Search Results (48)

Metasurfaces can realize the flexible manipulation of electromagnetic waves, which have the advantages of a low profile and low loss. In particular, the coding metasurface can flexibly manipulate electromagnetic waves through controllable sequence encoding of the coding units to achieve different functions. In this paper, a three-layer active coding metasurface is designed based on vanadium dioxide (VO2), which has an excellent phase transition. For the designed unit cell, the top patterned layer is composed of two split square resonant rings (SSRRs), whose gaps are in opposite directions, and each SSRR is composed of gold and VO2. When VO2 changes from the dielectric state to the metal state, the resonant mode changes from microstrip resonance to LC resonance, correspondingly. According to the Pancharatnam-Berry (P-B) phase, the designed metasurface can actively control terahertz circularly polarized waves in the near field. The metasurface can manipulate the order of the generated orbital angular momentum (OAM) beams: when the dielectric VO2 changes to metal VO2, the order l of the OAM beams generated by the metasurface changes from −1 to −2, and the purity of the generated OAM beams is relatively high. It is expected to have important application values in terahertz wireless communication, radar, and other fields. Full article

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. Full article

Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations. Full article

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity. Full article

In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the [email protected] of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority. Full article

Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%. Full article

Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability. Full article

By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized two-stage individual encoding and decoding scheme. Secondly, in order to realize the multi-scale search of the solution space, a multi-population update mechanism is designed, and a collaborative learning method is proposed to ensure the diversity of the population. Then, an adaptive mutation operation is introduced to enrich the diversity of the population, relying on the adaptive adjustment of the mutation operator to balance global search and local search capabilities. In order to realize a directional and efficient neighborhood search, this algorithm proposed a knowledge-driven variable neighborhood search strategy. Finally, the algorithm’s performance comparison experiment is carried out. The minimum makespans on the MK06 medium-scale case and MK10 large-scale case are 58 and 201, respectively. The experimental results verify the effectiveness of the hybrid algorithm. Full article

To ensure the success of energy transition and achieve the target of reducing the carbon footprint of energy systems, the management of energy systems needs to be decentralized. Public blockchains offer favorable features to support energy sector democratization and reinforce citizens’ trust, such as tamper-proof energy data registration and sharing, decentralization, transparency, and support for peer-to-peer (P2P) energy trading. However, in blockchain-based P2P energy markets, transactional data are public and accessible, which raises privacy concerns related to prosumers’ energy profiles while lacking scalability and featuring high transactional costs. In this paper, we employ secure multi-party computation (MPC) to assure privacy on a P2P energy flexibility market implementation in Ethereum by combining the prosumers’ flexibility orders data and storing it safely on the chain. We provide an encoding mechanism for orders on the energy market to obfuscate the amount of energy traded by creating groups of prosumers, by splitting the amount of energy from bids and offers, and by creating group-level orders. The solution wraps around the smart contracts-based implementation of an energy flexibility marketplace, assuring privacy features on all market operations such as order submission, matching bids and offers, and commitment in trading and settlement. The experimental results show that the proposed solution is effective in supporting P2P energy flexibility trading, reducing the number of transactions, and gas consumption with a limited computational time overhead. Full article

The mRubyFT is a monomeric genetically encoded fluorescent timer based on the mRuby2 fluorescent protein, which is characterized by the complete maturation of the blue form with the subsequent conversion to the red one. It has higher brightness in mammalian cells and higher photostability compared with other fluorescent timers. A high-resolution structure is a known characteristic of the mRubyFT with the red form chromophore, but structural details of its blue form remain obscure. In order to obtain insight into this, we obtained an S148I variant of the mRubyFT (mRubyFT^S148I) with the blocked over time blue form of the chromophore. X-ray data at a 1.8 Å resolution allowed us to propose a chromophore conformation and its interactions with the neighboring residues. The imidazolidinone moiety of the chromophore is completely matured, being a conjugated π-system. The methine bridge is not oxidized in the blue form bringing flexibility to the phenolic moiety that manifests itself in poor electron density. Integration of these data with the results of molecular dynamic simulation disclosed that the OH group of the phenolic moiety forms a hydrogen bond with the side chain of the T163 residue. A detailed comparison of mRubyFT^S148I with other available structures of the blue form of fluorescent proteins, Blue102 and mTagBFP, revealed a number of characteristic differences. Molecular dynamic simulations with the combined quantum mechanic/molecular mechanic potentials demonstrated that the blue form exists in two protonation states, anion and zwitterion, both sharing enolate tautomeric forms of the C=C–O⁻ fragment. These two forms have similar excitation energies, as evaluated by calculations. Finally, excited state molecular dynamic simulations showed that excitation of the chromophore in both protonation states leads to the same anionic fluorescent state. The data obtained shed light on the structural features and spectral properties of the blue form of the mRubyFT timer. Full article

The twisted Laguerre–Gaussian Schell-model (TLGSM) beam is a novel type of partially coherent beam embedded with both the second-order twist phase and the classical vortex phase. The intriguing properties induced by the interaction of the two types of phases have been demonstrated theoretically quite recently. In this work, we introduce a flexible way to experimentally synthesize a TLGSM beam with controllable twist strength. The protocol relies on the discrete pseudo-mode representation for the cross-spectral density of a TLGSM beam, in which the beam is viewed as an incoherent superposition of a finite number of spatially coherent modes. We show that all these pseudo modes endowed with random phases are mutually uncorrelated and can be encoded into a single frame of a dynamic computer-generated hologram. By sequentially displaying dynamic holograms on a single spatial-light modulator, the controllable TLGSM beam can be synthesized experimentally. The joint effect of the two phases on the propagation and self-reconstruction characteristics of the synthesized beam has also been studied in the experiment. Full article

Identification of novel drugs for anti-African swine fever (ASF) applications is of utmost urgency, as it negatively affects pig farming and no effective vaccine or treatment is currently available. African swine fever virus (ASFV) encoded pS273R is a cysteine protease that plays an important role in virus replication. E64, acting as an inhibitor of cysteine protease, has been established as exerting an inhibitory effect on pS273R. In order to obtain a better understanding of the interaction between E64 and pS273R, common docking, restriction docking, and covalent docking were employed to analyze the optimal bonding position between pS273R−E64 and its bonding strength. Additionally, three sets of 100 ns molecular dynamics simulations were conducted to examine the conformational dynamics of pS273R and the dynamic interaction of pS273R−E64, based on a variety of analytical methods including root mean square deviation (RMSD), root mean square fluctuation (RMSF), free energy of ligand (FEL), principal component analysis (PCA), and molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) analysis. The results show that E64 and pS273R exhibited close binding degrees at the activity center of ASFV pS273R protease. The data of these simulations indicate that binding of E64 to pS273R results in a reduction in flexibility, particularly in the ARM region, and a change in the conformational space of pS273R. Additionally, the ability of E64 to interact with polar amino acids such as ASN158, SER192, and GLN229, as well as charged amino acids such as LYS167 and HIS168, seems to be an important factor in its inhibitory effect. Finally, Octet biostratigraphy confirmed the binding of E64 and pS273R with a KD value of 903 uM. Overall, these findings could potentially be utilized in the development of novel inhibitors of pS273R to address the challenges posed by ASFV. Full article

Soft continuum robots are compliant mechanisms that rely on a deformable structure in order to achieve a desired posture. One of the challenges in designing and controlling this type of robot is to obtain the necessary proprioceptive information without resorting to external sensors, like cameras or 3D positioning devices. This requires a reliable and repeatable sensor that can be embedded in the highly deformable structure, distributed along its length, without imposing a significant change to the overall stiffness. This paper presents design considerations and practical results of estimating the tip position of a soft continuum manipulator module using embedded linear magnetic encoders. Three flexible scales with incremental tracks and a magnetic pole pitch of 2 mm are embedded in the robot structure as passive tendons, and six pairs of Hall effect linear sensors are used to measure the relative displacement between points along the outer surface of the structure. The curvature and tip position are then estimated from these measurements. Results are compared with the ground truth measurement of the tip position provided by a commercial optical tracker system. Average error estimates lower than 2.0 mm, with 8.7 mm peak value, were obtained for a robot module with a motion span of approximately 100 mm. Full article

Student engagement is a flexible, complicated concept that includes behavioural, emotional, and cognitive involvement. In order for the instructor to understand how the student interacts with the various activities in the classroom, it is essential to predict their participation. The current work aims to identify the best algorithm for predicting student engagement in the classroom. In this paper, we gathered data from VLE and prepared them using a variety of data preprocessing techniques, including the elimination of missing values, normalization, encoding, and identification of outliers. On our data, we ran a number of machine learning (ML) classification algorithms, and we assessed each one using cross-validation methods and many helpful indicators. The performance of the model is evaluated with metrics like accuracy, precision, recall, and AUC scores. The results show that the CATBoost model is having higher accuracy than the rest. This proposed model outperformed in all the aspects compared to previous research. The results part of this paper indicates that the CATBoost model had an accuracy of approximately 92.23%, a precision of 94.40%, a recall of 100%, and an AUC score of 0.9624. The XGBoost predictive model, the random forest model, and the multilayer perceptron model all demonstrated approximately the same performance overall. We compared the AISAR model with Our model achieved an accuracy of 94.64% compared with AISAR 91% model and it concludes that our results are better. The AISAR model had only around 50% recall compared to our models, which had around 92%. This shows that our models return more relevant results, i.e., if our models predict that a student has high engagement, they are correct 94.64% of the time. Full article

Aiming at the problem that the previous flexible machine network integrated scheduling algorithm only considers positive sequence scheduling, which leads to the extension of product completion time, a reverse-order machine network integrated scheduling algorithm based on the dynamic root node operation set is proposed. In order to avoid the constraints of multi-predecessor operations in the process of forward-order scheduling, an encoding method based on dynamic root node operation set is proposed to ensure the validity of constraints among operations. The crossover methods based on crossover row vector and subtree are proposed to ensure the legitimacy of offspring individuals. The chaotic mutation method based on sibling operation and the random mutation method based on mutation row vector are proposed, respectively, to ensure the diversity of the population. A local search strategy based on the critical operation machine set is proposed, which enhances the search ability of the algorithm for optimal solutions. The comparative experimental results show that the proposed algorithm’s solving speed and solution quality outperform other comparison algorithms. Full article