Improved particle swarm optimization algorithm for multi-reservoir system operation

AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm

Acceptance Of Qr Code In Taiwan: An Extension Of The Technology Acceptance Model

The development and application of QR code is quite advanced in Japan and Korea; in contrast, at the time when the QR code was introduced in Taiwan, lack of mobile phone support and the limited applications of QR code meant that it was not immediately popularized. In recent years, the growing popularity of smart phones has enhanced the various applications of QR code, not only providing the information to the user, but also performing navigation, marketing and ticketing functions, to name a few. This study is based on the Technology Acceptance Model, with Social Influence, Facilitating Conditions, Awareness Knowledge, Operation Knowledge and Price as the usage factors, and by verifying the structural modelling through public surveys and analyzing the importance of the Acceptance Model and other influences of users in Taiwan as the driving factors in incorporating QR code applications in the government, telecommunication carrier and business spheres

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines

Theoretical foundations of studying criticality in the brain

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions

A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity

The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Some ANN technologies, such as multiple-neural network, time-delay neural network (TDNN), and adaptive time-delay neural network (ATNN), have proven successful in addressing various complicated problems. The purpose of this study was to investigate the applicability of neural network MS predictive models. Motivated by the above-mentioned technologies, we proposed a hybrid neural network model, which integrated characteristics decomposition units, and a dynamic spline interpolation unit into the multiple ATNNs. Inside the net, the regular and certain information were extracted to ATNN, while both time delays and weights were dynamically adapted. The yearly average of the sunspots, which has been considered by geophysicists, environment scientists, and climatologists as a complicated non-linear system, was selected to test the hybrid model. Comparative results were presented between a traditional MS predictive model based on TDNN and the proposed model. Validation studies indicated that the proposed model is quite effective in MS prediction, especially for single-factor time series.Department of Civil and Environmental EngineeringAuthor name used in this publication: Chun-Tian Chen

Co-infection of HIV and intestinal parasites in rural area of China

<p>Abstract</p> <p>Background</p> <p>Intestinal parasite infections (IPIs) are among the most significant causes of illness and disease of socially and economically disadvantaged populations in developing countries, including rural areas of the People's Republic of China. With the spread of the human immunodeficiency virus (HIV) among rural Chinese populations, there is ample scope for co-infections and there have been increasing fears about their effects. However, hardly any relevant epidemiological studies have been carried out in the country. The aim of the present survey was to assess the IPI infection status among a representative sample of HIV-positive Chinese in rural Anhui province, and compare the findings with those from a cohort of non-infected individuals.</p> <p>Methods</p> <p>A case control study was carried out in a rural village of Fuyang, Anhui province, China. Stool samples of all participants were examined for the presence of intestinal parasites. Blood examination was performed for the HIV infection detection and anemia test. A questionnaire was administered to all study participants.</p> <p>Results</p> <p>A total of 302 HIV positive and 303 HIV negative individuals provided one stool sample for examination. The overall IPI prevalence of intestinal helminth infections among HIV positives was 4.3% (13/302) while it was 5.6% (17/303) among HIV negatives, a non-significant difference. The prevalence of protozoa infections among HIV positives was 23.2% while the rate was 25.8% among HIV negatives. The species-specific prevalences among HIV positives were as follows: 3.6% for hookworm, 0.7% for <it>Trichuris trichiura</it>, zero for <it>Ascaris lumbricoides</it>, 0.3% for <it>Clonorchis sinensis</it>, 1.3% for <it>Giardia intestinalis</it>, 16.2% for <it>Blastocystis hominis</it>, 1.7% for <it>Entamoeba </it>spp. and 8.3% for <it>Cryptosporidium </it>spp.. <it>Cryptosporidium </it>spp. infections were significantly more prevalent among HIV positives (8.3%) compared to the HIV negative group (3.0%; <it>P</it> < 0.05). Among people infected with HIV, <it>Cryptosporidium </it>spp. was significantly more prevalent among males (12.6%) than females (4.4%; <it>P</it> < 0.05). According to multivariate logistic regression, the factors significantly associated with parasite infections of the people who were HIV positive included sex (male: OR = 6.70, 95% CI: 2.030, 22.114), younger age (less than 42 years old: OR = 4.148, 95% CI: 1.348, 12.761), and poor personal hygiene habits (OR = 0.324, 95% CI: 0.105, 0.994).</p> <p>Conclusions</p> <p>HIV positive individuals are more susceptible to co-infections with <it>Cryptosporidium </it>spp. than HIV negative people, particularly younger males with poor personal hygiene habits, indicating a need for targeted hygiene promotion, IPI surveillance and treatment.</p