Urbanisation and Crime: A Case Study of Pakistan

Crime is an activity which is against the law and the fact that the linkage between criminal activities and the socio-economic development of the society is undeniable. Moreover, the relationship between crime and evolution of mankind may also be considered a historical one as Cain (first son of Adam and Eve) committed first crime when he murdered his brother Able because of jealousy. Due to the complex nature of the subject of crime, for example, regarding its causes and consequences, various academic disciplines such as criminology, sociology, geography, psychology and demography study it from their own perspective. A relatively new emerging field, however, is the economics of crime which tries to identify the socio-economic causes and consequences of criminal activities in a society

The Cost of Unserved Energy: Evidence from Selected Industrial Cities of Pakistan

This study is an attempt to explore the cost of unserved energy due to power outages in Pakistan that started in 2007. The study is based on a survey conducted for four major industrial cities of Punjab—Gujrat, Faisalabad, Gujranwala, and Sialkot. In addition to quantification of output losses, the effect on employment, cost of production, and delay in supply orders are also examined. The output loss is quantified using two-dimensional analyses, controlling for variations in the duration of outages and in the shift hours. The survey data reveal that employment has not suffered any significant drop due to alternative energy arrangements. These arrangements, nevertheless, have increased the production cost of the firms. Delays in the delivery of supply orders are also due to energy shortage. The study reports that the total industrial output loss varies between 12 percent and 37 percent, with Punjab as the major affected province.Energy Crises, Output Loss, Pakistan

The Impact of Climate Change on Major Agricultural Crops: Evidence from Punjab, Pakistan

It is necessary for a country to make its agriculture sector efficient to enhance food security, quality of life and to promote rapid economic growth. The evidence from least developed countries (LDCs) indicates that agriculture sector accounts for a large share in their gross domestic product (GDP). Thus the development of the economy cannot be achieved without improving the agriculture sector. According to the Economic Survey of Pakistan (2011-12) its main natural resource is arable land and agriculture sector’s contribution to the GDP is 21 percent. The agricultural sector absorbs 45 percent of labour force and its share in exports is 18 percent. Given the role of agricultural sector in economic growth and its sensitivity to change in temperature and precipitation it is important to study the impact of climate change on major crops in Pakistan. There are two crops seasons in Pakistan namely, Rabi and Kharif. Rabi crops are grown normally in the months of November to April and Kharif crops are grown from May to October. These two seasons make Pakistan an agricultural economy and its performance depends on the climate during the whole year. Climate change generally affects agriculture through changes in temperature, precipitation

A High Gain Flexible Antenna for Biomedical Applications

In this paper, a miniaturized antenna is presented for biomedical applications due to its flexibility. The proposed antenna operates in the Industrial, Medical, and Scientific (ISM) 24.00 GHz to 24.25 GHz band. This antenna consists of a radiating element with circular and rectangular slots and the ground with cross plus four square slots. The dielectric material Rogers RO3003 with permittivity of 3, is used for substrate and superstrate. The miniaturization of the antenna is achieved by shorting pin and some other techniques. The total volume of the designed antenna is (6.8×6.8×0.26) mm 3 . The maximum gain achieved by the simulation of the proposed antenna is 5.44 dB at 24.25 GHz, and at the start of the band, the gain is 4.9 dB at 23.98 GHz, and at the end of the band, the gain is 5.1 dB at 24.47 GHz. The designed antenna has better results than the antennas discussed in the literature in terms of size, gain, and efficiency

Machine Learning in Chemistry

Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. Within the field of computational materials science, such an abundance of data is possible mainly due to the success of density functional theory (DFT) and High throughput (HT) methods. This article aims to show how Machine Learning approaches to modern computational chemistry are being used to uncover complexities in different fields

A Statistical Survey Report to Assess Patient Satisfaction with Performance of Hospital for Service Quality Improvement

Aim: To analyze how satisfied patients are with the performance of hospital and which services need improvement

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

Abstract The research of visibility detection in foggy days is of great significance to both road traffic and air transport safety. Based on the meteorological and video data collected from an airport, a deep Recurrent Neural Network (RNN) model was established in this study to predict the visibility. First, the Fourier Transform was used to extract feature variables from video data. Then, the Principal Component Analysis method was used to reduce the dimension of features. After that, 462 sets of sample data include image features, air pressure, temperature and wind speed, were used as inputs to train the RNN model. By comparing the predicted results with the actual visibility data as well as some other state‐of‐the‐art methods, it can be found that the proposed model makes up for the deficiency of models based only on meteorological or image data, and has higher accuracy in different grades of visibility. With considering the meteorological data, the accuracy of RNN model is improved by 18.78%. Besides, with aids of correlation analysis, the influence of the meteorological factors on the predicted visibility was analysed, for fog at night, temperature is the dominant factor affecting visibility

Experimental Analysis of Nano-Enhanced Phase-Change Material with Different Configurations of Heat Sinks

The demand for high-performance and compact electronic devices has been increasing day by day. Due to their compactness, excessive heat is generated, causing a decrease in efficiency and life. Thermal management of electronic components is crucial for maintaining excessive heat within the limit. This experimental research focuses on the combined effect of nano-enhanced phase-change material (NePCM) with different configurations of heat sinks for cooling electronic devices. Multi-walled carbon nanotubes (MWCNTs) are used as nanoparticles with concentrations of 3 wt% and 6 wt%, RT-42 as the phase-change material (PCM), and aluminum as the pin fin heat sink material. Different configurations of the heat sink, such as circular, square, and triangular pin fins, are used against the fixed volume fraction of the fins. It is found that the square configuration has the highest heat transfer with and without PCM. A maximum base temperature reduction of 24.01% was observed in square pin fins with RT-42 as PCM. At 6 wt% of NePCM, the maximum base temperature lessened by 25.83% in the case of a circular pin fin. It is concluded from the results that a circular pin fin with NePCM is effective for base temperature reduction, and all fin configurations with NePCM collectively reduce the heat sink base temperature