A comparative study of adaptability and cohesion in families with and without a disabled child

AbstractThe purpose of this study was to see the Family adaptability and cohesion in families with handicapped member(s) and to determine whether the adaptability differ normal families. For, 150 subjects (100 handicapped and 50 normal people) from Esfahan city were randomly chosen. The data was conducted by Family adaptability and cohesion Evaluation scale (FACES-III) and was analyzed by T-test and ANOVA. The Results indicated that Family adaptability, cohesion and communion in families with handicapped member(s) were higher than normal families. Also, there was significant relationship between family cohesion and the number of family members (p<.05)

ELECTROLYTIC DEPOSITION OF MgO ON STAINLESS STEEL SUBSTRATE

The main objective of this thesis was to fabricate and characterize magnesium oxide coating on stainless steel alloy (SAE 316L). Electrolytic processing, followed by sintering was used for making it. Coating the heater tubes used in refinery industry with magnesium oxide is expected to increase the oxidation and corrosion resistance of the tubes. Fabrication of magnesium oxide coating was a two-step process. First, a magnesium hydroxide coating was deposited on the SAE 316L stainless steel substrate in an electrochemical bath and then the magnesium hydroxide was converted to magnesium oxide during sintering. The substrate was electropolished in a 15% sulfuric acid solution before immersing in the electrochemical bath. Electrolytic processing parameters such as deposition time and voltage were optimized and others like the distance between anode and cathode, concentration of magnesium ions in the solution and the surface area of the cathode and the anode were kept constant. The microstructure and texture of the magnesium hydroxide coating was analyzed using scanning electron microscopy (SEM) and X-ray diffraction (XRD). Sintering parameters were also optimized. Corrosion and oxidation resistance of the fabricated coating were compared to that of the uncoated sample in both high and room temperature. The hardness of the magnesium oxide coating was measured using a nano-hardness tester. The developed coating has uniform and crack free surface

Perspectives on Tobacco Product Waste: A Survey of Framework Convention Alliance Members' Knowledge, Attitudes, and Beliefs.

Cigarette butts (tobacco product waste (TPW)) are the single most collected item in environmental trash cleanups worldwide. This brief descriptive study used an online survey tool (Survey Monkey) to assess knowledge, attitudes, and beliefs among individuals representing the Framework Convention Alliance (FCA) about this issue. The FCA has about 350 members, including mainly non-governmental tobacco control advocacy groups that support implementation of the World Health Organization's (WHO) Framework Convention on Tobacco Control (FCTC). Although the response rate (28%) was low, respondents represented countries from all six WHO regions. The majority (62%) have heard the term TPW, and nearly all (99%) considered TPW as an environmental harm. Most (77%) indicated that the tobacco industry should be responsible for TPW mitigation, and 72% felt that smokers should also be held responsible. This baseline information may inform future international discussions by the FCTC Conference of the Parties (COP) regarding environmental policies that may be addressed within FCTC obligations. Additional research is planned regarding the entire lifecycle of tobacco's impact on the environment

Charting New Directions in Entrepreneurship Research

Like all peer-reviewed journal articles, the papers published here were subjected to rigorous peer review and editorial oversight. This screening was in addition to the fact that authors could submit papers to the special issue of New England Journal of Entrepreneurship only if the paper had previously been presented at an Eastern Academy of Management (EAM) conference (either in the United States or internationally). Thus, each of the articles in this special issue has been through at least two independent peer-review processes, one at an EAM conference and another at NEJE. This rigorous two-tier procedure resulted in a selection of quality articles that we hope you will enjoy. These articles also represent the leading edge of knowledge in entrepreneurship research

Synthesis and characterization of a novel Fe3O4-SiO2@Gold core-shell biocompatible magnetic nanoparticles for biological and medical applications

Objectives: The study of core-shell magnetic nanoparticles has a wide range of applications because of the unique combination of the nanoscale magnetic core and the functional shell. Characterization and application of one important class of core-shell magnetic nanoparticles (MNPs), i.e., iron oxide core (Fe3O4/¿-Fe2O3) with a silica shell and outer of gold (Fe3O4-SiO2@Gold (FSG)) in Boron Neutrons Capture Therapy (BNCT) highlighted. The main problem dealing with cancer cells is that the tumor and normal cells ones are mixed without a map of the boron accumulation. Methods: Areas specifically discussed in this report include the possibility of a FSG mediated by liposome as the boron carriers for the transfer of boron compound to tumor tissue. Furthermore, folate receptor was considered as an appropriate substrate that has great potential to attach to tumor on the surface of cancer cells. The present work aimed to study boron biodistribution in the muscle cancer animal model in Bagg Albino (BALB/c) mice employing PEGylated liposome-encapsulated FSG formulations. Results: The predetermined boron concentration was obtained to be 20-35 mg 10B/g. Samples of the tumor tissue, such as kidney, liver, lung, heart, skin, spleen, brain, stomach, and bone were taken as post-administration at different times to measure boron content by Inductively Coupled Plasma (ICP) analysis. The results showed the existence of GLUT-5 expression as an erythrocyte-type glucose transporter protein in a wide variety of tumor cells. Conclusions: Fe3O4-SiO2 nanoparticles are highly biocompatible with biological materials and gold shell also imparts the magnetic nanoparticles with many intriguing functional propertiesPeer ReviewedPostprint (published version

Implementation of a Blind navigation method in outdoors/indoors areas

According to WHO statistics, the number of visually impaired people is increasing annually. One of the most critical necessities for visually impaired people is the ability to navigate safely. This paper proposes a navigation system based on the visual slam and Yolo algorithm using monocular cameras. The proposed system consists of three steps: obstacle distance estimation, path deviation detection, and next-step prediction. Using the ORB-SLAM algorithm, the proposed method creates a map from a predefined route and guides the users to stay on the route while notifying them if they deviate from it. Additionally, the system utilizes the YOLO algorithm to detect obstacles along the route and alert the user. The experimental results, obtained by using a laptop camera, show that the proposed system can run in 30 frame per second while guiding the user within predefined routes of 11 meters in indoors and outdoors. The accuracy of the positioning system is 8cm, and the system notifies the users if they deviate from the predefined route by more than 60 cm.Comment: 14 pages, 6 figures and 6 table

Application of machine learning and deep learning methods for load prediction in institutional buildings

Worldwide, the building sector consumes a significant amount of energy in different stages such as construction and operation. Depending on the type of energy source used, buildings have a considerable impact on air pollution and greenhouse gas emissions. To reduce the amount of emissions from the building sector and manage energy consumption, many tools and incentives are used around the world. One of the most recent and successful approaches in this regard is the application of machine learning techniques in building engineering. The increasing availability of real-time data measured by sensors and building automation systems enable the owner and energy planner to analyze the collected information and explore the hidden useful knowledge and use it to answer specific questions such as which parts need retrofit, how much energy can be saved and what would be the cost. At the building level, machine learning has different applications, such as pattern extraction and load prediction. Amongst those, load analysis and energy demand prediction are of specific importance for the building energy managers, as it can lead to a more efficient operation schedule of energy systems in the building. The analysis of load profiles can give a good overview of the energy use and user behavior in the building. Detailed load analysis and understanding is an essential step before the predictive analysis. In this study, electrical load data from three transformers installed in EV building, Concordia University and weather data collected from the weather station installed in EV building were used for load analysis and load prediction. EV building includes two main parts, which are Engineering (ENCS) and visual arts departments (VA). The three transformers considered in this study measure heating, ventilation, and air conditioning (HVAC) load from a mechanical room (located in 17th floor of the EV building) in addition to the plug and miscellaneous loads from ENCS and VA departments. In the load analysis part, the representative daily loads of these three transformers of the building are studied. The magnitude and trend of daily loads are extracted and discussed. The average load from 17th floor’s transformer is found to be 1,441 kW during office hours of weekdays in summer, whereas this load during office time in winter is 991 kW. Note that, this load does not include the gas consumption, used for meeting the heating load during the winter. Regarding the plug load from ENCS and VA department, the average load during office hours of weekdays in summer is 512 kW, and 453 kW, respectively. Moreover, the load reduction during the COVID19 pandemic is studied by comparing the two months (April and May) of 2019 and 2020 for all three transformers. There was a significant reduction of 42 % for the load of 17th floor between April 2019 and April 2020 (weekdays), while 24% and 40% load reduction was observed for ENCS and VA transformers, respectively. Based on the results during COVID 19 period, we see that the existence of people in the building affects the load, but a great part of the load is related to the schedule and policy of the building. That is why there is a good potential to save energy just by changing the schedule and plans that systems are running based on. The second part of the work deals with load prediction using regression analysis and long shortterm memory (LSTM) model. The importance of input variables for load prediction is evaluated in the regression section. In linear regression, twenty scenarios are considered. Each scenario is a different combination of input features. It was found from the results that the best scenario is when all calendar and weather data are considered as input attributes. The best scenario in winter has R2=0.29 and MAPE=24.46, while in summer, R2=0.64 and MAPE=10.47. The results are confirmed with correlation analysis. For this case study, adding meteorological data did not improve prediction in winter significantly because in winter, gas is used for heating and the considered data does not reflect it, but in summer, weather variables were of great importance. Also, specific and unusual events in consumption could be detected with polynomial regression. Regarding load forecasting, LSTM is used as a deep learning model, which considers the sequential load data and predicts future load for different time horizons. Regarding the size of the dataset and LSTM parameters, the best performance was obtained for one-year ahead forecasting with R2= 0.75, and MAPE= 10.97. Another result was that the type of load influences the performance of the LSTM model. Considering different load types, the plug and lighting loads from the ENCS and VA departments could be better predicted than the 17th floor HVAC load, since HVAC load is affected by weather variables that are fluctuating and not easy to predict, but plug loads are more related to the schedule of building. The other influencing factor on prediction performance is the choice of train-set and test-set. The lowest R-squared belongs to the model that has the year 2019 as test-set. The results of this project could be useful for building facility managers to adapt and optimize the schedule of the energy systems and give recommendations to the users to improve energy efficiency

Comparing the Causes of Infidelity in Marital Relationships among Men and Women: A Qualitative Research

Introduction: Today, the evidence and unofficial indications for disturbing society show the marital infidelity as one of the hidden social problems. In this study, the underlying factors of infidelity in marital relationships among women and men were identified and compared. Moreover, some basic guidelines and practical suggestions on preventing and reducing marital infidelity and strengthening the family relations were presented. Method: Using qualitative content analysis methodology, and according to the purposive sampling method and theoretical saturation criterion, 40 unfaithful married men and 32 unfaithful married women participated in the study. Data were collected through in-depth, semi-structured interviews. Results: Through data analysis, 5 main themes were extracted. These themes consisted of “emotional and behavioral problems in marital relationships”, “sexual dissatisfaction”, “attitudes and individual characteristics”, “paternal family problems”, and “social factors”. Conclusion: Based on the study findings, although majority of male and female participants mentioned sexual dissatisfaction (low quantity and quality) and emotional dissatisfaction as the main reason for their unfaithfulness, respectively, the impact of other paternal family problems, social factors, and attitudes and individual characteristics must be also regarded. Keywords: Sexual Behavior, Family Relations, Extramarital Relations, Qualitative Evaluatio