Smoking Behaviour Among Resident University Students In North India: Some Issues And Challenges

The use of tobacco as cigarettes has taken a form of epidemic. Unless it is not managed properly and in time it may become an uncontrollable behavior leading to long term health and social problem. This paper is based on a survey that was intended to explore the smoking habits of university students residing in hostels, their perception towards it, factors associated with it and their implications. The study was conducted using a questionnaire based survey among 200 students, who were in the habit of smoking, belonging to different classes and residing in the hostels of a residential central University of North India. It was found that smokers were mainly from urban background. More than a quarter of the smokers spent more than 600 rupees per month on smoking only. The most important reasons given by students for smoking behavior was peer pressure followed by tension. Most of them had started smoking between 14-17 years of age, followed by 17- 21 yrs. age group. The number of cigarettes used increased with seniority. Most of the surveyed students wanted to leave the habit but could not do so because of bad habit followed by tension. 51% faced health problems, the major ones being respiratory problems. The study suggests that most effective control of the habit can be achieved by targeting the students of adolescent age and minimizing the tension among them.   Keywords: Smoking, Adolescents, students’ behavior, central universit

Evaluation of the Suitability of Public-Private Partnership for Kowloon East Smart City Development Project in Hong Kong

Smart City has become increasingly popular around the world; Hong Kong is no exception. However, Hong Kong is currently lagging in smart city development, consequently, the government has launched a pilot project ‘Kowloon East Smart City Development’. However, due to fact that the implementation of smart solutions requires a high level of technical and managerial skills and Hong Kong’s public sector does not possess any experience in the smart city, therefore, this study investigated the suitability of public-private partnership (PPP) for the pilot project. Analytic Hierarchy Process is adopted to quantitatively assess the positive and negative impacts of PPP on the smart city project. The pairwise comparison was conducted by interviewing local experts experienced in both smart cities and PPPs. The local weighting of each positive and negative factor and likelihood measurement of alternatives were carried out. Subsequently, sensitivity analysis is administrated to identify the critical factor that can affect the final decision. The results indicated that the PPP is a suitable approach for the pilot project. ‘Greater benefit to the public’ is found to be the most critical factor

Seasonal Aerosol Classification Over South Asia by Satellite based Atmospheric Optical Data

Aerosol optical characteristics have been investigated to explore regional and seasonal inconsistencies of aerosols and to define the dominant type throughout South Asia from 2001 to 2021. MODIS aerosol products from collection 6.1 have been used in present study, that comprise daily values of Angstrom exponent (AE) and aerosol optical depth (AOD) data. MODIS-derived AODs are validated by using nine ground-based AERONET station data. Overall, an adequate correlation is found among the two datasets. However, an overestimation of the MODIS retrievals is found in one site named Jaipur and underestimations are found at two sites named as Gandhi-college and Karachi. The seasonal evaluation shows that aerosol distribution found between 0 and 1.05, depending on the change in geographical location. The highest AOD value originates over the Indo-Gangetic plain (IGP), mostly throughout warm season. The second maximum AOD value covers a large area of South Asia during spring, summer and autumn. The lowest values of AOD are found in winter season excluding the IGP. A region with high aerosol optical depth (AOD) values support a low value of angstrom exponent (AE) indicating the coarse aerosol during warm seasons (spring and summer) over IGP. The region with high AOD and high AE values is showing fine aerosol during the mild to cold seasons (autumn and winter). The threshold values for AOD and AE have been used to classify aerosols. The results demonstrate that urban/industrial aerosols prominent in every season across the region dominate in spring and summer due to frequent occurrence of dust events. The mixed type aerosol is second largest contributor in aerosol formation in all seasons. The Biomass burning/smoke aerosol is dominant over IGP due to open forest and crop burning in autumn. Clean and maritime aerosol has small unnoticeable involvement in the studied region

Residential Demand for Electricity in Pakistan

Undoubtedly, in the new millennium, the importance of energy sector for the development of a country is undeniable. Rapidly increasing knowledge along with speedy technological innovation has resulted in the provision of abundance of facilities. This has made the human beings, consumers or producers, much demanding for energy sources that are used to run mechanical processes. There are various sources of energy which include oil, electricity, gas, coal and nuclear. Countries differ in the usage of alternative energy sources. In Pakistan the major energy source is gas which is 41 percent of the total energy supplied. The other energy supply sources along with their percentage shares are as follow: oil (29 percent), hydro (12.70 percent), coal (12 percent) and nuclear (1 percent).1 Electricity is one of the most important source of energy in Pakistan. It has become a necessity in the present life, having a wide range of uses in residential as well as in commercial sector. Table 1 describes the major domestic users of electricity in Pakistan along with their respective shares of consumption. It is obvious from the table that residential consumption of electricity has the highest share

BIOSORPTION AND RECOVERY OF HEAVY METALS FROM AQUEOUS SOLUTIONS BY EICHHORNIA CRASSIPES (WATER HYACINTH) ASH

Heavy metal’s release without treatment poses a significant threat to the environment. Heavy metals are non-biodegradable and persistent. In the present study the ash of water hyacinth (Eichhornia crassipes), was used to remove six metals from aqueous solutions through biosorption. Results of batch and column experiments showed excellent adsorption capacity. Removal of lead, chromium, zinc, cadmium, copper, and nickel was 29.83, 1.263, 1.575, 3.323, 2.984 and 1.978 µgg-1, respectively. The biosorptive capacity was maximum with pH >8.00. Desorption in µgg-1 of ash for lead, chromium, zinc, cadmium, copper, and nickel was 18.10, 9.99, 11.99, 27.54, 21.09, and 3.71 respectively. Adsorption/desorption of these metals from ash showed the potential of this technology for recovery of metals for further usages. Hydrogen adsorption was also studied with a Sievert-type apparatus. Hydrogen adsorption experiments showed significant storage capacity of water hyacinth ash

Detection of citrus leaf diseases using a deep learning technique

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively

Automatic modulation classification based deep learning with mixed feature

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB

Design of smart wireless changeover for continuous electric current feeding from power sources of variable capacities

Electric power has become a vital element for life today. Despite this importance, electric power consumers in Iraq suffer from the problem of noncontinuity and daily electric power supply interruption. This problem led to the use of various sources of electric power as an alternative to compensate for the shortage of electric power provided by the Iraqi national grid. In this work, a smart wireless changeover device is designed using wireless sensor networks technology aiming to solve problem caused by the multiplicity of power sources received at home and governmental buildings in Iraq by controlling operation of some electrical devices (which consume high current) in the home or workplace automatically when changing source of electricity from one to another. This solution will help to ensure the continuity of electric current feeding from power sources of variable capacities, also, to rationalize power consumption by assigning an operation priority to electric devices. Furthermore, a statistical measurement as a case study was performed in a building with a total power consumption of 160.8 KW/h. The result showed that the device functions effectively and it is capable of achieving an average saving in power of about 50% to 86% depending on the applied priorities and case study scenario