The present high-tech landscape has allowed institutes to undergo digital transformation in addition to the storing of exceptional bulks of information from several resources, such as mobile phones, debit cards, GPS, transactions, online logs, and e-records. With the growth of technology, big data has grown to be a huge resource for several corporations that helped in encouraging enhanced strategies and innovative enterprise prospects. This advancement has also offered the expansion of linkable data resources. One of the famous data sources is social media platforms. Ideas and different types of content are being posted by thousands of people via social networking sites. These sites have provided a modern method for operating companies efficiently. However, some studies showed that social media platforms can be a source for misinformation at which some users tend to misuse social media data. In this work, the ethical concerns and conduct in online communities has been reviewed in order to see how social media data from different platforms has been misused, and to highlight some of the ways to avoid the misuse of social media data.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

Sleep disorders (SD) have a complex aetiology, and socioeconomic status (SES) as determined by social class, household income, ethnicity and education plays an important role in their development. As SD are associated with cognitive impairment and mood disorders, they in turn impact SES. Socioeconomic status also influences allostatic load caused by chronic accumulation of stress throughout life. Environmental and psychological stressors have a direct effect on SD, and they are modulated by SES, in combination with comorbidities like obesity and cardiovascular disease. This review explores the recent theories about the influence of SES on the development of SD in the general population, whether or not occurring with comorbidities, and also focusses on the interplay between socioeconomic status, circadian rhythms, aging and clinical outcomes like metabolic diseases and cancer.

The COVID-19 pandemic has resulted in a wide range of spatial interventions to slow down the spread of the virus. The spatial limitations of narrow public circulation spaces within informal settlements, which house over one billion people around the world, make it impossible for pedestrians to practice physical distancing (or social distancing). In this paper, we propose a flexible mathematical method, named the Cluster Lane Method, for turning a planar circulation network of any size or complexity into a network of unidirectional lanes, making physical distancing possible in narrow circulation spaces by limiting face-to-face interactions. New notions and theorems about oriented graphs in graph theory are introduced. The paper ends with a discussion of the potential implementation of this cost-efficient, low-tech, sustainable solution, and with the introduction of a novel unidirectional tactile paving for the visually impaired.

This article analyzes some available bike mobile applications for smartphones as an alternative to bike computers (Cycle Computers or speedometer or speed sensors). We have records of a large number of MTB (Mountain Bike) datasets, 219 datasets were recorded on 4 different routes. These applications create maps and profiles from geographic data. Inputs can be in GPS data (tracks and waypoints), driving routes, street addresses, or simple coordinates. Most applications estimate fields such as speed, heading, slope, distance, VMG (velocity made good) and pace (cadence). However, it is necessary to calculate the relationship between cadence and power in pedaling so that cyclists know the appropriate moment to apply power to their legs to improve the torque. This paper shows tables, comparative graphs, and performance evaluation of biking routes in four different cycling mobile applications.

Transposable elements (TEs), also known as mobile elements (MEs), are interspersed repeats that constitute a major fraction of the genomes of higher organisms. As one of their important functional impacts on gene function and genome evolution, TEs participate in regulating the expression of genes nearby and even far away at transcriptional and post-transcriptional levels. There are two known principal ways by which TEs regulate expression of genes. First, TEs provide cis-regulatory sequences in the genome with their intrinsic regulatory properties for their own expression making them potential factors for regulating the expression of the host genes. TE-derived cis-regulatory sites are found in promoter and enhancer elements, providing binding sites for a wide range of trans-acting factors. Second, TEs encode for regulatory RNAs with their sequences showed to be present in a substantial fraction of miRNAs and long non-coding RNAs (lncRNAs), indicating the TE origin of these RNAs. Furthermore, TEs sequences were found to be critical for regulatory functions of these RNAs including binding to the target mRNA. TEs thus provide crucial regulatory roles by being part of cis-regulatory and regulatory RNA sequences. Moreover, both TE-derived cis-regulatory sequences and TE-derived regulatory RNAs, have been implicated to provide evolutionary novelty to gene regulation. These TE-derived regulatory mechanisms also tend to function in tissue-specific fashion. In this review, we aim to comprehensively cover the studies regarding these two aspects of TE-mediated gene regulation, mainly focusing on the mechanisms, contribution of different types of TEs, differential roles among tissue types, and lineage specificity, based on data mostly in humans.

This theoretical article reviews the model describing processes in social systems based on the analysis of their resource base. Application of the system theory can help to explain why some systems are aimed at prevention of type I errors, while other seek to decrease quantity of type II errors. Such differences are manifested in investment of resources either into deep interaction or into wide coverage. Some examples of such strategies in economic, market and production systems are provided in the article. The article introduces some provisions of the system theory in the context of the resource flows. The main indicators that are considered in this article are the characteristics of the sources of the exchanging flows of resources. Their relative frequency and quality are investigated, on the basis of which the most effective strategy of the system is derived, as a mechanism for redistributing resources. The rigor of the system's strategy depends on the magnitude of the difference in characteristics. It is explained how exactly it influences the exchange processes, that in reality systems do not interact simultaneously, and one of the opposite resource flows is always delayed. It is shown how the system strategy depends on the risks linked with interactions. Also, there are grounds for the need to accumulate resources, including in the situation of their surplus. The model helps also explain shift of economic centers throughout history. Additionally, there is an analogy between systems strategies and the competitive strategies described by M. Porter, and outsourcing versus integration.

This article discusses how, in today’s world of disruptive and dramatic social change (DSC), non-sports related coaching, which includes a wide range of services such as life coaching, career coaching, executive coaching, and team coaching, can inadvertently fuel undesirable social dynamics. There is little or no awareness of this risk among coaches and coachees. The global, fast-growing, multi-billion-dollar industry aimed at supporting people and organisations to perform better and increase wellbeing while managing and adapting to change has been developed with limited sociological input. The article is based on 15 years of social constructionist-informed reflective practice by a sociologist-turned-coach, and uses a multi-layered autoethnographic account to argue for a sociologically informed paradigm shift in coaching, as well as relevant sociological knowledge and social and human sciences research methodology in coaches’ education. It presents the rationale and key features of a new coaching approach that places DSC-relevant sociological concepts at the heart of the coaching process, helps people develop psycho-sociological awareness, and uses a learning-to-develop through research design. A new definition of coaching to address DSC is derived. Coaching practitioners will find DSC-relevant sociological concepts, critical reflections, coaching questions, and procedures to expand coaching effectiveness. Interdisciplinary research topics combining coaching sociology and coaching psychology are suggested.

Indonesia is among the countries developing the concept of smart city. The national government envisions Indonesia 2030 which shall implement the smart city towards sustainable development. Many regions in Indonesia have already implemented the concept of smart city, but still on the level of smart government and smart technology, lacking on the smart citizen component of a smart city. This paper aims to analyze the citizens behavior of using technology on Sleman smart citizen 2021 through the lapor sleman mobile application. This research uses mixed method of quantitative and qualitative approaches in examining the data. The authors distributed 100 questionares and conducted interviews to the government which is department of communication and information, Sleman and the several community that ever use the lapor sleman application. The result of the study reveals the following: firstly, performance expectancy, social influence, and behavioral intention are three independent variables which affect the users’ behavior. Secondly, the behavioral intention has the biggest significant effect on users’ behavior. Thirdly, the citizen behavior of using technology has affect the making a smart citizen. Therefore, using technology in term of lapor sleman mobile application can make the citizens as active participants in public life, in terms of social cooperation, freedom of expression and flexibility. However, there are still many problems in the use of lapor sleman mobile application such as the limited access to only android smartphones, lack of technical know-how, and people’s reluctance to use the lapor sleman mobile application.

Nature-based solutions (NbS) - working with and enhancing nature to address societal challenges - are increasingly being featured in climate change adaptation policy and plans. While there is growing evidence that NbS can reduce vulnerability to climate change impacts in general, there is a lack of understanding on the mechanisms through which this can be achieved, particularly in the Global South. To address this, we analyse 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, and intervention types. We develop and apply an analytical framework of people’s social-ecological vulnerability to climate change, in terms of six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. Most cases (95%) report a reduction in vulnerability, primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (52%), reducing ecological exposure (36%), increasing social adaptive capacity (31%), increasing ecological adaptive capacity (19%) and/or reducing social exposure (14%). An analysis of mediating factors shows that social dimensions are equally important as technical factors in NbS to achieving equitable and effective outcomes. Attention to the distinct social and ecological pathways through which vulnerability is reduced helps to harness the multiple benefits of working with nature in a warming world.

The first two decades of the 21st-century have seen the emergence of the modern citizen science movement, increased demand for niche eco and wildlife tourism experiences, and the willingness of people to voluntarily share information and photographs online. To varying extents, the rapid growth of these three phenomena has been driven by the availability of portable smart devices, access to the Web 2.0 internet from almost anywhere on the planet, and the development of applications and services, including social media/networking sites (SNSs). In addition, the number of peer-reviewed publications that explore how text and images shared on SNSs can be data-mined for academic research has surged in recent years. This systematic quantitative review has two goals. The first goal is to provide an oversight of how the photographs that ecotourists share online are contributing to wildlife tourism research. The second goal is to promote the emerging photovoice technique as a theoretical context for social research based on the photographs and comments that ecotourists share on SNSs. From the perspectives of community benefits, conservation behaviours, and environmental education, there are many similarities between authentic ecotourism experiences and quality ecological citizen science programs. Much of the literature regarding the theory and practice of citizen science reports on the difficulties of attracting, training, motivating and retaining community members. The synthesis of this review is that crowdsourcing wildlife and tourism data from comments and photographs that ecotourists share on SNSs is a credible method of research that provides a self-replenishing pool of citizen scientists.

Horses’ ability to adapt to new environments and to acquire new information plays an important role in handling and training. Social learning in particular would be very adaptive for horses as it enables them to flexibly adapt to new environments. In the context of horse handling, social learning from humans has been rarely investigated but could help to facilitate management practices. We assessed the impact of human demonstration on spatial problem-solving abilities in horses using a detour task. In this task, a bucket with a food reward was placed behind a double-detour barrier and horses (n = 16) received a human demonstration or no demonstration. Horses were allocated to two test groups of 8 horses each, which experienced the two treatments in a counterbalanced order. We found that horses did not solve the detour task faster with human demonstration. However, both test groups improved rapidly over trials. Our results suggest that horses prefer to use individual rather than social information when being confronted with a spatial problem-solving task.

Estimation and planning play a vital role in the construction of an autonomous navigation framework. However, these problems are often considered separately, while planning gives robot a free-collision path towards the desired goal, estimation algorithm presents the executed trajectory in the sense that it has to be closed to the ground truth path as much as possible. Recently, a unified probabilistic framework, which supports solving these problems simultaneously, dubbed STEAP has been proposed. Nevertheless, its current version is only designated for an omni wheels robot, which allows robot to move and turn in vertical direction. Differential drive robot, on the other hand, though limited to move along only one direction, has been used in various situations due to its flexibility and lower cost in hardware designing. Thus, in this extension, our aim is to control a differential drive robot via STEAP. Moreover, in a more complicated environment such as labyrinth or maze, the original STEAP sometimes fails to find a path. Indeed, this problem is mainly caused by the poor initialization and the non-linearity in optimizer constraints. In our implementation, instead of dealing with these constraints, we employ a global planner algorithm such as Dijkstra or RRT to treat STEAP as an effective local planner module that focus on following the global path. Consequently, the experimental results show that the extended STEAP not only able to navigate a differential drive robot but also in a more complicated and unstructured environment.

Aggregative multicellularity occurs when dispersed cells join together to form a highly cooperative unit, in contrast to clonal multicellular organisms formed by cells that remain in contact after descent from a single cell. Because aggregative groups may include non-relatives, aggregative multicellular organisms should be particularly vulnerable to the rise of cheater cells that take advantage of social goods without paying the costs, reducing cooperation, and even threatening extinction. We review the key mechanisms by which aggregative multicellular organisms control cheaters with a focus on the best studied aggregative organisms, Myxococcus xanthus and Dictyostelium discoideum. These include various passive and active mechanisms to maintain high relatedness within aggregates, to enforce cooperation on aggregate members, and the costs of cheating on other key functions. Ultimately, aggregative multicellular organisms are not that different from clonal organisms descended from a single cell.

Place attachment is an emotion that people experience in connection to a specific place and it is needed to maintain sustainable neighborhood community. The emotion is affected by various factors, such as experience, function, environment, and satisfaction. This study focuses on commercial structures, which are one feature that characterize the physical environments of neighborhoods. The aim of this study is to determine the effects of commercial activities in different commercial environments on social bonding and place attachment in residents. Two sites were selected for analysis due to their different commercial environments, and path analysis was used to examine the relationships among factors. The results indicate that the type of commercial environment had effects on commercial activities, including shopping frequency, and that commercial activities affected social bonding and place attachment in residents. Also, commercial activities had indirect effects on place attachment through social bonding between residents. These results suggest that the commercial environment is an important element affecting the community and place attachment of residents in neighborhoods.

Social networking applications such as Twitter have increasingly gained significance in terms of socio-economic, political, and religious as well as entertainment sectors. This in turn, has witnessed a wide gamut of information explosion in the social networking realm that can tend to be both useful as well as misleading at the same point of time. Spam detection is one such solution that caters to this problem through identification of irrelevant users and their data. However, existing research has so far laid primary focus on user profile information through activity detection and relevant techniques that may underperform when these profiles exhibit characteristics of temporal dependency, poor reflection of generated content from the user profile, etc. This is the primary motivation for this paper that addresses the aforementioned problem of user profiles by focusing on both profile information and content-based spam detection. To this end, this work delivers three significant contributions. Firstly, exhaustive use of Natural language processing (NLP) techniques has been rendered towards creation of a new comprehensive dataset with a wide range of content-based features. Secondly, this dataset has been fed into a customized state-of-art hybrid machine learning model that has been exclusively built using a combination of both machine learning and deep learning techniques. Extensive simulation based analysis not only records over 98% accuracy but also establishes the practical applicability of this proposal by proving that modeling based on the mixed profile and content-generated data is more capable of spam detection in contrast to each of these standalone approaches. Finally, a novel methodology based on logistic regression is proposed and supported by analytical formulations. This paves the way for the custom-built dataset to be analyzed and corresponding probabilities to be obtained that differentiate legitimate users from spammers. The obtained mathematical outcome can henceforth be used for future prediction of user categories through appropriate parameter tuning for any given dataset. This makes our method a truly generic one capable of identifying and classifying different user categories.

The diversity and inclusion discussion permeates many sectors of society. Within this dialogue, science and scientists are acutely aware of the value of diversity and the need for inclusion. While demographic diversity in science has received considerable recent attention, very little research and understanding exists on inclusion. Our study presents empirical data on the meaning of inclusion using a crowdsourcing approach that sought responses to the question “What does inclusion mean to you?”. The most prominent concepts were those of empathy, warmth, support, love, acceptance and curiosity; diverse perspectives; and participation. We clustered conceptual elements of inclusion into four themes: access and participation, embracing diverse perspectives, a welcome environment, and team belonging. On the basis of these data, we theorize a conceptual framework model from which inclusion may be put into practice. Our model suggests a dynamic process of inclusion operating from principal structural elements of 1) a foundation that involves place, access and participation, and space; 2) reciprocal engagement as an engine for inclusion; and 3) expression of inclusion as culture. The framework model demonstrates a means by which the practice of diversity can be more than shifts in demographic statistics, and instead promote the full expression of benefits derived when the many dimensions of diversity are truly included.

Bacterial conjugation, also referred to as bacterial sex, is a major horizontal gene transfer mechanism where the DNA is transferred from a donor to a recipient bacterium by direct contact. Conjugation is universally conserved among bacteria and occurs in a wide range of environments (soil, plant surfaces, water, sewage, biofilms and host-associated bacterial communities). Within these habitats, conjugation drives the rapid evolution and adaptation of bacterial strains by mediating the propagation of various metabolic properties, including symbiotic life-style, virulence, biofilm formation, or resistance to heavy metals and, most importantly, resistance to antibiotics. These properties make of conjugation a fundamentally important process at the center of extensive study. Here, we review the key steps of conjugation by following the life-cycle of the F plasmid during transfer from the donor to the recipient cell. We also discuss our current knowledge of the extent and impact of conjugation within an environmentally and clinically relevant bacterial habitat, bacterial biofilms.

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human interpretation and machine classification of satellite, aerial, or drone imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

To understand the health impacts of “hikikomori” lifestyle and to establish its first comprehensive health profile, a cross-sectional study was designed to measure how well the cases of hikikomori youths of Hong Kong were living, in terms of social, mental and physical aspects. This study involved 104 eligible participants at age 19.02 year-old who had completed the set of questionnaires and a series of anthropometric and physical health measurements. Despite SF36 score of 84.0 indicated good physical functioning in general, participants were lived sedentarily with high incidence of hypertension at 15.4% and prehypertension at 31.7%. Occurrence of hypertension in cases living as hikikomori >6 months was 3-times higher than those newly onset cases. The blood pressure levels were correlated with age and all obesity index parameters measured including waist circumference and body mass index. Half of the hypertensive cases involved the elevation of systolic blood pressure, which suggested higher odds of cardiovascular complications. Participants were mentally stable living with moderate levels of perceived stress and state anxiety, but borderline clinical depression. In conclusion, the hikikomori lifestyle could be a risk behavior that may harm the younger generation physically by promoting obesity and hypertension and probably other chronic illnesses.

This paper extends previous research on the influence of social media and digital channels on customer purchase behaviour by presenting a new omnichannel purchasing model. We characterise that model as a “virtuous circle” as it centres around customer use of social media and has potential to benefit both customers and companies. We illustrate that model with a worked example, discuss approaches to its implementation and evaluate its use in the context of a business case study. The model creates a framework that combines elements of digital marketing, social CRM, omnichannel CRM, and customer experience and engagement. This paper bridges academic and industry practitioner communities across those fields.

Over the last few decades, urban expansion and population shifts have modified the existing landscape throughout the U.S. Protected areas and development are compatible lenses, yet stakeholders’ involvement in decision-making is often missing from environmental governance. We examine how stakeholders living and working in proximity to Everglades National Park (EVER) perceive environmental and social changes to the park and community park relations. EVER was selected as a study site for several reasons: proximity to urban areas, rich biological diversity, largest subtropical wilderness in the U.S., International Biosphere Reserve, World Heritage Site, and prominence as a tourist destination for the region. Forty-one semi-structured interviews were conducted with neighborhood groups, representatives from gateway communities, and conservation organizations. An analysis of the interview data generated six research themes: loss of native species, urban development, a shortage and contamination of water, hurricanes, climate change, and increased recreation use. The results of this study add to the literature by providing a better understanding of the relationships stakeholders have with national parks. The results will provide useable knowledge that may help stakeholders and public land managers design strategies related for sustainable plans for the park and its surrounding communities.

Streets are primary elements through which the character of urban neighborhoods are experienced and expressed. The “sense of safety” in neighborhood streets is paramount to social and psychological wellbeing of its residents and visitors. The intention of this study was to explore environmental and social cues of a neighborhood, which evoke fear of crime, which will help designers to prevent the generation of such negative feelings and promote more safe and comfortable spaces in our cities. This study used interviews, group discussions and observations to identify fear-generating factors with a sample of participants in the multi ethnic neighborhood of Kotahena in Colombo, Sri Lanka. Field data was analyzed through visual documentation and photographic surveys. Moreover, group discussions, interviews and personal observations were used to synergize the study objectives. The findings inform that fear of crime on streets is influenced by both environmental and social cues to varying degrees. Feelings of fear were associated with gender, ethnicity and less familiarity with the place as participants were from an ethnic minority within the community. Literature has emphasized that fear of crime has a connection to actual crime locations. The research findings, however, indicate that fear of crime spots identified by the residents do not have a direct relationship to the actual crime locations.

Social media and other web 2.0 tools have provided users the platform to interact and also disclose personal information not only with their friends and acquaintances, but also with relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites within the Ghanaian context. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that all variables in the proposed model with the exception of interaction and perceived control were significant predictors of self-disclosure with privacy risk being the most significant predictor. In all, the model accounted for 54.6 percent of the variance in self disclosure. The implications and limitations of the current study are discussed and directions for future research proposed.

Circular Economy (CE) is a growing topic among scholars, industry and government aiming at decoupling economic growth and development from the consumption of finite resources. CE incorporates different meanings, from reduce, reuse and recycle activities to environmental degradation or resource scarcity, and supported by specific indicators to attain sustainable development. However, so far, there has been no agreement to measure how effective an industry/product is in making the transition from linear to circular approaches, particularly those affected the society. This research work aims to perform a systematic literature review (n=60) to analyze and discuss how social aspects have been considered and integrated in the CE research so far. Moreover, this review provides and overview of the literature on social impact within CE, that resulted in three main outputs: a knowledge map of the CE, an analysis of social aspects within CE, and the theories/frameworks used to evaluate social impact of CE. Finally, this study brings to light how CE implementation can affect society and highlights the importance of social dimension in the domains of CE and policy-making community, which could help moving CE towards a sustainable development.

Social recommendation is almost as the integration of the business platform and social platform, and gradually become a top in recommendation system. Social recommendation algorithm solves the problem of cold start and data sparseness for traditional commodity, while the internal structure of the relationship graph in social relations has not been fully excavated. This paper proposes two models of Micro Relation Transfer Model and Macro Relation Transfer Model of social relations, and applies the social relations transfer models into the social recommendation system. A relationship graph is built from the relationship between customers on the Internet. Micro Relation Transfer Model establishes the transfer activation function by calculating the relationship between the two customers using the similarity of interests set. Micro Relation Transfer Model spreads the relationship of friends by calculating the proportion of common neighbors held by the customer's social relations. In order to effectively control the transmission range and effect of social relations graph, we introduce pruning algorithm based on Monte Carlo Decision Tree convergence algorithm. The experimental results show that SRRTC algorithm enhances the success rate and stability significantly.

COVID-19 has created anxiety not only in individuals but also in health organizations, and countries worldwide. Not a single industry is left un-influenced and loss is being estimated in billions of dollars. The widespread of this pandemic disease has challenged researchers all over the world. Some of the researchers are working to invent its cure while, others are applying computing technologies to stop its spread, by analyzing and identifying patterns for prediction and forecasting. This is by no doubt the hottest area of research for the last 100 years. This survey has targeted the research published in computing sub-domains to combat the pandemic. The survey has clustered the scientific efforts into logical groups: surveillance, metrological effects, social media analytics, image processing and business and economy, analysis and modeling. It will serve as a leading source for the followings: researchers who want to identify what has been achieved in different computing sub-domains, those who need fresh authenticated datasets openly accessible for different research contexts and what are future directions in this area of research. The findings of analysis and modeling can be also useful for government agencies who want to set priorities and formulate policies.

Misinformation can amplify humanity's most significant challenges. As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it and also people downplaying the severity of it are also growing. This article investigates social media activity in May 2020, specifically Twitter, with respect to COVID-19, the themes of tweets, where the discussion is emerging from, disinformation shared about the virus, and its relationship with COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity, population, and the spatial variability of disease incidence; our results suggest that MGWR could explain 96.7% of the variations. Moreover, Covid-19 related twitter dataset content analysis reveals a meaningful strong spatial relationship that exists between social media activity and known cases of COVID-19. Discourses analysis was conducted on tweets to index tweets downplaying the Pandemic or disseminating disinformation; the discourses analysis findings suggest that states in where twitter users spread more misinformation and showed more resistance to pandemic management measures in May are experiencing a surge in the number of cases in July.

The growth of social media has changed the face of many aspects of marketing such as online, digital, etc. It also has changed the way modern human communicates and connects with others. Moreover, the behavior on this platform could not and should not be justified with strategies of other marketing channels and media. Due to the nature of social media, they are rich in precise and lean data, but processing these data and extracting knowledge and insights from them are problematic. Evaluating the effectiveness of a marketing endeavor is also a task related to these data. The current research attempts to assess the effectiveness of an advertising campaign on Instagram via advertising cost and sentiment classification of audience opinion regarding the campaign. The methodology used in this research is the standard process of data mining, i.e., CRISP-DM. Furthermore, multiple machine learning models and approaches were studied to train a prediction model based on data. In order to find the most accurate algorithm, grid search was used among the trained models and different algorithms with different combinations of hyper-parameters. The obtained results revealed that although the number of not profitable advertising media was higher than the profitable media, the overall status of the campaign was profitable, both in the cost-effectiveness approach and sentiment analysis approach. The other valued outcome of this research was important general and specific insights which can be used to shape a better-performing and effective advertising campaign on Instagram.

Mobile Cloud Computing(MCC) is a recent technology used by various users worldwide. In 2015, more than 240 million users used mobile cloud computing which gives a profit of $5.2 billion to service providers. MCC is a combination of Mobile computing and cloud computing. By the combination of these two, it gives various challenges like network access, elasticity, management, availability, security, privacy, etc. Here security issues are considered because both the security issues of mobile computing and cloud computing are considered as important like data security, virtualization security, partitioning security, Mobile cloud application security, Mobile device security. This paper gives a detailed study of security issues in mobile cloud computing and its prevention measures.

The present study is an attempt to investigate the factors behind spinsterhood, the sort of perception popular mindset hold on spinsters and the consequences of being a spinster. It starts with a theoretical part as a background to the topic. Then, it moves to the practical part where the study depends a field work for testing the constructed hypothesis. The field work is conducted through distributing quiet a big number of representative questionnaires. Briefly, the findings proved that the majority of respondents, 51/° believe that the society considers spinsters as those who had never been proposed to. Moreover, 52/° of respondents believes that spinsters are old women who had never been married. Both may imply a negative connotation set by society and culture for unmarried women. As to the factors behind spinsterhood, the findings proved that the majority of the respondents, 75/° refer spinsterhood to the fact that women prioritize their education and careers on marriage. For the consequences of spinsterhood, the major result demonstrates that 53/° of respondents believe that spinsters’ isolation and embarrassment is one major impact of spinsterhood.

Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

It is well recognised that the mining industry in South Africa is highly rated for its substantial contribution to the country’s economic growth, including employment and infrastructural development. It is also known that mining and ore processing activities potentially pose a severe threat to public health and environmental well-being, in the way operations are carried out, mine wastes are disposed of (in dumps), local communities are relocated, mine management and the mining community in general, perceive established environmental standards and etiquette. This paper examines ethical practices and norms in the South African mining industry, with particular reference to the management of tailings dams. We analyse the modes of articulation of the country’s regulatory instruments for tailings management, and review the corporate social responsibility (CSR) approach of leading mining companies. Despite decades of research and resulting recommendations on tailings management, it is concluded that current legislations are largely ineffective, level of adherence by mine management and the mining community, low, and mechanisms for compliance monitoring, weak. New perspectives on legislative issues for unsolved problems in tailings handling are put forward, and directions for future research, indicated.

Background: Acceleration of mass vaccination strategies is the only pathway to overcome the COVID-19 pandemic. Healthcare professionals and students have a key role in shaping public opinion about vaccines. This study aimed to evaluate the attitudes of dental students globally towards COVID-19 vaccines and explore the potential drivers for students' acceptance levels; Methods: A global cross-sectional study was carried out in February 2021 using an online ques-tionnaire. The study was liaised by the scientific committee of the International Association of Dental Students (IADS), and data was collected through the national and local coordinators of IADS member organizations. The dependent variable was the willingness to take the COVID-19 vaccine, and the independent variables included demographic characteristics, COVID-19-related experi-ence, and the drivers of COVID-19 vaccine-related attitude suggested by the WHO-SAGE; Results: A total of 6639 students from 22 countries representing all world regions responded to the ques-tionnaire properly. Their mean age was 22.06 ± 2.79 (17-40) years, and the majority were females (70.5%), in clinical years (66.8%), and from upper-middle-income economies (45.7%). In general, 22.5% of dental students worldwide were hesitant, and 13.9% rejected COVID-19 vaccines. The students in low- and lower-middle-income (LLMI) economies had significantly higher levels of vaccine hesitancy compared to their peers in upper-middle- and high-income (UMHI) economies (30.4% vs 19.8%; p < 0.001); Conclusions: The global acceptance level of dental students for COVID-19 vaccines was suboptimal, and their worrisome level of vaccine hesitancy was influenced by the socioeconomic context where the dental students live and study. The media and social media, public figures, insufficient knowledge about vaccines, and mistrust of governments and the pharmaceutical industry were barriers to vaccination. The findings of this study call for further implementation of epidemiology (infectious diseases) education within undergraduate dental curricula.

The fear of missing out (FoMO) is characterized in the literature as a fear that others are having rewarding experiences while one is missing out, and a constant need to keep connected with one’s social network. Driven by Social Determination Theory (SDT) FoMO has been linked with Problematic Social Networking Sites use (PSNSU), negative affectivity (NA), self-esteem (SE) and sleep disturbances. The present study reports findings from 512 individuals (79.1% women, mean age 30.5 years, SD= 8.61). Structural equation modelling (SEM) suggests that the duration of SNSs use and the numbers of SNSs platforms actively used partially mediated the relationship between FoMO and PSNSU. In turns, PSNSU partially mediated the relationship between FoMO and NA. Furthermore, the present study has extended the literature by incorporating the Vulnerability Model in the FoMO concept, identifying that SE partially mediated the relationship between FoMO and NA, while NA fully mediated the relationship between FoMO and sleeping disturbances. Accordingly, the present has extended previous research findings in showing exercise as a potential protective factor to prevent against FoMO. Practical and theoretical implications are discussed.

Health literacy describes skills and competencies that enable people to gain access to, understand and apply health information to positively influence their own health and the health of those in the social environment. In an increasingly media saturated and digitized world, these skill sets are necessary for accessing and navigating sources of health information and tools, such as television, the Internet, and mobile apps. The concepts of Media Health Literacy (MHL) and eHealth Literacy (eHL) describe the specific competencies such tasks require. This article introduces the two concepts, and then reviews findings on the associations of MHL and eHL with several contextual variables in the social environment such as socio-demographics, social support, and system complexity, as a structural variable. As eHL and MHL are crucial for empowering people to actively engage in their own health, a growing body of literature reports of the potential and the effectiveness of intervention initiatives to positively influence these competencies. From an ethical standpoint, equity is emphasized, stressing the importance of accessible media environments for all - including those at risk of exclusion from (digital) media sources. Alignment of micro and macro contextual spheres will ultimately facilitate both non-digital and digital media to effectively support and promote public health.

Within the field of guilt and shame, two competing perspectives have been advanced. The first, the social-adaptive perspective, proposes that guilt is an inherently adaptive emotion and shame is an inherently maladaptive emotion; thus, those interested in moral character development and psychopathology should work to increase an individual’s guilt-proneness and decrease an individual’s shame-proneness. The functionalist perspective, in contrast, argues that both guilt and shame can serve a person adaptively or maladaptively—depending on the situational appropriateness, duration, intensity, and so forth. This paper reviews the research conducted supporting both positions, critiques some issues with the most widely used guilt- and shame-proneness measure in the social-adaptive research (the TOSCA), and discusses the differences in results found when assessing guilt and shame at the state versus trait level. The conclusion drawn is that although there is broad support for the functionalist perspective across a wide variety of state and trait guilt/shame studies, the functionalist perspective does not yet have the wealth of data supporting it that has been generated by the social-adaptive perspective using the TOSCA. Thus, before a dominant perspective can be identified, researchers need to (1) do more research assessing how the social-adaptive perspective compares to the functionalist perspective at the state level, and (2) do more trait research within the functionalist perspective to compare functionalist guilt- and shame-proneness measures with the TOSCA.

We examined the relationship between living alone and the prevalence of depressive symptoms in older Korean widows and assessed the individual contributions of health, social ties, and socioeconomic factors to the development of depressive symptoms. The study was a secondary analysis using data from widows 65 years of age and older who participated in the Living Profiles of Older People Survey (LPOPS). A logistic regression analysis was used to evaluate the contributions of health, social ties, and socioeconomic factors to the development of depressive symptoms. Working status and equivalent household income were significantly associated with depressive symptoms in both those living with others and those living alone. Adjustment for health status and social ties did not change the impact of living alone on the prevalence of depressive symptoms. However, adjustment for equivalent household income eliminated the negative association between living alone and depressive symptoms. Our findings indicate that economic resources are more important than health and social ties for alleviating the negative impact of living alone on the development of depressive symptoms in older widows.

How can neuroscience help everyone to live and care for our confined brains? Understanding and studying human behaviour allows us to measure the impact of such confinement on each of us and to identify those who need help. The challenges are to understand the psychological repercussions following confinement and to understand how neuroscience and neurotechnology can be very interesting tools for dealing with the health crisis. Neuroscience is changing our traditional philosophical and ethical views by providing information about the biological basis of our moral behaviour. The exception of the brain is at the heart of neuroethical thinking and discourse. Neuroethics is an interdisciplinary discipline placed at the intersection between the human sciences and the neurosciences, and aims to help understand how knowledge and research in neurosciences and neurotechnologies will affect the future of society, their impact on humans, relationships, daily life, the labour market ... but also how they can help and provide solutions and answers to the questions of citizens in a health crisis. But it's a double-edged sword, collecting and analyzing brain data in real time seems to be increasingly simple and within everyone's reach, without having the hindsight of the real meaning of these data, allowing one day to read people's thoughts, control them and manipulate them. It is becoming clear that the boundaries between medical and non-medical uses of neuroscience and neurotechnology are becoming very porous, inviting us to reflect on neuroethical issues in order to put safeguards on these uses.

(1) Background: Nursing homes’ preparedness in managing a public health crisis has been fragile, with effects on safety culture. The objective of this study was to assess nursing homes’ COVID-19 preparedness in Southern Portugal, including personnel’s work experiences during the pandemic. (2) Methods: We used a COVID-19 preparedness checklist to be completed by management teams, followed by follow-up calls to nursing homes. Thereafter, a survey to personnel was applied. Data analysis included descriptive statistics, exploratory factor analysis, and thematic analysis of open-end questions. (3) Results: In total, 71% (138/195) of eligible nursing homes returned the preparedness checklist. We conducted 83 follow-up calls and received 720 replies to the personnel survey. On average, 25% of nursing homes did not have an adequate decision-making structure to respond to the pandemic. Outbreak capacity and training were fragile areas among nursing homes’ contingency plans. We identified compliance with procedures and nonpunitive response to mistakes as fragile areas of safety culture, and teamwork as a strong safety area. (4) Conclusions: To strengthen how nursing homes cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, nursing homes’ preparedness and safety culture should be fostered and closely monitored.

This article assumes that collaborators who adopt a positive-sum, prosocial perspective can build trust-based relationships, which is equivalent to collaborative ability (CA). To improve a collaborator’s CA, we anticipated that content based upon a model of positive-sum collaboration, founded upon ancestral behaviours presented as social norms, might alter a zero-sum, proself, collaborator’s perspective. However, we also postulated that learner’s willingness to engage in positive-sum content may rely on pre-existing prosociality. Intervention participant’s prosociality was assessed via self-assessment of their emotional intelligence (EQ), quality of life (QoL), and goal orientation (GO). Learner’s engagement in online content was assessed by the three stage reached: examine website front matter; examine website content; learn website content. Tukey’s multiple comparison showed a statistically significant difference between stages one and three, in the mean of respondent’s self-assessed QoL. This suggests that participants who wished to learn content had, on average, higher quality of life, and potentially therefore greater prosociality. In general, these findings support a claim that any intervention designed to increase individual prosociality may need to consider the target population’s reticence to engage.

The study investigates the determinants of mobile Internet banking adoption among Gen-Y in Malaysia. Align the issue of the study and research objectives, and the study underpinned Technology Acceptance Model (TAM) as the main guideline or a blueprint to analyze the research model. The study applied survey research design and analyzed using Structural Equation Modelling (SEM) under the PLS-SEM technique with SmartPLS 2.0. A total of 358 mobile internet banking users in Malaysia were random proportional selected as respondents and analyzed. Results from the partial least square structural equation modeling (PLS-SEM) revealed that perceived ease of use, perceived usefulness, facilitating condition, and personal innovativeness in IT have significantly influenced the attitude of mobile internet banking adoption in Malaysia. Additionally, perceived ease of use and facilitating conditions found to have a significant influence on the perceived usefulness of Mobile Internet Banking. Lastly, facilitating conditions and personal innovativeness in IT are significantly influence the perceived ease of use of mobile internet banking among Gen-Y in Malaysia

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

COVID-19 has been a major issue in most countries throughout the world with 213 countries being affected till date due to the disease. The pandemic has raised concerns over the healthcare facilities available in various countries and question the government decisions made during this period of outbreak. Despite having the best healthcare facilities several countries across Europe and America have found it difficult to contain the disease outbreak questioning the available solutions to contain an area. This paper focuses on presenting information on solutions available to control outbreaks in order to prevent another pandemic occurring in the future. The paper also highlights the strategies and plans implemented by various governments who have been successful in combatting the disease with minimum damage. By using available resources such as technology, scientific innovation and digitalized healthcare this paper focuses on providing solutions which are already available to be utilized in the right manner.

Background: Social Anxiety Disorder (SAD) is among the most common anxiety disorders worldwide with data largely emerging from the Euro-American and Pacific Rim populations. In contrast, there is a dearth of studies among the populations of Arabian Gulf countries including Oman. This study has two interrelated aims: (i) to explore the prevalence of SAD among Omani adults, and (ii) to tease out the links between sociodemographic factors and SAD in Oman. Methods: A cross-sectional study via an online survey was conducted among 1019 adult Omani nationals residing in Oman. The presence of SAD was assessed using the Arabic version of the Liebowitz Social Anxiety Scale (LSAS). Result: Nearly half the participants (45.9%, n=468) endorsed themselves as having features of SAD as defined by LSAS. In the multivariate logistic analysis, participants below 40 years of age were 1.6 times (OR=1.568, p=0.026) more likely to have SAD than those who were 40 and older. Women were 1.3 times (OR=1.348, p=0.038) more likely to endorse SAD than men. Participants who had secondary or undergraduate education were respectively 1.5 times (OR=1.45, p=0.014) and 2.5 times (OR=2.509, p<.001) to have SAD than who were postgraduates. Conclusion: The present data suggest that 45.9% of the participants reached the cut-off for case-ness in LSAS, which is high compared to reports from other populations. As online survey respondents tend to belong to similar demographics, the current results need not be representative of the Omani adult population, which calls for studies that adopt more inclusive survey methods.

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

Green public space immensely provides ecological soundness (ES), social resilience, social inclusiveness, mental health, self-esteem, and cultural prosperity from privileged class to lower ones. This research aims at estimating the effect of social services, safety, and infrastructural amenity in green space (parks) on mental health building self-esteem for elderly population. Data were collected from four parks out of 21 in Dhaka selected by systematic random sampling. Two-hundred and three elderly participants were picked up for survey using snowball sampling. The survey was conducted in a pre-structured questionnaire consisted of 40 close-ended questions. Data was analyzed applying ordinal logistic model, multivariate regression analysis, and Partial Least Square (PLS) -Path Modeling. The study explored several key findings-1) Infrastructural amenities impacted social services of parks; 2) Social services contributed positively to mental health boosting up self-esteem for elderly population. 3) Likewise, mental health for elderly population was affected by safety measure of green space. Dhaka requires constructing age-friendly parks to promote elderly mental health.

COVID-19, caused by SARS-CoV-2, is responsible for widespread mortality and economic loss across almost 212 countries. The distribution of disease prevalence is, however, uneven and likely dependent on the range of human behavior and/or the pathogen and environmental variables. Here in this research we examined the correlation between daily and total COVID-19 case reports from Bangladesh and meteorological variables (average temperature, average humidity, and average wind speed) against one another using Spearman rank and Kendall correlation tests to highlight significant meteorological variables in relation to COVID-19. The tests revealed that both temperature and humidity had a non-significant correlation with COVID-19 transmission amid epicenter (EC) and non-epicenter (NEC) areas. However, a weak (KCC: 0.168, p < 0.05; SCC: 0.271, p < 0.05) correlation was found for temperature in ECs. Wind speed showed moderate to strong correlation in ECs with a very high statistical significance to both daily new cases [(KCC: 0.494, p < 0.01); (SCC: 0.689, p < 0.01)] and total cases [(KCC: 0.426, p < 0.01); (SCC: 0.617, p < 0.01)]. The COVID-19 transmission was found to have a weak (KCC: 0.354, p < 0.01) to moderate (SCC: 0.465, p < 0.01) correlation with daily new cases; however, a moderate (KCC: 0.497, p < 0.01) to strong (SCC: 0.712, p < 0.01) correlation with total cases was found in NECs. The results suggest that temperature and humidity have little influence on COVID-19 transmission, but wind speed may have some influence. Countries should prioritize social engineering to modify human behavior in order to combat the spread of COVID-19 and future pandemics.

Molecular autism research is evolving towards a biopsychosocial framework that is more informed by autistic experiences. In this context, research aims are moving away from correcting external autistic behaviors and towards alleviating internal distress. Autism Spectrum Conditions (ASCs) are associated with high rates of depression, suicidality and other comorbid psychopathologies, but this relationship is poorly understood. Here, we integrate emerging characterizations of internal autistic experiences within a molecular framework to yield insight into the prevalence of psychopathology in ASC. We demonstrate that descriptions of social camouflaging and autistic burnout resonate closely with the accepted definitions for early life stress (ELS) and chronic adolescent stress (CAS). We propose that social camouflaging could be considered a distinct form of CAS that contributes to allostatic overload, culminating in a pathophysiological state that is experienced as autistic burnout. Autistic burnout is thought to contribute to psychopathology via psychological and physiological mechanisms, but these remain largely unexplored by molecular researchers. Building on converging fields in molecular neuroscience, we discuss the substantial evidence implicating mitochondrial dysfunction in ASC to propose a novel role for mitochondrial allostatic load in the relationship between autism and psychopathology. An interplay between mitochondrial, neuroimmune and neuroendocrine signaling is increasingly implicated in stress-related psychopathologies, and these molecular players are also associated with neurodevelopmental, neurophysiological and neurochemical aspects of ASC etiology. Together, this suggests an increased exposure and underlying molecular susceptibility to ELS that increases the risk of psychopathology in ASC. This article describes an integrative framework shaped by autistic experiences that highlights novel avenues for molecular research into mechanisms that directly affect the quality of life and well-being of autistic individuals. Moreover, this framework emphasizes the need for increased access to diagnoses, accommodations, and resources to improve mental health outcomes in autism.

This study examines the relationship of fear of missing out (FOMO) with heavy social networking among Turkish university students (aged 17 - 55). The perception of the possible role of parental supervision on online activities is also investigated. Factor analysis of FOMO scale led us to evaluate the construct under two dimensions as (1) fear of missing experience and (2) fear of missing activity. The results revealed that fear of missing activity increases social media intrusion while fear of missing experience is found to have no significant effect. The reverse relationship is also valid: an urge to use social media predicts fear of missing out (activity and experience). Fear of missing experience is associated with problematic social media use (PSMU) and a high desire to use social media. The results additionally demonstrate that students aged 30 and older believe more in the requirement of parental control than those aged 17-22.

Social media HPV vaccination interventions show promise for increasing HPV vaccination rates. An important consideration for the implementation of effective interventions into real-world practice in the translation potential, or external validity, of the intervention. To this end, we conducted a systematic literature review to describe the current body of evidence regarding the external validity of social media HPV vaccination-related interventions. Constructs related to external validity were based on the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework. Seventeen articles published between 2006 and 2020 met inclusion criteria. Three researchers independently coded each article using a validated RE-AIM (reach, effectiveness/efficacy, adoption, implementation, maintenance) framework. Discrepant codes were discussed with a fourth reviewer to gain consensus. Of these 17 studies, three were pilot efficacy studies, 10 were RCTs to evaluate effectiveness, one was a population-based study, and three did not explicitly state which type of study was conducted. Reflecting this distribution of study types, across all studies the mean level of reporting RE-AIM dimensions varied with reach recording 90.8%, effectiveness (72.1%), adoption (40.3%), implementation (45.6%), and maintenance (26.5%). This review suggests that while the current HPV vaccination social media-driven interventions provide sufficient information on internal validity (reach and effectiveness), few have aimed to gather data on external validity needed to translate the interventions into real world implementation. Our data suggest that implementation research is needed to move HPV vaccination-related interventions into practice. Included in this review are recommendations for enhancing the design and reporting of these HPV vaccination social media-related interventions.

Conservation of natural habitats in human-dominated landscapes is critical for halting biodiversity loss. Maintaining habitat quantity and connectivity requires landscape-level collective action, which results from environmental decisions made by individual land owners. We investigate how individual decision making in a rural collective translates into quantitative differences in landscape-level environmental outcomes. Behavioral science has become a critical domain of knowledge in conservation, but little attention has been paid to how multiple behavioral drivers determine the success of collective environmental action. We developed a social-ecological model for landscape-level conservation using a detailed data set of 600 land owners in New Zealand. With the model, we tested whether the effect of social influence networks on collective conservation action was altered by their interplay with land owners’ personal characteristics, connections to cross-scale actors and local environmental contexts. Interactions between multiple behavioral drivers determined the environmental outcomes of collective action in unexpected ways by modifying, muting or amplifying the effects of single drivers. Importantly, we detected a social-ecological mechanism for rapid change in the extent of protected habitats, which can explain highly successful or failed environmental outcomes of collective conservation. Further, when environmentally desirable and undesirable behaviors spread simultaneously through the social network, homophily and network cohesion hinder desirable environmental outcomes. This effect can be modified by other drivers such as social responses to local environmental change. Thus, understanding how the antagonistic and synergistic effects of behavioral drivers can be best utilized in conservation will benefit biodiversity and ensure benefits that humans obtain from biodiversity.

This article presents description of social structures such as, for example, states, in the context of their consideration as systems. Moreover, it seeks to develop K. Marx’s model in terms of systematic approach, as well as to justify the application of the Fibonacci sequence to systems in general and, in particular, to social systems. The description covers a wide range of issues, ranging from the assertion of the basic principle of living systems as patterns in resources flows to description of functions of various aspects of states and their economics in terms of objective needs of such structures. In addition, this article justifies synergetic effect based on proposed model, and the way this effect is applied to state systems. Moreover, it includes the description of main changes of socio-economic formations throughout the history as a natural development of social systems, including possible modern stages. The fundamental tool of this description is description of essence of the mechanism of property rights and its formation options. The differences of two extreme directions of such development are also described on the basis of the objective factor of resource provision. Special attention is given to the concept of surplus value as a feature of social production systems.

In relation to organizational performance measurement, there is a growing concern about the creation of value for people, society and the environment. The traditional corporate reporting does not adequately satisfy the information needs of stakeholders for assessing an organization’s past and future potential performance. Practitioners and scholars have developed new non-financial reporting frameworks from a social and environmental perspective, giving birth to the field of Integrated Reporting (IR). The Economy for the Common Good (ECG) model and its tools to facilitate sustainability management and reporting can provide a framework to do it. The present study is the first one that empirically validates such metrics on a sample of 206 European firms. Consequently, it allows knowledge to advance as it checks their statistical validity and reliability.

The COVID-19 outbreak has severely affected the social and economic conditions across this globe. Little is known about the relationship of COVID-19 with countries’ economic and socio-demographic status. Publicly available data on COVID-19 test rate, attack rate, case fatality rate, and recovery rate were analyzed in relation to country’s economic status, population density, median age, and urban population ratio. We also conducted multinomial logistic regression analysis to predict the influence of countries’ social and economic factors on COVID-19. The results revealed that the median age had significant positive correlation with attack rate (r=0.2389, p=0.003), case fatality rate (r=0.3207, p=0.000) and recovery rate (r=0.4847, p=0.000). The urbanization has positive significant correlation with recovery rate (r=0.1957, p= 0.016). The multinomial logistic regression analysis revealed low-income countries are less likely to have an increased recovery rate (p=0.000) and attack rate (p=0.016) compare to high-income countries. The lower-middle-income and upper-middle-income countries are less likely to have an increased recovery rate (p=0.000 and p=0.001, respectively) compared to high-income countries. Based on the result of this study, these economic and socio-demographic factors should consider in designing appropriate preventive measures as a next step. The low and lower-middle-income countries should invest more in health care services to lower the case fatality rate and increase test and recovery rates as part of pandemic preparation like COVID-19. As the number of COVID-19 attacks, death and recovery rates are constantly changing; however, the intensive study is required to obtain a clear picture.

Since the public inauguration of the URP (Urban and Regional Planning) Bill in 2009, which is now law (The Urban and Regional Planning Act No. 3 of 2015), urban planning in Zambia has undergone changes. In partnership with the Volunteer Service Overseas (VSO) Federation, the Zambian parliament put into effect pilot urban planning assistance programs to assist districts around the country, including Chipata District in 2011, transition to a more decentralized, integrated and locally-defined approach to urban planning. However, the presence of discrimination, corruption, and negative attitudes towards urban planning engagement, social maladies prominently displayed in Zambian society, pose challenges to implementing the ideal goals of the 2009 URP Bill. The extreme, widespread poverty in Zambia merely exacerbates the propensity towards corrupt and discriminatory behavior, and influences poor attitudes toward urban planning engagement. This paper describes the projects undertaken by the VSO volunteer from the USA between 2011 and 2012 in the light of the specific urban problems facing Chipata District, and discusses the ways the social maladies play out in Zambian society to pose challenges to implementing the recommended changes to the planning system scribed in the 2009 URP Bill.

B chromosomes are supernumerary chromosomes which are found in addition to the normal standard chromosomes (A chromosomes). B chromosomes are well known to accumulate several types of repeats. Although the evolution of B chromosomes has been subject of numerous studies, the mechanisms of accumulation and evolution of repetitive sequences is not fully understood. Recently, new genomic approaches have shed light on the origin and accumulation of different classes of repetitive sequences in the process of B chromosome formation and evolution. Here we discuss the impact of repetitive sequences accumulation on the evolution of plant B chromosomes.

The corona (COVID-19) pandemic offers an opportunity for dealing with persistent problems, through a transformative recovery process. It is a crisis that offers opportunities for dealing with three interrelated crises: the ecological crisis (climate change, loss of biodiversity, resource depletion, pollution and ecosystem destruction), the confidence crisis (people losing trust in government, politics, companies, regular news channels, science, each other and the future), and the inequality crisis (the widening of the gap between rich and poor). Our argument is that sustainability transitions will not succeed without a different economy and another social contract with the associated rights and duties of care (for the environment and the well-being of others, including future generations). A different social contract is not only desirable from the point of view of sustainability and fairness, justice and equality, but is also necessary to restore citizens' trust in politics, government, companies and each other. In the paper we discuss mechanisms towards a Natural Social Contract, systemic leverage points for system transformations and possibilities for co-evolutionary governance by actor coalitions interested in transformative change. The combination of those three elements helps to synchronize different agendas and reduce the chance that they will work against each other.

The role of Social Media Marketing (SMM) in marketing strategies is rapidly growing. Because the use of social media is growing, the industry of SMM will grow bigger in the coming years; the pace of this growth is faster than ever. To survive in the modern competitive world, effective use of SMM for a firm is a must; for that, every SMM channel needs to be used to its full potential. For a marketing campaign to be effective, there is a need for some metrics to measure the success of the SMM campaign. These metrics measure if the campaign is successfully implemented or not. This would help firms understand the market, gain a competitive advantage, and ultimately get a positive impact on the overall business. This study categorizes SMM strategy into 4 dimensions and associates 10 broad categories of SMM metrics to these dimensions. The proposed model of this study suggests the application of Social Media Analytics (SMA) ineffective use of metrics to measure SMM campaigns. There are so many SMA Tools available for free and time-efficient data analysis that can lead to faster and better results than manual analysis. Following this model, the importance of SMA tools in devising an effective SMM strategy is highlighted. The implication of this research is towards a better understanding of the application of SMA for any firm to have a solid SMM Strategy, especially small and medium-sized enterprises that have limited resources.

Customers make purchases through a multi-stage, omnichannel process, which now extends to social media. A loyalty loop can be achieved when an existing customer bypasses the initial stages –consider and evaluate– to make a direct purchase. However, to build that loyalty loop, a retailer must embrace techniques and software across social media marketing, social CRM, and omnichannel CRM. Reviewing literature from academic researchers and industry practitioners, this paper identifies emerging industry trends and discusses an illustrative case study of a UK digital retailer. The importance of social media for omnichannel retailing and building loyalty is highlighted and discussed.

Text book ought to be viewed as one of the numerous sources educators can attract after making a powerful exercise and may offer a system of direction and introduction. The analysis of textbook forms a necessary part in academics so as to devise the most appropriate content. The present study made a comparative account of textbooks for Grade 5 Social studies taught in public (i.e., Punjab textbook) and private sector (Oxford textbook) of Pakistan. The comparison was made on key features and on themes suggested by National Curriculum Council (2017) as a standard for textbook publishers. The findings demonstrated that both books contain activities and are clearly written, however, PTB is cheaper, possess low paper quality than OTB and was revised in 2018 whereas OTB has not revised since 2010. It is also suggested that both textbooks do not contain enough information as per NCC’s standard. It is suggested that government should take notice about it and publishers should be bound to deliver only that material in education sector that is according to the devised set of standards.

This pilot study explores the potential of using a citizen science approach for sourcing volunteered geographic information via social media to research wildlife tourism interactions with endangered Borneo Pygmy Elephants on the lower Kinabatangan River in Sabah, Malaysia. Such information is critical if the lower Kinabatangan region is to achieve the United Nations Sustainable Development Goals through a sustainable tourism industry based around viewing the pygmy elephants. Guests and guides from the Sukau Rainforest Lodge were encouraged to become close-range remote sensors by sharing geotagged photographs of pygmy elephant sightings on Flickr. A ten week on-ground trail generated 247 photographs shared by 17 individual contributors with approximately two-thirds (65%) of photographs being georeferenced for the time and location of the elephant sighting. Plotting those sighting to explore the vegetation matrix (i.e. remnant forest or oil palm plantation) showed almost three-quarter (73%) of the sightings occurred within 1 km of an oil palm plantation. Of greater concern is that one in two sightings (50%) along the river occurred within the 500 m of an oil palm planation, which is inside the riparian buffer that the Sabah Government recommended for conservation of the elephants in their Lower Kinabatangan range. This study therefore demonstrates proof of concept for this research method and its further application at the nexus of wildlife conservation and sustainable ecotourism research.

The spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 150 countries in a matter of months. Even though containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, as the US, Spain and Italy have come off worse-off by the spread of the virus, and there is serious concern regarding the national health systems' capacity around the world and Africa, in particular, to effectively respond to the needs of infected patients who require intensive care for the COVID-19 illness. As a result, authorities around the world have resorted to the lockdown strategy to curb the spread of the virus. Therefore, the study aimed at investigating the effect of the lockdown strategy in curbing the spread of the COVID-19 virus in Nigeria. Consequently, the study found daily relative risk increase in cases, and daily relative risk increase in mortality. Also observed is the growth in cases in areas where active measures were not taken. Further, halt in business activities has rendered many penniless and unable to provide for themselves basic amenities. The study recommends that there is need to implement community-level measures of social distancing which may include closing schools, need for individuals with COVID-19 case or respiratory symptoms be properly taken care of, trace and quarantine those who must have come in contact with affected persons and introducing stay at home palliatives for the general public.

Clubhouse is an auditory app that allows users to host various rooms surrounding a diverse range of topics from Artificial Intelligence to Philosophy. Along with its educational and serene approach, it is known for its popularity amongst celebrities, including Elon Musk, Mark Zuckerburg, CEO of Shopify, and its elusive invite and ios-only pass into gaining access into Clubhouse. Waiting lists are available in the case of not achieving an invite, but to further speed the process, various sellers on eBay, Reddit, Twitter, etc., charging invites from $10-$200. This research paper covers the phenomenon of Clubhouse and the emergence of audio-only rooms, along with a hypothesis of why Clubhouse and other apps of a similar kind are experiencing a harsh downfall despite its seemingly successful business model.

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

We present the detailed calculations of social distancing requirement. A comparative study of the growth pattern and death tolls in different communities indicates that the growth pattern of infected patients and death rate follow the similar distribution with different parametrizations. Every distribution follows an exponential growth pattern curve, like other microbes, then reaches the saturation point an d eventually decay s However, the argument for the exponential function depends on several parameters unbeknownst, as of yet. However, the slope varies different ial ly for various epicenter s and seems to have a relationship with parameters such as accessibi lity to healthcare facilities, pre existing medical conditions socio economic conditions and lifestyle. The mismatch of the growth pattern is also linked with the impact of various other factors and a premature interpretation of limit ed data. Novel behavi or of the virus brought many surprises, opened up new venues for medical research, and the need for the more detailed study of pathogens in the light of the interaction of RNA and DNA The adaptability to diverse ecological condition s and the relevant modification in the structure is also worth investigation The genetic modification can be studied using quantum mechanical probabilistic approach.

Responsible innovation combines philanthropic and economic aspects and it is common to refer to entrepreneurs who lead it as "social entrepreneurs". The present study of 100 Mexican SMEs, provides knowledge of exploratory nature about what the models of organization are conducive to SMEs in the generation and development of responsible innovations. Through the statistical technique of cluster analysis, this study identified and characterized four models of organization according to the level of social entrepreneurship reached: (1) “The techno-scientific organization”, (2) “The techno-social organization”, (3) “The capitalist-social organization” and (4) “The capitalist organization”. While in Europe the dominant discourse about responsible innovation focuses on the control of the risk of social rejection of the advance of science and technology; in contexts such as the Mexican, the phenomenon is configured as the mechanism through which entrepreneurs articulate its technological and scientific capabilities to solve priority and specific problems of the society, however, the social impact does not crucially affect their business initiatives. The techno-scientific organization (50% of studied SMEs) is proposed as the model of organization with greater viability for Mexican entrepreneurs.

This article analyzes the emergence of partnerships between corporations and nongovernmental organizations (NGOs) through voluntary product labeling schemes. The economics, management, and business literatures are reviewed to highlight cross-checking, consistencies, and complementarities among these disciplines. The objective is to identify and analyze the motives, risks, and joint benefits of partnering via voluntary product labeling, using examples from the agri-food sector. This study is an attempt to offer a framework of corporate-NGO partnerships by showing that the drivers and risks of each partner merge because each takes a step into the sphere of the other. The main risks – namely, a loss of profitability, credibility and legitimacy – are related to the financial and existential dependency and the asymmetric information between the partners in favor of corporations, inducing an ``NGO-capture'' risk.

The success of many viruses depends upon cooperative interactions between viral genomes. For example, viruses that coinfect the same cell can share essential gene products, such as replicase, the enzyme that replicates the viral genome. However, when cooperation occurs, there is the potential for ‘cheats’ to exploit that cooperation. We suggest that: (1) the biology of viruses makes viral cooperation particularly susceptible to cheating; (2) cheats are common across a wide range of viruses, including viral entities that are already well studied, such as defective interfering genomes, and satellite viruses. Consequently, evolutionary theory developed to explain cheating offers a conceptual framework for understanding and manipulating viral dynamics. At the same time, viruses offer unique opportunities to study how cheats evolve, because cheating is relatively common in viruses, compared with taxa where cooperation is more usually studied, such as animals.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

Growth hormone (GH) deficiency affects up to 1 in 4,000 children and is usually treated with daily injections of GH whilst the child is still growing. With children typically diagnosed around 5 years old, this can mean over 10 years of therapy, which can place a considerable burden on the child and parent. Over three-quarters of children are estimated to be not fully compliant with therapy, which can compromise their chances of attaining their target height. In recent years, interactive mobile health (smart phone or tablet) interventions using game-like concepts, so called ‘gamification’, have increased in popularity and have demonstrated success in promoting positive self-management behaviour in children with chronic conditions, such as diabetes. The application of gamified interventions has the potential to support adherence to therapy and positive behaviour in children with GH deficiency in a number of ways: 1) By providing education in a format that the child understands and accepts (e.g. using behavioural constructs to facilitate explaining why adherence is important); 2) By providing a mechanism to reduce the anxiety and stress associated with administering the injection (e.g. diversion with a virtual pet); and 3) By providing feedback to encourage on-going engagement (e.g. rewards, progression through levels).

Smartphone devices, particularly android devices used by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attackers that is a network of interconnected nodes operated by command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks carried Distributed denial of services (DDoS) that causes to steal sensitive data, remote access, spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet using static or dynamic analysis. In this paper, we have proposed a novel hybrid model, which is a combination of static and dynamic method that relies on machine learning to identify android botnet applications having C&C capability. The results evaluated through machine learning classifiers in which Random forest classifier outperform other ML techniques, i.e. Naïve Bayes, Support Vector Machine, and Simple logistics. Our proposed framework can achieve 97.48% accuracy in detecting such harmful applications. Furthermore, we highlight some research directions and possible solutions regarding botnet attacks for the entire community.

Heavy caffeine consumption is associated with adverse health effects. The effects of moderate and high doses of caffeine mixed with drinking water on the motor coordination, learning and memory and the social behavior in mice were studied in mice. Animals were divided into 3 groups: control group, moderate dose group (Ac MD) and high dose group (Ac HD). The animals were tested after 7 days of caffeine administration. Rota rod test for motor coordination showed that the mice of the moderate dose group could stay more time on the rotating rod before they fall than the control group and the high dose group. Water maze test for learning and memory showed better performance of mice receiving moderate dose of caffeine compared to the other groups. Animals that were administered moderate as well as high doses of caffeine showed no sociability and no preference for social novelty in the three-chamber test used to test the social behavior. In elevated plus maze, control animals showed no anxiety- like behavior while mice administered with caffeine were both showing anxiety-like behaviors. We concluded that acute administration of moderate dose of caffeine to mice could enhance their spatial memory and motor coordination. High doses however caused defects in memory and learning. The social behavior as the level of anxiety and sociability was affected negatively by moderate as well as high dose caffeine administration.

In Bangladesh, microcredit program has been in function for almost half a century. Though popularly termed as a tool for poverty alleviation and women empowerment, criticism about microcredit program’s actual effectiveness isn’t new. This study attempts to evaluate the impact of such programs by measuring borrowers’ social mobility. Using a multidimensional approach, indicators like household income, economic susceptibility, living conditions, consumption pattern, children’s education, healthcare facilities, women’s participation in decision-making were taken into account for measurement. Quantitative method was used. Based on a non-probability sampling, 107 microcredit borrowers were selected for conducting interview schedules who live in a rural, a semi-urban and an urban area. Statistical analysis of data reveals that those who have been taking loans for several number of years have all managed to increase their income level to a variety of extent, but only those have gained some sort of mobility who have been taking loans for more than 4-5 years and have taken 5 times or more. Few of them have actually moved from microcredit to become a microfinance client by taking bigger amount of loans and having savings. Positive responses about the indicators being used in the study were found among the handful of those who have mobilized significantly. In true sense, most of the borrowers are stuck at the bottom end of socio-economic ladder and are struggling to manage a better living standard.

Abstract: Understanding social media networks and group interactions are crucial to the ad-vancement of linguistic and cultural behaviour. This includes the manner in which people ac-cessed advice on health, especially during the global lockdown periods. Some people turned to social media to access information on health where most activities were curtailed with isolation rules, especially for older generations. Facebook public pages, groups and verified profiles, using "senior citizen health", "older generations", and "healthy living" keywords were analysed over a 12-month period to analyse the engagement promoting good mental health. CrowdTangle was used to source English language status updates, photo and video sharing information which resulted in an initial 116,321 posts and 6,462,065 interactions Data analysis and visualisation were used to explore large datasets, including natural language processing for “Message” content discovery, word frequency and correlational analysis and co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media which showed correla-tions to global daily confirmed case and daily death totals. The results can be used to identify public concerns early on and address mental health issues in the senior generation on Facebook.

The world is currently facing an outbreak of the monkeypox virus and confirmed cases have been reported from 74 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of more than 255,000 Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employee to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

In the smart city, information and communications technologies (ICTs) are proposed as solutions to urban challenges, including sustainability concerns. While sustainability commonly refers to economic and environmental dimensions, the concept also contains a social component. Our study asked how smartphone applications (apps) address social sustainability challenges in urban transport, if at all. We focused on transport disadvantages experienced due to low income, physical disability, and language barriers. A review of 60 apps showed that transport apps respond to these equity and inclusion issues in two ways: (a) by employing universal design in general-use apps; including cost-conscious features; and providing language options (b) by specifically developing smartphone apps for persons with disabilities. The article discusses the study by positioning it in the literature of smart cities as well as socially sustainable transport.

We first review the literature on machine behavior which may contain the interaction between machines, and then discuss the methodology and the related case involving collaboration between robots to instantiate the concept with AI empowerment. The navigation case aims to a security application with autonomous control and roadside agent embedded in a mobile edge computing structure. In the studied navigation based on the artificial potential field method, robots need to use position information to calculate the moving direction frequently. In the case of high motion speed as well as GPS positioning error, the path trajectory may show a sharp change of direction. In order to mitigate the trajectory oscillation, this paper proposes a path planning design where training process and motion direction prediction are integrated by using artificial neural network. The auxiliary navigation agent near multiple obstacles can first extract the past movement information of the robot and then determines whether there is a serious path jitter event. Computer simulation analysis shows that the combination of autonomous control and cooperative behavior can effectively reduce the path jitter so as to achieve a fast and safe path planning.

This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.

Users on the social networking platform have the freedom to express themselves freely. Towards the same time, this has created a forum for disagreement and hate directed at someone, society, racism, sexual orientation, and so on. Identifying hate online is a challenging task. Researchers from all around the world have contributed major methods for detecting hate speech, but owing to the issue's complexity, there are still many unresolved issues. In this research, we offer a multi-model learning strategy for detecting hate speech on Twitter. We utilised the Kaggle TwitterHate dataset, which had 31962 tweets categorised as binary hate or non-hate, to evaluate our technique. The suggested method is tested using commonly used machine learning classifiers with multi-model technique. Using TF-IDF features, we acquired detection results of 96.29 %, precision of 96%, recall of 96%, and f1-score of 96%.

The present study has been done using the Survey Research. The research sample scale equals 400 people, besides its statistical population is included the 15-year population and most of the city of Kerman in 2020. The method of multistage-cluster-stratified sampling was used in five districts of the city of Kerman, moreover the results have been analyzed by SPSS and AMOSS16 software, and only is one model fitted with reality among five models of designed path. The results of analysis of path diagram indicate that other coefficients of the path all of them are significant except the direct impact of one's image of the body on sport-based cultural capital and social class on the tendency toward the public sport. Other results of the study suggest that sport-based socio-economic capital leaves an indirect effect on sport-based cultural capital by which the tendency of citizens toward the sport grows up. At that showing athletic advertisements in the media are effective on the tendency of citizens to public sport.

The COVID-19 pandemic and associated restrictive measures implemented may considerably affect people’s lives. This study aimed to assess the well-being of Vietnamese people after COVID-19 lockdown measures were lifted and life gradually returned to normal. An online survey was organized from 21st to 25th April 2020 among Vietnamese residents aged 18 and over. Besides collecting socio-demographic and COVID-19-related data, the WHO-5 Well-Being Index (scored 0–25) was used to score participants’ well-being. A multivariate logistic regression model was used to determine the predictors of well-being. A total of 1922 responses were analyzed (mean age: 31 years; 30.5% male). Mean well-being score was 17.35±4.97. Determinants of high well-being score (≥13) included older age, eating healthy food, practising physical exercise, working from home, and adhering to the COVID-19 preventive measures. Female participants, persons worried about their relatives’ health, and smokers were more likely to have a low well-being score. In conclusion, after the lockdown measures were lifted, the Vietnamese people continued to follow COVID-19 preventive measures and most of them scored high on the well-being scale. Waiting to achieve large scale COVID-19 vaccine coverage, promoting preventive COVID-19 measures remains important, together with strategies to guarantee the well-being of the Vietnamese people.

All over the world, development of micro blog and other social platform indicate that Social Media is now the focus and trend of the Internet. Daily life, study and work are influenced by news in Social Medias . Micro blog is new emergent type of media and it spreads information rapidly in the crowd in recent years. Suppose an user searches for specific information about one topic on micro blog. He/she found easily plenty of information related to his/her search in social medias. The problem is to find out the correct information. Normally, multi-document summarization method deals with a collection of documents about one topic for extracting the valuable points and discards useless information. Actually, it needs to extract the topic content by adding topic factors and social patterns. Topic factor is the lexical information related to the topic. Social pattern relates to special interactive mode owned by online social network, such as comment and repost. People has been seen the fake news on mobile/internet during lockdown period. It is of no doubt that anyone with a social media account has seen at least one example of this.Humanity’s greatest challenges are to detect false information. Fake news are collected from 150 persons using social media The aim of the paper is to investigate the truthfulness of the news people share on social media using K-nearest Neighbour (KNN) based Classifier method.

Excessive cell phone use contributes to distracted driving, may increase risk for automobile accidents, and a minority of mobile phone users exhibit behaviors consistent with technological addiction. The purpose of this study was to determine whether cell phone beliefs and behaviors could be changed by a brief educational encounter. The Theory of Reasoned Action provided a lens for viewing attitudes and behavior. A one-week pre-post design with a thirty-day follow-up was used with participants (N = 215, 67.0% female, age = 20.0 + 1.6) assigned to a peer led intervention or comparison groups. The intervention included cell-phone educational materials. A short index of cell phone behavior was developed which showed good internal consistency with a Cronbach’s alpha of .81. The intervention group “agreed” or “strongly-agreed” more than the comparison group on five of the seven areas of cell phone beliefs and behaviors ( p < 0.05, item Cohen’s d = .32 to .47, total d = .50) at one-week following receipt of informational materials. We conclude that attitudes and behaviors regarding cell phones are malleable and susceptible to change in young-adults following a brief psychoeducational intervention.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

This research contributes to the development of theories regarding the relationship between Corporate Social Responsibility (CSR) and investment decisions. Acquisition of stock returns that exceed normal predictions depends on the successful implementation of Good Corporate Governance (GCG). This study aims to examine investors' reactions to information on CSR disclosure in several countries that are members of the Association of Southeast Asian Nations (ASEAN). Furthermore, this study also examines the role of implementing GCG in strengthening the impact of CSR disclosure on investor relations as measured by abnormal stock returns. The sampling technique used was purposive Sampling. The research was conducted on Manufacturing Companies in countries that are members of ASEAN during 2017-2019. The estimation model used to analyze data is a multiple regression model. The results showed that CSR information was able to increase investors' positive reactions. Meanwhile, GCG practice is proven to strengthen the impact of CSR information on investment decisions. Other variables involved in this study, namely audit quality, company size, debt level, and sales growth, are not proven to influence abnormal stock returns.

The Social-Ecological Systems (SES) framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. Yet, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change that institutions and natural resources systems co-evolve to provide a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how Data Cube technology could overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users.

Tinnitus is a complex and heterogeneous psycho-physiological disorder responsible for causing a phantom ringing or buzzing sound albeit the absence of an external sound source. It has a direct influence on affecting the quality of life of its sufferers. Despite being around for a while, there hasn’t been a cure for tinnitus, and the usual course of action for its treatment involves use of tinnitus retaining and sound therapy, or Cognitive Behavioral Therapy (CBT). One positive aspect about these therapies is that they can be administered face-to-face as well as delivered via internet or smartphone. Smartphones are especially helpful as they are highly personalized devices, and offer a well-established ecosystem of apps, accessible via respective marketplaces of differing mobile platforms. Note that current therapeutic treatments such as CBT have shown to be effective in suppressing the tinnitus symptoms when administered face-to-face, their effectiveness when being delivered using smartphones is not known so far. A quick search on the prominent market places of popular mobile platforms (Android and iOS) yielded roughly 250 smartphone apps offering tinnitus-related therapies and tinnitus management. As this number is expected to steadily increase due to high interest in smartphone app development, a contemporary review of such apps is crucial. In this paper, we aim to review scientific studies validating the smartphone apps, particularly to test their effectiveness in tinnitus management and treatment. We use the PRISMA guidelines for systematic identification of studies on major scientific literature sources and delineate the outcomes of identified studies.

This paper introduces both a hardware and a software system designed to allow low-cost electronic monitoring of social insects using RFID tags. Data formats for individual insect identification and their associated experiment are proposed to facilitate data sharing from experiments conducted with this system. The antenna's configuration and their duty cycle ensure a high degree of detection rates. Other advantages and limitations of this system are discussed in detail in the paper.

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

The purpose of this study is to explore the intensity of emotions and social skills in a physical education teaching unit (PE). Two instruments of analysis are used: the GES (Games and Emotion Scale) to evaluate emotions and an ad-hoc questionnaire to measure the social skills of 21 students in the sixth year of primary education. The data analysis was carried out using a generalised estimating equation model (GEE), taking into account the correlation between the different scores of the same subject and the asymmetry of the data. The results show positive emotions to be significantly more intense than negative ones and ambiguous emotions are revealed throughout all the PE sessions.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life.Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and so on are common tools of conversations grouping. On the other hand, crowdsourcing is a concept that involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We build upon earlier work where we analyzed online forum posts from various geographical regions in the USA and Canada and characterized the readability scores of such users. Specifically, we collected 1000 tweets from the members of the US Senate and computed the Flesch-Kincaid readability score for the Senators. Comparing the Senators’ tweets to the ones from average citizens, we note the following. 1) US Senators’ readability based on their tweets rate is much higher affirming the gap between the academic performance of US Senators and their average citizen, and 2) the immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

This document aims to adapt the dimension of the Socially Responsible Brand Personality (SRBP), proposed by Mayorga (2017), in the midst of the Covid-19 pandemic. The interest of this work is to contribute with the management of the CSR, contributing from a communicational perspective with a tool that optimizes the construction of a socially responsible image by the different stakeholders. The results allow us to conclude that there is a structural modification of the brand personality, which can be presumed to be generated by the current environment and can be established as a pillar of CSR management in the new normality.

Background Due to the Covid-19 pandemic and the strict national policies regarding social distancing behavior in Europe, America and Australia, people became reliant on social media as a means for gathering information and a tool for staying connected to family, friends and work. This is the first trans-national study exploring the qualitative experiences and challenges of using social media while in lockdown or shelter in place during the current pandemic. Methods This study was part of a wider cross-sectional online survey conducted in Norway, UK, USA and Australia during April/May 2020. The manuscript reports on the qualitative free text component of the study asking about the challenges of social media users during the Covid-19 pandemic in UK, USA and Australia. 1991 responses were included in the analysis. Thematic analysis was conducted independently by two researchers. Results Three overarching themes identified were: Emotional/Mental Health, Information and Being Connected. Participants experienced that using social media during the pandemic amplified anxiety, depression, fear, panic, anger, frustration and loneliness. They felt that there was information overload and social media was full of misleading or polarized opinions from which was difficult to switch off. Nonetheless, participants also thought that there was an urge for connection and learning which was positive and stressful and the same time. Conclusion Using social media while in shelter in place or lockdown could have a negative impact on the emotional and mental health of some of the population. To support policy and practice in strengthening mental health care in the community, social media could be used to deliver practical advice on coping and stress management. Communication with the public should be strengthened by unambiguous and clear messages and clear communication pathways. We should be looking at alternative ways of staying connected.

In late February 2020, Coronavirus disease-2019 (COVID-19) has aggressively spread around many bordering provinces of the three most productive regions of northern Italy (Lombardy, Veneto and Emilia Romagna). The first outbreak exploded in the municipality of Codogno (Lombardy). The province of Ferrara (Emilia Romagna) has been indicated as an anomaly due to the lower number of confirmed cases (1·065 cases per 1000 population). We argue that the spread of the virus throughout Emilia Romagna has a possible explanation in the geographical location of most of its provinces along the Via Emilia, an ancient Roman road that runs throughout the region, which we consider as a proxy for citizens’ movement, number of contacts, and social interactions. In order to test this hypothesis, we used a non-linear multiple regression analysis on aggregate province data to investigate the association between the rates of confirmed cases and the distance from the Via Emilia. The results indicate that the infection rate decreases proportionally to the distance from Via Emilia (-14% every 10 km, p<0·001). Further studies are needed, but Ferrara’s “peculiarity” might have a geographical/behavioral explanation, due to its isolation from the regional main connection routes, which are still revolving around a road built by the ancient Romans 2,000 years ago.

The COVID-19 pandemic has had devastating consequences for health, social, and economic domains, but what has received far less focus is the effect on people’s relationship to vital ecological supports, including access to greenspace. We assessed patterns of greenspace use in relation to individual and environmental factors and their relationship with experiencing psychological symptoms under the pandemic. We conducted an online survey recruiting participants from social media for adults in Korea for September–December 2020. The survey collected data on demographics, patterns of using greenspace during the pandemic, and major depression (MD) and generalized anxiety disorder (GAD) symptoms. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder 2-item (GAD-2) were applied to identify probable cases of MD and GAD. A logistic regression model assessed the association decreased visits to greenspace after the outbreak compared to 2019 and probable MD and GAD. Among the 322 survey participants, prevalence of probable MD and GAD were 19.3% and 14.9%, respectively. High rates of probable MD (23.3%) and GAD (19.4%) were found among persons currently having job-related and financial issues. Of the total participants, 64.9% reported decreased visits to greenspace after the COVID-19 outbreak. Persons with decreased visits to greenspace had 2.06 higher odds (95% CI: 0.91, 4.67) of probable MD at the time of the survey than persons whose visits to greenspace increased or did not change. Findings suggest that barriers to greenspace use could deprive people of mental health benefits and affect mental health during pandemic; an alternative explanation is that those experiencing poor mental health may be less likely to visit greenspaces during pandemic. This implies the need of adequate interventions on greenspace uses under an outbreak especially focusing on how low-income populations may be more adversely affected by a pandemic and its policy responses.