|Commenced in January 1999||Frequency: Monthly||Edition: International||Paper Count: 622|
In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.
There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.
Today’s workers face more numerous and complex challenges and are required to be increasingly interdependent and faster learners. Knowledge sharing activities between people have been understood as a significant element affecting organizational innovation performance. While they do have the potential to spark cognitive conflict, disagreement is important from an organizational perspective because it can stimulate the development of new ideas and perhaps pave the way for creativity, innovation, and competitive advantage. How teams cope with the cognitive conflict dimension of knowledge sharing and the associated interpersonal risk is what captures our attention. Specifically, assertive conflict management strategies have a positive influence on knowledge sharing behaviors, and team psychological safety has a positive influence on knowledge sharing intention. This paper focuses on explaining the impact that these factors have on the shaping of an individual’s decision to engage or not in knowledge sharing activities. To accomplish this, we performed an empirical analysis on the results of our questionnaire about knowledge-sharing related conflict management and team psychological safety in pharmaceutical enterprises located in North America, Europe, and Asia. First, univariate analysis is used to characterize behavior regarding conflict management strategy into two groups. Group 1 presents assertive conflict management strategies and group 2 shows unassertive ones. Then, by using SEM methodology, we evaluated the relationships between them and the team psychological safety construct with the knowledge sharing process. The results of the SEM analysis show that assertive conflict management strategies affect the knowledge sharing process the most with a small, but significant effect from team psychological safety. The findings suggest that assertive conflict management strategies are just as important as knowledge sharing intentions for encouraging knowledge sharing behavior. This paper provides clear insights into how employees manage the sharing of their knowledge in the face of conflict and interpersonal risk and the relative importance of these factors in sustaining productive knowledge sharing activities.
Rail-station area development that is based on the concept of TOD (Transit Oriented Development) is principally oriented to pedestrian accessibility for daily mobility. The aim of this research is elaborating how far the existing physical elements of a rail-station district could facilitate pedestrian mobility and establish a pedestrian friendly district toward implementation of a TOD concept. This research was conducted through some steps: (i) mapping the rail-station area pedestrian sidewalk and pedestrian network as well as activity nodes and transit nodes, (ii) assessing the level of pedestrian sidewalk connectivity joining trip origin and destination. The research area coverage in this case is limited to walking distance of the rail station (around 500 meters or 10-15 minutes walking). The findings of this research on the current condition of the street and pedestrian sidewalk network and connectivity, show good preference for the foot modal share (more than 50%) is achieved. Nevertheless, it depends on the distance from the trip origin to destination.
The tribological behavior of commercially used Perspex was evaluated under dry and wet sliding condition using a pin-on-disc wear tester with different applied loads ranging from 2.5 to 20 N. Experiments were conducted with varying sliding distance from 0.2 km to 4.6 km, wherein the sliding velocity was kept constant, 0.64 ms-1. The results reveal that the weight loss increases with applied load and the sliding distance. The nature of the wear rate was very similar in both the sliding environments in which initially the wear rate increased very rapidly with increasing sliding distance and then progressed to a slower rate. Moreover, the wear rate in wet sliding environment was significantly lower than that under dry sliding condition. The worn surfaces were characterized by optical microscope and SEM. It is found that surface modification has significant effect on sliding wear performance of Perspex.
In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.
In this paper, following the study-case of an inclined plane gravitational machine, efficiency of a double-cone gravitational motor and generator is evaluated. Two types of efficiency ratios, called translational efficiency and rotational efficiency, are defined relative to the intended duty of the gravitational machine, which can be either the production of translational kinetic energy, or rotational kinetic energy. One proved that, for pure rolling movement of the double- cone, in the absence of rolling friction, the total mechanical energy is conserved. In such circumstances, as the motion of the double-cone progresses along rails, the translational efficiency decreases and the rotational efficiency increases, in such way that sum of the rotational and translational efficiencies remains unchanged and equal to 1. Results obtained allow a comparison of the gravitational machine with other types of motor-generators, in terms of the achievable efficiency.
Brakes are one of the most important safety and performance components in automobiles and airplanes. Development of brakes has mainly focused on increasing braking power and stability. Nowadays, brake noise, vibration and harshness (NVH) together with brake dust emission and pad life are very important to vehicle drivers. The main objective of this research is to define the relationship between compressibility of friction materials and their tendency to generate vibration. An experimental study of the friction-induced vibration obtained by the disc brake system of a passenger car is conducted. Three commercial brake pad materials from different manufacturers are tested and evaluated under various brake conditions against cast iron disc brake. First of all, compressibility test for the brake friction material are measured for each pad. Then, brake dynamometer is used to simulate and reproduce actual vehicle braking conditions. Finally, a comparison between the three pad specimens is conducted. The results showed that compressibility have a very significant effect on reduction the vibration occurrence.
Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.
The patronage of e-government services (demand side of e-government) is vital to the successful implementation of e-government initiatives. The purpose of this study is to explore the predictors determining the willingness of African students in China to adopt and use e-government services. The Unified Theory of Acceptance and Use of Technology (UTAUT) will be used as the theoretical foundation for this research. Research instrument will be developed and administered to 500 African students in China. Factors such as performance expectancy, effort expectancy, social influence, facilitating conditions and culture will be investigated to determine its significant impact on the willingness to use e-government services. This study is a research in progress. The outcome of this study will provide valuable recommendations to improve the provision of public services through e-government.
The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.
The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system. The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.
There is a necessity among rail transportation authorities for a superior understanding of the rail track degradation overtime and the factors influencing rail degradation. They need an accurate technique to identify the time when rail tracks fail or need maintenance. In turn, this will help to increase the level of safety and comfort of the passengers and the vehicles as well as improve the cost effectiveness of maintenance activities. An accurate model can play a key role in prediction of the long-term behaviour of railroad tracks. An accurate model can decrease the cost of maintenance. In this research, the rail track degradation is predicted using an autoregressive moving average with exogenous input (ARMAX). An ARMAX has been implemented on Melbourne tram data to estimate the values for the tram track degradation. Gauge values and rail usage in Million Gross Tone (MGT) are the main parameters used in the model. The developed model can accurately predict the future status of the tram tracks.
Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.
The expansion of telecommunication and progress of electronic media constitute important elements of our times. The recent worldwide convergence of information and communication technologies (ICT) and dynamic development of the mass media is leading to noticeable changes in the functioning of contemporary states and societies. Currently, modern technologies play more and more important roles and filter down to almost every field of contemporary human life. It results in the growth of online interactions that can be observed by the inconceivable increase in the number of people with home PCs and Internet access. The proof of it is undoubtedly the emergence and use of concepts such as e-society, e-banking, e-services, e-government, e-government, e-participation and e-democracy. The newly coined word e-democracy evidences that modern technologies have also been widely used in politics. Without any doubt in most countries all actors of political market (politicians, political parties, servants in political/public sector, media) use modern forms of communication with the society. Most of these modern technologies progress the processes of getting and sending information to the citizens, communication with the electorate, and also – which seems to be the biggest advantage – electoral procedures. Thanks to implementation of ICT the interaction between politicians and electorate are improved. The main goal of this text is to analyze electronic voting (e-voting) as one of the important forms of electronic democracy in terms of security aspects. The author of this paper aimed at answering the questions of security of electronic voting as an additional form of participation in elections and referenda.
Friction stir welding is a modern and an environmentally friendly solid state joining process used to joint relatively lighter family of materials. Recently, friction stir spot welding has been used instead of resistance spot welding which has received considerable attention from the automotive industry. It is environmentally friendly process that eliminated heat and pollution. In this research, friction stir spot welding has been used to study the possibility to weld AA1100 aluminum alloy sheet with 3 mm thickness by overlapping the edges of sheet as lap joint. The process was done using a drilling machine instead of milling machine. Different tool rotational speeds of 760, 1065, 1445, and 2000 RPM have been applied with manual and automatic compression to study their effect on the quality of welded joints. Heat generation, pressure applied, and depth of tool penetration have been measured during the welding process. The result shows that there is a possibility to weld AA1100 sheets; however, there is some surface defect that happened due to insufficient condition of welding. Moreover, the relationship between rotational speed, pressure, heat generation and tool depth penetration was created.
Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km2, and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.
As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.
We are witnessing the rapid development of technologies that change the world around us. However, curriculums and teaching processes are often slow to adapt to the change; it takes time, money and expertise to implement technology in the classroom. Therefore, the University of Split, Croatia, partnered with local school Marko Marulić High School and created the project "Modern competence in modern high schools" as part of which five different curriculums for STEM areas were developed. One of the curriculums involves combining information technology with physics. The main idea was to teach students how to use different circuits and microcomputers to explore nature and physical phenomena. As a result, using electrical circuits, students are able to recreate in the classroom the phenomena that they observe every day in their environment. So far, high school students had very little opportunity to perform experiments independently, and especially, those physics experiment did not involve ICT. Therefore, this project has a great importance, because the students will finally get a chance to develop themselves in accordance to modern technologies. This paper presents some new methods of teaching physics that will help students to develop experimental skills through the study of deterministic nature of physical laws. Students will learn how to formulate hypotheses, model physical problems using the electronic circuits and evaluate their results. While doing that, they will also acquire useful problem solving skills.
This paper presents a proposed analytical model for predicting the shear strength of reinforced concrete beams strengthened with CFRP composites as external reinforcement. The proposed analytical model can predict the shear contribution of CFRP composites of RC beams with an acceptable coefficient of correlation with the tested results. Based on the comparison of the proposed model with the published well-known models (ACI model, Triantafillou model, and Colotti model), the ACI model had a wider range of 0.16 to 10.08 for the ratio between tested and predicted ultimate shears at failure. Also, an acceptable range of 0.27 to 2.78 for the ratio between tested and predicted ultimate shears by the Triantafillou model. Finally, the best prediction (the ratio between the tested and predicted ones) of the ultimate shear capacity is observed by using Colotti model with a range of 0.20 to 1.78. Thus, the contribution of the CFRP composites as external reinforcement can be predicted with high accuracy by using the proposed analytical model.
Health for all is considered as a sign of well-being and inclusive growth. New healthcare technologies are contributing to the quality of human lives by promoting health education and awareness, leading to the prevention, early diagnosis and treatment of the symptoms of diseases. Healthcare technologies have now migrated from the medical and institutionalized settings to the home and everyday life. This paper explores these new technologies and investigates how they contribute to health education and awareness, promoting the objective of high-value health system for all. The methodology used for the research is literature review. The paper also discusses the opportunities and challenges with futuristic healthcare technologies. The combined advances in genomics medicine, wearables and the IoT with enhanced data collection in electronic health record (EHR) systems, environmental sensors, and mobile device applications can contribute in a big way to high-value health system for all. The promise by these technologies includes reduced total cost of healthcare, reduced incidence of medical diagnosis errors, and reduced treatment variability. The major barriers to adoption include concerns with security, privacy, and integrity of healthcare data, regulation and compliance issues, service reliability, interoperability and portability of data, and user friendliness and convenience of these technologies.
Background: Maternal and child health (MCH) cares are the health services provided to mothers and children. It includes the health promotion, preventive, curative and rehabilitation health care for mothers and children. Materials and method: The present study sample comprises of 208 women within the age range 15-69 years from two remote villages of Tamenglong District in Manipur. They were randomly chosen for assessing their health as well as the child’s health adopting an interview schedule method. Results: The findings of the study revealed that majority (80%) of the women have their first conception in their first year of married life. A decadal change has been observed with regard to the last pregnancy i.e., antenatal check-up, place of delivery as well as the service provider. However, irrespective of age of the women, home delivery is still preferred though very few are locally trained. Pre- and post-delivery resting period vary depending on the busy schedule of the agricultural works as the population under study is basically agriculturist. Postnatal care remains to be traditional as they are strongly associated with cultural beliefs and practices that continue to prevail in the studied community. Breast feeding practices such as colostrums given, initiation of breastfeeding, weaning was all taken into account. Immunization of children has not reached the expected target owing to a variety of reasons. Maternal health care also includes use of birth control measures. The health status of women would invariably improve if family planning is meaningfully adopted. Only 10.1% of the women adopted the modern birth control implying its deep-rooted value attached to the children. Based on the self-assessment report on their health treatment a good number of the respondents resorted to self-medication even to the extent of buying allopathic medicine without a doctor’s prescription. One important finding from the study is the importance attributed to the traditional health care system which is easily affordable and accessible to the villagers. Conclusion: The overall condition of maternal and child care is way behind till now as no adequate/proper health services are available.
A facility exploiting only electronic gambling machines (EGMs) opened in 2007 in Quebec City, Canada under the name of Salons de Jeux du Québec (SdjQ). This facility is one of the first worldwide to rely on that business model. This paper models the performance of such EGMs. The interest from a managerial point of view is to identify the variables that can be controlled or influenced so that a comprehensive model can help improve the overall performance of the business. The EGM individual performance model contains eight different variables under study (Game Title, Progressive jackpot, Bonus Round, Minimum Coin-in, Maximum Coin-in, Denomination, Slant Top and Position). Using data from Quebec City’s SdjQ, a linear regression analysis explains 90.80% of the EGM performance. Moreover, results show a behavior slightly different than that of a casino. The addition of GameTitle as a factor to predict the EGM performance is one of the main contributions of this paper. The choice of the game (GameTitle) is very important. Games having better position do not have significantly better performance than games located elsewhere on the gaming floor. Progressive jackpots have a positive and significant effect on the individual performance of EGMs. The impact of BonusRound on the dependent variable is significant but negative. The effect of Denomination is significant but weakly negative. As expected, the Language of an EGMS does not impact its individual performance. This paper highlights some possible improvements by indicating which features are performing well. Recommendations are given to increase the performance of the EGMs performance.
In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium.