can benefit from existing infrastructure, including airports, highways, educational and medical services. Suggested locations also align well with earlier relevant studies. This study demonstrates the capabilities of integrating remotely sensed data with geospatial analysis in urban planning and development. Results are expected to help policymakers and planners to prioritize national-scale urban development. Full article

Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications. Full article

Particulate matter (PM2.5) is a critical indicator of air quality and has significant health implications. This study presents the development and evaluation of a custom-built PM2.5 device, named the P-Tracker, designed to offer an accessible alternative to commercially available air quality monitors. This paper presents the design framework used to address the requirements of a low-cost, accessible device which meets the performance of existing commercial systems. Step-by step build instructions are provided for hardware and software development and connection to the P-tracker open access website which displays the data and interactive map. To demonstrate the performance, the P-Tracker was compared against leading consumer devices, including the AtmoTube Pro by AtmoTech Inc., Flow by Plume Labs, View Plus by Airthings, and the Smart Citizen Kit 2.1 by Fab Lab Barcelona, across four controlled tests. The tests included: (1) a controlled paper combustion test in which all devices were exposed to combustion aerosols in a sealed environment alongside the DustTrak 8530 (TSI Incorporated, Shoreview, MN, USA), used as the gold standard reference, where the P-Tracker achieved a Pearson correlation of 0.99 with DustTrak over the final measurement period; (2) an outdoor test comparing readings with a stationary reference sensor, Osiris (Turnkey Instruments Ltd., Rudheath, UK), where the P-Tracker recorded a mean PM2.5 concentration of 3.08 µg/m³, closely aligning with the Osiris measurement of 3.53 µg/m³ and achieving a Pearson correlation of 0.77; (3) a controlled indoor air quality assessment, where the P-Tracker displayed stable readings with a standard deviation of 0.11 µg/m³, comparable to the AtmoTube Pro; and (4) a real-world kitchen environment test, where the P-Tracker effectively captured fluctuations in PM2.5 levels due to cooking activities, maintaining a consistent response with the DustTrak reference. The results indicate varied degrees of agreement across devices in different conditions, with the P-Tracker demonstrating strong correlation and low error margins in high-pollution and controlled scenarios. This research underscores the potential of open-source, low-cost, custom-built air quality sensors which may be developed and deployed by communities to provide hyperlocal measurements of air pollution. Full article

The current research on smart construction sites is mainly from the perspective of the whole life cycle of the project, and often focuses on the identification of factors at the macro level. It lacks in-depth quantitative analysis of the complex interdependence between influencing factors, and it is difficult to accurately identify key driving factors and weight distribution. This paper takes engineering project management as the perspective, constructs a smart site construction model. The advantages of DEMATEL method and ANP method are innovatively combined to construct the DEMATEL-ANP evaluation model, which overcomes the limitations of single method in weight determination and relationship analysis, and provides a more detailed and scientific analysis framework for the evaluation of smart site construction, and, taking the Urumqi region as an example, its smart construction is evaluated and analyzed. The results of the study show that the correlation between the indicators affecting the construction of smart construction sites is strong, in which the comprehensive influence of personnel safety management, construction quality management, and construction safety management play a greater role, with the comprehensive weights of 0.0917, 0.0817 and 0.0767, respectively; the total score of smart construction site construction of Urumqi region is 63.959, which is in the primary construction stage. Among them, the construction and application of meteorological monitoring are the best, scoring 70.26; the construction and application of most indicators, such as personnel safety management, cost comparison decision-making, dust monitoring, and noise monitoring, are the second best; the construction progress control and wastewater monitoring construction are poor, scoring 55.21 and 57.741. The results of this study can provide direct value to key audiences such as construction enterprise managers, government regulators, and smart site solution providers. This paper considers regions with unique climate types to provide a reference for the construction of intelligent construction sites in the same type of regions. Full article

Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose Stellar-YOLO, a YOLO11-based detection framework with asymmetrical architecture optimizations tailored for real-world conditions. The backbone is replaced by the lightweight StarNet to enhance computational efficiency, while the C3k2-CAS module, integrating convolution and additive attention, is embedded in the neck to improve feature expressiveness. The head incorporates the SEAM module, forming the Detect-SEAM, to boost the recognition of complex mineral details. Moreover, to robustly adapt to real mining environments, we apply simulated data augmentation techniques involving motion blur, dust noise, and low brightness conditions. Stellar-YOLO achieves 93.6% mAP based on a custom-built graphite ore dataset, outperforming the baseline by 4.5% and reducing the FLOPs, parameters, and model size by 27%, 26%, and 23%, respectively. This work explores how asymmetrical architectural innovations and robustness-oriented evaluation contribute to a lightweight and effective approach for computer vision-based mineral quality assessment, demonstrating strong potential for practical applications in real-world industrial environments. Full article

Diseases in pig houses not only hinder the growth and productivity of pigs but also result in significant economic losses for farmers due to high mortality rates. Although viral infections, including PRRS and PCV-2, are the primary causes, the likelihood of disease onset is closely linked to the pigs’ immune status, which is often compromised by environmental stressors. This study aimed to investigate the relationship between environmental conditions and pig mortality through detailed field monitoring in a commercial pig house with 600 growing pigs. The facility, which experienced a surge in mortality after a ventilation system change, was analyzed for various environmental parameters, including ammonia concentration (range: 7.0–10.7 ppm), dust levels (PM10: 106 µg/m³, PM2.5: 45 µg/m³), ventilation rates (0.49 AER, 67% of design capacity), air temperature (mean: 22.3 °C, range: 18.1–28.7 °C), and relative humidity (mean: 67.4%, range: 55.3–83.2%). Pig mortality and its spatial distribution were recorded, while viral infections were identified using RT-PCR, detecting pathogens such as PRRS, PCV-2, Mycoplasma hyopneumoniae, and Salmonella. Our findings revealed that although dust and ammonia concentrations remained within permissible limits, mortality was significantly correlated with thermal instability. Chronic respiratory diseases were observed in regions where ventilation was concentrated, resulting in daily temperature variations as high as 6.64 °C. The combination of improper ventilation and frequent temperature fluctuations weakened the pigs’ immunity, facilitating the onset of disease. This research underscores the critical role of maintaining stable microclimatic conditions in reducing mortality and highlights the need for advanced automated environmental control systems in smart livestock barns. The insights gained from this study provide a foundational framework for developing precision ventilation and thermal management strategies to enhance productivity and animal welfare. Full article