An accurate, real-time monitoring of the occupancy state of each space is a necessity for applications such as energy-aware smart buildings. In this paper, we have studied the feasibility of using Doppler Radar Sensors (DRS) and Infrared Thermal Array Sensors (ITA) to build an effective occupancy detection framework. The proposed sensor types are cost-effective and protect the privacy of the occupants. We have utilized Deep Neural Networks (DNN) to analyze the sensor data without any need for specialized feature extraction that is necessary for classical machine learning approaches. The results are indicative of the feasibility and the reliability of using both sensor types for detection of the occupancy state. While a threshold-based approach reached an average accuracy of 84.3% and 86% for the DRS and ITA sensors respectively, DNN models were able to achieve average accuracies of 98.9% and 99.96% for the DRS and ITA sensors respectively, thereby demonstrating the feasibility and success of the proposed framework.

In this research, the suitability of a vibrating dual bent-share cultivator was studied. Therefore, an eccentric pin-slider mechanism was designed to vibrate the two shanks laterally, using a tractor power take-off. The present study investigates the field performance of the vibrating dual bent-share cultivator with three different vibration frequencies (0, 0.88, and 2 Hz) in a clay loam soil at two working depths (100 and 200 mm) and having a water content of a 0.7 or 0.9 plastic limit. The lowest values of the draught, specific draught, and MWD were recorded at a vibration frequency of 2 Hz and a working depth of 100 mm. The draught force, specific draught, and MWD of the non-vibration implement were reduced by using a vibration frequency of 2 Hz. The coefficient of determination and F-values proved that the vibration frequency was more effective than the soil water content and the working depth on the draught, specific draught, and MWD. Although a dual bent-share cultivator needs low energy compared with a mould-board plough, the vibration of the dual bent-share cultivator may be recommended as an efficient energy-demanding implement in the soil manipulation process.

Denkard VII which considered as the “Legend of Zoroaster” has been the subject of several investigations. The first translation was by E.W. West (1897: The Sacred Books of the East. Vol. 47. Clarendon: Oxford University Press: 26); Many years later Marijan Molé (1967) published a French version of Book VII; in Persian, Ahmad Tafazzolī and Žāleh Āmūzgār (1993: 55–110) translated some parts of the book VII; the last version which is in Persian belongs to Rashed Muhassel (2012: Denkard VII. Tehran: Pažuheshgāh-e olūm-e ensāni). Chapter two, sentence 34 of Denkard VII contains a word transcribed as tōšn/tušn of which this essay aims to have a critical view.

Iran’s banking industry as a developing country is comparatively very new to risk management practices. An inevitable predictive implication of this rapid growth is the growing concerns with regard to credit risk management which is the motivation of conducting this research. The paper focuses on the credit scoring aspect of credit risk management using both logit and probit regression approaches. Real data on corporate customers are available for conducting this research which is also a contribution to this area for all other developing countries. Our questions focus on how future customers can be classified in terms of credibility, which models and methods are more effective in better capturing risks. Findings suggest that probit approaches are more effective in capturing the significance of variables and goodness-of-fitness tests. Seven variables of the Ohlson O-Score model are used: CL_CA, INTWO, OENEG, TA_TL, SIZE, WCAP_TA, and ROA; two were found to be statistically significant in logit (ROA, TL_TA) and three were statistically significant in probit (ROA, TL_TA, SIZE). Also, CL_CA, ROA, and WCAP_TA were the three variables with an unexpected correlation to the probability of default. The prediction power with the cut-off point is set equal to 26% and 56.91% for defaulted customers in both logit and probit models. However, logit achieved 54.85% correct estimation of defaulted assets, 0.37% more than what probit estimated.