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The fabrication of a Poly (vinylidene fluoride) membrane (PVDF) and ceramic-assisted bismuth vanadate-polyvinylidene fluoride (BiVO4-PVDF) composite membrane was achieved through the utilization of the electrospinning technique. The composition and structure of the fabricated membranes were characterized by X-ray powder diffraction, Raman analysis, scanning electron microscopy, Thermo gravimetric analyzer, Fourier transform infrared spectroscopy, and UV–Vis spectroscopy techniques. The prepared polymeric membranes were then utilized for catalytic investigation and to explore, how structure affects catalytic activity using 5 mg/L, 10 mL methylene blue (MB) dye solution. Ultrasonication, visible light irradiation, and the combination were used to study piezocatalysis, photocatalysis, and piezo-photocatalysis, moreover, degradation intermediates were also explored using scavengers. Electrospun BiVO4-PVDF (BV-PVDF) composite has been found to have better piezocatalytic and photocatalytic properties than PVDF. The experimental findings reveal that the composite of BiVO4-PVDF demonstrates the highest efficiency in dye degradation, achieving a maximum degradation rate of 61% within a processing time of 180 min. The rate of degradation was calculated to be 0.0047 min−1, indicating a promising potential for the composite in the field of dye degradation. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively. [ABSTRACT FROM AUTHOR]

WTe2 is one of the wonder layered materials, displays interesting overlapping of electron–hole pairs, opening of the surface bandgap, anisotropy in its crystal structure and very much sought appealing material for room temperature broadband photodection applications. Here we report the photoresponse of WTe2 thin films and microchannel devices fabricated on silicon nitride substrates. A clear sharp rise in photocurrent observed under the illumination of visible (532 nm) and NIR wavelengths (1064 nm). The observed phoresponse is very convincing and repetitive for ON /OFF cycles of laser light illumination. The channel length dependence of photocurrent is noticed for few hundred nanometers to micrometers. The photocurrent, rise & decay times, responsivity and detectivity are studied using different channel lengths. Strikingly microchannel gives few orders of greater responsivity compared to larger active area investigated here. The responsivity and detectivity are observed as large as 29 A/W and 3.6 × 108 Jones respectively. The high performing photodetection properties indicate that WTe2 can be used as a broad band material for future optoelectronic applications. [ABSTRACT FROM AUTHOR]

WTe2 is one of the wonder layered materials, displays interesting overlapping of electron–hole pairs, opening of the surface bandgap, anisotropy in its crystal structure and very much sought appealing material for room temperature broadband photodection applications. Here we report the photoresponse of WTe2 thin films and microchannel devices fabricated on silicon nitride substrates. A clear sharp rise in photocurrent observed under the illumination of visible (532 nm) and NIR wavelengths (1064 nm). The observed phoresponse is very convincing and repetitive for ON /OFF cycles of laser light illumination. The channel length dependence of photocurrent is noticed for few hundred nanometers to micrometers. The photocurrent, rise & decay times, responsivity and detectivity are studied using different channel lengths. Strikingly microchannel gives few orders of greater responsivity compared to larger active area investigated here. The responsivity and detectivity are observed as large as 29 A/W and 3.6 × 108 Jones respectively. The high performing photodetection properties indicate that WTe2 can be used as a broad band material for future optoelectronic applications. [ABSTRACT FROM AUTHOR]

In Mobile Ad hoc Network (MANET), network topology changes as devices/users/nodes move and nodes can serve as a source, destination, or router for the information. One of the challenging tasks in MANET is secure routing because of the change of network topology, open nature of wireless communications and frequent breakage in communication links. Thus, it is critically important to develop a robust and secure routing protocol for sending data from the source to the destination in MANET. In this paper, we investigate black hole attacks where compromised nodes utilize routing protocols for self-exposing which has the shortest route for data forwarding to the destination node. Specifically, the proposed approach enhances the Ad hoc On-Demand Distance Vector routing protocol for combating black hole attacks by leveraging the Firefly Algorithm with Artificial Neural Network. Performance evaluation is carried out using numerical results obtained from several experiments and considering different metrics such as computation overhead, packet delivery rate, throughput and delay. Numerical results show that the proposed approach outperforms the traditional approaches. [ABSTRACT FROM AUTHOR]

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT) based solutions are gaining prominence and innovative Peer To Peer (P2P) energy trading at micro-level are being deployed. Such advancement, however leave traditional security models vulnerable and pave the path for Blockchain, an Distributed Ledger Technology (DLT) with its decentralized, open and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, massive volumes of micro transactions are required to be supported, which become a performance bottleneck with existing Blockchain solution such as Hyperledger, Ethereum and so on. In this paper, a lightweight 'Tangle' based framework, namely IOTA (Third generation DLT) is employed for designing an energy trading market that uses Directed Acyclic Graph (DAG) based solution that not only alleviates the reward overhead for micro-transactions but also provides scalability, quantum-proof, and high throughput of such transactions at low confirmation latency. Furthermore the Masked Authentication Messaging (MAM) protocol is used over the IOTA P2P energy trading framework that allows energy producer and consumer to share the data while maintaining the confidentiality, and facilitates the data accessibility. The Raspberry Pi 3 board along with voltage sensor (INA219) used for the setting up light node and publishing and fetching data from the Tangle. The results of the obtained benchmarking indicate low confirmation latency, high throughput, system with Hyperledger Fabric and Ethereum. Moreover, the effect of transaction rate decreases when the IOTA bundle size increases more than 10. For bundle size 5 and 10 it behaves absolutely better than any other platform. The speedy confirmation time of transactions in IOTA, is most suitable for peer to peer energy trading scenarios. This study serves as a guideline for deploying, end-to-end transaction with IOTA Distributed Ledger Technology (DLT) and improving the performance of Blockchain in the energy sector under various operating conditions. [ABSTRACT FROM AUTHOR]

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'. [ABSTRACT FROM AUTHOR]

Software effort estimation is a significant part of software development and project management. The accuracy of effort estimation and scheduling results determines whether a project succeeds or fails. Many studies have focused on improving the accuracy of predicted results, yet accurate estimation of effort has proven to be a challenging task for researchers and practitioners, particularly when it comes to projects that use agile approaches. This work investigates the application of the adaptive neuro-fuzzy inference system (ANFIS) along with the novel Energy-Efficient BAT (EEBAT) technique for effort prediction in the Scrum environment. The proposed ANFIS-EEBAT approach is evaluated using real agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that ANFIS-EEBAT worked best as compared to various state-of-the-art meta-heuristic and machine learning (ML) algorithms such as fireworks, ant lion optimizer (ALO), bat, particle swarm optimization (PSO), and genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

We present Wolverine2, an integrated Debug-Localize-Repair environment for heap manipulating programs. Wolverine2 provides an interactive debugging environment: while concretely executing a program via on an interactive shell supporting common debugging facilities, Wolverine2 displays the abstract program states (as box-and-arrow diagrams) as a visual aid to the programmer, packages a novel, proof-directed repair algorithm to quickly synthesize the repair patches and a new bug localization algorithm to reduce the search space of repairs. Wolverine2 supports "hot-patching" of the generated patches to provide a seamless debugging environment, and also facilitates new debug-localize-repair possibilities: specification refinement and checkpoint-based hopping. We evaluate Wolverine2 on 6400 buggy programs (generated using automated fault injection) on a variety of data-structures like singly, doubly, and circular linked lists, AVL trees, Red-Black trees, Splay Trees and Binary Search Trees; Wolverine2 could repair all the buggy instances within realistic programmer wait-time (less than 5 s in most cases). Wolverine2 could also repair more than 80% of the 247 (buggy) student submissions where a reasonable attempt was made. [ABSTRACT FROM AUTHOR]

Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices. In such devices, automatic face recognition requires a low powered memory efficient visual computing system. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. The proposed technique extracts the features for visual recognition using local binary pattern histogram. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. The validation of proposed technique is proved on the basis of well-known databases. The technique shows significant results for all databases when implemented on Raspberry Pi. A comparison of the proposed technique with already existing/reported techniques for the similar applications has been provided. Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. Therefore, the proposed algorithm is useful for real time visual recognition for smart city surveillance. [ABSTRACT FROM AUTHOR]

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-18603-z, published online 25 August 2022 The original version of this Article contained an error in Affiliation 1, which was incorrectly given as 'School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vijayawada 522237, Andhra Pradesh, India'. The correct affiliation is listed below: School of Computer Science and Engineering (SCOPE), VIT-AP University, Vijayawada 522237, Andhra Pradesh, India. [Extracted from the article]