An Improved Deep Learning Model for Traffic Crash Prediction

Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively. Document type: Articl

A Comprehensive Context-Aware Interruption Management System

The interruption system is an application that prevents the user from noticing phone calls when he/she is busy, by turning off the ringtone. In a previous project, the user can enter his/her class and work schedule on Google Calendar. The intelligent interruption system can detect if the current time matches the range of one of the events in the user\u27s Google Calendar. Other contexts considered were: driving, relationship of the callers, and proximity of Bluetooth devices. This project is a continuation of the interruption system. We consider additional context, social media such as Twitter. Research is done on when is the best time to turn off the ringer when the user is using Twitter. If the user is using social media, the user isn\u27t as busy compared to, if the user is in class or at work. We further granularize social media activity such as reading messages, writing messages, and use these to help predict interruptions. We use the feedback provided by the user and employ machine learning approach which takes as input the different contexts and predicts if the user should be interrupted. We implemented a prototype application on Android operating system

DETERMINANTS AFFECTING THE CAPITAL STRUCTURE DECISION OF A FIRM (A CASE STUDY OF TEXTILE SECTOR IN PAKISTAN)

In this paper, we have examined the influence of specific factors based on a capital structure sample of five Pakistani textile sector (Leveraged) companies. The secondary data came from an analysis of the balance sheets of five companies listed on the Karachi Stock Exchange between 2004 and 2014.Regression and correlation analysis on the panel data shows that profitability is negatively correlated with leverage ratio, while tangibility is positively correlated with leverage ratio, but not significantly. Firm size and firm growth are also positively and significantly correlated with leverage. Return on equity is also negatively correlated with leverage. Our findings also show that large textile firms, compared with small ones, finance long-term through debt. Keywords: Capital Structure, Return on equity, Profitability, Tangibility, Leverage, Debt to equity ratio, Pakistan

Energy – Growth Nexus- A Case of South Asian Countries

The relationship between energy consumption and economic growth is a hot issue in today's society. This paper aims to empirically verify the relationship between energy consumption and economic growth. This article analyzes the relation of energy consumption with the economic growth taking the case of South Asian countries (Afghanistan, Bangladesh, Bhutan, India, Pakistan, Sri Lanka, and Nepal) along with the macroeconomic determinants that affect the total economic growth – FDI growth, CPI rate and population growth in order to avoid omitted variable bias and misleading results. The time span of this study covers the period of 1980–2019. To examine the significant relation of these determinants and impact of energy consumption on economic growth, In-pooled regression, Fixed-effects, Bidirectional fixed effect, Random-effects, and GLS estimation regression model are used. The estimated results show a positive correlation of energy consumption and all other economic determinants with economic growth except CPI, where there is a negative correlation founded

Strategies for Accomplishing the Benefits of China-Pakistan Economic Corridor for Pakistan

CPEC represents a new form of China-Pakistan alliance with the aptitude to broaden and further enhance the political and economic ties of both these countries through mutual trade and development. The total projects, (presently) worth US$ 70billion offer an all times biggest opportunity to Pakistan to tackle the main hitches to its economic development i.e. energy crisis, poor infrastructure, low foreign direct investment (FDI), limited industrial production, limited and old fashioned  technology , unemployment and security issues etc. CPEC’s estimated socio-economic changes is going to bring harmony, contentment and stability in the country in general and particularly in the undeveloped and retrograded provinces like Baluchistan and somewhat khaiber Pukhtunkhwa (KPK) by providing employment opportunities in different commercial, construction and production activities. Despite the substantial consequences of CPEC, the project is collared by various internal and external confronts and disputes like India’s stances on the project, terrorism and instability of Afghanistan and its spillover to Pakistan and feeling unsecure of other countries in the region, Internally political conflicts between the provinces, security challenges and political controversies regarding the route selection etc. which are to be coped with by Pakistan so that to execute of the mega project of CPEC a beneficial endeavor for Pakistan. Keywords: CPEC, OBOR, Silk Rode, Economic Policy, Foreign Policy DOI: 10.7176/JESD/12-2-07 Publication date: January 31st 202

Toward an RSU-unavailable lightweight certificateless key agreement scheme for VANETs

Vehicle ad-hoc networks have developed rapidly these years, whose security and privacy issues are always concerned widely. In spite of a remarkable research on their security solutions, but in which there still lacks considerations on how to secure vehicle-to-vehicle communications, particularly when infrastructure is unavailable. In this paper, we propose a lightweight certificateless and one-round key agreement scheme without pairing, and further prove the security of the proposed scheme in the random oracle model. The proposed scheme is expected to not only resist known attacks with less computation cost, but also as an efficient way to relieve the workload of vehicle-to-vehicle authentication, especially in no available infrastructure circumstance. A comprehensive evaluation, including security analysis, efficiency analysis and simulation evaluation, is presented to confirm the security and feasibility of the proposed scheme

CHINA-PAKISTAN ECONOMIC CORRIDOR (CPEC’S) SOCIO-ECONOMIC IMPACTS ON PAKISTAN

CPEC being a mega project of the recent era not only in South Asia but throughout the world. CPEC is going to take place between Pakistan and China by bringing enormous economic betterment and lifestyle changes for the people living in Pakistan. Both countries, China and Pakistan are agreed to build one road one belt which is commonly known as China Pakistan Economic Corridor (CPEC) with the intentions to bring peace and prosperity as well as to enhance business activities by promoting trade with each other and with rest of the world, which will directly affect on economic growth. Being a mega economic project for both countries it will bring prosperity and economic stability. It will enhance Pakistan’s infrastructure (road, railway and telecommunication), overcome energy crises, develop trade, modernize and develop agriculture and manufacturing industry and mutual connectivity between people of both countries which is a very important factor for trade. China being the strongest economy of the present day will support the new economically arising country (Pakistan). China will pull Pakistan from the crises by applying modern technology and high financial support, which every country faces during the difficult time of raising its economy. This study helps to overlook and analyze the benefits of CPEC for the people of Pakistan. Keywords: China Pakistan Economic Corridor, One Belt One Road, Gwadar, Socio-Economic Developmen

Exploring the future Electric Vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China

This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs

Landslides Caused by Climate Change and Groundwater Movement in Permafrost Mountain

Climate change induced warming results in permafrost degradation. Melting permafrost subsequently leads to an increased incidence of landslides. The study area was within the northwest section of the Lesser Khingan Range in northern China along the Bei\u27an-Heihe Highway. We analyzed the impact of climate change on landslide movement in the permafrost zone via a combination of geological survey and meteorological data. The average annual temperature of the study area has increased by 3.2°C in last 60 years, and permafrost degradation is severe. Loose soil on the hillside surface provides appropriate conditions for the infiltration of atmospheric precipitation and snowmelt, and seepage from thawing permafrost. As it infiltrates downwards, water is blocked by the underlying permafrost or dense soil, and infiltrates along this barrier layer toward lower positions, forming a potential sliding zone. The combination of high density resistivity (HDR) methods based on soil resistivity values, ground-penetrating radar (GPR) methods based on characteristics of radar wave reflection, respectively, and geological drilling can be utilized to determine the regional stratigraphic distribution. This will allow the exact location of the landslide sliding surface to be precisely determined. Field test results indicate that radar reflectivity characteristics and the resistivity values of the soil in the landslide mass is significantly different from surrounding soil. There are sudden decreases in the apparent resistivity values at the sliding surface location. In addition, the radar exhibits strong reflection at the sliding surface position, with a sudden increase in the amplitude of the radar wave. Drilling results indicate that the soil has high water content at the location of the sliding surface of the landslide mass in the study area, which is entirely consistent with the GPR and HDR results. Thus, abnormal radar wave reflection and abrupt changes in apparent resistivity values can be used in practice to identify the location of landslide sliding surfaces in this region. We produce a detailed analysis of a representative landslide within the study area. Displacement monitoring locations were positioned at the trailing edge of the landslide mass and on the landslide mass surface. We then used this data to determine the relationships of landslide movement with both ground temperature and the trailing edge pore water pressure. The results suggest seasonal variation in the landslide movement process and characteristics of an annual cyclical trend. Landslide movement can be described by intermittence and low angles. The slip rate and the timing of slide occurrence exhibit relationships with the trailing edge pore water pressure of the landslide mass. The seepage of thaw water into the landslide mass will impact the trailing edge pore water pressure of the landslide mass. This phenomenon is identified as the primary cause of landslide movement