Decision Metrics Research Articles

Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

When selecting a lc classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would save those designing an analysis pipeline from eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process This study tests the assumption that lc classification metrics are an appropriate proxy for cosmology metrics. We emulated photometric SN Ia cosmology lc samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. Based on these findings, we discourage any exclusive reliance on classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.

AI’s effect on innovation capacity in the context of industry 5.0: a scoping review

The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from “how innovation fosters AI” to “how AI fosters innovation”. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI’s effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Using the phase characteristic for source depth discrimination with a horizontal line array for a broadband source

A solution to the problem of source depth discrimination in a shallow water waveguide is proposed for the case of a horizontal line array and low-frequency broadband sources. The method divides the horizontal line array into two subarrays with the same number of elements and then calculates the cross-power spectrum of beamforming pressure outputs of the subarrays. Depth-dependent features of phase fluctuation in the cross-power spectrum are analyzed using longitudinal correlation, and depth-separable decision metrics are defined. These decision metrics are poorly impacted by the energy distribution of the signal spectrum. Depth discrimination relies on the phase variation of the cross-power spectrum, and thus does not require mode space information or geoacoustic parameters as the prior knowledge, but sufficient effective horizontal apertures. Due to the application of beamforming, array gain can be obtained to improve the input signal-to-noise ratio of the phase feature-based discriminators. Simulations and Monte Carlo methods are applied to performance predictions and method comparison. The impact on the performance of signal-to-noise ratio, horizontal aperture, and array overlap is numerically studied. Finally, the method is experimentally demonstrated with data from a bottom-mounted horizontal line array deployed in the South China Sea, and the applicability of array overlap is illustrated.

Utilization of machine learning for dengue case screening

Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.

Hydraulic analysis for assessing environmental flow selection and ecological model formulation

AbstractEcosystem management depends on transforming qualitative observations (e.g., slow‐moving shallow conditions provide nursery refugia for silvery minnow larvae) into management actions to increase habitat quantity or improve habitat quality. To be effective, decision metrics that are developed for management objectives should be validated with field observations. Model assumptions, precision and parameter importance can be refined by comparing the fidelity of selected parameters computed as habitat quality metrics and the correlation of these metrics to real‐world observations. Validated environmental metrics are more credible for management and can be compatible with ecosystem monitoring and project design processes. In this study, streamflow monitoring data and hydraulic modelling are used to quantify fish habitat extent for 15 years of spring runoff. The spring runoff event coincides with larval maturation to a free‐swimming juvenile phase for the silvery minnow, a critical period in Rio Grande habitat management. Different methods to estimate habitat availability (i.e., hydrology statistics, inundation extents based on hydraulic modelling and areal habitat availability based on different formulations of a habitat suitability index curve) were used to test the efficacy of different metrics relative to species population monitoring. This analysis finds that flow–ecology relationships based on hydraulic modelling or hydrology statistics are both effective and highly correlated to larval production. The investigation shows how seasonal hydrologic characterization and hydraulic discretization have varying levels of correlation with seasonal fish production. This study demonstrates how hydraulic modelling data and hydrologic characterization of riverine environments can be used to validate or develop conceptual ecological models.

Framework of managerial wisdom: an analysis using the TOPSIS and MICMAC process

PurposeBehavioral characteristics and the existence of personal knowledge and skills that are essential in forming a capable manager with specialized insight, is one of the most important evaluation processes in appointing a manager. Wisdom as a trait of a set of personal characteristics and cognitive knowledge of a capable manager will gain competitive advantages for the company and external stakeholders. The purpose of this study is to present the framework of managerial financial wisdom through qualitative and quantitative research methodologies.Design/methodology/approachThe study uses grounded theory methodology to identify factors for managerial financial wisdom and uses a MICMAC process to evaluate the power of driving and dependence among factors. MaxQDA software was used for data coding and analysis. The MICMAC process by determining the location of each of the sub-themes in the four-dimensional matrix, seeks to determine the most effective criterion in the context of the study. The method of data collection is mixed and in terms of philosophical nature, this study is inductive/comparative.FindingsThe results of the research in the qualitative part indicate the presentation of a framework based on two categories, four components, and thirty themes. The results of the quantitative part of the study also showed that the most important factor of managerial financial wisdom is market-oriented values. Finally, it was found that the social trust caused by managerial financial wisdom is considered a factor in gaining more shares in the capital market due to the positive stimulus in this dimension.Originality/valueThe study is relevant for both practitioners and academia and has significant implications. For practitioners, the structural linkages identified will help enhance managerial wisdom effectiveness and drive the critical metrics for financial decisions and shareholders' rights protection. The study may help financial market practitioners better understand future policies. Academia and researchers can take reference from methodologies used in this study for exploring factors of interest and developing matrix linkages among them.

On the enhancement of semi-supervised deep learning-based railway defect detection using pseudo-labels

In recent years, the use of deep learning methods in railway defect detection has expanded rapidly due to the technology’s ability to improve accuracy and efficiency in fault diagnosis. However, modeling deep learning applications requires a large amount of data, and the labeling process for such datasets demands significant manpower. To address this gap this paper introduces a semi-supervised learning approach using a student–teacher model with YOLOv4 for automatically labeling images, aiming to reduce the manual effort required for image classification. We also present a novel dataset generated from images provided by the TCDD (The State Railways of the Republic of Turkey), which includes five distinct railway defects. Results of the experiments on the same test dataset show that the proposed student–teacher model not only improves YOLOv4’s detection performance according to several decision metrics but also extends the training set with high-confidence pseudo-labeled images.

Anti-price-gouging law is neither good nor bad in itself: a proposal of narrative numeric method for transdisciplinary social discourses

Multiple authorities have introduced an anti-price-gouging law to prevent sellers from raising prices higher than what is considered reasonable. Effectiveness of the law has been heatedly debated in various disciplines such as economics, ethics and politics. In this article, we investigate its effectiveness by developing a model that simulates a post-earthquake situation and apply the model to San Francisco, CA, USA. The model accounts for various competing forces, i.e. post-disaster increase in production cost and demands, assets damage, donation and hoarding. Thereby, it returns multiple decision metrics, i.e. unfulfilled needs in basic goods, repair periods and well-being loss caused by insufficient supplies and increased prices. The result shows that the optimal level of a price cap depends on a decision metric and local conditions. This indicates that the problem does not have a single optimal decision, but rather a compromise needs to be made between conflicting decision metrics. Generalising this observation, we propose a narrative numeric (NN) method as a new social discourse method. The objective of the NN method does not lie in concluding the most truthful argument, but rather in identifying a decision scenario that yields an agreeable compromise to (hopefully) all stakeholder groups.

Evaluation Methodology for Selecting Data Exchange Technologies for Plant Design Processes

The plant design process involves numerous engineering disciplines that generate and process various types of information. To guarantee an effective data and information exchange diverse technologies are available for a successful collaboration among the engineering disciplines. This paper aims to establish an evaluation methodology for selecting data exchange technologies for the plant design process. For this purpose, several surveys with experts from the plant design process are conducted and lead to a four step guide. The first two steps include the elicitation of relevant generalistic and project-individual requirements for the specific use case. The third step involves prioritizing all collected requirements with project-specific experts, which finally results in a predictable decision metric. The developed methodology is applied and validated by the use case of the research project DIAMOND (Digital Plant Modeling with Neutral Data Formats). For this, the Web-API is selected as the most suitable data exchange technology.

To Build or Not To Build: Determining a Quantitative Metric for Land Planning and Allocation

Land planning is crucial to ensure that urban development occurs with consideration to the economic, social, and environmental interests of a community. Many conflicting factors must often be considered to adhere to optimal land planning. In this paper, our team makes a quantitative decision metric that can analyze these factors and determine the “best” choice from a given set of development options and the allocation of those choices. First, linear programming is used to determine two “best” development options: one that maximizes both economic and social factors and one that minimizes negative environmental factors while maximizing social. The maxima and minima from linear programming are then applied to the Technique for Order of Preference by Similarity to the Ideal Solution to obtain a third real-world “overall best” option that balances economic and environmental factors with a desired weighting. A genetic algorithm is then used to determine the optimal positioning of the three established “bests” by analyzing opportunity costs based on an environmental degradation penalty index. Finally, the Cobb-Douglas Function is used to conduct a short- and long-term analysis of each result’s profit by solving differential equations about inflation. This model is then applied to the parcel of land in Victory, NY, using data obtained from research. The ideal option and positioning are found to be 267 acres of a sports complex in the northern half of the land, 129 acres of regenerative farm directly west of the sports complex, 344 acres of a solar array in the southernmost region of the land, and 1 acre of agritourism center on the eastern side of the land. Conducting a sensitivity analysis on our model reveals that the linear programming results are most affected by the area and societal benefit restrictions but that the TOPSIS results remain relatively stable regardless of the changing parameters. Our model is adjusted to account for Micron Technology, Inc. building a nearby fabrication facility. As this facility brings more jobs and thus more people, the profit of facilities that involve tourism will increase. However, nature-based facilities will suffer detriment due to pollution caused by the facility. With these adjustments, the model is re-run, and the results are compared to the previous results. In this scenario, there would be a greater area of the solar array and agritourist center, a smaller sports complex, no regenerative farm, and 128 acres of ranch. Finally, the generalizability of our model is discussed by first discussing its application in Shenzhen, China, and then widening the scope to any location in any country. Our model will provide the most implementable results in rural environments due to its quantitative nature that cannot consider complicated urban planning laws but that the model can be applied to nearly any scenario as long as data is provided.

Retraction of: "Authorship growth and self- citations: a scholarly expedient that demonstrates that the use of the metrics for career decisions generates malpractice and misbehavior?"

The paper entitled "Authorship growth and self- citations: a scholarly expedient that demonstrates that the use of the metrics for career decisions generates malpractice and misbehavior?" by Renato De Vecchis et al., which was published in Minerva Cardiology and Angiology 2021 October;69(5):619-20, has been retracted by the Publisher due to self-plagiarism.

A source depth discrimination method based on wavenumber measured by bottom-mounted HLA

In research into underwater acoustics, the ability to differentiate submerged sources from surface sources could meet multiple applications. By using a bottom-mounted Horizontal Line Array (HLA), a solution to the problem of acoustic source depth discrimination in a downward-refracting underwater environment is presented. The testing problem is considered by beamforming in the modal domain. According to wavenumber offsets in the wavenumber spectra, a source depth feature is detected. The phenomenon is explained in terms of mode theory, using the concept of mode trapping. The decision metric is built based on the Accumulated Relative Wavenumber Offset (ARWO) derived by subarrays and broadband processing, then the simulation result reveals that the source can be correctly discriminated. Without mode matching or mode filtering, the method has high computational efficiency. In addition, the applicability for bottom-mounted HLA and the sediment independence of this method is proved. Finally, a moving surface source and a dipping submerged source during an experiment conducted in the South China Sea are taken into consideration. The source-depth characteristic is magnified by the ARWO. Over test trials, the classifier provides a satisfactory effect, with which the depth discrimination method is well explained.

Enhancing perspectives on lake impairments using satellite observations: A case study on High Rock Lake, North Carolina

AbstractStakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in‐situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state‐wide nutrient management planning processes. An existing in‐situ database was used to calibrate semi‐analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat‐7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in‐situ match‐ups for model calibration and validation. Model r‐squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in‐situ data and simulated TSS values produced by a calibrated hydrologic‐hydrodynamic model. The process‐based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy.

A Quantitative Model to Characterize and Measure Granular Flow Behavior and Grain Layer Mixing

Abstract. Highlights Introduction of a practical model for evaluating granular flow behavior. Results show that in-bin behavior of granular flow is consistent regardless of flow amount. Implications for traceability and recall of bulk products are addressed. Food quality and safety concerns have led to the national and global implementation of regulation and legislation focused on food and feed practices. The food and feed industry also benefit from traceability tools because data collected from such systems are useful as metrics for internal decision making. Bulk commodity grain traceability systems are challenging because most shipments are aggregated mixtures of grain from multiple sources. The amount of mixing that occurs due to the layering of grain and the flow regimes present as grain is removed has not been previously quantified. This lack of certainty on where specific lots are located leads to costly and inefficient recalls. This experiment is the first step in developing an understanding of how much mixing is occurring in grain storage bins to better predict flow regimes for traceability purposes. Accordingly, a small model similar in structure to a flat-floored cylindrical grain bin was designed to test grain flow. The experimental model presented consistent flow behavior data that aligned with expected regimes for flat-floored structures across 30 replications. These outcomes suggest that grain bin mixing and flow remain consistent regardless of flow amount. The data also provide the basis for the development of a probability model. Keywords: Bulk grain, Food security, Grain traceability, Granular flow, Grain mixing, Traceability systems.

Metrics for minimising environmental impacts while maximising circularity in biobased products: The case of lignin-based asphalt

Achieving a circular economy (CE) is seen by society and policymakers as crucial to achieving a sustainable, resource-efficient, renewable and competitive economy. Given the current threat of climate change, we must develop new products that not only maximise the circularity of resources but also minimise climate change impacts. While these two goals are usually aligned, trade-offs exist. For instance, recycling biobased asphalt is a better end-of-life option than landfilling from a resource efficiency perspective. However, landfilling of biogenic non-biodegradable material leads to permanent carbon storage and, therefore, climate benefits. To fully understand the potential benefits and impacts of biobased circular innovations, we need metrics to capture their complexity from both a circular and climate point of view. This study explores the use of different circularity and sustainability metrics to understand the impacts and trade-offs of lignin-based versus bitumen-based asphalts. The analysis is done by calculating the Material Circularity Index (MCI) and two newly developed indicators quantifying the biogenic carbon storage (BCS) of products (BCS100 and c-BCS) while following the CE principles. In addition, the impacts regarding climate change, life cycle costs and ECI (environmental costs indicator) are also provided. Based on the MCI, it can be concluded that lignin-based asphalt roads have slightly higher material circularity than their bitumen-based counterparts. The BCS analysis indicated that the least circular lignin-based alternative sequesters the highest amount of carbon in the long term due to permanent storage in foundations. Despite these trade-offs, the results from the newly developed BCS indicators allowed to align both climate and circularity goals, guiding policymakers and industry actors to implement circular biobased strategies where the value of biobased materials is optimised. Finally, this article discusses the use of different circularity and environmental metrics for decision making in the context of a circular biobased economy.

Lifetime risk, life expectancy, and years of life lost to type 2 diabetes in 23 high-income jurisdictions: a multinational, population-based study

Diabetes is a major public health issue. Because lifetime risk, life expectancy, and years of life lost are meaningful metrics for clinical decision making, we aimed to estimate these measures for type 2 diabetes in the high-income setting. For this multinational, population-based study, we sourced data from 24 databases for 23 jurisdictions (either whole countries or regions of a country): Australia; Austria; Canada; Denmark; Finland; France; Germany; Hong Kong; Hungary; Israel; Italy; Japan; Latvia; Lithuania; the Netherlands; Norway; Scotland; Singapore; South Korea; Spain; Taiwan; the UK; and the USA. Our main outcomes were lifetime risk of type 2 diabetes, life expectancy in people with and without type 2 diabetes, and years of life lost to type 2 diabetes. We modelled the incidence and mortality of type 2 diabetes in people with and without type 2 diabetes in sex-stratified, age-adjusted, and calendar year-adjusted Poisson models for each jurisdiction. Using incidence and mortality, we constructed life tables for people of both sexes aged 20-100 years for each jurisdiction and at two timepoints 5 years apart in the period 2005-19 where possible. Life expectancy from a given age was computed as the area under the survival curves and lifetime lost was calculated as the difference between the expected lifetime of people with versus without type 2 diabetes at a given age. Lifetime risk was calculated as the proportion of each cohort who developed type 2 diabetes between the ages of 20 years and 100 years. We estimated 95% CIs using parametric bootstrapping. Across all study cohorts from the 23 jurisdictions (total person-years 1 577 234 194), there were 5 119 585 incident cases of type 2 diabetes, 4 007 064 deaths in those with type 2 diabetes, and 11 854 043 deaths in those without type 2 diabetes. The lifetime risk of type 2 diabetes ranged from 16·3% (95% CI 15·6-17·0) for Scottish women to 59·6% (58·5-60·8) for Singaporean men. Lifetime risk declined with time in 11 of the 15 jurisdictions for which two timepoints were studied. Among people with type 2 diabetes, the highest life expectancies were found for both sexes in Japan in 2017-18, where life expectancy at age 20 years was 59·2 years (95% CI 59·2-59·3) for men and 64·1 years (64·0-64·2) for women. The lowest life expectancy at age 20 years with type 2 diabetes was observed in 2013-14 in Lithuania (43·7 years [42·7-44·6]) for men and in 2010-11 in Latvia (54·2 years [53·4-54·9]) for women. Life expectancy in people with type 2 diabetes increased with time for both sexes in all jurisdictions, except for Spain and Scotland. The life expectancy gap between those with and without type 2 diabetes declined substantially in Latvia from 2010-11 to 2015-16 and in the USA from 2009-10 to 2014-15. Years of life lost to type 2 diabetes ranged from 2·5 years (Latvia; 2015-16) to 12·9 years (Israel Clalit Health Services; 2015-16) for 20-year-old men and from 3·1 years (Finland; 2011-12) to 11·2 years (Israel Clalit Health Services; 2010-11 and 2015-16) for 20-year-old women. With time, the expected number of years of life lost to type 2 diabetes decreased in some jurisdictions and increased in others. The greatest decrease in years of life lost to type 2 diabetes occurred in the USA between 2009-10 and 2014-15 for 20-year-old men (a decrease of 2·7 years). Despite declining lifetime risk and improvements in life expectancy for those with type 2 diabetes in many high-income jurisdictions, the burden of type 2 diabetes remains substantial. Public health strategies might benefit from tailored approaches to continue to improve health outcomes for people with diabetes. US Centers for Disease Control and Prevention and Diabetes Australia.

From participatory process to robust decision-making: An Agriculture-water-energy nexus analysis for the Souss-Massa basin in Morocco

The Water-Energy-Food (WEF) framework is widely used to address sustainability and resource management questions. However, many WEF methods miss engaging with stakeholders in the process. In this study, we introduce a stakeholder-driven and model-supported robust nexus decision-making framework. This methodology is exemplified by a case study in the Souss-Massa basin (SMB) which has significant importance for the agricultural sector in Morocco. However, the water scarcity exacerbated by climate change, overexploitation of groundwater and heavy use of fossil fuels for pumping is threatening the future of this fertile land. An integrated agriculture, water and energy model was developed to explore various potential solutions or scenarios such as desalination, wastewater reuse and improved water productivity. The analysis revealed that engaging with stakeholders and developing common robust nexus decision metrics is essential to establishing a shared and transparent approach to address the complicated nexus challenges. It also showed that no one solution can address all nexus challenges and highlighted the need for an integrated strategy that stimulates the contributions from different sectors. Finally, the transition from fossil fuel groundwater pumping to solar pumping is shown to be economically and environmentally viable.

Evaluating the Performance of Centers for Disease Control and Prevention COVID-19 Community Levels as Leading Indicators of COVID-19 Mortality.

Centers for Disease Control and Prevention (CDC) defines low, medium, and high "COVID-19 community levels" to guide interventions, but associated mortality rates have not been reported. To evaluate the diagnostic performance of CDC COVID-19 community level metrics as predictors of elevated community mortality risk. Time series analysis over the period of 30 May 2021 through 4 June 2022. U.S. states and counties. U.S. population. CDC "COVID-19 community level" metrics based on hospital admissions, bed occupancy, and reported cases; reported COVID-19 deaths; and sensitivity, specificity, and predictive values for CDC and alternative metrics. Mean and median weekly mortality rates per 100000 population after onset of high COVID-19 community level 3 weeks prior were, respectively, 2.6 and 2.4 (interquartile range [IQR], 1.7 to 3.1) across 90 high episodes in states and 4.3 and 2.1 (IQR, 0 to 5.4) across 7987 high episodes in counties. In 85 of 90 (94%) episodes in states and 4801 of 7987 (60%) episodes in counties, lagged weekly mortality after onset exceeded 0.9 per 100000 population, and in 57 of 90 (63%) episodes in states and 4018 of 7987 (50%) episodes in counties, lagged weekly mortality after onset exceeded 2.1 per 100000, which is equivalent to approximately 1000 daily deaths in the national population. Alternative metrics based on lower hospital admissions or case thresholds were associated with lower mortality and had higher sensitivity and negative predictive value for elevated mortality, but the CDC metrics had higher specificity and positive predictive value. Ratios between cases, hospitalizations, and deaths have varied substantially over time. Aggregate mortality does not account for nonfatal outcomes or disparities. Continuing evolution of viral variants, immunity, clinical interventions, and public health mitigation strategies complicate prediction for future waves. Designing metrics for public health decision making involves tradeoffs between identifying early signals for action and avoiding undue restrictions when risks are modest. Explicit frameworks for evaluating surveillance metrics can improve transparency and decision support. Council of State and Territorial Epidemiologists.

Alternative metrics for design decisions based on separating aleatory and epistemic probabilistic uncertainties

There is still much philosophical debate about whether a frequentist or subjective view of probability should be adopted. Some uncertainties (typically aleatory uncertainties) are naturally modelled using a frequentist approach, while others (epistemic uncertainties) are clearly subjective in nature. In light of this it has been argued, for example by the German philosopher Rudolf Carnap, that both potential descriptions of uncertainty should be maintained and treated separately. However, in current engineering practice it is common to make no distinction between these two types of uncertainty. Generally, uncertainty is represented by a single figure or distribution, for example a probability of failure, which incorporates both aleatory and epistemic uncertainties. This paper explores the idea of treating aleatory and epistemic uncertainties separately and proposes alternative metrics –based on the epistemic probability of an aleatory probability - which can potentially provide greater insight for the designer in engineering problems. The metrics are illustrated using two example engineering dynamics problems; the prediction of wind induced accelerations in a tall building and optimizing the design of soundproofing in a car. It is shown that as well as providing further insight on the underlying contributing causes of uncertainty, treating aleatory and epistemic uncertainties as separate quantities, as opposed to in the traditional combined manner, can potentially lead to different design outcomes.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV’s limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users’ requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network’s coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)’ formation and UAVs’ deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users’ access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users’ requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users’ access delay, cache redundancy, and UAVs’ energy consumption.