A cluster-based baseline load calculation approach for individual industrial and commercial customer

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified

RoCE based 100GbE RDMA network stack on FPGA hardware

Big data analytics is one of the foundations for booming technologies such as machine learning, genetics/genomics, and computer vision. These big data applications require a large amount of data transfers for distributed and parallel processing. Networking is thus a crucial facilitator and could make big impact on big data processing.In a computing system with a common network stack such as the TCP/IP protocol suite, many expensive memory operations are necessary to process networking traffic. This means a large percentage of CPU resources are occupied by networking rather than data processing. The memory copying overhead introduced by networking not only reduces the throughput but also increases the latency. In this case, networking is becoming a major bottleneck for big data applications. This problem can be solved by applying Remote Direct Memory Access (RDMA) technology to the network stack. RDMA enables a zero-copy mechanism and has CPU bypass ability. With RDMA implemented, both the throughput and latency can be improved.In this work, we developed an open source 100 Gbps RDMA network stack on Field Programmable Gate Array (FPGA) hardware. The developed stack follows the RDMA over Converged Ethernet (RoCE) architecture and targets the Alveo FPGA platform. The stack includes a User kernel that can be customized for user applications. This means that computing applications can also be offloaded to this RoCE stack. Finally, we evaluate the stack and compare it with existing TCP/IP and RDMA stacks like the EasyNet and StRoM. The results show that the developed RDMA stack achieves a throughput of 100 Gbps and an RDMA READ operation latency around 4 us and an RDMA WRITE latency around 3.5 us for 64B data. It shows a great throughput advantage over the TCP/IP stack for message sizes smaller than 1 MB. The latency is also slightly lower than the TCP/IP stack.Big data analytics is one of the foundations for booming technologies such as machine learning, genetics/genomics, and computer vision. These big data applications require a large amount of data transfers for distributed and parallel processing. Networking is thus a crucial facilitator and could make big impact on big data processing.In a computing system with a common network stack such as the TCP/IP protocol suite, many expensive memory operations are necessary to process networking traffic. This means a large percentage of CPU resources are occupied by networking rather than data processing. The memory copying overhead introduced by networking not only reduces the throughput but also increases the latency. In this case, networking is becoming a major bottleneck for big data applications. This problem can be solved by applying Remote Direct Memory Access (RDMA) technology to the network stack. RDMA enables a zero-copy mechanism and has CPU bypass ability. With RDMA implemented, both the throughput and latency can be improved.In this work, we developed an open source 100 Gbps RDMA network stack on Field Programmable Gate Array (FPGA) hardware. The developed stack follows the RDMA over Converged Ethernet (RoCE) architecture and targets the Alveo FPGA platform. The stack includes a User kernel that can be customized for user applications. This means that computing applications can also be offloaded to this RoCE stack. Finally, we evaluate the stack and compare it with existing TCP/IP and RDMA stacks like the EasyNet and StRoM. The results show that the developed RDMA stack achieves a throughput of 100 Gbps and an RDMA READ operation latency around 4 us and an RDMA WRITE latency around 3.5 us for 64B data. It shows a great throughput advantage over the TCP/IP stack for message sizes smaller than 1 MB. The latency is also slightly lower than the TCP/IP stack.Electrical Engineerin

Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch⁻Tung⁻Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm

Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak

During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist-90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak

A fluorescence ratio-based method to determine microalgal viability and its application to rapid optimization of cryopreservation

The utility of microalgal biomass and bioproducts depends on long-term maintenance of certain physiological or biochemical features of the species. While unique characteristics may not be durably maintained with general subculture, cryopreservation methods better prevent alterations from desired characteristics. Post-thaw viability is critical to establishing microalgal cultures, and there is a critical need to effectively and rapidly evaluate microalgal viability after the post-thawing process. In the present study, we developed a rapid assay based on the change of fluorescence ratio to determine microalgal viability post-thaw. It was shown that the assessment of microalgal viability by the fluorescence ratio method correlated well with that of the FDA-staining (R-2 = 0.978) and regrowth method (R-2 = 0.976), demonstrating that the present method could be applied in the high throughput detection of viability of microalgal strains. Subsequent to establishing this method, we aimed to find out optimal cryopreservation protocol for each strain from a group of 125 microalgal strains. The viability of these strains under different treatments was quickly evaluated by the fluorescence ratio method. Of these strains, 95 attained post-thaw viability over 60%. DMSO was a suitable cryoprotectant for most strains at a concentration &lt;= 10%. Based on the dataset, the relative contribution of 3 variables-genus, cryoprotectants and concentration to post-viability was analyzed with the Random Forest (RF) classification method. All variables together could explain 97.8% of the viability, and type and concentration of cryoprotectant could explain 59.1% in Chlorophyta. This study provided a new approach for viability assay and demonstrated that this method can facilitate to find, out the optimal protocols for cryopreservation of microalgal strains.</p

Aliterella shaanxiensis (Aliterellaceae), a new coccoid cyanobacterial species from China

A new coccoid cyanobacterial species Aliterella shaanxiensis sp. nov. is described from a freshwater inland lake in Shaanxi Province, China. The new Aliterella species is defined by using a polyphasic approach that combines morphological, ultrastructural and ecological characteristics, as well as genetic analyses. It is morphologically similar to A. antarctica and A. atlantica but can be distinguished from the latter by its smaller cell, parietal thylakoids, freshwater habitat and 16S rRNA sequence difference. Phylogenetic analyses based on 16S rRNA sequence support the monophyly of the genus Aliterella. The novel taxa described in this study contribute to a better understanding of the diversity of the genus Aliterella in different habitats

Morphology and phylogeny of three planktonic Radiococcaceae sensu lato species (Sphaeropleales, Chlorophyceae) from China, including the description of a new species Planktosphaeria hubeiensis sp. nov.

The family Radiococcaceae sensu lato, defined as colonial autospore-producing mucilaginous coccoid green algae, is widespread in terrestrial and freshwater habitats. Three species of Radiococcaceae sensu lato, including two Radiococcus species and one Planktosphaeria species, were described from China by light and electron microscopy. A new species of Planktosphaeria, Planktosphaeria hubeiensis sp. nov. was erected based on morphological comparisons and genetic analyses. Our phylogenetic analyses indicated that Radiococcaceae sensu lato is polyphyletic, and separated into three lineages. The Radiococcus species did not cluster into a monophyletic group in phylogenetic analyses; therefore the taxonomy of the genus Radiococcus should be revised in the future.</p

Comparative Analysis of Secondary Organic Aerosol Formation during PM2.5 Pollution and Complex Pollution of PM2.5 and O3 in Chengdu, China

Nowadays, many cities in China are suffering from both fine particulate matter (PM2.5, particulate matter with an aerodynamic diameter smaller than 2.5 &micro;m) and ozone (O3) pollution. Secondary organic aerosol (SOA) is an important component of PM2.5 and is closely related to the oxidation processes. To investigate the characteristics and formation pathways of SOA during different types of haze pollution episodes, carbonaceous components of PM2.5 and volatile organic compounds (VOCs) were monitored continuously in Chengdu in April 2019, when Chengdu experienced not only PM2.5 pollution (SPP) but also a complex pollution of PM2.5 and O3 (CoP). In the CoP episode, the concentrations of SOA increased by 51.2% as compared to SPP, and the SOA concentrations were positively correlated with PM2.5 mass concentrations. These suggest that SOA drove the increase in PM2.5 levels during the haze event to some extent. The preliminary VOC source analysis based on the feature ratio showed that vehicle emission and fuel volatilization sources were the main sources of VOCs at this urban site. In addition, coal emissions and biomass burning were also important contributors. High-carbon alkanes and aromatic hydrocarbons significantly contributed to the SOA formation. These results provide a preliminary understanding of SOA formation during different types of pollution episodes in Chengdu, which can help us to further understand air pollution in this typical region

Comparative Analysis of Secondary Organic Aerosol Formation during PM_2.5 Pollution and Complex Pollution of PM_2.5 and O₃ in Chengdu, China

Nowadays, many cities in China are suffering from both fine particulate matter (PM2.5, particulate matter with an aerodynamic diameter smaller than 2.5 µm) and ozone (O3) pollution. Secondary organic aerosol (SOA) is an important component of PM2.5 and is closely related to the oxidation processes. To investigate the characteristics and formation pathways of SOA during different types of haze pollution episodes, carbonaceous components of PM2.5 and volatile organic compounds (VOCs) were monitored continuously in Chengdu in April 2019, when Chengdu experienced not only PM2.5 pollution (SPP) but also a complex pollution of PM2.5 and O3 (CoP). In the CoP episode, the concentrations of SOA increased by 51.2% as compared to SPP, and the SOA concentrations were positively correlated with PM2.5 mass concentrations. These suggest that SOA drove the increase in PM2.5 levels during the haze event to some extent. The preliminary VOC source analysis based on the feature ratio showed that vehicle emission and fuel volatilization sources were the main sources of VOCs at this urban site. In addition, coal emissions and biomass burning were also important contributors. High-carbon alkanes and aromatic hydrocarbons significantly contributed to the SOA formation. These results provide a preliminary understanding of SOA formation during different types of pollution episodes in Chengdu, which can help us to further understand air pollution in this typical region