Life cycle assessment and carbon reduction potential prediction of electric vehicles batteries.

Electric vehicles (EVs) battery is a crucial component of energy storage components for electric vehicles. However, the environmental impact of EVs battery is still not clear. Therefore, this paper establishes a cradle-to-cradle life cycle assessment (LCA) frame and clarifies the environmental impacts on the entire lifespan of EVs battery in China. Specifically, the environmental impact of battery production, battery use, and recycling & disposal stages are analyzed and measured. In addition, the carbon reduction potential of recycling and secondary use under a future electricity mix is estimated. Results show that: (1) The production stage of EVs battery with the carbon emission of 105 kgCO2-eq/kWh, which has the most significant impact on the environment. (2) In the recycling process, cascade utilization can reduce 1.536 kgCO2-eq/kWh carbon emission. In terms of recycling methods, hydrometallurgy can reduce the most carbon emission (13.3 kgCO2-eq/kWh), followed by the combined hydro-pyrometallurgical process (8.11 kgCO2-eq/kWh) and pyrometallurgy (0.57 kgCO2-eq/kWh). (3) Under the estimated electricity mix in 2030, 2040, and 2050, the carbon emission in battery production can be approximately reduced by 31.9 %, 45 %, and 48.1 %, respectively.

Two New Zhang Neural Networks for Solving Time-Varying Linear Equations and Inequalities Systems.

Recently, Xu et al. solved a class of time-varying linear equations and inequalities systems (LEIESs) by using a Zhang neural network (ZNN) model through introducing a nonnegative relaxation vector. However, the introduction of this unknown nonnegative slack vector will increase the size and complexity of the model, thereby increasing the cost of computation. In this article, we propose two new ZNN models (called traditional Zhang neural network (TZNN) and variant Zhang neural network (VZNN) models, respectively) in which no additional relaxation vector is needed. The convergence analysis of these two new models are performed, and two simulation experiments are given to illustrate their efficiency and effectiveness for solving the time-varying LEIESs, including the applicability of our proposed models to robot manipulator.

Comparison of reduced field-of-view DWI and conventional DWI techniques for the assessment of lumbar bone marrow infiltration in patients with acute leukemia.

Objectives: To compare the imaging quality, apparent diffusion coefficient (ADC), and the value of assessing bone marrow infiltration between reduced field-of-view diffusion-weighted imaging (r-FOV DWI) and conventional DWI in the lumbar spine of acute leukemia (AL). Methods: Patients with newly diagnosed AL were recruited and underwent both r-FOV DWI and conventional DWI in the lumbar spine. Two radiologists evaluated image quality scores using 5-Likert-type scales qualitatively and measured signal-to-noise ratio (SNR), contrast-to-noise (CNR), signal intensity ratio (SIR), and ADC quantitatively. Patients were divided into hypo- and normocellular group, moderately hypercellular group, and severely hypercellular group according to bone marrow cellularity (BMC) obtained from bone marrow biopsies. The image quality parameters and ADC value between the two sequences were compared. One-way analysis of variance followed by LSD post hoc test was used for the comparisons of the ADC values among the three groups. The performance of ADC obtained with r-FOV DWI (ADCr) and conventional DWI(ADCc) in evaluating BMC and their correlations with BMC and white blood cells (WBC) were analyzed and compared. Results: 71 AL patients (hypo- and normocellular: n=20; moderately hypercellular: n=19; severely hypercellular: n=32) were evaluated. The image quality scores, CNR, SIR, and ADC value of r-FOV DWI were significantly higher than those of conventional DWI (all p<0.05), and the SNR of r-FOV DWI was significantly lower (p<0.001). ADCr showed statistical differences in all pairwise comparisons among the three groups (all p<0.05), while ADCc showed significant difference only between hypo- and normocellular group and severely hypercellular group (p=0.014). The performance of ADCr in evaluating BMC (Z=2.380, p=0.017) and its correlations with BMC (Z=-2.008, p = 0.045) and WBC (Z=-2.022, p = 0.043) were significantly higher than those of ADCc. Conclusion: Compared with conventional DWI, r-FOV DWI provides superior image quality of the lumbar spine in AL patients, thus yielding better performance in assessing bone marrow infiltration.

Using cooperative game model of air pollution governance to study the cost sharing in Yangtze River Delta region.

To solve the increasingly serious air pollution problem, regional air pollution collaborative governance as an effective way is introduced in this paper. Firstly, this study constructs a game model of regional air pollution collaborative governance, and three classic cost sharing methods are used to allocate the cost of air pollution governance; Then the emission reduction cost of regional cooperative governance is obtained; Finally, the study makes an empirical analysis on the cost sharing of SO2 emission reduction by four local governments of the Yangtze River Delta region in 2017. The results show that: (i) The emission reduction cost of regional cooperative governance is better than that of individual governance, and in 2017 the cost of SO2 cooperative governance in Yangtze River Delta region is reduced by about 1.8% compared with the individual governance; (ii) The Shapley value method is utilized to allocate the benefits of the cooperative SO2 governance of four provinces in the Yangtze River Delta region, which indicates that all local governments can obtain certain profits from this cooperation and the cooperative alliance is stable; (iii) Collaborative governance of regional air pollution can meet the requirements of both collective rationality and individual rationality at the same time.

[Characterization of Metal Pollution of Regional Atmospheric PM2.5 and Its Sources Based on the PMF Model].

As important components of PM2.5, metal elements are extremely harmful to people and also have source specificity. Understanding the characteristics of PM2.5 metal pollution in the two different types of cities can help adjust the layout of regional industrial structure and improve the environment. PM2.5 samples during haze/non-haze periods were collected in Chengdu City and Renshou County. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the mass concentrations of eighteen metal elements in collected PM2.5 samples. The positive matrix factorization (PMF) model was used for source apportionment analysis for metal elements in PM2.5. The analysis showed that the ratio of trace elements from fugitive dust, motor vehicle emissions, and coal burning to the total elements is greater in Chengdu City than that in Renshou County. The proportion of trace elements from biomass combustion, industrial, and fuel sources in Renshou County is higher than that in Chengdu City. In addition, concentrations of Cd, As, and Cr in both areas exceeded the standards, indicating the occurrences of heavy metal pollution. During the haze period, the total concentrations of compositional metal elements in PM2.5 increased, although the rate was much lower than that for PM2.5. The ratios of elements between haze and non-haze periods ranged from 0.7 (Al) to 2.8 (Ba) in Chengdu City, and from 0.8 (Al) to 3.1 (Mn) in Renshou County. Among all metal elements, the increase rate for trace elements from coal burning and industrial activities was relatively large but small for those from fugitive dust, with the growth in trace elements from motor vehicles being modest. The results of this study indicated that the characteristics of pollution and source of metal elements in PM2.5 varied by economic scale, development mode, and industrial layout. In large cities such as Chengdu City, where economic development is mainly focused on tertiary industry, air pollution is mainly caused by transportation and urban construction, while in suburban area such as Renshou County, where secondary or heavy industry are the focus for economic development, the pollution is mainly affected by energy consumption and industrial production.

Exploring the effect of economic and environment factors on PM2.5 concentration: A case study of the Beijing-Tianjin-Hebei region.

Air pollution, especially haze pollution is a serious environment problem that directly affects the sustainable development in China. Identifying the key factors affecting PM2.5 concentration and the interaction mechanism between them through quantitative analysis can greatly help a city devise PM2.5 pollution control strategy. Using the geographical detector model, we quantitative measured 13 cities in the Beijing-Tianjin-Hebei region's social factors and their interaction impacts on PM2.5 concentration in 2016. In the analysis process, factor analysis method is used to separate the factors preliminary. According to the results, the factors mainly divided into two categories, i.e. economic factor and environment factor. R&D ranks top in the studied cities in terms of factor detection results, presenting closely relationship between PM2.5 concentration and R&D. We also find the interaction between any two factors all enhance impact on PM2.5 concentration than any one alone. This study provided a scientific basic and guidance for measure the driving degree of social factors and their interaction effects.

A New Continuous Rotation IMU Alignment Algorithm Based on Stochastic Modeling for Cost Effective North-Finding Applications.

Based on stochastic modeling of Coriolis vibration gyros by the Allan variance technique, this paper discusses Angle Random Walk (ARW), Rate Random Walk (RRW) and Markov process gyroscope noises which have significant impacts on the North-finding accuracy. A new continuous rotation alignment algorithm for a Coriolis vibration gyroscope Inertial Measurement Unit (IMU) is proposed in this paper, in which the extended observation equations are used for the Kalman filter to enhance the estimation of gyro drift errors, thus improving the north-finding accuracy. Theoretical and numerical comparisons between the proposed algorithm and the traditional ones are presented. The experimental results show that the new continuous rotation alignment algorithm using the extended observation equations in the Kalman filter is more efficient than the traditional two-position alignment method. Using Coriolis vibration gyros with bias instability of 0.1°/h, a north-finding accuracy of 0.1° (1σ) is achieved by the new continuous rotation alignment algorithm, compared with 0.6° (1σ) north-finding accuracy for the two-position alignment and 1° (1σ) for the fixed-position alignment.

A stationary north-finding scheme for an azimuth rotational IMU utilizing a linear state equality constraint.

The Kalman filter (KF) has always been used to improve north-finding performance under practical conditions. By analyzing the characteristics of the azimuth rotational inertial measurement unit (ARIMU) on a stationary base, a linear state equality constraint for the conventional KF used in the fine north-finding filtering phase is derived. Then, a constrained KF using the state equality constraint is proposed and studied in depth. Estimation behaviors of the concerned navigation errors when implementing the conventional KF scheme and the constrained KF scheme during stationary north-finding are investigated analytically by the stochastic observability approach, which can provide explicit formulations of the navigation errors with influencing variables. Finally, multiple practical experimental tests at a fixed position are done on a postulate system to compare the stationary north-finding performance of the two filtering schemes. In conclusion, this study has successfully extended the utilization of the stochastic observability approach for analytic descriptions of estimation behaviors of the concerned navigation errors, and the constrained KF scheme has demonstrated its superiority over the conventional KF scheme for ARIMU stationary north-finding both theoretically and practically.

Retrograde intrarenal surgery with combined spinal-epidural vs general anesthesia: a prospective randomized controlled trial.

OBJECTIVE: Retrograde intrarenal surgery (RIRS) involves a minimally invasive stone surgery, lending itself potential to combined spinal-epidural anesthesia (CSEA), although it is performed preferably under general anesthesia (GA). This prospective randomized study was undertaken to evaluate the feasibility and efficacy of CSEA for patients undergoing RIRS. PATIENTS AND METHODS: Seventy consecutive patients who were scheduled for RIRS were randomized to receive CSEA (n=35) or GA (n=35). Operative time, stone clearance rate, visual analog scale (VAS) of pain, complication rate, anesthetic cost, and hospital stay were compared between the two groups. RESULTS: A total of 65 patients randomized to CSEA (31) or GA (34) completed the study. In the CSEA group, each procedure was completed and there was no anesthetic conversion. Although based on the prospective randomized method, the GA group still had a little larger stone size (p=0.059) and more multiple caliceal stones (p=0.037). Overall, there were no statistically significant differences in operative time (p=0.088), stone fragmentation time (p=0.074), postoperative VAS pain score at 6 and 24 hours (p=0.156, 0.146), incidence of complications (p=0.870), stone-free rate (p=0.804), and hospital stays (p=0.907) between the two groups. The patients in the GA group experienced a higher mean hemoglobin drop (6.5±3.2 vs 8.6±2.7 g/L, p=0.012). In addition, the anesthetic cost was much cheaper in the CSEA group (183.8±31.4 vs 391.9±59.1 dollars, p<0.001). CONCLUSION: RIRS with CSEA can be completed with no anesthetic conversions and with the same efficacy and safety compared with GA. When considering economical aspects, CSEA appears to be a preferable alternative to GA for the patient whose general health status permits it.