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The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]

general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to analyze the diagnostic performance and clinical application of diffusion-weighted imaging (DWI) in patients with suspected pleural malignancy (PM). Methods: A retrospective review of patients with suspected PM was performed from March 2014 to August 2018 (NCT 02320617). All patients underwent chest DWI and computed tomography (CT) with cytological or histopathological findings as reference standards. The diagnostic performance of DWI and CT was analyzed and compared. A DWI diagnostic algorithm with three sequential steps was established. Results: Seventy patients (61.6 ± 13.6 years; 47 males and 23 females) were included. The sensitivity of DWI (94.2%, 49/52) for the diagnosis of PM was significantly higher compared with CT (67.3%, 35/52), with similar specificity (72.2% vs. 72.2%, respectively). The apparent diffusion coefficient of malignant lesions (1.15 ± 0.32 × 10−3 mm2/s) was lower compared with benign lesions (1.46 ± 0.68 × 10−3 mm2/s), but the cutoff value was difficult to define for overlap between groups. Approximately 62.5% (5/8) of invasive procedures were avoided when using the DWI diagnostic algorithm in patients with suspected PM without N3 lymph node or extra-thoracic metastasis. Conclusion: Including DWI into the diagnostic algorithm of suspected PM can effectively identify malignancy and avoid unnecessary invasive procedures, which may have some potential in clinical application. Key Points: • Diffusion-weighted imaging can identify pleural malignancy much more efficiently than CT. • A diffusion-weighted imaging diagnostic algorithm helped to avoid unnecessary invasive procedures in patients without N3 lymph node or extra-thoracic lesions. • A hyperintense signal on DWI at a high b value (800 s/mm2) but not at a low b value (50 s/mm2) was a reliable signature of PM. [ABSTRACT FROM AUTHOR]

Tacit collusion gives rise to major practical challenges for the enforcement of competition law. A problem which is well known from the gas station markets, now acquires new topicality with the intervention of institutional investors ('common ownership') and the use of online pricing algorithms. This article connects the previous strands of discussion and establishes a link to the discussion regarding a New Competition Tool. [ABSTRACT FROM AUTHOR]

Multi-objective clustering algorithms have superiority over its single objective counterparts as they include additional knowledge on properties of data in the form of objectives to discover the underlying clusters present in various datasets. This paper proposes a distributed clustering algorithm using multi-objective whale optimization (DMOWOA) for peer to peer network. The algorithm minimizes two objective functions to perform clustering, namely : Total Euclidean Deviation and Total Symmetrical Deviation. Both objective values are shared using diffusion method of cooperation to obtain correct partitioning at each peer. A single solution from the non-dominated solutions is selected as final solution based on its minimum distance to origin in the normalized objective space. The proposed algorithm's performance is evaluated on four synthetic and five real-life wireless sensor network datasets (Canada weather station dataset, Delhi air pollution content dataset, Intel laboratory dataset, Washington cook agronomy farm dataset and Thames river water quality dataset). The comparison is carried out with multi-objective distributed particle swarm optimization (DMOPSO), distributed K-Means (DK-Means) and other seven recently developed nature inspired multi-objective clustering techniques. The proposed algorithm in most of the cases outperforms the existing techniques in terms of statistical measures Minkowski Score, Dunn index and Silhouette index. The average rank of the proposed algorithm is also better in Kruskal–Wallis statistical test. • Distributed multi-objective clustering method is proposed in peer to peer network. • The algorithm is based on multi-objective whale optimization. • An optimal solution is chosen from the obtained Pareto optimal front. • The proposed algorithm is validated on nine distributed datasets. • Proposed approach shows superiority over existing clustering approaches. [ABSTRACT FROM AUTHOR]

Thermal energy dynamics of a large building can be turned up as a substantial source of ancillary service for the grid. This paper presents that how the potential of Air Handling Unit (AHU) of Heating Ventilation and Air Conditioning (HVAC) system can be utilized as a virtual source of an ancillary service by regulating it in real time according to its thermal load. Internet of Things (IoT) enabled real time controller is proposed where the thermal comfort of occupants is used as feedback to control the AHU fans. The key challenge corresponding to the AHU is, nonlinear torque characteristics of fan with respect to required air flow rate at a thermal load on AHU. To address this problem an Adaptive Neuro Fuzzy Inference System is used to model the nonlinear property of the AHU fan. In this system it has two-part integration, IoT based controller and ANFIS based model with thermal model of building. To evaluate the algorithm, first, the reserved ancillary potential is calculated based on weekly class schedule. Secondly, it has been controlled in real time as per the thermal comfort of occupants to maximize the potential of ancillary service through the HVAC system. To analyze the response of the algorithm for a building, this set-up is scaled up at the level of AHU network installed in the lecture hall complex of an educational building in Delhi. The proposed algorithm has been tested and analyzed on an experimental set-up, synchronized with the thermal-dynamic model of building in MATLAB. It shows that there is a potential of ancillary service up to 97 kW in non-scheduled period and for the scheduled period it can be increased by 20% to 59% of available reserve power. [ABSTRACT FROM AUTHOR]