1,906 research outputs found
The Study of Green Human Resource Management and its effect on employees’ health in Automobile Industry
As of late, there has been an expanding mindfulness inside business networks on the essentialness of making strides toward environmental friendliness and receiving different condition management methods. As the corporate world is going worldwide, the business is encountering a move from a regular money related structure to a cutting edge limit based economy which is prepared to investigate green financial aspects of business. Today, Green Human Resource Management (GHRM) has turned into a key business methodology for the noteworthy associations where Human Resource Departments have a functioning influence in making strides toward environmental friendliness at the workplace. The main objective of this study is to review Green Human Resource Management and its effect on Employees Health in Automobile Industry. This study focuses on how and why Green Human Resource Management practices are implemented by different organizations. This study also reviews the negative and positive impacts of green-HRM practices on employee health and the employee commitment levels in organizations.
 
Implications of binary black hole detections on the merger rates of double neutron stars and neutron star-black holes
We show that the inferred merger rate and chirp masses of binary black holes
(BBHs) detected by advanced LIGO (aLIGO) can be used to constrain the rate of
double neutron star (DNS) and neutron star - black hole (NSBH) mergers in the
universe. We explicitly demonstrate this by considering a set of publicly
available population synthesis models of \citet{Dominik:2012kk} and show that
if all the BBH mergers, GW150914, LVT151012, GW151226, and GW170104, observed
by aLIGO arise from isolated binary evolution, the predicted DNS merger rate
may be constrained to be ~\rate~ and that of NSBH mergers will be
constrained to ~\rate. The DNS merger rates are not constrained much
but the NSBH rates are tightened by a factor of as compared to their
previous rates. Note that these constrained DNS and NSBH rates are extremely
model dependent and are compared to the unconstrained values \rate~
and \rate, respectively, using the same models of
\citet{Dominik:2012kk}. These rate estimates may have implications for short
Gamma Ray Burst progenitor models assuming they are powered (solely) by DNS or
NSBH mergers. While these results are based on a set of open access population
synthesis models which may not necessarily be the representative ones, the
proposed method is very general and can be applied to any number of models
thereby yielding more realistic constraints on the DNS and NSBH merger rates
from the inferred BBH merger rate and chirp mass.Comment: 5 pages, no figures, 4 tables, v2: matches published versio
Aluminium and Its Interlinking Properties
Aluminium and its alloys are preferred materials, because of its varied desirable properties, availability and inexpensiveness. Aluminium alloys exist in several different grades available in the market commercially, from pure (about 99% Al content) to specific varieties based on the impurities contained in it by chemical composition. The properties are differing in nature which can be scientifically seen and justified in different perspectives. The properties such as forming, fracture mode, tensile, etc. can be seen through the metallurgical aspect, chemical aspect, crystallographic texture, forming limits and mechanical properties. The truth of its properties can be viewed by interlinking/correlating nature of its different studies. The purpose of this chapter is to show the correlating nature of different properties of aluminium of same and different grades
Charge order suppression and antiferromagnetic to ferromagnetic switch over in Pr_0.5Ca_0.5MnO_3 nanowires
We have prepared crystalline nanowires (diameter ~ 50 nm, length ~ a few
microns) of the charge ordering manganite Pr_0.5Ca_0.5Mn_O3 using a low
reaction temperature hydrothermal method and characterized them using X-ray
diffraction, transmission electron microscopy, SQUID magnetometry and electron
magnetic resonance measurements. While the bulk sample shows a charge ordering
transition at 245 K and an antiferromagnetic transition at 175 K, SQUID
magnetometry and electron magnetic resonance experiments reveal that in the
nanowires phase, a ferromagnetic transition occurs at ~ 105 K. Further, the
antiferromagnetic transition disappears and the charge ordering transition is
suppressed. This result is particularly significant since the charge order in
Pr_0.5Ca_0.5MnO_3 is known to be very robust, magnetic fields as high as 27 T
being needed to melt it.Comment: 12 pages including 4 figures. submitted to Applied Physics Letter
Strategy for Foreground Movement Identification Adaptive to Background Variations
Video processing has gained a lot of significance because of its applications in various areas of research. This includes monitoring movements in public places for surveillance. Video sequences from various standard datasets such as I2R, CAVIAR and UCSD are often referred for video processing applications and research. Identification of actors as well as the movements in video sequences should be accomplished with the static and dynamic background. The significance of research in video processing lies in identifying the foreground movement of actors and objects in video sequences. Foreground identification can be done with a static or dynamic background. This type of identification becomes complex while detecting the movements in video sequences with a dynamic background. For identification of foreground movement in video sequences with dynamic background, two algorithms are proposed in this article. The algorithms are termed as Frame Difference between Neighboring Frames using Hue, Saturation and Value (FDNF-HSV) and Frame Difference between Neighboring Frames using Greyscale (FDNF-G). With regard to F-measure, recall and precision, the proposed algorithms are evaluated with state-of-art techniques. Results of evaluation show that, the proposed algorithms have shown enhanced performance
An Overview of the Different Kinds of Vector Space Partitions
In a finite vector space V (n,q), where V is n-dimensional over a finite field with q elements, a collection P of subspaces is called a vector space partition. The property of this set P is that any vector that is not zero may be found in exactly one element of P. Partitions of vector spaces have strong ties to design theory, error-correcting algorithms, and finite projective planes.
The first portion of my talk will focus on the mathematical fields that share common ground with vector space partitions. The rest of the lecture will go over some of the most well-known results on vector space partition classification. Heden and Lehmann's result on vector space partitions and maximal partial spreads, as well as El-Zanati et al.'s recent findings on the types found in spaces V(n, 2) for n = 8 or less, the Beutelspacher and Heden theorem on T-partitions, and their newly established condition for the existence of a vector space partition will all be covered. Furthermore, I will demonstrate Heden's theorem about the tail length of a vector space split. Finally, I shall provide some historical notes
Impact of Training on Perceived Stress of Parents with Intellectually Disabled Children
The present study focuses on the perceived stress in 353 parents, whose Children with Intellectual disability underwent training in special education schools of (Bavitha Readiness Centres), Chittoor dist., Andhra Pradesh. The overall perceived stress (scale) score was measured through family assessment scheduled (FAS) developed by NIMHANS. The concerned mean scores were computed and compared at two stages of special training given to children i.e., at the time of joining and after two years of training with a paired t-test of significance. The overall findings reveal that the overall score, as well as its four sub–scales score of parent’s perceived stress, have been reduced after providing 2 years of special training to their Children with Intellectual disability as compared to joining the school (training)
A Prolific Scheme for Load Balancing Relying on Task Completion Time
In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks‘t’ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network
A Novel Method for Optimal Solution of Fuzzy Chance Constraint Single-Period Inventory Model
A method is proposed for solving single-period inventory fuzzy probabilistic model (SPIFPM) with fuzzy demand and fuzzy storage space under a chance constraint. Our objective is to maximize the total profit for both overstock and understock situations, where the demand D~j for each product j in the objective function is considered as a fuzzy random variable (FRV) and with the available storage space area W~, which is also a FRV under normal distribution and exponential distribution. Initially we used the weighted sum method to consider both overstock and understock situations. Then the fuzziness of the model is removed by ranking function method and the randomness of the model is removed by chance constrained programming problem, which is a deterministic nonlinear programming problem (NLPP) model. Finally this NLPP is solved by using LINGO software. To validate and to demonstrate the results of the proposed model, numerical examples are given
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