Pakistan: Prospects for Private Capital Flows and Financial Sector Development

In less than a decade after the debt crisis of 1982, developing countries have experienced a surge of capital inflows in recent years. This trend became more pronounced in the 1990s resulting in overall balance of payments surpluses and accumulation of reserves. Total private capital inflows to developing countries exceeded $173 billion in 1994, compared to annual average inflows of$34 billion during 1983–90 [World Bank (1995)]. Although the characteristics of capital inflows in this episode are different than in the period prior to the last debt crisis, nevertheless concerns about macroeconomic stability, loss in competitiveness, financial sector vulnerability and excessive borrowing remain the same. While the rise in inflows during 1991–93 was supported in part by low interest rates and weak economic activity in industrial countries, improved economic policies and prospects in most recipient countries also played an important role. The larger share in inflows of those countries that achieved greater progress in economic reforms, is evidence of the importance of recipient country policies. During this period, the composition of private flows to developing countries also became more diversified. Foreign direct investment (FDI) accounted for 45 percent of total equity inflows in 1994, with debt accounting for 32 percent and portfolio flows accounting for the remaining 23 percent

Zero-tillage Technology and Farm Profits: A Case Study of Wheat Growers in the Rice Zone of Punjab

The rice-wheat cropping zone of Punjab is the main producer of high-valued and fine quality basmati rice in Pakistan. The rice produced in this area is famous for its grain length and aromatic characteristics. Being an important export item, rice contributes significantly to the national foreign exchange earnings. Wheat is the other major crop of the rice-wheat system and being the staple food is central to national agricultural policies. Rice is grown on a vast area in this zone during Kharif mostly followed by wheat in the Rabi season. Studies have shown that a large gap exists between the potential and yields actually realised by the wheat growers of the area [Byerlee, et al. (1984); Hobbs (1985) and Sheikh, et al. (2000)]. Farmers’ practices regarding land preparation for paddy, wheat planting time, and other conflicts endogenous to the rice-wheat based cropping system were identified as the major factors limiting wheat yield in the area. The flooded and puddled soils that are well suited for paddy production as compared to well-drained conditions required for wheat is such an example of the system conflicts. The farmers in the rice-wheat zone of the Punjab predominantly grow basmati varieties, which are late maturing as compared to coarse varieties of rice. Therefore, paddy harvest is generally delayed at most of the farms in this zone. The late paddy harvest coupled with poor soil structure and loose plant residues create problems for preparation of a good seedbed and planting of wheat often gets late [Byerlee, et al. (1984)]. The farmers also had to resort to the broadcast method for wheat sowing which results in poor and patchy plant stands.

gpusvcalibration: A R Package for Fast Stochastic Volatility Model Calibration Using GPUs

In this paper we describe the gpusvcalibration R package for accelerating stochastic volatility model calibration on GPUs. The package is designed for use with existing CRAN packages for optimization such as DEOptim and nloptr. Stochastic volatility models are used extensively across the capital markets for pricing and risk management of exchange traded financial options. However, there are many challenges to calibration, including comparative assessment of the robustness of different models and optimization routines. For example, we observe that when fitted to sub-minute level midmarket quotes, models require frequent calibration every few minutes and the quality of the fit is routine sensitive. The R statistical software environment is popular with quantitative analysts in the financial industry partly because it facilitates application design space exploration. However, a typical R based implementation of a stochastic volatility model calibration on a CPU does not meet the performance requirements for sub-minute level trading, i.e. mid to high frequency trading.We identified the most computationally intensive part of the calibration process in R and off-loaded that to the GPU.We created a map-reduce interface to the computationally intensive kernel so that it can be easily integrated in a variety of R based calibration codes using our package. We demonstrate that the new R based implementation using our package is comparable in performance to aC=C++ GPU based calibration code

Sustainable green nanoadsorbents for remediation of pharmaceuticals from water and wastewater: A critical review

In the last three decades, pharmaceutical research has increased tremendously to offer safe and healthy life. However, the high consumption of these harmful drugs has risen devastating impact on ecosystems. Therefore, it is worldwide paramount concern to effectively clean pharmaceuticals contaminated water streams to ensure safer environment and healthier life. Nanotechnology enables to produce new, high-technical material, such as membranes, adsorbent, nano-catalysts, functional surfaces, coverages and reagents for more effective water and wastewater cleanup processes. Nevertheless, nano-sorbent materials are regarded the most appropriate treatment technology for water and wastewater because of their facile application and a large number of adsorbents. Several conventional techniques have been operational for domestic wastewater treatment but are inefficient for pharmaceuticals removal. Alternatively, adsorption techniques have played a pivotal role in water and wastewater treatment for a long, but their rise in attraction is proportional with the continuous emergence of new micropollutants in the aquatic environment and new discoveries of sustainable and low-cost adsorbents. Recently, advancements in adsorption technique for wastewater treatment through nanoadsorbents has greatly increased due to its low production cost, sustainability, better physicochemical properties and high removal performance for pharmaceuticals. Herein, this review critically evaluates the performance of sustainable green nanoadsorbent for the remediation of pharmaceutical pollutants from water. The influential sorption parameters and interaction mechanism are also discussed. Moreover, the future prospects of nanoadsorbents for the remediation of pharmaceuticals are also presented

Performance Analysis of Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC Protocol for C-V2X

Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC protocol is proposed as part of the latest cellular vehicle to everything (C-V2X) standard for medium access between vehicles. As C-V2X uses LTE based frame structure, mode 4 of the C-V2X standard uses SB-SPS to allocate resource blocks effectively. C-V2X shows great potential for the future as it brings many improvements such as enhanced range, reliability, and the ability to support and evolve with emerging technologies such as 5G. In this article, the SB-SPS protocol’s performance was analyzed in different scenarios using OMNET++, SUMO, and Veins simulator. Different vehicle speeds and densities were used to observe the effect on packet loss and throughput. It was found that as packet loss decreased, throughput increased when the mobility of vehicles decreased. The effects of changing some important parameters of SB-SPS were also observed. The results showed that while parameters such as increasing the number of subchannels increased the packet delivery ratio (PDR), the change in the probability of resource reselection parameter did not affect the PDR

An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model

The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and  CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool