Parallel Continuous Preference Queries over Out-of-Order and Bursty Data Streams

Techniques to handle traffic bursts and out-of-order arrivals are of paramount importance to provide real-time sensor data analytics in domains like traffic surveillance, transportation management, healthcare and security applications. In these systems the amount of raw data coming from sensors must be analyzed by continuous queries that extract value-added information used to make informed decisions in real-time. To perform this task with timing constraints, parallelism must be exploited in the query execution in order to enable the real-time processing on parallel architectures. In this paper we focus on continuous preference queries, a representative class of continuous queries for decision making, and we propose a parallel query model targeting the efficient processing over out-of-order and bursty data streams. We study how to integrate punctuation mechanisms in order to enable out-of-order processing. Then, we present advanced scheduling strategies targeting scenarios with different burstiness levels, parameterized using the index of dispersion quantity. Extensive experiments have been performed using synthetic datasets and real-world data streams obtained from an existing real-time locating system. The experimental evaluation demonstrates the efficiency of our parallel solution and its effectiveness in handling the out-of-orderness degrees and burstiness levels of real-world applications

Spherical GEMs for parallax-free detectors

We developed a method to make GEM foils with a spherical geometry. Tests of this procedure and with the resulting spherical \textsc{gem}s are presented. Together with a spherical drift electrode, a spherical conversion gap can be formed. This would eliminate the parallax error for detection of x-rays, neutrons or UV photons when a gaseous converter is used. This parallax error limits the spatial resolution at wide scattering angles. The method is inexpensive and flexible towards possible changes in the design. We show advanced plans to make a prototype of an entirely spherical triple-GEM detector, including a spherical readout structure. This detector will have a superior position resolution, also at wide angles, and a high rate capability. A completely spherical gaseous detector has never been made before.Comment: Contribution to the 2009 IEEE Nuclear Science Symposium, Orlando, Florid

Use of smartphone and crash risk among cyclists

High percentages of cyclists admit using smartphone devices while cycling. Moreover, such use has been found to be associated with near crashes and crashes, representing a risk factor for cyclists. This study examines the relationship between such type of behaviours, comprising calling and manipulating the screen, and the frequency of near crashes and actual crashes among Italian cyclists. We administered an online survey measuring smartphone-specific violation, errors, near crash and crash to Italian cyclists (N = 298; age range: 19–72). We hypothesised that the relationship between smartphone use and near crashes would be explained by an increase in the number of errors committed, thus increasing the likelihood of being involved in near crashes. Moreover, we hypothesised that near crashes will predict actual crashes. Results of path analysis showed that smartphone-specific violations predicted crashes throughout their consecutive effects on errors and near crashes only in the subsample of men. These findings offer an explanation of how smartphone use contributes to incrementing the likelihood of getting involved in near crashes and actual crashes. To our knowledge, the present study is the first in building a path model explaining how smartphone-specific violations lead to more near crashes among cyclists

Social Influence and Different Types of Red-Light Behaviors among Cyclists

Accident analysis and studies on traffic revealed that cyclists’ violation of red-light regulation is one typical infringement committed by cyclists. Furthermore, an association between cyclists’ crash involvement and red-light violations has been found across different countries. The literature on cyclists’ psychosocial determinants of red-light violation is still scarce. The present study, based on the classification of cyclists’ red-light behavior in risk-taking (ignoring the red-light and traveling through the junction without stopping), opportunistic (waiting at red-lights but being too impatient to wait for green signal and subsequently crossing the junction) and law-obeying (stopping to obey the red-light), adopted an eye-observational methodology to investigate differences in cyclists' crossing behavior at intersections, in relation to traffic light violations and the presence of other cyclists. Based on the social influence explanatory framework, which states that people tend to behave differently in a given situation taking into consideration similar people’s behaviors, and that the effect of social influence is related to the group size, we hypothesized that the number of cyclists at the intersection will have an influence on the cyclists’ behavior. Furthermore, cyclists will be more likely to violate in an opportunistic way when other cyclists are already committing a violation. Two researchers at a time registered unobtrusively at four different intersections during morning and late afternoon peak hour traffic, 1381 cyclists approaching the traffic light during the red phase. The 62.9% violated the traffic control. Results showed that a higher number of cyclists waiting at the intersection is associated with fewer risk-taking violations. Nevertheless, the percentage of opportunistic violation remained high. For the condition of no cyclist present, risk-taking behaviors were significantly higher, whereas, they were significantly lower for conditions of two to four and five or more cyclists present. The percentage of cyclists committing a red-light violation without following any other was higher for those committing a risk-taking violation, whereas those following tended to commit opportunistic violations more often

Is correction of the deformity sufficient for complete recovery from idiopathic scoliosis?

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The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations

Aim/Purpose This paper aims to investigate the recent developments in research and practice on the transformation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges that arise. Background The implementation of AI in various organisational sectors has the potential to automate tasks that are currently performed by humans or to reduce cognitive workload. While this can lead to increased productivity and efficiency, these rapid changes have significant implications for organisations and workers, as AI can also be perceived as leading to job losses. Successfully adapting to this transformation will lead companies and institutions to new working and organisational models, which requires implementing measures and strategies to upskill or reskill workers. Organisations, therefore, face considerable challenges such as guiding employees towards the change process, dealing with the cost of training, and ensuring fairness and inclusion posed by age, gender, and cultural diversity. Methodology A narrative review has been conducted to analyse research and practice on the impact of AI on human skills in organisations. Contribution This work contributes to the body of knowledge by examining recent trends in research and practice on how AI will transform professional skills and workplaces, highlighting the crucial role played by transversal skills and identifying strategies that can support organisations and guide workers toward the upskilling and reskilling challenges. Findings This work found that introducing AI in organisations combines many organisational strategies simultaneously. First, it is critical to map the transversal skills needed by workers to mitigate the current skills gap within the workplace. Secondly, organisations can help workers identify the skills required for AI adoption, improve current skills, and develop new skills. In addition, the findings show that companies need to implement processes to support workers by providing ad hoc training and development opportunities to ensure that workers’ attitudes and mental models towards AI are open and ready for the changing labour market and its related challenges. Recommendation for Researchers AI is a complex and multifaceted field that encompasses a wide range of disciplines, including computer science, mathematics, engineering, and behavioural and social sciences. Researchers should take a transdisciplinary approach to enable the integration of knowledge and perspectives from different fields that are essential to understanding the full range of implications and applications of AI. Future Research Further research is needed to understand the impact of AI on human skills and the role of soft skills in the adoption of AI in organisations. Future studies should also consider the challenges presented by Industry 5.0, which is likely to involve the integration of new technologies and automation on an even greater scale

High prevalence of human cytomegalovirus in a population of periodontally healthy subjects

Background: Human cytomegalovirus (HCMV) appears to be more frequent in periodontally affected patients than in healthy control groups. Based on this assumption, it has been suggested that HCMV may play a role in the pathogenesis of periodontal disease. Objective: The objective of this uncontrolled study was to assess the occurrence of HCMV in a large unselected population of periodontally healthy subjects. Study Design: Fifty consecutive periodontally healthy patients satisfied the inclusion criteria. Two samples of gingival crevicular fluids were taken from two non-bleeding on probing sites for each patient. Samples were collected from the anterior and the posterior area. Polymerase chain reaction (PCR) was used to identify the presence of HCMV. Results: HCMV was detected in 17 (33%) out of 50 participants. Ten subjects showed presence of HCMV on both anterior and posterior sites, whereas the remaining 7 only had HCMV present in the anterior sites. No differences were noticed between HCMV positive and HCMV negative in terms of smoking (p = 0.33), drinking habits (p=0,94) or the presence of prosthodontic restorations (p= 0,89). Conclusions: HCMV was detected in a high proportion of periodontally healthy subjects. Its presence was not found to be influenced by smoking or drinking habits