Cost-Benefit Analysis of Phase Balancing Solution for Data-scarce LV Networks by Cluster-Wise Gaussian Process Regression

Phase imbalance widely exists in the UK’s low voltage (415V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g. deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices

Cost-Benefit Analysis of Phase Balancing Solution for Data-scarce LV Networks by Cluster-Wise Gaussian Process Regression

Phase imbalance widely exists in the UK’s low voltage (415V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g. deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices

Efficient wide-bandgap perovskite solar cells with open-circuit voltage deficit below 0.4 V via hole-selective interface engineering

Wide-bandgap mixed-halide perovskite solar cells (WBG-PSCs) are promising top cells for efficient tandem photovoltaics to achieve high power conversion efficiency (PCE) at low cost. However, the open-circuit voltage (VOC) of WBG-PSCs is still unsatisfactory as the VOC-deficit is generally larger than 0.45 V. Herein, we report a buried interface engineering strategy that substantially improves the VOC of WBG-PSCs by inserting amphophilic molecular hole-selective materials featuring with a cyanovinyl phosphonic acid (CPA) anchoring group between the perovskite and substrate. The assembly and redistribution of CPA-based amphiphilic molecules at the perovskite-substrate buried interface not only promotes the growth of a low-defect crystalline perovskite thin film, but also suppresses the photo-induced halide phase separation. The energy level alignment between wide-bandgap perovskite and the hole-selective layer is further improved by modulating the substituents on the triphenylamine donor moiety (methoxyls for MPA-CPA, methyls for MePA-CPA, and bare TPA-CPA). Using a 1.68 eV bandgap perovskite, the MePA-CPA-based devices achieved an unprecedentedly high VOC of 1.29 V and PCE of 22.3% under standard AM 1.5 sunlight. The VOC-deficit (&lt;0.40 V) is the lowest value reported for WBG-PSCs. This work not only provides an effective approach to decreasing the VOC-deficit of WBG-PSCs, but also confirms the importance of energy level alignment at the charge-selective layers in PSCs.</p

Protein quality control and aggregation in the endoplasmic reticulum: From basic to bedside

Endoplasmic reticulum (ER) is the largest membrane-bound compartment in all cells and functions as a key regulator in protein biosynthesis, lipid metabolism, and calcium balance. Mammalian endoplasmic reticulum has evolved with an orchestrated protein quality control system to handle defective proteins and ensure endoplasmic reticulum homeostasis. Nevertheless, the accumulation and aggregation of misfolded proteins in the endoplasmic reticulum may occur during pathological conditions. The inability of endoplasmic reticulum quality control system to clear faulty proteins and aggregates from the endoplasmic reticulum results in the development of many human disorders. The efforts to comprehensively understand endoplasmic reticulum quality control network and protein aggregation will benefit the diagnostics and therapeutics of endoplasmic reticulum storage diseases. Herein, we overview recent advances in mammalian endoplasmic reticulum protein quality control system, describe protein phase transition model, and summarize the approaches to monitor protein aggregation. Moreover, we discuss the therapeutic applications of enhancing endoplasmic reticulum protein quality control pathways in endoplasmic reticulum storage diseases

Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval

With the explosive growth of web videos in recent years, large-scale Content-Based Video Retrieval (CBVR) becomes increasingly essential in video filtering, recommendation, and copyright protection. Segment-level CBVR (S-CBVR) locates the start and end time of similar segments in finer granularity, which is beneficial for user browsing efficiency and infringement detection especially in long video scenarios. The challenge of S-CBVR task is how to achieve high temporal alignment accuracy with efficient computation and low storage consumption. In this paper, we propose a Segment Similarity and Alignment Network (SSAN) in dealing with the challenge which is firstly trained end-to-end in S-CBVR. SSAN is based on two newly proposed modules in video retrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module to reduce redundant frame features, (2) A robust Similarity Pattern Detection (SPD) module for temporal alignment. In comparison with uniform frame extraction, SKE not only saves feature storage and search time, but also introduces comparable accuracy and limited extra computation time. In terms of temporal alignment, SPD localizes similar segments with higher accuracy and efficiency than existing deep learning methods. Furthermore, we jointly train SSAN with SKE and SPD and achieve an end-to-end improvement. Meanwhile, the two key modules SKE and SPD can also be effectively inserted into other video retrieval pipelines and gain considerable performance improvements. Experimental results on public datasets show that SSAN can obtain higher alignment accuracy while saving storage and online query computational cost compared to existing methods.Comment: Accepted by ACM MM 202

Exploring the interaction between renewables and energy storage for zero-carbon electricity systems

Many countries have set ambitious targets to achieve zero-carbon electricity systems by the Mid-21st Century. In their pathways, the renewable mix and the energy storage mix have been considered as two important facets. Current literature mostly focuses on how the storage mix is affected by the renewable mix, but few studied the inverse impact and the dynamic interaction between the storage and renewable mixes. We, therefore, developed an electricity system optimisation model with hourly resolution to investigate how the interaction between renewable and storage mixes could accelerate the decarbonisation in future 30 years. This study considered the decarbonisation roadmap in the UK designed by the National Grid with variable factors such as cost structure of renewables and storages, annual investment budget, and load growth. Our research finds that short-duration energy storages with duration time at 6–8 h are preferred for providing cheap and rapid ramping power to meet the daily fluctuation in the early stage (2020–2030) of the decarbonisation process. In the late stage of retiring fossil fuels (2040–2050), high-share wind energy plus with long-duration storages (with duration time longer than 38 h) can solve the problem of great-quantity and long-lasting energy shortage caused by renewables, thereby achieving high-renewable penetration.</p

Specific, simple and rapid detection of porcine circovirus type 2 using the loop-mediated isothermal amplification method

<p>Abstract</p> <p>Background</p> <p>Porcine circovirus type 2 (PCV2) is the causative agent of postweaning multisystemic wasting syndrome (PMWS), and porcine dermatitis and nephropathy syndrome (PDNS). It has caused heavy losses in global agriculture in recent decades. Rapid detection of PCV2 is very important for the effective prophylaxis and treatment of PMWS.</p> <p>Results</p> <p>A loop-mediated isothermal amplification (LAMP) assay was used to detect PCV2 in this study. Three pairs of primers were specially designed for recognizing eight distinct sequences of the ORF2 gene. This gene lies in the PCV2 virus genome sequence, and encodes the Rep protein that is involved in virus replication. Time and temperature conditions for amplification of PCV2 genes were optimized to be 55 min at 59°C. The analysis of clinical samples indicated that the LAMP method was highly sensitive. The detection limit for PCV2 by the LAMP assay was 10 copies, whereas the limit by conventional PCR was 1000 copies. The assay did not cross-react with PCV1, porcine reproductive and respiratory syndrome virus, porcine epidemic diarrhea virus, transmissible gastroenteritis of pigs virus or rotavirus. When 110 samples were tested using the established LAMP system, 95 were detected as positive.</p> <p>Conclusion</p> <p>The newly developed LAMP detection method for PCV2 was more specific, sensitive, rapid and simple than before. It complements and extends previous methods for PCV2 detection and provides an alternative approach for detection of PCV2.</p

The Rice Xa3 Gene Confers Resistance to Xanthomonas oryzae pv. oryzae in the Model Rice Kitaake Genetic Background

The rice XA21 and XA3 pattern receptor kinases, derived from Oryza longistaminata and an Oryza. sativa japonica cultivar Wase Aikoku 3, respectively, confer resistance to strains of the Gram-negative bacterium Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of rice bacterial blight disease. Previously, we showed that transfer of Xa21 to the model rice cultivar Kitaake enhances resistance to Xoo. In this manuscript we demonstrate that Kitaake expressing Xa3 confers resistance to Xoo strain PXO79 and that the stress-related marker genes PR10b and KO5 are upregulated in Xoo-infected Xa3 rice leaves. We also show that rice somatic embryogenesis receptor kinase 2 (OsSERK2) positively regulates XA3-mediated immunity in Kitaake. We found that overexpression of XA21 binding protein 15 (XB15) and XB24, two negative regulators of XA21-mediated immunity, do not affect XA3-mediated immunity in the Kitaake genetic background. Our results indicate that the rice immune receptors XA21 and XA3 employ both shared and distinct signaling components in their response to Xoo. The results are important to further understand pathogen-associated molecular pattern (PAMP)-triggered immunity in rice. Furthermore, the presence of Kitaake rice carrying Xa3 will facilitate genetic research to study the XA3-mediated immunity.Supported by NIH GM59962 and GM122968 to PR