Buildings consume up to 40% of the total global energy. By the year 2030, the consumption is expected to increase to 50%. In Malaysia, buildings consume a total of 48% of the electricity generated in the country. Commercial buildings consume up to 38,645 Giga watts (GWh) while Residential buildings consume 24,709 Gwh. Demand for electricity in the country is expected to rise from 91,539 GWh in the year 2007 to 108,732 GWh in 2011. By the year 2020, the energy demand in Malaysia is expected to reach 116 Million tons of oil equivalents (Mtoe). Carbon dioxide (CO2) emission in the country has increased by 221% ,which lists the nation at 26th among the top 30 greenhouse gas emitters in the world.  Literature studies indicate more than 50% of this energy is used in buildings for occupants comfort (air conditioning and refrigeration). Energy consumptions by residential occupants can be minimized if energy usage is considered. This paper aimed at reviewing some literatures on energy consumption in the residential buildings in Malaysia and suggests ways of improving the energy usage by the occupants.

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Internet of Things (IoT) fostered a new epoch of innovation by interconnecting digital devices to make human life more convenient and attractive. These smart objects are largely deployed as low power and lossy networks (LLNs) and use routing protocol for LLNs (RPL) for routing. Unfortunately, it is extremely vulnerable to a large variety of external and internal attacks to cause devastating and calamitous effects. However, this article's scope revolves around internal attacks only, where nodes are already part of a legitimate network. Various trust-based mechanisms have been proposed to secure the RPL protocol from insider attackers. Existing trust mechanisms cause high energy depletion due to complex computation on the node level, which consequently decreases the performance of LLNs. Therefore, this article presents a novel hierarchical trust-based mechanism "CTrust-RPL" by assessing the trust of nodes based on their forwarding behaviors. This study ships complex trust-related computations to the higher layer, known as the controller, to save computational, storage, and energy resources at the node level. We also compare the proposed mechanism with a state-of-the-art technique called Sec-trust. Our mechanism demonstrates superior performance in detecting and isolating blackhole attacks. The results depict that CTrust-RPL detects and isolates 10% more malicious nodes than Sec-trust in the same time-lapse. The average packet loss ratio difference is less for our proposed mechanism, with 35% more energy efficiency. 1 INTRODUCTION Internet of Things (IoT) has become an essential part of our personal lives. It is emerging as an epoch of innovation, where devices belonging to digital and machine ecosystems are interconnected over the Internet to yield efficacy and convenience in academia, industries, and human lives. 1-3 Technologies like 5G and 6G enable the next generation of wireless communication systems in compliance with sophisticated techniques for security. 4,5 They support the huge IoT infrastructures, which can be defined as a link, management, and communication of a large number of smart and sensing things (also known as objects). These things are capable of interacting with each other, especially, for transferring information in a network. 6-8 They are largely deployed as low power and lossy networks (LLN). The LLN is a class of networks where Trans Emerging Tel Tech.

The capabilities of four two-equation turbulence models in predicting film cooling effectiveness were investigated and their limitations as well as relative performance are presented. The four turbulence models are the standard, RNG, and realizable k-ε models as well as the standard k-ω model all found in the FLUENT CFD code. In all four models, the enhanced wall treatment has been used to resolve the flow near solid boundaries. A systematic approach has been followed in the computational setup to insure grid-independence and accurate solution that reflects the true capabilities of the turbulence models. Exact geometrical and flow-field replicas of an experimental study on discrete-jet film cooling were generated and used in FLUENT. A pitch-to-diameter ratio of 3.04, injection length-to-diameter ratio of 4.6 and density ratios of 0.92 and 0.97 were some of the parameters used in the film cooling analysis. Furthermore, the study covered two levels of blowing ratio (M = 0.5 and 1.5) at an environment of low free-stream turbulence intensity (Tu = 0.1%). The standard k-ε model had the most consistent performance among all considered turbulence models and the best centerline film cooling effectiveness predictions with the results deviating from experimental data by only ±10% and about 20–60% for the low (M = 0.5) and high (M = 1.5) blowing ratio cases, respectively. However, centerline side-view and surface top-view contours of non-dimensional temperature for the standard k-ε cases revealed that the good results for film cooling effectiveness η compared to the experimental data were due to a combination of an over-prediction of jet penetration in the normal direction with an under-prediction of jet spread in the lateral direction. The standard k-ω model completely failed to produce any results that were meaningful with under-predictions of η that ranged between 80 and 85% for the low blowing ratio case and over-predictions of about 200% for the high blowing ratio case. Even though the RNG and realizable models showed to have better predicted the jet spread in the lateral direction compared to the standard k-ε model, there were some aspects of the flow, such as levels of turbulence generated by cross-flow and jet interaction, that were not realistic resulting in errors in the η prediction that ranged from −10% to +80% for the M = 0.5 case and from −80% to +70% for the M = 1.5 case. As a result of this study at this point it was concluded that the standard k-ε model have the most promising potential among the two-equation models considered. It was chosen as the best candidate for further improvement for the simulation of film cooling flows.

Purpose The purpose of this paper is to study the relationship between reported sigma levels and actual failure rates (FRs) of gamma-distributed processes. The added complexity of the non-normality behavior of the gamma distribution is analyzed for the case of the cycle time (CT) of a real procurement process from the oil and gas industry. Then, recommendations and guidelines for the application of Six Sigma methodology for the case study are proposed. Design/methodology/approach Sensitivity analysis is conducted to study the relationship between gamma distribution parameters and FRs considering different quality levels. Then, adjustments for implementing Six Sigma programs for gamma processes are proposed. These adjustments consist of first determining the appropriate probability distribution, the standard CT and the due date, followed by setting performance zones and improvement strategies on target gamma parameters that yield the minimal FR. Findings For gamma-distributed processes, simply reporting the sigma level is not sufficient to capture the main characteristics of the process. These characteristics include process FR, mean setting, shape, spread and amount of variation reduction (i.e. improvement effort) required. That is why caution must be exercised when dealing with one-sided non-normal quality characteristics such as CT. Originality/value To the authors’ knowledge, this is the first time that the Six Sigma performance has been evaluated for gamma processes to analyze the link between Six Sigma FRs and gamma distribution parameters leading to the development of a modified Six Sigma methodology for non-normal processes.