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Transactive energy is an effective way to share and trade energy among peers. A transactive energy framework is composed of several integrated blocks such as an energy market, service providers, generation companies, transmission and distribution networks, prosumers, etc. The success of such a framework can be measured by analyzing the effectiveness of its major building blocks. This paper provides a basic definition of transactive energy which helps in identifying its primary building blocks. Based on the identified building blocks, each block of the transactive energy system is discussed briefly to highlight its significant role and effectiveness. Some enabling technologies for such systems are then reviewed along with pilot projects. In the end, some policy guidelines are presented to speed up their implementation.

Vehicle re-identification is one of the essential applications for intelligent transportation systems and urban surveillance. However, enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles with different views. Additionally, diversified illumination and complicated environments create significant hurdles for the existing methods. We present a multi-guided learning method in this paper that uses multi-attribute and view point information, while also enhancing the robustness of feature extraction. The multi-attribute sub-network learns discriminative features like, i.e. color and type of vehicle. Moreover, the view predic-tor network adds extra information to the feature embedding and To validate the effectiveness of our framework, experiments on two benchmark datasets VeRi-776 and VehicleID are conducted. Experimental results illustrate our framework achieved comparative performance. VAAG CAR A CAR B Fig. 1 The figure shows two separate cars from distinct views from VeRi-776. Car A on the top row illustrates several views from various angles. Car B in the lower row has a diversity of viewpoints as well. These cars have very similar appearances, and it’s difficult to tell them apart. Car A appears to be different than its other views and identical to Car B from various perspectives.

A large number of videos available on the internet belong to the category of sports. Generally, a sports video has a long duration and consists of only a few exciting moments. Sports enthusiasts keep themselves updated on the current happenings, in less time, by means of a summarized version of the sports video known as highlights. For the past few years, sports video summarization is regaining attention among the research community. Automatic generation of highlights form a sports video is a challenging task as different sports games have different rules and situations. In this paper, we propose a method for automatically generating highlights from broadcast sports videos. The proposed method generates highlights by extracting audio and visual features from a sports video. Our method automatically learns the scorebox template from a broadcast sports video using SIFT features, and then locates and extracts the template from a video stream. The extracted template is further analyzed to find out all the possible text regions. Afterward, the information is extracted from all the text regions by means of deep neural network. Based on user preferences, the most relevant information is extracted and converted to a keyframe representation which helps to generate highlights. Extensive experiments were performed to evaluate the effectiveness of the proposed method. Results of the experiments reveal the effectiveness and superiority of the proposed method.

Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.

With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.

3D-printed platforms integrated with smartphone light sensors have become a powerful tool for the development of frugal and facile biosensing systems. Herein, we report a 3D-printed platform for multimodal analysis (3D-PMA) based on a smartphone ambient light sensor (ALS). The proposed approach was first validated using a microtiter plate reader, and then its application potential was demonstrated by measuring halohydrin dehalogenase activities employing three different sample modes, viz. using enzyme-linked immunosorbent assay (ELISA) plate strips, cuvettes, and the glass fiber membrane as sample containers. For real-sample analysis, 1,3-dichloro-2-propanol (1,3-DCP), a toxic halogenated compound, was successfully detected in spiked river water samples with recoveries in the range of 101.95–109.70%. After demonstrating the suitability for liquid-phase assays, the 3D-PMA was also optimized for 1,3-DCP detection using a glass fiber membrane. The detection could be done using a 10 μ L reaction system with an assay time of 2 min. The assay showed two linear ranges. The calibration equation of Y = 81.687x + 140.060 was obtained from the lower part of the calibration curve and was used to calculate the detection limit of the system. Under optimized conditions, a detection limit of 80 μ M was achieved for 1,3-DCP. The small size of the device endows it with great promise for use as a benchtop and an on-demand detection system. The proposed low-cost and portable system has great potential for the (bio) sensing of different biological and environmental samples where biocomponent-analyte interaction leads to a color change of the reaction system, indicating its applicability in resource-constrained settings.

Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.

Enzymes, being remarkable catalysts, are capable of accepting a wide range of complex molecules as substrates and catalyze a variety of reactions with a high degree of chemo-, stereo- and regioselectivity in most of the reactions. Biocatalysis can be used in both simple and complex chemical transformations without the need for tedious protection and deprotection chemistry that is very common in traditional organic synthesis. This current review highlights the applicability of one class of biocatalysts viz. ‘‘lipases’’ in synthetic transformations, the resolution of pharmaceutically important small molecules including polyphenols, amides, nucleosides and their precursors, the development of macromolecular systems (and their applications as drug/gene carriers), flame retardants, polymeric antioxidants and nanocrystalline solar cells, etc.

The chain-breaking antioxidant activities of eight coumarins [7-hydroxy-4-methylcoumarin (1), 5,7-dihydroxy-4-methylcoumarin (2), 6,7-dihydroxy-4-methylcoumarin (3), 6,7-dihydroxycoumarin (4), 7,8-dihydroxy-4-methylcoumarin (5), ethyl 2-(7,8-dihydroxy-4-methylcoumar-3-yl)-acetate (6), 7,8-diacetoxy-4-methylcoumarin (7) and ethyl 2-(7,8-diacetoxy-4-methylcoumar-3-yl)-acetate (8)] during bulk lipid autoxidation at 37 °C and 80 °C in concentrations of 0.01–1.0 mM and their radical scavenging activities at 25 °C using TLC–DPPH test have been studied and compared. It has been found that the o-dihydroxycoumarins 3–6 demonstrated excellent activity as antioxidants and radical scavengers, much better than the m-dihydroxy analogue 2 and the monohydroxycoumarin 1. The substitution at the C-3 position did not have any effect either on the chain-breaking antioxidant activity or on the radical scavenging activity of the 7,8-dihydroxy- and 7,8-diacetoxy-4-methylcoumarins 6 and 8. The comparison with DL-α-tocopherol (TOH), caffeic acid (CA) and p-coumaric acid (p-CumA) showed that antioxidant efficiency decreases in the following sequence: TOH > CA > 3 > 4 > 6 > 5 > 2 > 1 = 7 = 8 = p-CumA. Theoretical calculations and the “Lipinski’s Rule of Five” were used for explaining the structure–activity relationships and pharmacokinetic behavior. A higher TGSO oxidation stability was observed in the presence of equimolar (1:1) binary mixtures of coumarins with TOH (1 + TOH, 3 + TOH and 5 + TOH). However, the synergism (14%) was observed only for the binary mixture of 5 + TOH.