Nonvolatile Memristive Effect in Few-Layer CrI3 Driven by Electrostatic Gating.

The potential of memristive devices for applications in nonvolatile memory and neuromorphic computing has sparked considerable interest, particularly in exploring memristive effects in two-dimensional (2D) magnetic materials. However, the progress in developing nonvolatile, magnetic field-free memristive devices using 2D magnets has been limited. In this work, we report an electrostatic-gating-induced nonvolatile memristive effect in CrI3-based tunnel junctions. The few-layer CrI3-based tunnel junction manifests notable hysteresis in its tunneling resistance as a function of gate voltage. We further engineered a nonvolatile memristor using the CrI3 tunneling junction with low writing power and at zero magnetic field. We show that the hysteretic transport observed is not a result of trivial effects or inherent magnetic properties of CrI3. We propose a potential association between the memristive effect and the newly predicted ferroelectricity in CrI3 via gating-induced Jahn-Teller distortion. Our work illuminates the potential of 2D magnets in developing next-generation advanced computing technologies.

CPACK: An Intelligent Cyber-Physical Access Control Kit for Protecting Network.

Access Control Lists (ACL) are critical to protecting network and cyber-physical systems. Traditional firewalls mostly use reactive methods to enforce ACLs, so that new ACL updates cannot take effect immediately. In this paper, based on our previous work, we propose CPACK, an intelligent cyber-physical access control kit, which uses a smart algorithm to upgrade the ACL list. CPACK adopts a proactive way to enforce ACL and reacts to a new ACL update and network view update in real time. We implement CPACK on both Floodlight and ONOS controller. We then conduct a large number of experiments to compare CPACK with the Floodlight firewall application. The experimental results show that CPACK has a better performance than the existing Floodlight firewall application. CPACK is also integrated into the new version of Floodlight and ONOS controller.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging.

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

Soil Metal Immobilization in Agricultural Land Contaminated with Cadmium and Lead: A Case Study of Effectiveness Evaluation in Lanping, Southwest China.

The growth of edible crops on land that is highly polluted with potentially toxic elements is prohibited in many developed countries, but the growth of fiber or energy crops may be permitted. Here, we have evaluated metal immobilization in a maize field polluted with cadmium (Cd) and lead (Pb) to determine the thresholds of soil CaCl2-extractable Cd and Pb and to assess management options designed to maximize food safety. Based on geographical and statistical methods we found that when the soil pH was increased from 5.24 to 6.24, the soil CaCl2-extractable Cd and Pb values decreased by 47.8 and 74.7%, respectively. Soil CaCl2-extractable Pb concentrations need to be < 2.14 mg kg-1 in order to comply with the Chinese maximum permissible grain Pb concentration (< 0.2 mg kg-1). Immobilization increased the percentage of samples that were below permissible levels from 77.4% to 96.2% (grain Cd) and 90.6% to 96.2% (grain Pb) during the period 2017 to 2019. To avoid excessive or inadequacy immobilization, the spatial distribution of correlation coefficients of soil pH, CaCl2-extractable or grain Cd/Pb may be helpful in the precise management of immobilization for long-term remediation.

Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection.

Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models.

Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks.

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models' effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of "Adam". The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.

WatchPose: A View-Aware Approach for Camera Pose Data Collection in Industrial Environments.

Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.

A Secure Mutual Batch Authentication Scheme for Patient Data Privacy Preserving in WBAN.

The current advances in cloud-based services have significantly enhanced individual satisfaction in numerous modern life areas. Particularly, the recent spectacular innovations in the wireless body area networks (WBAN) domain have made e-Care services rise as a promising application field, which definitely improves the quality of the medical system. However, the forwarded data from the limited connectivity range of WBAN via a smart device (e.g., smartphone) to the application provider (AP) should be secured from an unapproved access and alteration (attacker) that could prompt catastrophic consequences. Therefore, several schemes have been proposed to guarantee data integrity and privacy during their transmission between the client/controller (C) and the AP. Thereby, numerous effective cryptosystem solutions based on a bilinear pairing approach are available in the literature to address the mentioned security issues. Unfortunately, the related solution presents security shortcomings, where AP can with ease impersonate a given C. Hence, this existing scheme cannot fully guarantee C's data privacy and integrity. Therefore, we propose our contribution to address this data security issue (impersonation) through a secured and efficient remote batch authentication scheme that genuinely ascertains the identity of C and AP. Practically, the proposed cryptosystem is based on an efficient combination of elliptical curve cryptography (ECC) and bilinear pairing schemes. Furthermore, our proposed solution reduces the communication and computational costs by providing an efficient data aggregation and batch authentication for limited device's resources in WBAN. These additional features (data aggregation and batch authentication) are the core improvements of our scheme that have great merit for limited energy environments like WBAN.

Carrier transport dynamics in Mn-doped CdSe quantum dot sensitized solar cells.

In this work quantum dot sensitized solar cells (QDSSCs) were fabricated with CdSe and Mn-doped CdSe quantum dots (QDs) using the SILAR method. QDSSCs based on Mn-doped CdSe QDs exhibited improved incident photon-to-electron conversion efficiency. Carrier transport dynamics in the QDSSCs were studied using the intensity modulated photocurrent/photovoltage spectroscopy technique, from which transport and recombination time constants could be derived. Compared to CdSe QDSSCs, Mn-CdSe QDSSCs exhibited shorter transport time constant, longer recombination time constant, longer diffusion length, and higher charge collection efficiency. These observations suggested that Mn doping in CdSe QDs could benefit the performance of solar cells based on such nanostructures.

Three-dimensional SnO2/carbon on Cu foam for high-performance lithium ion battery anodes.

SnO2 is an attractive anode material for lithium-ion batteries (LIBs) due to its high theoretical specific capacity (1491 mAh g(-1)), low cost, and environmental benignity. The main challenges for SnO2 anodes are their low intrinsic conductivity and poor cycling stability associated with their large volume changes during the charge and discharge process. Here, we present a simple chemical vapor deposition method to fabricate three-dimensional SnO2/carbon on Cu foam electrodes for LIBs. Such a three-dimensional electrode combines multiple advantages, including a continuous electrically conductive network, short pathways for electron transport and ion diffusion, and porous space to allow for the volume expansion of SnO2 nanoparticles. With this anode, superior electrochemical performance is achieved with a high reversible specific capacity of 1171 mAh g(-1) at a current density of 100 mA g(-1). A stable cycling performance as well as an excellent rate capability is also achieved. These outstanding lithium-storage properties suggest the strategy is a reliable approach for fabricating high-performance LIB electrodes.

Passivation effects on quantum dots prepared by successive ionic layer adsorption and reaction.

ZnS is typically used to passivate semiconductor quantum dots (QDs) prepared by the successive ionic layer adsorption and reaction (SILAR) method for solar cell applications, while for colloidal QDs, organic ligands are usually used for this passivation purpose. In this study we utilized oleylamine and oleic acid ligands, besides ZnS, to passivate QDs prepared by the SILAR approach, and investigated their effects on the incident photon-to-current efficiency (IPCE) performance of the solar cells. It was observed that oleylamine passivation decreased device performance, while oleic acid passivation improved the IPCE of the cells. Redshift of the IPCE onset wavelength was also observed after oleic acid coating, which was attributed to the delocalization of excitons in the CdS QDs.

Large enhancement in photocurrent by Mn doping in CdSe/ZTO quantum dot sensitized solar cells.

We find a large enhancement in the efficiency of CdSe quantum dot sensitized solar cells by doping with manganese. In the presence of Mn impurities in relatively small concentrations (2.3%) the photoelectric current increases by up to 190%. The average photocurrent enhancement is about 160%. This effect cannot be explained by a light absorption mechanism because the experimental and theoretical absorption spectra demonstrate that there is no change in the absorption coefficient in the presence of the Mn impurities. To explain such a large increase in the injection current we propose a tunneling mechanism of electron injection from the quantum dot LUMO state to the Zn2SnO4 (ZTO) semiconductor photoanode. The calculated enhancement is approximately equal to 150% which is very close to the experimental average value of 160%. The relative discrepancy between the calculated and experimentally measured ratios of the IPCE currents is only 6.25%. For other mechanisms (such as electron trapping, etc.) the remaining 6.25% cannot explain the large change in the experimental IPCE. Thus we have indirectly proved that electron tunneling is the major mechanism of photocurrent enhancement. This work proposes a new approach for a significant improvement in the efficiency of quantum dot sensitized solar cells.

Variational first hyperpolarizabilities of 2,3-naphtho-15-crown-5 ether derivatives with cation-complexing: a potential and selective cation detector.

Crown ethers, as a kind of heterocycle, have been the subject of great interest over recent decades due to their selective capability to bind to metal cations. The use of a constant crown ether, such as naphtho-15-crown-5 (N15C5), and varied metal cations (Li+, Na+, K+, Be2+, Mg2+, Ca2+, Co2+, Ni2+, Cu2+) makes it possible to determine the contributions of the metal cations to nonlinear optical (NLO) responses and to design an appropriate NLO-based cation detector. N15C5 and its metal cation derivatives have been systematically investigated by density functional theory. It is found that the dependency of the first hyperpolarizability relies on the metal cation, especially for transition metals. The decrease of the first hyperpolarizabilities for alkali metal cation derivatives is due to their relatively low oscillator strengths, whereas the significant increase of the first hyperpolarizabilities for transition metal cation derivatives can be further illustrated by their low transition energies, large amplitudes and separate distributions of first hyperpolarizability density. Thus, the alkali metal and transition metal cations are distinguishable and the transition metal cations are easier to detect by utilizing the variations in NLO responses.

Coaxial Zn2GeO4@carbon nanowires directly grown on Cu foils as high-performance anodes for lithium ion batteries.

A single-step chemical vapor deposition method is utilized to prepare a novel electrode structure composed of coaxial Zn2GeO4@carbon nanowires directly grown on a Cu foil current-collector (ZGO@C/Cu), and the obtained ZGO@C/Cu hybrid electrode is employed as additive-free anode in lithium ion battery studies. The ZGO@C/Cu electrode exhibits a high reversible capacity of 1162 mA h g(-1) between 0.01 and 3.0 V at a current density of 0.2 A g(-1). It also shows a remarkable cycling stability and an excellent high-rate capability. At a current density of 2.0 A g(-1), a stable capacity of 790 mA h g(-1) is obtained without any noticeable decay over 100 cycles. Even at a high current density of 10 A g(-1), a reversible capacity of 465 mA h g(-1) is still obtained. The synthesis approach developed here could be utilized for the fabrication of other high-performance heterogeneous electrode structures for lithium ion battery applications.

Redox control of ferrocene-based complexes with systematically extended π-conjugated connectors: switchable and tailorable second order nonlinear optics.

The studies of geometrical structures, thermal stabilities, redox properties, nonlinear responses and optoelectronic properties have been carried out on a series of novel ferrocenyl (Fc) chromophores with the view of assessing their switchable and tailorable second order nonlinear optics (NLO). The use of a constant Fc donor and a 4,4'-bipyridinium acceptor and varied conjugated bridges makes it possible to systematically determine the contribution of organic connectors to chromophore nonlinear optical activities. The structures reveal that both the reduction reactions and organic connectors have a significant influence on 4,4'-bipyridinium. The potential energy surface maps along with plots of reduced density gradient mirror the thermal stabilities of the Fc-based chromophores. The first and second reductions take place preferentially at the 4,4'-bipyridinium moieties. Significantly, the reduction processes result in the molecular switches with large NLO contrast varying from zero or very small to a large value. Moreover, time-dependent density functional theory results indicate that the absorption peaks are mainly attributed to Fc to 4,4'-bipyridinium charge transfer and the mixture of intramolecular charge transfer within the two respective 4,4'-bipyridinium moieties coupled with interlayer charge transfer between the two 4,4'-bipyridinium moieties. This provides us with comprehensive information on the effect of organic connectors on the NLO properties.

Tunneling current-controlled spin states in few-layer van der Waals magnets.

Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI3. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI3, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI3 layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI3 tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing.

Strategy for enhancing second-order nonlinear optical properties of the Pt(II) dithienylethene complexes: substituent effect, π-conjugated influence, and photoisomerization switch.

The second-order nonlinear optical (NLO) properties of a series of Pt(II) dithienylethene (DTE) complexes possessing the reversible photochromic behavior have been investigated by density functional theory (DFT) combined with the analytic derivatives method. The results show that the calculated static first hyperpolarizabilities (ßtot) of the open-ring and closed-ring systems significantly increase in the range of 2.1-4.5 times through strengthening of the electron-withdrawing ability of the substituent R (R = H, CF3, NO2) and an increase of the number of thiophene rings. Moreover, there is a large enhancement of the ßtot values from the open-ring systems to the corresponding closed-ring systems. This efficient enhancement is attributed to the better delocalization of the π-electron system, the more obvious degree of charge transfer, and the larger f(os)/E(gm)(3) (f(os) is the oscillator strength, and E(gm) is the transition energy between the ground and the excited states) values in the closed forms according to the bond length alternation (BLA) and time-dependent density functional theory (TDDFT) calculations. In addition, the dispersion has less influence on the frequency-dependent first hyperpolarizabilities (ßtot(ω)) of the studied systems at the low-frequency area ω (0.000-0.040 au). Our present work would be beneficial for further theoretical and experimental studies on large second-order NLO responses of metal complexes.

Pulsed laser deposition of CdSe Quantum dots on Zn2SnO4 nanowires and their photovoltaic applications.

In this work we report a physical deposition-based, one-step quantum dot (QD) synthesis and assembly on ternary metal oxide nanowires for photovoltaic applications. Typical solution-based synthesis of colloidal QDs for QD sensitized solar cells involves nontrivial ligand exchange processing and toxic wet chemicals, and the effect of the ligands on carrier transport has not been fully understood. In this research using pulsed laser deposition, CdSe QDs were coated on Zn(2)SnO(4) nanowires without ligand molecules, and the coverage could be controlled by adjusting the laser fluence. Growth of QDs in dense nanowire network structures was also achieved, and photovoltaic cells fabricated using this method exhibited promising device performance. This approach could be further applied for the assembly of QDs where ligand exchange is difficult and could possibly lead to reduced fabrication cost and improved device performance.

Carbon responsibility allocation method based on complex structure carbon emission flow theory.

A carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution characteristics, and mechanism of carbon emission flow were analyzed. The "carbon potential of complex structure" concept was introduced to track "carbon trajectory" and "green trajectory" by harnessing the ability of complex structures to retain two-dimensional information. Subsequently, the carbon responsibility allocation methods for network loss and users' electricity consumption behavior were developed to realize the accurate carbon responsibility allocation of each system link. Finally, the effectiveness and advancement of the proposed carbon responsibility allocation method were verified using the improved IEEE 6-bus and 30-bus test systems. The application of the proposed complex structure carbon potential in the carbon emission flow theory expands the research dimension of electric power carbon emission for low-carbon development from the "carbon perspective," provides a novel optimization space for the operation of the distribution network and realizes the carbon emission flow theory, which serves as a bridge from calculation evaluation to optimization decision.

Selenium-enriched Polysaccharides from Pyracantha fortuneana (SePFP) Ameliorated Hepatic Lipid Accumulation through Enhancing Mitochondrial Fatty Acid ß-Oxidation in Aging Mice.

Selenium-enriched polysaccharides from Pyracantha fortuneana (SePFP) has many beneficial physiological activities, but how it improves the aging associated abnormal lipid metabolism is still unclear. Therefore, we explored the mechanisms of the regulatory role of SePFP on liver lipid accumulation in aging mice. METHODS: 60 naturally aged C57BL/6J male mice were divided into 6 groups: adult group, aging group (21-month-old mice), aging mice treated with low-, medium- and high-doses of SePFP (SePFP-L, SePFP-M, SePFP-H), and aging mice treated with resveratrol (RSV). SePFP and RSV were administrated daily via oral gavage from 16 to 21 months old. The parameters of energy metabolism were measured in all mice before sacrifice, and liver tissues were collected to determine the levels of metabolism-related enzymes by real-time PCR and Western blot. RESULTS: We found that SePFP significantly reduced the body weight, liver to bodyweight ratio, and white fat to body weight ratio in aging mice. SePFP also down-regulated the triglycerides and cholesterol levels in liver and serum, and decreased respiratory quotient in aging mice. The mechanism of SePFP regulating lipid metabolism was mainly through promoting fatty acid transportation to mitochondria and enhancing mitochondrial ß-oxidation and ketone body production. CONCLUSION: SePFP attenuates liver lipid deposition in aging mice by enhancing hepatic mitochondrial ß-oxidation.