The SDS22:PP1:I3 complex: SDS22 binding to PP1 loosens the active site metal to prime metal exchange.

SDS22 and Inhibitor-3 (I3) are two ancient regulators of protein phosphatase 1 (PP1) that regulate multiple essential biological processes. Both SDS22 and I3 form stable dimeric complexes with PP1; however, and atypically for PP1 regulators, they also form a triple complex, where both proteins bind to PP1 simultaneously (SPI complex). Here we report the crystal structure of the SPI complex. While both regulators bind PP1 in conformations identical to those observed in their individual PP1 complexes, PP1 adopts the SDS22-bound conformation, which lacks its M1 metal. Unexpectedly, surface plasmon resonance (SPR) revealed that the affinity of I3 for the SDS22:PP1 complex is ∼10-fold lower than PP1 alone. We show that this change in binding affinity is solely due to the interaction of I3 with the PP1 active site, specifically PP1's M2 metal, demonstrating that SDS22 likely allows for PP1 M2 metal exchange and thus PP1 biogenesis.

Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions.

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR-SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR-SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms.

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing.

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

How COVID-19 pandemic has shaped buyer-supplier relationships in engineering companies with ethical perception considerations: A multi-methodological study.

In business-to-business (B2B) operations, prior studies have mainly explored transaction-based relationships with both buyers and suppliers opportunistic behaviors, driven largely by their intent to maximize their own benefits. These studies have also found that dependency on partners increases when supply materials are scarce. However, research is scant on how this relationship changes in the face of exogenous forces such as the COVID-19 pandemic, keeping in mind the ethical perception considerations. This study aims to bridge this gap in the literature by studying how buyers and sellers leverage collaboration and resource-sharing to tide over pandemic-like situations similar to the current COVID-19 pandemic while considering their ethical perceptions. We conduct a multi-methodological study consisting of an industrial survey and an interview-based thematic analysis. In the first phase, we collect primary data using a structured questionnaire and conduct a covariance-based structural equation modeling (CB-SEM) analysis. In the second phase, we conduct a post-hoc test. We find that non-regular suppliers will share strategic resources with buyers during uncertain times (e.g. COVID-19 pandemic) if they have a high ethical perception of the buying firm and share a candid relationship despite being their irregular customers. Our findings propose that B2B firms should maintain healthy relationships with alternative suppliers to build trust and avoid supply crises in times of disruptions.

Identifying health related occupations of Twitter users through word embedding and deep neural networks.

BACKGROUND: Twitter is a popular social networking site where short messages or "tweets" of users have been used extensively for research purposes. However, not much research has been done in mining the medical professions, such as detecting the occupations of users from their biographical contents. Mining such professions can be used to build efficient recommender systems for cost-effective targeted advertisements. Moreover, it is highly important to develop effective methods to identify the occupation of users since conventional classification methods rely on features developed by human intelligence. Although, the result may be favorable for the classification problem. However, it is still extremely challenging for traditional classifiers to predict the medical occupations accurately since it involves predicting multiple occupations. Hence this study emphasizes predicting the medical occupational class of users through their public biographical ("Bio") content. We have conducted our analysis by annotating the bio content of Twitter users. In this paper, we propose a method of combining word embedding with state-of-art neural network models that include: Long Short Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit, Bidirectional Encoder Representations from Transformers, and A lite BERT. Moreover, we have also observed that by composing the word embedding with the neural network models there is no need to construct any particular attribute or feature. By using word embedding, the bio contents are formatted as dense vectors which are fed as input into the neural network models as a sequence of vectors. RESULT: Performance metrics that include accuracy, precision, recall, and F1-score have shown a significant difference between our method of combining word embedding with neural network models than with the traditional methods. The scores have proved that our proposed approach has outperformed the traditional machine learning techniques for detecting medical occupations among users. ALBERT has performed the best among the deep learning networks with an F1 score of 0.90. CONCLUSION: In this study, we have presented a novel method of detecting the occupations of Twitter users engaged in the medical domain by merging word embedding with state-of-art neural networks. The outcomes of our approach have demonstrated that our method can further advance the process of analyzing corpora of social media without going through the trouble of developing computationally expensive features.

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach.

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation.

There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

Privacy Preserving Multi-Party Key Exchange Protocol for Wireless Mesh Networks.

Presently, lightweight devices such as mobile phones, notepads, and laptops are widely used to access the Internet throughout the world; however, a problem of privacy preservation and authentication delay occurs during handover operation when these devices change their position from a home mesh access point (HMAP) to a foreign mesh access point (FMAP). Authentication during handover is mostly performed through ticket-based techniques, which permit the user to authenticate itself to the foreign mesh access point; therefore, a secure communication method should be formed between the mesh entities to exchange the tickets. In two existing protocols, this ticket was not secured at all and exchanged in a plaintext format. We propose a protocol for handover authentication with privacy preservation of the transfer ticket via the Diffie-Hellman method. Through experimental results, our proposed protocol achieves privacy preservation with minimum authentication delay during handover operation.

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization.

High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network's external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent.

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review.

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

An edge-driven multi-agent optimization model for infectious disease detection.

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.

ProBlock: a novel approach for fake news detection.

The world is diving deeper into the digital age, and the sources of first information are moving towards social media and online news portals. The chances of being misinformed increase multifold as our reliance on sources of information are getting ambiguous. Traditional news sources followed strict codes of practice to verify stories, whereas today, users can upload news items on social media and unverified portals without proving their veracity. The absence of any determinants of such news articles' truthfulness on the Internet calls for a novel approach to determine the realness quotient of unverified news items by leveraging technology. This study presents a dynamic model with a secure voting system, where news reviewers can provide feedback on news, and a probabilistic mathematical model is used for predicting the truthfulness of the news item based on the feedback received. A blockchain-based model, ProBlock is proposed; so that correctness of information propagated is ensured.

Internet of Things Based Blockchain for Temperature Monitoring and Counterfeit Pharmaceutical Prevention.

The top priority of today's healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.

Realizing Efficient Security and Privacy in IoT Networks.

In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper's information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.

Deep learning disease prediction model for use with intelligent robots.

Deep learning applications with robotics contribute to massive challenges that are not addressed in machine learning. The present world is currently suffering from the COVID-19 pandemic, and millions of lives are getting affected every day with extremely high death counts. Early detection of the disease would provide an opportunity for proactive treatment to save lives, which is the primary research objective of this study. The proposed prediction model caters to this objective following a stepwise approach through cleaning, feature extraction, and classification. The cleaning process constitutes the cleaning of missing values ,which is proceeded by outlier detection using the interpolation of splines and entropy-correlation. The cleaned data is then subjected to a feature extraction process using Principle Component Analysis. A Fitness Oriented Dragon Fly algorithm is introduced to select optimal features, and the resultant feature vector is fed into the Deep Belief Network. The overall accuracy of the proposed scheme experimentally evaluated with the traditional state of the art models. The results highlighted the superiority of the proposed model wherein it was observed to be 6.96% better than Firefly, 6.7% better than Particle Swarm Optimization, 6.96% better than Gray Wolf Optimization ad 7.22% better than Dragonfly Algorithm.

A Node Density Control Learning Method for the Internet of Things.

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

A Decentralized Privacy-Preserving Healthcare Blockchain for IoT.

Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.

Exploring Janus kinase inhibitors for alopecia areata: a comprehensive review.

INTRODUCTION: Alopecia areata poses a significant challenge due to its chronic autoimmune nature, leading to psychosocial impacts. Recent strides in understanding the disease have spotlighted Janus kinase (JAK) inhibitors as potential therapies. This comprehensive review aims to assess Baricitinib's efficacy and safety in treating scalp, eyebrow, and eyelash alopecia areata, and compare the effectiveness of Ritlecitinib and Brepocitinib. EVIDENCE ACQUISITION: Conducting a thorough electronic literature search, we focused on clinical studies of JAK inhibitors for moderate to severe alopecia areata from 2015 onward. Key databases, including MEDLINE, PubMed, Cochrane Library, EMBASE, Google Scholar, and Medscape, were utilized. Primary outcomes included changes in the Severity of Alopecia Tool (SALT) score, with safety data evaluating adverse events and serious adverse events. The risk of bias was assessed using the Cochrane Risk of Bias Tool. EVIDENCE SYNTHESIS: Among the twelve studies identified, Baricitinib demonstrated superior efficacy over placebo at 24 weeks, with both 2mg and 4mg dosages significantly reducing SALT scores. Comparative efficacy at 24 weeks for Baricitinib, Brepocitinib, and Ritlecitinib showed similar effectiveness compared to placebo, with a marginal superiority observed for Baricitinib 4mg. All JAK inhibitors were well-tolerated, with reported adverse events primarily being mild and manageable. CONCLUSIONS: Collectively, the reviewed studies affirm JAK inhibitors, particularly Baricitinib, as promising treatments for moderate to severe alopecia areata. These inhibitors exhibit superior efficacy, as indicated by notable reductions in SALT scores, and are well-tolerated, with predominantly mild and manageable adverse events.

Anomaly detection using deep convolutional generative adversarial networks in the internet of things.

Advanced 5 G and 6 G technologies have accelerated the adoption of the Internet of Things (IoT) and are a priority in providing support for high-speed communication and fast data analysis. One of IoT networks benefits is automated networking, which unfortunately increases the risk of security, integrity, and privacy breaches. Therefore, in this paper, we propose a weighted stacked ensemble model combining deep convolutional generative adversarial and bidirectional long short-term memory networks. The proposed model has been regularized, and hyperparameter tuning has been performed. The tuned model is then evaluated on four publicly available current IoT datasets. The proposed model exhibits significant improvement in standard performance measures for both binary and multiclass classification. Generalization error has been reduced by a rate of 0.005% and to overcome the issue of overfitting, a L2 regularization technique has been deployed. The overall Accuracy of the model on various datasets is 99.99% for BOT-IoT, 99.08% for IoT23, 99.82% for UNSWNB15, and 99.96% for ToN_IoT, respectively, alongside improvements in Precision, Recall, and F1-score.

SAGA: Stability-Aware Gait Analysis in constraint-free environments.

BACKGROUND: Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network. METHODS: It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach. RESULTS: The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%. SIGNIFICANCE: Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.