Regulating the Water Molecular in the Solvation Structure for Stable Zinc Metal Batteries

Rechargeable aqueous zinc batteries are promising energy storage devices because of their low cost, high safety, and high energy density. However, their performance is plagued by the unsatisfied cyclability due to the dendrite growth and hydrogen evolution reaction (HER) at the Zn anode. Herein, it is demonstrated that the concentrated hybrid aqueous/non-aqueous ZnCl₂ electrolytes constitute a peculiar chemical environment for not only the Zn-ions but also water molecules. The high concentration of chloride ions substitutes the H₂O molecular in the solvation structure of Zn²⁺, while the acetonitrile further interacts with H₂O to decrease its activity. The hybrid electrolytes both inhibit the dendrite formation and HER, enabling an ultrahigh average Coulombic efficiency of 99.9% in the Zn||Cu half-cell and a highly reversible Zn plating/stripping with a low overpotential of 21 mV. Using this hybrid electrolyte, the Zn||polytriphenylamine (PTPAn) full cell deliveres a high discharge capacity of 110 mAh g⁻¹, a high power density of 9200 W kg⁻¹ at 100 °C and maintains 85% of the capacity for over 6000 cycles at 10 °C. This study provides a deep understanding between the solvation structure and columbic efficiency of Zn anode, thus inspiring the development for stable Zn batteries.     

Dispersion-Enabled Symmetry Switching of Photonic Angular-Momentum Coupling

Photonic spin-orbit interactions describe the interactions between spin angular momentum and orbital angular momentum of photons, which play essential roles in subwavelength optics. However, the influence of frequency dispersion on photonic angular-momentum coupling is rarely studied. Here, by elaborately designing the contribution of the geometric phase and waveguide propagation phase, the dispersion-enabled symmetry switching of photonic angular-momentum coupling is experimentally demonstrated. This notion may induce many exotic phenomena and be found in enormous applications, such as the spin-Hall effect, optical calculation, and wavelength division multiplexing systems. As a proof-of-concept demonstration, two metadevices, a multi-channel vectorial vortex beam generator and a phase-only hologram, are applied to experimentally display optical double convolution, which may offer additional degrees of freedom to accelerate computing and a miniaturization configuration for optical convolution without collimation operation. These results may provide a new opportunity for complex vector optical field manipulation and calculation, optical information coding, light-matter interaction manipulation, and optical communication.     

Surface Oxygen Coordination of Hydrogen Bond Chemistry for Aqueous Ammonium Ion Hybrid Supercapacitor

Aqueous ammonium ion hybrid supercapacitor (A-HSC) combines the charge storage mechanisms of surface adsorption and bulk intercalation, making it a low-cost, safe, and sustainable energy storage candidate. However, its development is hindered by the low capacity and unclear charge storage fundamentals. Here, the strategy of phosphate ion-assisted surface functionalization is used to increase the ammonium ion storage capacity of an α-MoO₃ electrode. Moreover, the understanding of charge storage mechanisms via structural characterization, electrochemical analysis, and theoretical calculation is advanced. It is shown that NH₄⁺ intercalation into layered α-MoO₃ is not dominant in the A-HSC system; rather, the charge storage mainly depends on the adsorption energy of surface “O” to NH₄⁺. It is further revealed that the hydrogen bond chemistry of the coordination between “O” of surface phosphate ion and NH₄⁺ is the reason for the capacity increase of MoO₃. This study not only advances the basic understanding of rechargeable aqueous A-HSC but also demonstrates the promising future of surface engineering strategies for energy storage devices.     

A Hybrid Strategy-Based Ultra-Narrow Stretchable Microelectrodes with Cell-Level Resolution

Stretchable ultra-narrow (e.g., 10 µm in width) microelectrodes are crucial for the electrophysiological monitoring of single cells providing the fundamental understanding to the working mechanism of neuro network or other electrically functional cells. Current fabrication strategies either focus on the preparation of normal stretchable electrodes with hundreds of micrometers or millimeters in width by using inorganic conductive materials or develop conductive organic polymer gel for ultra-narrow electrodes which suffer from low stretchability and instability for long-term implantation, therefore, it is still highly desirable to explore bio-interfacial ultra-narrow stretchable inorganic electrodes. Herein, a hybrid strategy is reported to prepare ultra-narrow multi-channel stretchable microelectrodes without using photolithography or laser-assisting etching. A 10 µm × 10 µm monitoring window is fabricated with enhanced interfacial impedance by the special rough surface. The stretchability achieves to 120% for this 10 µm-width stretchable electrode. Supported by these superior properties, it is demonstrated that the stretchable microelectrodes can detect electrophysiological signals of single cells in vitro and collect electrophysiological signals more precisely in vivo. The reported strategy will open up the accessible preparation of the fine-size stretchable microelectrode. It will significantly improve the resolution of monitoring and stimulation of inorganic stretchable electrodes.     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.     

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class. This will mitigate the feature vector's dimensionality by eliminating redundant, irrelevant, or noisy data. This work presents a new hybrid recommender system based on optimized feature selection and systolic tree. The features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), feature selection with the utilization of River Formation Dynamics (RFD), and the Particle Swarm Optimization (PSO) algorithm. The systolic tree is used for pattern mining, and based on this, the recommendations are given. The proposed methods were evaluated using the MovieLens dataset, and the experimental outcomes confirmed the efficiency of the techniques. It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborative filtering, the precision of 0.89 was achieved.     

融合指标分组的高维混合多目标进化优化

高维混合多目标优化问题因包含多个不同类型指标,目前尚缺乏有效求解该问题的进化优化方法。提出一种基于目标分组的高维混合多目标并行进化优化方法。采用深度学习神经网络预测种群隐式性能指标;基于指标相关性,将高维混合多目标优化问题分解为若干子优化问题;采用多种群并行进化算法,求解分解后的每一子优化问题,并基于各子种群的非被占优解构建外部保存集;采用聚合函数对外部保存集个体进一步优化,得到Pareto最优解集。在室内布局优化问题中验证所提方法,实验结果表明,所提方法的Pareto最优解在收敛性、分布性以及延展性等方面均优于对比方法。     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.     

融合双注意力机制的人群计数算法

针对背景复杂、遮挡、人群分布不均等人群计数常见问题,提出了一种结合联合损失的空间-通道双注意力机制卷积神经网络模型(joint loss-based space-channel dual attention network, JL-SCDANet).该网络前端进行图像粗粒度特征提取,中间加入空间注意力机制以及通道注意力机制突出图像重点区域,后端使用可加大感受野且不丢失图像分辨率的空洞卷积提取深层二维特征.此外,该模型结合联合损失函数进行训练,以增强模型的鲁棒性.为了验证模型的改进效果,在3个公共数据集(ShanghaiTech Part B、mall和UCF＿CC＿50)上分别进行了对比实验,在ShanghaiTech Part B数据集中平均绝对误差(MAE)和均方误差(MSE)分别达到了8.13和13.13;在mall数据集中MAE、MSE达到了1.78和2.28;在UCF＿CC＿50数据集中MAE、MSE分别达到了182.12和210.24,实验结果证明了该网络在提高人数统计准确率上的有效性.