Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

SaBRe: load-time selective binary rewriting

Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe, a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for x86_64 and RISC-V—which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.

     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

The clinical treatment of bacterial keratitis: A review of drop instillation regimes

Bacterial keratitis (BK) presentations are often treated using the commercially available second-generation fluoroquinolones ciprofloxacin 0.3% and ofloxacin 0.3% as monotherapy. The guidelines available for instillation regimes are often not supported by data from clinical studies.This review examines the peer-reviewed clinical studies and compared treatment failure rates for ciprofloxacin 0.3% and ofloxacin 0.3% for BK in relation to Day-1 drop-regimes. From the statistical analysis, this review derived evidence-based clinically applicable minimum drop-regimes for the treatment of BK on Day-1.Lower numbers of drops of ciprofloxacin on Day-1 were significantly associated with increased treatment failure rates (p < 0.002). The derived minimum number of drops on Day for ciprofloxacin on Day-1 was 47 drops, and for ofloxacin 24 drops. The mean number of drops used in the clinical studies was significantly lower than the manufacturers’ recommended Day-1 regimes for both ciprofloxacin (p = 0.0006) and ofloxacin (p = 0.048). From Day-3 to ?6 of treatment the drop rates for ciprofloxacin relative to recommended rates were higher, and for ofloxacin lower (p = 0.014).The findings of this review were then compared with a representative sample of published guidelines and case studies to determine the validity of applying those drop-regimes in clinical practice. Although the manufacturers’ suggested minimum drop-regimes on Day-1 were significantly different (120 drops ciprofloxacin, 34 drops ofloxacin, p < 0.0001), many of the published guidelines suggested the same drop-regime for both fluoroquinolones. The suggested drop numbers on Day-1 for ciprofloxacin in these guidelines and case studies were significantly less than those used in the clinical studies (p = 0.043).Increased treatment failure rates for ciprofloxacin are associated with lower drop numbers on Day-1. The Day-1 dosing rates for ciprofloxacin and ofloxacin should be considered separately, and the regimes suggested in published guidelines and case studies may need be re-considered in light of the findings of this review.     

Italian translation,validation, and repeatability of Standard Patient Evaluation of Eye Dryness (SPEED) Questionnaire

PurposeThe assessment of symptoms of dry eye disease using a questionnaire is an effective and simple method of quantifying symptoms. The aim of this study was to translate the SPEED questionnaire and adapt it for the Italian language and verify the main psychometric performance of the translated version, including repeatability and agreement.MethodsThe SPEED questionnaire was translated into Italian following a standard methodology. The resulting questionnaire was administered to 206 adult participants in order to perform a validation analysis. A subgroup of 82 participants was retested after one week to give a repeatability and agreement assessment.ResultsInternal consistency showed an alpha of 0.852 (95% CI 0.818–0.881) and no unnecessary items. The factor analysis showed a saturation for three main factors related to (i) Dryness and Soreness, (ii) Fatigue, and (iii) Burning. Repeatability was high, with a CCC of 0.896 (95% CI 0.844–0.931). Agreement analysis showed no significant bias between sessions and an interval of agreement of 5 points for SPEED score.ConclusionThe translation and adaptation of the SPEED questionnaire for the Italian language have demonstrated good psychometric properties for the translated questionnaire, confirming and expanding the original psychometric characteristics. Consequently, the SPEED questionnaire could be used to measure the presence of symptoms of dry eye quantitatively.     

RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells

Wireless Networks - Femto Cells offer higher data rates to users within closed spaces. Dense deployment of small cells is a characteristic of pre-5G/LTE-Advanced Pro (LTE-A Pro) networks and is a...     

Community involvement critical for revitalization: Grass-roots initiative key to environmental remediation and restoration in the Great River (St. Lawrence River)

This paper describes the creation of an environmentally conscious community group, the Great River Network, and the role that it has played in the remediation and restoration process as part of one of the Great Lakes environmental programs. Community engagement was initiated in the region as part of the Remedial Action Plan for the Area of Concern at Cornwall/Akwesasne/Massena within the Upper St. Lawrence River. The community group formalised as a network representing 50+ organisations in response to perceived inadequacies in the agency of the community to respond to new environmental concerns outside of the scope of the existing programs. As a grass-roots initiative, the Great River Network has successfully completed remediation and restoration actions of significant value to the environment. These include a series of river clean ups (>42 tonnes of garbage removed), fish habitat restoration, and addressing shoreline erosion issues. Success has been achieved through partnering with a range of organisations, including Indigenous, non-profit, governmental, Conservation Authorities, businesses and industry partners. The action-oriented approach showcases how remediation and restoration led by, and embedded in, the community can result in true revitalization. A simplified framework for adaptive management practices for remediation and restoration efforts that lead to revitalization, including knowledge translation, is proposed. This case study highlights the transformational opportunities that remediation and restoration initiatives can bring. In this instance, the process is intensely local and cooperative and lays the foundation for moving towards a collective impact approach for the region.