Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Suggestions to the article: medical professionalism research characteristics and hotspots

Scientometrics - The article published on 5 July 2021 is well-written and of interest. However, some improvements could be made, such as ten Tables/Figures can be shortened to highlight the focused...     

Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Configurational Isomers Induced Significant Difference in All-Polymer Solar Cells

The design of polymer acceptors plays an essential role in the performance of all-polymer solar cells. Recently, the strategy of polymerized small molecules has achieved great success, but most polymers are synthesized from the mixed monomers, which seriously affects batch-to-batch reproducibility. Here, a method to separate γ-Br-IC or δ-Br-IC in gram scale and apply the strategy of monomer configurational control in which two isomeric polymeric acceptors (PBTIC-γ-2F2T and PBTIC-δ-2F2T) are produced is reported. As a comparison, PBTIC-m-2F2T from the mixed monomers is also synthesized. The γ-position based polymer (PBTIC-γ-2F2T) shows good solubility and achieves the best power conversion efficiency of 14.34% with a high open-circuit voltage of 0.95 V when blended with PM6, which is among the highest values recorded to date, while the δ-position based isomer (PBTIC-δ-2F2T) is insoluble and cannot be processed after parallel polymerization. The mixed-isomers based polymer, PBTIC-m-2F2T, shows better processing capability but has a low efficiency of 3.26%. Further investigation shows that precise control of configuration helps to improve the regularity of the polymer chain and reduce the π–π stacking distance. These results demonstrate that the configurational control affords a promising strategy to achieve high-performance polymer acceptors.     

Non-Halogenated-Solvent Processed and Additive-Free Tandem Organic Solar Cell with Efficiency Reaching 16.67%

Organic solar cells (OSCs) have recently reached a remarkably high efficiency and become a promising technology for commercial application. However, OSCs with top efficiency are mostly processed by halogenated solvents and with additives that are not environmentally friendly, which hinders large-scale manufacture. In this study, high-performance tandem OSCs, based on polymer donors and two small-molecule acceptors with different bandgaps, are fabricated by solution processing with non-halogenated solvents without additive. Importantly, the two active layers developed from non-halogenated solvents show better phase segregation and charge transport properties, leading to superior performance than halogenated ones. As a result, a tandem OSC with high efficiency of up to 16.67% is obtained, showing unique advantages in future massive production.     

Revealing the relationships between alloy structure,composition and plastic deformation in a ternary alloy system by a combinatorial approach

A high-throughput approach based on magnetron co-sputtering of alloy libraries is employed to inves-tigate mechanical properties of crystalline and amorphous alloys in a ternary palladium(Pd)-tungsten(W)-silicon(Si)system with the aim to reveal the difference in plastic deformation response and extract the relevant structure-property relationships of the alloys in the system.It was found that in contrast to crystalline alloys,the amorphous ones,i.e.,metallic glasses,exhibited a much smaller fluctuation range in the plasticity parameters(Er2/H and Wp/Wt),indicating a significant difference in the plastic deformation mechanism controlling the mechanical properties for the respective alloys.We propose that the inho-mogeneous deformation of amorphous alloys localized in thin shear bands is responsible for the weaker compositional dependence of both plasticity parameters,while dislocation gliding in crystalline materials is significantly more dependent on the exact structure,thus resulting in a larger scattering range.Based on the representative efficient cluster packing model,a set of composition-dependent atomic structural models is proposed to figure out the structure-property relationships of amorphous alloys in Pd-W-Si alloy system.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Sofosbuvir Selects for Drug-Resistant Amino Acid Variants in the Zika Virus RNA-Dependent RNA-Polymerase Complex In Vitro

The nucleotide analog sofosbuvir, licensed for the treatment of hepatitis C, recently revealed activity against the Zika virus (ZIKV) in vitro and in animal models. However, the ZIKV genetic barrier to sofosbuvir has not yet been characterized. In this study, in vitro selection experiments were performed in infected human hepatoma cell lines. Increasing drug pressure significantly delayed viral breakthrough (p = 0.029). A double mutant in the NS5 gene (V360L/V607I) emerged in 3 independent experiments at 40–80 µM sofosbuvir resulting in a 3.9 ± 0.9-fold half- maximal inhibitory concentration (IC₅₀) shift with respect to the wild type (WT) virus. A triple mutant (C269Y/V360L/V607I), detected in one experiment at 80 µM, conferred a 6.8-fold IC₅₀ shift with respect to the WT. Molecular dynamics simulations confirmed that the double mutant V360L/V607I impacts the binding mode of sofosbuvir, supporting its role in sofosbuvir resistance. Due to the distance from the catalytic site and to the lack of reliable structural data, the contribution of C269Y was not investigated in silico. By a combination of sequence analysis, phenotypic susceptibility testing, and molecular modeling, we characterized a double ZIKV NS5 mutant with decreased sofosbuvir susceptibility. These data add important information to the profile of sofosbuvir as a possible lead for anti-ZIKV drug development.