Exact solution of anisotropic compact stars via mass function

Studying relativistic compact objects is important in modern astrophysics to understand several astrophysical issues. It is therefore natural to ask for an internal structure and physical properties of specific classes of compact stars from astrophysical observations. We obtain a class of new relativistic solutions with anisotropic distribution of matter for compact stars. More specifically, stellar models, described by an anisotropic fluid, establishing a relation between metric potentials and generating a specific form of mass function, are explicitly constructed within the framework of General Relativity. New solutions can be used to model compact objects, which adequately describe compact strange star candidates like SMC X-1, Her X-1 and 4U 1538-52, with observational data taken from Gangopadhyay et al. (Mon. Not. R. Astron. Soc. 431:3216, 2013). As a possible astrophysical application the obtained solutions could explain the physics of selfgravitating objects, and might be useful for strong-field regimes where data are currently inadequate.     

Study of some chaotic inflationary models in $$ gravity

In this paper, we discuss an inflationary scenario via scalar field and fluid cosmology for an anisotropic homogeneous universe model in \(f(R)\) gravity. We consider an equation of state which corresponds to a quasi-de Sitter expansion and investigate the effect of the anisotropy parameter for different values of the deviation parameter. We evaluate potential models like linear, quadratic and quartic models which correspond to chaotic inflation. We construct the observational parameters for a power-law model of \(f(R)\) gravity and construct the graphical analysis of tensor–scalar ratio and spectral index which indicates the consistency of these parameters with Planck 2015 data.     

Projections of the impacts of climate change on the water deficit and on the precipitation erosive indexes in Mantaro River Basin,Peru

Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change (IPCC) A1B greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 20-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index (MFI) shows a statistically significant downward trend in the Mantaro River Basin, which suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index (PCI) shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.     

Review of pre-processing technologies for ice cores

High-resolution ice core records covering long time spans enable reconstruction of the past climatic and environmental conditions allowing the investigation of the earth system’s evolution. Preprocessing of ice cores has direct impacts on the data quality control for further analysis since the conventional ice core processing is time-consuming, produces qualitative data, leads to ice mass loss, and leads to risks of potential secondary pollution. However, over the past several decades, preprocessing of ice cores has received less attention than the improvement of ice drilling, the analytical methodology of various indices, and the researches on the climatic and environmental significance of ice core records. Therefore, this papers reviews the development of the processing for ice cores including framework, design as well as materials, analyzes the technical advantages and disadvantages of the different systems. In the past, continuous flow analysis (CFA) has been successfully applied to process the polar ice cores. However, it is not suitable for ice cores outside polar region because of high level of particles, the memory effect between samples, and the filtration before injection. Ice core processing is a subtle and professional operation due to the fragility of the nonmetallic materials and the random distribution of particles and air bubbles in ice cores, which aggravates uncertainty in the measurements. The future developments of CFA are discussed in preprocessing, memory effect, challenge for brittle ice, coupling with real-time analysis and optimization of CFA in the field. Furthermore, non-polluting cutters with many different configurations could be designed to cut and scrape in multiple directions and to separate inner and outer portions of the core. This system also needs to be coupled with streamlined operation of packaging, coding, and stacking that can be implemented at high resolution and rate, avoiding manual intervention. At the same time, information of the longitudinal sections could be scanned and identified, and then classified to obtain quantitative data. In addition, irregular ice volume and weight can also be obtained accurately. These improvements are recorded automatically via user-friendly interfaces. These innovations may be applied to other paleomedias with similar features and needs.     

秦皇岛近岸海域绿藻微观繁殖体的分布以及在绿潮形成中的作用

2015年以来，秦皇岛近岸海域暴发了绿潮，对北戴河旅游区的环境和生态系统造成了严重影响。绿藻微观繁殖体在绿潮的形成过程中起到重要作用，主要包括孢子、配子、幼苗和营养片段。绿藻微观繁殖体作为绿潮的“种源”，其分布规律可以反映绿潮的“藻源”位置。本研究于2016年4-9月和2017年1月对秦皇岛近岸海域绿藻微观繁殖体的调查，探究了其分布规律以及生物量变化。结果显示，绿藻微观繁殖体主要分布在近岸海域，由近岸向远岸海域逐渐降低。绿藻微观繁殖体的数量在7、8月份最高，在冬季最低。受绿潮影响严重的海域微观繁殖体数量高于其它海域。秦皇岛近岸海域的绿藻微观繁殖体为该海域绿潮的种源，其分布规律表明秦皇岛近岸海域绿潮起源于本地。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

基于细分行业的安徽省创新型企业集聚研究

创新型企业作为塑造创新地理格局的重要力量，其区位选择的外部特征及内在机理亟须予以系统化研究。基于对安徽省创新型企业的细分行业与空间建库，采用核密度估计和地理探测器等方法，对细分行业下的创新型企业空间集聚特征及影响因素进行研究。结果表明：（1）安徽省创新型企业类型以先进制造和自动化为主，并以生物与新医药、新材料、电子信息为辅；（2）创新型企业空间集聚的不均衡特征显著，呈现出“一核一带多节点”的集聚分布特征，且合肥、芜湖是两个重要集聚区；不同细分行业视角下创新型企业的空间分布形态各异，可归纳为点-轴式、极核式、双核式及多核分散式等4种空间组织形态。（3）创新型因子对于创新型企业的空间集聚最为重要，但传统型和政策型因子的重要性仍不容忽视。其中，产业基础、人力资本、创新投入是最核心的因素，但各行业创新型企业的影响因素作用力则表现出一定的异质性。     

EFFECTS OF FISH OIL,DHA OIL AND LECITIHIN IN MICROPARTICULATE DIETS ON STRESS TOLERANCE OF LARVAL GILTHEAD SEABREAM（SPARUS AURATA）

The effects of natural fish oil,DHA oil and soybean lecithin in microparticulate diets on stress tolerance of larval gilthead seabream(Sparus aurata)were investigated after 15 days feeding trials.The tolerance of larval gilthead seabream to various stress factors such as exposure to air(lack of dissolved oxygen),changes in water temperature(low)and salinity(high) were determined.This study showed that microparticulate diet with natural fish oil and soybean lecithin was the most effective for in-creasing the tolerance of larval gilthead seabream to various stresses,and that microparticulate diet with natural fish oil and palmitic acid(16:0)was more effective than microparticulate diet with DHA oil and soybean lecithin.     

抽油机悬点投影测量仪

油田抽油机的抽油杆幌动幅度过大，是引起抽油杆断裂的一个重要因素，检测这种幌动幅度是防止抽油杆断裂的一种有效手段。设计了一种基于面阵CCD和普通半导体激光器（LD）测量这种幌动的悬点投影测量方法，通过数字卷积滤波，达到了范围为0－40mm，误差＜0.2mm的技术检测指标。     

AN ILLUSTRATIVE STUDY ON LOCAL LANDSCAPEAND ITS LONG-TERM CHANGES BASED ON IKONOS AND HISTORICAL AERIAL PHOTO

Nowadays, the research works on landscape at fine scales using high-resolution images are uncommon.This research is based on the analysis of the combination of remote sensing data (1KONOS imagery acquired in 2002 and historical aerial photo taken in 1942). In the paper, the ecotopes in Qiujiadou and Xishao villages in Yixing City of Jiangsu Province in 1942 and 2002 were compared and landscape changes as well as the causes of the considerable changes were analyzed. It was found that the ecotope changes were at greater level in some aspects such as water surface and perennial vegetation coverage etc. This study at fine scale is globally significant for the rural areas, especially for the subsistence agricultural land, which occupies larger percentage in the earth. And it analyzes the structure of landscape based on a new landscape classification system--stratifications method.