Success factors of smart cities: a systematic review of literature from 2000-2018

The aim of this paper is to review smart city literature to achieve these goals. More than 150 sources of literature were approached and analyzed with a view of finding out drivers and success indicators of smart cities on which future research policies are depend. The results pointed out several drivers that stimulate cities to be smart. These drivers are related to economy, environment, governance, safety, energy, living, technology, buildings, education and people. Interestingly, a smart city should be distinguished by smartness extent achieved to meet the requirements of these drivers. That is, a smart city is the one that marked by its own smart economy, smart environment, smart governance, smart safety, smart energy, smart living, smart technology, smart buildings, smart education and smart people. This paper contributes to smart city literature by showing drivers and indicators related to smart cities success

Success and Survival of Various Types of All-Ceramic Single Crowns: A Critical Review and Analysis of Studies with a Mean Follow-Up of 5 Years or Longer

Purpose: The aim of this critical review was to assess the survival and success rates of all-ceramic single crowns manufactured using different ceramic materials with a mean follow-up time of 5 years or longer. Materials and Methods: An electronic search of studies published between 1980 and 2014 complemented by manual searching was conducted in Medline and Scopus. The terms ceramic, crown, survival, success, longevity, and complications were selected as keywords. Predetermined inclusion and exclusion criteria guided the search. Data were extracted and assessed by two independent reviewers. The results were statistically analyzed according to the type of material, and survival/success rate was calculated by assuming a Poisson-distributed number of events. Results: The initial search yielded 972 articles. After subsequent filtering, 14 studies were selected. The inter-reviewer agreement was rated as good (κ = 0.65) and very high agreement (κ = 0.93) during the identification and screening phases, respectively. No studies on densely sintered zirconia or feldspathic crowns satisfied the minimum follow-up time. Only one study of each of the following materials satisfied the inclusion criteria: lithium disilicate, leucite reinforced, pressed Al2O3, and sintered Al2O3. Meta-analysis of the included studies on other materials resulted in the following estimated survival and success rates: for densely sintered alumina crowns, 93.8% and 92.75%, respectively; for fluoromica reinforced, 87.7% and 87.7%, respectively; and for glass-infiltrated alumina core, 94.4% and 92%, respectively. Crown fracture was considered the most frequent complication. Conclusion: Based on the present critical review, there was no evidence to support the superior application of a single ceramic system or material. Further long-term prospective studies are required

Towards Sustainable Energy Efficiency with Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively

Mechanical Properties of the Modified Denture Base Materials and Polymerization Methods: A Systematic Review

Amidst growing technological advancements, newer denture base materials and polymerization methods have been introduced. During fabrication, certain mechanical properties are vital for the clinical longevity of the denture base. This systematic review aimed to explore the effect of newer denture base materials and/or polymerization methods on the mechanical properties of the denture base. An electronic database search of English peer-reviewed published papers was conducted using related keywords from 1 January 2011, up until 31 December 2021. This systematic review was based on guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search identified 579 papers. However, the inclusion criteria recognized 22 papers for eligibility. The risk of bias was moderate in all studies except in two where it was observed as low. Heat cure polymethyl methacrylate (PMMA) and compression moulding using a water bath is still a widely used base material and polymerization technique, respectively. However, chemically modified PMMA using monomers, oligomers, copolymers and cross-linking agents may have a promising result. Although chemically modified PMMA resin might enhance the mechanical properties of denture base material, no clear inferences can be drawn about the superiority of any polymerization method other than the conventional compression moulding technique

Blockchain-enabled security and privacy for Internet-of-Vehicles

The evolution of Internet-of-Vehicles (IoV) has promised improvement in traffic management and road safety. However, with continuously increasing number of vehicles on road, there are numerous challenges associated with IoV. Communication among vehicles is needed to be secure and bandwidth efficient. Messages exchanged between vehicles must be authentic so as to maintain trust among the network. On the other hand, blockchain is a rapidly emerging technology for various IoT related applications. It ensures security by maintaining transaction history in the form of an encrypted and immutable ledger. Therefore, blockchain-based communications can potentially solve various challenges of IoV. However, there are certain constraints which make the practical implementation of blockchain in IoV difficult. For example, one of the most common consensus algorithms of blockchain, Proof-of-Work, is energy inefficient and time consuming. Various other consensus algorithms have been developed but they compromise security. This chapter conceptualises the implementation of blockchain in IoV, particularly useful in emergency situations, such as accident. We discuss some consensus algorithms of blockchains and their suitability in IoV, ultimately leading to a formation of voting based consensus integrated with a relay selection mechanism. Furthermore, we present an economic model to incentivise vehicles for safe driving and cooperation through blockchain-enabled transaction of rewards. The security capacity of proposed blockchain against collusion of relay nodes is analysed by game theory. Simulation results of the proposed approach demonstrate its efficiency in terms of latency and success rate in message transmission as compared to other blockchain-based solutions for IoV