Fast Moving Consumer Goods(FMCG) by the name has high demand in the market as well as heavy competition. Maximizing efficiency and effectiveness of the process in the industry by shrinking supply chain and making dynamic production to meet unpredictable demand is a challenging task faced by the industry. Logistics which is a major portion of supply chain, adds cost to product without adding value is a major area to focus, as customer is not willing to pay for it. In this case study we have considered a process industry which consumes COPRA and extracts Edible oil from it. A portion of supply chain which deals with IN-BOUND LOGISTICS is taken for improvement. The Aim of the study is to reduce the inbound Truck Turn Around Time (TTAT) by 70% with minimal cost. Presently the company has loss of Rs. 11.5Lakhs per month on average by paying the truck owners for delaying truck for more than a day. Since the demand is increasing rapidly the company has decided to double the productivity. Through this study scope of improvement is found by analysing detailed time study. Mistake proofing and standardization of work is also done to reduce the variation and improve the mean TTAT.

Shale gas contains three kinds of flow mechanism, which are seepage, analytical attachment and spread, the situation is very complicated, so there is no substantial and breakthrough progress about 3D geological modeling of shale gas. The unique complex fracture system of shale gas is directly related to the result of numerical simulation, so the accurate representation of the complex fracture network system and the characteristics of the shale gas flow is the key to the simulation of the natural fracture and hydraulic fracturing. In this paper, combined with the exploration and development of Sichuan shale gas Y block, the preliminary study on the geological modeling of shale gas can provide useful reference for the numerical simulation of shale gas fine reservoir.

Value Stream Mapping is a lean tool used for reducing lead time, uncovering waste in production and supply process by identifying non-value-adding steps first and then removing them. A flow diagram of the process and material is drawn which reflects the current state of the operation. The non-value added actions are identified in each step. The waste of time and resources between the steps are also come to light. The process is then analyzed to reduce lead time and simplify the process by taking some necessary actions or applying some lean tools. By reducing waste the proportion of value adding time in the whole process increases and the process throughput production lead time decreases. After that a new, improved, well planned diagram is made. This is called future state map which makes the redesigned process more effective and more efficient.

This paper is concerned with the study of Rayleigh fading SIMO multicasting wireless network. The total system is analyzed with and without diversity. At first, we characterize the multicast channel capacity. Then the closed form expressions of Probability Density Function (PDF), Cumulative Distribution Function (CDF) and Complementary Cumulative Distribution Function (CCDF) of the multicast capacity are derived in the case of without applying any diversity combining technique. Next, we derived these parameters by applying two diversity combining techniques, such as Selection Combining (SC) and Maximal Ratio Combining (MRC). Finally we present a comparison between these two cases, with and without diversity combining techniques.

A Microstrip patch antenna is used in various applications (like Radar, Satellites) with different length dipoles. The value of return loss must be substantially good enough to meet the required performance level for the particular application. For this, length of dipoles is adjusted according to the material used in ground reflector to get a good value of return loss. A conventional SDP antenna consisting of two dipoles of different length and a ground reflector, which are serially connected with a transmission line, is designed for operating frequency range 1.7-2.7 GHz. The proposed antenna is designed on FR-4 substrate having Є=4.3 using electromagnetic simulator software called CST Microwave Studio Suite®. To improve the performance characteristics of the designed antenna regarding Directivity, Return Loss and gain a Split Ring Resonator (SRR) is mounted on top of the designed SDP. Numerical results show the improved results with improved operating frequency up to 3.2 GHz, enhanced directivity of 9.29dBi and increased gain of 7.98 dB.

One of the major factors that determine the success of hip replacement is the primary stability which is the function of the micromotion on the bone-implant interface. Failure of hips replacement may arise from excessive motion at the implant-bone interface under the weight bearing loads. Minimizing the micromotion of the cementless prosthetic components is a key requirement for obtaining bone in-growth. If the initial movement is excessive, bone in-growth into the porous surface will not occur. Few experimental studies are available on implant micromotion largely due to difficulty of simulating loads in-vitro and in-vivo. Due to this reason, this research derived a theoretical model that relates the micro-velocity at which the implant moves down in the bone at the implant-bone surface at a specific time, the axial force applied on the head of the implant and the stiffness of the implants and the bones. The implant-bone interface (fibrous tissues) was taken as elastic surface that obeys Hooke’s law using spring analysis. Here, the displacement of the implant equals the micromotion depending on the stiffness of both cortical and trabecular (cancellous) bones. When the implant is axially loaded, due to the elastic modulus of the bone when compared to that of the implant (stainless steel), the deformation of the implant is neglected. That is, Ei >> (EC,ET) where Ei ,EC and ET are the elastic moduli of the implant, cortical part of the bone and the trabecular part of the bone respectively.

Publication of bond and stock through stock market, is a way in order to prepare the capital for investment. According to this fact that stock price is the first and important issue for an investor, evaluating and forecasting of the future price will be propounded. Artificial Neural Network (NN) is a way of stock price forecasting. High efficiency of NNs in forecasting and importance of forecasting in different fields, have caused researchers to search the ways of improving the strategies used for increase of the accuracy of forecasting by neural networks. Accuracy level of a NN greatly depends on its weight and bias values. In a NN, weight and bias values depend on the kind of training algorithm. In this article, the NN using for stock price forecasting is trained by a new metaheuristic training algorithm, named bird mating optimizer (BMO). Effectiveness and efficiency of this algorithmis compared with NN trained by Genetic Algorithm (GA) algorithm based on the average, median and standard deviation of the Mean Square Error (MSE).The experimental results show that the BMO lends itself very well to forecast of stock price.