Development and Usability Evaluation of an Opioid Management App.

We describe the development and usability evaluation of a novel patient engagement tool (OPY) in its early stage from perspectives of both experts and end-users. The tool is aimed at engaging patients in positive behaviors surrounding the use, weaning, and disposal of opioid medications in the post-surgical setting. The messaging and design of the application were created through a behavioral economics lens. Expert-based heuristic analysis and user testing were conducted and demonstrated that while patients found the tool to be easy to use and subjectively somewhat useful, additional work to enhance the user interface and features is needed in close partnership with developers and stakeholders.

Algorithms and heuristics of health technology assessments: A retrospective analysis of factors associated with HTA outcomes for new drugs across seven OECD countries.

CONTEXT: Positive health technology assessment (HTA) outcomes can have important implications for equity, efficiency and timely patient access to novel therapies. Several outcomes and dimensions of benefit beyond utility feed into HTA processes. OBJECTIVE: We analyse a proprietary dataset of HTA outcomes in 7 countries, to (a) test whether HTA decision-making is grounded in welfarist or extra-welfarist approaches; and (b) empirically determine the factors associated with positive HTA outcomes, the time to achieve these and establish the magnitude of inter-country differences in assessment processes. METHODS: Data were extracted from publicly available HTA reports on drugs that received marketing authorisation between 2009 and 2018 (N = 1415). The outcomes of interest were the probability of positive HTA outcomes and the time-to-HTA outcome; these were examined with respect to clinical, regulatory, product- and disease-related, evidence uncertainty and contextual variables. Econometric models utilising survival analysis and multinomial logistic regression were specified. FINDINGS: Positive HTA outcomes accounted for 87.3% of the sample (n = 1235), of which 71% (n = 1004) were restricted. Drugs with positive HTA outcomes were subject to clinical restrictions (n = 652, 46%), financial risk-sharing (n = 439, 31%) or had been rejected at least once (n = 282, 20%). Significant predictors of positive HTA outcomes were orphan drugs with cancer indications, high quality of evidence linked to clinical and economic evidence uncertainties which had been overcome, and contextual considerations, particularly innovativeness and unmet need. Comparative analyses revealed systematic differences between countries in their propensity to accept the same drugs, particularly oncology and orphan drugs. CONCLUSIONS: Our results are contextual and reinforce arguments in favour of explicitly accounting for social value judgements, establishing separate assessment frameworks for highly uncertain products, adopting risk mitigation strategies for novel therapies with early phase evidence, and sharing of HTA practices across settings. Lastly, HTA agencies have adopted an extra-welfarist approach to value assessment and resource allocation.

Influence of survey engagement and multiple-choice heuristics in the estimation of the value of a statistical life.

Estimating the value of non-market goods, such as reductions in mortality risks due to traffic accidents or air pollution, is typically done using stated choice (SC) data. However, issues with potential estimation biases due to the hypothetical nature of SC experiments arise, as protest choices are common and survey engagement is not constant across respondents. Further, if respondents choose to use different choice mechanisms and this is not considered, the results may also be biased. We designed an SC experiment to estimate the willingness to pay (WTP) for mortality risk reductions, that allowed us to simultaneously estimate the WTP to reduce the risk of traffic accident deaths and cardiorespiratory deaths due to air pollution. We formulated and estimated a multiple heuristic latent class model that also considered two latent constructs: Institutional Belief, to consider protest responses, and survey Engagement as a class membership covariate. We found, first, that individuals with lower institutional belief gave a higher probability of choice to the status-quo alternative, shying away from programs involving governmental action. Second, that not identifying respondents who do not appropriately engage in the experiment, biased the WTP estimators. In our case WTP decreased up to 26% when two different choice heuristics were allowed for in the model.

Humans decompose tasks by trading off utility and computational cost.

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition (N = 806) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic-betweenness centrality-that is justified by our approach. Taken together, our results suggest the computational cost of planning is a key principle guiding the intelligent structuring of goal-directed behavior.

Conceptualizing the Mechanisms of Social Determinants of Health: A Heuristic Framework to Inform Future Directions for Mitigation.

Policy Points A large body of scientific work examines the mechanisms through which social determinants of health (SDOH) shape health inequities. However, the nuances described in the literature are infrequently reflected in the applied frameworks that inform health policy and programming. We synthesize extant SDOH research into a heuristic framework that provides policymakers, practitioners, and researchers with a customizable template for conceptualizing and operationalizing key mechanisms that represent intervention opportunities for mitigating the impact of harmful SDOH. In light of scarce existing SDOH mitigation strategies, the framework addresses an important research-to-practice translation gap and missed opportunity for advancing health equity. CONTEXT: The reduction of health inequities is a broad and interdisciplinary endeavor with implications for policy, research, and practice. Health inequities are most often understood as associated with the social determinants of health (SDOH). However, policy and programmatic frameworks for mitigation often rely on broad SDOH domains, without sufficient attention to the operating mechanisms, and effective SDOH mitigation strategies remain scarce. To expand the cadre of effective SDOH mitigation strategies, a practical, heuristic framework for policymakers, practitioners, and researchers is needed that serves as a roadmap for conceptualizing and targeting the key mechanisms of SDOH influence. METHODS: We conduct a critical review of the extant conceptual and empirical SDOH literature to identify unifying principles of SDOH mechanisms and to synthesize an integrated framework for conceptualizing such mechanisms. FINDINGS: We highlight eight unifying principles of SDOH mechanisms that emerge from landmark SDOH research. Building on these principles, we introduce and apply a conceptual model that synthesizes key SDOH mechanisms into one organizing, heuristic framework that provides policymakers, practitioners, and researchers with a customizable template for conceptualizing and operationalizing the key SDOH mechanisms that represent intervention opportunities to maximize potential impact for mitigating a given health inequity. CONCLUSIONS: Our synthesis of the extant SDOH research into a heuristic framework addresses a scarcity of peer-reviewed organizing frameworks of SDOH mechanisms designed to inform practice. The framework represents a practical tool to facilitate the translation of scholarly SDOH work into evidence-based and targeted policy and programming. Such tools designed to close the research-to-practice translation gap for effective SDOH mitigation are sorely needed, given that health inequities in the United States and in many other parts of the world have widened over the past two decades.

The Behavioural Economics of Music: Systematic review and future directions.

Music-related decision-making encompasses a wide range of behaviours including those associated with listening choices, composition and performance, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically, these disciplines have been unconnected. Recently, however, researchers have begun to bridge this gap by employing tools from behavioural economics. This article contributes to the literature by providing a discussion about the benefits of using behavioural economics in music-decision research. We achieve this in two ways. First, through a systematic review, we identify the current state of the literature within four key areas of behavioural economics-heuristics and biases, social decision-making, behavioural time preferences, and dual-process theory. Second, taking findings of the literature as a starting point, we demonstrate how behavioural economics can inform future research. Based on this, we propose the Behavioural Economics of Music (BEM), an integrated research programme that aims to break new ground by stimulating interdisciplinary research in the intersection between music, psychology, and economics.

Partial repetition costs index a mixture of binding and signaling.

People respond more slowly in two-choice tasks when either a previous stimulus feature or the previous response repeats in partial repetition trials than when (a) both repeat in complete repetition trials or (b) both alternate in complete alternation trials. The binding account posits that such partial repetition costs index a memory-retrieval conflict, which occurs because partial repetition trials trigger the retrieval of a previous stimulus feature or response that conflicts with a current stimulus feature or response. However, such costs may additionally reflect a simple decision-making heuristic that uses the repetition or alternation of a previous stimulus feature as a "signal" to bias response selection toward a repetition or an alternation of the previous response. To determine whether signaling contributes to partial repetition costs, we employed a four-choice task. Here, a stimulus feature repetition still signals a response repetition, but a stimulus feature alternation does not signal which of the three remaining responses to make. Consistent with an influence of signaling, we sometimes observed complete repetition advantages without complete alternation advantages. Exploratory analyses further revealed that partial repetition costs measured more broadly were smaller in the four-choice task than in a matched two-choice task. These findings suggest that partial repetition costs index a mixture of binding and signaling.

A heuristic model for health-related autonomy based on health promotion ideas: the 'Health-Autonomy Interaction Model'.

Autonomy and health are central concepts in health promotion literature. Operational frameworks explaining their relationship are important to support health-related autonomy actions. This study presents a heuristic model describing the 'health-autonomy relationship' based on health promotion ideas, particularly Antonovsky's and Nordenfeldt's works. Central to the 'Health-Autonomy Interaction Model' is the existence of inter-related dimensions of autonomy: thought, intention and action, all influenced by negative and positive factors of physical and social environments (extrinsic factors) and personal resources to cope with life needs (intrinsic factors). Intrinsic factors comprise elements often included in definitions of negative (ill-health) and positive health (well-being and fitness), therefore establishing a bridge between conceptions of health and autonomy. Unique to this biopsychosocial framework is the differentiation between the potential for being autonomous and the degree of autonomy effectively achieved-the potential autonomy reflects the individual set of abilities to accomplish any task, while the expressed autonomy results from the extent to which this satisfies the demands of the environment and vital goals. Conceptualizing autonomy in this manner highlights that it is a multi-factorial capability referred to external conditions, reflecting a cluster of abilities to be and do things indicative of a self-empowered life. Concluding, our model moves the concept of health-related autonomy from a focus on 'disease' and 'incapacity' to a broader view in which the meaning of 'capacity' depends on the achievement of 'self-fulfillment' in given circumstances. The notion of autonomy is regarded as a precondition for health and an essential priority in health promotion.

Assessing Heuristic Bias During Care for Patients Hospitalized for Heart Failure: Get With The Guidelines-Heart Failure.

BACKGROUND: Heuristic biases are increasingly recognized, and potentially modifiable, contributors to patient care and outcomes. Left digit bias is a cognitive bias where continuous variables are categorized by their left-most digit. The impact of this heuristic bias applied to patient age on quality of care in heart failure has not been explored. METHODS: We examined participants admitted from 2005 to 2021 in the Get With The Guidelines-Heart Failure registry. To create 2 naturally randomized groups, isolating the effect of left digit bias, we dichotomized patients into those discharged within 60 days prior to their 80th birthday (N=4238) and those discharged within 60 days after their 80th birthday (N=4329). We performed multivariable logistic regression to assess the association between discharge date relative to 80th birthday and several in-hospital quality metrics and in-hospital outcomes. Among Medicare participants (N=2759), we performed adjusted Cox regression to analyze the relationship between discharge date and risk of 1-year mortality or readmission. RESULTS: Among 8567 patients, 50.4% were female, 73% were non-Hispanic White, and 42.9% had an ejection fraction ≤40%. Discharge date relative to 80th birthday was not associated with numerous in-hospital quality metrics or in-hospital outcomes on unadjusted or adjusted logistic regression. Among Medicare beneficiaries, there was no association between discharge date and risk of mortality or readmission at 1-year postdischarge (hazard ratio, 1.03 [95% CI, 0.95-1.12]; P=0.52). CONCLUSIONS: In a large registry of patients hospitalized for heart failure, we did not detect a left digit bias' with respect to age at discharge, which resulted in differential quality of care or outcomes.

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective.

Currently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient connections between a large number of remote radio heads (RRHs) in the cell sites and the baseband unit (BBU) pool in the central location, known as the fronthaul, has emerged as a new challenge. Many wired and wireless solutions have been proposed to address this bottleneck. Specifically, optical technologies presented by passive optical networks (PONs) are introduced as the best suitable solution for 5G and beyond network fronthaul due to their properties of providing high capacity and low latency connections. We considered time and wavelength division multiplexed passive optical networks (TWDM-PONs) as a fronthaul for 5G and beyond. Taking that into consideration, in this paper, we propose an integer linear program (ILP) that results in the optimal optical fronthaul deployment while minimizing the total cost of 5G and beyond instances. However, for larger network instances, solving the ILP problem becomes unscalable and time-consuming. To address that, we developed two heuristic-based algorithms (the K-means clustering algorithm and the one based on the genetic algorithm-GA). We evaluated the suitability of our proposed ILP and heuristic algorithms in simulations by utilizing them to plan different network instances (dense and sparse).

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

Examining social class as it relates to heuristics women use to determine the trustworthiness of information regarding the link between alcohol and breast cancer risk.

BACKGROUND: High rates of alcohol consumption by midlife women, despite the documented risks associated with breast cancer, varies according to social class. However, we know little about how to develop equitable messaging regarding breast cancer prevention that takes into consideration class differences in the receipt and use of such information. OBJECTIVE: To explore the heuristics used by women with different (inequitable) life chances to determine the trustworthiness of information regarding alcohol as a modifiable risk factor for breast cancer risk. METHODS AND MATERIALS: Interviews were conducted with 50 midlife (aged 45-64) women living in South Australia, diversified by self-reported alcohol consumption and social class. Women were asked to describe where they sought health information, how they accessed information specific to breast cancer risk as it relates to alcohol, and how they determined whether (or not) such information was trustworthy. De-identified transcripts were analysed following a three-step progressive method with the aim of identifying how women of varying life chances determine the trustworthiness of alcohol and breast cancer risk information. Three heuristics were used by women: (1) consideration of whose interests are being served; (2) engagement with 'common sense'; and (3) evaluating the credibility of the message and messenger. Embedded within each heuristic are notable class-based distinctions. CONCLUSIONS: More equitable provision of cancer prevention messaging might consider how social class shapes the reception and acceptance of risk information. Class should be considered in the development and tailoring of messages as the trustworthiness of organizations behind public health messaging cannot be assumed.

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design.

BACKGROUND: Trauma clinical decision support systems improve adherence with evidence-based practice but suffer from poor usability and the lack of a user-centered design. The objective of this study was to compare the effectiveness of user and expert-driven usability testing methods to detect usability issues in a rib fracture clinical decision support system and identify guiding principles for trauma clinical decision support systems. METHODS: A user-driven and expert-driven usability investigation was conducted using a clinical decision support system developed for patients with rib fractures. The user-driven usability evaluation was as follows: 10 clinicians were selected for simulation-based usability testing using snowball sampling, and each clinician completed 3 simulations using a video-conferencing platform. End-users participated in a novel team-based approach that simulated realistic clinical workflows. The expert-driven heuristic evaluation was as follows: 2 usability experts conducted a heuristic evaluation of the clinical decision support system using 10 common usability heuristics. Usability issues were identified, cataloged, and ranked for severity using a 4-level ordinal scale. Thematic analysis was utilized to categorize the identified usability issues. RESULTS: Seventy-nine usability issues were identified; 63% were identified by experts and 48% by end-users. Notably, 58% of severe usability issues were identified by experts alone. Only 11% of issues were identified by both methods. Five themes were identified that could guide the design of clinical decision support systems-transparency, functionality and integration into workflow, automated and noninterruptive, flexibility, and layout and appearance. Themes were preferentially identified by different methods. CONCLUSION: We found that a dual-method usability evaluation involving usability experts and end-users drastically improved detection of usability issues over single-method alone. We identified 5 themes to guide trauma clinical decision support system design. Performing usability testing via a remote video-conferencing platform facilitated multi-site involvement despite a global pandemic.

Evaluación de los contenidos de los gestores de información y conocimiento en el Instituto Superior de Tecnologías y Ciencias Aplicadas-Universidad de la Habana / Contents assessment of information and knowledge managers at the Higher Institute of Applied Sciences and Technologies-University of Havana

Resumen Evaluar el funcionamiento de los gestores de información y conocimiento implementados en el Instituto Superior de Tecnologías y Ciencias Aplicadas (InSTEC) es el objetivo de la investigación. Ello, no solo posibilita el mejoramiento futuro del desempeño de estos, sino también sirve como estudio preliminar para la inserción de otros gestores en el futuro. Los métodos de análisis documental y evaluación heurística sustentan las bases teóricas, mientras que las herramientas automáticas Nibbler, GooglePageRank, SEOptimer, Website Grader, la entrevista y las encuestas a usuarios evidencian el desempeño de los gestores. Los resultados revelaron que la calidad del sitio externo y de la intranet obtuvo 69,4% de competencia global. Varios de los indicadores de estos sitios se deben perfeccionar en aras de brindar un mejor servicio a los usuarios del Instituto en la gestión de la información y el conocimiento, tan necesaria en las universidades.

Abstract The papers goal is to assess the already implemented information and knowledge managers at InSTEC. This allows improving the managers future work, and serves as a preliminary study for including others in the future. Documentary analysis and heuristics evaluation methods are the theoretical basis; meanwhile, automatic tools Nibbler, GooglePageRank, SEOptimer, Website Grader, interviews and polls to users evidenced the actual development of both sites. The studys outcomes revealed the quality of InSTECs external website and its intranet achieved 69,4 % of general competency. These sites indicators must be enhanced to provide a better service to users in the information and knowledge management so needed in universities.

Aspectos neurobiológicos en la toma de decisiones afectivas y sociales e influencia del estrés: posibles implicancias en la toma de decisiones clínicas / Neurobiological aspects in affective and social decision-making: possible implications on clinical decision-making

RESUMEN: La toma de decisiones está influenciada por múltiples factores muchas veces no conscientes. En este artículo se sintetizan algunos correlatos neurobiológicos del componente afectivo y social sobre la toma de decisiones, incluyendo el impacto del estrés agudo y crónico y la perspectiva cognitiva de las heurísticas y los sesgos en la práctica clínica. A nivel afectivo, la hipótesis del marcador somático ha asociado la respuesta corporal periférica con estructuras nerviosas centrales en la configuración de las decisiones; intervienen estructuras como la corteza orbitofrontal y el hipocampo. En la toma de decisiones sociales se involucran las dimensiones recompensa y motivación. En este tipo de decisiones es crucial la capacidad de mentalizar a otro e integrar su perspectiva en la toma de decisiones. Esta función se ha relacionado con el surco temporal posterosuperior, la unión temporoparietal, la corteza cingulada anterior y la corteza prefrontal medial. No obstante, en la toma de decisiones sociales también se integra el cumplimiento de normas socialmente establecidas. El estrés agudo o crónico puede afectar la toma de decisiones, positiva o negativamente. En estos procesos se han involucrado al eje hipotálamo-hipófiso-adrenal junto con circuitos corticoestriados, prefrontales, amigdalinos e hipocampales. En la toma de decisiones clínicas se conjugan factores "prerreflexivos" emocionales, sociales y cognitivos que influyen directamente en las conductas adoptadas con los pacientes. Recomendamos enfatizar la investigación en esta área y fortalecer su enseñanza para reconocerlos adecuadamente.

ABSTRACT Decision-making is influenced by multiple unaware factors. We synthesize some neurobiological correlates of the affective and social components on decision-making, including the impact of acute and chronic stress. We also address the impact of heuristics and biases on clinical practice from the cognitive perspective. At an affective level, the hypothesis of the somatic marker has associated the peripheral body response with central nervous structures in the configuration of decisions; structures such as the orbitofrontal cortex and the hippocampus intervene. The reward and motivation dimensions are involved in social decision-making. In these types of decisions, the ability to mentally engage others and integrate their perspective into decision making is crucial. This function has been related to the posterior superior temporal sulcus, the temporoparietal junction, the anterior cingulate cortex, and the medial prefrontal cortex. However, compliance with socially established norms is also integrated into social decision-making. Acute or chronic stress may affect decision-making, positively or negatively. The hypothalamic-pituitary-adrenal axis has been involved in these processes together with corticostriatal, prefrontal, amygdala and hippocampal circuits. In clinical decision-making, "pre-reflective" emotional, social and cognitive factors are combined, influencing the decisions towards patients. We recommend emphasizing research in this field and strengthening education in this area to recognize these aspects adequately.

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task.

This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

Costly teaching contributes to the acquisition of spear hunting skill among BaYaka forager adolescents.

Teaching likely evolved in humans to facilitate the faithful transmission of complex tasks. As the oldest evidenced hunting technology, spear hunting requires acquiring several complex physical and cognitive competencies. In this study, we used observational and interview data collected among BaYaka foragers (Republic of the Congo) to test the predictions that costlier teaching types would be observed at a greater frequency than less costly teaching in the domain of spear hunting and that teachers would calibrate their teaching to pupil skill level. To observe naturalistic teaching during spear hunting, we invited teacher-pupil groupings to spear hunt while wearing GoPro cameras. We analysed 68 h of footage totalling 519 teaching episodes. Most observed teaching events were costly. Direct instruction was the most frequently observed teaching type. Older pupils received less teaching and more opportunities to lead the spear hunt than their younger counterparts. Teachers did not appear to adjust their teaching to pupil experience, potentially because age was a more easily accessible heuristic for pupil skill than experience. Our study shows that costly teaching is frequently used to transmit complex tasks and that instruction may play a privileged role in the transmission of spear hunting knowledge.

Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.

To control the spread of an infectious disease over a large network, the optimal allocation by a social planner of a limited resource is a fundamental and difficult problem. We address this problem for a livestock disease that propagates on an animal trade network according to an epidemiological-demographic model based on animal demographics and trade data. We assume that the resource is dynamically allocated following a certain score, up to the limit of resource availability. We adapt a greedy approach to the metapopulation framework, obtaining new scores that minimize approximations of two different objective functions, for two control measures: vaccination and treatment. Through intensive simulations, we compare the greedy scores with several heuristics. Although topology-based scores can limit the spread of the disease, information on herd health status seems crucial to eradicating the disease. In particular, greedy scores are among the most effective in reducing disease prevalence, even though they do not always perform the best. However, some scores may be preferred in real life because they are easier to calculate or because they use a smaller amount of resources. The developed approach could be adapted to other epidemiological models or to other control measures in the metapopulation setting.

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators.

In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system's parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50-85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system's operational data.