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With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs-RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.

Smart Agriculture (SA) incorporates low cost and low energy consuming sensors and devices to enhance quantitative and qualitative agricultural production. However, these devices uses an open channel i.e., Internet and generates large amount of data in real-time and thus, has the potential to be misused. As a consequence, the major concern in the implementation of SA is minimizing the risk of security and data privacy violation (e.g., adversaries performing inference attacks). To address these challenges, we propose PEFL, a deep privacy-encoding based Federated Learning (FL) framework that adopts a perturbation-based encoding and Long-Short Term Memory-AutoEncoder (LSTM-AE) technique to achieve the target of privacy. Then a FL-based Gated Recurrent Unit Neural Network algorithm (FedGRU) is designed using the encoded data for intrusion detection. The experiment results based on ToN-IoT dataset reveals that the PEFL can efficiently identify normal and attack patterns after transformation over other non-FL and FL methods.

Background & Aims Retreatment with glecaprevir/pibrentasvir (G/P) resulted in a rate of sustained virologic response 12 weeks after treatment completion (SVR12) of >90% in HCV genotype 1 (GT1) patients who previously failed a regimen of sofosbuvir plus an NS5A inhibitor (NS5Ai). This study investigated the prevalence and impact of baseline NS3 and NS5A resistance-associated substitutions (RASs) on the efficacy of G/P in prior GT1 sofosbuvir+NS5Ai failures and the persistence of treatment-emergent RASs. Methods Longitudinal samples from 177 patients enrolled in a phase IIIb, randomized pragmatic clinical trial were analyzed. Patients without cirrhosis were randomized to 12 or 16 weeks of G/P, and patients with compensated cirrhosis were randomized to G/P and ribavirin for 12 weeks or G/P for 16 weeks. Linkage of RAS was identified using Primer-ID next-generation sequencing at a 15% cut-off. Results Of 177 patients, 169 (95.5%) were PI-naive. All 33 GT1b-infected patients achieved SVR12. In GT1a-infected patients, baseline NS5A RASs were prevalent (74.5%, 105/141) but NS3 RASs were uncommon. Baseline NS3 RASs had no impact on G/P efficacy and patients with baseline NS5A RASs showed a numerically but not statistically significantly lower SVR12 rate compared to those without NS5A RASs (89% vs. 97%). SVR12 was achieved in 34 of 35 (97%) patients without NS5A baseline substitution, and 53 of 57 (93%), 35 of 40 (88%), 5 of 8 (63%) with single, double-linked, and triple-linked NS5A substitutions, respectively. Among 13 patients with virologic failure, 4 acquired treatment-emergent NS3 RASs and 10 acquired NS5A RASs. Conclusion Baseline NS5A RASs were highly prevalent. The presence of an increasing number of linked NS5A RASs in GT1a showed a trend in decreasing SVR12 rates, although no specific NS5A RASs or their linkage pattern were associated with lower SVR12 rates. Lay summary Direct-acting antivirals have revolutionized the treatment of chronic hepatitis C infection, but treatment failure occurs in some patients. Retreatment of patients who previously failed a regimen consisting of sofosbuvir and an NS5A inhibitor with a regimen of glecaprevir and pibrentasvir (G/P) is >90% effective. Herein, we analyzed samples from these patients and showed that retreatment efficacy with G/P is lower in patients with double- or triple-linked NS5A resistance mutations than in patients with single or no NS5A resistance mutations. Clinical trial number NCT03092375 .

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

Background and Aims Glecaprevir/pibrentasvir (GLE/PIB) has shown high efficacy and safety in chronic HCV‐infected adults and adolescents; data in children were limited. DORA part 2 is a phase 2/3, nonrandomized, open‐label study evaluating the pharmacokinetics, efficacy, and safety of a pediatric formulation of GLE and PIB in children ages 3 to < 12 years. Approach and Results Children with chronic HCV infection, genotype 1‐6, with or without compensated cirrhosis, were divided into three cohorts by age—cohort 2 (9 to < 12 years), cohort 3 (6 to < 9 years), and cohort 4 (3 to < 6 years)—and given weight‐based doses of GLE and PIB for 8, 12, or 16 weeks. Primary endpoints were sustained virologic response at posttreatment week 12 (SVR12) and steady‐state exposure; secondary endpoints were rates of persistent viremia, relapse, and reinfection. Safety and laboratory abnormalities were assessed. Final pediatric dosages determined to be efficacious were 250 mg GLE + 100 mg PIB (in children weighing ≥ 30 to < 45 kg), 200 mg GLE + 80 mg PIB (≥ 20 to < 30 kg), and 150 mg GLE + 60 mg PIB (12 to < 20 kg). Of 80 participants enrolled and dosed, 96% (77/80) achieved SVR12. One participant, on the initial dose ratio, relapsed by posttreatment week 4; no participants had virologic failures on the final dose ratio of GLE 50 mg/PIB 20 mg. Two nonresponders prematurely discontinued the study. Most adverse events (AEs) were mild; no drug‐related serious AEs occurred. Pharmacokinetic exposures were comparable to those of adults. Conclusions A pediatric formulation of GLE/PIB was highly efficacious and well tolerated in chronic HCV‐infected children 3 to < 12 years old.

Provenance provides information about how data came to be in its present state. Recently, many critical applications are working with data provenance and provenance security. However, the main challenges in provenance-based applications are storage representation, provenance security, and centralized approach. In this paper, the authors propose a secure trading framework which is based on the techniques of blockchain that includes various features like decentralization, immutability, and integrity in order to solve the trust crisis in centralized provenance-based system. To overcome the storage representation of data provenance, they propose JavaScript object notation (JSON) structure. To improve the provenance security, they propose the access control language (ACL) rule. To implement the JSON structure and ACL rules, permissioned blockchain based tool “Hyperledger Composer” has been used. They demonstrate that their framework minimizes the execution time when the number of transaction increases in terms of storage representation of data provenance and security.

Sparse mobile crowdsensing is a new crowdsensing paradigm which leverages the spatial and temporal correlation between data sensed at different locations over time to reduce the overall sensing cost by significantly reducing the number of sensing tasks. Consequently, only sparsely selected spatio-temporal cells would be reporting the sensed data, whereas data for the rest of the cells would have to be inferred from the sensed data. This process, which is largely known as missing data inference is the focus of this study. We examine the KNN (K-Nearest Neighbor) approach, which is known to be relatively faster and simpler. However, it is generally accepted to perform poorly when the sensed data is sparse. In the context of environmental crowd sensing, we examine whether it is a viable missing data inference approach if we incorporate the spatio-temporal correlation of data in the algorithm, instead of just exploiting either the spatial or the temporal correlation independently. Thus, we examine three variants of KNN: KNN-ST (KNN-Spatio-Temporal), KNN-S (KNN-Spatial), and KNN-T (KNN-Temporal) on sparse data. Besides, we find that voxelization is a natural way of exploiting the spatio-temporal properties of sensed data and thereby the spatio-temporal correlation between them. Interestingly, we find that KNN-ST indeed shows good performance (normalized absolute error of about 0.1) even when the loss probability is as high as 0.9. Additionally, we implement an existing method on the same experimental datasets and present corresponding comparative simulation results.

The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage. Today, this large volume of IoMT generated medical data is stored in the centralized storage system. However, centralization of patient sensitive information leads to a single point of failure, privacy, and security concern. To address these issues, we propose a smart contracts enabled consortium blockchain network. We integrated interplanetary file systems (IPFS) cluster node where smart contracts are deployed at the initial stage for authentication of patient’s and medical devices, the same cluster layer is also proposed as a distributed data storage layer for device-generated data after authentication and these data are securely transmitted over the consortium blockchain. The IPFS cluster node ensures the security and authentication of the devices and it also provides secure storage management in IoMT enabled healthcare system. The consortium network enables the privacy of data owing to hash-based storage in a block of IoMT enabled healthcare network.

Internet of Medical Things (IoMT), an application of Internet of Things (IoT), is addressing countless limitation of traditional health-care systems such as quality of patient care, healthcare costs, shortage of medical staff and inadequate medical supplies in an efficient manner. With the use of the IoMT systems, there are unparalleled benefits that are enhancing the quality and efficiency of treatments and thereby are improving patients health. However, the 2018 Ransomware cyber-attack on Indiana hospital system exposed the critical fault-lines among IoMT environment. The gravity and frequency of cyber-attacks are expanding at an alarming rate. Motivated from aforementioned challenges, we propose an ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. The ensemble design, combines Decision Tree, Naive Bayes, and Random Forest as first-level individual learners. In the next level, the classification results are used by XGBoost for identifying normal and attack instances. Second, for dynamic and heterogeneous networks such as IoMT, fog, and cloud, we present a deployment architecture for the proposed framework as, Software as a Service (SaaS) in fog side and Infrastructure as a Service (IaaS) in cloud side. Further, most of the existing work is evaluated using KDD CUP99 or NSL-KDD dataset. These datasets lack modern IoMT-based attacks. Therefore, the proposed model uses a realistic dataset namely, ToN-IoT which is collected from a heterogeneous and large-scale IoT network. The experimental result shows that the proposed framework can achieve detection rate of 99.98%, accuracy of 96.35%, and can reduce false alarm rate up to 5.59%.

Access control is a policy in data security that controls access to resources. The current access control mechanisms are facing many problems, due to the interference of the third-party, privacy, and security of data. These problems can be addressed by blockchain, the technology that gained major attention in recent years and has many capabilities. However, in the blockchain network, every peer maintains the same state of the ledger to view the complete history of transactions that leads to scalability issues in the blockchain network. To address the problem of scalability we propose an enhanced Bell–LaPadula model and categorized the peers and transactions in different clearance and security levels. The peers don’t have to maintain the complete history of transactions owing to the clearance level. To provide data security in the network we constructed a dynamic access control policies using a smart contracts. We test our model on a blockchain-based healthcare network. The Hyperledger Fabric tool is used to run a complete infrastructure of healthcare organization while the Hyperledger composer modeling tool is used to implement the smart contracts and to provide dynamic access control functionality on the blockchain network.

The data volume is growing rapidly in material science. Every year data volume is getting double in many context of material science. The growing rate of data in material science is demanding for new computational infrastructures that can speed-up material discovery and deployment. In this survey, we are focusing on the challenges in material science due to growing data rate, and how Big Data technology can play a major role in research of material science. This survey includes various disciplines that can be used with Big Data to provide better analysis in the material science research.

Modern big data analysis and business intelligence applications come with high resource demands, along with a requirement for large data transfer between storage and compute nodes. Since the available resources of a single data center might not be sufficient to host these applications, federated cloud systems present a promising solution. The objective of each cloud service provider in a federation is to maximize its own profit and to minimize its total operating cost. To achieve these objectives, spatial variation in the energy cost and bandwidth cost could be leveraged while allocating the workload. We propose a hierarchical approach for resource management in a federated cloud, catering to the requirements of each provider in the federation. We formulate the placement of a data-intensive applications as an optimization problem to minimize the total operating cost, including the energy and communication costs. We propose an algorithm to allocate the virtual components of a data-intensive application, in two phases; partitioning and mapping. While partitioning creates clusters of correlated nodes, mapping allocates the created clusters to data centers that minimize the total cost. Through extensive experiments in different scenarios, we demonstrate that the proposed algorithm achieves a significant reduction in the total operating cost.

The Software Defined Internet of Things-Fog (SDIoT-Fog) has provided a new connectivity paradigm for effective service provisioning. SDIoT-Fog uses network resource virtualization to provide services to heterogeneous IoT devices. However, data privacy, and security are the two major challenges that prevents faster realization of SDIoT-based frameworks. Motivated from the aforementioned challenges, we present a Privacy-Preserving based Intrusion Detection Framework (P2IDF) for protecting confidential data and to detect malicious instances in SDIoT-Fog network traffic. This framework has two key engines. Firstly, a Sparse AutoEncoder (SAE)-based privacy-preservation engine is suggested that transforms original data into a new encoded form that avoids inference attacks. Secondly, an intrusion detection engine is suggested that uses Artificial Neural Network (ANN) to train and evaluate the outcomes of the proposed privacy-preservation engine using an IoT-based dataset named ToN-IoT. Finally, experimental results showed that the proposed P2IDF framework outperforms with some recent state-of-the-art frameworks in terms of detection rate, accuracy and precision score.

Blockchain technology has been gaining great attention in recent years. Owing to its feature of immutability, the data volume of the blockchain network is continuously growing in size. As of now, the size of Bitcoin distributed ledger has reached about 200 GB. Consequently, the increasing size of the blockchain network ledger prevents many peers from joining the network. Hence, the growing size not only limits the expansion of network but also the development of the blockchain ledger. This calls for development of efficient mechanisms for storage and bandwidth synchronization. As a step toward this goal, we propose an IPFS-based decentralized off-chain storage called storage model for data sharing in blockchain (SMDSB), to store the data volume of the blockchain network. In the proposed model, the miners validate and deposit the transactions into an IPFS-based decentralized storage, whereas they store the hash of the transactions in a blockchain network. Thus, by utilizing the characteristics of IPFS-based off-chain storage and its feature of hash creation, the blockchain ledger size can be significantly reduced. Implementation of SMDSB results in reduction of Bitcoin storage by 81.54%, Ethereum by 53.86%, and Hyperledger by 62.59%. Additionally, the experimental results also highlight the impact on storage cost for 1 transaction per second (TPS) in a year.

Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.

With the advancement of blockchain technology, and the proliferation of Internet of things (IoT)‐driven devices, the blockchain‐IoT applications is changing the perception and working infr...

In current environment, many web services map to the distributed data centers, to respond to variable and increasing workloads. It is not cost efficient for one data center to increase number of servers in order to fulfill all requests coming to it. For this reason, federated data center is a key solution for sharing unused resources and gain some profit. In this paper, we have used demand multiplexing and exploits the spatio-temporal variation of electricity price of distributed data center across locations and also consider the power consumption cost of servers at data center. We develop an Integer Linear Programming Problem (ILPP) for Profit Maximization Problem of federated data centers and proposes Greedy algorithm for computing an optimal federation formation of data centers. The performance of the proposed Greedy federation formation algorithm (G-FFA) compares with two other algorithms which are Optimal Federation Formation Algorithm (OFFA) and Random Federation Formation Algorithm (RFFA). Experiments are performed on real world traffic demands at data center in which the proposed (G-FFA) algorithm gives an optimize solution to the optimization problem.

David Bernstein,1 Rakesh Tripathi,2 Daniel E Cohen2 1Zucker School of Medicine, Hofstra University, Hempstead, New York, NY, USA; 2AbbVie Inc., North Chicago, IL, USA Background: This international, phase 2, open-label, multicenter study (ClinicalTrials.gov Identifier: NCT01609933) was conducted to evaluate the safety and efficacy of an enhanced regimen consisting of the direct-acting antivirals (DAAs) ombitasvir, paritaprevir, and ritonavir administered for 24 weeks, combined with pegylated interferon-α2a plus ribavirin (pegIFN-α2a/RBV) for 48 weeks, in patients with chronic hepatitis C virus (HCV) genotype 1 infection who had experienced virologic failure with a prior DAA regimen. This study was undertaken at a time when options were limited for the retreatment of patients who had failed prior DAA therapy. Methods and results: Thirty-two patients were enrolled; the majority were male (78%) and White (94%), and the median age was 54.5 years. Twelve weeks after the last dose of study drug, sustained virologic response was achieved in 81.3% of patients. Five patients prematurely discontinued the study drugs and one patient relapsed. Safety and tolerability were similar to prior studies of pegIFN-α2a/RBV alone.Conclusion: Given the availability of highly efficacious DAA regimens that are both IFN- and RBV-free, this regimen is no longer relevant in today’s HCV treatment landscape. Keywords: direct-acting antiviral, retreatment, virologic failure

Introduction and aim: Approximately 650,000 people in Brazil have chronic hepatitis C virus (HCV) infection. We evaluated the safety and efficacy of ombitasvir (OBV)/paritaprevir (PTV)/ritonavir (r) plus dasabuvir (DSV) with/without ribavirin (RBV) in an open-label multicenter phase 3b trial in treatment-naive or interferon (IFN) treatment-experienced Brazilian patients with advanced hepatic fibrosis (METAVIR F3/4) and HCV genotype (GT) 1 infection. Materials and methods: All patients received coformulated OBV/PTV/r daily + DSV twice daily (3-DAA). GT1a-infected patients received 3-DAA plus RBV for 12 weeks, except for prior pegIFN/RBV nonresponders with cirrhosis who were treated for 24 weeks. GT1b-infected patients received 3-DAA alone (F3) or in combination with RBV (F4) for 12 weeks. The primary endpoint was sustained virologic response (HCV RNA

Treatment of HCV genotype (GT) 2-infected Japanese patients with paritaprevir (NS3/4A inhibitor boosted with ritonavir) and ombitasvir (NS5A inhibitor) without ribavirin for 12 weeks in the phase 2 study M12-536, and with ribavirin for 16 weeks in phase 3 study GIFT II resulted in SVR rates of 72.2% to 91.5%. Overall, 11 out of 125 patients with GT2a and 37 out of 79 patients with GT2b infection experienced virologic failure. The prevalence of baseline polymorphisms in NS3 and NS5A and their the impact on treatment outcome, as well as the development of viral resistance in GT2-infected patients experiencing virologic failure were evaluated by HCV NS3 and NS5A population and clonal sequence analyses. Baseline polymorphisms in NS3 that confer resistance to paritaprevir were rare in both GT2a- and GT2b-infected patients, while baseline polymorphisms in NS5A that confer resistance to ombitasvir were detected in 11.2% and 14.1% of the GT2a- and GT2b-infected patients, respectively. There was no significant impact of baseline polymorphisms on treatment outcome in Japanese patients. The most common treatment-emergent substitutions at the time of virologic failure occurred at amino acid positions 168 in NS3 and 28 in NS5A in both GT2a- and GT2b-infected patients. Although there was a higher rate of virologic failure in patients with GT2b infection, the resistance analyses presented in this report support the conclusion that testing for baseline resistance-associated polymorphisms is not warranted for HCV GT2-infected patients treated with a regimen of ombitasvir/paritaprevir/ritonavir + ribavirin for 16 weeks.

In this paper we propose an algorithm for load balancing in distributed data centers based on game theory. We model the load balancing problem as a non-cooperative game among the front-end proxy servers. We model the operating cost associated with a data center as a weighted linear combination of the energy cost and the latency cost. We propose a non-cooperative load balancing game with the objective of minimizing the operating cost and obtain the structure of Nash equilibrium. Based on this structure, a distributed load balancing algorithm is designed. We compare the performance of the proposed algorithm with the existing approaches. Numerical results demonstrate that the solution achieved by the proposed algorithm approximates the global optimal solution in terms of the cost and it also ensures fairness among the users.

Many critical e-commerce and financial services are deployed on geo-distributed data centers for scalability and availability. Recent market surveys show that failure of a data center is inevitable resulting in a huge financial loss. Fault-tolerance in distributed data centers is typically handled by provisioning spare capacity to mask failure at a site. We argue that the operating cost and data replication cost (for data availability) must be considered in spare capacity provisioning along with minimizing the number of servers. Since the operating cost and client demand vary across space and time, we propose cost-aware capacity provisioning to minimize the total cost of ownership (TCO) for fault-tolerant data centers. We formulate the problem of spare capacity provisioning in fault-tolerant distributed data centers using mixed integer linear programming (MILP), with an objective of minimizing the TCO. The model accounts for heterogeneous client demand, data replication strategies (single and multiple site), variation in electricity price and carbon tax, and delay constraints while computing the spare capacity. Solving the MILP using real-world data, we observed a saving in the TCO to the tune of 35% compared to a model that minimizes the total number of servers and 43% compared to the model that minimizes the average response time. We demonstrate that our model is beneficial when the cost of electricity, carbon tax, and bandwidth vary significantly across the locations, which seems to be the problem for most of the operators.

Integrating renewable energy and ensuring high availability are two major requirements for geo-distributed data centers. Availability is ensured by provisioning spare capacity across the data centers to mask data center failures (either partial or complete). We propose a mixed integer linear programming formulation for capacity planning while minimizing the total cost of ownership (TCO) for highly available, green, distributed data centers. We minimize the cost due to power consumption and server deployment while targeting a minimum usage of green energy. Solving our model shows that capacity provisioning considering green energy integration not only lowers carbon footprint but also reduces the TCO. Results show that up to 40% green energy usage is feasible with a marginal increase in the TCO compared with the other cost-aware models.

In the current scenario, healthcare organizations are facing a serious problem in sharing medical information among different stakeholders without sacrificing the privacy and integrity of information. To store and share such a large volume of healthcare information securely is an important research issue. Blockchain has been used successfully in the bitcoin for the decentralized exchange of cryptocurrency without the involvement of the third party. The new structure of blockchain has been designed to accommodate the need of another field such as healthcare. Blockchain technology has the potential of immutability, integrity and decentralized architecture to manage the health records of the patient. This paper aims to address the issues of data security and authentication in healthcare. We have proposed a blockchain based secure framework using public key cryptography.

Background: Glecaprevir/pibrentasvir results in high rates of sustained virological response in patients with chronic hepatitis C virus (HCV) genotype 1-6 infection. Data in non-Japanese Asian patients have been minimal. Methods: Two phase 3 studies were undertaken in treatment-naive and treatment-experienced patients with chronic HCV genotype 1-6 infection: a randomised, double-blind, placebo-controlled study recruited patients without cirrhosis at 47 sites across China, South Korea, and Singapore (VOYAGE-1; NCT03222583); a single-arm, open-label study recruited patients with compensated cirrhosis at 34 sites across China and South Korea (VOYAGE-2; NCT03235349). Glecaprevir/pibrentasvir (300 mg/120 mg) or placebo (VOYAGE-1, 2:1 ratio) was given for 8 or 12 weeks in patients without and with cirrhosis, respectively (16 weeks in treatment-experienced patients with genotype 3). Primary efficacy endpoint was sustained virological response at post-treatment week 12 (SVR12; HCV RNA

Interplanetary file system (IPFS) is a version-controlled file system in a peer-to-peer scheme that provides a content-addressable block storage model for storing and sharing files in a distributed environment. According to the feature of IPFS, we have proposed a blockchain-based framework to share the file using content-addressable block storage in the peer-to-peer model. To overcome the availability, reliability, storage overhead, and another issue of centralized service providers, we have introduced blockchain- and IPFS-based distributed storage model that ensures the immutability, integrity, and availability of the resource. In this framework, we are storing the file on IPFS and the addressable content (hash) to the blockchain as a transaction. Our proposed scheme can effectively solve the problem of availability, reliability, and storage of a centralized service provider. In this proposed framework, we are storing the academic record of the student (jpg format) on IPFS (peer-to-peer scheme). To implement the blockchain framework, we have used python(anaconda), python flask, postman, and IPFS.

The healthcare industry electronically maintains medical data which includes patients' information such as patients' personal information, diagnostic reports, and doctor prescriptions. However, the centralized storage model is currently used for storing such sensitive information. One main disadvantage of the centralized model is the difficulty in preserving user privacy. Threats relating to user (patient) privacy include unauthorized access of critical information such as identity details and diseases from which a patient is suffering, and misuse of patients' data and their medical reports. To address this issue, we propose a distributed off-chain storage of medical data using IPFS (Interplanetary File System) and blockchain technology. The proposed framework while preserving patient privacy facilitates easy access of medical data by authorized entities such as healthcare providers (e.g., doctors and nurses). Moreover, it achieves consistency, integrity, and availability.

Glecaprevir-pibrentasvir results in high rates of sustained virological response in patients with chronic hepatitis C virus (HCV) genotype 1-6 infection. Data for glecaprevir-pibrentasvir in non-Japanese Asian patients have been minimal. The aim of these studies was to assess the efficacy and safety of glecaprevir-pibrentasvir in treatment-naive and treatment-experienced Asian patients with chronic HCV genotype 1-6 infection without cirrhosis (VOYAGE-1) and with compensated cirrhosis (VOYAGE-2).We did two phase 3 studies in treatment-naive and treatment-experienced patients with chronic HCV genotype 1-6 infection. VOYAGE-1 was a randomised, double-blind, placebo-controlled study that recruited patients without cirrhosis at 47 sites across China, South Korea, and Singapore. Randomisation was 2:1 with a fixed block size of three and stratified by geographical region and HCV genotype. Investigators, study site personnel, the study sponsor, and patients were masked to treatment allocation. VOYAGE-2 was a single-arm, open-label study that recruited patients with compensated cirrhosis at 34 sites across China and South Korea. Glecaprevir (300 mg) and pibrentasvir (120 mg) or placebo (VOYAGE-1, 2:1 ratio), administered as three tablets daily, was given for 8 weeks in patients without cirrhosis and for 12 weeks in those with cirrhosis (and for 16 weeks in treatment-experienced patients with genotype 3). The primary efficacy endpoint was the proportion of patients with a sustained virological response, defined as HCV RNA below the lower limit of quantification 12 weeks after the last dose of glecaprevir-pibrentasvir. We analysed efficacy and safety in all patients who received at least one dose of the study drug. These trials are registered with ClinicalTrials.gov, NCT03222583 (VOYAGE-1) and NCT03235349 (VOYAGE-2); both trials have been completed. This Article reports the results of the primary analysis for each study, undertaken when all patients who received glecaprevir-pibrentasvir (during the double-blind period in VOYAGE-1) had been followed up for 12 weeks following their last dose of study drug. Data from the double-blind period for placebo patients in VOYAGE-1 are also summarised.Between Oct 4, 2017, and April 20, 2018, 546 patients with chronic HCV without cirrhosis were randomly assigned to treatment (363 to glecaprevir-pibrentasvir, 183 to placebo) in VOYAGE-1. One patient withdrew consent and did not receive treatment with glecaprevir-pibrentasvir. 352 of 362 patients who received glecaprevir-pibrentasvir achieved SVR12 (97·2% [95% CI 95·5-98·9]). Of 160 patients with compensated cirrhosis who were enrolled in VOYAGE-2 between Sept 29, 2017, and June 14, 2018, 159 of 160 achieved SVR12 (99·4%, 95% CI 98·2-100·0). 20 patients with HCV genotype 3b across both trials received glecaprevir-pibrentasvir; six of these patients were among the 11 patients who did not achieve SVR12. Upper respiratory tract infection was the most common adverse event (35 [10%] of 362 receiving glecaprevir-pibrentasvir and 18 [10%] of 183 receiving placebo in VOYAGE-1; 19 [12%] of 160 in VOYAGE-2). For patients receiving glecaprevir-pibrentasvir, serious adverse events occurred in three (1%) of 362 patients in VOYAGE-1 and five (3%) of 160 patients in VOYAGE-2. Grade 3-4 adverse events in patients receiving glecaprevir-pibrentasvir occurred in five (1%) of 362 patients in VOYAGE-1 and six (4%) of 160 patients in VOYAGE-2; each type of event was experienced by at most one patient within a study. One patient with cirrhosis discontinued study drug because of an adverse event.Glecaprevir-pibrentasvir showed high efficacy and an acceptable safety profile in these studies although responses were less common in the few patients with HCV genotype 3b. The results support the use of glecaprevir-pibrentasvir in these Asian populations.AbbVie.

Many critical applications are designed on the distributed structure using the blockchain technology to ensure the availability, immutability, and security. However, these applications are facing the storage problem owing to the data volume growth of transaction. The number of transactions and its size in a block is growing in the blockchain day by day because of the feature of immutability and append-only. The growing nature of transactions in a block is not only making the problem for storage but also in access to the block transactions. In this paper, we propose an IPFS based blockchain storage model to solve the storage problem of transaction in a block along with access of transaction of a particular block. In the propose storage model, the miners stores transaction on IPFS distributed file system storage and get the returned IPFS hash of transaction into the block of the blockchain. The feature of the IPFS network and its resultant hash reduce the size of transactions in a block. To secure access of transaction for a particular block content-addressed (IPFS hash) storage technique has been proposed. We have applied this scheme on a transaction which includes image storage on IPFS and hash storage into the blockchain. In this paper, we have also proposed the content-addressed technique in contrast to the location addressed for the access of transaction. To implement the framework we have used anaconda python, python flask, and IPFS.

The current advancement in Unmanned Aerial Vehicles (UAVs) and the proliferation of the Internet of Things (IoT) devices is revolutionizing conventional farming operations into precision agriculture. The agricultural UAVs combined with IoT use an open channel i.e., the Internet to assist cultivators with data collection, processing, monitoring, and making correct decisions on the farm. However, the use of the Internet opens up a wide range of challenges such as security (e.g., performing cyber-attacks), risk of data privacy (e.g., data poisoning and inference attacks), etc. The usage of current conventional centralized security measures has limitations in terms of a single point of failure, verifiability, traceability, and scalability. Motivated from the aforementioned challenges, we propose a Secured Privacy-Preserving Framework (SP2F) for smart agricultural UAVs. The proposed SP2F framework has two main engines, a two-level privacy engine, and a deep learning-based anomaly detection engine. In the two-level privacy engine, a blockchain, and smart contract-based enhanced Proof of Work (ePoW) is designed for data authentication, and to mitigate data poisoning attacks. A Sparse AutoEncoder (SAE) is applied for transforming data into a new encoded format for preventing inference attacks. In the anomaly detection engine, a Stacked Long-Short-Term Memory (SLSTM) is used to train and evaluate the results of the proposed two-level privacy engine using two publicly accessible IoT-based datasets, namely ToN-IoT and IoT Botnet. Finally, based on thorough analysis, and comparison, we identify that the SP2F framework outperforms several state-of-the-art techniques in both non-blockchain and blockchain frameworks.

The main issues with drug safety in the counterfeit medicine supply chain, are to do with how the drugs are initially manufactured. The traceability of right and active pharmaceutical ingredients during actual manufacture is a difficult process, so detecting drugs that do not contain the intended active ingredients can ultimately lead to end-consumer patient harm or even death. Blockchain’s advanced features make it capable of providing a basis for complete traceability of drugs, from manufacturer to end consumer, and the ability to identify counterfeit-drug. This paper aims to address the issue of drug safety using Blockchain and encrypted QR(quick response) code security.

Glecaprevir (an NS3/4A protease inhibitor) and pibrentasvir (an NS5A inhibitor) are potent and pangenotypic hepatitis C virus (HCV) direct-acting antivirals. This report describes the baseline polymorphisms and treatment-emergent substitutions in NS3 or NS5A detected in samples from HCV genotype 1-infected patients receiving 3-day monotherapy of glecaprevir or pibrentasvir, respectively. None of the NS3 polymorphisms detected in the 47 baseline samples collected prior to glecaprevir monotherapy conferred reduced susceptibility to glecaprevir. The NS3 A156T substitution, which conferred resistance to glecaprevir but had low replication efficiency, emerged in one genotype 1a-infected patient among the 35 patients with available post-baseline sequence data. Baseline NS5A polymorphisms were detected in 12 of 40 patients prior to pibrentasvir monotherapy, most polymorphisms were single-position NS5A amino acid substitutions that did not confer resistance to pibrentasvir. Among the 19 patients with available post-baseline NS5A sequence data, 3 had treatment-emergent NS5A substitutions during pibrentasvir monotherapy. All treatment-emergent NS5A substitutions were linked multiple-position, almost exclusively double-position, substitutions that conferred resistance to pibrentasvir. Replicons engineered with these double-position substitutions had low replication efficiency. In conclusion, resistance-conferring substitutions emerged in a small number of genotype 1-infected patients during glecaprevir or pibrentasvir monotherapy, unlike other NS5A inhibitors, pibrentasvir did not select single-position NS5A substitutions during monotherapy.

Over 2,200 patients infected with hepatitis C virus (HCV) genotypes (GT) 1 to 6, with or without cirrhosis, who were treatment naive or experienced to interferon, ribavirin, and/or sofosbuvir were treated with glecaprevir/pibrentasvir for 8, 12, or 16 weeks in eight registrational phase 2 and 3 clinical studies. High rates of sustained virologic response at 12 weeks postdosing (SVR12) were achieved with a

Treatment of HCV genotype 1b (GT1b)-infected Japanese patients with paritaprevir (NS3/4A inhibitor boosted with ritonavir) and ombitasvir (NS5A inhibitor) in studies M12-536 and GIFT-I demonstrated high sustained virologic response (SVR) rates. The virologic failure rate was 3% (13/436) across the two studies. Analyses were conducted to evaluate the impact of baseline resistance-associated variants (RAVs) on treatment outcome and the emergence and persistence of RAVs in patients experiencing virologic failure. Baseline paritaprevir resistance-conferring variants in NS3 were infrequent, while Y93H in NS5A was the most prevalent ombitasvir resistance-conferring variant at baseline. A comparison of baseline prevalence of polymorphisms in Japanese and western patients showed that Q80L and S122G in NS3 and L28M, R30Q, and Y93H in NS5A were significantly more prevalent in Japanese patients. In the GIFT-I study, the prevalence of Y93H in NS5A varied between 13% and 21% depending on the deep-sequencing detection threshold. Among patients with Y93H comprising 40% of their preexisting viral population, the 24-week SVR (SVR 24 ) rates were >99% (276/277), 93% (38/41), and 76% (25/33), respectively, indicating that the prevalence of Y93H within a patient's viral population is a good predictor of treatment response. The predominant RAVs at the time of virologic failure were D168A/V in NS3 and Y93H alone or in combination with other variants in NS5A. While levels of NS3 RAVs declined over time, NS5A RAVs persisted through posttreatment week 48. Results from these analyses are informative in understanding the resistance profile of an ombitasvir- plus paritaprevir/ritonavir-based regimen in Japanese GT1b-infected patients.

Glecaprevir and pibrentasvir are hepatitis C virus (HCV) pangenotypic inhibitors targeting NS3/4A protease and NS5A, respectively. This once-daily, fixed-dose combination regimen demonstrated high sustained virologic response 12 weeks postdosing (SVR 12 ) rates in CERTAIN-1 and CERTAIN-2 studies in Japanese HCV-infected patients, with a low virologic failure rate (1.2%). There were no virologic failures among direct-acting antiviral (DAA)-treatment-naive genotype 1a (GT1a) ( n = 4)-, GT1b ( n = 128)-, and GT2 ( n = 97)-infected noncirrhotic patients treated for 8 weeks or among GT1b ( n = 38)- or GT2 ( n = 20)-infected patients with compensated cirrhosis treated for 12 weeks. Two of 33 DAA-experienced and 2 of 12 GT3-infected patients treated for 12 weeks experienced virologic failure. Pooled resistance analysis, grouped by HCV subtype, treatment duration, prior treatment experience, and cirrhosis status, was conducted. Among DAA-naive GT1b-infected patients, the baseline prevalence of NS3-D168E was 1.2%, that of NS5A-L31M was 3.6%, and that of NS5A-Y93H was 17.6%. Baseline polymorphisms in NS3 or NS5A were less prevalent in GT2, with the exception of the common L/M31 polymorphism in NS5A. Among DAA-experienced GT1b-infected patients (30/32 daclatasvir plus asunaprevir-experienced patients), the baseline prevalence of NS3-D168E/T/V was 48.4%, that of NS5A-L31F/I/M/V was 81.3%, that of the NS5A P32deletion was 6.3%, and that of NS5A-Y93H was 59.4%. Common baseline polymorphisms in NS3 and/or NS5A had no impact on treatment outcomes in GT1- and GT2-infected patients; the impact on GT3-infected patients could not be assessed due to the enrollment of patients infected with diverse subtypes and the limited number of patients. The glecaprevir-pibrentasvir combination regimen allows a simplified treatment option without the need for HCV subtyping or baseline resistance testing for DAA-naive GT1- or GT2-infected patients. (The CERTAIN-1 and CERTAIN-2 studies have been registered at ClinicalTrials.gov under identifiers NCT02707952 and NCT02723084, respectively.)

With an increase in the usage of data centers to power content distribution networks (CDN), minimizing the cost of deployment while handling fault-tolerance has become an important research issue. In this work, we demonstrate the importance of cost-aware capacity provisioning in fault-tolerant CDN data centers (that can tolerate failure at a single site). We propose an optimization model that exploits spatio-temporal variation in the electricity price and demand to minimize the total cost of ownership. We use real-world data to show that the cost reduction can be upto 40% compared to the existing model, which minimizes the expected response time by routing requests to a nearby data center.

Many critical e-commerce and financial services predominantly depend on geo-distributed data centers for scalability and availability. Recent market surveys show that failure of a data center is inevitable causing huge financial loss. Fault-tolerant distributed data centers are typically designed by provisioning spare capacity to mask failure at a site. At the same time, data center operators are trying to reduce their carbon footprint by using renewable energy power. Thus, a key challenge in spare capacity provisioning is to distribute the additional servers across the sites, minimizing the total cost of ownership (which includes the server acquisition cost and operating cost) while maximizing the usage of renewable energy. While the existing models minimize the number of servers, we demonstrate that brown electricity price and renewable energy availability should be considered for sustainable fault tolerant data center design. We use mixed integer linear programming formulation to minimize the total cost of ownership (TCO), while handling constraints such as delay bound, partial or complete failure, and intermittent supply of renewable energy. By solving the optimization problem, we demonstrate that the proposed model reduces the TCO by 48% compared to CDN model (minimize latency), and by 24% compared to minimum server model (minimizes server cost). Results also highlight the impact of latency, number of data centers, failure percentage, and demand variation on the TCO.

Many popular e-commerce applications run on geo-distributed data centers requiring high availability. Fault-tolerant distributed data centers are designed by provisioning spare compute capacity to support the load of failed data center, apart from ensuring data durability. The main challenge during the planning phase is how to provision spare capacity such that the total cost of ownership (TCO) is minimized. While the literature handled spare capacity provisioning by minimizing the number of servers, variation in electricity cost and PUE corroborate the need to minimize the operating cost for capacity provisioning. We develop an MILP model for spare capacity provisioning for geo-distributed data centers with durability requirements. We consider spare capacity provisioning problem with the objective of minimizing TCO. We model variation in the demand, fluctuation in electricity prices across locations, cost of state replication, carbon tax across different countries, and delay constraints while formulating the optimization model. Solving the model shows that TCO is reduced while leveraging the electricity price variation and demand multiplexing. The proposed model outperforms the CDN model by 50% and the minimum server model by 34%. Results also demonstrate the effect of power usage effectiveness (PUE), latency, number of data centers and demand on the TCO.

The extraction of roads from digital images is essential for automatic mapping, effective urban planning and updating of GIS databases. The very high spatial resolution images (VHR) taken by space and space probes are the main source of an accurate extraction of the route. The extraction of road networks in remote urban areas of images plays an important role in many urban applications (eg. Road traffic, geometric correction of remote sensing images in cities, updating geographical information, etc.). Because of the complex geometry of buildings and the geometry of sensor detection, it is generally difficult to distinguish the road from its background. In this research work the image segmentation techniques used for image remote sensing are discussed and evaluated. It has been found that there is no perfect method for image segmentation because the result of image segmentation depends on many factors, i.e. Pixel color, consistency, intensity, image similarity, image content and problem area. In this research work, a hybrid method is proposed for the extraction of paths from high resolution images based on the segmentation of the mean and HFT segmentation. The proposed method includes: noise suppression using the Lucy-Richardson algorithm, then a further improvement of the contrast between the initial segmentation of road and non-road pixels using the segmentation means k and finally HFT based segmentation. Simulation will be conducted on remote sensing images in urban, suburban and rural areas to demonstrate the proposed method and compare it with other similar approaches. The results show the validity and superior performance of the proposed extraction method.

Almost all modern online services run on geo-distributed data centers, and fault tolerance is one of the primary requirement that decides the revenue of the service provider. Recent experiences have shown that the failure of a data center (at a site) is inevitable. In order to mask the failure, spare compute capacity needs to be provisioned across the distributed data center, which leads to additional cost. While the existing literature addresses the capacity provisioning problem only to minimize the number of servers, we argue that the operating cost needs to be considered as well. Since the operating cost and client demand vary both across space and time, we consider cost-aware capacity provisioning to account for their impact on the operating cost of data centers. We propose an optimization framework to minimize the Total Cost of Ownership (TCO) of the cloud provider while designing fault-tolerant geo-distributed data centers. We model the variation in the demand, fluctuation of electricity price and carbon tax across different countries, and delay constraints while computing the spare capacity. On solving the proposed optimization model using real world data, we notice a saving in the TCO (that includes cost of servers and operating cost) of about 17% compared to the model that only minimizes the number of extra servers. Results also highlight the relationship of power usage effectiveness (PUE), over-provisioning for fault tolerance, choice of data center locations, and latency requirements on the TCO. In particular, we notice that the approach of minimizing TCO is beneficial when the electricity prices vary significantly and the PUE is high, which appears to be the case with most of the cloud providers.

AVIATOR, a phase 2 clinical trial, evaluated ritonavir-boosted paritaprevir (a protease inhibitor), ombitasvir (an NS5A inhibitor), and dasabuvir (a nonnucleoside polymerase inhibitor) (the three-drug [3D] regimen) with or without ribavirin (RBV) for 8, 12, or 24 weeks in 406 HCV genotype 1 (GT1)-infected patients. The rate of sustained virologic response 24 weeks after treatment ranged from 88% to 100% across the arms of the 3D regimen with or without RBV; 20 GT1a-infected patients and 1 GT1b-infected patient experienced virologic failure (5.2%). Baseline resistance-conferring variants in NS3 were rare. M28V in GT1a and Y93H in GT1b were the most prevalent preexisting variants in NS5A, and C316N in GT1b and S556G in both GT1a and GT1b were the most prevalent variants in NS5B. Interestingly, all the GT1a sequences encoding M28V in NS5A were from the United States, while GT1b sequences encoding C316N and S556G in NS5B were predominant in the European Union. Variants preexisting at baseline had no significant impact on treatment outcome. The most prevalent treatment-emergent resistance-associated variants (RAVs) in GT1a were R155K and D168V in NS3, M28T and Q30R in NS5A, and S556G in NS5B. The single GT1b-infected patient experiencing virologic failure had no RAVs in any target. A paritaprevir-ritonavir dose of 150/100 mg was more efficacious in suppressing R155K in NS3 than a 100/100-mg dose. In patients who failed after receiving 12 or more weeks of treatment, RAVs were selected in all 3 targets, while most patients who relapsed after 8 weeks of treatment did so without any detectable RAVs. Results from this study guided the selection of the optimal treatment regimen, treatment duration, and paritaprevir dose for further development of the 3D regimen. (This study has been registered at ClinicalTrials.gov under registration number NCT01464827.)

1Post doctoral research fellow, Johns Hopkins Hospital, Baltimore, MD. 2Superintendent in-Chief, Additional Director, Faizabad, India. 3ART Officer, District Hospital, Pratapgarh, India. Backorund: Tuberculosis (TB) remains a worldwide health problem. In 2013, an estimated 9 million people developed TB, most of which in South East Asian Region. Of these, 13 percent were HIV positives. India alone accounts for 24 percent of total TB cases making it the world's TB capital. HIV increases persons susceptibility to TB and in resource-strained country like India, TB could be one of the earliest manifestations in people with HIV. Study Geography, Design and Methods: A predesigned and pretested questionnaire was used to collect data in at ART clinic Pratapgarh, UP. Blood samples were tested for HIV infection using ELISA or Western Blot. All positive patients were transferred to RNTCP to be screened for pulmonary TB by using standard technique. Results

Every year, a tremendous amount of toner is produced for copiers and printers by toner manufacturing companies throughout the United States. Some of this toner does not meet quality specifications and becomes a waste of the manufacturing process. This waste and the spent toner from copiers and printer cartridges are dumped in landfills for lack of a better way of utilizing them. A cooperative research project was undertaken by Texas Department of Transportation and the University of Texas to investigate the feasibility and potential benefits of utilizing waste toner in hot mix asphalt concrete. The research program included procurement of a number of different waste toners, blending them with asphalt cement at different ratios, and evaluating binder and mixture properties. The results indicated that as the amount of waste toner in the blend increases, the stiffness and viscosity of the modified binder increase. The mixture analysis also indicates higher strength and stability for toner-modified asphalt concrete compared with unmodified mixtures. The toner-modified binder is expected to perform satisfactorily in areas where permanent deformation is of great concern and some increase in low-temperature stiffness is not regarded as posing any cracking problems. It was found that a PG64-28 asphalt will grade as PG70-22 with the addition of 10 percent waste toner.

A 23-day-old male baby with a history of perinatal hypoxia presented with refusal of feeds and abdominal distension. The child had a right-sided cystic upper abdominal mass and features of neonatal septicemia. Abdominal ultrasound (US) and contrast-enhanced CT scan showed bilateral adrenal abscesses. Laparotomy with drainage of the abscesses successfully treated the condition. The literature on the subject is reviewed.