The purpose of this paper is to introduce a new dimension of organizational collective engagement (OCE), namely spiritual engagement. This dimension proposes spiritual engagement, which is considered to increase the bundle of engagement as a whole at the organizational level. We collected data from 107 employees who worked in various agencies in Indonesia. We tested the validity and reliability of the proposed indicators of OCE and spiritual engagement using exploratory and confirmatory factor analysis. This study enhances the literature in the field of human resource development, especially in OCE, with the Islamic dimension of spiritual engagement. The findings reveal that there are 10 valid and reliable indicators that can be used to measure the concept of OCE among employees in Indonesia. OCE with four dimensions (physical, emotional, cognitive, and spiritual) can be an effort to increase organizational effectiveness through the collective engagement of all employees. Since this research is limited to Indonesia, further studies are needed in institutions around the world so that the consistency of the results can be justified.

This paper is devoted to the discussion of dynamical properties of anisotropic dark energy cosmological model of the universe in a Bianchi type-V space time in the framework of scale covariant theory of gravitation formulated by Canuto et al.(phys.Rev.Lett.39:429,1977).A  dark energy cosmological model is presented by solving the field equations of this theory by using some physically viable conditions. The dynamics of the model is  studied  by computing the cosmological parameters, dark energy density, equation of state(EoS) parameter, skewness parameters, deceleration parameter and the jerk parameter. This being a scalar field model gives us the quintessence model of the universe which describes a significant dark energy candidate of our accelerating universe. All the physical quantities discussed are in agreement with the recent cosmological observations.

Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.

Aiming at the problem of road network multi-scale matching, a multi-scale road matching method under the constraint of road mesh of small-scale data has been proposed. First, two road meshes with different scale data are constructed; Secondly, under the constraint of the small-scale road mesh, the composite mesh composed of several road meshes in the large-scale road is extracted, and the mesh matching with the small-scale road mesh is completed; Then, many-to-many matching of road meshes with different scales is realized; finally, the matching relationship between composite mesh and small-scale road mesh is transformed into the matching between multi-scale road mesh boundary roads and internal roads, and the matching of the whole road network is completed. The experimental results show that this method can better realize the matching of multi-scale road network.

Since 2013, the state has introduced a number of policies to strictly control the number and scale of public hospitals and to control the rapid expansion of public hospitals. After the introduction of this series of policies, the number of public hospitals in China did not continue to grow, but the number of beds in public hospitals continued to grow. This paper uses difference-in-difference (DID) method to analyze the number of public hospitals with the corresponding data of the development of private hospitals after the introduction of the policy, and the results proves that the introduction of relevant policies has an impact on the number of public hospitals, but has a limited impact on the expansion of the scale of public hospitals. At the end of the article, positive policy suggestions are given to the development of hospitals in China, such as controlling the expansion of public hospitals, strictly controlling the number of beds in public hospitals, and vigorously developing private hospitals. Promoting the development of private hospitals is an important economic supplement to China's health care.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.