CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning

Non-line-of-sight (NLOS) error is the main factor that reduces indoor positioning accuracy. Identifying NLOS signals and eliminating NLOS errors are the keys to improving indoor positioning accuracy. To better identify NLOS signals, a multi-stream model channel-impulse-response-neural-network (CIRNN) was proposed. The inputs of CIRNN include the channel impulse response (CIR) and a small number of channel parameters. To make a more obvious comparison between NLOS signals and line-of-sight (LOS) signals, a new energy normalization method is proposed. Fusing multi-dimensional features, the CIRNN network has a good convergence performance and shows stronger sensitivity to NLOS signals. Experimental results show that the CIRNN achieves the best accuracy on the open-source data set, the F1 score is 89.3%. At the same time, the working efficiency of CIRNN meets industry needs, CIRNN can refresh the target position at about 92.6 Hz per second

Supply Function Competition in Electricity Markets with Flexible, Inflexible, and Variable Generation

In this paper we study the supply function competition between power-generation firms with different levels of flexibility. Inflexible firms produce power at a constant rate over an operating horizon, while flexible firms can adjust their output to meet the fluctuations in electricity demand. Both types of firms compete in an electricity market by submitting supply functions to a system operator, who solves an optimal dispatch problem to determine the production level for each firm and the corresponding market price. We study how firms’ (in)flexibility affects their equilibrium behavior and the market price. We also analyze the impact of variable generation (such as wind and solar power) on the equilibrium, with the focus on the effects of the amount of variable generation, its priority in dispatch, and the production- based subsidies. We find that the classic supply function equilibrium model overestimates the intensity of the market competition, and even more so as more variable generation is introduced into the system. The policy of economically curtailing variable generation intensifies the market competition, reduces price volatility, and improves the system’s overall efficiency. Moreover, we show that these benefits are most significant in the absence of the production-based subsidies.http://deepblue.lib.umich.edu/bitstream/2027.42/102571/1/2014Jan28OWu.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102571/4/1218_Wu_Apr14.pd

Pyrrolidine Dithiocarbamate Attenuates Paraquat-Induced Lung Injury in Rats

Paraquat (PQ) has been demonstrated that the main target organ for the toxicity is the lung. This study aimed to investigate the potential protective effect of PDTC on the PQ-induced pulmonary damage. Fifty-four rats were divided into control, PQ-treated and PQ+PDTC-treated groups. Rats in the PQ group were administrated 40 mg/kg PQ by gastric gavage, and PDTC group with 40 mg/kg PQ followed by injection of 120 mg/kg PDTC (IP). On the days 3, 7, 14 and 21 after treatments, the activities of GSH-Px, SOD, MDA level and the content of HYP were measured. TGF-β1 mRNA and protein were assayed by RT-PCR and ELISA. MDA level in plasma and BALF was increased and the activities of GSH-Px and SOD were decreased significantly in the PQ-treated groups (P < .05) compared with control group. While the activities of GSH-Px and SOD in the PQ+PDTC-treated groups was markedly higher than that of PQ-treated groups (P < .05), and in contrast, MDA level was lower. TGF-β1 mRNA and protein were significantly lower in the PQ+PDTC-treated groups than that of PQ-treated groups (P < .05). The histopathological changes in the PQ+PDTC-treated groups were milder than those of PQ groups. Our results suggested that PDTC treatment significantly attenuated paraquat-induced pulmonary damage

Construction of a Recombinant Eukaryotic Expression Plasmid Containing Human Calcitonin Gene and Its Expression in NIH3T3 Cells

Aim. To construct a recombinant eukaryotic expression plasmid containing human calcitonin (hCT) gene and express the gene in murine fibroblast NIH3T3 cells. Materials and Methods. A murine Igκ-chain leader sequence and hCT gene were synthesized and cloned into pCDNA3.0 to form the pCDNA3.0-Igκ-hCT eukaryotic expression vector, which was transfected into NIH3T3 cells. The mRNA and protein expressions and secretion of hCT were detected. Primarily cultured osteoclasts were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells, and their numbers were counted and morphology observed. Results. The expression and secretion of hCT were successfully detected in pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. The number of osteoclasts was decreased and the cells became crumpled when they were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. Conclusion. A recombinant eukaryotic expression vector containing hCT gene was successfully constructed and expressed in NIH3T3 cells. The secreted recombinant hCT inhibited the growth and morphology of osteoclasts

Di-μ-sulfato-bis­{[bis­(3,5-dimethyl­pyrazol-1-yl)methane]copper(II)}

The mol­ecule of the title compound, [Cu2(SO4)2(C11H16N4)2], sits on a center of symmetry. The CuII atom has a distorted trigonal–bipyramidal coordination geometry comprising three O atoms of the two symmetry-related SO4 2− anions and two N atoms from one bis­(3,5-dimethyl­pyrazol-1-yl)methane ligand

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource

Comparative Evaluation of Biomass Power Generation Systems in China Using Hybrid Life Cycle Inventory Analysis

There has been a rapid growth in using agricultural residues as an energy source to generate electricity in China. Biomass power generation (BPG) systems may vary significantly in technology, scale, and feedstock and consequently in their performances. A comparative evaluation of five typical BPG systems has been conducted in this study through a hybrid life cycle inventory (LCI) approach. Results show that requirements of fossil energy savings, and greenhouse gas (GHG) emission reductions, as well as emission reductions of SO2 and NOx, can be best met by the BPG systems. The cofiring systems were found to behave better than the biomass-only fired system and the biomass gasification systems in terms of energy savings and GHG emission reductions. Comparing with results of conventional process-base LCI, an important aspect to note is the significant contribution of infrastructure, equipment, and maintenance of the plant, which require the input of various types of materials, fuels, services, and the consequent GHG emissions. The results demonstrate characteristics and differences of BPG systems and help identify critical opportunities for biomass power development in China

Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization

Water wave optimization (WWO) is a novel metaheuristic method that is based on shallow water wave theory, which has simple structure, easy realization, and good performance even with a small population. To improve the convergence speed and calculation precision even further, this paper on elite opposition-based strategy water wave optimization (EOBWWO) is proposed, and it has been applied for function optimization and structure engineering design problems. There are three major optimization strategies in the improvement: elite opposition-based (EOB) learning strategy enhances the diversity of population, local neighborhood search strategy is introduced to enhance local search in breaking operation, and improved propagation operator provides the improved algorithm with a better balance between exploration and exploitation. EOBWWO algorithm is verified by using 20 benchmark functions and two structure engineering design problems and the performance of EOBWWO is compared against those of the state-of-the-art algorithms. Experimental results show that the proposed algorithm has faster convergence speed, higher calculation precision, with the exact solution being even obtained on some benchmark functions, and a higher degree of stability than other comparative algorithms

Genetic Engineering of Starch Biosynthesis in Maize Seeds for Efficient Enzymatic Digestion of Starch during Bioethanol Production

Maize accumulates large amounts of starch in seeds which have been used as food for human and animals. Maize starch is an importantly industrial raw material for bioethanol production. One critical step in bioethanol production is degrading starch to oligosaccharides and glucose by alpha-amylase and glucoamylase. This step usually requires high temperature and additional equipment, leading to an increased production cost. Currently, there remains a lack of specially designed maize cultivars with optimized starch (amylose and amylopectin) compositions for bioethanol production. We discussed the features of starch granules suitable for efficient enzymatic digestion. Thus far, great advances have been made in molecular characterization of the key proteins involved in starch metabolism in maize seeds. The review explores how these proteins affect starch metabolism pathway, especially in controlling the composition, size and features of starch. We highlight the roles of key enzymes in controlling amylose/amylopectin ratio and granules architecture. Based on current technological process of bioethanol production using maize starch, we propose that several key enzymes can be modified in abundance or activities via genetic engineering to synthesize easily degraded starch granules in maize seeds. The review provides a clue for developing special maize cultivars as raw material in the bioethanol industry