Recyclable Architecture: Prefabricated and Recyclable Typologies

Buildings are being demolished without taking into the account the waste generated, and the housing shortage problem is getting more critical as cities are growing and the demand for built space and the use of resources are increasing. Architectural projects have been using prefabrication and modular systems to solve these problems. However, there is an absence of structures that can be disassembled and reused when the structure’s life ran its course. This paper presents three building prototypes of new recyclable architectural typologies: (i) a Slab prototype designed as a shelf structure where wooden housing modules can be plugged in and out, (ii) a Tower prototype allowing for an easy change of layout and use of different floors and (iii) a Demountable prototype characterized by the entire demountability of the building. These typologies combine modularity, flexibility, and disassembling to address the increasing demands for multi-use, re-usable and resource-efficient constructions. Design, drawings, plans, and 3D models are developed, tested and analyzed as a part of the research. The results show that the implementation of the recyclable architectural concept at the first design stage is feasible and realistic, and ensures the adaptation through time, increases life span, usability and the material reusability, while avoiding demolition, which in turn reduces the construction waste and, consequently, the CO2 emissions

Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture

A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems

The Web of Things (WoT) plays an important role in the representation of the objects connected to the Internet of Things in a more transparent and effective way. Thus, it enables seamless and ubiquitous web communication between users and the smart things. Considering the importance of WoT, we propose a WoT-based emerging sensor network (WoT-ESN), which collects data from sensors, routes sensor data to the web, and integrate smart things into the web employing a representational state transfer (REST) architecture. A smart home scenario is introduced to evaluate the proposed WoT-ESN architecture. The smart home scenario is tested through computer simulation of the energy consumption of various household appliances, device discovery, and response time performance. The simulation results show that the proposed scheme significantly optimizes the energy consumption of the household appliances and the response time of the appliances

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism

Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture

Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user

A study of neuronal ceroid lipofuscinosis proteins CLN5 and CLN8

Master of ScienceBiochemistry and Molecular Biophysics Interdepartmental ProgramStella Yu-Chien LeeNeuronal ceroid lipofuscinoses (NCLs) are a group of neurodegenerative lysosomal storage disorders which is the most frequent group of inherited neurodegenerative disorders that affect children leading to severe pathological conditions such as progressive loss of motor neuron functions, loss of vision, mental retardation, epilepsy, ataxia and atrophy in cerebral, cerebella cortex and retina and eventually premature death. Among the many genes that cause NCL, mutations in CLN5 leads to different forms of NCL (infantile, late infantile, juvenile and adult) and mutations in CLN8 leads to progressive epilepsy with mental retardation (EPMR) and a variant late infantile form of NCL. The function(s) of both CLN5 and CLN8 proteins remain elusive. CLN5 is a glycosylated soluble protein that resides in the lysosome. We observed that endogenous CLN5 protein exist in two forms and identified a previously unknown C-terminal proteolytic processing event of CLN5. Using a cycloheximide chase experiment we demonstrated that the proteolytic processing of CLN5 is a post-translational modification. Furthermore treatment with chloroquine showed the processing occurs in low pH cellular compartments. After treatment with different protease inhibitors our results suggested the protease involved in the processing of CLN5 could be a cysteine protease. Using two glycosylation mutants of CLN5, retained in the endoplasmic reticulum (ER) or the Golgi we showed the proteolytic processing occurs in an organelle beyond the ER. This study contributes to understanding the characteristics of the CLN5 protein. CLN8 is an ER resident transmembrane protein that shuttles between the ER and the ER-Golgi intermediate compartment (ERGIC). In our study we identified a potential interaction between CLN8 and a PP2A holoenzyme complex consisting regulatory subunit A α isoform and regulatory subunit B α isoform. Using two CLN8 patient derived fibroblast cell lines we were able to show that the phosphorylated levels of PP2A target kinase Akt was reduced at both of its regulatory sites Ser473 and Thr308 and the activity of PP2A was increased. A delay of ceramide transport from ER to Golgi in CLN8 deficient patient cell lines was observed using BODIPY FL C5-Ceramide staining. Our results provide evidence for CLN8 protein being involved in the regulation of PP2A activity and trafficking of ceramide from ER to Golgi