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Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported only in 2020, posing a high burden for this population. In this paper, we describe the data structure of snakebite records from 2007 to 2020 in the Notifiable Disease Information System (SINAN), made available by the Brazilian Ministry of Health (MoH). In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work are related to clinical and demographic information on SBE cases. Also, data on outcomes, laboratory results, and treatment are available. The dataset is available and freely accessible; however, preprocessing, adjustments, and standardization are necessary due to incompleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for assessing different aspects and the national burden of envenoming. Dataset: The dataset is available at https://github.com/LabdataManaus/describe%5fsnakebite/tree/main. Accessed on 26 April 2024. Dataset License: CC BY-NC-SA [ABSTRACT FROM AUTHOR]

Exploring online criticisms of the "take the knee" protest during "Euro 2020", this article examines how alt- and far-right conspiracies were both constructed and communicated via the social media platform, Twitter. By providing a novel exploration of alt-right conspiracies during an international football tournament, a qualitative thematic analysis of 1,388 original tweets relating to Euro 2020 was undertaken. The findings reveal how, in criticisms levelled at both "wokeism" and the Black Lives Matter movement, anti-white criticisms of the "take the knee" protest were embroiled in alt-right conspiracies that exposed an assumed Cultural Marxist, "woke agenda" in the tournament's organisation and mainstream media coverage. In conclusion, it is argued that conspiratorial discourses, associated with the alt-right, provided a framework through which the protest could be understood. This emphasises how the significance of conspiracy functions to promote the wider dissemination of alt-right ideology across popular cultural contexts, such as sport. [ABSTRACT FROM AUTHOR]

The term sportswashing has been discussed and analysed within academic circles, as well as the mainstream media. However, the majority of existing research has focused on one-off event-based sportswashing strategies (such as autocratic states hosting major international sports events) rather than longer term investment-based strategies (such as state actors purchasing sports clubs and teams). Furthermore, little has been written about the impact of this latter strategy on the existing fanbase of the purchased team and on their relationship with sportswashing and the discourses surrounding it. This paper addresses this lacuna through analysis of a popular Manchester City online fan forum, which illustrates the manner in which this community of dedicated City fans have legitimated the actions of the club's ownership regime, the Abu Dhabi United Group – a private equity group operated by Abu Dhabi royalty and UAE politicians. The discursive strategies of the City fans are discussed, in addition to the wider significance of these strategies on the issue of sportswashing and its coverage by the media. [ABSTRACT FROM AUTHOR]

England's 'shock' exit from the Euro 2016 Football Championship and the UK electorate's decision to (Br)exit from the EU occurred almost simultaneously, providing an interesting lens through which to examine unfolding tensions in the UK and its component identities – all the more so given the presence of two of the three other component countries of the UK, Wales and Northern Ireland, at the Championship. Our analysis of 34,324 original tweets featuring both the hashtag #Euro2016 and #Brexit shows a clear tendency to conflate British and English identities in the context of Brexit, a conflation coloured by largely negative characteristics. We pay particular attention to how recurring themes concerning leadership, accountability and immigration are articulated in the context of both sporting and political events. In addition, we examine the significance of the structural logic of social media in these discussions of the sport-nation nexus, with reference to vortextuality and mediatization. [ABSTRACT FROM AUTHOR]

Trust is widely regarded as being foundational in workplace relationships. The violation of interpersonal trust results in a range of negative affective, cognitive and behavioural consequences for the injured party. However, research has yet to isolate the specific neural areas and processes activated when different types of interpersonal trust are breached. Using electroencephalogram with 68 participants, we identified the effects of three distinct types of trust violations—ability violation, integrity violation and benevolence violations—on electrical brain activity. Our findings indicate that trust violations are processed in social cognitive‐related brain areas. Specifically, our results identify the significance of the default mode network (DMN), relevant to the processing of social information, in trust violation and further isolated distinct activity for ability, integrity and benevolence trust violation, with integrity violations demonstrating the greatest reaction in the DMN. Benevolence violations generated the next greatest reaction but were not significantly different from the ability violations. This potential distinction may be worth further investigation in future research. Our findings highlight the potential importance of the DMN in processing cues regarding the trustworthiness of others and the distinctiveness of the processing of violation cues of the three facets of trustworthiness. [ABSTRACT FROM AUTHOR]

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages. [ABSTRACT FROM AUTHOR]

The rise of online hate speech in sports is a growing concern, with fans, players and officials subject to racist, sexist and homophobic abuse (in addition to many other prejudices) via social media platforms. While hate speech and discrimination have always been problems in sports, the growth of social media has seen them exacerbated exponentially. As a consequence, policy makers, sport governing bodies and grassroots anti-hate organisations are largely left playing catch-up with the rapidly shifting realm of online hate. Scholars have attempted to fill this vacuum with research into this topic, but such is the evolving nature of the issue that research has been diverse and fragmentary. We offer a scoping review into the scholarship of online hate in sport in order to encourage and facilitate further research into this urgent issue. Our review will achieve this through offering a comprehensive cataloguing of previously employed methodologies, case studies and conclusions. In doing so, it will not only equip future researchers with a concise overview of existing research in the field, but also illuminate areas and approaches in need of further examination. [ABSTRACT FROM AUTHOR]

Streets perform a number of important functions and have a wide range of activities performed in them. There is a small but growing focus on streets as a more generalisable, atomised, and therefore more manageable unit of development and analysis than cities. Despite the public realm being one of the largest physical spaces on streets, the impact and potential of digitalisation projects on this realm is rarely considered. In this article, the smartness of a street is derived from the cyber-physical social infrastructure in the public realm, including data obtained from sensors, the interconnection between different services, technologies and social actors, intelligence derived from analysis of the data, and optimisation of operations within a street. This article conceptualises smart streets as basic units of urban space that leverage cyber-physical social infrastructure to provide and enable enhanced services to and between stakeholders, and through stakeholders' use of the street, generate data to optimise its services, capabilities, and value to stakeholders. A proposed conceptual framework is used to identify and explore how streets can be augmented and create value through cyber-physical social infrastructure and digital enhancements. We conclude with a discussion of future avenues of research. [ABSTRACT FROM AUTHOR]

After COVID-19, tuberculosis (TB) is the leading cause of death by an infectious disease in the world. This work presents a data set based on data collected from the Brazilian Information System for Notifiable Diseases (SINAN) for the period from January 2001 to April 2020 relating to patients diagnosed with tuberculosis in Brazil. The data from SINAN was pre-processed to generate a new data set with two distinct treatment outcome classes: CURED and DIED. The data set comprises 37 categorical attributes (including socio-demographic, clinical, and laboratory data) as well as the target class. There are 927,909 records of patients classified as CURED and 36,190 classified as DIED, totaling 964,099 records. [ABSTRACT FROM AUTHOR]

Purpose: The General Data Protection Regulation (GDPR) introduces significant data protection obligations on all organizations within the European Union (EU) and those transacting with EU citizens. This paper presents the GDPR privacy label and uses two empirical studies to examine the effectiveness of this approach in influencing consumers' privacy perceptions and related behavioral intentions. Design/methodology/approach: The paper tests the efficacy of two GDPR privacy label designs, a consent-based label and a static label. Study 1 examines the effects of each label on perceptions of risk, control and privacy. Study 2 investigates the influence of consumers' privacy perceptions on perceived trustworthiness and willingness to interact with the organization. Findings: The findings support the potential of GDPR privacy labels for positively influencing perceptions of risk, control, privacy and trustworthiness and enhancing consumers' willingness to transact and disclose data to online organizations. Practical implications: The findings are useful for organizations required to comply with the GDPR and present a solution to requirements for transparent communications and explicit consent. Originality/value: This study examines and demonstrates the efficacy of visualized privacy policies in impacting consumer privacy perceptions and behavioral intentions. [ABSTRACT FROM AUTHOR]

During the last decade, sustainable fashion has attracted increasing attention from policymakers, firms, and consumers. This interest is also reflected in consumer behavior research in this area. Notwithstanding this, there are few systematic literature reviews from a consumer behavior perspective. The purpose of this paper is to systematically review and critically assess the studies on consumer behavior in sustainable fashion to identify the research gap in this context and inform a future research agenda. A total of 167 journal articles were identified, and a final sample of 88 articles were synthesized. A descriptive analysis was performed to examine the research methods, research trends, and theoretical underpinnings. Separately, a synthetic analysis was performed using the Stimulus‐Organism‐Response (S‐O‐R) framework. Findings reveal a significant increase in the volume of publications since 2009. Qualitative, experimental, cross‐cultural, and longitudinal studies are significantly less represented in the literature. No evidence of research using big data techniques were identified. Much of the research published is not adequately grounded in theory. The findings also suggest that the consumer attitude‐behavior gap requires further exploration. This review fills a need to summarize the current state of consumer behavior in sustainable fashion literature and provides valuable insights into the existing marketing corpus on the topic. Moreover, the review identifies a number of outstanding research gaps that can inform future research. [ABSTRACT FROM AUTHOR]

Mobile contact tracing applications have emerged as a potential solution to track and reduce the transmission of viruses such as Covid‐19. These applications require the disclosure of potentially sensitive personal information thus generating understandable implications for personal privacy. This research aims to determine the factors driving acceptance of these applications, with acceptance represented by three distinct variables, namely usage intentions, willingness to disclose personal data, and willingness to rely on health advice. The study examines the influence of perceived privacy, social influence, and benefits on acceptance of contact tracing applications among a sample of 1,114 Brazilian citizens. The study leverages social contract theory to demonstrate the importance of perceived control and perceived surveillance in the formation of individuals' perceptions of privacy. Integrating privacy calculus theory with social contract theory to include reciprocity and social influence, our findings suggest that perceived privacy, reciprocal benefits, and social influence all positively influence individuals' intentions to download or continue the use of contact tracing applications, while intentions to disclose information are influenced by adoption intentions, perceived privacy, and reciprocal benefits and individuals' willingness to rely on contact tracing applications for health advice is influenced by reciprocal benefits and disclosure intentions. [ABSTRACT FROM AUTHOR]

As signals of internal control weaknesses, cyber security incidents can represent significant risk factors to the quality of financial reporting. We empirically assess the audit quality implications of data breaches for a large sample of US firms. Using a difference-in-difference approach based on a matched sample of breached and non-breached firms, we find no evidence that cyber-security incidents result in a decline in audit quality. Instead, we observe positive shifts in four widely-used proxies for audit quality. We document that breached firms (i) experience a decrease in abnormal accruals, (ii) are less likely to report small profits or small earnings increases, (iii) are more likely to be issued a going concern report, and (iv) are less likely to restate their financial statements in the two years following a breach. Our results indicate that auditors effectively offset increases in audit risk through additional substantive testing and audit effort. Our evidence supports the view that auditors have increased their audit risk awareness and put in place adequate procedures to deal with the consequences of cyber-security incidents. [ABSTRACT FROM AUTHOR]

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine learning and deep learning techniques to identify fake news in online communications in the Brazilian Portuguese language relating to the COVID-19 pandemic. We build a dataset of 11,382 items comprising data from January 2020 to February 2021. Exploratory data analysis suggests that fake news about the COVID-19 vaccine was prevalent in Brazil, much of it related to government communications. To mitigate the adverse impact of fake news, we analyse the impact of machine learning to detect fake news based on stop words in communications. The results suggest that stop words improve the performance of the models when keeping them within the message. Random Forest was the machine learning model with the best results, achieving 97.91% of precision, while Bi-GRU was the best deep learning model with an F1 score of 94.03%. [ABSTRACT FROM AUTHOR]

One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical countries. Brazil has the largest absolute number of cases in Latin America. This work presents a unified data set with clinical, sociodemographic, and laboratorial data on confirmed patients of Dengue and Chikungunya, as well as patients ruled out of infection from these diseases. The data is based on case notification data submitted to the Brazilian Information System for Notifiable Diseases, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), from 2013 to 2020. The original data set comprised 13,421,230 records and 118 attributes. Following a pre-processing process, a final data set of 7,632,542 records and 56 attributes was generated. The data presented in this work will assist researchers in investigating antecedents of arbovirus emergence and transmission more generally, and Dengue and Chikungunya in particular. Furthermore, it can be used to train and test machine learning models for differential diagnosis and multi-class classification. Measurement(s) clinical data Technology Type(s) interview [ABSTRACT FROM AUTHOR]

Summary: Mobile network traffic prediction is an important input into network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non‐linear patterns, or other important correlations in time series mobile network data. We compare the performance of two deep learning (DL) architectures, long short‐term memory (LSTM) and gated recurrent unit (GRU), and two conventional machine learning (ML) architectures—Random Forest and Decision Tree—for predicting mobile Internet traffic using 2 months of Telecom Italia data for Milan. K‐Means clustering was used a priori to group cells based on Internet activity, and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error and mean absolute error. Both DL algorithms were effective in modeling Internet activity and seasonality, both within days and across 2 months. We find variations in performance across clusters within the city. Overall, the DL models outperformed the conventional ML models, and the LSTM outperformed the GRU in our experiments. [ABSTRACT FROM AUTHOR]

Background: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. Author summary: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

This study investigates the effect of news media coverage on trading activity in, and the liquidity of, target firms' shares around acquisition announcements. We use the number of articles published in four of the UK's main newspapers as a proxy for media coverage. Our dataset includes 350 UK domestic acquisition deals between 1996 and 2014. The results of our analysis suggest that media coverage is positively associated with target firms' trading activity and stock liquidity. This is consistent with the media playing a key role in mitigating information asymmetry in the financial markets. This study contributes to the literature on stock market reactions to acquisition announcements by investigating the effect of media coverage on trading activity and stock liquidity beyond the price run-up, and by providing additional insights into the UK market which traditionally attracts less attention than the US market. [ABSTRACT FROM AUTHOR]

License plates are the primary source of vehicle identification data used in a wide range of applications including law enforcement, electronic tolling, and access control amongst others. License plate detection (LPD) is a critical process in automatic license plate recognition (ALPR) that reduces complexity by delimiting the search space for subsequent ALPR stages. It is complicated by unfavourable factors including environmental conditions, occlusion, and license plate variation. As such, it requires training models on substantial volumes of relevant images per use case. In 2018, the new Mercosur standard came in to effect in four South American countries. Access to large volumes of actual Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for LPD, thereby adversely impacting the efficacy of ALPR in Mercosur countries. This paper presents a novel license plate embedding methodology for generating large volumes of accurate Mercosur license plate images sufficient for training supervised LPD. We validate this methodology with a deep learning-based ALPR using a convolutional neural network trained exclusively with synthetic data and tested with real parking lot and traffic camera images. Experiment results achieve detection accuracy of 95% and an average running time of 40 ms. [ABSTRACT FROM AUTHOR]

This study investigates the impact of cyber-security incidents on audit fees. Using a sample of 5,687 firms, we find that (i) breached firms are charged 12% higher audit fees, and (ii) firms operating in the same industry of a breached firm are charged 5% higher fees. Finally, using a difference-in-difference regression on a propensity score matched sample, we provide evidence suggesting that auditors do not revise their audit risk assessment following a breach. Overall, these results suggest that the increase in audit fees in the year of a breach is only temporary, and that auditors include cyber-security risk in their audit risk assessment even before an incident occurs. Higher cyber-security risk is ultimately reflected in higher audit fees paid by auditees. [ABSTRACT FROM AUTHOR]

This article adopts a work orientations perspective to consider how, through the medium of Twitter, employees voice the things they love and hate about their jobs. Using 817,235 tweets posted by 650,958 users in the calendar year 2014, the findings provide new insights into both employee voice and job satisfaction/dissatisfaction as well as an enhanced understanding of the employment relationship. First, the findings indicate that Twitter, in its expression of very personal and individualistic needs, might be considered a new form of employee voice. Second, Twitter captures the positive, the negative and the ambivalence in the notion of job satisfaction. Third, the description of the methodology used to access and analyse Twitter data illustrates how new methodological approaches, particularly those embedded within computer science, may be of value to social scientists in their analysis of 'Big Data'. [ABSTRACT FROM AUTHOR]

This paper considers agentic-activities relating to the Irish language on Twitter and who is engaged with them? To answer this question, this study draws on Twitter data and filters it using the linguistic identifiers, ‘#Gaeilge’ and the word ‘Gaeilge’ (Irish language). Tweets with these identifiers are conceptually considered as formulations of agentic practice with respect to language (Sabatier, P. [2007]. Theories of the Policy Process. Boulder, CO: Westview Press). Individuals who use the #Gaeilge identifier in particular are viewed within this study as micro-implementers. Their communications provide partial insight into agentic activities within the Twittersphere specifically, related to and about the Irish language. To explore this data, peak, network, content and sentiment analyses were conducted. The study’s findings illustrate that there are a number of influential hubs in this network. They are in the main micro-level individuals as opposed to macro-level or governmental institutions promoting the Irish language. This paper demonstrates the potential for in-depth research into agentic practices of minority linguistic communities within Twitter, and outlines the limitations of a computational approach in conducting such research. [ABSTRACT FROM AUTHOR]

Summary: Microservices architectures are becoming the defacto standard for building continuously deployed systems. At the same time, there is a substantial growth in the demand for migrating on‐premise legacy applications to the cloud. In this context, organizations tend to migrate their traditional architectures into cloud‐native architectures using microservices. This article reports a set of migration and rearchitecting design patterns that we have empirically identified and collected from industrial‐scale software migration projects. These migration patterns can help information technology organizations plan their migration projects toward microservices more efficiently and effectively. In addition, the proposed patterns facilitate the definition of migration plans by pattern composition. Qualitative empirical research is used to evaluate the validity of the proposed patterns. Our findings suggest that the proposed patterns are evident in other architectural refactoring and migration projects and strong candidates for effective patterns in system migrations. [ABSTRACT FROM AUTHOR]

Cloud computing is rapidly changing the digital service landscape. A proliferation of Cloud providers has emerged, increasing the difficulty of consumer decisions. Trust issues have been identified as a factor holding back Cloud adoption. The risks and challenges inherent in the adoption of Cloud services are well recognised in the computing literature. In conjunction with these risks, the relative novelty of the online environment as a context for the provision of business services can increase consumer perceptions of uncertainty. This uncertainty is worsened in a Cloud context due to the lack of transparency, from the consumer perspective, into the service types, operational conditions and the quality of service offered by the diverse providers. Previous approaches failed to provide an appropriate medium for communicating trust and trustworthiness in Clouds. A new strategy is required to improve consumer confidence and trust in Cloud providers. This paper presents the operationalisation of a trust label system designed to communicate trust and trustworthiness in Cloud services. We describe the technical details and implementation of the trust label components. Based on a use case scenario, an initial evaluation was carried out to test its operations and its usefulness for increasing consumer trust in Cloud services. [ABSTRACT FROM PUBLISHER]

Purpose This paper aims to provide a social network site influence (SNSI) profile of early adopters. This study explores the relationship between personality traits of early adopters of social network sites (SNS), their propensity to share information and rumors and their general SNSI.Design/methodology/approach An online survey was sent to the first users of Twitter (n = 200) and Google+ (n = 130) to assess their personality traits. Answers of each respondent were matched to their SNSI scores from Klout and PeerIndex, the industry standard for measuring SNSI.Findings Early adopters of SNS, in comparison to market mavens, are more likely to exert influence on one particular topic related to their profession: technology and the internet. Their levels of extraversion, openness and conscientiousness have a positive and significant impact on information sharing, and a negative impact on rumor sharing. Both, information sharing and rumor sharing have a positive and significant impact on the general SNSI of early adopters.Originality/value Firms struggle to decide whether to invest early in the life of newly created SNS as they are unsure about the characteristics of early adopters of such networks, and, more importantly, whether these sites are effective initial vectors for word-of-mouth. The findings demonstrate that early adopters’ influence (SNSI score) is on par with that of the rest of SNS users, suggesting their influence may be somewhat limited. The study also shows that the opinion leadership impact of the more influential early adopters is monomorphic in nature, being mainly confined to the related technology and internet domains. [ABSTRACT FROM AUTHOR]

This paper explores the factors which influence the business location decisions of start-ups, focusing in particular on the role of personal factors. Established explanations of industry location emphasize proximity to firms in the same or related industries and proximity to a wider set of business services, though recent research suggests that personal factors may play an important role in explanations of industry location—particularly in technology-enabled sectors. A survey of 97 new firms, founded between 2008 and 2012, in the Irish software services sector, shows that the business location decision is influenced by the personal motivation of entrepreneurs to attain a desired quality of life, and that this outweighs economic factors such as proximity to firms within the same or related industries, proximity to a broader set of supporting business services, infrastructure or the availability of government support schemes. Personal factors are particularly important to firms located outside the Dublin metropolitan area and to home-based businesses. This has important policy implications for national and regional governments seeking to encourage entrepreneurship in technology-enabled service sectors. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editor discusses various reports within the issue on topics including communication and war, influence of social media post Twitter, and mediatization of Gaza and the Palestinian crisis.

This study investigates resourcing of computer software companies that have adopted cloud computing for the development and delivery of application software. Use of this innovative technology potentially impacts firm financing because the initial infrastructure investment requirement is much lower than for packaged software, lead time to market is shorter and cloud computing supports instant scalability. We test these predictions by conducting in-depth interviews with founders of 18 independently owned nascent enterprises, of which three-quarters have adopted cloud computing. We identify particular bootstrapping methods used by start-ups in the computer software sector. Cloud computing enables firms to develop and launch products with minimal resources, reducing barriers to entry, with consequent increased competition. The primary business bootstrapping technique is foregoing wages, supplemented by small amounts of grant funding. Customers are a source of knowledge and expertise for product development, which occurs in an iterative process. Product bootstrapping techniques have changed in response to technological innovation, although methods to acquire tangible assets are identical over time. Astutely applied, financial bootstrapping is a resource management strategy essential to the growth and survival of high-technology firms. [ABSTRACT FROM PUBLISHER]

These authors contributed equally: Sebastião Rogério da Silva Neto, Thomás Tabosa de Oliveira, Igor Vitor Teixiera, Leonides Medeiros Neto, Vanderson Souza Sampaio, Theo Lynn and Patricia Takako Endo. Author Correction: Arboviral disease record data - Dengue and Chikungunya, Brazil, 2013-2020 Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01312-7, published online 10 May 2022 In this article the funding information relating to support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 was omitted. [Extracted from the article]

Purpose – The purpose of this study is to establish whether Irish listed firms comply with the substance of corporate governance guidance rather than the letter of the rule in the determination of director independence. The paper examines non-executive director independence from three perspectives: the first is from the viewpoint of the sample firms, the second from that given in corporate governance guidance when applied to the sample firms, and the third based on extensive financial statement analysis, prior research and prior literature. By exploring multiple perspectives of director independence, disparities in the interpretation of non-executive director independence can be identified.Design/methodology/approach – Descriptive statistics and non-parametric statistical analysis are used to examine for differences between multiple perspectives of non-executive director independence.Findings – The study identified significant disparities in the interpretation of independence by Irish listed firms. This may be explained by a misunderstanding of what director independence means, a deliberate choice to ignore pre-existing norms regarding NED independence or the desire to exceed such norms. The findings suggest Irish financial institutions exhibit higher levels of NED independence than the remaining sample firms explained in part by linkages with the institutions themselves. Findings suggest a lack of adequate oversight in the sample firms, which could ultimately lead to greater agency costs for shareholders.Practical implications – Policymakers and other stakeholders valuing director independence may need to reassess guidelines for interpreting director independence and related reporting, policies regarding adherence to such guidelines and associated director training requirements.Originality/value – This study is timely, topical and the first of its kind in relation to Irish listed firms and provides evidence into the lack of compliance within Irish listed companies with best practice guidance. The findings clearly identify a notable lack in a consistent means of interpretation in the sample firms as to what non-executive independence is and why it is an important part of good corporate governance. The paper provides a basis for future research. Such research may include studies of firm motivation in interpretation choice and comparative studies with other jurisdictions. [ABSTRACT FROM AUTHOR]

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst. [ABSTRACT FROM AUTHOR]

While information technology is playing a significant transformative role in virtually every industry, within the agriculture sector, family-operated farming enterprises have been slow to adopt IT solutions to manage their operations. This study adopts a sequential mixed-methods research design to examine the pre- and post-adoption phases of farmers' use of a mobile digital platform for farm management. Our findings show that farmers' initial acceptance of a mobile digital platform for farm management is shaped by social influence, which mediates the impact of performance and effort expectancy. Post-adoption continued use of the digital platform is influenced directly by performance and effort expectancy and indirectly by trust beliefs and social influence. Perceived work impediment indirectly influences post-adoption acceptance via effort expectancy. Our study untangles the direct and indirect influences of positive and negative perceptions on farmers' acceptance of a new innovative AgriTech digital platform in these different phases. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class. [ABSTRACT FROM AUTHOR]

The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts. [ABSTRACT FROM AUTHOR]

Simulation has become an indispensable technique for modelling and evaluating the performance of large-scale systems efficiently and at a relatively low cost. ElasticSearch (ES) is one of the most popular open source large-scale distributed data indexing systems worldwide. In this paper, we use the RECAP Discrete Event Simulator (DES) simulator, an extension of CloudSimPlus, to model and evaluate the performance of a real-world cloud-based ES deployment by an Irish small and medium-sized enterprise (SME), Opening.io. Following simulation experiments that explored how much query traffic the existing Opening.io architecture could cater for before performance degradation, a revised architecture was proposed, adding a new virtual machine in order to dissolve the bottleneck. The simulation results suggest that the proposed improved architecture can handle significantly larger query traffic (about 71% more) than the current architecture used by Opening.io. The results also suggest that the RECAP DES simulator is suitable for simulating ES systems and can help companies to understand their infrastructure bottlenecks under various traffic scenarios and inform optimisation and scalability decisions. [ABSTRACT FROM AUTHOR]