An AI-based disease detection and prevention scheme for COVID-19.

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.

Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System.

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid's energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.

GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection.

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.

Formulation and evaluation of novel functional snack bar with amaranth, rolled oat, and unripened banana peel powder.

The present pandemic situation has increased demand for functional foods that enhance all aged groups' people immunity against COVID-19. This factor has led to innovation in snack market because healthy and good quality snack products are lacking. In this study, attempt has been made to develop functional snack bar that is beneficial for malnourished population from various combinations of amaranth grain, oat, and banana peel powder. Among various combinations amaranth grain (60%), oats (25%) and banana peel powder (15%) was found better than other combinations in respect to organoleptic and nutritional quality. Oat and banana peel powder addition increased the contents of protein, mineral, ß-glucan, dietary fibre, essential amino acid, phenolic, and antioxidant activity of functional snack bar. TGA analysis shows that active components present in it were stable even at high temperature which adds benefit to its functional property. Microbial studies of FSB revealed it could be stored up to 60 days without microbial contamination and acceptable by consumer. The cost of a functional snack bar was 9.57 per bar, which was less than market snack bar. This study showed that developed functional snack bar snack increases market's revenue and enables snack market to develop new type snack bar.

Iron nanoparticles as a promising compound for food fortification in iron deficiency anemia: a review.

Abstract: Iron deficiency anemia (IDA) is a global health concern that is affecting all age groups significantly. Among many of the existing methods, the fortification of foods with iron salts is the best and most cost-effective strategy for targeting large-scale populations to provide nutritional security. The fortification of foods with iron salts is a challenging task because most iron complexes (ferrous sulfate, ferrous chloride) used in fortification are highly water-soluble, which impart unacceptable organoleptic changes in food vehicles and also causes gastrointestinal problems. However, insoluble iron salts (ferric pyrophosphate) do not cause unacceptable taste or color in food vehicles but low bioavailable. Nanosized iron salts can overcome these concerns. The particle size of iron salts has been reported to play an important role in the absorption of iron. Reduction in the particle size of iron compounds increases its surface area, which in turn improves its solubility in the gastric juice leading to higher absorption. Nanosized iron compound produces minimal organoleptic changes in food vehicles compared to water-soluble iron complexes. Thus nanosized iron salts find potential applications in food fortification to reduce IDA. This paper focuses on providing a complete review of the various iron salts used in IDA, including their bioavailability, the challenges to food fortification, the effects of nanosized iron salts on IDA, and their applications in food fortification. Graphic abstract: Fortification of foods with water-soluble Fe salts imparts unacceptable organoleptic changes in food vehicle and adverse impact on health. However, insoluble iron salts do not cause unacceptable taste or color in food vehicles but low bioavailable. Using Nano-sized iron compound produces minimal organoleptic changes in food vehicles compared to changes produced by water-soluble iron complexes, improves Fe absorption in the gastrointestinal tract and does not cause any health issues.

Development of water spinach powder and its characterization.

The study was performed on water spinach (Ipomea aquatica), a hidden gem with lots of nutritional attributes and medicinal properties. To check its feasibility as an ingredient in other food products and to know its nutritional attributes, powders were made by drying the leaves and stems of the plant using different drying techniques such as sun drying, freeze-drying, and tray drying. The physicochemical analysis of powder was done to get a comparative result, in which the freeze-dried powder showed the most significant result. The physicochemical analysis revealed that lyophilized water spinach powder has a good amount of carbohydrates (58.15%), ash (12.39%), protein (4.01%), and fat (4.46%) content. The powder also possessed a high antioxidant property of 77.25% and a total phenolic content of 32 µg/ ml. SEM and XRD results showed that the water spinach powder was amorphous in nature.

ξboost: An AI-Based Data Analytics Scheme for COVID-19 Prediction and Economy Boosting.

The coronavirus (COVID-19) outbreak has a significant impact on people's lives, occupations, businesses, and economies globally. The world economic market is experiencing a big shift and the share market has observed crashes day-by-day. Even, the Indian economy has witnessed a slowdown in the current pandemic, and recovery of it is quite difficult. The restrictions and restrain strategies (e.g., lockdown and social distancing) introduced by the government leave many professions and facilities in a dormant state, catalyzing economy downfall. It necessitates to improve economy along with control strategies of COVID-19, which is a challenging task. To handle the above-mentioned issues, this article proposes a novel economy-boosting scheme, i.e., [Formula: see text] boost, which is a fusion of artificial intelligence (AI) and big data analytics (BDA) integrated with the Internet-of-Things (IoT)-based data communication. Here, a bidirectional long short-term memory (LSTM) model is anticipated for early prediction of total positive cases as well as the economy. Then, it calculates an optimal subsegment of days, in which trade and commerce related restrictions could be reduced to control a sharp decline in the economy. Next, a spark-based pre and post unlock (PPU) analytics is carried out on the rise of COVID-19 cases to validate the intensity of testing in the country and deciding economy-boosting activities. Then, the [Formula: see text] boost scheme is evaluated based on various factors such as prediction accuracy and others while comparing to existing approaches. It facilitates healthy and profitable smart cities by the means to control pandemic with subsequent economy rise.

A rapid and cost-effective method based on dispersive liquid-liquid microextraction coupled to injection port silylation-gas chromatography-mass spectrometry for determination of morphine in illicit opium.

A simple, rapid, cost-effective and environment friendly analytical method based on dispersive liquid-liquid microextraction (DLLME) coupled to injection port silylation (IPS)-gas chromatography-mass spectrometry is described for the determination of morphine in illicit opium samples. Raw opium was dispersed in ultrapure water and 5 mL of aqueous sample was subjected to DLLME by rapidly injecting a mixture of chloroform and acetone (extraction and disperser solvent, respectively) followed by ultrasonication for 1 min and subsequent centrifugation for 3 min at 5000 rpm. The sedimented phase thus obtained was reconstituted in acetonitrile and 1 µL along with 1 µL of N,O-Bis(trimethylsilyl)acetamide (BSA) was injected manually into GC-MS injection port at a temperature of 250°C. The derivatization reaction was completed instantaneously inside the heated GC-MS injection port without any side product. Various parameters associated with IPS and DLLME have been thoroughly optimized. Under the optimized conditions, the method has been found linear in the range of 5-50 µg/mL with a correlation coefficient (R 2) of 0.997. The limit of detection (LOD) and limit of quantification (LOQ) for morphine-diTMS were found to be 1.6 and 4.8 µg/mL. The method has been successfully applied for the quantitative analysis of morphine in illicit opium samples. In conclusion, the proposed method has completely eliminated the time consuming and laborious steps of LLE and in-vial silylation and can be routinely used for analysis of opium and other polar analytes in forensic science laboratories.