Search Results (3,476)

Labor shortages and the push for higher greenhouse efficiency have accelerated interest in automated harvesting. However, the development of a universal harvesting robot has been constrained by large variations in crop architecture, especially plant height. In this review, a height-based stratification of greenhouse harvesting robots and transferable high-wire crop harvesters is presented, covering ground-level crops (<0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato), and high-wire crops (>1.5 m, e.g., trellised cucumber). For each height layer, key design features, technical progress, prototype performance, and common obstacles—including fruit occlusion, mechanical crop damage, unreliable operation, and high commercial costs—are analyzed. Future efforts should target intelligent perception, soft end-effectors, and height-specific solutions (swarm robotics for ground crops, modular hybrid designs for medium crops, infrastructure co-design for high-wire crops). By using plant height as the primary stratification criterion, a design-oriented framework is provided, distinct from conventional crop-type or mechanism-based categorizations. Full article

The deployment of Large Language Model (LLM)-generated SQL in Artificial Intelligence of Things (AIoT) systems introduces critical security risks, as prompt injection attacks can manipulate LLMs into producing unauthorized queries that expose sensitive data or execute destructive operations. Existing Natural Language to SQL (NL2SQL) research targets query accuracy, while current Model Context Protocol (MCP) servers offer only SQL-level protection without fine-grained, role-based access control. This paper proposes SecureMCP, a policy-enforced framework that integrates Role-Based Access Control (RBAC) with an MCP server to establish multi-layer defense for LLM-generated SQL execution. Grounded in an explicit threat model, the framework chains five defense modules in a sequential fail-closed pipeline addressing six prompt injection types spanning four adversary goals. We evaluate SecureMCP on the IoT-SQL dataset using Qwen3-8B, reporting filter performance—false positive rate (FPR) and false negative rate (FNR)—separately from LLM generation quality. On benign queries, the framework maintains a low false positive rate (0.3–2.2%) across four RBAC roles while keeping execution accuracy among allowed queries within 65.1–76.4%, matching the unprotected baseline of 63.8% and confirming that the defenses act as a transparent pre-execution filter. On 2400 adversarial queries, SecureMCP limits the effective false negative rate—computed over realized threats in which the injection payload was actually incorporated—to 3.98%, and an ablation confirms that RBAC and MCP-level defenses are complementary, as neither blocks the full range of injection vectors alone. The 72.5% injection incorporation rate confirms high LLM susceptibility, establishing the necessity of external policy enforcement. Full article

Integrated sensing and communication (ISAC) enables concurrent wireless communication and target sensing using shared hardware and spectrum resources, and is regarded as a key technology for next-generation mobile networks. To address the challenge of coordinating sensing and communication in multiuser scenarios, this paper investigates an unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and proposes a joint beamforming and trajectory optimization framework. Specifically, ground users are first clustered subject to a capacity–diameter constraint, and the UAV employs RSMA to serve users within each cluster. The sensing performance is characterized by the composite transmit beampattern gain in the target direction contributed by the common stream and all private streams. Under constraints on users’ downlink rates, transmit power, and sensing quality, we formulate an optimization problem to maximize the average downlink rate. By adopting a block coordinate descent (BCD) framework, the resulting non-convex problem is decomposed into three subproblems, namely cluster scheduling, rate allocation and beamforming, and UAV trajectory optimization. These subproblems are then solved via linear programming, semidefinite relaxation, and successive convex approximation, respectively. Numerical results demonstrate that, while satisfying the sensing-quality requirement, the proposed algorithm significantly improves the achievable system downlink rate. Full article

Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C₄ carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment. Full article

To address the problem of lacking effective evaluation benchmarks for UAV navigation algorithms in complex-structured, GNSS-denied industrial environments (e.g., fully enclosed stockyards), this paper proposes and open-sources a multi-modal UAV navigation dataset. The dataset is collected in a real steel plant enclosed stockyard, integrating LiDAR point clouds, IMU, RGB images, and high-precision total station ground truth trajectories, and specially designs ArUco markers to aid visual localization. Different from existing datasets targeting urban or campus scenes, this dataset realistically reflects the challenges of GNSS-denied signal, weak texture, high dust, and complex spatial grid structures in industrial environments. Through the evaluation of various mainstream LiDAR odometry and fusion navigation algorithms, the difficulties encountered by existing methods in this scenario are highlighted, and the potential of the proposed dataset as a valuable benchmark for developing and quantitatively testing highly robust navigation algorithms is suggested. Full article

Adaptive locomotion across continuous terrain transitions remains difficult for hexapod robots because contact timing, body attitude, support height, and load distribution change simultaneously along a route. This paper presents a unified proprioception-driven deep reinforcement learning and central pattern generator (DRL-CPG) framework for terrain-transition locomotion without visual terrain classification, explicit terrain labels, or terrain-specific controller switching. A high-level proximal policy optimization policy maps a 46-dimensional proprioceptive observation to a three-dimensional CPG modulation action comprising oscillation amplitude, swing-phase frequency, and turn modulation. A coupled six-node Hopf oscillator network then expands these modulated parameters into phase-coordinated rhythmic commands, which are mapped to the 18 joint targets of a JetHexa hexapod and executed by a low-level proportional-derivative controller. The observation space contains body linear velocity, body angular velocity, relative joint positions, relative joint velocities, the previous three-dimensional policy action, and inertial measurement unit (IMU)yaw/heading relative to the initial track direction. A continuous route consisting of flat ground, uphill stairs, irregular terrain, downhill stairs, and a recovery segment is defined to evaluate transition-aware locomotion using route completion, velocity-tracking error, lateral deviation, and roll/pitch fluctuation. Compared with the fixed-parameter CPG and end-to-end DRL baselines, the proposed method increased the full-distance success rate at 4.7 m from 9% and 20%, respectively, to 88%, while maintaining smoother velocity, lateral deviation, and roll/pitch responses. The framework preserves the rhythmic prior of CPG control while reducing the exploration burden of reinforcement learning, providing a compact formulation for adaptive hexapod locomotion across terrain transitions. Full article

Ground-penetrating radar (GPR) is a rapid and non-destructive technique for road sub-surface distress (RSD) detection. However, reliable interpretation of GPR images remains challenging because subsurface anomalies often present weak boundaries, ambiguous textures, and high similarity to non-distress targets. This study proposes a cross-verification intelligent algorithm that exploits complementary information from different views of 3D GPR data. Three YOLO-based detectors are trained on view-specific GPR images to identify RSD-related targets, including voids, loose structures, and manholes. By sequentially verifying detection results across different views, the proposed method improves recognition reliability under vague subsurface imaging conditions. The models are trained and evaluated on an expert-annotated field 3D GPR dataset containing 2134 location-level multi-view samples. At the selected operational thresholds, the complete cross-verification procedure achieved 95.9% precision and 98.6% recall for RSD detection in the testing subset. In a field evaluation on 15 roads, all 69 RSD locations in the expert-identified reference set were matched by automatic indications. When integrated into an automatic detection system, the method reduced manual inspection workloads by approximately 90% while maintaining high field reliability. These results demonstrate the potential of multi-view cross-verification for post-survey RSD screening and expert-assisted review. Full article

The construction industry is inherently high-risk, with workers’ unsafe behaviors directly causing most safety incidents. As smart technologies are widely deployed on construction sites, new forms of risk-taking behavior have emerged, but their underlying mechanisms remain poorly understood. Grounded in Human–Technology–Organization (HTO) theory, this study establishes a multi-factor coupling analytical framework and employs a mixed NCA–fsQCA method to empirically analyze data from 312 workers across two smart construction sites in Beijing. The results show that no single antecedent variable acts as a necessary condition for either type of high-risk-taking behavior, though each variable exerts distinct bottleneck constraints. Five configurations driving high-risk behaviors are identified: smart technology adaptability serves as the core condition for automation trust bias behaviors, while individual risk-taking propensity and task situational pressure are universal core factors for both behavior types. These findings uncover the multi-dimensional coupling logic of risk-taking behaviors and offer theoretical and practical insights for targeted safety management in smart construction contexts. Full article

Rockets were launched from a freely ascending balloon, and the attitude dynamics of the rockoon system were investigated. Compared with ground-based or aircraft-based launches, rockoons offer reduced aerodynamic drag and pressure losses, leading to higher maximum altitudes of sub-orbital trajectory and improved launch capacity to earth orbits. To ensure trajectory accuracy and flight safety, an azimuth control system based on a control moment gyroscope (CMG) was implemented. The launcher, suspended beneath a helium balloon, was equipped with a CMG device for active azimuth control. Three model rocket launches were conducted, and attitude data were obtained using multiple accelerometers installed on the rocket and launcher. The results confirmed that azimuth control remained effective during free ascent, with the azimuth error at ignition within 7° of the target in all three launches. Oscillatory motion was observed in roll and yaw angles. It was identified as rotation about the launcher’s principal inertia axis, indicating no significant impact on the rocket’s flight trajectory. Additionally, pitch-up behavior during launch due to rail friction was observed, consistent with previous studies. Frequency analysis showed that a double-pendulum model reproduced the measured first-mode frequency within approximately 2%, while the measured second-mode frequencies were higher than the predictions, indicating an increase in the effective pendulum length due to the relaxed constraint of the balloon suspension. Under free-flight conditions, the first mode was no longer observed within the measurable frequency band, consistent with the removal of the ground constraint. These findings provide an experimental characterization of the attitude dynamics of rockoon launches with active azimuth control. Full article

Acoustic side-channel attacks (ASCAs) exploit unintended sound emitted by keyboards to infer typed input, but existing methods generally assume manually labelled training data and controlled environments, limiting their applicability to realistic scenarios such as online lectures. We develop a pipeline that automatically labels keystroke-sound samples captured from online coding tutorials: video frames are processed with optical character recognition (OCR) to extract the ground-truth character sequence, audio is segmented into clips centred on detected click events, and the two streams are aligned. A convolutional neural network (CNN) is trained on mel-spectrogram features, with transfer learning used to adapt the pretrained model to a target user with minimal samples. The classifier is trained on all 68 physical keys present in the recordings; of these, 50 produce a character or whitespace and the remaining 18 are control, navigation, and modifier keys. On a held-out test set, the CNN achieves 98.1% top-1, 99.4% top-2, and 100% top-3 accuracy. Transfer learning retains strong performance with as few as 13 samples per key. Pairing OCR-derived ground truth with acoustic CNN classification removes the labelling bottleneck that has limited previous ASCAs, and the transfer-learning stage makes the attack viable with minimal per-victim data. All code, trained models, and labelled datasets are released to support reproducible research. Full article

Despite growing evidence on sustainable food systems and children’s food choice behaviours, a persistent gap remains between research findings and their implementation in local institutional contexts, particularly in schools. Existing approaches often address behavioural, implementation, or governance dimensions separately, limiting real-world applicability. This conceptual review aimed to develop a theoretically grounded and practice-informed conceptual framework explaining how science–local government partnerships can support sustainable food system interventions in schools. An integrative conceptual approach was applied, combining a targeted narrative review of literature on sustainable food systems, consumer behaviour, and school-based interventions; theoretical integration of behavioural and implementation frameworks, including the COM-B model and Self-Determination Theory; and practice-informed implementation insights from a large-scale municipal nutrition education programme implemented in primary schools in Warsaw, Poland. Framework development involved component identification, domain synthesis, and model integration. The resulting Integrated Science–Policy–Practice (ISPP) Model integrates five interrelated components: Knowledge Translation, Institutional Embedding, Sustainability-Oriented Behavioural Activation Mechanisms, Multi-stakeholder Integration, and Feedback Loops. The model provides a system-level explanation of how evidence-informed interventions can be embedded within local governance structures and translated into mechanisms that may support responsible food-related behaviours. It offers implications for research, policy, and practice in sustainable school food systems. Full article

Detecting changes in coral and seagrass habitat composition from satellite imagery places exceptionally high demands on sensor design due to low underwater reflectance signals and variable water column conditions. Recent multispectral satellite-based attempts to assess such changes across large spatial extents illustrate this challenge through an inability to reliably distinguish live coral from algae, often resulting in broad confidence intervals. This study quantifies the radiometric sensitivity requirements for detecting 10% absolute changes in live coral and seagrass fractional cover from spaceborne imaging spectroscopy using representative parameters for an aquatic imaging spectrometer. The analysis combined representative benthic spectra with realistic, management-relevant co-occurrence scenarios informed by extensive regional knowledge and field measurements from Fiji, Australia, and the Solomon Islands to evaluate detection performance across depths from 0 to 30 m. We show that live coral is the most demanding of the benthic targets evaluated because of its low reflectance and spectral similarity to algal cover types, requiring SNRs of approximately 300–700 to detect 10% absolute changes in cover at depths up to 10 m. In contrast, the greater spectral separation between seagrass and adjacent sandy substrates allows detection of 10% absolute changes in cover to depths exceeding 20 m in clear water. These results highlight the importance of high radiometric sensitivity and contiguous spectral sampling for future aquatic imaging spectrometers intended to monitor benthic change. Approaches that increase effective SNR, such as ground motion compensation (GMC), can extend the depth and confidence with which changes in benthic composition are detected, supporting a transition from broad-area habitat mapping toward quantitative monitoring of benthic change from space. Full article

Portland cement production accounts for roughly 8% of anthropogenic CO₂ emissions, driving interest in low-carbon geopolymer binders. One-part (“just-add-water”) geopolymers, which replace hazardous liquid activators with a dry, pre-blended solid activator, are especially suited to field deployment where handling safety and logistics are decisive. However, their formulation space is combinatorially vast, and trial-and-error development cannot efficiently navigate it. This paper reviews one-part geopolymer science, presents a new comparative and interpretable ML analysis of a published 80-mixture one-part fly-ash/ground granulated blast-furnace slag (GGBS, hereafter slag) geopolymer dataset from twelve studies, and proposes an AI-assisted design framework. The ML demonstration targets 28-day compressive strength only. Under leave-one-source-out (LOSO) cross-validation—the appropriate test for a literature-pooled dataset—gradient-boosted trees achieved R² = 0.61 (RMSE = 15.5 MPa; 95% bootstrap confidence interval on R², 0.44–0.75), well above a linear baseline (0.36), suggesting that non-linear structure transfers across studies; a random split gives a higher but less reliable R² = 0.90 on only 16 test mixtures. Because fly-ash and slag contents are near-perfectly anti-correlated ($r=-0.99$), we model the precursor axis as a single slag fraction descriptor; SHAP then identifies this precursor balance and the activator’s Na₂O dosage as the dominant statistical predictors of strength in this dataset, an ordering consistent with known activation chemistry; causal confirmation of these associations awaits the experimental validation stage of the proposed framework. Demonstrated for strength only, at paste level, the framework offers a transferable route toward multifunctional low-carbon binders for protective and infrastructure applications; the multifunctional extensions are proposed, but not yet demonstrated. Full article

Seawater intrusion (SWI) represents an increasingly critical challenge for coastal groundwater systems, with particularly pronounced impacts observed along the Indian coastline. Coastal aquifers constitute a vital freshwater resource supporting domestic, agricultural, and industrial demands. This study evaluates SWI vulnerability along the entire Indian coast, extending from Gujarat to West Bengal, covering approximately 7517 km of shoreline and up to 100 km inland. The assessment applies the GALDIT vulnerability framework that combines several hydrogeological and hydrochemical criteria such as groundwater occurrence, aquifer hydraulic conductivity, depth to groundwater, distance from shoreline, hydrochemical data, and groundwater quality data. We also assessed the intrusion of existing seawater, shoreline location, and aquifer thickness. However, conventional vulnerability assessments are inherently static and often fail to capture anthropogenic influences. To address this limitation, the present study integrates multi-temporal land use and land cover (LULC) datasets derived from ESA WorldCover remote sensing data for the period 2017–2024. Incorporating LULC dynamics enables a more comprehensive evaluation of the impacts of urban expansion and agricultural intensification on coastal susceptibility to SWI. Accordingly, a modified GALDIT-LU framework is developed to assess the spatiotemporal evolution of coastal vulnerability. The outcomes suggest that huge parts of the Indian coastline are vulnerable to moderate or very high classes, with the very high vulnerability class growing from 13,295 km² in 2017 to 38,257 km² in 2024, a 188% increase in vulnerability over the course of seven years. Groundwater chloride concentrations from Central Ground Water Board (CGWB) monitoring well locations have been used for validation over the proposed assessment, and show good spatial agreement between areas identified as high vulnerability and the spatial distribution of groundwater salinity for all three assessment periods, lending support to the robustness and predictive power of the proposed groundwater salinity assessment. The findings carry direct implications for the United Nations 2030 Agenda, demonstrating that the identified vulnerability patterns intersect with critical targets related to clean water and sanitation, food security, public health, climate action, and poverty reduction along one of the world’s most densely populated coastlines. Full article