The present study focuses on the influence of social media (SM) on an individual’s self-esteem and self-evaluation. The construction of virtual identities in cyberspace affect individual’s mental ...health. The article introduces the concepts of self-esteem and self-evaluation from a psychological perspective and explores the multifaceted factors that influence the formation of virtual identities on social media. Previous research suggests that prolonged exposure to idealized images on SM may lead to dissatisfaction and mood swings in individuals, especially among young women. This phenomenon may be due in part to the fact that social media allows users to edit and present an idealized self-image. The article highlights the fact that the association between social media, self-esteem and self-evaluation is a complex and worthwhile area of in-depth research, which not only has important implications for an individual’s psychological well-being, but also involves a number of important issues in the fields of social psychology and psychological science.
In recent years, the rapid urbanization of world's population causes many economic, social, and environmental problems, which affect people's living conditions and quality of life significantly. The ...concept of "smart city" brings opportunities to solve these urban problems. The objectives of smart cities are to make the best use of public resources, provide high-quality services to the citizens, and improve the people's quality of life. Information and communication technology plays an important role in the implementation of smart cities. Blockchain as an emerging technology has many good features, such as trust-free, transparency, pseudonymity, democracy, automation, decentralization, and security. These features of blockchain are helpful to improve smart city services and promote the development of smart cities. In this paper, we provide a comprehensive survey on the literature involving blockchain technology applied to smart cities. First, the related works and background knowledge are introduced. Then, we review how blockchain technology is applied in the realm of smart cities, from the perspectives of smart citizen, smart healthcare, smart grid, smart transportation, supply chain management, and others. Finally, some challenges and broader perspectives are discussed.
In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and ...heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.
Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially ...limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.
Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are ...unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.
To better cope with the Internet usage shift from host-centric end-to-end communication to receiver-driven content retrieval, innovative information-centric networking (ICN) architectures have been ...proposed. With the explosive increase in global network traffic, the energy efficiency issue in ICN is a growing concern. A number of approaches have been proposed to address the energy-efficiency issue in ICN. However, several significant research challenges remain to be addressed before its widespread deployment, including shutdown, slowdown, mobility, and cloud computing. In this paper, we present a brief survey on some of the works that have been already done to achieve green ICN and discuss some research issues and challenges. We identify several important aspects of green ICN, i.e., overview, energy efficiency metrics, network planning, enabling technologies, and challenges. Finally, we explore some broader perspectives for green ICN.
The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge ...due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.
Enterococcus faecalis is considered a predominant pathogen for persistent periapical infections and in addition is reportedly resistant to calcium hydroxide medication. The WalRK 2-component system ...of E. faecalis is essential for environmental adaptation, survival, and virulence. The goal of this study was to investigate the potential roles of walR in the regulation of biofilm aggregation, alkaline stress, and susceptibility to calcium hydroxide (CH) medication.
Antisense walR RNA (aswalR) overexpression strains were constructed. Exopolysaccharide (EPS) production and bacterial viability of E. faecalis biofilms were evaluated by confocal laser scanning microscopy. Quantitative real-time polymerase chain reaction was used to investigate the expressions of virulent factor genes. The proportion of viable bacteria and EPS production in dentin were assessed after CH medication.
We showed that walR interference by aswalR RNA leads to a reduction in the dextran-dependent aggregation in E. faecalis biofilm. The overexpression of aswalR reduced the transcripts of the virulence genes and alkaline stress tolerance ability. Furthermore, the down-regulation of walR sensitized E. faecalis in infected canals to CH medication associated with inhibiting EPS synthesis.
The data suggest a role for the walR regulator in the susceptibility to CH associated with dispelling the EPS matrix, which could be explored as a potential supplementary therapy for the management of root canal infection.
•Antisense walR negatively affects the expression production of WalR.•Antisense walR leads to a reduction in E. faecalis biofilm aggregation.•Inhibiting of walR sensitized E. faecalis in infected canals to calcium hydroxide.•Antisense walR could be explored as a supplementary therapy for the medication.
Streptococcus mutans (S. mutans) has been proved to crucial cariogenic pathogens. Antisense vicR RNA reduced the transcription of virulence genes and lead to a reduction in biofilm formation. In the ...current study, a graphene-oxide plasmid transformation system was developed using interacted GO-polyethylenimine (PEI) complexes loaded with antisense vicR-expressing plasmid (GO-PEI-ASvicR). The particle size distribution and zeta potential of the GO-PEI-based ASvicR were evaluated. Quantitative real-time PCR assays were used to investigate the expression of S. mutans virulence genes. The exopolysaccharide (EPS) production in biofilm were evaluated by confocal laser scanning microscopy and anthrone method. We showed that GO-PEI could efficiently deliver the ASvicR-expressing plasmid into S. mutans cells and support excellent transcripts of ASvicR. Furthermore, GO-PEI-ASvicR significantly reduced virulent-associated gene expressions, suppressed biofilm aggregation and inhibited EPS accumulation. Our reports demonstrated that preserving nano-graphene oxide with antisense vicR RNA will be a more effective strategy for dental caries management.
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