Penilaian Kecekapan Pengutipan Sisa Pepejal di Malaysia Satu Kajian Kes di Majlis Perbandaran Seremban

The efficiency of solid waste collection is influenced by the type of vehicle used, methods of collection and the type of area served with the r2 values equal to 100%. Mechanical collection with a lifter has been proven to be the most efficient (0.39 minute/lot) compared to manual collection by open lorry which is the least efficient (5.11 minute/lot). In terms of area served, collection of waste in residential areas is the most efficient at about 0.19 minute/lot. The semimechanical collection method is relatively cheaper (RM415,720/lorry.year) than manual collection (RM435,290/lorry.year). In addition, the collection of solid waste by private agencies is relatively cheaper than collection by government agencies

Development Of Municipal Solid Waste Generation And Recyclable Components Rate Of Kuala Lumpur: Perspective Study.

This paper presents a forecasting study of municipal solid waste generation (MSWG) rate and potential of its recyclable components in Kuala Lumpur, the capital city of Malaysia. The generation rates and composition of solid wastes of various classes such as street cleansing, landscape & garden, industrial & constructional, institutional, residential and commercial are analyzed

Sustainable rural electrification through solar PV DC microgrids—An architecture-based assessment

Solar photovoltaic (PV) direct current (DC) microgrids have gained significant popularity during the last decade for low cost and sustainable rural electrification. Various system architectures have been practically deployed, however, their assessment concerning system sizing, losses, and operational efficiency is not readily available in the literature. Therefore, in this research work, a mathematical framework for the comparative analysis of various architectures of solar photovoltaic-based DC microgrids for rural applications is presented. The compared architectures mainly include (a) central generation and central storage architecture, (b) central generation and distributed storage architecture, (c) distributed generation and central storage architecture, and (d) distributed generation and distributed storage architecture. Each architecture is evaluated for losses, including distribution losses and power electronic conversion losses, for typical power delivery from source end to the load end in the custom village settings. Newton–Raphson method modified for DC power flow was used for distribution loss analysis, while power electronic converter loss modeling along with the Matlab curve-fitting tool was used for the evaluation of power electronic losses. Based upon the loss analysis, a framework for DC microgrid components (PV and battery) sizing was presented and also applied to the various architectures under consideration. The case study results show that distributed generation and distributed storage architecture with typical usage diversity of 40% is the most feasible architecture from both system sizing and operational cost perspectives and is 13% more efficient from central generation and central storage architecture for a typical village of 40 houses. The presented framework and the analysis results will be useful in selecting an optimal DC microgrid architecture for future rural electrification implementations

2-Bromo-N-(dibenzyl­carbamothioyl)benzamide

The 2-bromo­benzoyl group in the title compound, C22H19BrN2OS, adopts an E conformation with respect to the thiono S atom across the N—C bond. In the crystal structure, the mol­ecule is stablized by N—H⋯O inter­molecular hydrogen bonds, forming a one-dimensional chain along the b axis

Message Coding and Compression with Artificial Neural Networks

The need to overcome data preprocessing inherent in much of the classical data coding techniques commonly available led to the search for a free, easy-to-use, but flexible and powerful method. Artificial Neural networks have been attracting more and more researchers since the past decades. The distinct properties, such as learning ability,nonlinearity, fault tolerance, generalization etc., make it suitable for information protection, such as data encryption, data authentication, data detection, etc. In this paper a simple and low-cost coding method based on neural networks is proposed to be used to patterns compression. The goal of the developers is to build a tool able to store and send a coded and compressed message. The formed two-dimensional patterns are coded and compressed using the multilayer neural network with Back-propagation training algorithm. Hidden layer outputs of a trained network are sent as two-dimensional data,which represents the encoded vectors. To reconstruct the original patterns, this requires the output weights matrix and the output nodes functions which are unknown and not available in the encoded sent vectors. A compression rate of about 6:1 has been achieved