Search Results (3,805)

Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article

The construction industry is increasingly challenged by an aging workforce and persistent labor shortages, underscoring the need for automation and the integration of construction robotics. However, the high uncertainty and variability of real construction environments impose significant constraints on robot design and deployment. In particular, accurately estimating the required operational force—without unnecessary overdesign—is essential for ensuring operational safety, energy efficiency, and battery endurance. Conducting on-site experiments that reflect diverse field conditions is often impractical, making simulation-based approaches a viable alternative. This study proposes a simulation-driven method for deriving energy-efficient, task-appropriate operational forces for construction robots. As a case study, an aluminum formwork dismantling operation was modeled in NVIDIA Isaac Sim, and a dataset of environmental variables was generated through random sampling. Sensitivity analysis revealed that the dynamic friction coefficient at the aluminum–aluminum interface had the greatest impact on the required dismantling force. To mitigate this influence, a lubrication strategy was introduced to reduce surface friction. With a 10% safety margin applied, the dismantling operation achieved a 99.5% success probability at an operational force of 50 N-representing an 11.71 N reduction and an 18.97% decrease compared to the non-lubricated scenario. These results demonstrate a practical and evidence-based approach for optimizing operational forces in construction robotics, contributing to reduced energy consumption, improved operational efficiency, and mitigation of construction schedule delays. Full article

Accurate early-stage assessment of building energy and carbon performance is essential for informed sustainable design yet remains challenging due to limited design detail and simulation effort. This study presents a Building Information Modeling–Machine Learning (BIM-ML) framework for predicting office building energy and carbon performance at early design stages using simulation-based datasets. A reduced-factorial Design of Experiments (DOE) generated 210 parametric office building models for Orlando, Florida (ASHRAE Climate Zone 2A), complemented by additional climate scenarios. Systematic variations in geometry, envelope, building systems, and operational schedules produced a dataset with 14 independent variables and five performance indicators: Energy Use Intensity, Operational Energy, Operational Carbon, Embodied Carbon, and Total Carbon. Four regression methods—Linear Regression, Model Tree (M5P), Sequential Minimal Optimization Regression, and Random Forest—were trained and evaluated using 10-fold cross-validation. Random Forest showed the strongest overall predictive performance. Feature-importance analysis identified HVAC system type, Window-to-Wall Ratio, and operational schedule as the most influential parameters, while geometric factors had lower impact. Cross-climate analysis and validation with measured data from two university office buildings indicate that the framework is adaptable and generalizable, supporting reliable early-stage evaluation of energy and carbon performance. Full article

In agricultural plant protection spraying, dynamic occlusion by droplet swarms on leaf surfaces poses a major challenge to accurately acquiring leaf motion parameters, limiting the optimization of precision spraying and pesticide utilization. Traditional contact-based methods interfere with natural leaf dynamics, while non-contact optical approaches suffer from tracking failures under occlusion. This study proposes an improved framework combining YOLOv8 integrated with a Spatial Attention Module (SAM) and optimized DeepSORT for robust non-contact tracking of marked points on pepper leaves. High-speed binocular cameras were used to collect leaf motion data under controlled droplet occlusion conditions. Results demonstrate that, under 5% occlusion, the improved model achieves a 19.6% increase in detection mAP@0.5 and significantly enhances tracking MOTA, with trajectory breakage rate reduced to 3.2% and ID switches decreased by approximately 71.4% in long-sequence tracking. Quantitative analysis of leaf midrib motion reveals a clear spatial gradient: average speed increases from 0.012 m s⁻¹ at the base to 0.153 m s⁻¹ at the tip, with intensified fluctuations toward the tip and a consistent dominant vibration frequency of 0.403 Hz across all points. This method provides an efficient, reliable non-contact solution for measuring leaf motion parameters in complex spraying scenarios, offering valuable data support for targeted spray parameter optimization and improved deposition efficiency in precision agriculture. Full article

As wind penetration rises, the share of synchronous generation declines, reducing system inertia and increasing uncertainty in frequency stability; wind-output disturbances, power-electronic control characteristics, and stochastic load variations can further amplify frequency deviations caused by power imbalance. To improve frequency security under high wind penetration, this study optimizes BESS control parameters and evaluates their impact on system dynamic stability using a PSS®E V34 dynamic model of the IEEE New England 39-bus system that includes three wind turbines and two BESS units under four disturbance scenarios: (i) derating one turbine to 50%, (ii) tripping one turbine, (iii) derating all three turbines to 50%, and (iv) an N-1 contingency corresponding to the tripping of the largest conventional generator in the system. Morris sensitivity analysis is first applied to identify key parameters affecting frequency response and reduce the optimization dimension, and the selected parameters are then tuned using an improved genetic algorithm (IGA) and grey wolf optimization (GWO). Simulation results show the minimum frequency improves from 59.957 Hz (baseline) to 59.961 Hz with IGA and to 59.966 Hz with GWO, while the maximum equivalent power-angle difference in the BESS unit relative to the center of inertia decreases from 266.3° to 250.1° (IGA) and 251.2° (GWO), indicating that the proposed approach strengthens BESS frequency support and enhances dynamic stability under various wind-power and N-1 contingency disturbance conditions. Full article

To address the challenges of low manual harvesting efficiency and high difficulty in automated picking of safflower filaments in the unstructured field environments of Xinjiang, this study proposes an intelligent harvesting method that integrates lightweight visual detection and adaptive localization. Firstly, a safflower image dataset covering multiple scenarios and growth stages was constructed. An improved lightweight detection model, named SSO-YOLO, was proposed based on the YOLOv11n model. By introducing the StarNet backbone network, the SEAttention mechanism, and structural optimization, this model achieves a high detection accuracy (mAP@0.5 of 97.4%) while reducing the model size by 29.4% to 3.94 MB, significantly enhancing its deployment feasibility on mobile devices. Secondly, based on the detection results, an adaptive localization algorithm for picking points was developed. This algorithm achieves precise localization of picking points at the filament–flower head junction by integrating geometric analysis of filament growth posture, dynamic judgment of connection conditions, and intersection calculation of rotated bounding boxes. Experimental results demonstrate that this algorithm achieves an average localization success rate of 87.3% across various unstructured scenarios such as occlusion and backlighting, representing an improvement of approximately 10.7 percentage points over traditional methods. The estimation error for filament posture angle is merely 0.6°, and the localization success rate remains above 90% across the entire growth cycle. This study provides an efficient and robust visual solution for the automated harvesting of safflower filaments and offers valuable insights for advancing intelligent harvesting technologies for specialty cash crops. Full article

This article discusses the fundamental limitations of Light Fidelity (Li-Fi) systems, an emerging visible light communication technology that is constrained by line-of-sight dependency and optical attenuation. Unlike existing adaptive modulation approaches that focus solely on improving signal processing, we present an integrated framework that combines three key contributions: (1) an adaptive modulation optimization algorithm that selects among OOK, PAM, and OFDM schemes based on instantaneous signal-to-noise ratio thresholds, achieving a 30–40% range extension compared to fixed modulation references; (2) a method for spatial optimization of access points (APs) using the L-BFGS-B algorithm to determine the optimal location of APs, taking into account lighting constraints and coverage uniformity; and (3) comprehensive system-level modeling incorporating shot noise, thermal noise, inter-symbol interference, and dynamic shadowing effects for realistic performance evaluation. Through extensive simulations on multiple room geometries (6 m × 5 m to 20 m × 15 m) and AP configurations (one to six APs), we demonstrate that the proposed adaptive system achieves an average throughput 60% higher than that of fixed OOK, while maintaining 98.7% coverage in a 10 m × 8 m environment with two optimally placed APs. The framework provides practical design guidelines for Li-Fi deployment, including an analysis of computational complexity $O(M\times N)$ for coverage assessment, $O(I\times D^3)$ for access point optimization) and a characterization of convergence behavior. A comparative analysis with state-of-the-art techniques (optical smart reflective surfaces, machine learning-based blockage prediction, and Li-Fi/RF hybrid configurations) positions our lightweight algorithmic approach as suitable for resource-constrained deployment scenarios, where system-level integration and practical feasibility take precedence over innovation in individual components. Full article

This study presents an integrated modeling and optimization framework for laser transmission welding (LTW) of transparent polymethyl methacrylate (PMMA) joints using single- and multi-core copper wires as energy absorbers. The highly nonlinear relationships between laser power, welding speed, and spot diameter and the resulting shear force and weld width were modeled using a hybrid neuro-regression strategy combining data-driven learning with physically interpretable analytical formulations. A wide range of candidate mathematical models were systematically evaluated based on training and testing performance, residual behavior, and physical consistency. The results demonstrate that models exhibiting near-perfect training accuracy frequently suffered from severe overfitting and poor generalization, whereas intermediate-complexity formulations provided a more reliable balance between accuracy and robustness. Comparative analysis further showed that multi-core absorbers consistently produced higher shear strength and more uniform weld seams than single-core configurations. The selected robust models were subsequently integrated into a two-level ensemble meta-optimization framework employing Differential Evolution, Nelder–Mead, Random Search, and Simulated Annealing algorithms under multiple design scenarios. The meta-optimization process successfully eliminated model- and algorithm-dependent extreme solutions and identified stable consensus parameter regions. For the multi-core system, an optimal combination of 30 W laser power, 20 mm/s welding speed, and 0.7 mm spot diameter was obtained, achieving improved mechanical performance while remaining within experimentally validated limits. The proposed framework provides a physically grounded and reliable strategy for surrogate-based optimization of nonlinear welding processes. Full article

Signage systems are an integral part of modern urban environments, and they influence both city aesthetics and information flow. But their growing use also adds to the embodied carbon footprint of urban infrastructure, a factor that is often overlooked in sustainable city planning. The present study investigates the environmental impact of signage within the context of urban development and climate-responsive design using two Australian case studies, including one installed at a national bank. The assessment is limited to the cradle-to-site (A1–A4) stages, focusing on material production and transportation impacts only. In each case study, one installed signage unit is used as the functional unit, with the results scaled to a nationwide-deployment scenario in Case Study 2. The results show that aluminium and steel dominate signage materials in both mass and embodied carbon. The study also proposes several mitigation strategies, including the use of low-carbon aluminium, higher-grade steel, and design optimization methods. A quantitative analysis also demonstrates the potential reductions in embodied carbon, ranging from 18% to 80.3%, with low-carbon material substitution achieving up to an 83.4% reduction in one case study. The findings also highlight that targeted material and design choices in the signage sector can significantly advance urban sustainability goals. Full article

Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, $Erro{r}_{\mathrm{peak}}=0.19896\pm 0.39400$, while remaining competitive in global reconstruction, $MS{E}_{\mathrm{map}}=0.00144\pm 0.00348$, $R^2=0.96066\pm 0.09861$. In addition, a turning analysis based on the absolute heading change between consecutive segments $\left|\Delta \alpha \right|$ shows that ACO-Path produces smoother trajectories, with fewer sharp turns $\left|\Delta \alpha \right|\ge {45}^{\circ }$ than counterpart baselines under the same mission constraints. Full article

This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural-network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers an effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications. Full article

This study assessed the techno-economic performance and energy efficiency of scrap tyre valorization through pyrolysis in Nigeria, comparing two configurations: a pyrolysis plant integrated with power generation (Scenario 1) and a standalone pyrolysis plant (Scenario 2). Process simulations were carried out using Aspen Plus V12, and cost estimations were performed with the Aspen Process Economic Analyzer. For a feed capacity of 20 tons per hour, the pyrolysis process yielded steel wire (15.04%), char (35.57%), pyro-diesel (37.94%), gas (7.91%), and heavy oil (3.54%). Scenario 2 achieved a higher energy efficiency (94.44%) than Scenario 1 (51.23%). However, Scenario 1 demonstrated superior economic performance, with a Net Present Value (NPV) of USD 28.65 million and an Internal Rate of Return (IRR) of 34.48%, despite its higher capital investment of USD 27.63 million. Sensitivity analysis revealed that the selling price of pyro-diesel and the cost of scrap tyres were the most influential parameters affecting profitability. The findings provide useful insights for optimizing scrap tyre pyrolysis systems toward sustainable waste-to-energy applications in developing regions. Full article

With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control (MPC) emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning (RL) and deep reinforcement learning (DRL) have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems. Full article

Reinforcement Learning (RL) enables learning optimal decision-making strategies by maximizing cumulative rewards. Deep reinforcement learning (DRL) enhances this process by integrating deep neural networks (DNNs) for effective feature extraction from high-dimensional input data. Unlike prior studies focusing on algorithm design, we investigated the impact of different feature extractors, DNNs, on DRL performance. We propose an enhanced feature extraction model to improve control effectiveness based on the proximal policy optimization (PPO) framework in autonomous driving scenarios. Through a comparative analysis of well-known convolutional neural networks (CNNs), MobileNet, SqueezeNet, and ResNet, the experimental results demonstrate that our model achieves higher cumulative rewards and better control stability, providing valuable insights for DRL applications in autonomous systems. Full article

In regions with abundant solar energy, solar water disinfection (SODIS) offers a sustainable strategy to improve drinking water access, especially in rural, off-grid communities. This study presents a numerical modeling approach to assess the thermal and microbial disinfection performance of glass-free parabolic trough collectors (PTCs). The model integrates geometric sizing, one-dimensional thermal energy balance, and first-order microbial inactivation kinetics, supported by optical simulations in SolTRACE 3.0. Simulations applied to a representative case in the Colombian Caribbean (Gambote, Bolívar) highlight the influence of rim angle, focal length, and optical properties on system efficiency. Results show that compact PTCs can achieve fluid temperatures above 70 °C and effective pathogen inactivation within short exposure times. Sensitivity analysis identifies key geometric and environmental factors that optimize performance under variable conditions. The model provides a practical tool for guiding the design and local adaptation of SODIS systems, supporting decentralized, low-cost water treatment solutions aligned with sustainable development goals. Furthermore, it offers a framework for future assessments of PTC implementations in different climatic scenarios. Full article