Search

Objective To provide the latest evidence for the efficacy and safety of arbidol (umifenovir) in COVID‐19 treatment. Methods A literature systematic search was carried out in PubMed, Cochrane Library, Embase, and medRxiv up to May 2021. The Cochrane risk of bias tool and Newcastle–Ottawa scale were used to assess the quality of included studies. Meta‐analysis was performed using RevMan 5.3. Results Sixteen studies were met the inclusion criteria. No significant difference was observed between arbidol and non‐antiviral treatment groups neither for primary outcomes, including the negative rate of PCR (NR‐PCR) on Day 7 (risk ratio [RR]: 0.94; 95% confidence interval (CI): 0.78–1.14) and Day 14 (RR: 1.10; 95% CI: 0.96–1.25), and PCR negative conversion time (PCR‐NCT; mean difference [MD]: 0.74; 95% CI: −0.87 to 2.34), nor secondary outcomes (p > .05). However, arbidol was associated with higher adverse events (RR: 2.24; 95% CI: 1.06–4.73). Compared with lopinavir/ritonavir, arbidol showed better efficacy for primary outcomes (p .05). No remarkable therapeutic effect was observed between arbidol and other agents (p > .05). Conclusion The present meta‐analysis showed no significant benefit of using arbidol compared with non‐antiviral treatment or other therapeutic agents against COVID‐19 disease. High‐quality studies are needed to establish the efficacy and safety of arbidol for COVID‐19., The evidence on the antivirus effect of Arbidol against COVID‐19 is controversial. The present meta‐analysis showed no significant benefit of using arbidol compared with non‐antiviral treatment or other therapeutic agents against COVID‐19 disease

Software-Defined Networking (SDN) is a modern paradigm that provides a platform for implementing reliable, centrally managed, and automated security solutions for conventional and new generation networks, such as IoT, cloud computing, 5G/6G mobile communication networks, and vehicular communications. In these complex systems, manual security operations can delay or obstruct the identification, mitigation, and prevention of ever-increasing sophisticated threats. Thus, the idea of security automation for networks using the SDN paradigm has become fundamental, given that SDN was created to facilitate the operation and management of complex networks with minimal human intervention, which is considered error-prone. This survey studies the state-of-the-art research efforts concerned with security automation in SDN environments. We identified and ranked various classes of security solutions with different levels of automation and complexity. The level of automation is measured using four well-defined qualitative parameters: self-healing, self-adaptation, self-configuration, and self-optimization. The complexity is characterized by the amount of processing and storage resources and implementation requirements. This work represents the first endeavor to analyze the level of automation and complexity of security solutions in SDN environments. Our findings reveal important advances in the area of security automation in SDN. However, there are still several open problems and challenges, which we detail in this work.

Objective: Type 2 diabetes (T2DM) is a chronic disease associated with many psychological complications, which require psychological interventions. This study aimed to examine the effect of mindfulness-based cognitive therapy (MBCT) on cognitive emotion regulation, perseverative thinking, and glycemic index in patients with T2DM Materials and Methods: This quasi-experimental study was done on T2DM patients visiting Yazd diabetes center in 2019, 30 men and women were randomly divided into experimental (n=15) and control (n=15) groups. The experimental group received 8 sessions (90 minutes) of MBCT and the control group received no interventions. In the pretest and posttest phase, data were collected using the cognitive emotion regulation questionnaire (CERQ), Perseverative Thinking Questionnaire and fasting blood sugar test. Data were analyzed in statistical package (SPSS 21) using multivariate analysis of covariance. Results: Results showed a significant difference in mean scores of cognitive emotion regulation (F= 9.866 & P-value= 0.0001), perseverative thinking (F= 13.745 & P-value= 0.0001), and fasting blood sugar (FBS) (F= 3.679 & P-value= 0.034) in the experimental group before and after the intervention. Conclusion: Based on findings of the study MBCT by specialists in public and private centers can help improve cognitive emotion regulation strategies, perseverative thinking, and ultimately blood sugar control in patients with T2DM.

In the last decade, due to wireless technology’s enhancement, the people’s interest is highly increased in Wireless Body Sensor Networks (WBSNs). WBSNs consist of many tiny biosensor nodes that are continuously monitoring diverse physiological signals such as BP (systolic and diastolic), ECG, EMG, SpO2, and activity recognition and transmit these sensed patients’ sensitive information to the central node, which is straight communicate with the controller. To disseminate this sensitive patient information from the controller to remote Medical Server (MS) needs to be prolonged high-speed wireless technology, i.e., LTE, UMTS, WiMAX, WiFi, and satellite communication. It is a challenging task for the controller to choose the optimal network to disseminate various patient vital signs, i.e., emergency data, normal data, and delay-sensitive data. According to the nature of various biosensor nodes in WBSNs, monitor patient vital signs and provide complete intelligent treatment when any abnormality occurs in the human body, i.e., accurate insulin injection when patient sugar level increased. In this paper, first, we select the optimal network from accessible networks using four different fuzzy attribute-based decision-making techniques (Triangular Cubic Hesistent Fuzzy Weighted Averaging Operator, Neutrosophic Linguistic TOPSIS method, Trangualar Cubic Hesistent Fuzzy Hamacher Weighted Averaging Operator and Cubic Grey Relational Analysis) depending upon the quality of service requirement for various application of WBSNs to prolong the human life, enhanced the society’s medical treatment and indorse living qualities of people. Similarly, leakage and misuse of patient data can be a security threat to human life. Thus, confidential data transmission is of great importance. For this purpose, in our proposed scheme, we used HECC for secure key exchange and an AES algorithm to secure patient vital signs to protect patient information from illegal usage. Furthermore, MAC protocol is used for mutual authentication among sensor nodes and Base Stations (BS). Mathematical results show that our scheme is efficient for optimal network selection in such circumstances where conflict arises among diverse QoS requirements for different applications of WBSNs.

Millimeter wave (mmWave) relaying has been introduced recently as a solution to extend the coverage of mmWave communication systems and to deal with the blockage problem as well. In order for the relay probing process to identify the most suitable relays, we should maintain an intelligent trade-off between the number of probed relays and the overhead due to beamforming training (BT). This letter leverages an online learning tool, namely sleeping contextual multi-armed bandits (S-CMAB), to effectively address this problem. Thanks to the multi-band capability of WiGig devices that supports both mmWave and WiFi, the characteristics of the WiFi signal centered at 5.25 GHz are used as contexts for the candidate WiGig relays operating at 60 GHz. Moreover, the sleeping relays that are unable to construct a WiGig link, due to blockages for instance, could be identified during the online learning process and accordingly excluded. Extensive simulations prove that the proposed S-CMAB approach integrated with the proposed sleeping linear upper confidence bound (S-LinUCB) algorithm outperform the legacy approaches and the context-free UCB algorithm in terms of both the average throughput and energy efficiency.

In light of the vast potential for the application of modern energy absorption systems in the structures, the effectiveness of each system should be tested, analyzed, and proved before convincing the structures design companies to use them. In this research, the effectiveness of applying base isolators, viscous and friction dampers and a hybrid system consisted of lead rubber bearing with friction dampers are studied. To quantify the impact of each system, a control structure without any energy absorption system is considered. Base isolators and damper systems are designed according to available international codes. American concrete institute’s codes were used to design the concrete structure frames. All of the considered systems therewith hybrid systems including lead rubber base isolation and viscous dampers have been modeled in ETABS 2018 software. The control structure has an intermediate moment resisting frame with high importance located in a high seismic hazard zone. These models were subjected to time history analysis by using Manjil, Loma prieta, and Imperial Valley Earthquakes records. It was observed that the structural design parameters such as base shears and story drifts were significantly reduced by 76–86% and 44–92%, respectively, compared to the structure without any energy absorption system. Also, the total energy dissipated in the structures equipped with modern energy absorption systems was decreased between 32 and 86% compared to the friction dampers. It should be noted that the studied devices may be used either for the design of a new structure or seismic rehabilitation of the existing structures.

Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.

It is important to predict which astigmatic patients require separate refraction for near vision. This study compared cylindrical components changes by cyclopentolate 1% for the low and high amount of astigmatism. The right eyes of 1014 healthy individuals (307 males and 707 females) with cylindrical refractive power more than −0.5 diopter on autorefractometer were selected. Both male and female patients in the age range of 17–45 years were refracted before and after cycloplegia, using 1% cyclopentolate. All volunteers were classified into 2 subgroups including the lower astigmatism group (−2.25 to −0.50) and the higher astigmatic group (−2.50 to over). Alpines’ method was used to compare the effect of cycloplegic drop on cylindrical power. The mean age in the lower astigmatism group (29.58; 95% CI: 29.18 to 29.99 years) was not significantly different from the higher astigmatic group (29.85; 95% CI: 29.07 to 30.62) and there were no significant differences in gender between these two groups (P=0.54). Differences between wet and dry refraction in J0 (−0.03; 95% CI:−0.06 to −0.008) and J45 (−0.03; 95% CI:−0.06 to −0.01) were significant only in the higher astigmatic group. Axis changes by the cycloplegic drop in the lower astigmatism group were 3.51 (CI: 3.22 to 3.81) and axis changes by the cycloplegic drop in the higher astigmatism group were 2.21 (CI: 1.73 to 2.49). In patients with a lower amount of astigmatism (−2.25 to −0.50), additional near subjective refraction could be done for precise determination of axis and in patients with a higher amount of astigmatism (−2.50 to over), near subjective refraction might be done for precise determination of power.

OBJECTIVES Human leukocyte antigen-G is an immuno-modulatory factor that affects acute allograft rejection and autoimmune diseases such as type 1 diabetes mellitus. In this study, possible associations between human leukocyte antigen-G 14-bp insertion/deletion polymorphism and acute pancreas rejection were investigated. MATERIALS AND METHODS Human leukocyte antigen-G genotyping was assessed in 102 Iranian pancreas transplant recipients (including 41 with acute rejection and 61 with nonacute rejection). Results were compared with 100 individuals in a normal control group. RESULTS No significant differences in genotype frequencies of human leukocyte antigen-G 14-bp insertion / deletion were observed in recipients who had acute rejection episodes. On the other hand, the insertion / insertion genotype was a risk factor for susceptibility to type 1 diabetes mellitus (odds ratio = 3.82, 95% confidence interval, 1.37- 11.22; P = .005). CONCLUSIONS Our results provided evidence revealing that the human leukocyte antigen-G insertion / insertion genotype might be involved in the pathogenesis of type 1 diabetes mellitus.

A challenging issue in data-driven methods is the limitation of applying high-dimensional damage-sensitive features (HD-DSFs) to localize and quantify structural damage. This is because the use of such data causes a time-consuming process for feature classification. Low damage detectability is another important challenge that may cause unreliable and erroneous results. The main objective of this study is to deal with these challenging issues by proposing hybrid distance methods as combinations of a distance-based indicator called residual relative error (RRE) and Kullback–Leibler divergence (KLD) with the well-known Mahalanobis distance. Autoregressive modeling is used to model vibration responses and extract the residual samples from normal and damaged conditions as HD-DSFs. The major contribution of this article is to propose two hybrid distance-based methods in unsupervised learning manners for locating and quantifying damage using low-dimensional feature samples obtained from the RRE index and KLD. The great advantage of these methods is that they not only reduce the size of HD-DSFs and behave as dimensionality reduction tools but also can increase damage detectability by the low-dimensional feature samples that are distance quantities. For this reason, both the RRE and KLD are individually capable of detecting early damage. An ability to locate and quantify damage under varying environmental and operational conditions is the other advantage of these methods. To validate the proposed methods, experimental vibration datasets of two benchmark laboratory structures are utilized. Results demonstrate that the proposed methods are practical and reliable tools to accurately localize and quantitatively quantify damage by addressing the limitation of using HD-DSFs.

The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and non-blurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, to assess better the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.

The way services offered by cloud computing gets its unprecedented and undisputed popularity, so its security concerns. Among them the storage as service model (SaaS) is of the forefront of these concerns. SaaS liberates individuals and enterprises from management of IT infrastructure and data centers to concentrate on their core business. Because of untrusted and out-of-premise architecture users are reluctant to outsource their personal and important data. Encryption before outsourcing addresses some of these issues but at the same time strips the data of its useful operation such as sharing and searching. Now to address this issue, the combination of keyword based searchable encryption (KSE) and attribute-based encryption (ABE) leads to an attribute-based keyword searching (ABKS). The resultant combined concept is capable of fine-grained search operation in the multi-owner/multi-user (M/M) setting. However, the underlying costly pairing operation and complex secret sharing mechanism of ABE makes it unsuitable in practical application for resource-limited devices. On top of it, in most of the existing ABKS schemes the size of the secret key and its associated pairing operation linearly expands to the number of attributes. This paper aims at presenting a novel ABKS scheme with pairing-free access verification and constant size secret key based on AND gate access structure and ciphertext-policy (CP) framework. The security of the proposed work is reduced to the standard Decisional Diffie-Hellmen (DDH) assumption, and also collision free and error tolerant. Finally, the performance evaluation and experimental results shows that the proposed scheme improved the overall efficiency and communication overhead.

Emerging Unmanned Aerial Vehicles (UAVs) have applications for traffic monitoring, public safety, surveillance, agriculture, health services. Collaborative UAVs can form flying ad hoc networks, although such networks are especially vulnerable to security vulnerabilities due to open access media and limited power. Very recently, Khan et al. presented an Identity-Based Generalized Signcryption having Multi-access Edge computing to secure Flying Ad hoc Networks (FANETs). First, this paper presents the cryptanalysis of the Khan et al. scheme and shows that their scheme does not provide message confidentiality, Authenticity, and integrity. Second, it presents an improved scheme as well. The comparison of the improved scheme with the state of the art schemes based on security and cost shows, it is efficient, provably secure against security attacks, and suitable for multi-access edge computing empowered FANETs.

Recently, Attribute-Based Encryption (ABE) is used as a baseline technology for keyword searching mechanisms to cater users’ security and privacy-related concerns who outsource their data to the powerful and resource-rich cloud server. Almost all the existing Attribute-Based Keyword Searching (ABKS) schemes either operate on a single-keyword or multiple; conjunctive-keyword search setting. These schemes work on the premise of an “all-or-nothing” retrieval mode, which greatly limits users’ searching capabilities. The most recent work incorporates multi-keyword ranked search in attribute-based encryption, which improves user search results accuracy and the flexibility of retrieval mode. However, these schemes demand high computation overhead, which ultimately suffers searching efficiency. This dilemma prevents further research and application of ABKS schemes, especially mobile-resource constrained devices such as mobile phones. This paper proposes an Online/Offline-aided Attribute-Based Multi-keyword Search (OOABMS) scheme to delegate most laborious computation operations to the offline phase before acquiring the attribute-based access control policy or keywords. An online phase then quickly assembles the pre-computed index or trapdoor with the required specifics when it becomes known. Theoretical comparison and simulation results show that our scheme is more efficient and practical in the real world scenario for mobile cloud computing in terms of computation overhead and flexibility.

Spatial diversity plays a significant role in wireless communication systems, including the Fourth Generation (4G) and Fifth Generation (5G) systems, and it is expected to be a fundamental part of the future wireless communication systems as well. The Multiple-Input Multiple-Output (MIMO) technology, which is included in the IEEE 802.16j standard, still holds the most crucial position in the 4G spectrum as it promises to improve the throughput, capacity, spectral, and energy efficiency of wireless communication systems in the 2020s. This makes MIMO a viable technology for delay constrained medical and health care facilities. This paper presents an approximate closed-form expression of the ergodic capacity for the Decode-and-Forward (DF) protocol MIMO-Orthogonal Frequency Division Multiplexing (OFDM) relaying network. Although MIMO-OFDM is highly valuable for modern high-speed wireless communication systems, especially in the medical sciences, its performance degrades in multi-hop relay networks. Therefore, in this paper, an approximate closed-form expression is derived for an end-to-end ergodic capacity of multi-hop DF MIMO-OFDM wireless communication system has been presented. Monte-Carlo simulations are conducted to verify the performance of the presented analysis regarding the capacity (bits/s/Hz) for different SNR-dB values for single, 2 × 2, 4 × 4, and multi-hop DF MIMO-OFDM systems. The presented results provide useful insights for the research on the end-to-end ergodic capacity evaluation.

Diesel engines are the most commonly used internal-combustion engines. Due to incomplete combustion of fossil fuels, engine performance and emissions output are unsuitable. On the other hand Emissions of greenhouse gases using fossil fuels in internal-combustion engines are considered to be the main cause of climate change. Thus to find an alternative fuel to eliminate or reduce CO2 and exhaust emissions is an urgent concern worldwide. Superabundant and readily available, makes hydrogen an attractive alternative to fossil fuels. In this study, a 3-cylinder turbocharged diesel engine is converted into CNG direct-injection engine and then hydrogen as a fuel enrichment added to CNG. AVL Fire™ Software is used to simulate the engine at different engine speeds, injection pressure, and Air/Fuel ratios. The results confirm that adding hydrogen to CNG improves brake thermal efficiency and engine brake power, reduces brake specific fuel consumption as well as the diminution in HC and Soot but also increases the NOx.

The devastating effects of natural hazards uncover and exacerbate social inequalities, yet reproductive health outcomes are often overlooked. Despite a small but growing literature on gender and disaster-related impacts, there are no studies to date to our knowledge on the intersection of abortion and disasters, which is important because abortion is common in the U.S. and is a critical component of comprehensive reproductive healthcare yet is routinely inaccessible due to a lack of health insurance coverage and other policy barriers. This is a qualitative case study of 8 individuals who required abortion services in Texas at the time of Hurricane Harvey. The study sample comes from caller data from a local Texas abortion fund. We present caller demographics, which reveal nonwhite patients in later trimesters struggling economically. Callers display a need for funding, particularly for travel, and were affected by interpersonal and sexual violence. We conclude with policy and research implications for disaster planners, domestic violence organizations, state and federal officials, and health insurers.

Due to anti-inflammatory effects of vitamin D3, we aimed to explore the effects of supplementation with this vitamin on headache characteristics and serum levels of pro/anti-inflammatory markers in migraineurs. This placebo-controlled, double-blind study included 80 episodic migraineurs who randomly assigned into two equal groups to receive either daily dose of vitamin D3 2000 IU (50 μg) or placebo for 12 weeks. At baseline and after the trial, headache characteristics were determined using diaries and serum levels of interleukin (IL)-10, IL-6, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (Cox-2) were assessed via ELISA method. At the end of trial, analysis of covariance (ANCOVA) adjusted for baseline values, and confounders revealed that vitamin D3 supplemented group experienced significantly lower headache days per month (4.71), reduced attacks duration (12.99 h/attack), less severe headaches (5.47, visual analog scale), and lower analgesics use/month (2.85) than placebo group (6.43, 18.32, 6.38 and 4.87, respectively) (P values

Extracellular matrix (ECM) mechanical stiffness and its dynamic change is one of the main cues that directly affects the differentiation and proliferation of normal cells as well as the progression of disease processes such as fibrosis and cancer. Recent advancements in biomaterials have enabled a wide range of polymer matrices that could mimic the ECM of different tissues for a wide range of in vitro basic research and drug discovery. However, most of the technologies utilized to quantify the stiffness of such ECM are either destructive or expensive, and therefore are unsuitable for the in situ, long-term monitoring of variations in ECM stiffness for on-chip cell culture applications. This work demonstrates a novel noninvasive on-chip platform for characterization of ECM stiffness in vitro, by monitoring ultrasonic wave attenuation through the targeted material. The device is composed of a pair of millimeter scale ultrasonic transmitter and receiver transducers with the test medium placed in between them. The transmitter generates an ultrasonic wave that propagates through the material, triggers the piezoelectric receiver and generates a corresponding electrical signal. The characterization reveals a linear (r2 = 0.86) decrease in the output voltage of the piezoelectric receiver with an average sensitivity of -15.86 μV kPa-1 by increasing the stiffnesses of hydrogels (from 4.3 kPa to 308 kPa made with various dry-weight concentrations of agarose and gelatin). The ultrasonic stiffness sensing is also demonstrated to successfully monitor dynamic changes in a simulated in vitro tissue by gradually changing the polymerization density of an agarose gel, as a proof-of-concept towards future use for 3D cell culture and drug screening. In situ long-term ultrasonic signal stability and thermal assessment of the device demonstrates its high robust performance even after two days of continuous operation, with negligible (

The communications in the millimeter wave (mmWave) band, e.g., WiGig (60 GHz), is considered as one of the main components of 5G and beyond 5G (B5G) networks. However, it is characterized by short range transmissions along with high susceptibility to path blockage, e.g., human shadowing. Thus, mmWave multi-hop relaying using device to device (D2D) connections turns to be an efficient solution to extend its communication range and to route around blockages. In this context, relay probing is essential to discover/explore the candidate multi-hop routes from source-to-destination and then select the best route among them. However, a trade-off exists between relay probing and the required overhead coming from mmWave beamforming training. In this paper, taking advantage of the multi-band μW /mmWave relay nodes, an efficient multi-hop relay probing scheme is proposed for mmWave D2D routing. In this scheme, the collected μW received signal strengths (RSSs) among the distributed relay nodes are used to estimate the probability of their mmWave signal-to-noise power ratios (SNRs) while considering line-of-sight (LOS) and non-LOS (NLOS) path availabilities. Then, based on a proposed probabilistic metric, a hierarchical search algorithm is proposed to jointly discover the relay nodes along the candidate multi-hop routes and enumerate the number of routes expected to maximize the spectral efficiency of the whole path from source-to-destination. This is done in an offline phase, and only the relay nodes located within the pre-selected multi-hop routes are requested to do online relay probing. Numerical analysis confirms the superiority of the proposed mmWave multi-hop relay probing scheme over the candidate techniques.

In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting.

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant's hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

With the rising number of patients along with the same time, the comparative evolution in wireless technology has made Body Sensor Networks (BSNs) flourishing in the market. In BSNs, tiny biosensor nodes are deployed inside/outside of a human body to continuously monitor patient vital signs such as heartbeat rate, blood pressure, respiratory rate, and temperature. BSNs face various challenges due to their constrained nature environment. These are prioritized, delaying less, and secure patient data transmission using a public network, simultaneous reception of patient data by end-users, patient identity privacy, high overhead, and energy constraints. However, various researchers have worked in this domain, unable to cope with all these issues in one go. In this paper, we have proposed a novel cryptosystem covering the mentioned issues in a desirable way. For this purpose, we offer a novel concept of a lightweight and secure attribute-based multi-receiver generalized signcryption scheme that can adaptively work as an encryption mode, a signature mode, or a signcryption mode with a single algorithm that consume fewer resources by using shorter keys size of Hyper-elliptic Curve Cryptography (HECC). Similarly, we design a modified priority-based scheduling algorithm to delay less emergency patient data. Multiple end-users access the encoded data simultaneously by defining an access policy using AND & OR logic gates. Furthermore, we simulate our scheme using the AVISPA tool and demonstrate that our proposed scheme can meet the security requirements such as data confidentiality (IND-CCA), integrity, unforgeability (EUF-CMA), patient data authenticity, forward secrecy, and non-repudiation distinctly in the Random Oracle Model (ROM). Due to lower processing costs and transmission overhead, this scheme is more efficient and suitable for the resource-constrained environment of BSNs.

Proxy signcryption is essential security primitive for emerging secure communication such as e-business, mobile agents, online voting, contract signing, and online auction. It combines the functionality of a proxy signature and encryption to achieve basic security features maintaining a low computational and communicational cost. Ming proposed Proxy Signcryption (PSC) scheme in the standard computational model, claimed it to be secured against: (1) Indistinguishable Chosen Ciphertext Attack (IND-CCA) under the Decisional Bi-linear Diffie-Hellman (DBDH) assumption (2) Existentially Unforgeable Chosen Message Attack (EUF-CMA) under the Computational Diffie Hellman (CDH) assumption. This paper first provides a security analysis to check the correctness and validity of the said PSC scheme. Furthermore, it proves PSC is vulnerable to the launched cryptanalysis attacks. It is established that the PSC is neither semantically secured against IND-CCA nor existentially secured against EUF-CMA in its defined security model. Secondly, we propose an improved new proxy signcryption scheme (N-PSC) based on Elliptic Curve Cryptosystem (ECC) without bi-linear pairing secure against IND-CCA and EUF-CMA for Type-1 adversary A1 in the standard computational model. It is also proved that the new proposed N-PSC scheme achieves an extra security property of judge verification in case of signature dispute between the proxy correspondents, as well as it outperforms the existing states of the art schemes including the Ming scheme in terms of cost efficiency which makes the new proposed scheme suitable for scarce resources constraint proxy enabled communication applications.

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid's capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO2 emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

Wireless Body Area Network is an emerging technology that is used primarily in the area of healthcare applications. It is a low-cost network having the capability of transportability and adaptability. It can be used in location independent and long-term remote monitoring of people without disturbing their daily activities. In a typical WBAN system, sensing devices are either implanted or etched into the human body that continuously monitors his physiological parameters or vital signs. In such a network, trusts among the stakeholders (healthcare providers, users, and medical staff, etc.) are found of high importance and regarded as the critical success factor for the reliability of information exchange among them. In remote patient monitoring, the implementation of trust and privacy preservation is crucial, as vital parameters are being communicated to remote locations. Nonetheless, its widespread use, WBAN, has severe trust and privacy risks, limiting its adaptation in healthcare applications. To address trust and privacy-related issues, reliable communication solutions are widely used in WBANs. Given the motivation, in this paper, we have proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. To ensure reliability, a cooperative communication approach is used, while for privacy preservation, a cryptography mechanism is used. The performance of the proposed scheme is evaluated using MATLAB simulator. The output results demonstrated that the proposed scheme increases service delivery ratio, reliability, and trust with reduced average delay. Furthermore, a fuzzy-logic method used for ranking benchmark schemes, that has been concluded that the proposed scheme has on top using comparative performance ranking.

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of protein-protein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F- measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.

The IoT devices produce a massive amount of content compared to the host-centric (IP-based) network. The IP-based network has no strengthened capabilities to handle the growing traffic of the IoT network. The existing research is focusing on proposing Information Centric Network (ICN) framework with potential replacement of IP-based network. Using the caching technique in ICN based IoT significantly strengthen the resource-constrained IoT devices, by reducing the access time with optimized energy consumption. Thus, this paper proposes a caching scheme for IoT contents, known as Central Control Caching (CCC) Scheme. This scheme positions a central entity between different autonomous systems that manage caching and updates the content, by keeping information in a table regarding different contents present on different autonomous systems. This information is utilized when cached contents are exchanged among different autonomous systems. Furthermore, when contents becomes stale, the same information is used to identify the autonomous systems with a copy of the outdated content. The simulation results show that the proposed scheme has outperformed in terms of reduced energy consumption, response latency, and cache-hit compared to state-of-the-art works.

We are introducing a wireless and passive strain sensing scheme that utilizes ultrasound imaging of a highly stretchable hydrogel embedded with zinc oxide (ZnO) nanoparticles, named "ZnO-gel". The incorporation of ZnO nanoparticles into a polymer network of the hydrogel improves both its elasticity and strength. It also serves as an ideal biocompatible ultrasound contrast agent that allows remote interrogation of the changes in volume or dimensions of the hydrogel in response to mechanical strains through simple ultrasound imaging. A systematic study of various ratios of ZnO nanoparticle fillers (ranging from 0 to 40% w/w), cross-linked within the poly (DMA

Background: In vitro fertilization (IVF) is one of the most important treatments of infertility for couples. The purpose of this study was in vitro fertilization experiences of women in Maybod city. Methods: This study is based on the qualitative approach and Grounded theory method. Under-study populations were infertile females of Maybod City. Cases of the study were 14 women which were selected with purposive sampling up to data saturation. Data of this investigation were collected from semi-structure interviews. After data collection, open, axial and selective coding was performed. Results: After the first step of coding 348 primary codes were collected and they categorized into five main groups including turning to spirituality, keeping the stability life, overcoming on mutual challenges, mental occupation, and common acceptance. Conclusion: According to the findings of this study, for women with several unsuccessful experiences of IVF, the main problem related to their infertility is unstable family.

BackgroundOnychomycosis is one of the most common and recurrent dermatological diseases worldwide. The antimycotic activity of prescribed medications varies according to the causative agents, and treatment failure rates exceeding 30%. This study aimed to assess the epidemiological profile of onychomycosis in Iran. Also, the susceptibilities to conventional and new antifungals were investigated.MethodsIn this descriptive cross-sectional study, during the period of 18 months starting from September 2019 until March 2020, 594 nail specimens were obtained from patients who presented nail changes compatible with a clinical diagnosis of onychomycosis. The patients were referred from different cities, including Tehran, Kermanshah, Arak, Kashan, Rasht, Qom, Urmia, Zahedan, Hamadan, Zanjan, Borujerd, Bushehr, and Yazd. All the samples were subjected to microscopic examination and fungal culture. Fungi identified were confirmed through the PCR-sequencing method. The susceptibility to itraconazole, fluconazole, terbinafine, griseofulvin, posaconazole, ravuconazole, efinaconazole, luliconazole, and tavaborole was evaluated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines, document M38-A2 for filamentous fungi, and document M27-A3 for yeasts.Results594 patients were included. Of these, in 179 cases (30.1%) (95% CI:0.3 ± 0.037) onychomycosis was confirmed. The majority of patients were ≥ 60 years of age (n=58, 32.6%) and female (n=113, 63.1%). Saprophytic fungi accounted for the vast majority of the nail isolates (n=92, 51.4%) (95% CI:0.051 ± 0.0.073), followed by dermatophytes (n=45, 25.1%) (95% CI:0.25 ± 0.063), and yeasts (n=42, 23.5%) (95% CI:0.23 ± 0.061). Diabetes mellitus (77.3%), hypothyroidism (18.2%), and solid tumors (4.5%) were documented as the most prevalent underlying conditions. Antifungal susceptibility testing was performed against 60 fungal isolates (20 each of Candida species, saprophytic fungi, and dermatophytes). Efinaconazole, ravuconazole, and luliconazole were the most active agents against Candida species. Also, luliconazole, posaconazole, and efinaconazole were most potent against dermatophytes. Luliconazole had the greatest antifungal activity against saprophytic fungi.ConclusionsThe prevalence of onychomycosis in Iranian patients was relatively high. LUL exhibited potent antifungal activity against the three groups of fungi tested, determining its broad-spectrum antimycotic activity and its probable use as the first-line therapy for onychomycosis.

In this article, in order to find some reliable connection types for avoiding the infill failure, an experimental study is conducted on six half-scale single-story single-bay specimens, including one bare frame and five steel frames with autoclave-cured aerated concrete walls, each having different frame-to-wall connection types. Polyurethane adhesive was used in bed joints of the autoclave-cured aerated concrete walls as a mortar. The specimens were tested under displacement-controlled loading to examine their effect on the in-plane behavior of steel frames. The results suggested that V- and T-shaped connectors can be used as prequalified connectors between the wall and the frame. In specimens with these connectors, the interaction between infill and frame was ignorable and the infill remained intact up to life safety performance level (2.5% drift ratio). Furthermore, the application of these connectors resulted in a two-level performance of the infill whereby the frame stiffness degradation and strength deterioration after the drift ratio of 2.5% was compensated.

Background: Elderly is an era of life, which affects quality of life; aging changes the thinking ways and reduces the self-confidence. The present study was conducted to evaluate the effect of hope therapy on the elderly quality of life in Mehriz, Yazd in 2016 - 2017. Methods: In this controlled study with pre-test post-test design, the study population consisted of all elderly people who referred to daily care centers of Mehriz. The sample consisted of 24 elderly women in the daily care centers, who were divided into two groups of experimental (n = 12) and awaiting (n = 12).Hope therapy was provided in eight sessions for the experimental group. The instrument used in this research was Quality of Life Scale for the Elderly. Results: The MANOVA results showed a significant difference between the experimental and awaiting groups. In the experimental group, physical function, depression, anxiety, mental performance, and life satisfaction improved, but education did not affect the sex dimension. Hope therapy, was effective on improving the elderly quality of life. Conclusion: The studied training can be used as an effective treatment to improve the elderly quality of life.

The aim of the study was to investigate the effect of ignition timing and hydrogen fraction on the performance and exhaust emissions of an engine. The engine was converted from port injection system to direct injection with enrichment hydrogen to natural gas at WOT condition and engine operation at 4000 rpm. The results showed that when hydrogen volume fraction was raised from 0 up to 50% at 20 bar injection pressure and at the ignition timing from 19 to 28 °CA BTDC, a good stratified effect was achieved. Also, performance results demonstrated that fuel conversion efficiency, power output, and torque were increased up to 3.7% and the brake thermal efficiency first rose and then fell with an increment of hydrogen enrichment to CNG. The peak cylinder pressure and temperature with the advance of ignition timing increased. In addition, the advance of the ignition timing caused NOx, HC, and CO emissions to decrease but with the increase in hydrogen fraction in the blend of fuel, HC, and CO dropped and NOx emission rose. This study concluded that the 30% blends of hydrogen and 21° BTDC ignition timing can optimize engine performance and emission without any engine modification.

Polyomavirus BK infection is a common complication and a major cause of morbidity after kidney transplantation. Surveillance of kidney transplant recipients was threatened by reactivation of polyomavirus BK infection can lead to polyomavirus BK-associated nephropathy (PVN). Antiviral immunoregulatory markers like Gamma interferon (IFN-γ) might also affect the polyomavirus BK pathogenesis for its role in antiviral host defense, graft rejection, and regulative of the adaptive immune responses. After screening polyomavirus BK infection, using Real time PCR (Taq-Man), the possible association between polyomavirus BK infection with IFN-γ gene expression was assessed. The mRNA levels of IFN-γ was examined in (n = 23) polyomavirus BK infected and (n = 23) non-infected kidney transplant patients in comparison with healthy controls (n = 23), using an in-house Real time PCR (SYBR Green) assay. The correlation of IFN-γ expression with viral load as well as other variables was also performed. The mRNA expression level of IFN-γ was significantly higher in polyomavirus BK infected patients (fold = 58.47) compared with non-infected ones (fold = 4.62), and healthy controls (p = 0.002). IFN-γ expression was higher in patients with higher viral load (p = 0.001). IFN-γ expression was correlated with viral load (r = 0.7, p 0.0001). Accordingly, polyomavirus BK infection can induce IFN-γ gene over expression in kidney transplant infected patients. The results emphasized on the determinative role of IFN-γ in the pathogenesis of activated polyomavirus BK infection and also its importance in managing the clinical complications after kidney transplantation due to virus reactivation, requiring further investigation.

Background: Depression and anxiety are the most common reaction in infertile couples. Several studies have been conducted to examine the psychiatric disorders among infertile and fertile couples. Objective: This meta-analysis was conducted to compare the depression and anxiety in fertile and infertile couples in various studies. Materials and Methods: The authors searched articles published in multiple databases including World Health Organization, PubMed, Cochrane Library, Scopus, Science Direct, Medline EMBASE and Persian databases including Scientific Information Database (SID) and IranMedx between 2005 and 2017. The main keywords used for searching the databases were: depression, anxiety, infertility, and fertility. Statistical analyses were performed using Comprehensive Meta-Analysis/2.0 software. Results: The authors found 42 related articles after searching the databases. 11 articles entered the meta-analysis after considering the inclusion and exclusion criteria. Finally, eight articles were chosen for the comparison of depression and anxiety, two published articles for the comparison of depression, and one published article to compare anxiety in fertile and infertile couples. The results of the heterogeneity test showed a significant heterogeneity among all articles that were analyzed in this meta-analysis in the field of depression and anxiety. The results showed that depression (p = 0.0001; Hedges’g= 1.21; 95% CI 0.63–1.78) and anxiety (p = 0.00001; Hedges’g = 0.63; 95% CI 0.54–0.73) were higher in infertile couples than fertile couples and that the possibility of a publication bias does not exist in this study. Conclusion: The analysis of articles used in this meta-analysis showed that depression and anxiety scores in infertile couples were higher than fertile couples. Key words: Depression, Anxiety, Infertility, Meta-analysis.

The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one's car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloud-based applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Recently, by the rapid development of Vehicular Ad Hoc Networks (VANETs) and the advancement of Software Defined Networking (SDN) as an emerging technology, the Software-Defined Vehicular Network (SDVN) has a tremendous attraction in the academia and research community. SDN’s unique properties and features, such as its flexibility, programmability, and centralized control, make the network scalable and straightforward. In VANETs, traffic management and secure communication of vehicle information using the public network are the main research dimensions in the current era for the researchers to be considered while designing an efficient and secure VANETs architecture. This paper highlights the possible identified threat vectors and efficiently resolves the network vulnerabilities to design a novel and secure hierarchic architecture for SDVN. To solve the above problem, we proposed a Public Key Infrastructure-based digital signature model for efficient and secure communication from Vehicle to Vehicle. We also used the public key authority infrastructure for Vehicle to Infrastructure and the three-way handshake method for secure session creation and secure data communication in the SDN controller. The proposed security is validated through the well-known simulation tool AVISPA. Additionally, a formal security model is applied to validate the design hierarchic architecture’s fundamental security properties for SDVN in an efficient and desirable way. In a comparative analysis, we prove that our proposed scheme fulfills all the essential security properties compared to other states of the art schemes.

Studies have shown sexual intimacy enhancement-training with cognitive-behavioral can affect women's sexual intimacy. Interventional study to compare online and face-to-face sexual enhancement-training with cognitive-behavioral approach on sexual intimacy was conducted on 48 pregnant women with sexual intimacy scores < 75 who were randomly divided online (n = 25) and face-to-face (n = 23). Six 90-minute session sexual intimacy enhancement-training were conducted. Data were collected Botlani's sexual intimacy questionnaire (primary outcome) and counseling satisfaction scale (secondary outcome) measured baseline and after 6 and 10 weeks. In each group sexual intimacy in 10th week increased significantly compared to baseline (65.88 ± 5.51 vs 87.36 ± 8.39, p < 0.001) and (67.39 ± 5.26 vs 83.70 ± 5.61, p < 0.001) respectively. There was no significant difference between two groups in sexual intimacy scores in 6th (82.32 ± 9.25 vs 79.87 ± 6.35, p = 0.29) and 10th weeks (87.36 ± 8.39 vs 83.70 ± 5.61, p = 0.08) but totally intervention caused significant increase in sexual intimacy in 10th week compared to baseline (p = 0.04). Satisfaction from intervention was significantly different in 6th (65.72 ± 2.57 vs 61.21 ± 7.17, p = 0.021) and 10th weeks (68.92 ± 2.79 vs 64.26 ± 5.15, p = 0.001). Intervention improved sexual intimacy in pregnant women via both online and face-to-face counseling, with more sexual intimacy and satisfaction in online group, could be useful in COVID-19 pandemic.

Background Closantel is the best-known anti-parasitic medicine for veterinarians, which is contraindicated in humans. After reviewing the literature on ocular toxicity following mistaken usage of Closantel in humans, this report was found as the first complete restoration of visual function after Closantel intoxication. This report could be useful in anticipating the possibility of a further improvement based on a dose-response relationship. An important point of this report is the apparent reversibility of the vision and Electrophysiological parameters after Closantel intoxication and blindness. To conclude, the present case report demonstrates the importance of immediate referral and management in Closantel intoxication to avoid the long-term adverse effects of drug on visual function. Case presentation A 47-year-old man mistakenly took about 20 cc of Closantel 5% (15.87 mg/kg). Four hours after mistaken usage of Closantel, he was transferred to the district hospital due to dizziness and nausea. His stomach was washed out immediately after hospital arrival. He was being hospitalized in that hospital for 3 days. Then, he was referred to our clinic due to progressive vision loss. Methylprednisolone acetate 250 mg was injected once on 5th day after taking Closantel. His vision was reducing gradually so low that he could only detect hand motion (HM) on the 14th day after taking Closantel. ERG test was requested. It showed an exclusive reduction in b-wave amplitude under photopic and scotopic conditions. Later, his vision surprisingly improved gradually and his visual acuity was fully restored on the 28th day after the incident. After 3 years, we checked him again. His visual acuity was 20/20 in both eyes and the patient did not have any problem and his ERG report was completely normal. Conclusions In low dose of Closantel and immediate referral, ocular toxicity could be resolved.

A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now spread to all countries of the world, including Iran. Although new anti-coronavirus antibodies in patients may be identified by immunological methods with sufficient sensitivity and specificity, the conclusive diagnosis of the disease is by the molecular RT-PCR process. We used a population-based seroepidemiological survey to quantify the proportion of the exposed population with SARS-CoV-2 antibodies and evaluated whether the antibodies are a marker of total or partial immunity to the proportion of the population that remains susceptible to the virus. This cross-sectional study was conducted to investigate the seroprevalence of CoVID-19 in Tehran, the capital of Iran, between April and end of October 2020. Specimens of clotted and heparinized blood (2ml) were collected from the patients. The serum and plasma were separated and stored at − 80LJ°C until use. We examined serum anti-SARS-CoV-2 IgG and IgM antibodies from 1375 in-patients admitted to our hospitals using ELISA kits. In total, 1375 participants were enrolled in this study, and SARS-CoV-2 antibodies were detected using IgM-IgG antibody assay in 291 patients. Among the seropositive patients studied, 187 were men (64.3%), and 104 were women (35.7%) (P

The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.

Wireless Sensor Network (WSN) is a particular network built from small sensor nodes. These sensor nodes have unique features. That is, it can sense and process data in WSN. WSN has tremendous applications in many fields. Despite the significance of WSN, this kind of network faced several issues. The biggest problems rising in WSN are energy consumption and security. Robust security development is needed to cope with WSN applications. For security purposes in WSN, cryptography techniques are very favorable. However, WSN has resource limitations, which is the main problem in applying any security scheme. Hence, if we are using the cryptography scheme in WSN, we must first guarantee that it must be energy-efficient. Thus, we proposed a secure hybrid session key management scheme for WSN. In this scheme, the major steps of public key cryptography are minimized, and much of the operations are based on symmetric key cryptography. This strategy extensively reduces the energy consumption of WSN and ensures optimum security. The proposed scheme is implemented, and their analysis is performed using different parameters with benchmark schemes. We concluded that the proposed scheme is energy-efficient and outperforms the available benchmark schemes. Furthermore, it provides an effective platform for secure key agreements and management in the WSN environment.

Toll-like receptors (TLRs) have important role in transplant outcomes by activating the innate immune system and production of pro-inflammatory cytokines, leading to graft rejection. We assessed the expression level of TL2 and TLR4 in acute rejection (AR) on the 1st and 7th-day post-transplantation. TLR2 and TLR4 expressions were evaluated by real-time PCR in both the AR group (n = 50) and non-AR (n = 50), compared with the control group. Also, the correlation of the expression levels of TLRs between both the 1st and 7th day was analyzed. ROC curve analysis was used to determine the cut-off value for TLRs expression. TLR4 mRNA expression was significantly up-regulated in AR patients vs. the controls on the 1st day (p ≤ 0.05) and it was down-regulated in non-AR vs. controls on the 1st day (p ≤ 0.05). Also, TLR4 expression had decreased in both AR and non-AR groups vs. control on the 7th day (p ≤ 0.05). Both TLR2 and TLR4 expression in comparison to non-AR had increased in the AR group on the 7th day (p ≤ 0.05). TLR2 expression positively correlated between 1st and 7th day in AR (r = 0.3, (p ≤ 0.05) and non-AR group (r = 0.2, p ≤ 0.05). ROC curve analysis showed a cut-off value of TLR2 up to 0.98 with sensitivity 71.05 (95%CI = 54.1–84.6) and specificity 63.27 (95%CI = 48.3–76.6) that could distinguish between AR and non-AR group (p ≤ 0.05). The data support that both TLR2 and TLR4 expression have an effective role in AR after liver transplantation and could be used as possible biomarkers for AR to choose better therapeutic strategies based on immunological aspects.