Search Results (4,614)

This study focuses on existing pruning equipment; cutting blades show cutting resistance and lead to high energy consumption. Using finite element (FEA) numerical simulation technology, the branch stress wave propagation mechanism during pruning was studied. The cutting performance of the bionic blade was evaluated with cutting energy consumption as the test index and the branch diameter and branch angle as the test factors, respectively. The test results showed that the blades imitating the mouthparts of the three-pecten bull and the beak of the woodpecker performed well in pruning, and the energy consumption during cutting was reduced by 18.2% and 16.3% compared to traditional blades, making these blades significantly better. These two blades also effectively reduced the cutting resistance and branch splitting by optimizing the edge angle design and increasing the slip-cutting action. In contrast, the imitation shark’s tooth blade increased cutting energy consumption by 14.4% due to the large amount of cutting resistance in the cutting process when cutting larger-diameter branches, making it unsuitable for application in the pruning field. Therefore, the blades imitating the mouthparts of the three pectins and the beak of the woodpecker have significant advantages in reducing the cutting resistance and improving the pruning quality. These findings provide an important theoretical reference for the development of energy-efficient pruning equipment. Full article

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%. Full article

Difficult-to-cut titanium matrix composites (TiB+TiC)/Ti6Al4V have extensive application prospects in the fields of biomedical and aerospace metal microcomponents due to their excellent mechanical properties. Jet electrochemical micromilling (JEMM) technology is an ideal method for machining microstructures that leverages the principle of electrochemical anodic dissolution. However, the matrix Ti6Al4V is susceptible to passivation during electrochemical milling, and the inclusion of high-strength TiB whiskers and TiC particles as reinforcing phases further increases the machining difficulty of (TiB+TiC)/Ti6Al4V. In this study, a novel approach using NaCl+NaNO₃ mixed electrolyte for the JEMM of (TiB+TiC)/Ti6Al4V was adopted. Electrochemical behaviors were measured in NaCl and NaCl+NaNO₃ electrolytes. In the mixed electrolyte, a higher transpassive potential was required to break down the passive film, which led to better corrosion resistance of (TiB+TiC)/Ti6Al4V, and the exposed reinforcing phases on the dissolved surface were significantly reduced. The results of the JEMM machining indicate that, compared to NaCl electrolyte, using mixed electrolyte effectively mitigates stray corrosion at the edges of micro-grooves and markedly improves the uniformity of both groove depth and width dimensions. Additionally, the surface quality was noticeably improved, with a reduction in Ra from 2.84 μm to 1.03 μm and in Rq from 3.41 μm to 1.40 μm. Full article

With the advancement of CMOS technology, the susceptibility of SRAM to single node upset (SNU), double node upset (DNU), and multiple node upset (MNU) induced by radiation has increased. To address this issue, various cutting-edge solutions, such as radiation hardened sextuple cross coupled (RHSCC)-16T and DNU-completely-tolerant memory (DNUCTM) cells, have been proposed. While the RHSCC-16T cell is robust against SNU, it may be vulnerable to DNU. The DNUCTM cell is resistant to both SNU and DNU, but it remains susceptible to MNU. In this paper, we propose a radiation hardened read-stability and speed enhanced (RHRSE)-20T SRAM, which is immune to all potential cases of SNU, DNU, and MNU. Additionally, the proposed design demonstrates improvements in read and write delays compared to conventional SRAM designs. Experimental results confirm that the RHRSE-20T SRAM maintains stability under various charge levels for SEU, DNU, and MNU. The proposed integrated circuit is implemented in a 90-nm CMOS process and operates on a 1 V supply voltage, offering significant advantages for next-generation radiation-hardened memory applications. Full article

Significant research accomplishments have been made so far for the development and application of ZnO nanomaterials in enhanced optical biodetection. The unparalleled optical properties of ZnO nanomaterials and their reduced dimensionality have been successfully exploited to push the limits of conventional optical biosensors and optical biodetection platforms for a wide range of bioanalytes. ZnO nanomaterial-enabled advancements in optical biosensors have been demonstrated to improve key sensor performance characteristics such as the limit of detection and dynamic range. In addition, all nanomaterial forms of ZnO, ranging from 0-dimensional (0D) and 1D to 2D nanostructures, have been proven to be useful, ensuring their versatile fabrication into functional biosensors. The employment of ZnO as an essential biosensing element has been assessed not only for ensembles but also for individual nanomaterials, which is advantageous for the realization of high miniaturization and minimal invasiveness in biosensors and biodevices. Moreover, the nanomaterials’ incorporations into biosensors have been shown to be useful and functional for a variety of optical detection modes, such as absorption, colorimetry, fluorescence, near-band-edge emission, deep-level emission, chemiluminescence, surface evanescent wave, whispering gallery mode, lossy-mode resonance, surface plasmon resonance, and surface-enhanced Raman scattering. The detection capabilities of these ZnO nanomaterial-based optical biosensors demonstrated so far are highly encouraging and, in some cases, permit quantitative analyses of ultra-trace level bioanalytes that cannot be measured by other means. Hence, steady research endeavors are expected in this burgeoning field, whose scientific and technological impacts will grow immensely in the future. This review provides a timely and much needed review of the research efforts made in the field of ZnO nanomaterial-based optical biosensors in a comprehensive and systematic manner. The topical discussions in this review are organized by the different modes of optical detection listed above and further grouped by the dimensionality of the ZnO nanostructures used in biosensors. Following an overview of a given optical detection mode, the unique properties of ZnO nanomaterials critical to enhanced biodetection are presented in detail. Subsequently, specific biosensing applications of ZnO nanomaterials are discussed for ~40 different bioanalytes, and the important roles that the ZnO nanomaterials play in bioanalyte detection are also identified. Full article

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks. Full article

Advancements in technology have driven the miniaturization of embedded systems, making them more cost-effective and energy-efficient for wireless applications. As a result, the number of connectable devices in Internet of Things (IoT) networks has increased significantly, creating the challenge of linking them effectively and economically. The space industry has long recognized this challenge and invested in satellite infrastructure for IoT networks, exploiting the potential of edge computing technologies. In this context, it is of critical importance to enhance the onboard computing capabilities of satellites and develop enabling technologies for their advancement. This is necessary to ensure that satellites are able to connect devices while reducing latency, bandwidth utilization, and development costs, and improving privacy and security measures. This paper presents the GPU@SAT DevKit: an ecosystem for testing a high-performance, general-purpose accelerator designed for FPGAs and suitable for edge computing tasks on satellites. This ecosystem provides a streamlined way to exploit GPGPU processing in space, enabling faster development times and more efficient resource use. Designed for FPGAs and tailored to edge computing tasks, the GPU@SAT accelerator mimics the parallel architecture of a GPU, allowing developers to leverage its capabilities while maintaining flexibility. Its compatibility with OpenCL simplifies the development process, enabling faster deployment of satellite-based applications. The DevKit was implemented and tested on a Zynq UltraScale+ MPSoC evaluation board from Xilinx, integrating the GPU@SAT IP core with the system’s embedded processor. A client/server approach is used to run applications, allowing users to easily configure and execute kernels through a simple XML document. This intuitive interface provides end-users with the ability to run and evaluate kernel performance and functionality without dealing with the underlying complexities of the accelerator itself. By making the GPU@SAT IP core more accessible, the DevKit significantly reduces development time and lowers the barrier to entry for satellite-based edge computing solutions. The DevKit was also compared with other onboard processing solutions, demonstrating similar performance. Full article

Encapsulating Cs₄PbBr₆ quantum dots in silicon nano-sheets not only stabilizes the halide perovskite, but also takes advantage of the nano-sheet for a compatible integration with the traditional silicon semiconductor. Here, we report the preparation of un-passivated Cs₄PbBr₆ ellipsoidal nanocrystals and pseudo-spherical quantum dots in silicon nano-sheets and their enhanced photoluminescence (PL). For a sample with low concentrations of quantum dots in silicon nano-sheets, the emission from Cs₄PbBr₆ pseudo-spherical quantum dots is quenched and is dominated with Pb²⁺ ion/silicene emission, which is very stable during the whole measurement period. For a high concentration of Cs₄PbBr₆ ellipsoidal nanocrystals in silicon nano-sheets, we have observed Förster resonance energy transfer with up to 87% efficiency through the oscillation of two PL peaks when UV excitation switches between on and off, using recorded video and PL lifetime measurements. In an area of a non-uniform sample containing both ellipsoidal nanocrystals and pseudo-spherical quantum dots, where Pb²⁺ ion/silicene emissions, broadband emissions from quantum dots, and bandgap edge emissions (515 nm) appear, the 515 nm peak intensity increases five times over 30 min of UV excitation, probably due to a photon recycling effect. This irradiated sample has been stable for one year of ambient storage. Cs₄PbBr₆ quantum dots encapsulated in silicon nano-sheets can lead to applications of halide perovskite light emitting diodes (PeLEDs) and integration with traditional semiconductor materials. Full article

A rapid and feasible approach was used to develop visible-light-driven-type Dion–Jacobson perovskites by the modification of the RbLaTa₂O₇ host (RbLTO) with FeCl₂ through the molten salt route. X-ray diffraction (XRD) characterization showed that FeCl₂-modified layered perovskite (e.g., Fe@RbLTO) preserved its lamellar structure. SEM micrographs confirmed the layered morphology of both RbLTO and Fe@RbLTO perovskite materials. The UV-Vis spectra illustrated a significant red shift of the absorption edge after Fe²⁺ modification, with the band gap energy reducing from 3.88 to 1.82 eV. H₂-TPR measurements emphasized the anchorage of Fe²⁺ species located on the surface of the layered perovskite as well as in the interlayer space. The synthesized materials were valorized as photocatalysts for the degradation of phenol under both Xe lamp and simulated solar irradiation (SSL) conditions. The photocatalytic reaction follows first-order kinetics. By-product formations during phenol (Ph) degradation were identified and quantified using high-performance liquid chromatography (HPLC). Hydroquinone, 1,2-dihydroxi-benzene, benzoquinone, and pyrogallol were identified as the main Ph degradation intermediates. Pristine RbLaTa₂O₇ exhibited a phenol conversion value of about 17% using an Xe lamp, while a ≈ 11% conversion was achieved under SSL. A substantial increase in Ph conversion and selectivity was perceived after Fe²⁺ modification. Fe@RbLTO demonstrated superior photocatalytic performances (43% conversion of phenol under an Xe lamp, and 91% selectivity to aromatic intermediate compounds) at optimized reaction conditions. The stability of the Fe@RbLTO photocatalyst when exposed to an Xe lamp was also assessed. These results suggest that the existence of iron species on the layered perovskite’s surface is responsible for the improved redox properties of Fe@RbLTO, resulting in a valuable material for environmental applications. Full article

Flexible grippers are a promising and pivotal technology for robotic grasping and manipulation tasks. Remarkably, magnetorheological (MR) materials, recognized as intelligent materials with exceptional performance, are extensively employed in flexible grippers. This review aims to provide an overview of flexible robotic grippers and highlight the application of MR materials within them, thereby fostering research and development in this field. This work begins by introducing various common types of flexible grippers, including shape memory alloys (SMAs), pneumatic flexible grippers, and dielectric elastomers, illustrating their distinctive characteristics and application domains. Additionally, it explores the development and prospects of magnetorheological materials, recognizing their significant contributions to the field. Subsequently, MR flexible grippers are categorized into three types: those with viscosity/stiffness variation capabilities, magnetic actuation systems, and adhesion mechanisms. Each category is comprehensively analyzed, specifying its unique features, advantages, and current cutting-edge applications. By undertaking an in-depth examination of diverse flexible robotic gripper types and the characteristics and application scenarios of MR materials, this paper offers a valuable reference for fellow researchers. As a result, it facilitates further advancements in this field and contributes to the provision of efficient gripping solutions for industrial automation. Full article

Plastics are essential in modern civilization due to their affordability, simple manufacturing, and properties. However, plastics impact the environment as they decompose over a long period and degrade into microplastics. The construction sector has been exploring substituting conventional bricks with plastic bricks, as concrete and clay bricks consume natural resources and pollute the environment. The introduction of recycling plastic, and using plastic waste and sand mixtures to create Lego-like bricks has become a new trend. The bricks have superior properties to conventional bricks, such as a smoother surface, finer edges, easy application, crack-free, higher compression strength, almost zero water absorption, and reduced energy consumption. The study: compares the results of PE with sand and PET with sand samples to previous studies, confirms alignment, works as a control sample for PET and PE novel research, and validates the concept. Three plastic mixtures using two types of plastic waste (PE and PET) and sand were used. The plastic waste with sand was heated up to 200 °C. Plastic acts as a binder, while sand acts as a filler material. Optimized durability and cohesiveness were achieved at 30–40% plastic weight ratios. A mixture of PE and sand showed a maximum compressive strength of 38.65 MPa, while the PET and sand mixture showed 76.85 MPa, and the mix of PE and PET in equal proportions with sand resulted in 26.64 MPa. The plastic samples showed ductile behavior, with elongation between 20 and 30%, water absorption between 0 and 0.35%, and thermal conductivity from 0.8 to 1.05 W/(m/K). Carbon dioxide emissions are significantly reduced as compared to standard bricks. The CO₂ per brick (kg) was 0.008 and 0.0085 in the PE; 0.0085 and 0.009 in the PET; and 0.0065 and 0.007 in the PE mixed with PET. Full article

This manuscript delves into the transformative advancements in wind turbine blade technology, emphasizing the integration of innovative materials, dynamic aerodynamic designs, and sustainable manufacturing practices. Through an exploration of the evolution from traditional materials to cutting-edge composites, the paper highlights how these developments significantly enhance the efficiency, durability, and environmental compatibility of wind turbines. Detailed case studies of notable global projects, such as the Hornsea Project One, the Gansu Wind Farm, and the Block Island Wind Farm, illustrate the practical applications of these technologies and their impact on energy production and sustainability. Additionally, the manuscript examines the critical role of regulatory frameworks and industry standards in fostering these technological advancements, ensuring safety, and promoting global adoption. By analyzing the current trends and future directions, this study underscores the potential of modern turbine technologies to meet the increasing global demand for renewable energy and contribute to sustainable development goals. The findings advocate for continued innovation and policy alignment to fully harness the potential of wind energy in the renewable energy landscape. Full article

Accidents caused by vehicles changing lanes occur frequently on highways. Moreover, frequent lane changes can severely impact traffic flow during peak commuting hours and on busy roads. A novel framework based on a multi-relational graph convolutional network (MR-GCN) is herein proposed to address these challenges. First, a dynamic multilevel relational graph was designed to describe interactions between vehicles and road objects at different spatio-temporal granularities, with real-time updates to edge weights to enhance understanding of complex traffic scenarios. Second, an improved spatio-temporal interaction graph generation method was introduced, focusing on spatio-temporal variations and capturing complex interaction patterns to enhance prediction accuracy and adaptability. Finally, by integrating a dynamic multi-relational graph convolutional network (DMR-GCN) with dynamic scene sensing and interaction learning mechanisms, the framework enables real-time updates of complex vehicle relationships, thereby improving behavior prediction’s accuracy and real-time performance. Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian, showed that our algorithmic framework achieves significant performance improvements in vehicle behavior prediction tasks, with Map, Recall, and F1 scores reaching 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, the model achieved a Map of 91%, a Recall of 89%, and an F1 score of 90% under congested road conditions in a self-collected high-speed traffic scenario dataset, further demonstrating its robustness and adaptability in high-speed traffic conditions. These results show that the proposed model is highly practical and stable in real-world applications such as traffic control systems and self-driving vehicles, providing strong support for efficient vehicle behavior prediction. Full article

The global energy market is shifting toward renewable, sustainable, and low-carbon hydrogen energy due to global environmental issues, such as rising carbon dioxide emissions, climate change, and global warming. Currently, a majority of hydrogen demands are achieved by steam methane reforming and other conventional processes, which, again, are very carbon-intensive methods, and the hydrogen produced by them needs to be purified prior to their application. Hence, researchers are continuously endeavoring to develop sustainable and efficient methods for hydrogen generation and purification. Membrane-based gas-separation technologies were proven to be more efficient than conventional technologies. This review explores the transition from conventional separation techniques, such as pressure swing adsorption and cryogenic distillation, to advanced membrane-based technologies with high selectivity and efficiency for hydrogen purification. Major emphasis is placed on various membrane materials and their corresponding membrane performance. First, we discuss various metal membranes, including dense, alloyed, and amorphous metal membranes, which exhibit high hydrogen solubility and selectivity. Further, various inorganic membranes, such as zeolites, silica, and CMSMs, are also discussed. Major emphasis is placed on the development of polymeric materials and membranes for the selective separation of hydrogen from CH₄, CO₂, and N₂. In addition, cutting-edge mixed-matrix membranes are also delineated, which involve the incorporation of inorganic fillers to improve performance. This review provides a comprehensive overview of advancements in gas-separation membranes and membrane materials in terms of hydrogen selectivity, permeability, and durability in practical applications. By analyzing various conventional and advanced technologies, this review provides a comprehensive material perspective on hydrogen separation membranes, thereby endorsing hydrogen energy for a sustainable future. Full article

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. Full article