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The use of blockchain technology has been proposed to provide auditable access control for individual resources. Unlike the case where all resources are owned by a single organization, this work focuses on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may have event-driven constraints and can be overlapping in terms of the component conditions/rules as well as events. Existing work cannot handle event-driven constraints and does not sufficiently account for overlaps leading to significant overhead in terms of cost and computation time for evaluating authorizations over the blockchain. In this work, we propose an automata-theoretic approach for generating a cost-efficient composite access control policy. We reduce this composite policy generation problem to the standard weighted set cover problem. We show that the composite policy correctly captures all the local access control policies and reduces the policy evaluation cost over the blockchain. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and empirically validated the effectiveness and efficiency of our approach. Ablation studies were conducted to determine the impact of changes in individual service policies on the overall cost.

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

Quantum system attributes, notably entanglement, are indispensable in manipulating quantum information tasks. Ergo, machine learning applications to help harness the sophistication of quantum information theory have surged. However, relying on unsuited prototypes and not bridling quantum information data for usual processors has often resulted in sub-optimal efficaciousness and inconvenience. We develop a custom deep convolutional neural network, XpookyNet, which is streamlined with respect to the interrelationships of density matrices of two-qubit systems to underpin three-qubit systems, breaking new ground to more qubit systems by their subsystems. Comparative implementation of XpookyNet provides instantaneous and meticulous results with an accuracy of 98.53% in merely a few epochs. XpookyNet effectively handles the inherent complexity of quantum information, equipping deeper insights into many-body systems as a bedrock. The study also investigates quantum features and their relation to the purity of a density matrix directly related to noise and system decoherence in NISQ-era quantum computation. Preparing the density matrix in a compact and compatible format with customary convolutional neural networks plays a determining role in dissecting quantum features. The procedure renders a convex criterion that detects entanglement and is a yardstick for quantum system coherence.

Federal regulations require employees to protect themselves from electrical hazards when working at substations. Such protections, commonly called personal protective equipment (PPE), vary with the hazard types and nature of exposure or delivery. Over the past decades, personal injuries and fatalities from electrical hazards have remained relatively common despite regular risk assessments and controls. One reason for this is that adequate PPE is not appropriately used. Easy-to-deploy strategies to detect proper use of PPE for electrical hazards are not available. Here, an intelligent detection model is developed to check whether PPE is appropriately worn or not; warning alarms would be triggered when the usage does not follow safety regulations. Arc-flash analysis is employed to determine a reasonable and safe PPE guideline. Eight types of PPE are considered, which cover the major PPE categories utilized in practice, including medical masks recommended for the Covid-19 pandemic. The model's framework utilizes a few-shot based graph neural network (GNN) technique to detect PPE. In contrast to prior data-driven models, only 50 images were collected for each PPE type, a relatively small number compared with state-of-the-art research. The proposed model was trained with diversified samples within multiple environments, resulting in a robust, efficient, intelligent detection model with probability of similarity in the range of 79%–100%. To tackle the existing issues of computer-vision based PPE detection models, some technical suggestions on preserving personal privacy and PPE labels are provided.

Climate adaptation offers promising approaches to cope with the detrimental impacts of climate change on rainfed agriculture, intending to mitigate yield losses and safeguard food security worldwide. This is especially critical in the semi-arid tropics, characterized by highly variable and often intensified rainfall regimes. The current study explored the potential contribution of climate adaptation measures, specifically sowing windows and supplemental irrigation (SI), to offset the impacts of rainfall variability on rainfed crop yield in the Thondamuthur block, India. Under the proposed adaptation measures, this study analyzed the relationship between historical rainfall variability (1951–2019) and rice, maize, and sorghum yield anomalies. A seamlessly integrated framework was developed to link rainfall variability analysis with yield response simulations using the AquaCrop model. Rainfall classification analysis indicated an increase in rainfall availability during the Kharif season and a decrease during the Rabi season in Period III compared with the baseline. The late-sown Kharif and early-sown Rabi crops demonstrated effective climate adaptation, with the 27th and 38th SMWs identified as optimal climate-adapted sowing weeks, respectively. Significant decreases of 10% and 12% in SI requirements were observed in early-sown Rabi maize and sorghum, respectively. The results showed that substituting Rabi rice and maize with Rabi sorghum led to reductions of 90% and 75% in seasonal SI requirements, respectively. The early-sown Rabi rice and maize contributed to 88% and 12% relative yield increases, respectively, compared with the normal-sown. This study highlights the effectiveness of climate adaptation measures in enabling farmers to withstand climate-induced anomalies.

The COVID19 Pandemic has highlighted our dependence on online services (from government, e-commerce/retail, and entertainment), often hosted over external cloud computing infrastructure. The users of these services interact with a web interface rather than the larger distributed service provisioning chain that can involve an interlinked group of providers. The data and identity of users are often provided to service provider who may share it (or have automatic sharing agreement) with backend services (such as advertising and analytics). We propose the development of compliance-aware cloud application engineering, which is able to improve transparency of personal data use -- particularly with reference to the European GDPR regulation. Key compliance operations and the perceived implementation challenges for the realization of these operations in current cloud infrastructure are outlined.

Intentional controlled islanding (ICI) is a final resort for preventing a cascading failure and catastrophic power system blackouts. This paper proposes a controlled islanding algorithm that uses spectral clustering over multi-layer graphs to find a suitable islanding solution. The multi-criteria objective function used in this controlled islanding algorithm involves the correlation coefficients between bus frequency components and minimum active and reactive power flow disruptions. Similar to the previous studies, the algorithm is applied in two stages. In the first stage, groups of coherent buses are identified with the help of modularity clustering using correlation coefficients between bus frequency components. In the second stage, the ICI solution satisfying bus coherency with minimum active and reactive power flow disruptions is determined by grouping all nodes using spectral clustering on the multi-layer graph. Simulation studies on the IEEE 39-bus test system demonstrate the effectiveness of the method in determining an islanding solution in real time while addressing the generator coherency problem.

The significance of modern power grids is acknowledged every time there is a major threat. This paper proposes the novel approaches to aid power system planner to improve power grid resilience by making appropriate hardening strategies against man-made attack or natural hazards. The vulnerability indices are introduced, which return the most vulnerable component in the system based on a tri-level defender-attacker-operator (DAO) interdiction problem which solves iteratively. The output of DAO is the set of hardening strategies that optimally allocated along the network to mitigate the impact of the worst-case damages. By repeating DAO problem based on the proposed algorithm, the various crafted attack is imposed on the system, and the defender’s behavior demonstrates how an element is vulnerable to threats. The WSCC 9-bus, IEEE 24-bus, and IEEE 118-bus systems are employed to evaluate the model performance. The counter-intuitive results are proven by the proposed robust hardening strategy, which shows how the hardening strategy should be allocated to improve power network resilience against threats.

Experimental work was conducted to obtain the adsorption isotherms of safflower seed using a semi static gravimetric method at 25, 40, and 60 °C over a water activity range from 0.11 to 0.94. Fourteen isotherm equations and multilayer artificial neural network approach were employed to analyze the experimental data. In order to evaluate the goodness of fit of each model determination coefficient, Chi Square and root mean square error were used. The Peleg (at 25 and 40 °C) and Osvin (at 60 °C) models were selected to best describe the sorption isotherms of safflower seed. Accordingly, the results showed that the predicted values of ANN model were more accurate than those predicted by nonlinear regression method. The adsorption monolayer moisture content was also determined using Brunauer, Emmett and Teller equation. The monolayer moisture content values were 0.028, 0.024, and 0.022 gH2O/g solid at 25, 40, and 60 °C, and the corresponding constant values of the Brunauer, Emmett and Teller equation were found to be −13.029, −10.873, and −17.255, respectively. The safflower seeds heat of sorption values was also found. The heat of sorption values was large at low moisture content and decreased with an increase in the moisture content, whereas the magnitudes increase with increases in temperature and could be well adjusted by an exponential relationship. The experimental data revealed that enthalpy-entropy compensation theory was satisfactorily applicable to the moisture sorption behavior of the safflower seed.

•Techno-economic analysis of VVC in normal size solar inverter over a 2-year and 24 years period respectively•Technical and economic impacts of over-sizing solar inverter and comparing with the normal size smart inverter as well as traditional inverter•Same node collaborative-supportive VVC utilizing BSS for efficient reactive power support to the power grid.•Digital twin testbed to verify the results