9,139 research outputs found

    Current issues in tourism: Mitigating climate change in sustainable tourism research

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    This paper adopts a problematising review approach to examine the extent of mitigating climate change research in the sustainable tourism literature. As climate change has developed into an existential global environmental crisis and while tourism's emissions are still increasing, one would expect it to be at the heart of sustainable tourism research. However, from a corpus of 2573 journal articles featuring ‘sustainable tourism’ in their title, abstract, or keywords, only 6.5% covered climate change mitigation. Our critical content analysis of 35 of the most influential papers found that the current methods, scope and traditions of tourism research hamper effective and in-depth research into climate change. Transport, the greatest contributor to tourism's emissions, was mostly overlooked, and weak definitions of sustainability were common. Tight system boundaries, lack of common definitions and incomplete data within tourism studies appear to hamper assessing ways to mitigate tourism's contribution to climate change

    Some results concerning the valences of (super) edge-magic graphs

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    A graph GG is called edge-magic if there exists a bijective function f:V(G)E(G){1,2,,V(G)+E(G)}f:V\left(G\right) \cup E\left(G\right)\rightarrow \left\{1, 2, \ldots , \left\vert V\left( G\right) \right\vert +\left\vert E\left( G\right) \right\vert \right\} such that f(u)+f(v)+f(uv)f\left(u\right) + f\left(v\right) + f\left(uv\right) is a constant (called the valence of ff) for each uvE(G)uv\in E\left( G\right) . If f(V(G))={1,2,,V(G)}f\left(V \left(G\right)\right) =\left\{1, 2, \ldots , \left\vert V\left( G\right) \right\vert \right\}, then GG is called a super edge-magic graph. A stronger version of edge-magic and super edge-magic graphs appeared when the concepts of perfect edge-magic and perfect super edge-magic graphs were introduced. The super edge-magic deficiency μs(G) \mu_{s}\left(G\right) of a graph GG is defined to be either the smallest nonnegative integer nn with the property that GnK1G \cup nK_{1} is super edge-magic or ++ \infty if there exists no such integer nn. On the other hand, the edge-magic deficiency μ(G) \mu\left(G\right) of a graph GG is the smallest nonnegative integer nn for which GnK1G\cup nK_{1} is edge-magic, being μ(G) \mu\left(G\right) always finite. In this paper, the concepts of (super) edge-magic deficiency are generalized using the concepts of perfect (super) edge-magic graphs. This naturally leads to the study of the valences of edge-magic and super edge-magic labelings. We present some general results in this direction and study the perfect (super) edge-magic deficiency of the star K1,nK_{1,n}

    Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

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    The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.Comment: 32 pages, 6 figures, 5 tables. Accepted for publication in the Proceedings of the ACM on Human-Computer Interaction (PACM HCI), CSCW 202

    TeamSTEPPS and Organizational Culture

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    Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital

    Augmented Symbolic Execution for Information Flow in Hardware Designs

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    We present SEIF, a methodology that combines static analysis with symbolic execution to verify and explicate information flow paths in a hardware design. SEIF begins with a statically built model of the information flow through a design and uses guided symbolic execution to recognize and eliminate non-flows with high precision or to find corresponding paths through the design state for true flows. We evaluate SEIF on two open-source CPUs, an AES core, and the AKER access control module. SEIF can exhaustively explore 10-12 clock cycles deep in 4-6 seconds on average, and can automatically account for 86-90% of the paths in the statically built model. Additionally, SEIF can be used to find multiple violating paths for security properties, providing a new angle for security verification

    Learning in Repeated Multi-Unit Pay-As-Bid Auctions

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    Motivated by Carbon Emissions Trading Schemes, Treasury Auctions, and Procurement Auctions, which all involve the auctioning of homogeneous multiple units, we consider the problem of learning how to bid in repeated multi-unit pay-as-bid auctions. In each of these auctions, a large number of (identical) items are to be allocated to the largest submitted bids, where the price of each of the winning bids is equal to the bid itself. The problem of learning how to bid in pay-as-bid auctions is challenging due to the combinatorial nature of the action space. We overcome this challenge by focusing on the offline setting, where the bidder optimizes their vector of bids while only having access to the past submitted bids by other bidders. We show that the optimal solution to the offline problem can be obtained using a polynomial time dynamic programming (DP) scheme. We leverage the structure of the DP scheme to design online learning algorithms with polynomial time and space complexity under full information and bandit feedback settings. We achieve an upper bound on regret of O(MTlogB)O(M\sqrt{T\log |\mathcal{B}|}) and O(MBTlogB)O(M\sqrt{|\mathcal{B}|T\log |\mathcal{B}|}) respectively, where MM is the number of units demanded by the bidder, TT is the total number of auctions, and B|\mathcal{B}| is the size of the discretized bid space. We accompany these results with a regret lower bound, which match the linear dependency in MM. Our numerical results suggest that when all agents behave according to our proposed no regret learning algorithms, the resulting market dynamics mainly converge to a welfare maximizing equilibrium where bidders submit uniform bids. Lastly, our experiments demonstrate that the pay-as-bid auction consistently generates significantly higher revenue compared to its popular alternative, the uniform price auction.Comment: 51 pages, 12 Figure

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    The nexus between e-marketing, e-service quality, e-satisfaction and e-loyalty: a cross-sectional study within the context of online SMEs in Ghana

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    The spread of the Internet, the proliferation of mobile devices, and the onset of the COVID-19 pandemic have given impetus to online shopping in Ghana and the subregion. This situation has also created opportunities for SMEs to take advantage of online marketing technologies. However, there is a dearth of studies on the link between e-marketing and e-loyalty in terms of online shopping, thereby creating a policy gap on the prospects for business success for online SMEs in Ghana. Therefore, the purpose of the study was to examine the relationship between the main independent variable, e-marketing and the main dependent variable, e-loyalty, as well as the mediating roles of e-service quality and e-satisfaction in the link between e-marketing and e-loyalty. The study adopted a positivist stance with a quantitative method. The study was cross-sectional in nature with the adoption of a descriptive correlational design. A Structural Equation Modelling approach was employed to examine the nature of the associations between the independent, mediating and dependent variables. A sensitivity analysis was also conducted to control for the potential confounding effects of the demographic factors. A sample size of 1,293 residents in Accra, Ghana, who had previously shopped online, responded to structured questionnaire in an online survey via Google Docs. The IBM SPSS Amos 24 software was used to analyse the data collected. Positive associations were found between the key constructs in the study: e-marketing, e-service quality, e-satisfaction and e-Loyalty. The findings from the study gave further backing to the diffusion innovation theory, resource-based view theory, and technology acceptance model. In addition, e-service quality and e-satisfaction individually and jointly mediated the relationship between e-marketing and e-loyalty. However, these mediations were partial, instead of an originally anticipated full mediation. In terms of value and contribution, this is the first study in a developing economy context to undertake a holistic examination of the key marketing performance variables within an online shopping context. The study uniquely tested the mediation roles of both e-service quality and e-satisfaction in the link between e-marketing and e-loyalty. The findings of the study are novel in the e-marketing literature as they unearthed the key antecedents of e-loyalty for online SMEs in a developing economy context. The study suggested areas for further related studies and also highlighted the limitations
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