A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO 2 emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach

Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering

Semantic matching is a basic problem in natural language processing, but it is far from solved because of the differences between the pairs for matching. In question answering (QA), answer selection (AS) is a popular semantic matching task, usually reformulated as a paraphrase identification (PI) problem. However, QA is different from PI because the question and the answer are not synonymous sentences and not strictly comparable. In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. We apply explicit cross-pair matching based on Graph Convolutional Network (GCN) to help KCG recognize general domain-independent Q-to-A patterns better. And with the incorporation of domain-specific information from knowledge bases (KB), KCG is able to capture and explore various relations within Q-A pairs. Experiments show that KCG is robust against the diversity of Q-A pairs and outperforms the state-of-the-art systems on different answer selection tasks.</jats:p

Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering

Semantic matching is a basic problem in natural language processing, but it is far from solved because of the differences between the pairs for matching. In question answering (QA), answer selection (AS) is a popular semantic matching task, usually reformulated as a paraphrase identification (PI) problem. However, QA is different from PI because the question and the answer are not synonymous sentences and not strictly comparable. In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. We apply explicit cross-pair matching based on Graph Convolutional Network (GCN) to help KCG recognize general domain-independent Q-to-A patterns better. And with the incorporation of domain-specific information from knowledge bases (KB), KCG is able to capture and explore various relations within Q-A pairs. Experiments show that KCG is robust against the diversity of Q-A pairs and outperforms the state-of-the-art systems on different answer selection tasks

Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.</jats:p

Efficacy and safety of intravitreal injections of conbercept for the treatment of idiopathic choroidal neovascularization

Abstract Background To determine the efficacy and safety of intravitreally injected conbercept, a vascular endothelial growth factor receptor fusion protein, for the treatment of idiopathic choroidal neovascularization (ICNV). Methods This retrospective study analyzed outcomes in 40 patients (40 eyes) with ICNV who received intravitreal injections of conbercept 0.5 mg (0.05 ml) and were followed up for at least 12 months. All patients underwent full ophthalmic examinations, including best-corrected vision acuity (BCVA), intraocular pressure (IOP), slit-lamp examination, color fundus photography, optical coherence tomography angiography, multifocal electroretinogram, and fundus fluorescence angiography, if necessary, at baseline and after 1, 3, 6, and 12 months. BCVA, macular central retinal thickness (CRT), IOP, CNV blood flow area, thickness of the CNV-pigment epithelial detachment complex, thickness of the retinal nerve fiber layer (RNFL), and the first positive peak (P1) amplitude density in ring 1 before and after treatment were compared. Results Mean baseline BCVA (logMAR), CRT, CNV blood flow area, and CNV-pigment epithelial detachment complex thickness were significantly lower 1, 3, 6, and 12 months after than before conbercept treatment (P < 0.05 each). IOP and baseline RNFL thickness were unaffected by conbercept treatment. P1 amplitude density was significantly higher 1, 3, 6, and 12 months after than before conbercept treatment (P < 0.05 each). None of the 40 eyes showed obvious ocular adverse reactions, such as endophthalmitis, glaucoma, cataract progression, and retinal detachment, and none of the patients experienced systemic adverse events, such as cardiovascular and cerebrovascular accidents. Conclusions Intravitreal injection of conbercept is beneficial to eyes with ICNV, inducing the recovery of macular structure and function and improving BCVA, while not damaging the neuroretina. Intravitreal conbercept is safe and effective for the treatment of ICNV

Significant genetic differentiation between native and introduced farmed Burmese pythons and low risk of genetic introgression from escaped farmed individuals in Hainan Island

Abstract The Burmese python (Python bivittatus) is one of the most endangered pythons and is commonly traded in the international pet industry. In addition to wild P. bivittatus, domesticated individuals from farms have been living on Hainan Island for nearly 20 years. The intentional release or accidental escape of farmed P. bivittatus may lead to risks, such as genetic introgression or competition for space and food, owing to the genetic differences between the two populations and limited resources. Our objective was to better understand the genetic background of P. bivittatus and genetic introgression between native and introduced farmed populations. We conducted mitochondrial DNA (mtDNA) sequencing (138 specimens) and whole‐genome resequencing (110 specimens) of P. bivittatus from farm and wild populations on Hainan Island. Genetic analysis suggested two highly differentiated clusters (VN and HN clades), Fst = 0.22. The HN clade included samples that originated from southern China and Hainan and represented native individuals of Hainan Island, whereas the VN clade most likely originated from Vietnam, as expected based on the Vietnamese origin of the farm. The results of ADMIXTURE analysis indicated three possible genetic components, one of which can be viewed as the VN clade, and the remaining two genetic components both belong to the HN clade. We identified a small number of shared haplotypes between the farmed and wild populations, indicating that both farmed and wild samples included individuals from VN and HN clades. In addition, only one F1 generation hybrid individual between the two clades was found, suggestive of low gene flow. Thus, the probability of genetic introgression between HN and VN clades is low and poses a low threat to the genetic integrity of the native P. bivittatus on Hainan Island; however, we cannot underestimate the risk of escaped individuals, which should be closely monitored