Crystallization path of salts from brine in Zabuye Salt Lake, Tibet, during isothermal evaporation

Zabuye Salt Lake in Tibet, China is a carbonate-type lake, rich in Li, B, K and other useful trace elements that are of great economic value. We studied the concentration behavior of these elements and the crystallization paths of salts in the brine at 25 C, based on an isothermal evaporation experiment. The crystallization sequence of the primary salts from the brine is halite - aphthitalite - zabuyelite - sylvite - trona and thermonatrite in accordance with the metastable phase diagram. In the experiment, zabuyelite was precipitated in the early stage in the brine at 25 degrees C. Potassium was precipitated as sphthitalite in the intermediate stage and as sylvite in the late stage, while boron was concentrated in the early and intermediate stages and precipitated as borax in the late stage

N′-(Butan-2-yl­idene)furan-2-carbohydrazide

The title Schiff base compound, C9H12N2O2, was obtained from a condensation reaction of butan-2-one and furan-2-carbohydrazide. The furan ring and the hydrazide fragment are roughly planar, the largest deviation from the mean plane being 0.069 (2)Å, but the butanyl­idene group is twisted slightly with respect to this plane by a dihedral angle of 5.2 (3)°. In the crystal, inter­molecular N—H⋯O hydrogen bonds link pairs of inversion-related mol­ecules, forming dimers of R 2 2(8) graph-set motif

Bis(4-acetyl-3-methyl-1-phenyl-1H-pyrazol-5-olato-κ2 O,O′)bis­(N,N-dimethyl­formamide-κO)nickel(II)

The title complex, [Ni(C12H11N2O2)2(C3H7NO)2], lies on on an inversion center. The NiII ion is coordinated in a slightly distorted octa­hedral coordination enviroment by four O atoms from two bis-chelating 4-acety-3-methyl-1-phenyl-1H-pyrazol-5-olate ligands in the equatorial plane and two O atoms from two N,N-dimethyl­formamide ligands in the axial sites. In the crystal structure, weak inter­molecular π–π stacking inter­actions with centroid–centroid distances of 3.7467 (13) Å link mol­ecules into chains extending alongthe b axis

Methyl 2-meth­oxy-4-{[2-(4-nitro­phen­yl)hydrazinyl­idene]meth­yl}benzoate

The mol­ecule of the title Schiff base compound, C16H15N3O5, obtained from a condensation reaction of 4-acet­oxy-3-meth­oxy­benzaldehyde and 4-nitro­phenyl­hydrazine, adopts an E geometry with respect to the C=N double bond. The mol­ecule is roughly planar, with the two benzene rings twisted slightly with respect to each other by a dihedral angle of 6.90 (9)°. In the crystal, inter­molecular N—H⋯O hydrogen bonds link centrosymmetrically related pairs of mol­ecules, forming dimers of R 2 2(22) graph-set motif. The dimers are further connected through slipped π–π inter­actions between symmetry-related benzene rings [centroid–centroid distance of 3.646 (1) Å, offset angle of 15.4°]

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio

Use of low-dose computed tomography to assess pulmonary tuberculosis among healthcare workers in a tuberculosis hospital

BACKGROUND: According to the World Health Organization, China is one of 22 countries with serious tuberculosis (TB) infections and one of the 27 countries with serious multidrug-resistant TB strains. Despite the decline of tuberculosis in the overall population, healthcare workers (HCWs) are still at a high risk of infection. Compared with high-income countries, the TB prevalence among HCWs is higher in low- and middle-income countries. Low-dose computed tomography (LDCT) is becoming more popular due to its superior sensitivity and lower radiation dose. However, there have been no reports about active pulmonary tuberculosis (PTB) among HCWs as assessed with LDCT. The purposes of this study were to examine PTB statuses in HCWs in hospitals specializing in TB treatment and explore the significance of the application of LDCT to these workers. METHODS: This study retrospectively analysed the physical examination data of healthcare workers in the Beijing Chest Hospital from September 2012 to December 2015. Low-dose lung CT examinations were performed in all cases. The comparisons between active and inactive PTB according to the CT findings were made using the Pearson chi-square test or the Fisher’s exact test. Comparisons between the incidences of active PTB in high-risk areas and non-high-risk areas were performed using the Pearson chi-square test. Analyses of active PTB were performed according to different ages, numbers of years on the job, and the risks of the working areas. Active PTB as diagnosed by the LDCT examinations alone was compared with the final comprehensive diagnoses, and the sensitivity and positive predictive value were calculated. RESULTS: A total of 1 012 participants were included in this study. During the 4-year period of medical examinations, active PTB was found in 19 cases, and inactive PTB was found in 109 cases. The prevalence of active PTB in the participants was 1.24%, 0.67%, 0.81%, and 0.53% for years 2012 to 2015. The corresponding incidences of active PTB among the tuberculosis hospital participants were 0.86%, 0.41%, 0.54%, and 0.26%. Most HCWs with active TB (78.9%, 15/19) worked in the high-risk areas of the hospital. There was a significant difference in the incidences of active PTB between the HCWs who worked in the high-risk and non-high-risk areas (odds ratio [OR], 14.415; 95% confidence interval (CI): 4.733 – 43.896). Comparisons of the CT signs between the active and inactive groups via chi-square tests revealed that the tree-in-bud, cavity, fibrous shadow, and calcification signs exhibited significant differences (P = 0.000, 0.021, 0.001, and 0.024, respectively). Tree-in-bud and cavity opacities suggest active pulmonary tuberculosis, whereas fibrous shadow and calcification opacities are the main features of inactive pulmonary tuberculosis. Comparison with the final comprehensive diagnoses revealed that the sensitivity and positive predictive value of the diagnoses of active PTB based on LDCT alone were 100% and 86.4%, respectively. CONCLUSIONS: Healthcare workers in tuberculosis hospitals are a high-risk group for active PTB. Yearly LDCT examinations of such high-risk groups are feasible and necessary. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0274-6) contains supplementary material, which is available to authorized users

Stormwater Management in Southeast Detroit: Adaptive and Contextually Informed Green Infrastructure Strategies

This master’s project focuses on the planning, analysis, and design of contextually informed green infrastructure strategies for adaptive stormwater management in Detroit. The city has observed significant population loss over the last half century, which puts a strain on the tax base required for the upkeep of stormwater and other key infrastructure services. Aging combined sewer systems in need of maintenance combined with increases in the frequency of extreme storm events related to climate change create a scenario in which finding an adaptive solution to stormwater management is becoming progressively more important. The Jefferson-Chalmers neighborhood is located in Detroit’s Lower Eastside and serves as the central study area for this project. The study aims to develop a suite of planning and design concepts for a network of site-based green infrastructure strategies for stormwater management that take advantage of Detroit’s vacant land. Our approach is to create a networked system of a diverse array of green infrastructure stormwater controls. Stormwater management strategies are informed by the surrounding landscape context and respond to site-based opportunities and limitations. Primary research methods include GIS-based hydrologic modeling and studies of Detroit’s combined sewer infrastructure, vacancy data, innovative green infrastructure strategies, and community stabilization plans. A small set of design concepts specific to the Jefferson-Chalmers neighborhood were also developed to illustrate actionable stormwater management strategies.Master of Science Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/106566/1/2014 Master Project 701-242.pd

HMM-Based Emotional Speech Synthesis Using Average Emotion Model

Abstract. This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speech da-tabase. Applying a MLLR-based model adaptation method, we can transform the average emotion model to present the target emotion which is not included in the training data. A multi-emotion speech database including four emotions, “neutral”, “happiness”, “sadness”, and “anger”, is used in our experiment. The results of subjective tests show that the average emotion model can effectively synthesize neutral speech and can be adapted to the target emotion model using very limited training data