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The fundamental mission of a distribution network is to satisfy the customer demand by providing sufficient delivery capacity. However, the capacity of a distribution network is practically stochastic because of unexpected events, and moreover, the commodities may rot or be spoilt during delivery owing to inclement weather, traffic accidents, collisions, and so on, such that the intact commodity flow may not meet market demand. This paper focuses on the reliability of a multi-state distribution network (MSDN) with cost and spoilage characteristics, defined as the probability that the MSDN is able to distribute a sufficient quantity of goods to meet the market demand under delivery spoilage and budget limit considerations. A specific spoilage rate associated with each route is adopted to characterize the perishability of commodity flows, and the critical routes whose spoilage rate change has the biggest impact on network reliability are identified with the use of sensitivity analysis method. Apart from delivery cost, the cost involved with the disposal of spoilt goods is also incorporated into the reliability indicator. A minimal paths based algorithm is presented to calculate network reliability, together with an example to illustrate the procedure. A real fruit distribution network is accordingly discussed to demonstrate the utility of the algorithm and the managerial implication of network reliability.

Deep neural network (DNN) based face recognition system has become one of the most popular modalities for user identity authentication. However, some recent studies have indicated that, the malicious attackers can inject specific backdoors into the DNN model of a face recognition system, which is known as backdoor attack. As a result, the attacker can trigger the backdoors and impersonate someone else to log into the system, while not affecting the normal usage of the legitimate users. Existing studies use the accessories (such as purple sunglasses or bandanna) as the triggers of their backdoor attacks, which are visually conspicuous and can be easily perceptible by humans, thus result in the failure of backdoor attacks. In this paper, for the first time, we exploit the facial features as the carriers to embed the backdoors, and propose a novel backdoor attack method, named BHF2 (Backdoor Hidden in Facial Features). The BHF2 constructs the masks with the shapes of facial features (eyebrows and beard), and then injects the backdoors into the masks to ensure the visual stealthiness. Further, to make the backdoors look more natural, we propose BHF2N (Backdoor Hidden in Facial Features Naturally) method, which exploits the artificial intelligence (AI) based tool to auto-embed the natural backdoors. The generated backdoors are visually stealthy, which can guarantee the concealment of the backdoor attacks. The proposed methods (BHF2 and BHF2N) can be applied for those black-box attack scenarios, in which a malicious adversary has no knowledge of the target face recognition system. Moreover, the proposed attack methods are feasible for those strict identity authentication scenarios where the accessories are not permitted. Experimental results on two state-of-the-art face recognition models show that, the maximum success rate of the proposed attack method reaches 100% on DeepID1 and VGGFace models, while the accuracy degradation of target recognition models are as low as 0.01% (DeepID1) and 0.02% (VGGFace), respectively. Meantime, the generated backdoors can achieve visual stealthiness, where the pixel change rate of a backdoor instance relative to its clean face image is as low as 0.16%, and their structural and dHash similarity score are high up to 98.82% and 98.19%, respectively.

The determination of threshold and the construction of thresholding function would directly affect the signal denoising quality in wavelet transform denoising techniques. However, some deficiencies exist in the conventional methods, such as fixed threshold value and the inflexible thresholding functions. To overcome the defects of the traditional wavelet thresholding techniques, a modified particle swarm optimization (MPSO) algorithm-based parametric wavelet thresholding approach is proposed for signal denoising. Firstly, a kind of parametric wavelet thresholding function construction method is proposed on the basis of conventional thresholding functions. With mathematical derivation, the properties of the constructed function are proved. Three dynamic adjustment strategies are then employed to modify the PSO algorithm. The mean square error (MSE) between the original signal and the reconstructed signal is minimized by the MPSO algorithm. Finally, the performances of the proposed approach and the existing methods are simulated by denoising four benchmark signals with different noise levels. The simulation results show that the proposed MPSO-based parametric wavelet thresholding can obtain lower MSE, higher signal-to-noise ratio, and noise suppression ratio compared to the other algorithms. Besides, the denoising visual results also indicate the superiority of the proposed approach in terms of the signal denoising capability.

Background Cervical cancer is the most common cancer among women in Nepal. The prevalence of human papillomavirus (HPV) 16 and or HPV 18 among women with cervical pre-cancer and cancer is higher than the incidence of HPV in the world population. The population-based epidemiological data of HPV in the general population in most parts of the country remains unknown. The objective of this study was to assess the prevalence and type distribution of HPV infection and association of abnormal cytology with high risk HPV infection among women in mid-western rural, Nepal. Methods A population-based cross sectional study was conducted in Jumla, one of the most remote districts in Nepal. A total of 1050 cervical samples were collected from married and non- pregnant women aged 20–65 years during mobile Cervical Cancer Screening Clinics conducted from May 2016 to January 2017. The presence of HPV DNA was firstly confirmed by HPV consensus PCR using PGMY09/PGMY11 designed primers, then HPV positive samples were further genotyped by the membrane hybridization method to detect the 21 high-risk HPV (HR-HPV) and low-risk HPV types. The prevalence of HR-HPV among women with normal and abnormal cytology was calculated. Data were analyzed using SPSS software for Windows. P

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cable-stayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

The kinetics for complete iron release showing biphasic behavior from pig spleen ferritin-Fe (PSFF) was measured by spectrophotometry. The native core within the PSFF shell consisted of 1682 hydroxide Fe3+ and 13 phosphate molecules. Inhibition kinetics for complete iron release was measure by differential spectrophotometry in the presence of phosphate; the process was clearly divided into two phases involving a first-order reaction at an increasing rate of 46.5 Fe3+/PSFF/min on the surface of the iron core and a zero-order reaction at a decreasing rate of 6.67 Fe3+/PSFF/min inside the core. The kinetic equation [C(PSFF-Fe3+)max - C(PSFF-Fe3+)t](1/2) = Tmax - Tt gives the transition time between the two rates and represents the complex kinetic characteristics. The rate was directly accelerated twofold by a mixed reducer of dithionite and ascorbic acid. These results suggest that the channel of the PSFF shell may carry out multiple functions for iron metabolism and storage and that the phosphate strongly affects the rate of iron release.