Scattering Analysis of Electromagnetic Materials Using Fast Dipole Method Based on Volume Integral Equation

The fast dipole method (FDM) is extended to analyze the scattering of dielectric and magnetic materials by solving the volume integral equation (VIE). The FDM is based on the equivalent dipole method (EDM) and can achieve the separation of the field dipole and source dipole, which reduces the complexity of interactions between two far groups (such as group i and group j) from O(NiNj) to O(Ni+Nj), where Ni and Nj are the numbers of dipoles in group i and group j, respectively. Targets including left-handed materials (LHMs), which are a kind of dielectric and magnetic materials, are calculated to demonstrate the merits of the FDM. Furthermore, in this study we find that the convergence may become much slower when the targets include LHMs compared with conventional electromagnetic materials. Numerical results about convergence characteristics are presented to show this property

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Online and semi-online scheduling on two hierarchical machines with a common due date to maximize the total early work

In this study, we investigated several online and semi-online scheduling problems on two hierarchical machines with a common due date to maximize the total early work. For the pure online case, we designed an optimal online algorithm with a competitive ratio of $\sqrt 2$. For the case when the total processing time is known, we proposed an optimal semi-online algorithm with a competitive ratio of $\frac{4}{3}$. Additionally, for the cases when the largest processing time is known, we gave optimal algorithms with a competitive ratio of $\frac{6}{5}$ if the largest job is a lower hierarchy one, and of $\sqrt 5-1$ if the largest job is a higher hierarchy one, respectively

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolution Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

An overview of Antarctic polynyas: sea ice production, forcing mechanisms, temporal variability and water mass formation

Polynyas are irregular open water bodies within the sea ice cover in polar regions under freezing weather conditions. In this study, we reviewed the progress of research work on dynamical forcing, sea ice production (SIP), and water mass formation for both coastal polynyas and open-ocean polynyas in the Southern Ocean, as well as the variability and controlling mechanisms of polynya processes on different time scales. Polynyas play an irreplaceable role in the regulation of global ocean circulation and biological processes in regional ocean ecosystems. The coastal polynyas (latent heat polynyas) are mainly located in the Weddell Sea, the Ross Sea and on the west side of protruding topographic features in East Antarctica. During the formation of coastal polynyas, which are mainly forced by offshore winds or ocean currents, brine rejection triggered by high SIP results in the formation of high salinity shelf water, which is the predecessor of the Antarctic bottom water — the lower limb of the global thermohaline circulation. The open-ocean polynyas (sensible heat polynyas) are mainly found in the Indian sector of the Southern Ocean, which are formed by ocean convection processes generated by topography and negative wind stress curl. The convection processes bring nutrients into the upper ocean, which supports biological production and makes the polynya regions an important sink for atmospheric carbon dioxide. The limitations and challenges in polynya research are also discussed

Active Acquisition Methods for Single Cell Genomics

We introduce two novel computational methodologies, ActiveSVM and Active Cell Inference, aimed at reducing the costs and enhancing the efficiency of single-cell mRNA sequencing and spatial transcriptomics, respectively. ActiveSVM employs an active learning approach to identify minimal yet highly informative gene sets for cell-type classification, physiological state identification, and genetic perturbation responses in single-cell datasets. By focusing on misclassified cells through an iterative process, ActiveSVM efficiently scales to analyze over a million cells, demonstrating around 90% accuracy across various datasets, including cell atlas and disease characterization studies. Active Cell Inference complements this by utilizing ordered gene sets, developed through ActiveSVM, to streamline spatial genomics measurements. This end-to-end pipeline significantly reduces measurement time and costs by up to 100-fold in scientific and clinical settings. It optimizes the gene probing process by identifying well-classified cells early, allowing for targeted gene application based on cell classification certainty. This method's efficacy is further enhanced by a temporal scaling calibration scheme, improving calibration accuracy throughout its iterative process. Both methodologies were rigorously tested on the expansive Human Cell Atlas dataset, using the advanced computational tool, CellxGene-Census, involving over 60 million cells. This integration facilitated the creation of precise gene sets for various human tissues, dramatically improving the efficiency and reliability of these cutting-edge genomic techniques. Together, ActiveSVM and Active Cell Inference represent significant advancements in the application of genomics to clinical diagnostics, therapeutic discovery, and genetic screens, promising substantial reductions in the operational complexities and costs associated with next-generation sequencing technologies.</p