This is the first time we report that simply air plasma treatment can also enhances the optical absorbance and absorption region of titanium oxide (TiO₂) films, while keeping them transparent. TiO₂ thin films having moderate doping of Fe and Co exhibit significant enhancement in the aforementioned optical properties upon air plasma treatment. The moderate doping could facilitate the formation of charge trap centers or avoid the formation of charge recombination centers. Variation in surface species viz. Ti³⁺, Ti⁴⁺, O²⁻, oxygen vacancies, OH group and optical properties was studied using X-ray photon spectroscopy (XPS) and UV-Vis spectroscopy. The air plasma treatment caused enhanced optical absorbance and optical absorption region as revealed by the formation of Ti³⁺ and oxygen vacancies in the band gap of TiO₂ films. The samples were treated in plasma with varying treatment time from 0 to 60 seconds. With the increasing treatment time, Ti³⁺ and oxygen vacancies increased in the Fe and Co doped TiO₂ films leading to increased absorbance; however, the increase in optical absorption region/red shift (from 3.22 to 3.00 eV) was observed in Fe doped TiO₂ films, on the contrary Co doped TiO₂ films exhibited blue shift (from 3.36 to 3.62 eV) due to Burstein Moss shift.

This study demonstrates significant visible light photo-detection capability of pristine ZnO nanostructure thin films possessing substantially high percentage of oxygen vacancies $$({V}_{o}{\rm{s}})$$( V o s ) and zinc interstitials $$(Z{n}_{i}{\rm{s}})$$( Z n i s ) , introduced by simple tuning of economical solution method. The demonstrated visible light photo-detection capability, in addition to the inherent UV light detection ability of ZnO, shows great dependency of $${V}_{o}{\rm{s}}$$V o s and $$Z{n}_{i}{\rm{s}}$$Z n i s with the nanostructure morphology. The dependency was evaluated by analyzing the presence/percentage of $${V}_{o}{\rm{s}}$$V o s and $$Z{n}_{i}{\rm{s}}$$Z n i s using photoluminescence (PL) and X-ray photoelectron spectroscopy (XPS) measurements. Morphologies of ZnO viz. nanoparticles (NPs), nanosheets (NSs) and nanoflowers (NFs), as a result of tuning of synthesis method contended different concentrations of defects, demonstrated different photo-detection capabilities in the form of a thin film photodetector. The photo-detection capability was investigated under different light excitations (UV; 380~420 nm, white ; λ > 420 nm and green; 490~570 nm). The as fabricated NSs photodetector possessing comparatively intermediate percentage of $${V}_{o}{\rm{s}}$$V o s ~ 47.7% and $$Z{n}_{i}{\rm{s}}$$Z n i s ~ 13.8% exhibited superior performance than that of NPs and NFs photodetectors, and ever reported photodetectors fabricated by using pristine ZnO nanostructures in thin film architecture. The adopted low cost and simplest approach makes the pristine ZnO-NSs applicable for wide-wavelength applications in optoelectronic devices.

Blockchain is a revolutionary technology that enables users to communicate in a trust-less manner. It revolutionizes the modes of business between organizations without the need for a trusted third party. It is a distributed ledger technology based on a decentralized peer-to-peer (P2P) network. It enables users to store data globally on thousands of computers in an immutable format and empowers users to deploy small pieces of programs known as smart contracts. The blockchain-based smart contract enables auto enforcement of the agreed terms between two untrusted parties. There are several security vulnerabilities in Ethereum blockchain-based smart contracts, due to which sometimes it does not behave as intended. Because a smart contract can hold millions of dollars as cryptocurrency, so these security vulnerabilities can lead to disastrous losses. In this paper, a systematic review of the security vulnerabilities in the Ethereum blockchain is presented. The main objective is to discuss Ethereum smart contract security vulnerabilities, detection tools, real life attacks and preventive mechanisms. Comparisons are drawn among the Ethereum smart contract analysis tools by considering various features. From the extensive depth review, various issues associated with the Ethereum blockchain-based smart contract are highlighted. Finally, various future directions are also discussed in the field of the Ethereum blockchain-based smart contract that can help the researchers to set the directions for future research in this domain.

Blockchain technology and its applications are gaining popularity day by day. It is a ground-breaking technology that allows users to communicate without the need of a trusted middleman. A smart contract (self-executable code) is deployed on the blockchain and auto executes due to a triggering condition. In a no-trust contracting environment, smart contracts can establish trust among parties. Terms and conditions embedded in smart contracts will be imposed immediately when specified criteria have been fulfilled. Due to this, the malicious assailants have a special interest in smart contracts. Blockchains are immutable means if some transaction is deployed or recorded on the blockchain, it becomes unalterable. Thus, smart contracts must be analyzed to ensure zero security vulnerabilities or flaws before deploying the same on the blockchain because a single vulnerability can lead to the loss of millions. For analyzing the security vulnerabilities of smart contracts, various analysis tools have been developed to create safe and secure smart contracts. This paper presents a systematic review on Ethereum smart contracts analysis tools. Initially, these tools are categorized into static and dynamic analysis tools. Thereafter, different sources code analysis techniques are studied such as taint analysis, symbolic execution, and fuzzing techniques. In total, 86 security analysis tools developed for Ethereum blockchain smart contract are analyzed regardless of tool type and analysis approach. Finally, the paper highlights some challenges and future recommendations in the field of Ethereum smart contracts.

The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users’ privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient’s medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.