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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.

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.

Many studies have been devoted to sports video summarization and content-based video search. However, the semantic importance of caption box or scorebox (SB) appearing in broadcast sports videos has been almost neglected as SB holds key elements for conducting these research tasks. SB localization is challenging as there exists a huge variety of SBs, and almost every broadcast sports video contains a different SB with unique features such as geometry, font, colors, location, and texture. Every time a new sports series emerges, it contains a new type of scorebox that never resembles any other sports series. One can say that, SBs are evolving with unexpected features and novel challenges. Thus, traditional learning-based methods alone are not suitable for detection. This paper proposes a robust method for detecting and localizing SBs appearing in broadcast sports videos. It automatically learns the template of SB and further utilizes the template, as the SB may translate from the usual location and may disappear for a short time. We performed comprehensive experiments on a real-life dataset SP-1 and comparison with state-of-the-art methods shows that the proposed method achieves better performance.

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.

The development of Location Base Services (LBS) poses new challenges for the protection of user's privacy. Users have to send their current location information to the service provider. The current location of the user can expose critical information such as home/work address, other sensitive information, etc. thus, it is important to protect the users private and sensitive information. As a solution to this problem, different techniques have been proposed over the past few years. Among them, one of the most widely used technique is dummy location generation. However, current dummy location generation methods hardly account for the factor that an attacker has prior knowledge and spatiotemporal information about the user. In this paper, we identify shortcomings and vulnerabilities of the existing techniques and provide a robust solution to maintain the user's data privacy. Furthermore, we propose a robust dummy location generation method capable of averting the negative effects of an attacker having prior knowledge and spatiotemporal information. Additionally, we present some attacker strategies and remedies to avert such attacks. Experiment results show that our proposed method successfully preserves the user's private information, where other state of the art techniques fails to do so.

Global warming has made the transition to zero-carbon emissions generation a necessity for a healthy and green society. This requires that the dependability on the main grid, which is mostly driven by fossil fuel, natural gas, and coal, be reduced. Peer-to-peer (P2P) energy trading has been proposed to achieve this objective. However, the success of such trading depends on the fulfillment of energy-related objectives of participants. The identification of energy-related objectives of participants and their fulfillment is a challenging task. For this purpose, this paper proposes a game-theoretic approach to motivate prosumers for P2P energy trading in both the islanded and grid-connected modes. First, a model that addresses the basic energy-related objectives of both producers and consumers is proposed along with the layered architecture of P2P market to identify and categorize the existing technologies. Then, an extensive form game based on non-cooperative game theory is described and the existence of its strict Nash equilibrium is evaluated. An energy allocation policy has also been devised that motivates prosumers by ensuring that none of them exits the market without trading irrespective of the balance between supply and demand. The proposed energy trading scheme is evaluated on the IEEE-14 bus system with 8 producers and 11 consumers as market participants. We performed comprehensive experiments and the results of these experiments are compared with the previous studies and found that 33 % to 7 % reduction in energy bills of the consumers is achieved from the proposed scheme which boldly shows the effectiveness of the proposed scheme.

With the increase in energy demand and the integration of renewable energy resources, price determination of energy trading in the P2P energy market became more challenging. Participants need a fair mechanism that ensures the basic energy-related objectives i.e. savings on energy bills for consumers and profitability for the seller. To achieve these goals, this paper presents a uniform pricing scheme for energy trading along with energy allocation policy for P2P energy trading market. Based on consumer preferences, a novel method for determining energy trading price is proposed which ensures the increase in utility for consumers (saving in energy bill) and sellers profitability. Considering the fluctuating supply of energy in the P2P energy market, due to the sporadic nature of renewable energy resources, an effective scheme for energy allocation is proposed. The proposed method promotes competition among sellers and brings fairness in the energy market by selecting an optimal price. False declaration from the participants leads to a decrease in utilities. The proposed method provides the necessary information for assessing the consumers’ requirements and increase the social welfare of the consumers by reducing the trading prices. Extensive experiments were performed and the results show the effectiveness of the proposed method as the method helps to reduce the energy bills of the consumers and increases the satisfaction level as compared to previously proposed methods.

P2P electricity trading is a promising new approach, which achieves a higher level of satisfaction for prosumers and consumers, and reduces the amount of line losses in smart-grid. However, uncertainties in demand and supply of energy may result in contract instability for prosumers and consumers in P2P market. This paper proposes a framework which incorporates both non-cooperative and cooperative game, to facilitate the P2P electricity trading while maintaining stability of the contract. The proposed cooperative game ensures the survival of small-scale prosumers at a risk of market-exit when they do not own enough surplus electricity to cater the power needs of a single consumer. Hence the stability of the contract can be guaranteed. In the proposed approach, a pivotal player is identified with the concept of Shapely-Shubik power index, to distribute the shared revenue in a fair manner. An IEEE 14-bus system, which includes total 22 players (11 prosumers and 11 consumers), is used to test the effectiveness of the proposed approach. One year of dataset is tested and it is shown that both prosumers and consumers can get benefit from the P2P electricity trading while maintaining the stability of the market.

A new class of twinned amphiphiles was developed by conjugating a pair of hydrophilic head groups from mPEG chains (Mn : 350 or 1000) and a pair of hydrophobic segments from linear alkyl chains (C11 or C18 ) through a novel spacer synthesized from glycerol and p-hydroxybenzoic acid. The aggregation phenomena of the amphiphiles were proven by DLS and fluorescence experiments, whereas size and morphology of the aggregates were evaluated by cryo-TEM. The measurements proved the formation of globular, thread-like or rod-like micelles as well as planar double-layer assemblies, depending on the amphiphile's molecular structure. The applicability of these non-ionic amphiphilic systems as nanocarriers for hydrophobic guest molecules was demonstrated by encapsulating a hydrophobic dye, Nile Red, and a hydrophobic drug, Nimodipine. The transport capacity results for both Nimodipine and Nile Red prove them as a promising candidate for drug delivery.

The adverse impact of chemical and biochemical waste on the environment and human health poses a serious challenge in today's World. The best way to address these challenges is to reduce the waste by developing more efficient processes and technologies, based on the principles of "green chemistry". Some of these synthetic approaches involving the chemoenzymatic synthetic methodologies are discussed herein. These lead to the formation of unique nanomaterials with diverse applications, such as drugs/gene delivery systems, flame retardant materials, conducting polymers, controlled release systems, diagnostic agents, and polymeric electrolytes for nanocrystalline solar cells.

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.

Crosslinking of polymers using crosslinking agents has been widely utilized to further improve the mechanical and thermal properties of the polymers. We have explored various aliphatic and aromatic dicarboxylic acids such as malonic acid, glutaric acid, isophthalic acid, terephthalic acid and terephthaloyl chloride as crosslinking agents to crosslink polydimethylsiloxane copolymer [poly{poly(dimethylsiloxane-1000)-propylamine-5-aminoisophthaloyl}] to improve its flame retardant properties. We have also used nanoclays [Cloisite 20A, 2C18 MMT (dimethylditallowammonium-/ dimethyldioctadecylammonium-modified montmorillonite)] along with the crosslinker during crosslinking process to further reduce the flammability of the crosslinked polymers. Among all the crosslinkers investigated, isophthalic acid with the nanoclay was found to have the best performance for flame retardant applications. The present work provides a basis to further improve the performance of silicone-based polymers for the flame retardant ap...

Nano-particles with controllable particle size and shape are of great interest in biomedical applications (1). From the standpoint of drug carrier design with wide applicability to a variety of hydrophobic drugs, an effective strategy would be to prepare a simple copolymer having the property to form stable polymeric micelles which can entrap hydrophobic drugs in the core (2). In this study, we have attached saturated and unsaturated long chain acid chlorides to the copolymer of PEG and dimethyl-5-aminoisophthalate via an amide linkage in order to make the copolymers amphiphilic. Further, bioactive compounds like β-carotene, which has considerable double bond unsaturation and curcumin, which has considerable aromatic unsaturation, were encapsulated in these amphiphilic copolymers which successfully demonstrated their drug loading ability.

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.