Search Results (34,508)

With the exponential growth of Internet of Things (IoT) and wearable devices for home automation and industrial applications, vast volumes of data are continuously generated, requiring efficient data collection methods. IoT devices, being resource-constrained and typically battery-dependent, require lightweight protocols that optimize resource usage and energy consumption. Among such IoT devices, this article focuses on Bluetooth-based beacons due to their low latency and the advantage of not requiring pairing for communications. Specifically, to tackle the limitations of beacons in terms of bandwidth and transmission frequency, this article proposes a protocol that modifies beacon frames to include up to three parameters per frame and that allows for making use of configurable beaconing intervals based on the specific requirements of the communications scenario. Moreover, the use of the proposed protocol leads to increased data rates for beaconing transmissions, providing a low latency and a flexible configuration that permits adjusting different parameters. The proposed solution enables end-to-end interoperability in Opportunistic Edge Computing (OEC) networks by integrating a lightweight bridge module to transparently manage BLE advertisement segments. To demonstrate the performance of the devised opportunistic protocol, it is evaluated across multiple scenarios (i.e., in a short-distance reference scenario, inside a home with diverse obstacles, inside a building, outdoors and in an industrial scenario), showing its flexibility and ability to collect substantial data volumes from heterogeneous IoT devices. Full article

Accurate estimation of tree diameter at breast height (DBH) from LiDAR point clouds is essential for forest inventory, biomass assessment, and ecological monitoring. This paper presents a perimeter-based DBH estimation framework that achieves competitive accuracy against geometric fitting methods across three datasets. The proposed approach partitions the trunk cross-section into angular sectors and employs Gaussian Mixture Models (GMMs) to identify representative boundary points in each sector, weighted by radial proximity and statistical confidence. To handle occlusion and partial scans, missing sectors are reconstructed using symmetry-aware proxy generation. The final perimeter is modeled via either convex hull or B-spline interpolation, from which DBH is derived. Extensive experiments were conducted on two public TreeScope datasets and a custom mobile LiDAR dataset. Compared to the Density-Based Clustering Ring Extraction (DBCRE) baseline, our method reduced RMSE by 22.7% on UCM-0523M (from 2.60 to 2.01 cm), 34.3% on VAT-0723M (from 3.50 to 2.30 cm), and 29.6% on the Custom Dataset (from 2.16 to 1.52 cm). Ablation studies confirmed the individual and synergistic contributions of GMM clustering, radial consistency filtering, and proxy synthesis. Overall, the method provides a flexible alternative that reduces dependence on strict geometric assumptions, offering improved DBH estimation performance with moderate occlusion and incomplete, uneven boundary coverage. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

Conductive hydrogels hold great promise for biomedical and electronic applications. However, their practical use is often limited by poor self-healing capability, which can affect long-term stability and durability. To address this, we developed alginate/polyacrylamide-based conductive hydrogels incorporating FeCl₃ and AlCl₃, named CH-Fe and CH-Al, respectively. We systematically studied the influence of metal cations on the hydrogels’ mechanical and electrical properties. CH-Al showed the most optimized performance, with a 329% increase in tensile strength and a 323% improvement in conductivity compared to the blank hydrogel. Additionally, CH-Al demonstrated excellent self-healing ability, with nearly 100% recovery after damage. The introduction of Al³⁺ improved conductivity by forming dynamic electron-conductive pathways through interactions with the polymer network. The self-healing behavior arises from reversible metal–ligand coordination bonds, which enable rapid recovery of the hydrogel’s structure after mechanical disruption. This study successfully developed a conductive hydrogel that combines high electrical conductivity, robust mechanical strength, and an intrinsic self-healing ability, offering significant potential for applications in bioelectronic devices, flexible sensors, and implantable medical technologies. Full article

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article

While calcium (Ca²⁺) is a universal cellular messenger, the ionic properties of magnesium (Mg²⁺) make it less suited for rapid signaling and more for structural integrity. Still, besides being a passive player, Mg²⁺ is the only active Ca²⁺ antagonist, essential for tuning the efficacy of Ca²⁺-dependent cardiac excitation–contraction coupling (ECC) and for ensuring cardiac function robustness and stability. This review aims to provide a comprehensive framework to link the structural and molecular mechanisms of Mg²⁺/Ca²⁺ antagonistic binding across key proteins of the cardiac ECC machinery to their physiopathological relevance. The pervasive “dampening” effect of Mg²⁺ on ECC activity is exerted across various players and mechanisms, and lies in the ions’ physiological competition for multiple, flexible binding protein motifs across multiple compartments. Mg²⁺ profoundly modulates the cardiac action potential waveform by inhibiting the L-type Ca²⁺ channel Cav1.2, i.e., the key trigger of cardiac ryanodine receptor (RyR2) opening. Cytosolic Mg²⁺ favors RyR2 closed or inactive conformations not only through physical binding at specific sites, but also indirectly through modulation of RyR2 phosphorylation by Camk2d and PKA. RyR2 is also potently inhibited by luminal Mg²⁺, a vital mechanism in the cardiac setting for preventing excessive Ca²⁺ release during diastole. This mechanism, able to distinguish between Ca²⁺ and Mg²⁺, is mediated by luminal partners Calsequestrin 2 (CASQ2) and Triadin (TRDN). In addition, Mg²⁺ favors a rearrangement of the RyR2 cluster configuration that is associated with lower Ca²⁺ spark frequencies. Full article

Due to the high cost of hydrogen utilization and the uncertainties in renewable energy generation and load demand, significant challenges are posed for the operation optimization of hydrogen-containing integrated energy systems (IESs). In this study, a robust operational model for an electric–heat–gas IES (EHG-IES) is proposed, considering the hydrogen energy system heat recovery (HESHR) and multiple uncertainties. Firstly, a heat recovery model for the hydrogen system is established based on thermodynamic equations and reaction principles; secondly, through the constructed adjustable robust optimization (ARO) model, the optimal solution of the system under the worst-case scenario is obtained; lastly, the original problem is decomposed based on the column and constraint generation method and strong duality theory, resulting in the formulation of a master problem and subproblem with mixed-integer linear characteristics. These problems are solved through alternating iterations, ultimately obtaining the corresponding optimal scheduling scheme. The simulation results demonstrate that our model and method can effectively reduce the operation and maintenance costs of HESHR-EHG-IES while being resilient to uncertainties on both the supply and demand sides. In summary, this study provides a novel approach for the diversified utilization and flexible operation of energy in HESHR-EHG-IES, contributing to the safe, controllable, and economically efficient development of the energy market. It holds significant value for engineering practice. Full article

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance. Full article

Generative video diffusion models (GVDs) generate high-fidelity, text-conditioned videos but risk producing unsafe or copyrighted content due to training on large, uncurated datasets. Concept erasure techniques aim to remove such harmful concepts from pre-trained models while preserving overall generative performance. However, existing methods mainly target single-concept erasure and thus cannot satisfy the demand for simultaneously eliminating multi-concept in real-world scenarios. On the one hand, naively applying single-concept erasure sequentially to multi-concept often yields suboptimal results due to conflicts among target concepts; on the other hand, methods that alter concept mappings exhibit very poor adaptability and fail to accommodate the dynamic concept changes. To address these, we propose ConceptVoid, a scalable multi-concept erasure framework formulated as a constrained multi-objective optimization problem. For each target concept, an erasure loss is defined as the discrepancy between noise predictions conditioned and unconditioned on the concept. Non-target generation capabilities are preserved via output-distribution alignment regularization. We apply the multiple gradient descent algorithm (MGDA) to obtain Pareto-optimal solutions, aiming to minimize conflicts among different concept erasure objectives. In addition, we improve MGDA by introducing an importance-weighting mechanism, which adjusts the weights of gradients corresponding to each erasure objective, enabling flexible control over the priority and intensity of erasing different concepts, thereby enhancing the scalability of ConceptVoid. Extensive experiments demonstrate the effectiveness of ConceptVoid, validating our key contributions: (1) a scalable framework for multi-concept erasure in GVDs; (2) the integration of per-concept erasure with distribution alignment to retain non-target quality; and (3) an enhanced MGDA for conflict-aware, controllable erasure. Full article

Recently, plastic and agricultural waste have gained attention as sustainable alternatives. Despite efforts to recycle these materials, much still ends up in landfills, raising environmental concerns. To optimize their potential, these wastes ought to be transformed into value-added products for diverse industrial applications. This work focused on producing thin composite material films using olive oil solid waste called JEFT and recycled plastic bottles. JEFT was cleaned, dried, and processed mechanically via ball milling to produce nano- and micron-sized particles. Composite films were produced via melt compounding and compression molding with a rapid cooling process for controlled crystallinity and enhanced flexibility. Their density, water absorption, tensile strength, thermal stability, water permeability, functional groups, and biodegradation were comprehensively analyzed. Results indicated that 50% JEFT in recycled plastic accelerated thermal deterioration (42.7%) and biodegradation (13.4% over 60 days), highlighting JEFT’s role in decomposition. Peak tensile strength (≈32 MPa) occurred at 5% JEFT, decreasing at higher concentrations due to agglomeration. Water absorption and permeability slightly increased with JEFT content, with only a 1% rise in water permeability for 50% JEFT composites after 60 days. JEFT maintained the recycled plastic’s surface chemistry, ensuring stability. The findings of this study suggest that JEFT/r-HDPE films show potential as greenhouse coverings, enhancing crop production and water efficiency while improving plastic biodegradation, offering a sustainable waste management solution. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article

Implantable electronic devices are driving innovation in modern medical technology and have significantly improved patients’ quality of life. This review comprehensively analyzes the latest technological trends in implantable electronic devices used in major organs, including the heart, brain, and skin. Additionally, it explores the potential for application in the gastrointestinal system, particularly in the field of biliary stents, in which development has been limited. In the cardiac field, wireless pacemakers, subcutaneous implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices have been commercialized, significantly improving survival rates and quality of life of patients with cardiovascular diseases. In the field of brain–neural interfaces, biocompatible flexible electrodes and closed-loop deep brain stimulation have improved treatments of neurological disorders, such as Parkinson’s disease and epilepsy. Skin-implantable devices have revolutionized glucose management in patients with diabetes by integrating continuous glucose monitoring and automated insulin delivery systems. Future development of implantable electronic devices incorporating pressure or pH sensors into biliary stents in the gastrointestinal system may significantly improve the prognosis of patients with bile duct cancer. This review systematically organizes the technological advances and clinical outcomes in each field and provides a comprehensive understanding of implantable electronic devices by suggesting future research directions. Full article

Electronic devices (EDs) exhibit complex failure patterns throughout their lifetime, with failure modes (FaM) can be monotonic, non-monotonic, or a combination of both. This complexity is increased by using advanced semiconductors and flexible electronics, which introduce variability in degradation mechanisms. Although multiple reliability models exist, many lack flexibility or practical applicability in this context. This work proposes a novel competing risk (CR) model that combines the Fréchet and Chen distributions, called Fréchet-Chen Competitive Risk (FCCR). This model allows for modeling the minimum time to failure between two relevant FaMs. Its key mathematical properties and applicability to real-life scenarios are analyzed. Parameter estimation is performed using maximum likelihood (MLE) and Bayesian inference (BEM) using Hamiltonian Monte Carlo (HMC), which provides a robust basis for analysis. Two case studies with real-life ED data validate the model, demonstrating its superior fit and predictive capability compared to classical models. Furthermore, the effect of FCCR parameters on system behavior is explored, highlighting its usefulness in accurately modeling complex failure patterns in EDs. Full article

Unmanned Aerial Vehicles (UAVs) are known to have limited payloads, which challenges their widespread use in transporting heavy goods. Meanwhile, collaboration between multiple UAVs in performing such a task may be a promising solution. To address the issues associated with the simultaneous use of UAVs, this paper presents a formation control system for transporting a payload suspended via a cable using two UAVs. The control structure is based on a layered scheme that combines a null-space-based kinematic controller with a PID controller associated with each UAV (quadcopters) with a neural correction system. The null-space supervisor controller is designed to generate the desired velocity for the UAV system to maintain formation. This proposal aims to avoid obstacles, balance the weight distribution across each vehicle, and also reduce the payload trajectory tracking error. The PID controller associated with the neural correction system receives these desired speeds and performs dynamic compensation, taking into account parametric uncertainties and dynamic disturbances caused by the movement of the payload coupled to the UAV systems. The stability analysis of the entire control system is performed using Lyapunov theory. Detailed dynamic models of each UAV in the system, the flexible cables, and the payload are presented in a realistic scenario. Finally, numerical simulations demonstrate the good performance of the UAV system control in formation. Full article

High-entropy oxides, HEOs, represent a relatively new class of ceramic materials characterized by the incorporation of multiple cations, typically four or more, into a single-phase crystal structure. This extensive compositional flexibility allows for the introduction of specific chemical elements into a crystal lattice that would normally be unable to accommodate them, making it difficult to predict a priori their properties and crystal structures. Consequently, studying the phase stability of these single-phase materials presents significant challenges. This work examines the key parameters commonly employed to predict the stabilization of HEOs and introduces a unified framework for analyzing their stability. The proposed approach incorporates a normalized configurational entropy per mole of atoms and the relative volume occupied by cations into the mean atomic size deviation. By combining these parameters, the approach enables, as a first approximation, the identification of compositional ranges that favor the formation of single-phase and multi-phase HEO compounds with rock salt, spinel, fluorite, pyrochlore, and perovskite structures. Full article