An integrated analytical model for the forecasting of the molten pool dimensions in Selective Laser Melting

Selective laser melting (SLM) is one of the most promising processes in the Additive Manufacturing of metals because of the possibility to fabricate complex geometry parts with a wide range of materials. The molten pool dimensions are notoriously crucial for the production of high quality parts, in terms of mechanical properties and roughness, and to control the ‘balling’ phenomenon. In the past, several studies have been conducted to monitor temperature gradient and thermal history, stress and deformation field, balling occurrence, effect of volume shrinkage and the effect of process parameters on temperature evolution. Up to now, very few works are available in literature on the effects of the process parameters on the molten pool shape and dimensions: moreover, they also neglect the simultaneously effect of various physical factors. In this work, an integrated analytical model, consisting of three sub-models (thermal, optical and melting sub-models), with the purpose to forecast the molten pool dimensions in terms of width and depth, was developed. An experimental plan has been carried out, processing the 18 maraging 300 steel, to demonstrate the capability and the accuracy of the presented model. The obtained results demonstrate that the integrated analytical model led to optimal forecasting.

On some statistics of fractional Brownian motion

Fractional Brownian motion as a method for estimating the parameters of a stochastic process by variance and one-step increment covariance is proposed and substantiated. The root-mean-square consistency of the constructed estimates has been proven. The obtained results complement and generalize the consequences of limit theorems for fractional Brownian motion, that have been proved in the number of articles. The necessity to estimate the variance is caused by the absence of a base unit of time and the estimation of the covariance allows one to determine the Hurst exponent. The established results let the known limit theorems to be used to construct goodness-of-fit criteria for the hypothesis “the observed time series is a transformation of fractional Brownian motion” and to estimate the error of optimal forecasting for time series.

Perencanaan Produksi Pada Kain Rayon, Polyster dan Cotton di Departemen D/F PT. Sinar Pangjaya Mulia

The opportunities for the national textile industry depend on the ability to compete with producers from other countries. Not only in the world market but also in the domestic market. This is indicated by the flood of imported textile products, especially from China, which offers low prices for their products. However, local textile products are still able to compete because they have better quality. PT. Sinar Pangjaya Mulia is an export-oriented textile company engaged in the manufacture of various types of fabrics with the main products being rayon, polyester and cotton fabrics. In carrying out its production, PT. Sinar Pangjaya Mulia is still unable to perform optimal forecasting and efficient production planning as the basis for carrying out production activities, so all planning in the company is carried out suddenly. This has caused problems in the production activities carried out by the company in determining the production, machines and time needed to fulfill demand. To overcome these conditions, the company must be able to compile a demand forecast and production planning as a statement regarding how much demand is in a certain period and when the production demand must be made. From the results of data processing, it can be seen that the demand for fabrics produced for these three product items is within the next one year. Demand in January 2012 was 106,277 kg of rayon, 41,586 kg of polyester cloth and 78,552 kg of cotton, while the production planning for January 2012 was 226,415 kg with an inventory value of 80,000 kg, so in the January 2012 period there were no additional hours. /working days.

Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

Finance Leasing and ARMA Forecasting: Evidence from Albania

Ten financial institutions are offering finance leasing-loans in Albania. Even though finance leasing is a potential financing resource for small and medium enterprises in Albania (which are on average 95% of national enterprises), the value of finance leasing is one thousand times smaller than other forms of medium and long-term loans or real estate loans. Developing of finance leasing is a challenge for the progress of the financial sector, and untapped potential as well. Currently, the finance leasing portfolio is dominated by financing for personal vehicles and work-vehicles, therefore diversification of leasing products is an immediate need of consumers. This study analyzes the value of finance leasing in Albania with time series from 2008 to 2020 (with quarterly frequency). The methodology applied for data processing is the co-integration method of finance leasing and other forms of medium-term and long-term financing. Also, the ARMA method is used to forecast the value of finance leasing. We found out that there is no long-run relationship between finance leasing with medium and long-term loans. Therefore, econometric tests suggest optimal forecasting ARMA (1,1) modeling. The parameters of ARMA model are positive statistically significant with autocorrelation AR (1) and negative statistically significant with the moving average MA (1), and forecasting values have a short-run equilibrium with a wide interval.

Box-Jenkins ARIMA Modelling: Forecasting FDI in India

In the rapidly advancing dynamics of the economy trends of countries, the forecasting econometric techniques hold significant importance in the field of advance economics and management. Thus, this study intends to create Box Jenkins time series ARIMA model for analysing and predicting the trend of net FDI (Foreign Direct Investment) in India. The model was generated on the dataset of FDI inflow of India from the year 1950 to 2020. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF test (Augmented Dickey-Fuller) to transform FDI data into stationary form for model generation. It proposes ARIMA (1,1,4) model for optimal forecasting of net FDI inflow in India with an accuracy of 96.5%. The model thus predicts the steady-state exponential growth of FDI inflow in the coming 2020-25.In the rapidly advancing dynamics of the economy trends of countries, the forecasting econometric techniques hold significant importance in the field of advance economics and management. Thus, this study intends to create Box Jenkins time series ARIMA model for analysing and predicting the trend of net FDI (Foreign Direct Investment) in India. The model was generated on the dataset of FDI inflow of India from the year 1950 to 2020. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF test (Augmented Dickey-Fuller) to transform FDI data into stationary form for model generation. It proposes ARIMA (1,1,4) model for optimal forecasting of net FDI inflow in India with an accuracy of 96.5%. The model thus predicts the steady-state exponential growth of FDI inflow in the coming 2020-25.

Status Quo, Monetary Loss-Aversion and Forecasting - An Approach to Investment During Amygdala Damages & Asymmetry

The research essay aims to understand investor's ability to forecast having the perception of status quo and monetary loss-aversion in the situation of amygdala damages and asymmetry during decisions regarding stock's investment and use of several techniques to make efficient investment decisions based on optimal forecasting. The objectives of this study are to inquire about the irrationalities in investors at the time of stock's investment, having status quo and monetary loss-averse bias of investors at the time of amygdala damages and asymmetry and find-out the ways to deal with these situations. A qualitative research style was used for data collection for the subject study. Partially-organized discussions were arranged to get information in detail. A sample of 15 experienced stock marketers and brokers and 35 investors from the Pakistan stock exchange were selected for this study. This inquiry found the definite type of edgy and biased investor's attitude in the market and also found their solutions. This study perceptibly peaks the ways to deal with stress and biasness through optimal forecasting techniques and some other suggestions.

AN OPTIMAL FORECASTING OF LATENT SIMILARITY BASED RELATIONSHIP ON USER ATTRIBUTES USING LINK PREDICTION

Communal is one of the common words. A billion of peoples share or have certain attitudes and interests in common. By sharing and receiving the information such as text, image, audio, video etc., this kind of information, it improves the global knowledge, easily distinct the good and bad things. Now days, Social media is a good platform to share the content collectively with collaboration. Digital technologies are spread all over the global rapidly. It is an efficient way to improve the knowledge via Communal. People do not show the attention to join the community because of addiction, hacks the personal data and get misused. So that people have lack of awareness to join and use the communities. To overcome the above reasons and also all the peoples have to access and gain the information without any dilemmas. The proposed system provides the platform to link the peoples via Communal much more and gather the information all over the world with secure authentication. Anywhere in the world, every person can share and learn their thoughts with everyone. It consist of two phase to implement the proposed system. The first phase is to identify the neighbourhood and link the data. Here use Interest based FGM algorithm to predict the neighbour and link within the environment. So that each person will know all the information. Second phase, decision process to detect the person who are all link with particular communities across globally with the help of decision tree. People from anywhere to access all the data with anyone. It is easy way to equip people in all kind of innovative ideas as soon as possible.