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"Engineering (General). Civil engineering (General)"
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From landfill gas to energy : technologies and challenges
\"A comprehensive description of technologies available for converting old landfills to energy producers, and capturing the green house gases emitting from them. Its key assets are the case studies of successful landfill gas (LFG) recovery for energy projects around the world, and that it highlights why this has not been done in many more landfills around the world. Technical, financial, and social challenges facing the conversion of landfills to energy producers will be detailed, and solutions offered to either remine the landfill for recovering useful land (as is planned in dense urban areas of India) or close them properly while recovering the methane for energy use. Intended as a guide with background information and instructive tools to educate, guide and establish a basis for decision-making, technical feasibility assessment, economic assessment, and market evaluation of all aspects necessary for developing successful LFG management projects. \"-- Provided by publisher.
Book
Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.
Journal Article
Electroencephalography (EEG) Technology Applications and Available Devices
The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are.
Journal Article
exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)
News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.
Journal Article
The Metaverse as a Virtual Form of Smart Cities: Opportunities and Challenges for Environmental, Economic, and Social Sustainability in Urban Futures
Data infrastructures, economic processes, and governance models of digital platforms are increasingly pervading urban sectors and spheres of urban life. This phenomenon is known as platformization, which has in turn given rise to the phenomena of platform society, where platforms have permeated the core of urban societies. A recent manifestation of platformization is the Metaverse, a global platform project launched by Meta (formerly Facebook) as a globally operating platform company. The Metaverse represents an idea of a hypothetical “parallel virtual world” that incarnate ways of living and working in virtual cities as an alternative to smart cities of the future. Indeed, with emerging innovative technologies—such as Artificial Intelligence, Big Data, the IoT, and Digital Twins—providing rich datasets and advanced computational understandings of human behavior, the Metaverse has the potential to redefine city designing activities and service provisioning towards increasing urban efficiencies, accountabilities, and quality performance. However, there still remain ethical, human, social, and cultural concerns as to the Metaverse’s influence upon the quality of human social interactions and its prospective scope in reconstructing the quality of urban life. This paper undertakes an upper-level literature review of the area of the Metaverse from a broader perspective. Further, it maps the emerging products and services of the Metaverse, and explores their potential contributions to smart cities with respect to their virtual incarnation, with a particular focus on the environmental, economic, and social goals of sustainability. This study may help urban policy makers to better understand the opportunities and implications of the Metaverse upon tech-mediated practices and applied urban agendas, as well as assess the positives and negatives of this techno-urban vision. This paper also offers thoughts regarding the argument that the Metaverse has disruptive and substantive effects on forms of reconstructing reality in an increasingly platformized urban society. This will hopefully stimulate prospective research and further critical perspectives on the topic.
Journal Article
A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning
Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.
Journal Article
FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.
Journal Article
Ulvan, a Polysaccharide from Macroalga Ulva sp.: A Review of Chemistry, Biological Activities and Potential for Food and Biomedical Applications
The species of green macroalga belonging to the genus Ulva (family: Ulvaceae) are utilized in various fields, from food supplements to biomedical applications. Ulvan, a polysaccharide obtained from various Ulva species, has shown various biological activities, including antioxidant, anti-inflammatory, anticancer, antibacterial, and antiviral activities. To obtain the polysaccharide ulvan that can be utilized in various fields, it is necessary to understand the critical points that affect its physicochemical nature, the extraction procedures, and the mechanism of action for biological activities. This article discusses the physicochemical properties, extraction, isolation and characterization procedures and benefits in food and biomedical applications of ulvan. In conclusion, ulvan from Ulva sp. has the potential to be used as a therapeutic agent and also as an additional ingredient in the development of tissue engineering procedures.
Journal Article
Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, electronic noses have evolved significantly since their inception in the 1980s. They have transitioned from bulky, costly, and energy-intensive devices to today’s streamlined, economical models with minimal power requirements. This paper presents a comprehensive and systematic review of the electronic nose technology domain, with a special focus on advancements over the last five years. It highlights emerging applications, innovative methodologies, and potential future directions that have not been extensively covered in previous reviews. The review explores the application of electronic noses across diverse fields such as food analysis, environmental monitoring, and medical diagnostics, including new domains like veterinary pathology and pest detection. This work aims to underline the adaptability of electronic noses and contribute to their continued development and application in various industries, thereby addressing gaps in current literature and suggesting avenues for future research.
Journal Article