Search results for: panel regression techniques

Authors: Elnaz Bandeh

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This study aimed to predict marital burnout based on irrational beliefs and sexual dysfunction of couples. The research method was descriptive-correlational, and the statistical population included all couples who consulted to counseling clinics in the fall of 2016. The sample consisted of 200 people who were selected by convenience sampling and answered the Ahwaz Irrational Beliefs Questionnaire, Pines Couple Burnout, and Hudson Marital Satisfaction Questionnaire. The data were analyzed using regression coefficient. The results of regression analysis showed that there was a linear relationship between irrational beliefs and couple burnout and dimensions of helplessness toward change, expectation of approval from others, and emotional irresponsibility were positive and significant predictors of couple burnout. However, after avoiding the problem of power, it was not a significant predictor of marital dissatisfaction. There was also a linear relationship between sexual dysfunction and couple burnout, and sexual dysfunction was a positive and significant predictor of couple burnout. Based on the findings, it can be concluded that irrational beliefs and sexual dysfunction play a role in couple dysfunction.

Keywords: couple burnout, irrational beliefs, sexual dysfunction, marital relationship

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Authors: Amara Rafik, Mostefa Belhadj Aissa

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Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques.

Keywords: ATM, GIS, radar data, simulation

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Authors: Mehmet Izzettin Yilmazer, Emin Cadirli

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Zn-7wt.%Al-2.96wt.%Cu eutectic alloy was directionally solidified upwards with different temperature gradients (from 6.70 K/mm to 10.67 K/mm) at a constant growth rate (16.4 Km/s) and also different growth rate (from 8.3 micron/s to 166 micron/s) at a constant temperature gradient (10.67 K/mm) using a Bridgman–type growth apparatus.The values of eutectic spacing were measured from longitudinal and transverse sections of the samples. The dependency of microstructures on the G and V were determined with linear regression analysis and experimental equations were found as λl=8.953xVexp-0.49, λt=5.942xVexp-0.42 and λl=0.008xGexp-1.23, λt=0.024xGexp-0.93. The measurements of microhardness of directionally solidified samples were obtained by using a microhardness test device. The dependence of microhardness HV on temperature gradient and growth rate were analyzed. The dependency of microhardness on the G and V were also determined with linear regression analysis as HVl=110.66xVexp0.02, HVt=111.94xVexp0.02 and HVl=69.66xGexp0.17, HVt=68.86xGexp0.18. The experimental results show that the microhardness of the directionally solidified Zn-Al-Cu alloy increases with increasing the growth rate. The results obtained in this work were compared with the previous similar experimental results.

Keywords: directional solidification, eutectic alloys, microstructure, microhardness

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Authors: Pawthawala Nancy Manish, Syed Alay Hashim

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A hybrid rocket is a rocket engine which uses propellants in two different states of matter- one is in solid and the other either gas or liquid. A hybrid rocket exhibit advantages over both liquid rockets and solid rockets especially in terms of simplicity, stop-start-restart capabilities, safety and cost. This paper deals the design and development of a hybrid rocket having paraffin wax as solid fuel and liquid oxygen as oxidizer. Due to variation of pressure in combustion chamber there is significantly change in mass flow rate, burning rate and uneven regression along the length of the grain. This project describes the working model of a hybrid propellant rocket motor. We have designed a hybrid rocket thrust chamber based on the predetermined combustion chamber pressure and the properties of hybrid propellant. This project is all ready in working condition with normal oxygen injector. Now we have planned to modify the injector design to improve the combustion property. We will use spray type injector for injecting the oxidizer. This idea will increase the performance followed by the regression rate of the solid fuel. By employing mass conservation law, oxygen mass flux, oxidizer/fuel ratio and regression rate the thrust coefficient can be obtained for our current design. CATIA V5 R20 is our design software for the complete setup. This project is fully based on experimental evaluation and the collection of combustion and flow parameters. The thrust chamber is made of stainless steel and the duration of test is around 15-20 seconds (Maximum). These experiments indicates that paraffin based fuel provides the opportunity to satisfy a broad range of mission requirements for the next generation of the hybrid rocket system.

Keywords: burning rate, liquid oxygen, mass flow rate, paraffin wax and sugar

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Authors: Rashid Mahmood, Muhammad Junaid Sarwar

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Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields.

Keywords: MANET, IDS, intrusions, signature, detection, prevention

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

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Authors: Waseem Al Talalwah, Shorok Al Dorazi, Roger Soames

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Persistent sciatic artery is a rare anatomical vascular variation resulting from a lack of regression of the embryonic dorsal axial artery. The axial artery is the main artery supplying the lower limb during development in the first trimester. The current research includes 206 sciatic artery cases in 171 patients between 1864 and 2012. It aims to identify the risk factor of sciatic artery aneurysm in congenital limb anomalies. Sciatic artery aneurysm was diagnosed incidentally in amniotic band syndrome (ABS) existing with no congenital anomaly in 0.7% or with double knee in 0.7%, with the tibia in 0.7% and with hemihypertrophy or soft tissue hypertrophy in 1.4%. Therefore, the current study indicates a relationship the same gene responsible for the congenital limb deformities may be responsible for non-regression of the sciatic artery. Furthermore, pediatricians should refer cases of congenital limb anomalies for vascular evaluation prior to corrective surgical intervention.

Keywords: amniotic band syndrome, congenital limb deformities, double knee, sciatic artery, sciatic artery aneurysm , soft tissue hypertrophy

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Authors: Mikel Alonso López

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In the first decade of the twenty-first century, advanced imaging techniques began to be applied for neuroscience research. The Functional Magnetic Resonance Imaging (fMRI) is one of the most important and most used research techniques for the investigation of emotions, because of its ease to observe the brain areas that oxygenate when performing certain tasks. In this research, we make a review about the main research carried out on the influence of the emotions in the decision-making process that is exposed by using the fMRI.

Keywords: decision making, emotions, fMRI, consumer behaviour

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Authors: Mohsen Amiri

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This study aimed at investigating the relationship between familial functioning, child characteristics, demographic variables and parenting stress and mental health among parents of children with mental disabilities. 200 parents (130 mothers and 70 fathers) were studied and they completed the Parenting Stress Index, General Health Questionnaire, Family Assessment Device and demographic questionnaires for parents and children. Data were analyzed using correlation and regression analysis. Regression analysis showed that child characteristics, familial functioning and parents demographic factors could predict 8, 4 and 17 percent of variance in parental stress and 3.6, 16 and 10 percent of variance in mental health, respectively. Familial functioning, child characteristics and parental demographic variables correlated with mental health and parental stress and could predict them.

Keywords: parenting stress, mental health, mentally disabled children, familial functioning, demographic variables

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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Authors: Kaisy Xinhong Ye, Su Lin Lim, Jialiang Li, Lei Feng

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background: Multiple healthful dietary patterns have been linked with dementia, but limited studies have looked at the role of diet in cognitive health in Asians whose eating habits are very different from their counterparts in the west. This study aimed to derive a dietary pattern that is associated with the risk of cognitive impairment (CI) in the Singaporean population. Method: The analysis was based on 719 community older adults aged 60 and above. Dietary intake was measured using a validated semi-quantitative food-frequency questionnaire (FFQ). Reduced rank regression (RRR) was used to extract dietary pattern from 45 food groups, specifying sugar, dietary fiber, vitamin A, calcium, and the ratio of polyunsaturated fat to saturated fat intake (P:S ratio) as response variables. The RRR-derived dietary patterns were subsequently investigated using multivariate logistic regression models to look for associations with the risk of CI. Results: A dietary pattern characterized by greater intakes of green leafy vegetables, red-orange vegetables, wholegrains, tofu, nuts, and lower intakes of biscuits, pastries, local sweets, coffee, poultry with skin, sugar added to beverages, malt beverages, roti, butter, and fast food was associated with reduced risk of CI [multivariable-adjusted OR comparing extreme quintiles, 0.29 (95% CI: 0.11, 0.77); P-trend =0.03]. This pattern was positively correlated with P:S ratio, vitamin A, and dietary fiber and negatively correlated with sugar. Conclusion: A dietary pattern providing high P:S ratio, vitamin A and dietary fiber, and a low level of sugar may reduce the risk of cognitive impairment in old age. The findings have significance in guiding local Singaporeans to dementia prevention through food-based dietary approaches.

Keywords: dementia, cognitive impairment, diet, nutrient, elderly

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Authors: Breanna Wright, Peter Bragge

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Diagnostic error rates in healthcare are approximately 10% of cases. Diagnostic errors can cause patient harm due to inappropriate, inadequate or delayed treatment, and such errors contribute heavily to medical liability claims globally. Therefore, addressing diagnostic error is a high priority. In most cases, diagnostic errors are the result of faulty information synthesis rather than lack of knowledge. Specifically, the majority of diagnostic errors involve cognitive factors, and in particular, cognitive biases. Emergency Departments are an environment with heightened risk of diagnostic error due to time and resource pressures, a frequently chaotic environment, and patients arriving undifferentiated and with minimal context. This project aimed to develop a behavioural, evidence-informed intervention to reduce diagnostic error in Emergency Departments through co-design with emergency physicians, insurers, researchers, hospital managers, citizens and consumer representatives. The Forum Process was utilised to address this aim. This involves convening a small (4 – 6 member) expert panel to guide a focused literature and practice review; convening of a 10 – 12 person citizens panel to gather perspectives of laypeople, including those affected by misdiagnoses; and a 18 – 22 person structured stakeholder dialogue bringing together representatives of the aforementioned stakeholder groups. The process not only provides in-depth analysis of the problem and associated behaviours, but brings together expertise and insight to facilitate identification of a behaviour change intervention. Informed by the literature and practice review, the Citizens Panel focused on eliciting the values and concerns of those affected or potentially affected by diagnostic error. Citizens were comfortable with diagnostic uncertainty if doctors were honest with them. They also emphasised the importance of open communication between doctors and patients and their families. Citizens expect more consistent standards across the state and better access for both patients and their doctors to patient health information to avoid time-consuming re-taking of long patient histories and medication regimes when re-presenting at Emergency Departments and to reduce the risk of unintentional omissions. The structured Stakeholder Dialogue focused on identifying a feasible behavioural intervention to review diagnoses in Emergency Departments. This needed to consider the role of cognitive bias in medical decision-making; contextual factors (in Victoria, there is a legislated 4-hour maximum time between ED triage and discharge / hospital admission); resource availability; and the need to ensure the intervention could work in large metropolitan as well as small rural and regional ED settings across Victoria. The identified behavioural intervention will be piloted in approximately ten hospital EDs across Victoria, Australia. This presentation will detail the findings of all review and consultation activities, describe the behavioural intervention developed and present results of the pilot trial.

Keywords: behavioural intervention, cognitive bias, decision-making, diagnostic error

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Authors: Davod Ahmadigheidari

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The main objective of this study was to explore prevalence of anthropometric indicators as well the factors associated with the anthropometric indications in Mali. Data on 2015, downloaded from the website of Unicef, were analyzed. A total of 16,467 women (ages 15-49 years) and 16,467 children (ages 0-59 months) were selected for the sample. Different statistical analyses, such as descriptive, crosstabs and binary logistic regression form the basis of this study. Child anthropometric indicators (i.e., wasting, stunting, underweight and BMI for age) were used as the dependent variables. SPSS Syntax from WHO was used to create anthropometric indicators. Different factors, such as child’s sex, child’s age groups, child’s diseases symptoms (i.e., diarrhea, cough and fever), maternal education, household wealth index and area of residence were used as independent variables. Results showed more than forty percent of Malian households were in nutritional crises (stunting (42%) and underweight (34%). Findings from logistic regression analyses indicated that low score of wealth index, low maternal education and experience of diarrhea in last two weeks increase the probability of child malnutrition.

Keywords: Mali, wasting, stunting, underweight, BMI for age and wealth index

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: green home, resident aware, resident profile, activity learning, machine learning

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Piotr Kacejko, Mariusz Duk, Miroslaw Wendeker

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This paper presents an adaptive controller to track the maximum power point of a photovoltaic modules (PV) under fast irradiation change on the city-bus roof. Photovoltaic systems have been a prominent option as an additional energy source for vehicles. The Municipal Transport Company (MPK) in Lublin has installed photovoltaic panels on its buses roofs. The solar panels turn solar energy into electric energy and are used to load the buses electric equipment. This decreases the buses alternators load, leading to lower fuel consumption and bringing both economic and ecological profits. A DC–DC boost converter is selected as the power conditioning unit to coordinate the operating point of the system. In addition to the conversion efficiency of a photovoltaic panel, the maximum power point tracking (MPPT) method also plays a main role to harvest most energy out of the sun. The MPPT unit on a moving vehicle must keep tracking accuracy high in order to compensate rapid change of irradiation change due to dynamic motion of the vehicle. Maximum power point track controllers should be used to increase efficiency and power output of solar panels under changing environmental factors. There are several different control algorithms in the literature developed for maximum power point tracking. However, energy performances of MPPT algorithms are not clarified for vehicle applications that cause rapid changes of environmental factors. In this study, an adaptive MPPT algorithm is examined at real ambient conditions. PV modules are mounted on a moving city bus designed to test the solar systems on a moving vehicle. Some problems of a PV system associated with a moving vehicle are addressed. The proposed algorithm uses a scanning technique to determine the maximum power delivering capacity of the panel at a given operating condition and controls the PV panel. The aim of control algorithm was matching the impedance of the PV modules by controlling the duty cycle of the internal switch, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PV system is a general solution and can be used for other types of PV systems of both high and low power. Experimental results obtained from comparison of algorithms by a motion loop are presented and discussed. Experimental results are presented for fast change in irradiation and partial shading conditions. The results obtained clearly show that the proposed method is simple to implement with minimum tracking time and high tracking efficiency proving superior to the proposed method. This work has been financed by the Polish National Centre for Research and Development, PBS, under Grant Agreement No. PBS 2/A6/16/2013.

Keywords: adaptive control, photovoltaic energy, city bus electric load, DC-DC converter

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Authors: Liwiusz Wojciechowski

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Research background: The current knowledge does not give a clear answer to the question of the impact of FDI on productivity. Results of the empirical studies are still inconclusive, no matter how extensive and diverse in terms of research approaches or groups of countries analyzed they are. It should also take into account the possibility that FDI and productivity are linked and that there is a bidirectional relationship between them. This issue is particularly important because on one hand FDI can contribute to changes in productivity in the host country, but on the other hand its level and dynamics may imply that FDI should be undertaken in a given country. As already mentioned, a two-way relationship between the presence of foreign capital and productivity in the host country should be assumed, taking into consideration the endogenous nature of FDI. Purpose of the article: The overall objective of this study is to determine the causality between foreign direct investment and total factor productivity in host county in terms of different relative absorptive capacity across countries. In the classic sense causality among variables is not always obvious and requires for testing, which would facilitate proper specification of FDI models. The aim of this article is to study endogeneity of selected macroeconomic variables commonly being used in FDI models in case of Visegrad countries: main recipients of FDI in CEE. The findings may be helpful in determining the structure of the actual relationship between variables, in appropriate models estimation and in forecasting as well as economic policymaking. Methodology/methods: Panel and time-series data techniques including GMM estimator, VEC models and causality tests were utilized in this study. Findings & Value added: The obtained results allow to confirm the hypothesis states the bi-directional causality between FDI and total factor productivity. Although results differ from among countries and data level of aggregation implications may be useful for policymakers in case of providing foreign capital attracting policy.

Keywords: endogeneity, foreign direct investment, multi-equation models, total factor productivity

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Authors: Arun Dhakal, Geoff Cockfield, Tek Narayan Maraseni

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This paper attempts to fulfil the gap in measuring adoption in agroforestry studies. It explains the derivation of an index of adoption rate in a Nepalese context and examines the factors affecting adoption of agroforestry-based land management practice (AFLMP) in the Dhanusha District of Nepal. Data about the different farm practices and the factors (bio-physical, socio-economic) influencing adoption were collected during focus group discussion and from the randomly selected households using a household survey questionnaire, respectively. A multivariate regression model was used to determine the factors. The factors (variables) found to significantly affect adoption of AFLMP were: farm size, availability of irrigation water, education of household heads, agricultural labour force, frequency of visits by extension workers, expenditure on farm inputs purchase, household’s experience in agroforestry, and distance from home to government forest. The regression model explained about 75% of variation in adoption decision. The model rejected ‘erosion hazard’, ‘flood hazard’ and ‘gender’ as determinants of adoption, which in case of single agroforestry practice were major variables and played positive role. Out of eight variables, farm size played the most powerful role in explaining the variation in adoption, followed by availability of irrigation water and education of household heads. The results of this study suggest that policies to promote the provision of irrigation water, extension services and motivation to obtaining higher education would probably provide the incentive to adopt agroforestry elsewhere in the terai of Nepal.

Keywords: agroforestry, adoption index, determinants of adoption, step-wise linear regression, Nepal

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Authors: Peter Temitope Agboola

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Food insecurity continues to affect a large number of U.S households during this coronavirus COVID-19 pandemic. The pandemic has threatened the livelihoods of people, making them vulnerable to severe hardship and has had an unanticipated impact on the U.S economy. This study attempts to identify the food insecurity status of households and the determinant factors driving household food insecurity. Additionally, it attempts to discover the mitigation measures adopted by households during the pandemic in the city of Huntsville, Texas. A structured online sample survey was used to collect data, with a household expenditures survey used in evaluating the food security status of the household. Most survey respondents disclosed that the COVID-19 pandemic had affected their life and source of income. Furthermore, the main analytical tool used for the study is descriptive statistics and logistic regression modeling. A logistic regression model was used to determine the factors responsible for food insecurity in the study area. The result revealed that most households in the study area are food secure, with the remainder being food insecure.

Keywords: food insecurity, household expenditure survey, COVID-19, coping strategies, food pantry

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Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

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Authors: Nicola Colaninno, Eugenio Morello

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The urban environment affects local-to-global climate and, in turn, suffers global warming phenomena, with worrying impacts on human well-being, health, social and economic activities. Physic-morphological features of the built-up space affect urban air temperature, locally, causing the urban environment to be warmer compared to surrounding rural. This occurrence, typically known as the Urban Heat Island (UHI), is normally assessed by means of air temperature from fixed weather stations and/or traverse observations or based on remotely sensed Land Surface Temperatures (LST). The information provided by ground weather stations is key for assessing local air temperature. However, the spatial coverage is normally limited due to low density and uneven distribution of the stations. Although different interpolation techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Multiple Linear Regression (MLR) are used to estimate air temperature from observed points, such an approach may not effectively reflect the real climatic conditions of an interpolated point. Quantifying local UHI for extensive areas based on weather stations’ observations only is not practicable. Alternatively, the use of thermal remote sensing has been widely investigated based on LST. Data from Landsat, ASTER, or MODIS have been extensively used. Indeed, LST has an indirect but significant influence on air temperatures. However, high-resolution near-surface air temperature (NSAT) is currently difficult to retrieve. Here we have experimented Geographically Weighted Regression (GWR) as an effective approach to enable NSAT estimation by accounting for spatial non-stationarity of the phenomenon. The model combines on-site measurements of air temperature, from fixed weather stations and satellite-derived LST. The approach is structured upon two main steps. First, a GWR model has been set to estimate NSAT at low resolution, by combining air temperature from discrete observations retrieved by weather stations (dependent variable) and the LST from satellite observations (predictor). At this step, MODIS data, from Terra satellite, at 1 kilometer of spatial resolution have been employed. Two time periods are considered according to satellite revisit period, i.e. 10:30 am and 9:30 pm. Afterward, the results have been downscaled at 30 meters of spatial resolution by setting a GWR model between the previously retrieved near-surface air temperature (dependent variable), the multispectral information as provided by the Landsat mission, in particular the albedo, and Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM), both at 30 meters. Albedo and DEM are now the predictors. The area under investigation is the Metropolitan City of Milan, which covers an area of approximately 1,575 km2 and encompasses a population of over 3 million inhabitants. Both models, low- (1 km) and high-resolution (30 meters), have been validated according to a cross-validation that relies on indicators such as R2, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). All the employed indicators give evidence of highly efficient models. In addition, an alternative network of weather stations, available for the City of Milano only, has been employed for testing the accuracy of the predicted temperatures, giving and RMSE of 0.6 and 0.7 for daytime and night-time, respectively.

Keywords: urban climate, urban heat island, geographically weighted regression, remote sensing

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Authors: Ali Soroush, Mehdi Mirzaei Alavijeh, Touraj Ahmadi Jouybari, Fazel Zinat-Motlagh, Abbas Aghaei, Mari Ataee

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Diabetes is a disease with long cardiovascular, renal, ophthalmic and neural complications. It is prevalent all around the world including Iran, and its prevalence is increasing. The aim of this study was to determine the factors related to self-care behavior based on health belief model among sample of Iranian diabetic patients. This cross-sectional study was conducted among 301 type 2 diabetic patients in Gachsaran, Iran. Data collection was based on an interview and the data were analyzed by SPSS version 20 using ANOVA, t-tests, Pearson correlation, and linear regression statistical tests at 95% significant level. Linear regression analyses showed the health belief model variables accounted for 29% of the variation in self-care behavior; and perceived severity and perceived self-efficacy are more influential predictors on self-care behavior among diabetic patients.

Keywords: diabetes, patients, self-care behaviors, health belief model

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Authors: Zaynab El Khattabi, Youness Tabii, Abdelhamid Benkaddour

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Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research.

Keywords: video summarization, static summarization, video skimming, semantic features

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Authors: Hamed Rashid Sarand, Kemal Selçuk Öğüt

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Speed is one of the fundamental variables of road traffic flow that stands as an important evaluation criterion for traffic analyses in several aspects. In particular, varieties of speed variable, such as average speed, free flow speed, optimum speed (capacity speed), acceleration/deceleration speed and so on, have been explicitly considered in the analysis of not only road safety but also road capacity. In the purpose of realizing 'road speed – maximum speed difference across lanes' and 'road flow rate – maximum speed difference across lanes' relations on freeway traffic, this study presents a case study conducted on a basic freeway segment of O-2 in Istanbul. The traffic data employed in this study have been obtained from 5 remote traffic microwave sensors operated by Istanbul Metropolitan Municipality. The study stretch is located between two successive freeway interchanges: Ümraniye and Kavacık. Daily traffic data of 4 years (2011-2014) summer months, July and August are used. The speed data are analyzed into two main flow areas such as uncongested and congested flows. In this study, the regression analyses were carried out in order to examine the relationship between maximum speed difference across lanes and road speed. These investigations were implemented at uncongested and congested flows, separately. Moreover, the relationship between maximum speed difference across lanes and road flow rate were evaluated by applying regression analyses for both uncongested and congested flows separately. It is concluded that there is the moderate relationship between maximum speed difference across lanes and road speed in 50% cases. Additionally, it is indicated that there is the moderate relationship between maximum speed difference across lanes and road flow rate in 30% cases. The maximum speed difference across lanes decreases as the road flow rate increases.

Keywords: maximum speed difference, regression analysis, remote traffic microwave sensor, speed differentiation, traffic flow

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Authors: Abdelhak Bouchakour, Mustafa Brahami, Layachi Zaghba

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The use of photovoltaic energy offers an inexhaustible supply of energy but also a clean and non-polluting energy, which is a definite advantage. The geographical location of Algeria promotes the development of the use of this energy. Indeed, given the importance of the intensity of the radiation received and the duration of sunshine. For this reason, the objective of our work is to develop a data-processing tool (software) of calculation and optimization of dimensioning of the photovoltaic installations. Our approach of optimization is basing on mathematical models, which amongst other things describe the operation of each part of the installation, the energy production, the storage and the consumption of energy.

Keywords: solar panel, solar radiation, inverter, optimization

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Authors: Bedilu Tafesse

Abstract:

Home gardens are important social and cultural spaces where knowledge related to agricultural practice is transmitted and through which households may improve their income and livelihood. High levels of inter- and intra-specific plant genetic diversity are preserved in home gardens. Plant diversity is threatened by rapid and unplanned urbanization, which increases environmental problems such as heating, pollution, loss of habitats and ecosystem disruption. Tropical home gardens have played a significant role in conserving plant diversity while providing substantial benefits to households. This research aimed to understand the relationship between household characteristics and plant diversity in western Ethiopia home gardens and the contributions of plants to the household economy. Plant diversity and different uses of plants were studied in a random sample of 111 suburban home gardens in the Ilu Ababora, Jima and Wellega suburban area, western Ethiopia, based on complete garden inventories followed by household surveys on socio-economic status during 2012. A total of 261 species of plants were observed, of which 41% were ornamental plants, 36% food plants, and 22% medicinal plants. Of these 16% were sold commercially to produce income. Avocado, bananas, and other fruits produced in excess. Home gardens contributed the equivalent of 7% of total annual household income in terms of food and commercial sales. Multiple regression analysis showed that education, time spent in gardening, land for cultivation, household expenses, primary conservation practices, and uses of special techniques explained 56% of the total plant diversity. Food, medicinal and commercial plant species had significant positive relationships with time spent gardening and land area for gardening. Education and conservation practices significantly affected food and medicinal plant diversity. Special techniques used in gardening showed significant positive relations with ornamental and commercial plants. Reassessments in different suburban and urban home gardens and proper documentation using same methodology is essential to build a firm policy for enhancing plant diversity and related values to households and surroundings.

Keywords: plant genetic diversity, urbanization, suburban home gardens, Ethiopia

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Authors: Fuchun Li, Hong Xiao

Abstract:

We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Anna M. Pretorius

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The emerging markets category for portfolio investment was introduced in 1986 in an attempt to promote capital market development in less developed countries. Investors traditionally diversified their portfolios by investing in different developed markets. However, high growth opportunities forced investors to consider emerging markets as well. Examples include the rapid growth of the “Asian Tigers” during the 1980s, growth in Latin America during the 1990s and the increased interest in emerging markets during the global financial crisis. As such, portfolio flows to emerging markets have increased substantially. In 2002 7% of all equity allocations from advanced economies went to emerging markets; this increased to 20% in 2012. The stronger links between advanced and emerging markets led to increased synchronization of asset price movements. This increased level of stock market integration for emerging markets is confirmed by various empirical studies. Against the background of increased interest in emerging market assets and the increasing level of integration of emerging markets, this paper focuses on the determinants of stock market integration of emerging market countries. Various studies have linked the level of financial market integration with specific economic variables. These variables include: economic growth, local inflation, trade openness, local investment, budget surplus/ deficit, market capitalization, domestic bank credit, domestic institutional and legal environment and world interest rates. The aim of this study is to empirically investigate to what extent trade-related determinants have an impact on stock market integration. The panel data sample include data of 16 emerging market countries: Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Malaysia, Pakistan, Peru, Philippines, Poland, Russian Federation, South Africa, Thailand and Turkey for the period 1998-2011. The integration variable for each emerging stock market is calculated as the explanatory power of a multi-factor model. These factors are extracted from a large panel of global stock market returns. Trade related explanatory variables include: exports as percentage of GDP, imports as percentage of GDP and total trade as percentage of GDP. Other macroeconomic indicators – such as market capitalisation, the size of the budget deficit and the effectiveness of the regulation of the securities exchange – are included in the regressions as control variables. An initial analysis on a sample of developed stock markets could not identify any significant determinants of stock market integration. Thus the macroeconomic variables identified in the literature are much more significant in explaining stock market integration of emerging markets than stock market integration of developed markets. The three trade variables are all statistically significant at a 5% level. The market capitalisation variable is also significant while the regulation variable is only marginally significant. The global financial crisis has highlighted the urgency to better understand the link between the financial and real sectors of the economy. This paper comes to the important finding that, apart from the level of market capitalisation (as financial indicator), trade (representative of the real economy) is a significant determinant of stock market integration of countries not yet classified as developed economies.

Keywords: emerging markets, financial market integration, panel data, trade

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