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Abstract In this paper, a hybrid features based support vector machine (SVM) model is proposed using infrared thermography technique for hotspots detection and classification of photovoltaic (PV) panels. A novel hybrid feature vector consisting of RGB, texture, the histogram of oriented gradient (HOG), and local binary pattern (LBP) as features is formed using a data fusion approach. A machine learning algorithm SVM is employed to classify the obtained thermal images of PV panels into three different classes (i.e., healthy, non-faulty hotspot, and faulty). The comparison of different machine learning algorithms and datasets is also carried out to validate the superiority of the proposed model and hybrid feature dataset. The experimental results reveal that the proposed hybrid features with SVM resulted in 96.8% training accuracy and 92% testing accuracy with lesser computational complexity and storage space than other machine learning algorithms. The proposed approach is easily implementable for efficient monitoring and fault diagnosis of PV panels.

Growing concerns over the use of limited fossil fuels and their negative impacts on the ecological niches have facilitated the exploration of alternative routes. The use of conventional plastic material also negatively impacts the environment. One such green alternative is polyhydroxyalkanoates, which are biodegradable, biocompatible, and environmentally friendly. Recently, researchers have focused on the utilization of waste gases particularly those belonging to C1 sources derived directly from industries and anthropogenic activities, such as carbon dioxide, methane, and methanol as the substrate for polyhydroxyalkanoates production. Consequently, several microorganisms have been exploited to utilize waste gases for their growth and biopolymer accumulation. Methylotrophs such as Methylobacterium organophilum produced highest amount of PHA up to 88% using CH4 as the sole carbon source and 52–56% with CH3OH. On the other hand Cupriavidus necator, produced 71–81% of PHA by utilizing CO and CO2 as a substrate. The present review shows the potential of waste gas valorization as a promising solution for the sustainable production of polyhydroxyalkanoates. Key bottlenecks towards the usage of gaseous substrates obstructing their realization on a large scale and the possible technological solutions were also highlighted. Several strategies for PHA production using C1 gases through fermentation and metabolic engineering approaches are discussed. Microbes such as autotrophs, acetogens, and methanotrophs can produce PHA from CO2, CO, and CH4. Therefore, this article presents a vision of C1 gas into bioplastics are prospective strategies with promising potential application, and aspects related to the sustainability of the system.

The generally accepted hypothesis for the pathogenesis of alcoholic liver disease (ALD) is the two-hit model, which proposes that fat accumulation in the liver increases the sensitivity of the liver to a second hit that leads to inflammatory liver cell damage. In this study we evaluated the effects of Magnolia officinalis (MO), which contains honokiol and magnolol as the primary pharmacological components, to eradicate fatty liver in rats fed an ethanol diet. In vitro studies showed that MO was able to protect RAW 264.7 cells from ethanol-induced production of tumor necrosis factor-alpha, reactive oxygen species, and superoxide anion radicals; the activation of NADPH oxidase; and subsequent cell death. We also investigated the therapeutic effects of MO on alcoholic fatty liver in Lieber-DeCarli ethanol diet-fed rats. MO treatment of the rats for the last 2 weeks of ethanol feeding completely reversed all the serum, hepatic parameters, and fatty liver changes. The increased maturation of sterol regulatory element-binding protein-1c in the liver by ethanol treatment was completely inhibited by treatment with MO. Therefore, MO may be a promising candidate for development as a therapeutic agent for ALD.

AbstractThe susceptibility of porphyrin derivatives to light‐harvesting and charge‐transport operations have enabled these materials to be employed in solar cell applications. The potential of porphyrin derivatives as hole‐transporting materials (HTMs) for perovskite solar cells (PSCs) has recently been demonstrated, but knowledge of the relationships between the porphyrin structure and device performance remains insufficient. In this work, a series of novel zinc porphyrin (PZn) derivatives has been developed and employed as HTMs for low‐temperature processed PSCs. Key to the design strategy is the incorporation of an electron‐deficient pyridine moiety to down‐shift the HOMO levels of porphyrin HTMs. The porphyrin HTMs incorporating diphenyl‐2‐pyridylamine (DPPA) have HOMO levels that are in good agreement with the perovskite active layers, thus facilitating hole transfers from the perovskite to the HTMs. The DPPA‐containing zinc porphyrin‐based PSCs gave the best performance, with efficiency levels comparable to those of PSCs using spiro‐OMeTAD, a current state‐of‐the‐art HTM. In particular, PZn–DPPA‐based PSCs show superior air stability, in both doped and undoped forms, to spiro‐OMeTAD based devices.

A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of our algorithm, we implement a multi-agent system (MAS) on top of a conventional development framework and assess the algorithm’s adaptability, satisfaction metric, and running time.

This study examines whether related-party transactions (RPTs) impact the value of firms’ cash holdings. Using a 2011–2018 sample of Korean public firms, we find that greater RPTs lead to lower cash holdings value. In particular, this decline is more pronounced in Korean chaebol firms than in non-chaebol firms. Our findings suggest that a unique and complex corporate governance structure of chaebol firms makes it difficult for outside investors to monitor firms’ internal cash management decisions, resulting in a negative valuation of cash holdings. This study contributes to the extant literature by providing additional evidence that RPTs in chaebol firms with severe agency problems may lower the value of cash holdings.

Abstract Waste heat recovery has attracted a significant attention because of the world growth in energy demand. In this paper, we report the study on an energy recovery system utilizing low-grade waste heat below 100 °C. This system called a thermal-driven electrochemical generator is composed of reverse electrodialysis (RED) power generation and thermal separation using waste heat. We especially focus on the experimental characterization of the RED process with ammonium bicarbonate (NH4HCO3) solution, which is known to be easily decomposed at the temperature around 60 °C. We characterized this NH4HCO3-RED system with various parameters including the concentration difference, the membrane type, the inlet flow rate, and the compartment thickness. We found the best power density at the concentrated solution of 1.5 mol L−1 and the diluted solution of 0.01 mol L−1. The maximum power density increases as the inlet flow rate increases or the compartment thickness decreases owing to the decrease in the internal resistance. We obtained the excellent power density of 0.77 W m−2, compared with the previous studies.

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.