Emerging Issues in the Implementation of Irrigation and Drainage Sector Reforms in Sindh, Pakistan

Ever increasing demand for food, electricity and domestic water use due to rapid growth in population has remained a key challenge for Pakistan since the 1950s. The country has invested heavily in water engineering projects to establish the world’s largest gravity-driven irrigation network on the Indus [Bandaragoda (2006); Bengali (2009)]. Besides fulfilling a significant proportion of the country’s energy demand from hydro-power installations, the system irrigates about 14 million hectares of farmlands and supports agriculture sector to contribute about 21 percent of the GDP, 60 percent of the exports and 45 percent of the labour force [Bhutta (2006); Pakistan (2012)]. Amidst its development, the elaborated irrigation facility has left a deep footprint on productivity and environment of the basin itself in the form of the rising levels of water-logging and salinity and the degradation of deltaic ecology [Briscoe and Qamar (2009); Memon and Thapa (2011)]. By the 1960s, every year about 40,000 hectares of fertile farmlands were turning into wastelands because of water-logging and salinity in the basin [Bhutta (2006); Mulk (2009); Qureshi, et al. (2008)]. Therefore, the country had no option but to develop a remedial drainage network of thousands of kilometres of drains and numerous tube wells parallel to the existing irrigation infrastructure

MEASURING REGIONAL PUBLIC HOSPITAL (RSUD) DAYA MAKASSAR PERFORMANCE WITH CONTEMPORARY MANAGEMENT ACCOUNTING APPROACH

This research aims to look at how the mechanism of performance evaluation and measurement using contemporary management accounting methods to provide comprehensive and long-term insightful performance information and identify critical success factors. This research uses primary and secondary data through questionnaires, interviews and written data documentation. Data analysis method used is strategy mapping approach using the Balanced Scorecard. The results showed that the performance of Daya Makassar Hospital was stated to be good. It refers to the four perspectives of the assessed Balanced Scorecard. From the customer's perspective, it can be stated as good at the level of satisfaction of patients who are satisfied. From an internal business perspective it is also said to be good by looking at each of the overall assessment indicators as well. In the perspective of growth and development is also expressed well by looking at indicators that meet the standard. From a financial perspective it is declared good because it has reached a level of efficiency

Digital Predistortion Based Experimental Evaluation of Optimized Recurrent Neural Network for 5G Analog Radio Over Fiber Links

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits

Performance evaluation of High Definition video streaming over Mobile Ad Hoc Networks

© 2018 Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data

Reshaping Tomorrow through Green Governance and Circular Economy: An Emerging Paradigm

Circular economy and green governance are gaining momentum and traction with industry and policymakers. The circular economy is viewed as a restoration and regeneration system in which resources, energy usage, and greenhouse gases are minimized. At the same time, the Green Governance structure is a good transition to a circular economy and helps enterprises to move toward sustainable development. This study’s objective is to explore the hidden possible relationship that exists between the Green Governance of companies and the much-anticipated Circular Economy for low waste and carbon society. Furthermore, an extensive literature review was undertaken to create the first green governance circular economy framework (GGCE) for businesses to integrate the proposed model into their operational activities. This GGCE framework will be developed by exploring the similarities between green governance and circular economy. This study has three expected findings; firstly, the proposed framework will help firms to change their business approach to addressing climate change. Secondly, the GGCE framework will help policymakers to develop policies for circular economy governance. Lastly, it will be the point of reference to the academician for further extension of the GGCE framework. Keywords: green governance, circular economy, GGCE framework, Nigeria oil and gas secto

DISSOLUTION BEHAVIOR OF BIOACTIVE GLASS CERAMICS WITH DIFFERENT CaO/MgO RATIOS

In this work, powders of three different compositions, each having 34 SiO2-14.5 P2O5-1 CaF2-0.5 MgF (% wt) and ratio of CaO/MgO varying from 11.5:1 to 1:11.5 were thoroughly mixed and melted under oxy-acetylene flame in a fire clay crucible that made the glass formation cheaper in time and cost. The melt of each composition was quenched in water to form three different glasses. Every glass was sintered at 950°C to form three glass ceramics named G1, G2 and G3 respectively. To study the dissolution behavior, each sample was immersed in a simulated body fluid (SBF) for 2, 5, 10, 20 and 25 days at room temperature. Thin film XRD analysis revealed that the samples with larger CaO/MgO ratio exhibited better bioactivity. pH of SBF increased efficiently in case of G1 whereas in case of G2 and G3, this increase was slower due to greater amount of MgO. The concentrations of Ca, P, Mg and Si ions were measured by Atomic Absorption Spectroscopy. EDS analysis showed the increase in P and Ca ions and presence of C in G1 after 5 days immersion and after 10 days, in case of G2 indicating the higher formation rate of hydroxycarbonate Apatite layer in G1 as compared to G2 due to greater CaO/MgO ratio whereas in G3 Mg-hydroxycarbonate apatite (Ca(Mg)5(CO3)(PO4)3(OH)) (heneuite) layer was recognized after 20 days showing the least bioactivity due to very large amount of Mg and the least CaO/MgO ratio

Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel

The Gaussian Process (GP)-based surrogate model has the inherent capability of capturing the anomaly arising from limited data, lack of data, missing data, and data inconsistencies (noisy/erroneous data) present in the modeling and simulation component of the digital twin framework, specifically for the accident tolerant fuel (ATF) concepts. However, GP will not be very accurate when we have limited high-fidelity (experimental) data. In addition, it is challenging to apply higher dimensional functions (>20-dimensional function) to approximate predictions with the GP. Furthermore, noisy data or data containing erroneous observations and outliers are major challenges for advanced ATF concepts. Also, the governing differential equation is empirical for longer-term ATF candidates, and data availability is an issue. Physics-informed multi-fidelity Kriging (MFK) can be useful for identifying and predicting the required material properties. MFK is particularly useful with low-fidelity physics (approximating physics) and limited high-fidelity data - which is the case for ATF candidates since there is limited data availability. This chapter explores the method and presents its application to experimental thermal conductivity measurement data for ATF. The MFK method showed its significance for a small number of data that could not be modeled by the conventional Kriging method. Mathematical models constructed with this method can be easily connected to later-stage analysis such as uncertainty quantification and sensitivity analysis and are expected to be applied to fundamental research and a wide range of product development fields. The overarching objective of this chapter is to show the capability of MFK surrogates that can be embedded in a digital twin system for ATF

Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multi-scale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes meso-scale uncertainties. It is also possible for macro-scale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for the multi-scale modeling of advanced composite materials. This chapter proposes a multi-scale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed meso-scale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms