Zhijiang College of Zhejiang University of Technology

In this paper, a Blockchain-driven platform for supply chain finance, BCautoSCF (Zhi-lian-che-rong in Chinese), is introduced. It is successfully established as a reliable and efficient financing platform for the auto retail industry. Due to the Blockchain built-in trust mechanism, participants in the supply chain (SC) networks work extensively and transparently to run a reliable, convenient, and traceable business. Likewise, the traditional supply chain finance (SCF), partial automation of SCF workflows with fewer human errors and disruptions was achieved through smart contract in BCautoSCF. Such open and secure features suggest the feasibility of BCautoSCF in SCF. As the first Blockchain-driven SCF application for the auto retail industry in China, our contribution lies in studying these pain points existing in traditional SCF and proposing a novel Blockchain-driven design to reshape the business logic of SCF to develop an efficient and reliable financing platform for small and medium enterprises (SMEs) in the auto retail industry to decrease the cost of financing and speed up the cash flows. Currently, there are over 600 active enterprise users that adopt BCautoSCF to run their financing business. Up to October 2019, the BCautoSCF provides services to 449 online/offline auto retailors, three B2B asset exchange platforms, nine fund providers, and 78 logistic services across 21 provinces in China. There are 3296 financing transactions successfully completed in BCautoSCF, and the amount of financing is ¥566,784,802.18. In the future, we will work towards supporting a full automation of SCF workflow by smart contracts, so that the efficiency of transaction will be further improved.

BACKGROUND: It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. RESULTS: In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. CONCLUSIONS: The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

Supply Chain Finance (SCF) refers to the financial service in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream enterprises. SCF adopts a self-testing and closed-loop credit model to control funds and risks. The key factor in a successful SCF service is the deployment of SCF business-oriented information systems that allow businesses to form partnerships efficiently and expedite cash flows throughout the supply chain. Blockchain Technology (BCT), featuring decentralization, tamper-proofing, traceability, which is usually paired with the Internet of Things (IoT) in real-world contexts, has been widely adopted in the field of finance and is perfectly positioned to facilitate innovative collaborations among participants in supply chain networks. In this paper, we propose a BCT and IoT-based information management framework (named BC4Regu), which works as the regulatory to improve the information transparency in the business process of SCF. With BC4Regu, the operation cost of the whole supply chain can be significantly reduced through the coordination and integration of capital flow, information flow, logistics and trade flow in the supply chain. The contributions in this paper include: (1) proposing a novel information management framework which leverages Blockchain and IoT to solve the problem of information asymmetry in the trade of SCF; (2) proposing the technical design of BC4Regu, including the Blockchain infrastructure, distributed ledger-based integrated data flow service, and reshaped SCF process; and (3) applying BC4Regu to a group of scenarios and conducting theoretical analysis by introducing the principal-agent model to validate the BC4Regu.

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Abstract Tactile sensing is essentially required for dexterous manipulation in robotic applications. Mimicking human perception of softness identification in a non-invasive fashion, thus achieving satisfactory interaction with fragile objects remains a grand challenge. Here, a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported. This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm, contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback. The integration of multifunctional sensing system allows the manipulator to hold capabilities of self-sensing and adaptive grasping motility in response to objects with the various softness (i.e., kPa-MPa). As a proof-of-concept demonstration, the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness, paving the way for the development of microsurgery robots, human-machine interfacing, and advanced prosthetics.

To overcome the disadvantages of the rotating bed available in open literature, a novel kind of high gravity device-rotating zigzag bed (RZB) was developed, which exhibits many superior features owing to its unique rotor combining a rotational part with a stationary one. The outstanding characteristics of RZB are its capability of middle-feed and easily realizing multirotor configuration in one unit by simply installing multiple rotors along the same axis. Thus one unit of RZB can be applied to continuous distillation processes with a higher mass transfer capacity. A preliminary test of mass transfer performance of the RZB in a pilot-scale system using methanol−water was carried out in this study. Experimental results show its excellent mass transfer behavior with an acceptable pressure drop. Comparison with rotating packing bed (RPB) shows that RZB provides equivalent mass transfer efficiency to RPB but exhibits excellent operability with a higher turndown ratio than that of RPB. Comparison with valve tray indicates that the baffle efficiency of RZB is slightly lower than the plate efficiency of valve tray. But if the difference between the tray space and baffle space is taken into consideration, RZB provides much higher efficiency than that of valve tray. Therefore the RZB is a kind of high efficiency gas−liquid contactor and a promising alternative in chemical process industries.

Abstract The sophistication, adaptability, and complexity of biological systems have provided enormous inspiration and have been a continuous source of numerous innovations. Soft living organisms like drosera capensis have amazing predatory behavior that can capture prey of ideal size, enabling them to interact with environmental stimuli efficiently. Mimicking such natural intelligence in artificial systems with systematical functions of multiple information perception, neuronal transmission, and adaptive motility remains a grand challenge. Here, a biomimetic drosera capensis is reported that is capable of multifunctional self‐sensing, automatic regulation, and adaptive actuation in response to diverse stimuli with intelligent predation capability in an entirely closed‐loop fashion. The functional system heterogeneously integrates the thermal‐responsive soft actuator as the muscle‐like motor and flexible tactile, strain, and piezoelectric multimodal sensors as somatosensory receptors. With the synergistic effect of multifunctional sensing and fast actuating schemes, the artificial drosera capensis deconvolutes multiple characteristics of the catching process (e.g., strain rate, magnitude, and direction) and thus holds impressive predatory behavior for ideal‐sized prey. This electronically innervated artificial drosera capensis with multimodal sensing and self‐regulated actuating capability through the closed‐loop control of sensing and actuating system paves the way for the development of adaptive soft robots.

A flexible piezoelectric sensor with frequency-response behaviour can enable the detection of the magnitude and loading rate of stimuli.

Abstract Melt blowing is an industrial approach for producing microfibrous nonwoven materials utilizing high-speed air to attenuate polymer melt. The melt-blowing air flow field which is widely believed to be turbulence determines the process of fiber formation. In this study, the turbulent air flow field in slot-die melt blowing was experimental measured by hot-wire anemometer. The fluctuations of air velocity and temperature, the mean velocity and mean temperature were measured and analyzed; moreover, the relationship between turbulent air flow field and fiber formation in melt blowing was discussed and predicted. In the last part of this paper, the coupling effect of air temperature and velocity was studied tentatively, results showed that air temperature not only had an enhanced effect on velocity, but contributed to the fluctuation of velocity. This work shows that the fluctuating characteristics of air velocity and temperature have dominant effect on fiber motion and the evenness of fiber diameter.

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations.

Abstract

This research aims to examine the psychology and behavior of users when customizing avatars from the standpoint of user experience and to provide constructive contributions to the Metaverse avatar customization platform. This study analyzed the factors that affect the behavior of user-customized avatars in different virtual environments and compared the differences in public self-consciousness, self-expression, and emotional expression among customized avatars in multiple virtual contexts. Methods: Using a between-subjects experimental design, two random groups of participants were asked to customize avatars for themselves in two contexts, a multiplayer online social game (MOSG) and a virtual meeting (VM). Results: When subjects perceived a more relaxed environment, the customized avatars had less self-similarity, and the subjects exhibited a stronger self-disclosure willingness and enhanced avatar wishful identification; nevertheless, public self-consciousness was not increased. When subjects perceived a more serious environment, the customized avatars exhibited a higher degree of self-similarity, and the subjects exhibited a greater self-presentation willingness, along with enhanced identification of avatar similarity and increased public self-consciousness. Conclusions: Participants in both experiment groups expressed positive emotions. The virtual context affects the self-similarity of user-customized avatars, and avatar self-similarity affects self-presentation and self-disclosure willingness, and these factors will affect the behavior of the user-customized avatar.

Based on empirical mode decomposition (EMD) and least squares support vector machine (LS-SVM), a new method is proposed to identify the flow pattern of gas-liquid two-phase flow in a minichannel. Four flow patterns are observed in three pipes with inner diameters of 4.0, 3.1, and 1.8 mm. For each flow pattern, the capacitance signals are obtained by a two-electrode capacitance sensor. The EMD method is applied to the capacitance signal to obtain intrinsic mode functions (IMFs) with different characteristic time scales. For each IMF, the autoregression (AR) model is built to extract multiscale features. Combining the extracted features with the energy feature of each IMF, the flow patterns are identified by the multiclassification LS-SVM classifier. The experimental results indicate that the presented method is effective for flow pattern identification and has identification rates higher than 91%.

The renewable energy projects which can be used as an alternative to traditional energy industries can not only be able to bring enormous economic benefits to people, but also produce positive environmental effects. Although the RE projects have obvious advantages, but countries have many obstacles during the development of RE projects, especially the lack of financial support. Based on the panel data of top 55 global financial countries and regions, this paper has conducted the analysis to test the important influence from the financial intermediation sector to the development of the RE sector in these countries during 1980-2008, which has confirmed that there is positive correlation between the development level of financial intermediation and the total power output of the renewable energy projects in these countries, and this positive correlation in the power output of the hydropower project is more evident. © 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of RIUDS

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

Bearings are one of the critical components widely used in rotary machines. Bearing failure can be catastrophic and may lead to a lengthy downtime of systems for maintenance. Bearing fault prognostics can help reduce the cost for maintenance and avoid catastrophic failures of the systems. This paper proposes a new data-driven approach for bearing fault prognostics, which is based on the Kolmogorov-Smirnov test, self-organizing map, and unscented Kalman filter (UKF). The proposed approach has two steps. The first step is to detect bearing's degradation process by learning the historical data and the second step is to predict the remaining useful life (RUL) with the aid of a degradation model and the UKF. The proposed approach is validated by bearing's life data obtained from a run-to-failure experiment. Results show that the proposed approach can detect the bearing degradation process successfully and predict the RUL effectively.

This research explains the conversion of waste tobacco stalks into nitrocellulose to try to recover as much chemical potential contained in the biomass material as possible. Simple pioneering experiments were conducted using pulping tobacco stalks with a moisture content of 10.17 wt% and the soda pulping method being applied to produce cellulose pulp. The cellulose pulp was bleached using calcium hypochlorite to produce a dry white lignin-free pulp, which was subjected to nitration. The mixture used was 67% nitric acid and 98% sulphuric acid, and the acid ratio was varied between 3:7 and 7:3 v/v. Nitration time was varied between 5 and 25 min. This process produced nitrocellulose with all the various conditions. The nitrocellulose obtained with nitrogen content between 11 - 11.5% v/v was characterized using its solubility in acetone. An optimum nitrating mixture of 1:1 v/v with a nitration time of 5 min was used to produce nitrocellulose from tobacco stalks using soda pulping. The results show a great potential for tobacco farming countries to reduce their nitrocellulose import bill using this process.

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

Abstract Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer‐aided pathology research. In this article, Mask RCNN is presented for the automatic detection of nuclei on high‐resolution histopathological images of breast cancer. Mask RCNN uses the ResNet network and effectively combines modules such as feature pyramid networks (FPN), ROIAlign, and fully convolutional networks (FCN). FPN can efficiently extract features of various dimensions in images. ROIAlign can improve the accuracy of the detection model in the detection task. FCN renders the prediction results more detailed. The experiment results show that the application of this algorithm is superior to other algorithms in terms of its intuitive vision, as well as in performance indicators such as accuracy, recall, and F‐Measure.

Supply chain finance refers to the financial service model in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream small- and medium-sized enterprises (SMEs). It adopts the self-testing and closed-loop credit model to control funds and risks. It is an efficient route for SMEs to solve the problem of financing. However, at present, the market of supply chain finance in China still faces problems such as the inability of credit disassembly of core enterprises, which seriously hinder the development of the supply chain finance industry. Blockchain technology featuring decentralization, tamper-proofing and traceability had been widely adopted in the field of finance and provided a new vision to solve the bottleneck for the development of supply chain finance. In this paper, with regard to the characteristics of supply chain finance, we propose a novel blockchain-driven architecture to reshape the business process of supply chain finance, and we introduce the underlying technical implementations in detail. Our contributions in this paper include (1) proposing a novel technical architecture of blockchain-driven supply chain finance management system and detailing its underlying implementations; (2) designing the mechanism of the credit disassembly, which is implemented by blockchain technology, to improve financing efficiency; and (3) exploring the impact and potential of blockchain technology in traditional business models (e.g., receivables financing, inventory financing and prepayment financing) in supply chain finance.