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Widespread adoption of internet of things (IoT) for automation, monitoring and control in various engineering, business and industrial applications poses serious challenges of data security and cyber-attacks to IoT networks. Blockchain enabled IoT networks, due to their immutability, transparency, and accountability, are commonly employed to ensure safe and secure implementation of the IoT networks. However, blockchain technology is prone to degradation in performance and efficiency in the presence of large number of IoT devices and massive data generated by these networks and does not scale well with the growing size of the networks. This research proposes a Scalable EdgeIoT Blockchain (SEB) framework using EOSIO to enhance the performance and efficiency of the blockchain enabled IoT networks. The proposed framework leverages upon the concepts of sharding for parallel execution of smart contracts, Delegated Proof of Stake (DPoS) for consensus in the network with large number of devices and Interplanetary File System (IPFS) for the data storage and management of massive data in the IoT networks. The proposed framework is implemented in the EOSIO blockchain. This study experiments show significant improvement in the throughput, latency and resource utilization compared to the state-of-the-art solutions in the blockchain enabled IoT networks.

Leukemia inhibitory factor (LIF), a member of the IL-6 cytokine family, is highly expressed throughout the body in multiple tissues and cell types. LIF is primarily known to induce the differentiation of myeloid leukemia cells, but recent studies show that LIF has many other functions, including playing multiple roles in cancer and normal physiology. LIF expression is linked to cellular proliferation, metastasis, inflammation, and chemoresistance. LIF expression and secretion are triggered by many means and its downstream signaling can vary based on tissue types. Recent publications suggest that LIF may play a role in pulmonary diseases and its regulation is altered through external factors, such as cigarette smoke, inflammation stimuli, or infections. This review outlines the current knowledge of the function of LIF protein, mediators of LIF expression, receptors it interacts with, downstream LIF signaling, and possible pulmonary outcomes mediated by LIF.

Introduction: The negative effects of media exposure to interpersonal violence (IPV) have been a controversial topic for decades. Our aims were to identify the prevalence and trend of maxillofacialrelated IPV in top grossing movies 50 years apart to look for changing patterns of portrayal in mainstream cinema. Methods: The 20 highest-grossing movies from 1970 to 2020 were identified. Each instance of maxillofacial-related IPV was identified and the following data points were analyzed: type of IPV (blunt/penetrating), the injury sustained, gender of the assailant and the victim as well as their relationship, the use of drugs and/or alcohol, any physical and/or legal consequences and if hospitalisation was required. Results: From the movies examined, 13/20 in 1970 and 14/20 in 2020 showed episodes of maxillofacial-related IPV. There was a higher number overall of maxillofacial-related IPV in 2020 (n=54) compared to 1970 (n=30). In 1970s movies, assailants were more frequently known to their victims (96.7 % vs 66.7 %, p=0.01) and maxillofacial-related IPV was more likely to involve alcohol and/or drugs (16.7 % vs 1.9 %, p=0.01). Victims were rarely hospitalized in either 1970 or 2020 cinema (0 % and 3.7 % respectively), assailants rarely suffered legal consequences (13.3 % and 5.6 % respectively) and these differences were not significant. Conclusion: Little has changed in mainstream cinema concerning the prevalence of maxillofacial-related IPV between 1970 and 2020, but there are subtle differences in how this is portrayed. The overarching notion that such injuries do not culminate in either hospitalisation and/or legal ramifications remains.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes’ scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target’s service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Cost and utility modeling of economics agents based on the differential theory is fundamental to the analysis of the microeconomics models. In particular, the first and second-order derivative tests are used to specify the desired properties of the cost and utility models. Traditionally, paper-and-pencil proof methods and computer-based tools are used to investigate the mathematical properties of these models. However, these techniques do not provide an accurate analysis due to their inability to exhaustively specify and verify the mathematical properties of the cost and utility models. Additionally, these techniques cannot accurately model and analyze pure continuous behaviors of the economic agents due to the utilization of computer arithmetic. On the other hand, an accurate analysis is direly needed in many safety and cost-critical microeconomics applications, such as agriculture and smart grids. To overcome the issues pertaining to the above-mentioned techniques, in this paper, we propose a theorem proving based methodology to formally analyze and specify the mathematical properties of functions used in microeconomics modeling. The proposed methodology is primarily based on a formalization of the derivative tests and root analysis of the polynomial functions, within the sound core of the HOL-Light theorem prover. We also provide a formalization of the first-order condition, which is used to analyze the maximum of the profit function in a higher-order-logic theorem prover. We then present the formal analysis of the utility, cost and first-order condition based on the polynomial functions. To illustrate the usefulness of proposed formalization, the proposed formalization is used to formally analyze and verify the quadratic cost and utility functions, which have been used in an optimal power flow problem and demand response (DR) program, respectively.

A large-scale integration of plug-in electric vehicles (PEVs) into the power grid system has necessitated the design of online scheduling algorithms to accommodate the after-effects of this new type of load, i.e., PEVs, on the overall efficiency of the power system. In online settings, the low computational complexity of the corresponding scheduling algorithms is of paramount importance for the reliable, secure, and efficient operation of the grid system. Generally, the computational complexity of an algorithm is computed using asymptotic analysis. Traditionally, the analysis is performed using the paper-pencil proof method, which is error-prone and thus not suitable for analyzing the mission-critical online scheduling algorithms for PEV charging. To overcome these issues, this paper presents a formal asymptotic analysis approach for online scheduling algorithms for PEV charging using higher-order-logic theorem proving, which is a sound computer-based verification approach. For illustration purposes, we present the complexity analysis of two state-of-the-art online algorithms: the Online cooRdinated CHARging Decision (ORCHARD) algorithm and online Expected Load Flattening (ELF) algorithm.

Bacterial transposons are known to move to new genomic sites using either a replicative or a conservative mechanism. The behavior of transposon Tn5 is anomalous. In vitro studies indicate that it uses a conservative mechanism while in vivo results point to a replicative mechanism. To explain this anomaly, a model is presented in which the two mechanisms are not independent--as widely believed--but could represent alternate outcomes of a common transpositional pathway.

OBJECTIVE: To determine the frequency of UTIs in people with diabetes and identify local bacterial flora and antibiotic resistance. METHODOLOGY: An observational, analytical, cross-sectional study was conducted at the Medicine Ward of Jinnah Medical and Dental College Hospital (now Sohail Trust Hospital) from January-December 2020. A convenient sampling technique was applied, and the sample size was 132. Diabetic patients >18 years of age were included. Data was collected on a structured questionnaire. A urine sample was sent for culture sensitivity. Any growing flora and their sensitivity/resistance to antibiotics were recorded. All the data were analysed using SPSS version 21. RESULTS: UTI was found in 31(23.5%) patients. Female gender, insulin therapy, and lower creatinine clearance were related to UTI, as shown by significant p values. Most common flora was E. Coli 26(84%), followed by Klebsiella 4(13%) and S. aureus 1(3%). E.Coli was 100% resistant to 3rd generation cephalosporins, 83.9% resistant to quinolones and 67.7% resistant to Penicillin (amoxicillin/clavulanic acid) and 43% resistant to aminoglycosides. Klebsiella was 100% resistant to Penicillin and quinolones and 85% to 3rd generation cephalosporin. E. coli and Klebsiella were 100% sensitive to carbapenems, nitrofurantoin and Fosfomycin, whereas S. Aureus was resistant to cloxacillin, clindamycin and clindamycin and quinolones, while sensitive to vancomycin., linezolid and aminoglycosides. CONCLUSION: UTI was found in 23.5% of our diabetic patients; the most common organisms prevalent were E. Coli, Klebsiella and S. aureus, which was primarily resistant to Penicillins, Cephalosporins and quinolones while sensitive to nitrofurantoin, Fosfomycin and carbapenems. [ABSTRACT FROM AUTHOR]