A reassessment of the Global Food Security Index by using a hierarchical data envelopment analysis approach

This study proposes a hierarchical data envelopment analysis (H-DEA) approach to construct a multi-dimensional indicator, and applies it to reassess the 2014 Global Food Security Index created by the Economist Intelligence Unit (EIU) across 110 countries. Instead of using expert opinions to assign weights, the proposed model endogenises the weights, and thus avoids the problems of subjective weighting for international comparisons. The results show that although the ranking is not significantly different from that of the EIU, the optimal scores and weights differ by income levels. Additionally, this work articulates the value of a well-founded performance evaluation method by leveraging experts’ opinions and data-driven techniques through constructing a best-practice frontier with observation-specific weights. It is suggested that food availability should be the top policy priority in low- to medium-income and Sub-Saharan African countries where food deficits are most prevalent. The findings can serve as guidance to improve the design of the ongoing efforts for global food security.     

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In this paper, the insurer is allowed to buy reinsurance and allocate his money among three financial securities: a defaultable corporate zero-coupon bond, a default-free bank account, and a stock, while the instantaneous rate of the stock is described by an Ornstein-Uhlenbeck process. The objective is to maximize the exponential utility of the terminal wealth. We decompose the original optimization problem into two subproblems: a pre-default case and a post-default case. Using dynamic programming principle, and then solving the corresponding HJB equations, we derive the closed-form solutions for the optimal reinsurance and investment strategies and the corresponding value functions     

新型城镇化对养老金收支的影响分析

随着人口老龄化的不断加重,养老金短缺状况日益严重.通过构建城镇化建设对养老金的影响机制,并采用31个省市自治区2009-2018年的统计数据,采用系统广义矩估计方法,分析新型城镇化建设对养老金收支水平的影响作用,发现城镇化对养老金收入的促进作用大于对养老金支出的影响,说明新型城镇化的建设可以有效缓解养老金缺口,实现养老金的累积效应.     

Physical layer secrecy of a cognitive radio with spatially correlated Alamouti OSTBC system

In this paper, an underlay spectrum-sharing system with Alamouti orthogonal space–time block coding (OSTBC) is considered to analyze and evaluate the physical layer security (PLS) performance of the cognitive radio system under a practical scenario with spatially correlated transmit antennas. It is assumed that there exists a passive eavesdropper and the cognitive channel and the wiretap channel follow Rayleigh fading distribution. To investigate and study the PLS performance of the cognitive system, first closed-form expressions are derived for three PLS metrics, namely: the probability of strictly-positive secrecy capacity (PSPSC), the secrecy outage probability (SOP), and the average secrecy capacity (ASC). Then numerical results obtained from the derived closed-form expressions are presented and validated by the computer simulations, to study the effects of spatial correlation on the PLS performance of the considered cognitive radio system under different parameters. It is shown that increasing the SNR of the cognitive system (Alice-to-Bob) channel yields an improvement in the PLS of the cognitive system. Moreover, a smaller value of the eavesdropping (Alice-to-Eve) channel SNR always leads to a better PLS for the cognitive system. It is also observed that the spatial correlation related to Alice-to-Bob channel degrades the PLS, and the spatial correlation related to Alice-to-Eve channel has less impact on the PLS performance.     

Channel indicator-based intelligent network selection scheme for device-to-device communication

Device-to-device (D2D) communication has captured the researchers attention working in data-intensive applications. It has various benefits, such as low communication latency, load balancing, high spectral efficiency, and many more. However, despite these benefits, it has significant issues like efficient resource allocation, device discovery, and interference mitigation. Various solutions have given by the researchers to tackle these issues and the research community accepts them well. Here, we are targeting the issues associated with the device discovery, i.e., the base station assisted discovery. The initial step for D2D communication is the device discovery that the base station can perform. But, if the channel indicator parameters of the base station are not good, then the device discovery and further data sharing will be affected. Thus, there is a need for the best base station selection that improves the efficiency of the overall network. Many network selection solutions (for cellular networks) are available in the literature, but none of it talked about in the D2D communication scenario. So, motivated by this, this paper proposes an AI-based intelligent and efficient network selection scheme for D2D users to improve the device discovery experience and overall system’s sum rate. We then evaluate the performance of the proposed scheme using various evaluation metrics, such as accuracy, precision, recall, receiver operating curve (ROC), computation time, and sum rate.     

Deep reinforcement learning based IRS-assisted mobile edge computing under physical-layer security

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) network under physical-layer security, where users can partially offload confidential and compute-intensive tasks to a computing access point (CAP) with the help of the IRS. We consider an eavesdropping environment, where an eavesdropper steals information from the communication. For the considered MEC network, we firstly design a secure data transmission rate to ensure physical-layer security. Moreover, we formulate the optimization target as minimizing the system cost linearized by the latency and energy consumption (ENCP). In further, we employ a deep deterministic policy gradient (DDPG) to optimize the system performance by allocating the offloading ratio and wireless bandwidth and computational capability to users. Finally, considering the impacts from different resources, based on DDPG, seeing our optimization strategy as one criterion, we designed other criteria with different resource allocation schemes. And some simulation results are given to demonstrate that our proposed criterion outperforms other criteria.     

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

On the interaction of encapsulated pH indicator species within a silica matrix produced by three sol-gel routes

Solid acid-base sensors were prepared by encapsulating two pH indicators (brilliant yellow or acridine) within a silica matrix by the sol-gel method using three different routes: (1) non-hydrolytic, (2) acid catalyzed and (3) base catalyzed. The interactions of the silica-indicator with the resulting materials were then investigated by cyclic and differential pulse voltammetry. Complementary, ultraviolet-visible, photoacoustic spectroscopy was employed for the characterization of the interactions by monitoring the band shifts (bathochromic or hypsochromic, depending on the sol-gel route) between the neat pH indicators and those encapsulated within the silica network. Furthermore, X-ray photoelectron spectroscopy showed that the N 1s binding energy in brilliant yellow was shifted for the material resulting from the acid route. The electrochemical behavior and the pH indicator interactions with the silica network were dependent on the nature of the employed sol-gel route. For the sensors prepared with acridine, the interactions with the silica network took place through the nitrogen group from the pyridinic ring. For the brilliant yellow indicator, different behaviors were observed depending on the route, suggesting different processes during preparation or analysis. For the basic catalyzed and non-hydrolytic routes, it was not possible to assign a specific interaction. Nevertheless, it seemed that interactions might have taken place through the hydroxyl and/or sulphonic groups. Furthermore, for the brilliant yellow sensor prepared through the acid route, it was possible to show that the interaction probably or partially occurred through the azo groups.     

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood.In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson’s correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset.In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.     

Homotherapy for heteropathy active components and mechanisms of Qiang-Huo-Sheng-Shi decoction for treatment of rheumatoid arthritis and osteoarthritis

Qiang-Huo-Sheng-Shi decoction (QHSSD), a classic traditional Chinese herbal formula, which has been reported to be effective in rheumatoid arthritis (RA) and osteoarthritis (OA). However, the concurrent targeting mechanism of how the aforementioned formula is valid in the two distinct diseases OA and RA, which represents the homotherapy-for-heteropathy principle in traditional Chinese medicine (TCM), have not yet been clarified. In the present study, network pharmacology was adopted to analyze the potential molecular mechanism, and therapeutic effective components of QHSSD on both OA and RA. A total of 153 active ingredients in QHSSD were identified, 142 of which associated with 59 potential targets for the two diseases were identified. By constructing the protein-protein interaction network and the compound-target-disease network, 72 compounds and 10 proteins were obtained as the hub targets of QHSSD against OA and RA. The hub genes of ESR1, PTGS2, PPARG, IL1B, TNF, MMP2, IL6, CYP3A4, MAPK8, and ALB were mainly involved in osteoclast differentiation, the NF-κB and TNF signaling pathways. Moreover, molecular docking results showed that the screened active compounds had a high affinity for the hub genes. This study provides new insight into the molecular mechanisms behind how QHSSD presents homotherapy-for-heteropathy therapeutic efficacy in both OA and RA. For the first time, a two-disease model was linked with a TCM formula using network pharmacology to identify the key active components and understand the common mechanisms of its multi-pathway regulation. This study will inspire more innovative and important studies on the modern research of TCM formulas.