The Impact of Trade Liberalization on Economic Growth: A case study of Pakistan

This study empirically analyzes the impact of trade liberalization on the economic growth of Pakistan over the period 1972 to 2014 .Gross fixed capital formation, Trade liberalization, Labor force participation, inflation, interest rate are important explanatory variables. While economic growth which is measured by (GDP) is dependent variable used for the model specification. The study used Johensen co-integration approach developed by Johensen and Jeselius (1990).The results show that trade liberalization and gross fixed capital have positive and significant impact on economic growth. Inflation and interest rate have negative impact on economic growth. And labor force has positive impact on economic growth. Positive correlation between trade liberalization and economic growth has been explored in this study. Keywords: Trade liberalization (LIBE), Economic growth (GDP), Co-Integration, Labour force participation (LFP), Interest rate (INT), Inflation (INF)

Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques

Eco-Friendly Management of Nausinoe Geometralis Through Botanical Extracts on Jasmine Plant

Jasmine leaf webworm, Nausinoe geometralis, is a significant pest of Jasminum spp. commonly known as Jasmine plant. This plant holds a special place in Pakistan\u27s culture; as it is declared as its national flower. N. geometralis feeds on the leaves of the jasmine plant; leaving it damaged and unattractive. Current study aimed to evaluate the efficacy of four botanical extracts (i.e. Neem, Taramira, Lemon grass, and Cactus) against N. geometralis; to explore an effective and eco-friendly method to protect the jasmine plant. Different concentrations of extracts were prepared using distilled water. Bioassays were performed on third instar larvae of N. geometralis following leaf dip method for various exposure intervals. Outcomes revealed that Neem extract was highly effective to manage the test insect pest followed by Taramira, Lemon grass, and Cactus. LC50 values of Neem after 24, 48, 72, and 96 hours were 22.25, 11.11, 11.31, and 15.82 ppm, respectively. It was concluded that botanical extracts can be utilized as promising agents in developing effective management strategies against N. geometralis. Future research should focus on optimizing the application methods and exploring the synergistic effects of these botanical extracts with other eco-friendly control measures to enhance their effectiveness against N. geometralis in field conditions

UV-Light Mediated Biosynthesis of Silver Nanowires; Characterization, Dye Degradation Potential and Kinetic Studies

Herrin, a simple and eco-friendly method for the synthesis of silver nanowires (Ag-NWs) has been reported. Silver nanowires were synthesized using Psidium guajava seed extract that acted as a reducing agent as well as a stabilizing agent for silver nitrate solution. Synthesis was carried out at 50 °C temperature under continuous UV-irradiation. Silver nanowires were initially characterized by a UV-visible and FTIR spectrophotometer. In addition, morphology and particle size of synthesized Ag-NWs were determined using Field Emission Scanning Electron Microscopy and X-ray diffraction (XRD) techniques. Nanowires were found to have 12.8 μm length and 200–500 nm diameter and cubic phase morphology. Furthermore, the catalytic potential of Ag-NWs for the degradation of methyl orange dye (MO) was determined. The selected dye was degraded successfully that confirmed the catalytic potential of Ag-NWs. The authors concluded that Ag-NWs can be synthesized using plant extract having excellent morphological features as well as impressive catalytic potential

Homeobox regulator Wilms Tumour 1 is displaced by androgen receptor at cis-regulatory elements in the endometrium of PCOS patients

Decidualisation, the process whereby endometrial stromal cells undergo morphological and functional transformation in preparation for trophoblast invasion, is often disrupted in women with polycystic ovary syndrome (PCOS) resulting in complications with pregnancy and/or infertility. The transcription factor Wilms tumour suppressor 1 (WT1) is a key regulator of the decidualization process, which is reduced in patients with PCOS, a complex condition characterized by increased expression of androgen receptor in endometrial cells and high presence of circulating androgens. Using genome-wide chromatin immunoprecipitation approaches on primary human endometrial stromal cells, we identify key genes regulated by WT1 during decidualization, including homeobox transcription factors which are important for regulating cell differentiation. Furthermore, we found that AR in PCOS patients binds to the same DNA regions as WT1 in samples from healthy endometrium, suggesting dysregulation of genes important to decidualisation pathways in PCOS endometrium due to competitive binding between WT1 and AR. Integrating RNA-seq and H3K4me3 and H3K27ac ChIP-seq metadata with our WT1/AR data, we identified a number of key genes involved in immune response and angiogenesis pathways that are dysregulated in PCOS patients. This is likely due to epigenetic alterations at distal enhancer regions allowing AR to recruit cofactors such as MAGEA11, and demonstrates the consequences of AR disruption of WT1 in PCOS endometrium

Web-based dynamic visualization of 3D spatial data

Abstract True 3D GIS requires lot of efforts from GIS research community both in academia as well as from software vendors. Visualizing 3D spatial data from DBMS for such 3D system is one of the issues and problems in 3D GIS domain. This paper discusses the dynamic visualization of 3D spatial data such as buildings and other large objects using geo-DBMS couples with Web that works with VRML and X3D modeling languages. The modeling of the 3D objects are based on the Simplified Data Model (SDM). The experiment will be presented by using digital 3D building datasets and the approach is applicable for tasks like 3D strata title and buildings management. Finally, it discusses the limitations and shortcomings of the current approach, and identifies future research and development tasks. Key words: 3D spatial data, VRML, X3D, visualization, and GeoDBMS

Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques