An Interdisciplinary Analysis of Physical-spatial Changes in Contemporary Markets and Business Centers of Iran with an Emphasis on Religious Texts

In this research this question is addressed: are the physical-spatial changes in contemporary markets of Iran, as a pioneer in promoting the noble values of Islam, in accordance with Islamic principles and values? In this article, through an analysis of religious texts, using qualitative content analysis, four physical-spatial indicators are deduced for markets; these indicators include the need to mention spiritual truths in physical-spatial structure of market, avoiding the collective social spaces in the market, the alignment of physical market patterns with Islamic identity, and locating the market in meta-local situations. An interdisciplinary analysis was carried out which indicated that the decline in these indicators in contemporary markets is rooted in the contemporary intellectual foundations of the West, whose economic impacts have resulted in the liberal capitalist economic system; one of the most important features of this system is the promotion of the culture of consumerism and a decline in Islamic physical-spatial indicators in contemporary markets, which is in accordance with the requirements of this culture

Experiments and dimensional analysis of waste tire-based permeable pavements

This study investigates the stress-strain response of a novel high-porosity semi-bound soft-rigid permeable pavement blend prepared using rock- and tire-derived aggregates (RDA and TDA) bonded by a polyurethane (PUR) binder. A series of unconfined compression tests were performed on 36 mix designs (with different RDA and TDA proportions, PUR contents and curing durations) to identify the variables governing the stress-strain response. The greater the TDA content, the lower the mobilized strength (UCS) and stiffness (E50), both following an exponentially-decreasing trend. Meanwhile, an increase in PUR content (i.e. increase in the number of inter-particle bonds) and/or curing duration enhanced the UCS and E50. Unlike the UCS which often achieved a stabilized state at seven days of curing, the development of stiffness extended into higher curing durations. Applying the dimensional analysis concept, a practical modeling framework was proposed and validated (using an independent database) for the UCS and E50, allowing these parameters to be simulated as a function of the blend's basic properties - that is, RDA (or TDA) content and its mean particle size, PUR content, curing duration, and dry density. The proposed models can be used with confidence for preliminary design assessments and/or semi-bound soft-rigid optimization studies. © 2021 Thomas Telford Ltd

GIS BASED SYSTEM FOR POST-EARTHQUAKE CRISIS MANAGMENT USING CELLULAR NETWORK

Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the destroyed areas. The search and rescue phase usually is maintained for many days. Time reduction for surviving people is very important. A Geographical Information System (GIS) can be used for decreasing response time and management in critical situations. Position estimation in short period of time time is important. This paper proposes a GIS based system for post–earthquake disaster management solution. This system relies on several mobile positioning methods such as cell-ID and TA method, signal strength method, angel of arrival method, time of arrival method and time difference of arrival method. For quick positioning, the system can be helped by any person who has a mobile device. After positioning and specifying the critical points, the points are sent to a central site for managing the procedure of quick response for helping. This solution establishes a quick way to manage the post–earthquake crisis

TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE

Short time prediction is one of the most important factors in intelligence transportation system (ITS). In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day

Preliminary analysis of the 21 February 2008 Svalbard (Norway) seismic sequence

The Svalbard Archipelago is situated in the northwestern part of the Barents shelf, in close proximity to the passive continental margin. This intraplate region is characterized by some of the highest seismicity in the entire Barents Sea and adjoining continental shelf, surpassed only by the Knipovich ridge (e.g., Engen et al. 2003; International Seismological Centre 2001), which, as a spreading plate boundary, is the structure that dominates the regional stress field. Most of the seismic activity (Figure 1) is characterized by smaller events, which often occur in small concentrations sparsely distributed in time. However, earthquakes of moderate to stronger magnitudes do occur in the Svalbard area, such as the 4 July 2003 mb 5.7 event close to Hopen Island (e.g., Stange and Schweitzer 2004)

Using data mining for survival prediction in patients with colon cancer

Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients. Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis. Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48, 81, and 87, and the accuracy, specificity, and sensitivity of Bagging was 83.95, 78, and 88, respectively. Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy. © 2018, Iranian Epidemiological Association. All rights reserved