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The preparation of a new series of luminescent one dimensional coordination polymers based on the association of a photoactive tetrametallic metallacycle Cu(I) precursor with pyridyl‐caped ditopic linkers is reported. In spite of related molecular architectures, these coordination polymers present upon Ultraviolet visible light photoexcitation markedly contrasted solid‐state luminescence behaviors including eye‐perceived colors of the emitted light spanning all over the visible spectrum. Solid‐state temperature‐dependent photophysical measurements and Time‐dependent density‐functional theory calculations have been conducted to identify the relaxation pathways lying in these assemblies. Very importantly, thermal stability studies at high temperature for all these derivatives reveal an irreversible post‐synthetic solid‐state transition that impacts dramatically the photophysical properties of these newly obtained phases, highlighting an innovative family of stimuli‐sensitive materials that can witness the exceeding of critical temperatures in their environment. [ABSTRACT FROM AUTHOR]

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A thermoelectric module is composed of n- and p-type materials, and its performance is evaluated by the materials' dimensionless figure of merit, zT, which is expected to be as high as possible. Moreover, because a thermoelectric module is designed to operate in various temperature ranges, the rate of each material's deformation in response to a change in temperature, also called thermal expansion, should be considered. If n- and p-type materials in a thermoelectric module have a too large difference in the thermal expansion, it could lead to risks of several device failures and reduction in performance over time. Thus, to achieve practical use, not only zT but also the compatibility of n- and p-type materials are also crucial parameters. In this study, we demonstrated a technique used to minimize the difference of thermal expansion rate between n-type Mg2Si and p-type higher manganese silicide, which are the promising materials used for a practical thermoelectric module. [ABSTRACT FROM AUTHOR]

Modular reconfigurable machines offer the possibility to efficiently produce a family of different parts. This paper formalises a cost optimisation problem for flow lines equipped with reconfigurable machines which carry turrets, machining modules and single spindles. The proposed models take into account constraints related to: (i) design of machining modules, turrets, and machines, (ii) part locations, and (iii) precedence relations among operations. The goal is to minimise equipment cost while reaching a given output and satisfying all the constraints. A mixed integer programming model is developed for the considered optimisation problem. The approach is validated through an industrial case study and extensive numerical experiments. [ABSTRACT FROM AUTHOR]

In the area of contemporary construction the modules of each material and the units are very different. If harmony can be introduced lots of wastage can be avoided. Because in this era of climate change the construction industry is one of the biggest unsustainable and pollutant industries. This paper aims to discuss the unit of the least module for contemporary construction. The study is supported by standards and case studies and various practicing modules. Drawings are made the prove the least module for construction of housing of LIG, MIG, and HIG units. [ABSTRACT FROM AUTHOR]

We describe the fabrication of thermoelectric modules based on Half-Heusler TiNiSn and FeNbSb and their performance assessment under different boundary conditions. Module design is guided by a multiphysics model implementing experimentally determined thermoelectric materials properties. We consider two types of boundary conditions: first, imposing fixed cold- and hot-side temperatures onto the module, and second, imposing fixed values for the heat transfer coefficients between module and heat sink and source, representative for a waste-heat-recovery system using the exhaust heat of an internal combustion engine. We compare the modeling results with experimental data obtained from Half-Heusler modules integrated into a heat exchanger mounted to the exhaust of a compact van. [ABSTRACT FROM AUTHOR]

The aim of this editorial is to introduce the scope of the call for papers for the special issue. The submission statistics are then provided. A brief summary of the accepted papers is synthesised. The editorial ends with calls for future research in the areas of modularity and supply chain management. [ABSTRACT FROM AUTHOR]

Multichip silicon carbide power modules with integrated snubbers are promising for large-capacity converters with high speed and cost efficiency. In the design stage, the parasitic inductance of module layout generally attracts the primary efforts of designers because of its severe impact on dynamic performance, e.g., voltage overshoot and transient current imbalance. However, due to the complexity of the heterogeneous layout, the solution space and development efficiency are always limited by the traditional manual design approach. Thus, this article proposes a generative method to optimize the layout autonomously. First, a graph model is built to describe heterogeneous layouts with all interconnectivity and design constraints retained. Based on the model, integer programming is introduced to generate layout templates with variable geometric topologies. Then, coupled with a self-developed discrete extractor, the Pareto-front is obtained by genetic algorithm, providing a tradeoff boundary for loop inductance and branch mismatch. The proposed method is systematic, flexible, and requires few designers’ expertise. A 1200 V/240 A half-bridge module is designed and fabricated to validate its capability. The experimental results show that the loop inductance of 5.59 nH is achieved, and less than 5% of the transient current imbalance is realized under 6.2 A/ns turn-on in the rated. [ABSTRACT FROM AUTHOR]

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers. [ABSTRACT FROM AUTHOR]

Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive waveform models for binary black hole inspiral and merger. Surrogate modelling yields fast and accurate approximations of gravitational waves and neural networks have been used in the final step of interpolating the coefficients of the surrogate model for arbitrary waveforms outside the training sample. We investigate the existence of underlying structures in the empirical interpolation coefficients using autoencoders. We demonstrate that when the coefficient space is compressed to only two dimensions, a spiral structure appears, wherein the spiral angle is linearly related to the mass ratio. Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models. A thorough experimental study is conducted and the final result is a surrogate model which can evaluate millions of input parameters in a single forward pass in under 1 ms on a desktop GPU, while the mismatch between the corresponding generated waveforms and the ground-truth waveforms is better than the compared baseline methods. We anticipate the existence of analogous underlying structures and corresponding computational gains also in the case of spinning black hole binaries. [ABSTRACT FROM AUTHOR]

Membrane fouling is one of the challenging issues surrounding membrane‐based separation. Here in, we demonstrate direct 3D printing of electrically conductive interdigitated spacer on an ultrafiltration membrane for membrane cleaning and chlorination via periodic electrolysis. The spacers were 3D printed on the membrane surface via material extrusion using a composite slurry of graphite and silver. This 3D printed spacer, which was found to be mechanically robust, promoted turbulence, and increased the permeate flux. Moreover, as the potential is applied across the spacer segments in the presence of saline water, bubbling at the cathodic segment due to hydrogen evolution causes physical removal of the foulant from the membrane surface leading to flux recovery. In parallel, chlorine evolves at the anodic segment which decomposes the organic foulants and can potentially disinfect the membrane. Therefore, direct 3D printing of electrically conductive spacers on the membrane offers four benefits simultaneously: (1) spacer effect by promoting turbulence, (2) in‐situ membrane cleaning, (3) in‐situ chlorination, and (4) a reduced build‐envelope due to the integration of both anode and cathode with the membrane as spacer. Our work, therefore, demonstrates dual‐action in‐situ membrane cleaning while simplifying the design of the membrane module. [ABSTRACT FROM AUTHOR]

The stitch wire configuration is widely adopted for large-area IGBT chips. However, an inhomogeneous wire current density introduces uneven self-heating and nonuniform chip heating, which exacerbates the chip thermal stress. In this article, a multicellular electro-thermal model considering the stitch-bonding wires is derived to optimize the wire layout parameters. Furthermore, the current density alleviation of wire bonds is investigated to be the most sensitive and effective method to comprehensively mitigate the thermal nonequilibrium and local overheating. Consequently, an improved multitier layout is proposed to further achieve thermal stress suppression without any additional components and advanced materials. Finally, a triple-tier-bonded IGBT module with optimal design parameters is fabricated to validate the thermal mitigation performance. The prototype experimental results demonstrate that the modeling error is less than 3.0%. The multitier layout decreases the current density by 57.6%, hence, the maximum and average chip temperature are reduced by 21.8% and 12.4%, respectively. [ABSTRACT FROM AUTHOR]

Images taken under low light conditions cause people's sensory vision to fall into blind areas, and the essential properties of objects are hidden in the dark. There are many excellent algorithms that can adjust the brightness of high and low light region, but they still have room for improvement in realizing the function of denoising and similarity restoration of internal attributes of objects. Inspired by the work principle of human retina, the image is decomposed into two modules, firstly, the enhancement network which is guided by the Attention Mask module completed the enhancement by the U‐net network structure. In this structural network, an RISS module is designed here to effectively reduce the loss of image feature information, so as to improve the structural similarity of reconstructed images. Full convolutional network is used to maintain the position relation of illumination feature information. This network is trained on LOL‐dataset, and the experimental results prove that the network can recover images with different illuminations. In the later stage, the effectiveness of this method is verified by the subjective and objective comparison with other methods on different datasets. This method can not only realize the function of low‐light image enhancement, but also the function of image similarity restoration and image denoising. [ABSTRACT FROM AUTHOR]

Micro-crack anomaly detection is a crucial part of the quality inspection of photovoltaic (PV) module cells. However, due to the complex background and the lack of sufficient anomaly samples, it is a challenging task to identify and locate micro-crack accurately. This paper presents a novel method for detecting micro-crack anomaly in PV module cells by designing an attention classification-and-segmentation network. Specifically, the proposed network consists of a classification network and a segmentation network. In the classification network, the real-time micro-crack anomaly discrimination task in the detection process is performed at first. The classification network introduces transfer learning and deep supervision mechanism to effectively extract and fuse multi-scale features to accurately predict the anomaly probability score. In the segmentation network, the pixel-level micro-crack detection is conducted on the sample determined as defects. The M-shaped structure is used to better extract and fuse shallow-level and deep-level features in the segmentation network, effectively solving the "All Black" issue in the training process. And the attention mechanism module is inserted into the M-shaped structure to more effectively extract micro-crack anomaly features and suppress background noise, thereby significantly improving the accuracy of segmentation. By the design of two-stage network architecture and the utilization of the attention module, the proposed network presents a strong capability for learning from a small set of labeled and annotated samples. Comprehensive experiments are conducted on the real PV electroluminescence (EL) images dataset. Experimental results show that, compared with InceptionV4 classification network and U-net segmentation network, the proposed network has superior performance with ACC of 100.0% and DICE of 0.541 in the micro-crack anomaly classification and segmentation task. Moreover, the experiments show the attention module inserted in the proposed network plays a significant role in improving the classification and segmentation accuracy. • A compact architecture is designed to coordinate the classification and segmentation networks. • The two-stage network reduces the cost of model training and labeling data by transfer learning. • It can perform multiple detection tasks through structural design and attention mechanism. [ABSTRACT FROM AUTHOR]

Compared with RGB salient object detection (SOD) methods, RGB-D SOD models show better performance in many challenging scenarios by leveraging spatial information embedded in depth maps. However, existing RGB-D SOD models prone to ignore the modality-specific characteristics and fuse multi-modality features by simple element-wise addition or multiplication. Thus, they may induce noise-degraded saliency maps when encountering inaccurate or blurred depth images. Besides, many models adopt the U-shape architecture to integrate multi-level features layer-by-layer. Despite the fact that low-level features can be gradually polished, little attention has been paid to enhance high-level features, which may lead to suboptimal results. In this paper, we propose a novel network named CFIDNet to tackle the above problems. Specifically, we design the feature-enhanced module to excavate informative depth cues from depth images and enhance the RGB features by employing complementary information between RGB and depth modalities. Besides, we propose the feature refinement module to exploit multi-scale complementary information between multi-level features and polish these features by applying residual connections. The cascaded feature interaction decoder (CFID) is then proposed to refine multi-level features iteratively. Equipped with these proposed modules, our CFIDNet is capable of segmenting salient objects accurately. Experimental results on 7 widely used benchmark datasets validate that our CFIDNet achieves highly competitive performance over 15 state-of-the-art models in terms of 8 evaluation metrics. Our source code will be publicly available at https://github.com/clelouch/CFIDNet. [ABSTRACT FROM AUTHOR]

Motivation Cancer survival prediction can greatly assist clinicians in planning patient treatments and improving their life quality. Recent evidence suggests the fusion of multimodal data, such as genomic data and pathological images, is crucial for understanding cancer heterogeneity and enhancing survival prediction. As a powerful multimodal fusion technique, Kronecker product has shown its superiority in predicting survival. However, this technique introduces a large number of parameters that may lead to high computational cost and a risk of overfitting, thus limiting its applicability and improvement in performance. Another limitation of existing approaches using Kronecker product is that they only mine relations for one single time to learn multimodal representation and therefore face significant challenges in deeply mining rich information from multimodal data for accurate survival prediction. Results To address the above limitations, we present a novel hierarchical multimodal fusion approach named HFBSurv by employing factorized bilinear model to fuse genomic and image features step by step. Specifically, with a multiple fusion strategy HFBSurv decomposes the fusion problem into different levels and each of them integrates and passes information progressively from the low level to the high level, thus leading to the more specialized fusion procedure and expressive multimodal representation. In this hierarchical framework, both modality-specific and cross-modality attentional factorized bilinear modules are designed to not only capture and quantify complex relations from multimodal data, but also dramatically reduce computational complexity. Extensive experiments demonstrate that our method performs an effective hierarchical fusion of multimodal data and achieves consistently better performance than other methods for survival prediction. Availability and implementation HFBSurv is freely available at https://github.com/Liruiqing-ustc/HFBSurv. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A hybrid fault current limiter (FCL) for medium-voltage direct current power systems is introduced in this article. It achieves low operation loss and high current limiting resistance through the combination of a fast mechanical switch, a magnetic induction module, and a number of liquid metal units (LMUs). First, the parameters of the magnetic induction module are designed to achieve a desired fault current commutation process. Then, the working principle of LMU is introduced, and its resistance properties corresponding to the liquid metal arc are studied in detail. Equations are deduced to model the V-I characteristic of LMU. Thus, the current limiting performance of the topology can be simulated. To verify the feasibility of the FCL, a 5-kV/5-kA prototype is developed and laboratory tests are implemented. Comparisons with other FCLs are also conducted to demonstrate the advantages of the proposed one. It is concluded that the prototype provides the basis for further commercial product development. [ABSTRACT FROM AUTHOR]

Non-cooperative aerial target classification is one of the most attractive but challenging tasks in radar remote sensing applications. Multiview high-range resolution profiles (HRRPs) of the aerial target contain abundant information and will benefit to classification. In this paper, a new aerial target classification method based on an end-to-end lightweight feature learning network (LFL-Net) with multiview HRRPs is proposed. The aerial target classification scenario using multiview HRRPs is first studied and modelled. Then a LFL-Net with multi-inputs and some distinct modules is designed to effectively learn the target classification information from the multiview HRRPs. Therefore, the proposed method can achieve accurate and reliable classification results under different signal-to-noise ratios (SNRs). Experimental results have shown the superiorities of the proposed non-cooperative aerial target classification method. [ABSTRACT FROM AUTHOR]

In this work, we have proposed and experimentally demonstrated a true full-duplex input–output (TFD-IO) for high-speed high-density interfaces. The proposed TFD-IO can be used as an independent module that converts a unidirectional IO/interconnect to a fully bidirectional IO/interconnect, to ideally double the throughput of the high-speed interface. The TFD-IO uses a correlation-based technique to cancel the self-interference (SI) and echoes adaptively in the background. The signals transmitted from the near-end and the far-end can use independent baud-rates and signaling schemes in a TFD-IO. A proof-of-concept design of the TFD-IO module has been fabricated in a 65-nm CMOS technology, and demonstrated with bidirectional throughputs of up to 12.8 Gb/s. [ABSTRACT FROM AUTHOR]

In today's era of rapid development of science and technology, people want to have more portable devices for learning and work in life and study. For this, an English learning system based on Android is proposed. This article first introduces the relevant technologies of the Android system and gives a detailed introduction according to the characteristics, architecture, development type, life cycle, network communication technology, and database technology of the Android platform and studies the functional requirements and overall architecture design of the English learning system. The detailed design and development of the user management module, online examination module, online word search module, and online video module are carried out in accordance with the functional requirements, and the detailed design and implementation methods of the user management module, online examination module, online word search module, and online video module are designed. It is tested proof that the Android-based English learning system has user learning. [ABSTRACT FROM AUTHOR]

The vibration compensation control of a hovering helicopter rescue simulator mounted on a crane beam is studied in this research. A Stewart platform is used as the motion generator of the helicopter simulation cabin and the vibration compensation device of the beam, simultaneously. This study describes how the dynamic model of the Stewart platform with consideration of the beam vibration is established. To determine the interference of the Stewart platform motion control in the special application of a large component flexible base requiring large-scale movement, a hybrid vibration controller composed of a feed-forward compensation module and a PD (proportional-derivative) feedback control module is designed. The experimental results show that this method can effectively compensate for the beam vibration and improve the accuracy of the motion reproduction of a helicopter simulation cabin. [ABSTRACT FROM AUTHOR]

In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper. [ABSTRACT FROM AUTHOR]

This study aims to solve the problem that the traditional online foreign language teaching system focuses on function development, ignoring system security, and has certain risks. An online foreign language education system is designed and developed based on the blockchain technology. First, the blockchain technology and key technologies of system design are described in detail. Second, the overall technical architecture of the system, functional modules, and business logic of each module are designed. Finally, the basic performance of the system is tested. The results show that the system can realize the user's unrestricted office work and zero maintenance of the system. The separation of presentation logic and business logic facilitates the development and maintenance of the system. The system mainly includes six functional modules: user management, course management, course order, course study, course certificate, and credit authentication. These modules are guaranteed for daily teaching use. The event processing success rate of the six functional modules of the system is greater than 99%, and the processing success rate is relatively high. The central processing unit (CPU) usage and memory usage are both below 30%. The host throughput of the six major modules is greater than 100 times/s when processing services. The average response time on the terminal side is maintained below 0.5 s. The average response time of business-side processing is maintained below 0.4 s, which is in line with the standard. The event processing success rate of the constructed system is 10.75% higher than that of other systems, and the average response time, CPU usage, and memory usage are 53.38%, 51.49%, and 50% lower than other systems, respectively. Therefore, the proposed system has better performance. To sum up, the designed system has excellent throughput, event processing capability, response speed, and low CPU and memory occupancy when processing business and is suitable for promotion and use in foreign language online education in colleges and universities. The use of the proposed system can improve its overall teaching efficiency and quality. The purpose is to provide important technical support for the improvement of the security of the online foreign language teaching system. [ABSTRACT FROM AUTHOR]

In renewable energy source applications, multilevel inverters with lower power components have become more popular in recent decades. This work exhibits a novel topology for high-quality output in PV applications, along with low-power switches and isolated dc sources. The core module of the suggested design may create a 13-level output waveform with two unequal voltage source values. The cascaded structure is intended to boost the voltage levels, and the related parameters are obtained analytically. The even and odd levels of voltage can be created natively without the usage of an additional H bridge circuit. Furthermore, the switches, gate driver circuits, dc sources, and standing voltage are fewer in number when compared to other recent topologies. Power losses and cost comparisons are calculated and given in monetary terms. This new research supports the idea that nearest level control (NLC) is used as a modulation scheme in the simulation modeling and experimental validation of the proposed topology. [ABSTRACT FROM AUTHOR]

This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. For high-throughput processing, the FFT and IFFT modules are designed with the pipeline FFT architecture of radix-2 single-path delay feedback (R2SDF) and mixed-radix multipath delay commutator (MRMDC) architecture, respectively. In addition, the IFFT module and the complex multiplier module perform a four-channel operation to reduce the processing time from repeated operations. Simultaneously, the MRMDC IFFT module minimizes the circuit area by reducing the number of non-trivial multipliers by using a mixed-radix algorithm. In addition, the proposed CWT processor can support variable lengths of 8, 16, 32, 64, 128, 256, 512, and 1024 to analyze various vital signals. The proposed CWT processor was implemented in a field-programmable gate array (FPGA) device and verified through the measurement of heartbeat and respiration from an FMCW radar sensor. Experimental results showed that the proposed CWT processor can reduce the processing time by 48.4-fold and 40.7-fold compared to MATLAB software with Intel i7 CPU. Moreover, it can be confirmed that the proposed CWT processor can reduce the processing time by 73.3% compared to previous FPGA-based implementations. [ABSTRACT FROM AUTHOR]

The intelligent programming analysis system based on Internet has attracted more and more attention in the field of education management. The problem of how to flexibly master the application capabilities of intelligent programming and development tools, debugging and optimizing programs, and project deployment is becoming more and more serious. Based on the intelligent programming analysis method, this paper systematically introduces the research and design process of education management model and designs modules such as distributed parallel PhpDig, information analyzer, and information resource database. The system adopts client/middleware/server (C/M/S) three-tier architecture planning and design. The server-side carries the resource manager, the middleware carries the intelligent work tasks (information analyzer, resource collection agent), and the client implements user interaction and data representation solves the storage load problem of a large amount of data. The experimental results show that the semantic processing of search object attributes can improve the retrieval performance, and the retrieval rate and tolerance factor reach 87.6% and 0.041, respectively, which effectively promotes the integration of the analysis data on the chain level and the education system. [ABSTRACT FROM AUTHOR]

The technology of remote sensing image segmentation has made great progress in recent years. However, there are still several challenges which need to be addressed (e.g., ground objects blocked by shadows, higher intra-class variance and lower inter-class variance). In this paper, we propose a novel high-resolution boundary-constrained and context-enhanced network (HBCNet), which combines boundary information to supervise network training and utilizes the semantic information of categories with the regional feature presentations to improve final segmentation accuracy. On the one hand, we design the boundary-constrained module (BCM) and form the parallel boundary segmentation branch, which outputs the boundary segmentation results and supervises the network training simultaneously. On the other hand, we also devise a context-enhanced module (CEM), which integrates the self-attention mechanism to advance the semantic correlation between pixels of the same category. The two modules are independent and can be directly embedded in the main segmentation network to promote performance. Extensive experiments were conducted using the ISPRS Vahingen and Potsdam benchmarks. The mean F1 score (m-F1) of our model reached 91.32% and 93.38%, respectively, which exceeds most existing CNN-based models and represents state-of-the-art results. [ABSTRACT FROM AUTHOR]

As the existing object detection algorithms have the problem of low detection accuracy of power components and inspection defects in complex inspection scenarios of high-voltage power, defect detection in transmission line based on scale- invariant feature pyramid networks is proposed. Firstly, by applying current baseline detection methods to the problem in this article, the RepPoints v2 networks has the highest accuracy. Secondly, as FPN structure cannot effectively extract cross- level semantic information and ignoring the scale normalization in the corner point verification process of RepPoints v2, the scale- invariant feature pyramid networks (SI- FPN) structure is proposed by combining the efficient channel attention (ECA)mechanism and the scale-equalizing pyramid convolution (SEPC). In SI-FPN, the ECA attention module enhances the features of FPN at the channel level, and then SEPC extracts scale-invariant features from FPN and fuses cross-level pyramid features. Through training and testing on the self-built data set of six objects including insulator, shock hammer, suspension clamp, insulator self explosion, shock hammer falling off and bird' s nest, the proposed method improves 1.9 percentage points on the baseline of RepPoints v2, and the mAP reaches 96.3%. The detection accuracy is far beyond the current baseline detection models. Moreover, the SI-FPN module designed in this paper can be used as an independent structure to improve other detection models, which has certain universality. [ABSTRACT FROM AUTHOR]

Ideological and political education in colleges and universities is an important way to enhance students' ideological and political quality and promote their all-around education. It plays an important and positive role in personnel training. To improve the effectiveness of online ideological and political education, this study builds a web-based ideological and political education system and adopts a three-layer system architecture including a data layer, an interface layer, and an application layer. In addition, the system is designed with multiple modules to guarantee the active operation of the complete system and combines algorithms to implement system resource recommendation and practical student-teaching functions. Finally, a control experiment is designed to examine the performance of the proposed web-based ideological and political education system. Results show that the system constructed in this study has certain practical effects. The proposed system can be used to strengthen the work of ideological and political education, truly achieve the correct guidance of ideological, political, and moral aspects, and improve the teaching quality of ideological and political education as much as possible. [ABSTRACT FROM AUTHOR]

Modeling user's fine-grained preferences and dynamic preference evolution from their chronological behaviors are challenging and crucial for sequential recommendation. In this paper, we develop a Hierarchical Self-Attention Incorporating Knowledge Graph for Sequential Recommendation (HSRec). HSRec models not only the user's intrinsic preferences but also the user's external potential interests to capture the user's fine-grained preferences. Specifically, the intrinsic interest module and potential interest module are designed to capture these two preferences respectively. In the intrinsic interest module, user's sequential patterns are characterized from their behaviors via the self-attention mechanism. As for the potential interest module, high-order paths can be generated with the help of the knowledge graph. Therefore, a hierarchical self-attention mechanism is designed to aggregate the semantic information of user interaction from these paths. Specifically, an entity-level self-attention mechanism is applied to capture the sequential patterns contained in the high-order paths while an interaction-level self-attention mechanism is designed to further capture the semantic information from user interactions. Moreover, according to the high-order semantic relevance, HSRec can explore the user's dynamic preferences at each time, thus describing the user's dynamic preference evolution. Finally, experiments conducted on three real world datasets demonstrate the state-of-the-art performance of the HSRec. [ABSTRACT FROM AUTHOR]

A solar bifacial photovoltaic (PV) module is designed so that it permits the addition of the back electrode to the prevailing silicon PV on the front side. Hence, it has the ability to harvest energy using its front and back faces. This study presents an optimization model for calculating the extra energy yield (EY) that can be harvested from the backside of a bifacial PV module using the Firefly Algorithm (FA). Mathematical modelling of the various parameters that influence the extra EY of the backside of a bifacial module was carried out using SIMULINK. Moreover, the mathematical model of the EY of the module was also carried out and then optimized using FA. The optimization model was confined to two orientation states namely the vertical south–north and vertical east–west at Ogbomosho (8.1227° N, 4.2436° E), Nigeria, with different values of albedo and mounting heights. The simulation result shows that the vertical east–west oriented modules outperform the vertical south–north oriented modules in terms of the EY generated. The result also showed that the maximum value of the EY is harvested at a mounting height of 1 m above the ground with row spacing of 2.5 m and a tilt angle of 25 degrees. Therefore, an optimal selection of the mounting surface (albedo) and mounting elevation values can harvest an extra EY of 5 to 45 per cent and help minimize the cost of energy generated using bifacial PV modules for electricity generation. [ABSTRACT FROM AUTHOR]

Deep learning methods have gained rapid development in hyperspectral pansharpening (HP) due to powerful spatial–spectral feature extraction ability. However, most of these methods are optimized using a single reconstruction objective. It is difficult for these methods to find a balance between spectral preservation and spatial preservation. Furthermore, these methods adopt interpolation or convolution to upsample the hyperspectral images (HSIs), which tends to cause noticeable spectral distortion. To conquer these issues, a novel multiobjective guided divide-and-conquer network (MO-DCN) is proposed for HP. It consists of a deconvolution long short-term memories (LSTMs) network (DLSTM) and a divide-and-conquer network (DCN). DLSTM leverages bi-direction learning to upsample HSIs by considering 3-D spatiotemporal dependencies. Then, DCN designs a two-branch architecture to reconstruct spatial and spectral information from upsampled HSIs and panchromatic images (PANIs), respectively, where the spatial branch designs an attention-in-attention module (AIAM) to emphasize complementary attention in a coarse-to-fine way. Finally, co-improvement of spatial and spectral information is formulated as an Epsilon-constraint-based multiobjective optimization. The Epsilon constraint method transforms one objective into a constraint and regards it as a penalty bound to make an excellent tradeoff between different objectives. Experimental results demonstrated that the proposed method markedly improves pansharpening performance in both the spatial and spectral domains and has superior fusion performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

High-level semantic features and low-level detail features matter for salient object detection in fully convolutional neural networks (FCNs). Further integration of low-level and high-level features increases the ability to map salient object features. In addition, different channels in the same feature are not of equal importance to saliency detection. In this paper, we propose a residual attention learning strategy and a multistage refinement mechanism to gradually refine the coarse prediction in a scale-by-scale manner. First, a global information complementary (GIC) module is designed by integrating low-level detailed features and high-level semantic features. Second, to extract multiscale features of the same layer, a multiscale parallel convolutional (MPC) module is employed. Afterwards, we present a residual attention mechanism module (RAM) to receive the feature maps of adjacent stages, which are from the hybrid feature cascaded aggregation (HFCA) module. The HFCA aims to enhance feature maps, which reduce the loss of spatial details and the impact of varying the shape, scale and position of the object. Finally, we adopt multiscale cross-entropy loss to guide network learning salient features. Experimental results on six benchmark datasets demonstrate that the proposed method significantly outperforms 15 state-of-the-art methods under various evaluation metrics. [ABSTRACT FROM AUTHOR]

Achieving accurate crowd counting still faces many challenges due to continuous scale variations. To this end, we present an innovative Context-Aware Pyramid Attention Network for crowd counting which is realized by extracting rich contextual features and dealing with dependencies on space and channels. To extract rich contextual features, we propose a context-aware pyramid feature extraction module. With this module, the rich contextual feature extraction is implemented by dividing the input features into four blocks with different scales. In addition, we design an attention module consisting of a space attention block and a channel attention block which are responsible for dealing with the interdependence on feature information in the spatial dimension and the channel dimension, respectively. We first perform an ablation study to evaluate the effectiveness of each component on ShanghaiTech Part_A. Then we conduct several comparative experiments with several state-of-art methods on five challenging crowd counting datasets, including the ShanghaiTech, the UCF_CC_50, the UCF-QNRF, the WorldExpo'10, and NWPU-Crowd datasets. Experimental results demonstrate that the mean absolute error and mean square error of CAPAN are significantly reduced compared to the state-of-art methods in counting accuracy, and at the same time, the quality of density maps and the false recognition rate are improved. These results validate the effectiveness of CAPAN in crowd counting. [ABSTRACT FROM AUTHOR]

• Quinoline-based carboxylic acid and Cu(II) complexes were synthesized. • They were characterized by magnetic moment measurements, spectral (1H NMR, UV–Vis and IR) and X–ray crystallographic analysis. • Single crystals X-ray diffraction studies all complexes. A new quinoline-based carboxylic acid called 3-benzoyl-2-oxo-1,2-dihydro-quinoline-4-carboxylic acid (HQC) (1a) was synthesized. In addition, one dimensional polymeric {[Cu(QC) 2 (BPE) 2 DMSO]} n.2DMSO.H 2 O (2) (BPE = 4,4′-ethylenebipyridine, DMSO = dimethyl sulfoxide) and dimeric [Cu 2 (QC) 2 (AcO) 2 (DMSO) 2 ] (3) (QC = 3-benzoyl-2-oxo-1,2-dihydro-quinoline-4-carboxylate, AcO = Acetate ion) two Cu(II) complexes were synthesized by hydrothermal reaction. The structure of 1a was elucidated using elemental analysis and spectral techniques (NMR, UV–Vis and FTIR), while X-ray study, UV–Vis, FTIR, magnetic susceptibility and elemental analysis were used for complexes (2 and 3). X-ray analysis revealed that complex 2 has square-pyramidal geometry and complex 3 has octahedral geometry. From X-ray analysis for one dimensional coordination polymer 2 , HQC (1a) was found to have a monodentate coordination mode. In contrast to 2 , in dimeric complex 3 , QC acts as a bidentate ligand, bridging two Cu(II) atoms. [Display omitted] [ABSTRACT FROM AUTHOR]

A recent trend in research on reconfigurable manufacturing systems is the development of reconfigurable machine tools (RMTs). A RMT can be used as a group of machines through change of its configuration to satisfy different manufacturing requirements. A literature review is provided in this paper to demonstrate the state-of-the-art advances and challenges on research and development of RMTs from the perspectives of architecture design, configuration design and optimisation, and system integration and control. In architecture design, semi-open and open architectures based on modular design approach are often selected to allow different modules of the machine to be added and removed. In configuration design and optimisation, operations of reconfiguration processes are analysed and optimised to achieve variety of configurations with the minimum reconfiguration effort. In system integration and control, transfers of motion, energy and data among different modules of the RMT are carried out. The challenges in research on RMTs are also discussed. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of simultaneous standardisation of a set of modules and of several sets of components that may be combined in these modules. The aim is to minimise future costs of production and procurement. We introduce a multilevel standardisation that permits to link the level of forecasts used in manufacturing companies with the modules. This formulation determines the composition of the module i.e. their Bill of Material instead of using a predetermined one. The model takes into account coupling restrictions between components and also permits to modelise the possibility of lifting some of them through ‘junction components’. Our model proposes an efficient approach to deal with temporal dimension in the standardisation process. This approach is illustrated with a real case from the automotive industry and some of the related implementation problems are discussed. [ABSTRACT FROM PUBLISHER]

Background: Though Internet- and mobile-based interventions (IMIs) and mindfulness-based interventions (generally delivered in-situ) appear effective for people with substance use disorders, IMIs incorporating mindfulness are largely missing, including those targeting frequent cannabis use. Methods: This paper details the protocol for a three-arm randomized controlled trial comparing a mindfulness-based self-help IMI (arm 1) and cognitive-behavioral therapy (CBT)-based self-help IMI (arm 2) versus being on a waiting list (arm 3) in their effectiveness reducing cannabis use in frequent cannabis users. Predictors of retention, adherence and treatment outcomes will be identified and similarities between the two active intervention arms explored. Both active interventions last six weeks and consist of eight modules designed to reduce cannabis use and common mental health symptoms. With a targeted sample size of n = 210 per treatment arm, data will be collected at baseline immediately before program use is initiated; at six weeks, immediately after program completion; and at three and six months post baseline assessment to assess the retention of any gains achieved during treatment. The primary outcome will be number of days of cannabis use over the preceding 30 days. Secondary outcomes will include further measures of cannabis use and use of other substances, changes in mental health symptoms and mindfulness, client satisfaction, intervention retention and adherence, and adverse effects. Data analysis will follow ITT principles and primarily employ (generalized) linear mixed models. Discussion: This RCT will provide important insights into the effectiveness of an IMI integrating mindfulness to reduce cannabis use in frequent cannabis users. Trial registration: International Standard Randomized Controlled Trial Number Registry: ISRCTN14971662; date of registration: 09/09/2021. [ABSTRACT FROM AUTHOR]

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF). [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is still a challenging task, which is mainly due to the reason that variable locations and diverse viewpoints in UAVs platform are always resulting in more appearance ambiguities among the intra-objects and inter-objects. To address the above issues, in this paper, we proposed an adaptively attention-driven cascade part-based graph embedding framework (AAD-CPGE) for UAV object Re-ID. The AAD-CPGE aims to optimally fuse node features and their topological characteristics on the multi-scale structured graphs of parts-based objects, and then adaptively learn the most correlated information for improving the object Re-ID performance. Specifically, we first executed GCNs on the parts-based cascade node feature graphs and topological feature graphs for acquiring multi-scale structured-graph feature representations. After that, we designed a self-attention-based module for adaptive node and topological features fusion on the constructed hierarchical parts-based graphs. Finally, these learning hybrid graph-structured features with the most correlation discriminative capability were applied for object Re-ID. Several experimental verifications on three widely used UAVs-based benchmark datasets were carried out, and comparison with some state-of-the-art object Re-ID approaches validated the effectiveness and benefits of our proposed AAD-CPGE Re-ID framework. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification (ASC), as a subtask of Aspect-based sentiment analysis (ABSA), aims to analyze the sentiment polarity of different aspect terms in a sentence. Although the existing methods have been proven to be effective, they fail to effectively identify the range of local context and fully leverage the nonequivalent property of dependency relation. Hence, we propose a concept of dependency cluster and design two modules named Dynamic Local Context Focus (DLCF) and Dependency Cluster Attention (DCA) respectively. The DLCF can dynamically capture the range of local context based on the different max distance from the target aspect term to its context words and the DCA allows the model to pay more attention to the cluster which is more critical for sentiment classification. Together with the two modules, we then propose the DLCF-DCA model, in which the DLCF is equipped before DCA. Considering the DLCF has masked or weighted down the less-semantic-relative words, the semantic information can therefore be better extracted in DCA. Experiments conducted on six benchmark datasets demonstrate that DLCF-DCA achieves the state-of-the-art results. Moreover, the ablation experiment results also verify the effectiveness of each part in DLCF-DCA. [ABSTRACT FROM AUTHOR]

Deep-brain stimulation (DBS) is an emerging research topic aiming to improve the quality of life of patients with brain diseases, and a great deal of effort has been focused on the development of implantable devices. This paper presents a low-noise amplifier (LNA) for the acquisition of biopotentials on DBS. This electronic module was designed in a low-voltage/low-power CMOS process, targeting implantable applications. The measurement results showed a gain of 38.6 dB and a −3 dB bandwidth of 2.3 kHz. The measurements also showed a power consumption of 2.8 μW. Simulations showed an input-referred noise of 6.2 μVRMS. The LNA occupies a microdevice area of 122 μm × 283 μm, supporting its application in implanted systems. [ABSTRACT FROM AUTHOR]

There has been significant progress in skeleton-based action recognition. Human skeleton can be naturally structured into graph, so graph convolution networks have become the most popular method in this task. Most of these state-of-the-art methods optimized the structure of human skeleton graph to obtain better performance. Based on these advanced algorithms, a simple but strong network is proposed with three major contributions. Firstly, inspired by some adaptive graph convolution networks and non-local blocks, some kinds of self-attention modules are designed to exploit spatial and temporal dependencies and dynamically optimize the graph structure. Secondly, a light but efficient architecture of network is designed for skeleton-based action recognition. Moreover, a trick is proposed to enrich the skeleton data with bones connection information and make obvious improvement to the performance. The method achieves 90.5% accuracy on cross-subjects setting (NTU60), with 0.89M parameters and 0.32 GMACs of computation cost. This work is expected to inspire new ideas for the field. [ABSTRACT FROM AUTHOR]

A dual-servo drive and control integrated platform based on VNet neural network development is aimed to address the concerns of poor realization function, operational stability, and lengthy reaction time in the presently built dual-servo drive and control integrated platform. The VNet neural network model structure is established, the VNet neural network model is trained, and the VNet neural network adaptive control method is introduced. Based on VNet neural network development, the hardware design of the dual servo drive control integrated platform has been completed through the overall hardware architecture of the platform, power drive circuit design, and control unit circuit design. The control program was developed using the C language based on a combination of STM32F4 library functions. Through the overall structure of the platform software, the design of functional modules and the design of the servo drive controller, the software design of the dual-servo drive and control integrated platform is completed, and the dual-servo drive and control integrated platform design is realized. The experimental results show that the proposed method design platform's implementation function and operational stability are good, and the platform response time can be effectively shortened. [ABSTRACT FROM AUTHOR]