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Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts. [ABSTRACT FROM AUTHOR]

Effecient green tools of β-(3,4-dichlorophenyl-acrylic acid, thioglycolic acid and β-(2-oxopyrrolidin-1-yl) ethanol under ultrasonic and microwave-assisted irradiation (MW-US) as found to be a simple approach for preparing interesting Spiro derivatives. A novel class Spiro-pyrrolidine-2,3'-thieno[2,3-d]pyridazine derivatives (2–6) were efficiently synthesized via one-pot multicomponent reactions. Ethoxy acetyl Spiro pyrrolidine-2,3'-thieno[2,3-d]pyridazin-hydrazide (5) assessed as ecofriendly microbicidal and insecticidal activities to establish a structure-activity relationship. Elemental analysis and spectroscopic data were used to describe the structures of all synthesized products. The Spiro-heterocyclic compounds are exclusive non-planar structures and branded for binding to biomolecules. The microbicidal and insecticidal activities of the compounds were investigated on ATCC coded test microorganisms upon the applied dose and time was studied through two kinetic study. The interaction with these antibacterial and insect's third instar larvae was medicinal and economically valuable. The preliminary testing demonstrated flourishing microbicidal capability to prevent the growth of threatening pathogenic bacteria as well as blossoming insecticidal efficacy. Moreover, Molecular docking and density functional theory simulations can be used to validate the experimental results. Also, we utilized the docking approach with secondary bacterial infection indicated that Ethoxy acetyl Spiro hydrazide (5) and Spiro arylidene hybrid (6) have exerted the strongest docking binding value against the active sites of 6LU7. [ABSTRACT FROM AUTHOR]

Background: Vancomycin is one of the main therapies for methicillin-resistant S. aureus (MRSA), however emergence of resistant strains is rising. Nanoparticles can offer promising solution. So, we studied the effect of gold nanoparticles (AuNPs) in enhancing vancomycin potential against MRSA clinical isolates from different types of infections. Methods: The susceptibility patterns of vancomycin, AuNPs, and vancomycin-conjugated gold nanoparticles (V-AuNPs) were evaluated on 30 MRSA isolates obtained from various clinical samples by using the microtiter broth dilution method. AuNPs and V-AuNPs were prepared by using a straightforward technique of microwave-assisted synthesis. Results: Twenty-seven (90%) MRSA isolates were susceptible to vancomycin and three (10%) showed intermediate susceptibility, with mean minimum inhibitory concentration (MIC) 2.68 ± 2.07 µg/ml, inhibitory concentration (conc.) to 50% of MRSA (IC50) 1.95 µg/ml and inhibitory conc. to 90% of the isolates (IC90) 3.9 µg/ml. While the synthesized AuNPs demonstrated a bactericidal effect on all the tested isolates with mean MIC 164.6 ± 62.5 µg/ml, IC50 125 µg/ml, and IC90 250 µg/ml. Meanwhile, V-AuNPs demonstrated synergistic bactericidal effect on all thirty (100%) tested MRSA isolates with mean MIC 0.40 ± 0.46 µg/ml, IC50 0.24 µg/ml, and IC 90 0.48 µg/ml. Moreover, V-AuNPs demonstrated minimal cytotoxic concentration up to 100 and 98.65 percent viability on human foreskin fibroblast (HFF-1) cell line at conc. of 2 µg/ml and 3.9 µg/ml respectively. Conclusions: The V-AuNPs display superior antibacterial activity as compared to vancomycin and AuNPs alone as a potentially effective therapy against MRSA. [ABSTRACT FROM AUTHOR]

The interaction between epigallocatechin gallate (EGCG) and whey protein isolate (WPI) under various pH and ion concentrations conditions was examined by ultraviolet-visible spectrophotometer and fluorescence spectra. The results showed that EGCG with WPI was linked mainly by electrostatic force, and EGCG caused the fluorescence quenching of WPI through a static quenching. Furthermore, in vitro static release results showed that the release process of EGCG from EGCG-WPI gel in release medium of different pH and ion concentrations follows the Korsmeyer-Peppas model and conform to the non-Fick diffusion law. Finally, the binding constant KA and the constant release Kapp kinetic model were established. The model followed the equation LnKA=A - 1 n LnKapp. [ABSTRACT FROM AUTHOR]

The present work described a novel synthesis of spiro[indoline-3,3′-pyrazol]-2-one derivatives 2, 4, 5, and 7 via a one-pot reaction of chalcones or β-aroyl acrylic acid 1a–c, isatin, and hydrazine derivatives e.g. hydrazine hydrate, methyl hydrazine, phenylhydrazine or acetohydrazide in sonicated reaction condition. The unexpected product 1,3-oxazino[3,2-a]indol-10-yl-ethanone derivative 6 can be generated instead of N-acetyl spiro[indoline-3,3′-pyrazol]-2-one as desired. Also, the Spiropyrazole-fused furanone and pyridazinone ring can be made. Owing to their unique 3-dimensional structures, spirooxindoles have been found as lucky chemotypes for antiviral drug enhancement in an attempt to achieve these spiropyrazole derivatives 2–10 with hydrophobic groups. Furthermore, the density functional theory (DFT) was used to elucidate the electronic and structural features of spiropyrazole derivatives and their reactive species, which supports the proposed mechanism of antibacterial and antiviral capabilities. [ABSTRACT FROM AUTHOR]

Energy consumption reduction under changing climate conditions is a major challenge in buildings design, where excessive energy consumption creates an economic and environmental burden. Improving thermal performance of the buildings through support applying phase change material (PCM) is a promising strategy for reducing building energy consumption under future climate change. Therefore, this study aims to investigate the energy saving potentials in buildings under future climate conditions in the humid and snowy regions in the hot continental and humid subtropical climates of the east Asia (Seoul, Tokyo and Hong Kong) when various PCMs with different phase change temperatures are applied to a lightweight building envelope. Methodology in this work is implemented in two phases: firstly, investigation of energy saving potentials in buildings through inclusion of three types of PCMs with different phase temperatures into the building envelop separately and use weather file in the present (2017); and, secondly, evaluation of the effect of future climate change on the performance of PCMs by analyzing energy saving potentials of PCMs with 2020, 2050 and 2080 weather data. The results show that the inclusion of PCM into the building envelope is a promising strategy to increase the energy performance in buildings during both heating and cooling seasons in Seoul, Tokyo and Hong Kong under future climate conditions. The energy savings achieved by using PCMs in those regions are electricity savings of 4.48–8.21%, 3.81–9.69%, and 1.94–5.15%, and gas savings of 1.65–16.59%, 7.60–61.76%), and 62.07–93.33% in Seoul, Tokyo and Hong Kong, respectively, for the years 2017, 2020, 2050 and 2080. In addition, BioPCM and RUBITHERMPCM are the most efficient for improving thermal performance and saving energy in buildings in the tested regions and years.