Pragmatic Scenarios Research Articles

Eligibility for omecamtiv mecarbil in a real-world heart failure population: Data from the Swedish Heart Failure Registry.

We assessed eligibility for omecamtiv mecarbil (OM) in a real-world cohort with heart failure with reduced ejection fraction (HFrEF) according to the selection criteria of the GALACTIC-HF trial (trial scenario) and selected trial´s criteria more likely to impact real-world use (pragmatic scenario). We included 31,015 patients with HFrEF lasting ≥3 months and registered in the Swedish HF registry between 2000-2021. Trial eligibility was calculated by applying all the GALACTIC-HF selection criteria. The pragmatic scenario considered only the New York Heart Association class, history of worsening HF, N-terminal pro-B-type natriuretic peptides (NT-proBNP), blood pressure and renal failure criteria defined as in the trial. Eligibility for OM in chronic HFrEF was 21% and 36% in the trial and pragmatic scenarios, respectively. Eligibility was higher in those with EF<30% (trial: 27%, pragmatic: 44%), in-patients (trial:30%, pragmatic:57%), severe HF (trial: 35%, pragmatic: 60%), NYHA class III-IV (trial: 26%, pragmatic: 45%), and NT-proBNP≥5,000pg/mL (trial: 30%, pragmatic: 51%). The criteria that most limited eligibility were history of a recent worsening HF event (60% eligible in chronic HFrEF), elevated NT-proBNP (82% eligible), and deviating blood pressure (82% eligible). Overall, eligible patients were characterized by more severe HF and higher CV event-rates in both scenarios, and higher comorbidity burden in the pragmatic scenario. Approximately 21% of real-world chronic HFrEF patients would be eligible for OM according to the GALACTIC-HF selection criteria, and 36% according to the criteria more likely to affect OM use in clinical practice. Criteria in both scenarios identified a patient-group with severe HF and high CV event-rates.

Impact analysis of environmental policies on shipping fleet planning under demand uncertainty

Considerations to decarbonize maritime shipping by implementing the Emissions Trading System (ETS) are underway at the International Maritime Organization (IMO). This study seeks to investigate the impact of ETS on short-term fleet deployment decisions of liner shipping companies. Considering pragmatic scenarios of demand uncertainty and transshipment, we extend state-of-the-art maritime operational planning under climate policies and propose a fleet planning model accounting for carbon emissions to evaluate the decision-making nuances of a liner company. Numerical experiments are conducted to analyze ETS implementations with varying policy design parameters for their efficacy in mitigating emissions and potential impact on stakeholders. The findings indicate that, for liner shipping which is cross-regional by nature, regional ETS implementations are inadequate due to carbon leakage; the efficacy of open ETS policies is limited, particularly when carbon prices are low; strict carbon purchase caps integrated with robust carbon price-setting mechanisms are critical to designing an effective ETS.

Cost-Effectiveness of SGLT2 Inhibitors in a Real-World Population: A MICADO Model-Based Analysis Using Routine Data from a GP Registry.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to reduce the risk of cardiovascular complications, which largely drive diabetes' health and economic burdens. Trial results indicated that SGLT2i are cost effective. However, these findings may not be generalizable to the real-world target population. This study aims to evaluate the cost effectiveness of SGLT2i in a routine care type 2 diabetes population that meets Dutch reimbursement criteria using the MICADO model. Individuals from the Hoorn Diabetes Care System cohort (N = 15,392) were filtered to satisfy trial inclusion criteria (including EMPA-REG, CANVAS, and DECLARE-TIMI58) or satisfy the current Dutch reimbursement criteria for SGLT2i. We validated a health economic model (MICADO) by comparing simulated and observed outcomes regarding the relative risks of events in the intervention and comparator arm from three trials, and used the validated model to evaluate the long-term health outcomes using the filtered cohorts' baseline characteristics and treatment effects from trials and a review of observational studies. The incremental cost-effectiveness ratio (ICER) of SGLT2i, compared with care-as-usual, was assessed from a third-party payer perspective, measured in euros (2021 price level), using a discount rate of 4% for costs and 1.5% for effects. From Dutch individuals with diabetes in routine care, 15.8% qualify for the current Dutch reimbursement criteria for SGLT2i. Their characteristics were significantly different (lower HbA1c, higher age, and generally more preexisting complications) than trial populations. After validating the MICADO model, we found that lifetime ICERs of SGLT2i, when compared with usual care, were favorable (<€20,000/QALY) for all filtered cohorts, resulting in an ICER of €5440/QALY using trial-based treatment effect estimates in reimbursed population. Several pragmatic scenarios were tested, the ICERs remained favorable. Although the Dutch reimbursement indications led to a target group that deviates from trial populations, SGLT2i are likely to be cost effective when compared with usual care.

Environmental flow estimation for regulated rivers under data-scarce condition

For mitigating human-induced river alterations and reviving the dying rivers to preserve natural ecosystem services, the assessment of environmental flow (e-flow) is essential. However, e-flow estimation under data-scarce conditions for the rivers being heavily regulated has become a major issue worldwide under changing environmental conditions, particularly in developing countries. To address this challenge, this study was carried out for estimating time-varying e-flows for a heavily regulated river of Eastern India under data-scarce conditions. In the absence of any pre-impact streamflow data and tributary inflow to the main river reach, this study proposes a framework integrating three methods to estimate e-flow for river reaches. A hydrologic model (first method) was employed to estimate tributary inflows to the main river, which was used in the mass-balance based naturalization approach (second method) for reconstructing the daily long-term natural streamflow from the observed post-impact streamflows at two gauging stations on the main river. Thereafter, the naturalized streamflow was used for estimating e-flows by the GA optimization technique (third method) for two pragmatic scenarios (‘Low Hydrologic Alteration’, and ‘Moderate Hydrologic Alteration’) at the two stations. Analysis of the results revealed that the major impounding and diversion infrastructures built on the river have caused profound alteration to the river reach having an overall degree of hydrologic alteration index greater than 70. The mean post-impact streamflows tend to vary less and have decreased by 35–50 % with 2–3 times reduction in the peak flow as compared to the pre-impact condition. The estimated monthly e-flows for the river basin range from 1.2 to 185 m3/s (50–60 % of the natural flow) and 0.5 to 80 m3/s (22 to 30 % of the natural flow) for the ‘Low’ and ‘Moderate’ alteration scenarios, respectively. Results of this study can aid in formulating efficient strategies for sustainable water resources management in the river basin.

Graph-based representations of clarification strategies supporting automatic dialogue management

This paper aims at presenting a dialogue-oriented approach to the construction of a graph knowledge base (KB) supporting task-oriented human-machine interactions. In particular, we focus on different pragmatic scenarios, facing the Common Ground issue and arguing that knowledge bases (in the form of graphs) are needed to make a clarification and recover pieces of information when inconsistencies occur during the communicative exchange.The main contributions of this work are: 1) a flexible dialog system architecture designed to be plugged into existing service infrastructures, 2) a graph-based knowledge representation protocol to manage both dialog domain and dialog management, 3) a detailed investigation of clarification requests forms with respect to their functions. After a brief introduction (see Section 1), we present: the theoretical underpinnings of the paper and the background work (see Section 2) our system architecture (see Sections 3 and 4) and the clarification requests (CRs) issue (see Section 5); our CRs classification, and some examples in context (see Section 6).

Deep Learning-Based Scheduling Scheme for IEEE 802.15.4e TSCH Network

IEEE 802.15.4e time-slotted channel hopping (TSCH) is one of the most reliable resources of the Industrial Internet of Things (IIoT). TSCH operates on the slot-frame structure consisting of multiple channel-offsets and multiple slot-offsets. It is gaining acceptance due to its simple architecture and consume low power in industrial applications. The performance of TSCH is mainly dominated by the media access control (MAC) mechanism, which covers the refitment, enumeration, composition, and data transmission. However, in many cases, the data transmission schedules are not accurately prescribed. Therefore, most researchers are trying to define many pragmatic scenarios of scheduling. Their fundamental approach is to schedule TSCH network in a centralized way while framing scheduling based on network performance such as throughput and delay. In this work, a deep learning (DL)-based scheme has been proposed. TSCH network schedules for links to cell assignment of a slot-frame can be constructed as a maximum edge weighted bipartite matching approach. In this paper, we design bipartite edge weight to be composed of throughput and delay, and we use the Hungarian algorithm for proper cell assignment. With the Hungarian scheduling algorithm, we generate the training data and train a deep neural network (DNN) accordingly. In the simulation, we consider a simple TSCH network with 5 nodes where 12 links are formulated, and we consider 16 cells for the link assignment. The simulation results show that the proposed deep learning-based scheduling scheme can provide performance similar to the Hungarian algorithm-based scheduling scheme with above 90% accuracy and nearly 80% execution time reduction.

Modelling geographical accessibility to urban centres in Kenya in 2019.

BackgroundAccess to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting.MethodsUrban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed.ResultsA total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya’s population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant.ConclusionTravel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

Enabling realistic health data re-identification risk assessment through adversarial modeling.

Re-identification risk methods for biomedical data often assume a worst case, in which attackers know all identifiable features (eg, age and race) about a subject. Yet, worst-case adversarial modeling can overestimate risk and induce heavy editing of shared data. The objective of this study is to introduce a framework for assessing the risk considering the attacker's resources and capabilities. We integrate 3 established risk measures (ie, prosecutor, journalist, and marketer risks) and compute re-identification probabilities for data subjects. This probability is dependent on an attacker's capabilities (eg, ability to obtain external identified resources) and the subject's decision on whether to reveal their participation in a dataset. We illustrate the framework through case studies using data from over 1000000 patients from Vanderbilt University Medical Center and show how re-identification risk changes when attackers are pragmatic and use 2 known resources for attack: (1) voter registration lists and (2) social media posts. Our framework illustrates that the risk is substantially smaller in the pragmatic scenarios than in the worst case. Our experiments yield a median worst-case risk of 0.987 (where 0 is least risky and 1 is most risky); however, the median reduction in risk was 90.1% in the voter registration scenario and 100% in the social media posts scenario. Notably, these observations hold true for a wide range of adversarial capabilities. This research illustrates that re-identification risk is situationally dependent and that appropriate adversarial modeling may permit biomedical data sharing on a wider scale than is currently the case.

Identifying the Topology of Undirected Networks From Diffused Non-Stationary Graph Signals

We address the problem of inferring an undirected graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics that depend on the structure of the unknown network. Using the so-called graph-shift operator (GSO), which is a matrix representation of the graph, we first identify the eigenvectors of the shift matrix from observations of the diffused signals, and then estimate the eigenvalues by imposing desirable properties on the graph to be recovered. Different from the stationary setting where the eigenvectors can be obtained directly from the covariance matrix of the measurements, here we need to estimate first the unknown diffusion (graph) filter - a polynomial in the GSO that preserves the sought eigenbasis. To carry out this initial system identification step, we exploit different sources of information on the arbitrarily-correlated input signal driving the diffusion on the graph. We first explore the setting where the observations, the input information, and the unknown graph filter are linearly related. We then address the case where the relation is given by a system of matrix quadratic equations, which arises in pragmatic scenarios where only the second-order statistics of the inputs are available. While such a quadratic filter identification problem boils down to a non-convex fourth-order polynomial minimization, we discuss identifiability conditions, propose algorithms to approximate the solution, and analyze their performance. Numerical tests illustrate the effectiveness of the proposed topology inference algorithms in recovering brain, social, financial, and urban transportation networks using synthetic and real-world signals.

Sustainable inventory policy for an imperfect production system with energy usage and volume agility

A sustainable production inventory model that addresses some of the pragmatic scenarios of the production process is developed. The production process is considered imperfect which results in the production of some imperfect quality items with known distribution. The imperfect products are managed through an efficient rework process that makes the items suitable to be vended at their original markup price only. The agile nature of the manufacturing process is considered. And, the energy usage and carbon emission in the production process is also considered. Undoubtedly, the fundamental nature of demand is sensitive to price. Thus, the model incorporates the demand to be price-contingent. The objective is to maximize the overall inventory turnover by conjointly optimizing the selling price, production rate, and production time. A numerical example is included to validate the model. Further, the sensitivity analysis unfolds valuable managerial insights for decision-makers. Findings suggest that the model with the insertion of volume agility effect performs better when compared with the one without the volume agility.

The Pragmatics of Pragmatic Language and the Curse of Ambiguity: An fMRI Study

In pragmatic language, there is an intentional distinction between the literal meaning of what is said, and what the speaker actually means. Previous neuroimaging investigations of pragmatic language have contrasted it with literal language; however, such contrasts may have been confounded by the higher levels of ambiguity in pragmatic language. Here, we used functional magnetic resonance imaging (fMRI) to compare pragmatic sentences (specifically requiring the interpretation of nonliteral meaning in the form of hints) with unintentionally ambiguous scenarios. Analysis showed that ambiguous language activated brain areas recognized to play a role in generating a theory of mind (ToM) that have previously been argued to support understanding of pragmatic language, specifically medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and temporoparietal junction (TPJ). In contrast, the pragmatic scenarios drew on anterior temporal, superior parietal lobule, in addition to precuneus. While no effect of gender was found for unintentionally ambiguous stimuli, females showed greater activity than males within mPFC and inferior frontal gyrus (IFG) for pragmatic scenarios – regions thought to be involved in cognitive and affective empathy, respectively. Findings suggest that while areas underpinning ToM are sufficient to support meaning derivation in the context of ambiguity, reasoning about pragmatic intent is more reliant on access to self-referential memory.

Brazilian Modernism and Cultural Criticism

The purpose of this paper is to disclose some issues of Brazilian Modernism (in the 1920’s) and to discuss how the leading exponents of this movement attempted to shape a new and authentic way of evaluating the Brazilian national project, showing their views on cultural criticism as a whole. This paper is also an attempt to understand how contemporary criticism re-evaluates this aesthetic and critical project. The late 1970`s and early 1980`s can be seen as fundamental to all the changes in scenarios, images and discourses that took place in Brazil. These changes were not limited to the composition of pragmatic, academic and disciplinary scenarios, but there was an attempt to bring about changes in the face of Brazil. The new agenda of civil society began to be redefined and the relations between public and private worlds were in the spotlight again. The end of the authoritarian military rule was not only a rupture with the current institutional pattern, but it altered deeply the way of thinking about the country.

Associative Relevance Based Eye Fixations Enhance Decision Making Processes in Scene Perception

&lt;p&gt;Recent research studies on eye movements in area of information processing task, such as scene perception have magnificently advanced towards understandings of underlying visual perception mechanism and human cognitive dynamics. Besides, business applications of eye tracking are unceasingly revealing innovative trends based on pragmatic scenarios. In this study, we conducted a number of eye tracking experiments to establish our hypothesis that the eye fixations based on the associative relevance found within the contexts of scenes during scene perception significantly bettered the processes of decision-making. The collected eye movement data from participants who viewed artistic scenes discovered that the tracks of eye fixations traversed along the existing associative relevance among the elements of scenes for decision-making processes. These experimental evidences confirmed our hypothesis that the associative relevance based eye fixations enhanced decision-making processes in scene perception.&lt;/p&gt;

A NOVEL METHOD FOR PARTITIONING FEATURE SPACES ACCORDING TO THEIR INHERENT CLASSIFICATION COMPLEXITY

The evaluation of the intrinsic complexity of a supervised domain plays an important role in devising classification systems. Typically, the metrics used for this purpose produce an overall evaluation of the domain, without localizing the sources of complexity. In this work we propose a method for partitioning the feature space into subsets of different complexity. The most important outcome of the method is the possibility of preliminarily identifying hard and easy regions of the feature space. This possibility opens interesting theoretical and pragmatic scenarios, including the analysis of the classification error and the implementation of robust classification systems. A first group of experiments has been performed on synthetic datasets, devised to separately highlight specific and recurrent problems often found in real-world domains. In particular, the focus has been on class boundaries, noise, and density of samples. A second group of experiments, performed on selected real-world datasets, confirm the validity of the proposed method. The ultimate goal of our research is to devise a method for estimating the classification difficulty of a dataset. The proposed method makes a significant step in this direction, as it is able to partition a given dataset according to the inherent complexity of the samples contained therein.

Primary Prevention of Osteoporosis: Mass Screening Scenario or Prescreening With Questionnaires? An Economic Perspective

This study focuses on the controversy surrounding selective approaches to screen for osteoporosis. Seven screening approaches were compared in terms of cost-effectiveness and incremental cost-effectiveness ratios in a sample of 4035 postmenopausal women. Our results show that certain prescreening strategies are more efficient than DXA-based approaches. These results are of considerable value for health policy decision-makers and the scientific community. There is no general consensus on the most efficient strategy to use bone densitometry for osteoporosis screening. Two distinct approaches have progressively emerged: mass screening using DXA and prescreening strategies using user-friendly risk indices. This study was designed to compare the efficiency of these approaches. A database of 4035 medical records from postmenopausal women above 45 years was analyzed. In the first scenario, women were systematically referred to DXA if above 45, 50, or 65 years of age. The second scenario involved the validated prescreening tools SCORE, ORAI, OST, and OSIRIS and assessed two separate ways of handling their results (theoretical and pragmatic). The cost of a DXA test was set as the median Belgian value: 40.14 Euros. All strategies were compared in terms of cost exposed per osteoporotic patient detected and in terms of incremental cost-effectiveness ratios. In the systematic DXA strategies, the cost per patient detected ranged from 123 Euros when measuring all women >45 years of age to 91 Euros when focusing on women >65 years of age. The corresponding percentage of cases detected ranged from 100% (age > 45 years) to 50% (age > 65 years). When considering prescreening under the theoretical and pragmatic scenarios, the OSIRIS index provided the best efficiency, with costs of 74 Euros (theoretical) to 85 Euros (pragmatic) per case detected, followed by ORAI (75 Euros and 96 Euros), OST (84 Euros and 94 Euros), and SCORE (96 Euros and 103 Euros). The corresponding percentage of cases detected ranged from 89% (SCORE) to 75% (OSIRIS). The cost-effectiveness analysis showed that mass screening strategies over 50 and 65 years of age and using ORAI were best. Our study sets the grounds for considering, in a health economics perspective, prescreening tools as valuable, cost-effective, approaches to significantly reduce the economic burden of osteoporosis screening.

Static and adaptive distributed data replication using genetic algorithms

Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NP-complete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today's distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the trade-off between running time and solution quality.

Optimum Transmitter/Receiver Design for a Narrowband Overlay in Noncoordinated Subscriber Lines

Transmitter/receiver optimization for a generic narrowband overlay communication scenario is addressed. The overlay and existing legacy systems are assumed to occupy the same frequency bands for spectral efficiency, thus introducing cochannel interference; however, the new and legacy systems are assumed to be noncooperative, as is appropriate for some pragmatic scenarios. A composite figure of merit is used consisting of a weighted sum of the mean-squared error (MSE) of the (new) overlay system plus the excess MSE in the legacy system caused by the introduction of the overlay system. Necessary conditions on the transmitter and receiver that jointly optimize the above metric are derived. The effects of varying key parameters such as the loop length (range) and transmitter power are investigated via computational examples.