Cooling load prediction model during the design of mosques in Madinah

The building sector accounts for almost 40% of the total global energy consumption. Saudi Arabia, along with other developed countries have expressed their concern on the increasing energy demand and established several related policies focusing on the building sector. Mosques are one category of buildings that consume huge amounts of energy above other public sector buildings such as hospitals. An extensive review of literature has revealed that there is an increased demand to build new mosques. The majority of previous research works focused on operational and maintenance stages. In term of energy reduction, not much can be done to existing mosques as the solutions are both costly and time consuming. The importance of making right design at the design stage has been stressed which may save up to 70% of total energy consumption. The literature also revealed that there is a gap and absence of design stage integration for mosque projects, due to the complexity of the stage, the lack of information, and limited support tools. This research aims to develop a prediction model known as the Mosque Cooling Load Prediction Model (MCLPM) to assist designers and local authorities in reducing the energy consumption of mosques during the design stage. The process began by identifying significant structural and architectural design parameters that influence the energy consumption of mosques, using a three rounds of Delphi approach with 33 local experts. Thirteen significant parameters were identified, of which mosque orientation was found to be the most significant. Integration between Rhinoceros/Grasshopper parametric model, EnergyPlus™ simulation of selected medium-sized mosques, and optimization through Genetic Algorithm (GA) and Galapagos were made to generate the dataset required for developing mosque cooling load prediction model based on the Artificial Neural Network (ANN) approach. Two thousand five hundred simulations were performed to achieve the optimum (approximately 58%) of total energy reduction, and 23 non-repetitive design alternatives with the least demand for cooling load were generated. The Mean Square Error (MSE) and correlation coefficient (R) were obtained for the developed ANN prediction model. Based on the findings, the least MSE and R values were at 6.27 * 1 0 _ 9 and 0.99888, respectively. Validation of the results revealed that the back-propagation strategy and Levenberg-Marquardt algorithm have the highest accuracies in predicting the exact total cooling load, in comparison to the actual values, and the absolute difference is less than 1%. The comparison with other methods and algorithms showed that the proposed prediction model has the highest accuracy, effectiveness, and least time required, to complete a given task. Hence, the developed prediction model act as a powerful tool to support the decision-making process that helps mosque designers provide a range of lowest cooling load design alternatives, thus facilitating the design process, and easy-quick estimation of total cooling load

Current Challenges in Modeling Cellular Metabolism

Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results

Biologically Inspired Connected Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) have become commonplace in the automotive industry over the last few decades. Even with the advent of ADAS, however, there are still a significant number of accidents and fatalities. ADAS has in some instances been shown to significantly reduce the number and severity of accidents. Manufacturers are working to avoid ADAS plateauing for effectiveness, which has led the industry to pursue various avenues of investment to ascend the next mountain of challenges – vehicle autonomy, smart mobility, connectivity, and electrification – for reducing accidents and injuries. A number of studies pertaining to ADAS scrutinize a specific ADAS technology for its effectiveness at mitigating accidents and reducing injury severity. A few studies take holistic accounts of ADAS. There are a number of directions ADAS could be further progressed. Industry manufacturers are improving existing ADAS technologies through multiple avenues of technology advancement. A number of ADAS systems have already been improved from passive, alert or warning, systems to active systems which provide early warning and if no action is taken will control the vehicle to avoid a collision or reduce the impact of the collision. Studies about the individual ADAS technologies have found significant improvement for reduction in collisions, but when evaluating the actual vehicles driving the performance of ADAS has been fairly constant since 2015. At the same time, industry is looking at networking vehicle ADAS with fixed infrastructure or with other vehicles’ ADAS. The present literature surrounding connected ADAS be it with fixed systems or other vehicles with ADAS focuses on the why and the how information is passed between vehicles. The ultimate goal of ADAS and connected ADAS is the development of autonomous vehicles. Biologically inspired systems provide an intriguing avenue for examination by applying self-organization found in biological communities to connecting ADAS among vehicles and fixed systems. Biological systems developed over millions of years to become highly organized and efficient. Biological inspiration has been used with much success in several engineering and science disciplines to optimize processes and designs. Applying movement patterns found in nature to automotive transportation is a rational progression. This work strategizes how to further the effectiveness of ADAS through the connection of ADAS with supporting assets both fixed systems and other vehicles with ADAS based on biological inspiration. The connection priorities will be refined by the relative positioning of the assets interacting with a particular vehicle’s ADAS. Then based on the relative positioning data distribution among systems will be stratified based on level of relevance. This will reduce the processing time for incorporating the external data into the ADAS actions. This dissertation contributes to the present understanding of ADAS effectiveness in real-world situations and set forth a method for how to optimally connect local ADAS vehicles following from biological inspiration. Also, there will be a better understanding of how ADAS reduces accidents and injury severity. The method for how to structure an ADAS network will provide a framework for auto-manufacturers for the development of their proprietary networked ADAS. This method will lead to a new horizon for reducing accidents and injury severity through the design of connecting ADAS equipped vehicles.Ph.D

The evolution of the urban pattern of Southeast Asia during the nineteenth and twentieth centuries.

This is the first attempt of its kind made to analyse the evolution of the urban pattern of Southeast Asia as a whole during the 19th and 20th centuries. The main objective of this study is to collect sufficient information and data to fill in the "gaps" in our knowledge of the development of Southeast Asian cities. It empliasizes the influence of political and socio-economic changes on the growth of urban centres both in space and time. The study brings out the essential character of the Southeast Asian cities, i.e. it shows their growth, their decay and the different stages reached in the process of urban evolution. Chapter I outlines the general characteristics of the region which have direct or indirect influence upon urban development in the past as well as present. Chapter II addresses itself to the historical development of urban settlements in the early centuries. Emphasis is placed on the genesis of sacred-capital cities on mainland Southeast Asia as well as port-cities in the maritime part of the region. The development of early colonial cities in the Philippines and Indonesia is also examined. Urban development in the last 160 years was largely the result of a long interplay of colonial economic forces. Chapter III is devoted to an analysis of the drastic political and socio-economic changes which took place in the 19th century and how they generated the new urban network in the region. The 20th century witnessed the rapid spread of towns and cities over the surface of Southeast Asia. Chapter IV examines the changing pattern and the factors which led to this unprecedented urban growth. Chapter V focusses upon the development of metropolises in the region. With the aid of maps, their growth patterns are examined and characteristics described. Within the framework of the rank-size rule, the city-size distribution of each country is examined and its hierarchal characteristics analysed. The future urban pattern is being formed by today's process of growth. The concluding chapter looks at the problems facing Southeast Asian cities. Some suggestions are raised for a national urban policy and for a more positive approach to urban and regional planning in the region

Object Cloning for Ownership Systems

Modern object-oriented programming languages frequently need the ability to clone, duplicate, and copy objects. The usual approaches taken by languages are rudimentary, primarily because these approaches operate with little understanding of the object being cloned. Deep cloning naively copies every object that has a reachable reference path from the object being cloned, even if the objects being copied have no innate relationship with that object. For more sophisticated cloning operations, languages usually only provide the capacity for programmers to define their own cloning operations for specific objects, and with no help from the type system. Sheep cloning is an automated operation that clones objects by leveraging information about those objects’ structures, which the programmer imparts into their programs with ownership types. Ownership types are a language mechanism that defines an owner for every object in the program. Ownership types create a hierarchical structure for the heap. In this thesis, we construct an extensible formal model for an object-oriented language with ownership types (Core), and use it to explore different formalisms of sheep cloning. We formalise three distinct operational semantics of sheep cloning, and for each approach we include proofs or proof outlines where appropriate, and provide a comparative analysis of each model’s benefits. Our main contribution is the descripSC formal model of sheep cloning and its proof of type soundness. The second contribution of this thesis is the formalism of Mojo-jojo, a multiple ownership system that includes existential quantification over types and context parameters, along with a constraint system for context parameters. We prove type soundness for Mojo-jojo. Multiple ownership is a mechanism which allows objects to have more than one owner. Context parameters in Mojo-jojo can use binary operators such as: intersection, union, and disjointness