Search

This paper studies the characteristics of internal waves induced by the motion of a submerged body through a combination of experimental and numerical methods. First, by deploying an array of conductivity probes in an experiment, the temporal evolution of the internal wave at the plane of the conductivity probe array is obtained and compared with numerical simulation results based on the Navier–Stokes equations, thereby validating the accuracy of the Computational Fluid Dynamics (CFD) model. Subsequently, utilizing CFD calculations, further exploration is conducted on the spatiotemporal distribution characteristics of the internal wave, encompassing its generation and evolution, velocity field distribution, wake angle, and wave profile features. Finally, the variation of the internal wave with the Froude number (Fr) is investigated, revealing that it varies apparently with Fr, including the wave component, wave mode, wake length, wake angle, wave amplitude, and so on. It is found that when Fr is large, the internal wave converges toward the centerline behind the body, until forming a line. The wave magnitude changes with Fr in four stages, i.e., increasing with Fr from zero in the first stage, reaching a peak and decreasing thereafter in the second stage, then changing little in the third stage, and finally increasing approximately linearly in the fourth stage. [ABSTRACT FROM AUTHOR]

Effective science communication is one of the key skills undergraduates must achieve and is one of the threshold learning outcomes for Science (TLO 4.1). In addition, presenting published research to their peers allows students to critically evaluate scientific research (TLO 3.1) and develop a deeper appreciation for the link between experimental methodologies and the contestable nature of scientific knowledge. Although it is recognised that feedback given to students has positive impacts on student learning, increasing workload pressures may restrict academics' capacity to provide effective feedback. An alternate approach is to facilitate the exchange of feedback between peers, where gaining experience in providing feedback can further develop students' skills in critique, which enhances their learning outcomes. In this study, 3rd year undergraduate biomedical science students were asked to provide anonymous, written feedback on the quality of an oral "journal club" presentation of a primary research article by a group of their peers. Students gave extensive, rich and detailed feedback to their peers. The quality of the feedback given was high, with most students receiving a grade of distinction or higher for the feedback they provided. In addition, the improvement in student learning outcomes was significantly greater with peer feedback than with academic feedback alone, suggesting that performing peer review provides students with additional benefits.

In response to the susceptibility of federated learning, which is based on a distributed training structure, to byzantine poisoning attacks from malicious clients, resulting in issues such as slowed or disrupted model convergence and reduced model accuracy, we propose a robust aggregation technique for defending against byzantine poisoning attacks in federated learning, known as FLRAM. First, we employ isolation forest and an improved density-based clustering algorithm to detect anomalies in the amplitudes and symbols of client local gradients, effectively filtering out gradients with large magnitude and angular deviation variations. Subsequently, we construct a credibility matrix based on the filtered subset of gradients to evaluate the trustworthiness of each local gradient. Using this credibility score, we further select gradients with higher trustworthiness. Finally, we aggregate the filtered gradients to obtain the global gradient, which is then used to update the global model. The experimental findings show that our proposed approach achieves strong defense performance without compromising FedAvg accuracy. Furthermore, it exhibits superior robustness compared to existing solutions. [ABSTRACT FROM AUTHOR]

TCL CSOT utilized inkjet printing technology to produce impressive medium and large‐size OLED products in the previous. In this work, high‐resolution notebook equipped with IGZO backplane is shown. This inkjet printing notebook offers 240 PPI along with good optical performance. Also, breakthrough in high‐resolution printing has been achieved, 274 PPI panel has been successfully printed. It is demonstrated that inkjet printing technology would adapt the trend of display products with various sizes toward higher resolution. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, a huge number of devices are connected to the network, network traffic is exhibiting massive and low latency characteristics. At the same time, it is becoming cheaper and cheaper to launch DDoS attacks, and the attack traffic is becoming larger and larger. Software-defined networking SDN is proposed as a new network architecture. However, the controller as the core of SDN is vulnerable to DDoS attacks and causes a single point of failure in the network. This paper combines the ideas of distributed and edge computing, firstly, a DDoS attack detection algorithm using heterogeneous integrated feature selection and random forest algorithm is proposed. Then, this DDoS attack detection algorithm is distributed and deployed on the edge equipment switches of SDN to perform distributed edge parallel computing using the residual computing power of the switches for fast and accurate detection of DDoS attacks. Finally, simulation experiments are conducted in the SDN environment using the CIC-DDoS2019 dataset to evaluate the effectiveness and feasibility of the proposed scheme. The experimental results show that the performance evaluation metrics of this solution: accuracy, precision, recall and F-value all reach 99.99%, while the prediction time is only 0.4 s, all metrics are better than other DDoS attack detection methods in the same category. Therefore, this solution is able to detect DDoS attacks in a timely and accurate manner. [ABSTRACT FROM AUTHOR]

In order to improve the detection probability of weak targets, tracking radar using sum and difference beams often adopt the method of long‐time coherent integration. However, the multidimensional migration of time‐varying targets will lead to the decline of parameter estimation accuracy. To solve this problem, this article proposes a refined angle estimation method for time‐varying targets with the traditional sum and difference beam echo model, this method compensates and searches the angle parameters of the targets based on subarray rotation invariant and focus process. In addition, this article also studies the masking problem of highly dynamic proximity group targets detection, and proposes an adaptive weighted LMS‐CLEAN based on Least Mean Square criterion, which effectively reduces the influence of masking effect on the parameter estimation accuracy of weak targets. Firstly, the proposed algorithm performs angle search and phase compensation on the pulse compression echo of sum and difference channels based on subarray rotation invariant. Secondly, focus the search matrix, reconstruct the strong target echo, and stripe it from both channels by adaptive weighting. Lastly, repeat the above steps until parameters of all targets are achieved precisely. The proposed two algorithms maintain a very low computational effort while effectively reducing the parameter estimation error, and are highly promising for engineering applications. In order to verify the effectiveness of the proposed algorithm, this article also provides some numerical experiments to compares with two existing algorithms in error performance, anti‐noise performance, and computational complexity. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) is still one of the main threats to network security today. Attackers are able to run DDoS in simple steps and with high efficiency to slow down or block users' access to services. In this paper, we propose a framework based on feature and model selection (FAMS), which is used for detecting DDoS attacks with the aim of identifying the features and models with a high generalization capability, high prediction accuracy, and short prediction time. The FAMS framework is divided into four main phases. The first phase is data pre-processing, including operations such as feature coding, outlier processing, duplicate elimination, data balancing, and normalization. In the second stage, 79 features are extracted from the dataset and selected by the feature selection algorithms filter, wrapper, embedded, variance, mutual information, backward elimination, Lasso.L1, and random forest. The purpose of feature selection is to simplify the model, avoid dimensional disasters, reduce computational costs, and reduce the prediction time. The third stage is model selection, which aims to select the most ideal algorithm from GD, SVM, LR, RF, HVG, SVG, HVR, and SVR using a model selection algorithm for the selected 21 features, and the results show that RF is far ahead in all evaluation indexes compared to the other models. The fourth stage is model optimization, which aims to further improve the performance of the RF algorithm in detecting DDoS attacks by optimizing the parameters max_samples, max_depth, n_estimators for the initially selected RF by the RF optimization algorithm. Finally, by testing the 100,000 CIC-IDS2018, CIC-IDS2017, and CIC-DoS2016 synthetic datasets, the results show that all the results have achieved excellent performance in the same category. Moreover, the framework also shows an excellent generalization performance by testing over 1 million synthetic datasets and over 330,000 CIC-DDoS2019 datasets. [ABSTRACT FROM AUTHOR]

Obtaining the balance between information loss and training accuracy is crucial in federated learning. Nevertheless, inadequate data quality will affect training accuracy. Here, to improve the training accuracy without affecting information loss, we propose a malicious data detection model using the genetic algorithm to resist model poisoning attack. Specifically, the model consists of three modules: (1) Participants conduct single point training on data and upload accuracy to the third-party server; (2) Formulate data scoring formula based on data quantity and quality; (3) Use the genetic algorithm to obtain the threshold which makes the score highest. Data with accuracy which exceeds this threshold can participate in cooperative training of federated learning. Before participating in training, participants' data is optimized to oppose data poisoning attacks. Experiments on two datasets validated the effectiveness of the proposed model. It was also verified in the fashion-MNIST data set and cifar10 data set that the training accuracy of GAFL is 7.45% higher than that of the federated learning model in the fashion-MNIST data set and 8.18% in the cifar10 data set. [ABSTRACT FROM AUTHOR]

Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10 − 4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved. [ABSTRACT FROM AUTHOR]

The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users' private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Wideband detection provides a way to estimate targets' motion parameters with a high degree of accuracy. However, some factors limit the promotion of velocity measurement performance, such as target's multiple scattering characteristics, change of target's posture, and so on. To solve this problem, an effective method based on phase analysis and scatterers association is put forward. Firstly, by building a segment model to present constant-acceleration motion in a straight line, and combining range alignment and adaptive Chirplet decomposition, we can get motion information of the target by analyzing change of phase. Secondly, the prediction-observation association method can be used to get rid of the influence of the not-translational factor, and lead to an improvement on estimate accuracy of translational velocity. Finally, experimental results using measured data confirm the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an analytical model of a cable-stayed shallow arch is developed in order to investigate the 1 : 1 internal resonance between modes of a cable and a shallow arch. Integrodifferential equations with quadratic and cubic nonlinearities are used to model the in-plane motion of a simple cable-stayed shallow arch. Nonlinear dynamic responses of a cable-stayed shallow arch subjected to external excitations with simultaneous 1 : 1 internal resonances are investigated. Firstly, the Galerkin method is used to discretize the governing nonlinear integral-partial-differential equations. Secondly, the multiple scales method (MSM) is used to derive the modulation equations of the system under external excitation of the shallow arch. Thirdly, the equilibrium, the periodic, and the chaotic solutions of the modulation equations are also analyzed in detail. The frequency- and force-response curves are obtained by using the Newton–Raphson method in conjunction with the pseudoarclength path-following algorithm. The cascades of period-doubling bifurcations leading to chaos are obtained by applying numerical simulations. Finally, the effects of key parameters on the responses are examined, such as initial tension, inclined angle of the cable, and rise and inclined angle of shallow arch. The comprehensive numerical results and research findings will provide essential information for the safety evaluation of cable-supported structures that have widely been used in civil engineering. [ABSTRACT FROM AUTHOR]

A new small‐molecule nonfullerene acceptor based on the benzo[1,2‐b:4,5‐b′]dithiophene (BDT) fused central core with asymmetrical alkoxy and thienyl side chains, namely TOBDT, is designed and synthesized. The alkoxy unit helps narrow the bandgap, and thienyl side chain helps enhance the intermolecular interaction. As a result, TOBDT is suitable to match the deep‐lying highest occupied molecular orbital (HOMO) of polymer donor PM6. Then, a strong crystalline acceptor IDIC is introduced as the third component to fabricate as‐cast nonfullerene ternary devices to achieve absorption and morphology control. Addition of IDIC not only mixes well with TOBDT but modulates the morphology of the blend film, which helps to balance the charge transport properties and reduce the photovoltage loss of ternary devices. All these contribute to synergetic improvement of Jsc, Voc, and fill factor parameters, leading to a power conversion efficiency of 14.0% for the as‐cast fullerene‐free ternary device. [ABSTRACT FROM AUTHOR]

A new all- d -metal full-Heusler alloy (Cd2MnPd) was designed based on density functional theory. Its crystal structure, band structure, density of states, magnetism, and the possibility of martensitic transformation were studied. The calculated total energy shows that the most stable cubic structure of this alloy is the L21 type at ferromagnetic (FM) state, and the equilibrium lattice constant is 6.57 Å. The band structure and density of states show that it is a FM metal with a total magnetic moment of 3.97 μ B , mainly originating from the Mn element. The results of the martensitic transformation investigation show that the martensitic phase has minimum energy when the c/a ratio is 1.35, and the absolute value of the energy difference (Δ E M ) between the martensitic and the austenitic phases is 0.07 eV. This indicates that there is a high possibility that a stable martensitic phase exists. Furthermore, Δ E M can be tuned by uniform strain, and the absolute value of Δ E M increases as the volume increases in the range of V opt ± 3 % V opt ( V opt is the optimal volume, namely, the cubic volume under its equilibrium lattice). In conclusion, Cd2MnPd may be a good candidate for ferromagnetic shape-memory alloys (FSMAs). We expect that our theoretical prediction can provide guidance for exploring valuable FSMAs experimentally. [ABSTRACT FROM AUTHOR]

An organic solar cell (OSC) usually contains a cathode interlayer (CIL) to enhance the device performance. However, if a cell without a CIL (CIL-free) still shows high performance, the cost of the cell can be reduced. In this study, we demonstrate that methanol soaking of the active layers is efficient to improve the performance of porphyrin small molecule-based bulk heterojunction photovoltaic devices. Compared with the CIL-free devices without treatment, the champion power conversion efficiency (PCE) of the CIL-free OSCs upon 15 s MeOH soaking treatment is improved by 18.7% from 7.55% to 8.96%. The PCE is even higher than those of the devices with widely used PFN-Br cathode interlayers (PCE: 8.55%) (PFN-Br: (poly[(9,9-bis(3′-((N,N-dimethyl)-N-ethylammonium)-propyl)-2,7-fluorene)-alt-2,7-fluorene)-alt-2,7-(9,9-dioctylfluorene)]dibromide). Methanol soaking of the active layers improves the exciton dissociation and charge carrier transport and collection in the devices. This work provides a simple approach for preparing highly efficient CIL-free organic solar cells, which can be beneficial for large-scale and commercial applications. [ABSTRACT FROM AUTHOR]

Abstract Cathode interlayer materials (CIMs) play very important roles in organic solar cells (OSCs). In this study, we synthesize three porphyrin CIMs Br − FNEZnP-OE , Br − FNEZnP-TC 16 and SO 3 − FNEZnP-TC 16 with the same conjugated backbones but different ionic side chains at the fluorene units and different substituents at the porphyrin cores. Because Br − FNEZnP-TC 16 and SO 3 − FNEZnP-TC 16 are substituted with hydrophobic alkyl-thienyls while Br − FNEZnP-OE contains more hydrophilic di-ethanediol ester phenyls at the porphyrin cores, Br − FNEZnP-TC 16 and SO 3 − FNEZnP-TC 16 are soluble in methanol but insoluble in water while Br − FNEZnP-OE soluble in not only methanol but also water. The bulk heterojunction (BHJ) OSCs based on PTB7:PC 71 BM (PTB7: poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-di-yl] [3-fluoro-2-[(2-ethylhexy)carbonyl]thienyl[3,4-b]thiophenediyl]], PC 71 BM: [6,6]-phenyl C 71 butyric acid methyl ester) with Br − FNEZnP-TC 16 and SO 3 − FNEZnP-TC 16 cathode interlayers (CILs) exhibit high power conversion efficiencies (PCEs) of 9.07% and 8.86%, which are enhanced by 66.4% and 62.6%, respectively, with simultaneously improved open circuit voltages, short circuit currents (J sc) and fill factors compared with those of the devices without any CIL (PCE: 5.45%), and even higher than those of the devices with widely used PFN CILs (PCE: 7.74%) (PFN: poly [(9,9-bis(3´-(N,N-dimethylamino) propyl)-2,7-fluorene)-alt-2,7-(9,9–dioctylfluorene)). In contrast, the OSCs with Br − FNEZnP-OE CILs only show a slightly enhanced PCE of 6.55% with a reduced J sc compared with that of the control devices with no CILs. We also investigate why the performance of the devices with Br − FNEZnP-OE CILs is inferior to those with Br − FNEZnP-TC 16 and SO 3 − FNEZnP-TC 16 CILs. Graphical abstract Conjugated ionic porphyrins have been designed and synthesized as the cathode interlayer materials in organic solar cells. Image 1 Highlights • Three ionic porphyrins are synthesized as cathode interlayer materials (CIMs). • The ionic porphyrins can effectively reduce the work function of Al cathode. • The super hydrophilicity of a CIM may be detrimental for an organic solar cell. [ABSTRACT FROM AUTHOR]

Solution processed bulk heterojunction organic photodetectors (OPDs) based on a conjugated dimeric porphyrin small molecule as the donor and PC71BM as the acceptor show a broad and strong spectral response from 300 to 1000 nm. At room temperature and zero bias, the panchromatic OPDs are responsive to the near infrared (NIR) region, exhibiting a very low dark current density of 4.25 × 10−10 A cm−2 with a very high external quantum efficiency (EQE) of 48% at 850 nm, leading to a responsivity of 0.33 A W−1 at 850 nm and detectivity of over 1013 Jones from 360 to 950 nm, with a peak detectivity value of 5.73 × 1013 Jones at 850 nm. All these parameters are among the best for small molecule NIR OPDs to date, and are comparable to the best NIR OPDs based on polymers, demonstrating the potential of small molecules in panchromatic photodetectors. [ABSTRACT FROM AUTHOR]

Strong electron-withdrawing materials of bis(trifluoromethanesulfonyl)amide (TFSA, [CF3SO2]2NH) and bis(trifluoromethane)sulfonimide lithium salt (LiTFSI, [CF3SO2]2NLi) are incorporated into the active layers of a porphyrin small molecule TDPPEZnP and PC61BM for bulk heterojunction solar cells. While the solar cells based on the undoped devices show a power conversion efficiency (PCE) of 6.11% with a Voc of 0.70 V, a Jsc of 13.92 mA cm−2 and a FF of 62.71%, doping the active layers with very low loadings of LITFSI and TFSA leads to improved PCEs of 6.85% and 7.01% with Jsc values of 14.97 and 15.39 mA cm−2 and FF values of 64.73% and 63.92%, accounting for 14% and 16% PCE enhancement, respectively. The improved performance is ascribed to the enhanced charge carrier transport, which is supported by charge mobility, impedance spectroscopy and transient spectroscopy analyses. [ABSTRACT FROM AUTHOR]

Three conjugated D–A porphyrin dimers (DPP-ZnP-E)2, (DPP-ZnP-E)2-2T and (DPP-ZnP-E)2-Ph linked with diethynylene, diethynylene-dithiophene and diethynylene-phenylene have been developed for bulk heterojunction solar cells with high power conversion efficiencies of 4.50%, 5.50% and 6.42%, respectively, when blended with PC61BM as the electron acceptor material. [ABSTRACT FROM AUTHOR]