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On crowdfunding platforms, the success of campaigns relies on the ability of projects to attract backers. While backers' motivations have been examined, the dynamics existing among the distinct profiles of backers remain underexplored. Combining quantitative and qualitative methods, this research offers a typology of backers' profiles and shows how these profiles contribute to the success of a crowdfunding campaign. Informed by the sociological theory of cooperation, these results explain how backers with different profiles influence each other's decisions to support crowdfunding campaigns. We provide guidelines to project leaders and platforms to attract backers with the best profiles at the best moment to optimise the campaign's success strategy. We also help crowdfunding platform managers build their crowd of backers and optimise their number of funded projects. [ABSTRACT FROM AUTHOR]

Objective: Misperception of sexual intent is linked to incidence of sexual assault. The present study tested the effects of gender, alcohol consumption, and leaving a party together, in isolation and in interaction, on perceptions of sexual intent. Participants: Undergraduates (N = 438) completed an online survey between Winter 2017 and Winter 2018. Methods: Participants read one of eight versions of a vignette about a social interaction between male and female college-aged acquaintances and rated the targets' sexual interest in each other. Who was consuming alcohol (both targets, female only, male only, or neither) and whether the targets left together were manipulated. Results: Alcohol consumption and leaving together interacted. Leaving together signified to participants that both targets had greater sexual intent. When targets left separately, participants viewed both targets as less sexually interested in each other when only the female was drinking. Conclusions: Implications for sexual assault on college campuses are discussed. [ABSTRACT FROM AUTHOR]

Accessible summary: What Is Known about the Subject?: Nurses' perspectives and consensus on the possible key factors contributing to aggression at inpatient units can be summarized into patients' related factors, staff related factors and environment related factors.Results of the possible factors contributing to aggression at inpatient units reflect the complicated nature of this problem. What the Paper Adds to Existing Knowledge: Perspectives of nurses as frontline mental health professionals on factors contributing to aggression as one of the psychiatric emergencies were considered through an iterative process. This approach gave nurses an opportunity to revisit their own views in each round to provide an in‐depth reflection in the light of the contribution of others. What Are the Implications for Mental Health Nursing Practice?: Nursing curricula should focus on nurses' communication skills and emotion regulation training.An open dialogue between nurses and people with mental health issues should be initiated to discuss the possible key factors contributing to aggressive behaviour at inpatient units from both standpoints.Mental health nurses' turnover at inpatient settings could be targeted through the design and implementation of aggression prevention protocols Introduction: Aggression at inpatient units is a universal problem leading to hazardous outcomes. Aim: To generate group consensus about factors contributing to aggressive behaviour among patients with mental health issues at inpatient units. Methods: Nurses working at inpatient psychiatric departments were approached, and purposive sampling was employed to implement Delphi technique. A total of three Delphi rounds were conducted online. The average percent of majority opinions method was followed to measure consensus in which questions with a cut‐off rate below 69.7% were included in the next round. Results: Twenty‐one nurse experts with different skills participated in this study. Consensus increased among nurse experts across rounds for the following items: Patients' misinterpretation of the attitude of the healthcare providers, severity of mental health issues, attitude and communication style of the healthcare providers, nurses limited emotional regulation capacity and the inadequate staff–patient ratio in psychiatric wards. Discussion: The complicated nature of aggressive behaviour displayed by people with mental health issues is reflected on the results of the current study; patients' related factors, staff related factors and environment related factors constitute interacting facets for this issue. Implications for Mental Health Nurses: Nurse scientists could use insights derived from this study to design studies aiming at assessment and management of aggression at inpatient units guided by implementation science frameworks. Additionally, open dialogues between nurses and people with mental health issues could be initiated about factors contributing to aggression at inpatient units. Mental health nursing training should focus on nurses' communication and emotion regulation skills. [ABSTRACT FROM AUTHOR]

How are terror threats and counterterrorism measures experienced in everyday urban spaces? We argue that thinking atmospherically about the spaces of urban encounters with (counter)terrorism is important, firstly, to identify and question feelings and dispositions shaped by discourses, practices, and infrastructures of (counter)terrorism; secondly, to contribute spatial perspectives of felt experience to literatures on security and (counter)terrorism in geography and beyond; thirdly, to connect official understandings of (counter)terrorism with its everyday felt experiences and materialities. We highlight two conceptual and empirical arenas – the crowd and the question of difference – where atmospheric approaches to urban (counter)terrorism can be developed. [ABSTRACT FROM AUTHOR]

Home advantage is a well-established phenomenon in professional football. However, the literature on the effect of the size of the crowd on home advantage has produced mixed results. The present study utilized data from all the games in the Chinese Super League between the 2011 and 2019 seasons. Using multivariate regressions, where we control for teams' abilities, different schedule related variables and referees' unobserved characteristics, we find that the size of attendance has a positive effect on home advantage in terms of yellow cards, scored goals and points per game. Our finding emphasizes the important role of crowd on home advantage, not only for the dichotomous comparison between games with and without crowd, but also for the continuous case with varying levels of crowd. [ABSTRACT FROM AUTHOR]

This paper examines how Stanford Hospital, a Level I trauma centre serving the metropolitan region of California's Bay Area, manages an acute surge of patients from a mass casualty incident, specifically within the context of the crowded and overburdened US emergency medicine system. The authors offer practical considerations for the rapid creation of space and bandwidth during an acute surge of injured patients as well as best practices for reorganising daily systems to care for those patients efficiently. The study also discusses how past mass casualty incidents were examined for lessons learned in order to build and refine the response plan at Stanford Hospital, with input from a multidisciplinary committee. [ABSTRACT FROM AUTHOR]

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert's knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds. [ABSTRACT FROM AUTHOR]

Background: Patients with dialysis‐dependent kidney failure and treated for hyperphosphatemia receive a combination of dietary advice, phosphate binders and prolonged dialysis. However, research focusing on the challenges patients meet in everyday life addressing diet and medication is sparse. Objective: The objective of this study is to explore the everyday challenges patients meet when following treatment for hyperphosphatemia. Design: Interpretive description was the methodological approach. Semistructured in‐depth interviews were employed to study the challenges patients experienced. Data were analysed using Braun and Clarke's reflexive thematic analysis. Participants: Patients (n = 14) receiving haemodialysis and treated for hyperphosphatemia from two hospitals in Southern Denmark. Findings: The analysis resulted in one over‐arching theme; separation in social gatherings and two subthemes; a new social code, and my food and their food. Participants experienced difficulty integrating diet and medication in daily life, especially at social gatherings. They felt separated from others when special menus were provided for them or struggled when choosing between high and low phosphate‐containing food. A new awareness of self and others arose, especially their position among families and friends, and how they presented themselves and their social identity to others. Likewise, a new social code manifested itself, which was difficult to accept. Most participants experienced that diet and medication were accompanied by a moral responsibility of whether to accept prepared food with high phosphorus content or not, which affected commensality. Conclusion: Patients were often nonadherent to hyperphosphatemia treatment at social gatherings. Hyperphosphatemia treatment led to new social identities with new social codes, which patients found difficult to accept. [ABSTRACT FROM AUTHOR]

We developed a security guard robot that is specifically designed to manage queues of people and conducted a field trial at an actual public event to assess its effectiveness. However, the acceptance of robot instructions or admonishments poses challenges in real-world applications. Our primary objective was to achieve an effective and socially acceptable queue-management solution. To accomplish this, we took inspiration from human security guards whose role has already been well received in society. Our robot, whose design embodied the image of a professional security guard, focused on three key aspects: duties, professional behavior, and appearance. To ensure its competence, we interviewed professional security guards to deepen our understanding of the responsibilities associated with queue management. Based on their insights, we incorporated features of ushering, admonishing, announcing, and question answering into the robot's functionality. We also prioritized the modeling of professional ushering behavior. During a 10-day field trial at a children's amusement event, we interviewed both the visitors who interacted with the robot and the event staff. The results revealed that visitors generally complied with its ushering and admonishments, indicating a positive reception. Both visitors and event staff expressed an overall favorable impression of the robot and its queue-management services. These findings suggest that our proposed security guard robot shows great promise as a solution for effective crowd handling in public spaces. [ABSTRACT FROM AUTHOR]

Evidence about the impact of mass gatherings during the coronavirus disease 2019 (COVID-19) pandemic on the number of disease cases and on the health-care systems of host countries is limited. Additionally, there have been few publications on the lessons identified from the adaptation of mass gatherings held during the pandemic, including the implementation of comprehensive public health and social measures aimed at reducing viral transmission. This article describes preparations made for the 2020 Union of European Football Associations (UEFA) European Football Championship (UEFA Euro 2020) by the World Health Organization's (WHO) Regional Office for Europe, UEFA and other stakeholders after the championship had been rescheduled because of the COVID-19 pandemic. Technical guidance on preparations for the football tournament and risk assessment tools were provided by WHO. A task force established by the WHO Regional Office for Europe conducted traditional and event-based disease surveillance before and during UEFA Euro 2020, monitored public health and social measures in the 11 host countries, and developed a risk communication and community engagement strategy that involved multimedia campaigns targeting news and social media, fans, athletes, event organizers and other stakeholders. The lessons and good practices identified during UEFA Euro 2020 are described to help guide preparations for future mass gatherings in health emergencies. Sharing data and recommendations on best practice from previous mass gatherings with the organizers and countries involved in planning for a major event is particularly important. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges faced in transforming the dietetics curriculum, particularly in relation to curriculum drift and overcrowding. Topics include the use of constructive alignment based on constructivist learning theory, the need for an outcome-based approach to curriculum design, and the impact of individual academic decisions on the consistency and effectiveness of the curriculum.

COVID-19 has compelled every sector to shift towards virtualizing, and to curb the spread of this deadly virus, technologies like cloud computing, artificial intelligence, and IoT have played a vital role. Cloud-based crowdsourcing is a sourcing method through which organizations may post application development tasks as a contest, and the crowd may participate to win prizes or earn monetary support through online means. Many IT firms hesitate to adopt this methodology for competitive software development despite its advantages. This study's primary purpose is to discover the inhibitors and criteria restricting IT firms from incorporating cloud-based crowdsourcing in their workflow. An extensive literature review has been conducted to identify the inhibitors and criteria. The authors applied a novel three-phased methodology to the identified inhibitors to analyze them with mixed method approach. Authors have used the m-TISM method to develop a hierarchal model and find the interrelation between the identified inhibitors, which were further analyzed and segmented with the help of MICMAC analysis. The PF-AHP method was used to find the weightage of the identified criteria, and the PF-CoCoSo method was applied to find the ranking of the inhibitors considering the weights of the criteria. The study reveals that Difficulty in Price Fixing, Labor Exploitation, and Risk of Dependency are the critical challenges to adoption. The study reflects how the Contingency Management Theory and the Resource Dependency Theory acts as frameworks for navigating complexity and reducing risks in the ever-changing world of software development. The study concludes with key inputs that may help IT firms adopt and promote cloud-based crowdsourcing methodology for software development. These inputs may help companies to attract experienced developers who prefer working from home and prove worthy even during disruptions caused due to unprecedented situations. [ABSTRACT FROM AUTHOR]

Aim: To explore how overcrowding affects children's health, environment and schooling. Methods: A qualitative study was conducted with individual interviews among 20 participants with occupational experience from overcrowded Stockholm areas but diverse in professions, locations and employers. The interviews were recorded, transcribed and analysed with Systematic Text Condensation. Results: Almost all participants expressed that overcrowding has a negative impact on children's health, environment and schooling – based on perceptions of precarious and different living conditions for children in overcrowded areas, for example, substandard homes, vulnerability, stress, exclusion, limited resources, lack of learning opportunities, gender differences, confinement, shame, insecurity, conflicts, risk of criminality, and bodily impact, both physical and psychological. Conclusions: Our qualitative evidence suggest that overcrowding has a negative impact on children's health, environment and schooling. [ABSTRACT FROM AUTHOR]

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability. [ABSTRACT FROM AUTHOR]

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study's significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment. [ABSTRACT FROM AUTHOR]

Recently, the research of crowd counting has attracted increasing attention but still faces many challenges, such as crowded scenes, scale variations and cluttered backgrounds. With the development of deep learning, density maps are widely used for crowd counting, where the quality of density maps plays a crucial role in counting performance. In this paper, we propose a new convolutional network architecture, called double multi-scale feature fusion network (DMFFNet), to generate high-quality density maps and accurate counting estimates. DMFFNet utilizes VGG19 to extract multi-scale feature maps from input images. The features from last three scales are further enlarged the receptive fields by three designed dilated feature pyramid modules, and then fused together. Moreover, a feature enhancement module composed of spatial attention and channel-wise attention is presented to weight the fused feature maps for effectively distinguishing between crowd and background. We also design a new dual-scale loss to optimize the network during training. Experimental results show that DMFFNet reduces MAEs by at least 1.5 % , 1.5 % , 1.2 % , 0.6 % and 0.5 % on UCF _ CC _ 50, UCF-QNRF, JHU-Crowd++, ShanghaiTech Part A and Part B datasets, and decreases MSEs by at least 1.8 % and 0.1 % on JHU-Crowd++ and ShanghaiTech Part B datasets, as compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

From 7 October 2023 to 7 June 2024, the Crowd Counting Consortium recorded nearly 12,400 pro-Palestine protests and over 2,000 pro-Israel protests in the United States. Since January 2017, when CCC first started counting protest events and their crowd sizes, the current pro-Palestine protest wave involves the largest, most sustained US protests sparked by a foreign event. We hypothesize three possible reasons for the size of the pro-Palestine mobilization: casualties and suffering motivate protestors and the Palestinian casualties have been much higher; protestors react to the US government’s position, which in this case strongly favors Israel; and protestors are driven by emotional outrage at Israeli policy and the resultant Palestinian suffering. By looking at property damage and police injuries, we also conclude that this pro-Palestine movement has not been violent. That is true of both the national protest wave in general and of the student encampments in spring 2024 in particular. The rhetorical core of this pro-Palestine movement has not been a call for violence against Jews, but rather a call for freedom for Palestinians and an end to violence being inflicted upon them. To substantiate this point, we considered two sources of evidence: 1) the banners, signs, and chants seen or heard at pro-Palestine events; 2) the demands issued by organizers of over 100 student encampments. One caveat: Although we use the terms pro-Israel and pro-Palestine as shorthand, we acknowledge that these binary terms are unsatisfying and misleading. [ABSTRACT FROM AUTHOR]

Overcrowding is a worldwide problem, and long waiting times are associated with increased morbidity and even mortality of patients regardless of triage classification. Although there are many tools published in the literature that contribute to the reduction of overcrowding, for the Colombian population there are not many tools evaluated to reduce the length of stay of patients in the emergency department. This is a retrospective analytical study that compared whether there was a difference in patient definition time and ED length of stay between a group attended under an early care protocol (PAT) versus the usual protocol. Of the total of 969 patients included it was found that the group attended under the PAT protocol had a shorter definition time than the usual protocol, also the Emergency department length of stay (EDLOS) was significantly lower in the PAT group compared to the usual protocol. The implementation of the PAT protocol performed by emergency physicians allows a faster contact with the patient by the physician, and leads to a significant reduction of EDLOS, contributing to the reduction of overcrowding in the emergency department. [ABSTRACT FROM AUTHOR]

Objective: Alcohol is a major public health issue and the ACEM funds regular 'snapshot' surveys of the prevalence of alcohol‐related presentations in EDs. The present study uses these data to investigate ED occupancy and alcohol‐ and methamphetamine‐related presentations at the time of the COVID‐19 pandemic. Methods: Survey‐based point prevalence study of EDs in Australia and New Zealand conducted at 02:00 hours local time on the Saturday of the weekend before Christmas in 2019–2022. Primary outcomes were ED occupancy, the number of alcohol‐related presentations and methamphetamine‐related presentations in each ED at the time of survey. Results: Seventy eight of a possible 152 hospitals answered all four surveys (51%, 95% confidence interval 43–59, individual yearly response rates ranged from 70.5% to 83.3%). The mean number of alcohol‐related presentations in EDs at the snapshot time was 4.2 (95% confidence interval 3.2–5.2) in the 2019 survey and 3.8 (3.1–4.6) in 2022 with no significant variation over time. There was also no change in methamphetamine‐related presentations which occurred at a lower level. There was a major increase in reported total ED occupancy – from 31.4 to 43.5 in Australia (P < 0.0001, paired t test) and from 22.8 to 38.7 in New Zealand (P = 0.0001). Subgroup analysis showed that both the number being treated and the number waiting to be seen increased, with little change in the number in observation units. Conclusions: The present study demonstrates that the COVID‐19 pandemic did not affect summer alcohol‐related ED presentations in Australasia but was associated with an unsustainable increase in ED crowding. [ABSTRACT FROM AUTHOR]

The widespread use of mobile phones and social media platforms provides valuable information about users' behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users' opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. [ABSTRACT FROM AUTHOR]

The wisdom of the crowd is the finding that aggregating the judgments of many people can lead to surprisingly accurate group judgments. Usually statistical methods are used to aggregate people's judgments, but there are advantages to using cognitive models instead. Crowd judgments based on cognitive modeling can (a) identify experts and amplify their judgments, (b) provide a representational structure for aggregating complicated multidimensional judgments, (c) debias judgments that are affected by heuristic cognitive processes or competitive social situations, and (d) diversify the crowd by incorporating predictions about judgments that have not been observed. Demonstrations of these advantages are provided in case studies involving ranking, probability estimation, and categorization problems. [ABSTRACT FROM AUTHOR]

Shared social identity and social norms are often un(der)recognized within mass gatherings health literature, yet they can increase infectious disease transmission by motivating people to engage in risk‐taking behaviours. Across three experiments (Ntotal = 1551), we investigated how shared social identities, perceived norms of resource‐sharing, and perceived riskiness of sharing interact to shape decisions that can lead to disease transmission. In Experiment 1 (N = 528), we examined how shared social identity and perceived descriptive norms affect the likelihood of crowd members sharing resources that may contribute to disease spread. We then replicated this in Experiment 2 (N = 511) using perceived injunctive norms. In Experiment 3 (N = 512), we explored how high shared social identity, perceived norms, and perceived health risks of resource‐sharing impact the likelihood of sharing that may, in turn, increase infectious disease transmission at mass gatherings. We found that high shared social identity interacts with perceived descriptive and injunctive norms, and low health risk perceptions, to increase the likelihood of accepting resources and giving resources at mass gatherings. We discuss the theoretical and practical implications for researchers, practitioners, and policymakers to build effective strategies to mitigate infectious disease transmission at mass gatherings. [ABSTRACT FROM AUTHOR]

Mainstream RGB-T crowd counting methods use cross-modal complementary information to improve the counting accuracy. However, most of them neglect the effect of lighting variation on cross-modal data fusion. In this paper, we propose a Light-sensitive and Adaptive Fusion Network (LAFNet) for RGB-T crowd counting. Specifically, we present a Modality-specific Feature Extraction Module (MFEM) that fuses the lighting information, and a Light-sensitive and Adaptive Fusion Module (LAFM) that adjusts the fusion strategies of different modalities according to the lighting conditions of the input crowd images. Moreover, we propose an Improved Multi-scale Extraction Module (IMEM) to extract and fuse multi-modal at different scales. We evaluate our method on the RGBT-CC dataset and the experiment results show the validity of the model and its effectiveness in various scenarios. [ABSTRACT FROM AUTHOR]

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings. [ABSTRACT FROM AUTHOR]

The article "Ramadan in the plague year: Indonesian Muslim responses to the onset of COVID-19" published in Contemporary Islam discusses how Indonesia's two largest Muslim organizations, Muhammadiyah and Nahdlatul Ulama, responded to the COVID-19 pandemic in 2020. The organizations promoted public health measures such as social distancing, quarantine, wearing masks, and altering religious practices to adapt to the pandemic. They combined bio-medical and religious strategies to ensure the safety of Muslims during Ramadan in the midst of the pandemic. The article highlights the historical context of pandemics, the similarities between COVID-19 and bubonic plagues, and the importance of cultural and religious components in health care systems during infectious disease outbreaks. [Extracted from the article]

We propose a novel contact-aware method to synthesize highly-dense 3D crowds of animated characters. Existing methods animate crowds by, first, computing the 2D global motion approximating subjects as 2D particles and, then, introducing individual character motions without considering their surroundings. This creates the illusion of a 3D crowd, but, with density, characters frequently intersect each other since character-to-character contact is not modeled. We tackle this issue and propose a general method that considers any crowd animation and resolves existing residual collisions. To this end, we take a physics-based approach to model contacts between articulated characters. This enables the real-time synthesis of 3D high-density crowds with dozens of individuals that do not intersect each other, producing an unprecedented level of physical correctness in animations. Under the hood, we model each individual using a parametric human body incorporating a set of 3D proxies to approximate their volume. We then build a large system of articulated rigid bodies, and use an efficient physics-based approach to solve for individual body poses that do not collide with each other while maintaining the overall motion of the crowd. We first validate our approach objectively and quantitatively. We then explore relations between physical correctness and perceived realism based on an extensive user study that evaluates the relevance of solving contacts in dense crowds. Results demonstrate that our approach outperforms existing methods for crowd animation in terms of geometric accuracy and overall realism. [ABSTRACT FROM AUTHOR]

Attracting a sufficiently diverse number of participants for the success of firms' creative crowdsourcing is a critical and persistent issue. Our analysis of 40 in-depth interviews capturing crowd heterogeneity (ordinary consumers and creative professionals), highlights seven barriers to participation. For example, ordinary consumers highlight scepticism towards the firm's genuine willingness to concede power and perceived illegitimacy; professionals complain about governance unfairness and the lack of regulation. Grouping these barriers into three higher-order themes (crowd-, process- and context-related) creates a novel perspective that encompasses the empowerment-related concerns of heterogeneous crowds better than previous research. The analysis reveals both specific and common (higher purpose) levers that can alleviate these concerns, and it also questions the empowerment promise of creative crowdsourcing and has important implications for business and policymakers. [ABSTRACT FROM AUTHOR]

For Muslims all across the world, the desire to participate in the religious rites of the Hajj (pilgrimage to Mecca) which stands as one of the five pillars of Islam is a heartfelt longing. It stands for the pinnacle of devotion and spiritual gratification, luring followers to the most sacred city in Islam for a life-changing journey of faith, comradery, and submission to Allah. Muslims hold Mecca in the highest regard; it is a source of endless inspiration and devotion throughout their lives, as seen by their desire to undertake the Hajj and Umrah. The pilgrimage encompasses a series of synchronized rituals and acts of worship, each holding its unique spiritual meaning, and serve as a powerful testament to the universal nature of Islamic teachings. These rituals have a significant impact on Muslims’ mental and spiritual well-being. Hajj elicits a spectrum of feelings; creates unity, humility, and thankfulness; and encourages self-reflection as well as personal development. It also instills a sense of spiritual fulfillment. Hajj transcends personal boundaries, strengthening a pilgrim’s sense of connection to the larger Muslim community that rejuvenates their hearts and souls toward the teaching of Islam. Hence, it is imperative to explore in depth this transformative journey, illuminating the mental, emotional, and spiritual dimensions that bind Muslims across the globe.

We discuss a relatively new meta-scientific research design: many-analyst studies that attempt to assess the replicability and credibility of research based on large-scale observational data. In these studies, a large number of analysts try to answer the same research question using the same data. The key idea is the greater the variation in results, the greater the uncertainty in answering the research question and, accordingly, the lower the credibility of any individual research finding. Compared to individual replications, the large crowd of analysts allows for a more systematic investigation of uncertainty and its sources. However, many-analyst studies are also resource-intensive, and there are some doubts about their potential to provide credible assessments. We identify three issues that any many-analyst study must address: 1) identifying the source of variation in the results; 2) providing an incentive structure similar to that of standard research; and 3) conducting a proper meta-analysis of the results. We argue that some recent many-analyst studies have failed to address these issues satisfactorily and have therefore provided an overly pessimistic assessment of the credibility of science. We also provide some concrete guidance on how future many-analyst studies could provide a more constructive assessment. [ABSTRACT FROM AUTHOR]

Accurately determining whether two images show the same person is a surprisingly difficult task, which becomes even harder if one or both faces are wearing medical face masks. Attempts to improve unfamiliar face matching accuracy have generally had limited success. Although one brief training program improved masked face matching accuracy by 4.9%, this increase would not overcome the entire performance deficit caused by masks. Here, we investigate whether combining independent identification decisions from different individuals can improve masked face matching performance through the wisdom of the crowd effect. Accuracy gains emerged reliably after combining the decisions of three individuals, culminating in significant improvements of 11%–26% among the largest crowds. Despite creating crowds of up to 80 people, half of the eventual improvement had generally already occurred in crowds of 6 individuals. The wisdom of the crowd effect is a highly effective approach to improving accuracy when identifying masked faces. [ABSTRACT FROM AUTHOR]

Introduction: In the United States, more chronic and preventive healthcare is being delivered in the emergency department (ED) setting. Understanding the availability of preventive health services in the ED setting is crucial. Our goal was to understand the availability of a subset of preventive health services in US EDs and explore how that has changed over time. Methods: In 2022–2023, using the National Emergency Department Inventory (NEDI)-USA, we surveyed a random 20% (1,064) sampling of all 5,613 US EDs. We asked directors of these EDs about the availability of and preference for 12 preventive health services, social worker availability, selfreported percentage of uninsured ED patients, and measures of ED crowding. We also asked about perceptions of barriers to implementing preventive health services in the ED. We used unadjusted and multivariable logistic regression models to compare service frequency in 2022–2023 to prior findings from 2008–2009 that represented a 5.7% random sampling of all EDs. Results: Among 302 responders to the 2022–2023 survey (5.4% random sampling, 28.4% response rate), 94% reported offering at least one preventive health service, with a median of five services. The most common service offered was intimate partner violence screening (83%), while the least common was routine HIV screening (19%). Seven services (eg, intimate partner violence, alcohol risk, and smoking cessation screening) had a higher odds of being offered in 2022–2023 than in 2008–2009; findings were unchanged in sensitivity analyses. A small proportion of directors opposed offering preventive health services. However, many expressed concerns that preventive health services in the ED would lead to longer lengths of stay (56%), increased costs to their ED (58%), a diversion of staff time from providing acute care (50%), or that their patients would not have access to adequate followup (49%). Conclusion: Nearly all EDs offer at least one preventive health service. Many offer multiple services; rates were higher than those identified in 2008–2009, in both unadjusted and multivariable models. Although limited by the response rate, this work provides the most recent and comprehensive snapshot of the type and frequency of a subset of preventive health services currently offered in US EDs. [ABSTRACT FROM AUTHOR]

Objective: Because admitted emergency department (ED) patients waiting for an inpatient bed contribute to dangerous ED crowding, we conducted a patient flow investigation to discover and solve outflow delays. After solution implementation, we measured whether the time admitted ED patients waited to leave the ED was reduced. Methods: In June 2022, a team using Lean Healthcare methodologies identified flow delays and underlying barriers in a Midwest, mid-sized hospital. We calculated barriers’ magnitudes of burden by the frequency of involvement in delays. During October–December 2022, solutions targeting barriers were implemented. In October 2023, we tested whether waiting time, defined as daily median time in minutes from admission disposition to departure (ADtoD), declined by conducting independent sample, singletailed t-test comparing pre- to post-intervention time periods, January 1–September 30, 2022 (273 days) to January 1–September 30, 2023 (273 days). Additionally, we regressed ADtoD onto pre-/post period while controlling for ED volume (total daily admissions and ED daily encounters) and hospital occupancy. A run chart analysis of monthly median ADtoD assessed improvement sustainability. Results: Process mapping revealed that three departments (ED, environmental services [EVS], and transport services) co-produced the outflow of admitted ED patients wherein 18 delays were identified. The EVS-clinical care collaboration failures explained 61% (11/18) of delays. Technology contributed to 78% (14/18) of delays primarily because staff’s technology did not display needed information, a condition we coined “digital blindness.” Comparing pre- and post-intervention days (3,144 patients admitted pre-intervention and 3,256 patients post), the median minutes a patient waited (ADtoD) significantly decreased (96.4 to 87.1 minutes, P = 0.04), even while daily ED encounter volume significantly increased (110.7 to 117.3 encounters per day, P < 0.001). After controlling in regression for other factors associated with waiting, the intervention reduced ADtoD by 12.7 minutes per patient (standard error 5.10, P = 0.01; 95% confidence interval −22.7, −2.7). We estimate that the intervention translated to ED staff avoiding 689 hours of admitted patient boarding over nine months (ADtoD coefficient [−12.7 minutes] multiplied by post-intervention ED admissions [3,256] and divided by 60). Run chart analysis substantiated the intervention’s sustainability over nine months. Conclusion: After systemwide patient flow investigation, solutions resolving digital blindness and environmental services-clinical care collaboration failures significantly reduced ED admitted patient boarding. [ABSTRACT FROM AUTHOR]

Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification. [ABSTRACT FROM AUTHOR]

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The paper addresses the problem of surveillance of dynamic crowds in a bounded arena using multiple drones. The strategy proposes a prediction-based path planning approach for drones, where the target's future position is predicted and used to generate a traverse path. The procedure for grouping numerous human beings in order to deal with a dense crowd scenario is also described. An algorithm to dynamically divide and allocate the arena to drones is also proposed to ensure equal workload distribution among all drones in the framework. The paper also discusses relevant concepts like detecting anomalous crowd activity and searching for any missing target in the arena. The proposed algorithms are validated on MATLAB environment numerically and ROS/Gazebo physics engine as a part of simulation-in-the-loop (SITL). [ABSTRACT FROM AUTHOR]

Background The 2022 African Cup of Nations (AFCON) took place in Cameroon from January 9th to February 5th, 2022, including Garoua in the north. We aimed to measure the impact of this event on the local COVID-19 epidemic given the implementation of a preventive strategy based on a health pass. Methods All players, staff and fans involved in the AFCON event were screened with PCR tests. Symptomatic cases were also continuously monitored in the general population and screened for variants of concern. Daily numbers of confirmed cases were compared to neighboring countries numbers retrieved from a public domain source. Results In total, 1479 and 2481 tests were performed in the general population and on asymptomatic AFCON attendees, respectively. From the latter, 12.5% were PCR-positive; 97% were infected with Omicron, with no significant difference compared to the passive program (G-test, P value = 0.162). Surveillance indicated the AFCON did not increase the number of symptomatic PCR-positive cases in the general population compared to neighboring countries. Conclusions Though the COVID-19 epidemic was fueled by asymptomatic cases infected with the Omicron variant at the time, the non-therapeutic preventive measures implemented for AFCON mitigated an increase in the epidemic in the local population. [ABSTRACT FROM AUTHOR]

Destination crowding has emerged as a serious issue for tourist sites and visitors alike. This research delves into the correlation between two‐dimensional perceived (spatial and human) crowding and two tourist behaviors (pro‐environmental and deviant behavior). Additionally, it explores the influence of deindividuation and environmental knowledge on these relationships. The study, based on 313 Chinese domestic tourists who recently visited the Great Wall, reveals that perceptions of spatial and human crowding significantly trigger deviant behavior. Conversely, pro‐environmental behavior is indirectly impeded by both forms of perceived crowding, with deindividuation acting as a mediating factor. The presence of environmental knowledge proves crucial in empowering tourists to make well‐informed behavioral decisions and mitigating the negative effects of deindividuation on their actions. This research contributes to the tourism literature by incorporating deindividuation to enhance the understanding of how perceived crowding affects tourist behaviors. It further advances the field by differentiating between pro‐environmental and deviant. [ABSTRACT FROM AUTHOR]

This article identifies a new legal stakeholder in the data economy: the data crowd. A data crowd is a collective that: (i) is unorganised, non-deliberate and unable to form an agenda; (ii) relies on productive aggregation that creates an interdependency among participants; and (iii) is subjected to an external authority. Notable examples of crowds include users of a social network, users of a search engine and users of artificial intelligence-based applications. The law currently only protects users in the data economy as individuals, and in certain cases may address broad public concerns. However, it does not recognise the collective interests of the crowd of users and its unique vulnerability to platform power. The article presents and defends the crowd's legal interests in a stable infrastructure for participation. It therefore reveals the need for a new approach to consumers' rights in the data economy. [ABSTRACT FROM AUTHOR]

The tolerance of pedestrians to red light signals is a crucial factor in urban residents' travel experiences and road traffic safety. To address the current lack of research on methods for measuring pedestrian red light tolerance time at crossings, this paper proposes a stacking model for predicting the red light tolerance time of individual pedestrians. The model employs the XGBoost (XGB), random forest (RF), support vector machine regression (SVR), and multilayer perceptron (MLP) models as primary models, with a multiple linear regression model as the secondary-layer meta-model, as well as Bayesian hyperparameter tuning. Using random survival forest (RSF) and K-means clustering methods, the dataset was divided into three categories: the low, medium, and high tolerance groups. The feature variables influencing the red light tolerance time were grouped accordingly. Stacking models were established for each tolerance and feature group. The experimental results demonstrated that the feature group voted on by multiple machine learning models performed the best in all three tolerance groups. In the low tolerance group, the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 6.58, 1.91, and 19.78%, respectively. For the medium tolerant group, the MSE, MAE, and MAPE were 4.82, 1.53, and 7.63%, respectively. For the high tolerance group, the MSE, MAE, and MAPE were 33.32, 3.89, and 10.14%, respectively. Individual XGB, RF, SVR, MLP, and ungrouped stacking models were established for comparative analysis of the test set. The results indicated that the proposed grouped stacking model outperformed the other models overall. The method provides a means of obtaining the probability distribution function of the length of the waiting crowd's tolerance time for each red light range and thus determining the duration of the red light at the crossing signal for different personnel compositions. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic, drought and internal conflict have worsened Ethiopia's already weak healthcare system. Antenatal and intrapartum care are especially prone to interruption under these circumstances. Objective: To explore women's experiences receiving antenatal and intrapartum care during the pandemic. Design: A descriptive qualitative approach was utilised. Methods: We conducted in-depth interviews with 17 women and held 4 focus group discussions with women who gave birth at 4 public hospitals during the pandemic. A study was conducted at four public hospitals in the Sidama region of Ethiopia, during which data were collected from 14 February to 10 May 2022. Thematic analysis was performed to generate themes. Results: The peak of the COVID-19 pandemic in Ethiopia presented several barriers to access and uptake of antenatal and intrapartum care at public hospitals. Four themes and 10 subthemes emerged from the thematic analysis. The themes were 'Barriers to maternity care uptake during COVID-19', 'Shortage of resources during COVID-19', 'Delays in maternity care uptake during COVID-19' and 'Mistreatment of women during maternity care during COVID-19'. The subthemes included 'Fear of contracting COVID-19', 'People in the hospital neglecting COVID-19 prevention', 'Women losing their job during COVID-19', 'Shortage of beds in the labour ward', 'Shortage of medical supplies', 'Delays in seeking care', 'Delays in receiving care', 'Complications during childbirth', 'disrespectful' and 'suboptimal care'. Conclusion: The findings of this study underscore the impact of COVID-19 on antenatal and intrapartum care, leading to delays in seeking and receiving care due to reduced rapport, resource shortages, companion restrictions, disrespectful care and suboptimal care. These factors contribute to increased obstetric complications during COVID-19. It is imperative for policymakers to prioritise essential resources for antenatal and intrapartum care in the present and future pandemics. Moreover, healthcare providers should maintain respectful and optimal care even amid challenges. Plain Language Summary: Women's experiences in receiving antenatal and intrapartum care during COVID-19 The COVID-19 pandemic has posed a significant challenge in seeking and receiving care of antenatal care and institutional childbirth. Women could experience delays in booking antenatal care appointments, postponing antenatal care visits, and facing delays in receiving care at hospitals, which has disrupted their access to routine antenatal care and institutional birth during the pandemic. This study highlights a range of barriers that hinder access to and utilisation of antenatal care and institutional birth, including lack of transport, shortage of resources, fear of contracting the virus, companion restrictions, disrespectful care and suboptimal care that leads to increased obstetric complications during COVID-19. This study provides recommendations for policymakers to prioritise resources and enhance respectful and optimal care for pregnancy and institutional birth to lessen the indirect impact of present and future pandemics on pregnant women and their unborn babies. [ABSTRACT FROM AUTHOR]

As a fundamental computer vision task, crowd counting plays an important role in public safety. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be well applied to this problem for three reasons: (1) Existing loss functions fail to address sample imbalance in highly dense and complex scenes; (2) Canonical object detectors lack spatial coherence in loss calculation, disregarding the relationship between object location and background region; (3) Most of the head detection datasets are only annotated with the center points, i.e. without bounding boxes. To overcome these issues, we propose a novel Mask Focal Loss (MFL) based on heatmap via the Gaussian kernel. MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths. Additionally, we introduce GTA_Head, a synthetic dataset with comprehensive annotations, for evaluation and comparison. Extensive experimental results demonstrate the superior performance of our MFL across various detectors and datasets, and it can reduce MAE and RMSE by up to 47.03% and 61.99%, respectively. Therefore, our work presents a strong foundation for advancing crowd counting methods based on density estimation. [ABSTRACT FROM AUTHOR]

AbstractThis study assessed the carrying capacity of an urban aerial trails, considering physical and psychosocial dimensions of crowding. To evaluate physical crowding, the research utilized a quantitative approach to measure tourist density. Psychosocial crowding was assessed using a visual assessment technique. Data collection involved presenting participants with simulated imagery, facilitating the assessment of social and psychological perceptions of crowding on the mountain and sea trails in Xiamen, China. The study found that the discrepancy between physical and psychological crowding primarily arises from the uneven spatial distribution of tourists along the trails. The findings confirm that the proximity of individuals on the trails significantly influences perceived congestion, thereby highlighting the unique nature of congestion analysis and assessment in the context of aerial trails. The research proposes strategies to address the challenges associated with both physical and psychosocial crowding phenomena. [ABSTRACT FROM AUTHOR]