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Bag-of-Tasks (BoT) applications consisting of multiple tasks are widely used in various fields.Different from the traditional deadline constraint in scheduling problems,a due date constraint allows the BoT applications to finish more than a predetermined due date,but would result in the tardiness penalty.In this case,in order to reduce the total cost,the efficient harmony search (EHS) algorithm is proposed to optimize the scheduling tasks in hybrid clouds.The algorithm obtains the initial task sequence by random search and fine tuning.By improving the harmony search steps and the way of generating new harmony memory,a large number of high-quality harmony can be obtained in one search process,which greatly improves the efficiency of harmony search and speeds up the convergence speed of the algorithm.Through continuous iteration,the global optimal solution is obtained,that is to say,BoT application scheduling scheme has the lowest total cost.Experimental results show that compared with other algorithms,the proposed algorithm has significant improvement in performance,which can effectively reduce the total cost of scheduling BoT applications.

At present, resonance demodulation and data envelopment analysis, etc are usually used for health evaluation and remaining life prediction of bearings, but there are some problems such as difficulty in extracting health degree, single fault prediction type, and lack of reliable models. Aiming at these deficiencies, this paper proposes a method which realizes bearing health assessment and remaining life prediction through two-stage cascaded long short term memory neural network. In this paper, the bearing data set of Xi'an Jiaotong University is used for experimental comparison. The results show that, compared with the resonance demodulation method, the monotonicity, robustness and trend of the health degree curve are comprehensive in the health degree evaluation of single fault data using this method. The evaluation indicators increase by 12%, 24.8% and 5% respectively, and the comprehensive evaluation index in the composite fault data increases by 15.1%, and the remaining service life can be predicted according to the health evaluation curve, which proves that the method is effective in health evaluation and remaining life.

Background: Cognitive impairment is a clinical manifestation that occurs in the course of dementia like Alzheimer's disease. The association between cognitive impairment and gut microbiota is unclear. Objective: We aimed to identify gut microbiota characteristics and key gut microbiota biomarkers associated with cognitive impairment in a relatively large cohort of older adults in China. Methods: A total of 229 adults aged ≥60 years from Shenzhen, China were recruited into this cross-sectional study. Participants were divided into cognitive impairment (CI) and no cognitive impairment (NCI) groups according to the results of the Mini-Mental State Examination. Diversity analysis and network analysis were used to characterize the gut microbiota between the two groups. The linear discriminant analysis effect size method and machine learning approaches were sequentially performed to identify gut microbiota biomarkers. The relationship between biomarkers and lifestyle factors was explored using Transformation-based redundancy analysis (tb-RDA). Results: A total of 74 CI participants and 131 NCI participants were included in the analysis. The CI group demonstrated lower α-diversity compared to the NCI group (Shannon: 2.798 versus 3.152, p < 0.001). The density of the gut microbiota interaction network was lower in the CI group (0.074) compared to the NCI group (0.081). Megamonas, Blautia, Pseudomonas, Stenotrophomonas, and Veillonella were key biomarkers for CI. The tb-RDA revealed that increased fruit intake and exercise contribute to a higher abundance of Megamonas, Blautia, and Veillonella. Conclusions: We identified a significantly reduced abundance of certain beneficial gut microbiota in older Chinese adults with cognitive impairment. [ABSTRACT FROM AUTHOR]

The South China Sea is a typical marginal sea characterized by the presence of numerous seamounts. However, the effect of seamounts on the air‐sea CO2 flux has not yet been well studied. In September 2021, the air‐sea CO2 flux was measured directly using eddy covariance (EC), and discrete waterside sampling was conducted. The results indicate that the northern South China Sea is a source of atmospheric CO2. Furthermore, EC measurements show that the seamount emits CO2 at an average rate of 0.34 mmol m−2 hr−1, nearly double that of non‐seamount areas. We suggest that the upwelling around the seamount transports deep water rich in dissolved inorganic carbon to the upper ocean, increasing the partial pressure of CO2 there. In addition, the increase in nutrients caused by the upwelling would increase the concentration of chlorophyll‐a, resulting in a productive area that emits CO2. Plain Language Summary: The South China Sea (SCS) serves as a minor CO2 source, and the air‐sea CO2 flux in its northern region demonstrates challenging spatiotemporal variations. In our investigation, we conducted underway assessments in the northern SCS and validated its emission of CO2, particularly in the vicinity of seamount ocean zones. CO2 emissions may have been intensified by the upward movement of seawater surrounding the seamount region. Key Points: The northern South China Sea emitted CO2 into the atmosphere at a mean rate of 0.28 mmol m−2 hr−1 in September 2021The CO2 flux almost doubled as the seamount was approached, indicating an increase in CO2 emissions near the seamountUpwelling releases high dissolved inorganic carbon, which increases the partial pressure of CO2 on the sea surface around the seamount [ABSTRACT FROM AUTHOR]

Spectral encoding is an important way of describing spectral features and patterns. Traditional methods focused on encoding the spectral amplitude information (SAI). Abundant spectral shape information (SSI) was wasted. In addition, traditional statistical encoding methods might only gain local adaptability since different objects should have their own best encoding scales. In order to obtain differential signals from hyperspectral images (HSI) for detecting ground objects correctly, a multi-scale encoding (MSE) method with SSI and two optimization strategies were proposed in this research. The proposed method concentrated on describing the SAI and SSI of the spectral reflectance signals. Four widely used open data sets were adopted to validate the performance of the proposed method. Experimental results indicated that the MSE method with SSI could describe the details of spectral signals accurately. It could obtain excellent performance for detecting similar objects with a small number of samples. In addition, the optimization strategies contributed to obtaining the best result from dynamic encoding scales. [ABSTRACT FROM AUTHOR]

Sulfide-based materials were widely investigated as electrolytes for all-solid-state lithium batteries owing to their good ionic conductivity and safety. However, their applications are hindered by the air sensitivity and Limited ionic conductivity. Here, a series of GeO2-doped Li3+xP1-xGexS4-2xO2x (x = 0,0.02,0.04,0.06,0.08) solid electrolytes were synthesized through a modified solid-state method and characterized comprehensively. The lithium ionic conductivity of Li3.04P0.96Ge0.04S3.92O0.08 solid electrolytes reached 3.54 mS cm–1 at room temperature and the H2S emission was only 0.31 cm−3 g when the electrolytes were exposed to air for 300 min. Importantly, the critical current density of Li3.04P0.96Ge0.04S3.92O0.08 electrolytes was 1.2 mA cm–2, which was twice of Li3PS4. In addition, the ASSLB of LiCoO2@SSEs/Li3.04P0.96Ge0.04S3.92O0.08/Li delivered an initial discharge capacity of 126.9 mAh g−1 at 0.1 C at room temperature, and it showed excellent rate performance, e.g., 126, 103, 55, 39, mAh g−1 at 0.1, 0.2, 0.5, and 1 C at RT. The cell achieved a reversible capacity of 84 mAh g−1 when the current reverted to 0.3 C. The electrochemical performance and chemical stability of Li3PS4 were improved by co-doping of Ge and O, and provided a promising approach to achieve excellent chemical and electrochemical stability for the large-scale application of sulfide-based solid state electrolytes. [ABSTRACT FROM AUTHOR]