Additional Metrics Research Articles

Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions.

The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient intelligence vision, in which the environment becomes intelligent and aware of its surroundings. WSN has unique features which create its own distinct network attributes and is deployed widely for critical real-time applications that require stringent prerequisites when dealing with faults to ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems, substantial enhancements of current FT mechanisms will ensure that the system constantly provides high network reliability and integrity. Fault tolerance structures contain three fundamental stages: error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of harnessing it has led to the development of new fault-tolerant structures and strategies based on artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on eight constraints is presented. Many prior studies have provided classifications for fault tolerance systems. However, this research has enhanced these reviews by proposing an extensively enhanced categorization that depends on the new and additional metrics which include the number of sensor nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal algorithm responsible for eliminating network errors. A new taxonomy of comparison that also extensively reviews previous surveys and state-of-the-art scientific articles based on different factors is discussed and provides the basis for the proposed open issues.

3D Enhancement Color Maps in the Characterization of Intracranial Atherosclerotic Plaques.

High-resolution MR imaging allows the identification of culprit symptomatic plaques after the administration of gadolinium. Current high-resolution MR imaging methods are limited by 2D multiplanar views and manual sampling of ROIs. We analyzed a new 3D method to objectively quantify gadolinium plaque enhancement. Patients with stroke due to intracranial atherosclerotic disease underwent 7T high-resolution MR imaging. 3D segmentations of the plaque and its parent vessel were generated. Signal intensity probes were automatically extended from the lumen into the plaque and the vessel wall to generate 3D enhancement color maps. Plaque gadolinium (Gd) uptake was quantified from 3D color maps as gadolinium uptake = (µPlaque T1 + Gd -µPlaque T1/SDPlaque T1). Additional metrics of enhancement such as enhancement ratio, variance, and plaque-versus-parent vessel enhancement were also calculated. Conventional 2D measures of enhancement were collected for comparison. Thirty-six culprit and 44 nonculprit plaques from 36 patients were analyzed. Culprit plaques had higher gadolinium uptake than nonculprit plaques (P < .001). Gadolinium uptake was the most accurate metric for identifying culprit plaques (OR, 3.9; 95% CI 2.1-8.3). Gadolinium uptake was more sensitive (86% versus 70%) and specific (71% versus 68%) in identifying culprit plaques than conventional 2D measurements. A multivariate model, including gadolinium uptake and plaque burden, identified culprit plaques with an 83% sensitivity and 86% specificity. The new 3D color map method of plaque-enhancement analysis is more accurate for identifying culprit plaques than conventional 2D methods. This new method generates a new set of metrics that could potentially be used to assess disease progression.

Performance Measurement Model for Sustainability Assessment of the Swine Supply Chain

In this paper, a model and a set of indicators for evaluating the sustainability in swine supply chains are presented and discussed. Using the Delphi method, environmental, social and economic indicators were identified (namely environmental performance indicators to evaluate soil, water, air, energy and environmental practices; social performance indicators related to human capital and social interaction; and economic performance indicators that address labor remuneration and return on investment). Subsequently, the proposed sustainability assessment model was applied for validation purposes in three different companies belonging to the swine supply chain in the southern region of Brazil. This study differs from previous ones by focusing on the sustainability assessment of the upstream and downstream of the supply chain, which are responsible for significant impacts. The performance of the studied companies from the three triple bottom-line (TBL) dimensions is significantly different. Both negative and positive impacts were found to be related to most of the specific metrics. The economic dimension presented a better performance than the environmental and social ones. Furthermore, there was a predominance of negative impacts in the environmental dimension, in relation to the soil, water, air, and energy indicators. Thus, specific actions, strategies, and policies must be designed for the different companies towards an effective and comprehensive sustainability throughout the swine supply chain. The proposed model can be extended to other companies in the same supply chain, replicated in other livestock and agribusiness industries and supply chains (such as cattle and poultry), and it can be used in different locations and including additional indicators and metrics.

Characterizing microplastic hazards: which concentration metrics and particle characteristics are most informative for understanding toxicity in aquatic organisms?

There is definitive evidence that microplastics, defined as plastic particles less than 5 mm in size, are ubiquitous in the environment and can cause harm to aquatic organisms. These findings have prompted legislators and environmental regulators to seek out strategies for managing risk. However, microplastics are also an incredibly diverse contaminant suite, comprising a complex mixture of physical and chemical characteristics (e.g., sizes, morphologies, polymer types, chemical additives, sorbed chemicals, and impurities), making it challenging to identify which particle characteristics might influence the associated hazards to aquatic life. In addition, there is a lack of consensus on how microplastic concentrations should be reported. This not only makes it difficult to compare concentrations across studies, but it also begs the question as to which concentration metric may be most informative for hazard characterization. Thus, an international panel of experts was convened to identify 1) which concentration metrics (e.g., mass or count per unit of volume or mass) are most informative for the development of health-based thresholds and risk assessment and 2) which microplastic characteristics best inform toxicological concerns. Based on existing knowledge, it is recommended that microplastic concentrations in toxicity tests are calculated from both mass and count at minimum, though ideally researchers should report additional metrics, such as volume and surface area, which may be more informative for specific toxicity mechanisms. Regarding particle characteristics, there is sufficient evidence to conclude that particle size is a critical determinant of toxicological outcomes, particularly for the mechanisms of food dilution and tissue translocation .

Ideal cardiovascular health status and risk of cardiovascular disease and all-cause mortality: over a decade of follow-up in the Tehran lipid and glucose study

ObjectiveTo quantify the association between ideal cardiovascular health (CVH) metrics and incident cardiovascular disease (CVD) including different subtypes [coronary heart disease (CHD), stroke, and sudden death], and all-cause mortality in an Iranian population.MethodsThe study population included 6,388 participants (2,726 men) aged 48.0 ± 12.4 years free of CVD at baseline. We utilized the American Heart Association’s 2020 impact target criteria of ideal, intermediate, and poor CVH. The multivariate Cox proportional Hazard model, adjusted for age, sex, educational level, marital status, and family history of CVD, was applied to estimate the hazard ratio (HR) of outcomes per one additional metric of ideal CVH metrics. Furthermore, the risk was also calculated for ideal and intermediate categories considering poor category as a reference.ResultsDuring the median follow-up of 11.26 years, 692 CVD, 589 CHD, 130 stroke, 111 sudden death, and 519 all-cause mortality events were reported. All of the individual ideal CVH metrics were independent predictors except intermediate physical activity level for CVD, BMI < 25 kg/m2, and intermediate physical activity for all-cause mortality. Each additional metrics of ideal CVH decreased the risk by 31 (0.69, 0.65–0.73) for CVD, 32 (0.68, 0.64–0.73) for CHD, 31 (0.69, 0.60–0.80) for stroke, 25 (0.75, 0.64–0.88) for sudden death, and 13% (0.87, 0.81–0.93) for all-cause mortality events. Moreover, intermediate and ideal categories of CVH metrics were associated with lower risk for different CVD outcomes, i.e., 44 (0.56, 0.48–0.65) and 76% (0.24, 0.17–0.35) for CVD; 43 (0.57, 0.47–0.67) and 75% (0.25, 0.16–0.37) for CHD, 58 (0.42, 0.29–0.61) and 86% (0.14, 0.04–0.44) for stroke; 56 (0.44, 0.29–0.66) and 55% (0.45, 0.21–0.99) for sudden death; and 25 (0.75, 0.62–0.90) and 46% (0.54, 0.37–0.80) for all-cause mortality events, respectively. We also assessed the impact of changes in ideal CVH status from phase III to phase IV (2008–2011) on CVD events among 5,666 participants. Accordingly, compared to those remaining in the poor category, all of the changes in ideal CVH categories showed a lower risk for CVD events.ConclusionAmong the Iranian population, meeting higher ideal CVH metrics is associated with a lower risk of different CVD events and mortality outcomes.

The Importance of Continuous Glucose Monitoring-derived Metrics Beyond HbA1c for Optimal Individualized Glycemic Control.

ContextCurrent guidelines recommend assessing glycemic control using continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) measurement.ObjectiveThis study aimed to clarify the characteristics of patients who might benefit from CGM metrics in addition to HbA1c monitoring.MethodsCGM metrics, specifically time in range (TIR), time below range (TBR), and time above range (TAR), were determined in 999 outpatients with type 2 diabetes and compared between HbA1c categories (HbA1c < 53 mmol/mol [7.0%, HbA1c < 53], HbA1c 53-63 mmol/mol [7.0-7.9%, HbA1c 53-63], HbA1c 64-74 mmol/mol [8.0-8.9%, HbA1c 64-74], and HbA1c ≥ 75 mmol/mol [9.0%, HbA1c ≥ 75]) and between patients with identical HbA1c categories who were stratified by age, types of antidiabetic agents, and renal function.ResultsFor HbA1c < 53 category, patients aged ≥ 65 years had a significantly higher nocturnal TBR than those aged < 65 years. For HbA1c < 53 and HbA1c 53-63 categories, patients receiving insulin and/or sulfonylureas had a significantly higher TAR and TBR, and a lower TIR than those not receiving these drugs, and for HbA1c 64-74 category, they had a significantly higher TBR. For HbA1c < 53, HbA1c 53-63, and HbA1c 64-74 categories, patients with an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 had a significantly higher TBR during some periods than those with an eGFR ≥ 60.ConclusionHigher HbA1c levels do not always protect against hypoglycemic episodes. Our data demonstrate that using CGM metrics to complement HbA1c monitoring is beneficial, especially in older people, users of insulin and/or sulfonylureas, and patients with chronic kidney disease.

Tools for the Economic Evaluation of Precision Medicine: A Scoping Review of Frameworks for Valuing Heterogeneity-Informed Decisions.

Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine. We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine. Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider. Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.

Reviewing local and integrated energy system models: insights into flexibility and robustness challenges

The electrification of heating, cooling, and transportation to reach decarbonization targets calls for a rapid expansion of renewable technologies. Due to their decentral and intermittent nature, these technologies require robust planning that considers non-technical constraints and flexibility options to be integrated effectively. Energy system models (ESMs) are frequently used to support decision-makers in this planning process. In this study, 116 case studies of local, integrated ESMs are systematically reviewed to identify best-practice approaches to model flexibility and address non-technical constraints. Within the sample, storage systems and sector coupling are the most common types of flexibility. Sector coupling with the transportation sector is rarely considered, specifically with electric vehicles even though they could be used for smart charging or vehicle-to-grid operation. Social aspects are generally either completely neglected or modeled exogenously. Lacking actor heterogeneity, which can lead to unstable results in optimization models, can be addressed through building-level information. A strong emphasis on cost is found and while emissions are also frequently reported, additional metrics such as imports or the share of renewable generation are nearly entirely absent. To guide future modeling, the paper concludes with a roadmap highlighting flexibility and robustness options that either represent low-hanging fruit or have a large impact on results.

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

BackgroundAlthough risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints. MethodsPatients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration. ResultsAmong 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, −0.657, 0.692, and calibration slope 0.954; 95% CI, 0.769, 1.139), and at 30 days (−0.026; 95% CI, −0.374, 0.322, and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches. ConclusionsLogistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.

Detecting overmature forests with airborne laser scanning (ALS)

AbstractBuilding a network of interconnected overmature forests is crucial for the conservation of biodiversity. Indeed, a multitude of plant and animal species depend on forest structural maturity attributes such as very large living trees and deadwood. LiDAR technology has proved to be powerful when assessing forest structural parameters, and it may be a promising way to identify existing overmature forest patches over large areas. We first built an index (IMAT) combining several forest structural maturity attributes in order to characterize the structural maturity of 660 field plots in the French northern Pre‐Alps. We then selected or developed LiDAR metrics and applied them in a random forest model designed to predict the IMAT. Model performance was evaluated with the root mean square error of prediction obtained from a bootstrap cross‐validation and a Spearman correlation coefficient calculated between observed and predicted IMAT. Predictors were ranked by importance based on the average increase in the squared out‐of‐bag error when the variable was randomly permuted. Despite a non‐negligible RMSEP (0.85 for calibration and validation data combined and 1.26 for validation data alone), we obtained a high correlation (0.89) between the observed and predicted IMAT values, indicating an accurate ranking of the field plots. LiDAR metrics for height (maximum height and height heterogeneity) were among the most important metrics for predicting forest maturity, together with elevation, slope and, to a lesser extent, with metrics describing the distribution of echoes' intensities. Our framework makes it possible to reconstruct a forest maturity gradient and isolate maturity hot spots. Nevertheless, our approach could be considerably strengthened by taking into consideration site fertility, collecting other maturity attributes in the field or developing adapted LiDAR metrics. Including additional spectral or textural metrics from optical imagery might also improve the predictive capacity of the model.

Scale and spread of quality improvement initiatives for bronchiolitis management in Alberta emergency departments

Background: Acute viral bronchiolitis is among the most common illnesses seen in the emergency department (ED) and is the leading cause of infant hospitalization in Canada. Practice guidelines do not recommend routine use of certain diagnostic tests and medications in managing bronchiolitis, yet prior studies suggest that low-value interventions are routinely administered to patients with bronchiolitis. Successful implementation of quality improvement at the Alberta Children’s Hospital suggested that low-value interventions and tests can be improved. Yet, practice variation and potential opportunities to improve bronchiolitis management are likely present in EDs in urban and rural settings.&#x0D; Implementation: The project is a collaboration between the Maternal Newborn Child and Youth Strategic Clinical Network (MNCY SCN), the Improving Health Outcomes Together Team (IHOT), and Physician Learning Program (PLP) under the umbrella of the AHS Ernst &amp; Young Clinical Appropriateness Theme recommendations to expand and scale initiatives to reduce unnecessary tests to improve patient safety. A provincial Bronchiolitis Steering Committee, led by two Physician Initiative Leads, was formed to guide the project and implementation at 16 facilities across Alberta.&#x0D; Site implementation includes two key aspects:&#x0D; &#x0D; Audit &amp; Feedback (A&amp;F) – review practice data, facilitated discussion with clinicians, and identify enablers and barriers to practice change&#x0D; Site Specific Implementation Plan – options for sites include use of posters, tools, resources; utilization of ConnectCare (order sets); and staff and physician education&#x0D; &#x0D; Qualitative interviews with site-champions will provide perspectives and feedback on enablers and barriers to change. Discussions from the A&amp;F sessions, in addition to the qualitative interviews will be coded and analyzed based on the Consolidated Framework for Implementation Research and Theoretical Domains Framework.&#x0D; Resources required included clinical leads, project management, data/dashboards, educational posters, updated order sets, and a central location for staff and physician to access bronchiolitis materials (via SharePoint).&#x0D; Evaluation Methods: The primary objective of the study is a reduction of chest x-ray utilization. Chest x-rays utilization can be readily obtained from administrative data at all sites in the project. Secondary measures include medications (PIN) and respiratory viral testing. There is strong evidence to support that medications and respiratory viral testing do not impact bronchiolitis management. The project addresses patient safety and outcomes by reducing the use of low-value interventions and tests in the ED and enables resources to be directed towards evidence-based care. As ConnectCare is phased into all facilities across Alberta, additional metrics will be incorporated into reports and updates to participating sites.&#x0D; Results: The first phase of the project took place from September to November 2021, with rollout to six facilities (four EDs and two inpatient units). A total of 151 physicians attended the audit and group feedback sessions. Site-specific planning sessions and qualitative interviews with site-champions are planned, and the next phase of the project will continue with spread and scale to regional EDs (n=5), urban (n=2) and rural (n=4) locations in fall 2022. &#x0D; Advice and Lessons Learned: &#x0D; &#x0D; The partnership with MNCY SCN, PLP, IHOT, and two Clinician Leads has been beneficial for establishing a team-based multi-disciplinary approach to address needs as they arise and the ability to work together with site champions.&#x0D; Identifying and collaborating with site champions is necessary for establishing relationships and trust prior to conducting audit and feedback sessions and addressing practice change. Site champions understand the contextual factors of their facility and how to best utilize enablers or address barriers for practice change. Initiation of these working relationships need to take place months before implementation and ideally develop through existing networks.&#x0D; Timing and flexibility are crucial for successful implementation. Rescheduling launch dates, adjusting session time, validating data, and adjusting to external factors such as delays in ConnectCare rollouts and pressures on the healthcare system brought on by the COVID-19 pandemic were experienced in the planning and initiating phases of the project. These lessons will be carried forward as we plan for the second part of spread and scale in Fall 2022.&#x0D;

Аддитивная регуляризация при тематическом моделировании текстов сообществ онлайновых социальных сетей

The task of modeling communities (groups) of users in social media is relevant in the framework of information support for decision-making at different levels of government. For automated extraction of the meaning of textual and related information, topic modeling methods are used. This article presents the experience of improving the results of social networks communities topic modeling using the Additive Regularization for Topic Modeling (ARTM). The improvement of the results is achieved through the use of basic regularizers available in the open-source software BigARTM. Topic models obtained using regularized ARTM are compared with topic models obtained by Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis. The experiments were carried out on a dataset, containing preprocessed texts of posts from communities of an online social network. In particular, the quality of topic models in terms of coherence, the purity of topics, and the sparsity of the distribution matrices are compared. Disadvantages of coherence metrics for assessing the quality of topic models obtained using the ARTM method are discussed. Additional metrics are proposed that can be used for assessing the quality of topic models. Conclusions are drawn about the suitability of the ARTM approach for modeling communities of online social networks. The results of this work can be applied in the development of information and analytical systems for supporting the management of regional development.

Vastus lateralis versus rectus femoris muscle flaps for recalcitrant hip joint infection: An anatomical study comparing the effectiveness of acetabular dead space control.

Eliminating recalcitrant prosthetic hip joint infections remains one of the greatest challenges in orthopedic surgery. In such cases, the salvage procedure of femoral head excision (the Girdlestone procedure) is often performed. There has been emerging surgical interest in filling the resulting acetabular dead space with a pedicled muscle flap, to enable antibiotic delivery. Both vastus lateralis (VL) and rectus femoris (RF) muscle flaps have been described for this purpose with good success. This study is the first anatomical investigation comparing VL and RF as candidates for interposition myoplasty following hip joint excision. Following standard surgical technique, the Girdlestone procedure and interposition myoplasty of both RF and VL were performed on 10 cadavers. The primary aim was to determine which muscle flap eliminated a greater volume of acetabular dead space. Secondary aims were to characterize the blood supply to RF and assess additional metrics indicative of the likelihood of flap success. The VL flap eliminated more dead space than RF. However, the use of the RF flap was feasible in all cases and has several benefits, including ease of harvest, mobility, and aesthetics. The location of the inferior vascular pedicle into RF was relatively consistent and the most effective predictor of flap success. Both VL and RF are effective in reducing acetabular dead space. While VL can fill a greater volume, the RF flap has technical advantages, related to the predictability of the blood supply.

A Simulation Study of the Comparative Performance of Partial Area under the Curve (pAUC) and Partial Area under the Effect Curve (pAUEC) Metrics in Crossover Versus Replicated Crossover Bioequivalence Studies for Concerta and Ritalin LA.

Concerta and Ritalin LA are methylphenidate (MPH) drugs with different release mechanisms. Generic bioequivalence (BE) to these conventionally uses pAUC (partial area under the curve) as metrics in addition to Cmax (maximum concentration), AUC0-t (area from time 0 to time t), and AUC0-infinity. The recommended BE design was a standard two-formulation, two-sequence, and two-period crossover; however, the currently recommended design is a replicated crossover to better define subject-by-formulation interaction variance. The current purpose was to compare via simulation, using literature MPH models, the performance of the pAUC metrics in establishing BE via the standard crossover design versus a replicated design, and the relationship of the pAUC metrics to PD (pharmacodynamics) metrics, e.g., SKAMP (Swanson, Kotkin, Agler, M-Flynn, and Pelham rating scale) composite scores pAUEC (partial area under the effect curve). One-thousand, 40-subject studies were simulated with model literature parameters. An indirect response model described the SKAMP composite scores corrected for placebo. Performance of the pAUC metrics was demonstrated by the calculation of 90% confidence intervals (CIs) for each k0fast (fast absorption rate constant) and kaslow (slow absorption rate constant) test/reference (T/R) ratio. The 90% CIs resulting from changes in the k0fast and kaslow ratios, e.g., T/R, showed greater sensitivity to changes in the ratios at quotients below 0.8 than above for both Concerta and Ritalin LA. Ritalin LA pAUC values were insensitive to increases in either ratio once the ratio exceeded 1.0 and the study design. Correlations between least squares means (LSM) for pAUC and the SKAMP pAUEC for the composite scores were near 90%.

Enhancing the Stability of Coalitions in Cross-Border Transmission Expansion Planning

Cooperative Game Theory has recently attracted attention in power systems research as a tool for expansion planning analysis. However, this analysis is usually performed in an ex-post manner, i.e., planning and operation decisions are separated from the allocation mechanisms themselves. The existing paradigm implies selecting an allocation rule (say, the Shapley value) to share the value of cooperation and verifying its rationality (e.g., by checking the Core of the cooperative game). Additional metrics of coalitional stability are omitted. Such an ex-post approach could lead to coalitional stability issues, which may hamper the establishment of cooperation. For example, there could be a severe imbalance in players’ positions, and the contribution of some players could be underestimated in the grand coalition. This paper proposes a bilevel optimization framework for explicitly incorporating Cooperative Game Theory principles into transmission expansion planning problems. Using a simple 4-system case study and a real-world case of cross-border power interconnections in Northeast Asia, we demonstrate that it is possible to make planning decisions in an anticipatory manner subject to the stability of coalitions. The identified expansion plans with enhanced coalitional stability indicate the ways of making parties more equally involved in electricity trading. Finally, we discuss the applicability of the bilevel TEP model, the properties of resulting cooperative games, and the compromise between coalitional stability and economic efficiency in transmission expansion planning.

Identifying biomarkers for epilepsy after cerebral malaria in Zambian children: rationale and design of a prospective observational study

IntroductionMalaria affecting the central nervous system (CM) is a major contributor to paediatric epilepsy in resource-poor settings, with 10%–16% of survivors developing epilepsy within 2 years of infection. Despite high...

Subseasonal-to-Seasonal Arctic Sea Ice Forecast Skill Improvement from Sea Ice Concentration Assimilation

Abstract The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in subseasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily gridcell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June-, July-, August-, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year, and IFD is the first date on which a grid cell is ice free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.

Techno‐economic, environmental, and social measurement of clean energy technology supply chains

AbstractIn addition to the criteria of reliability and cost, clean energy technologies, such as wind, solar, and batteries, need to strive to a higher standard of environmental and societal benefit along their entire supply chain. This means additional performance metrics for these technologies should be considered, such as embodied energy, embodied carbon, recycled content and recyclability, environmental impact of material sourcing, impact on land and ecosystems, materials recovery at end of life, and production through quality nonexploitive jobs with community benefit. Many commercial and emerging energy technologies have not yet been explicitly evaluated based on these environmental and social performance metrics, which presents multiple opportunities for researchers and analysts. In this paper, we review the importance and current limitations of techno‐economic and life‐cycle assessment models for research design and manufacturing decisions. We explore emerging manufacturing modeling options that could improve environmental and social performance and how they could be used to help guide research. Even with the deployment of low‐carbon energy‐generation technologies, the future of a successful clean energy transition requires collaboration between researchers, advanced manufacturers, independent standards and tracking organizations, local communities, and national governments, to ensure the financial, environmental, and social sustainability of the entire supply and manufacturing process of energy technologies.

An investigation of IBM quantum computing device performance on combinatorial optimisation problems

The intractability of deterministic solutions in solving \(\mathcal {NP}\)-Hard Combinatorial Optimisation Problems (COP) is well reported in the literature. One mechanism for overcoming this difficulty has been the use of efficient COP non-deterministic approaches. However, with the advent of quantum technology, these modern devices’ potential to overcome this tractability limitation requires exploration. This paper juxtaposes classical and quantum optimisation algorithms’ performance to solve two common COP, namely the Travelling Salesman Problem and the Quadratic Assignment Problem. Two accepted classical optimisation methods, Branch and Bound and Simulated Annealing, are compared to two quantum optimisation methods, Variational Quantum Eigensolver (VQE) algorithm and Quantum Approximate Optimisation Algorithm (QAOA). These algorithms are, respectively, executed on both classical devices and IBM’s suite of Noisy Intermediate-Scale Quantum (NISQ) devices. We have encoded the COP problems for the respective technologies and algorithms and provided the computational encodings for the NISQ devices. Our experimental results show that current classical devices significantly outperform the presently available NISQ devices, which both agree with and extend on those findings reported in the literature. Further, we introduce additional performance metrics to better compare the two approaches with respect to computational time, feasibility and solution quality. Our results show that the VQE performs better than QAOA with respect to these metrics, and we infer that this is due to the increased number of operations required. Additionally, we investigate the impact of a new set of basis gates on the quantum optimisation techniques and show they yield no notable improvement on obtained results. Finally, we highlight the present shortcomings of state-of-the-art NISQ IBM quantum devices and argue for continued future work on improving evolving devices.

Simulation as Soothsayer: Simulated Surgical Skills MMIs During Residency Interviews are Associated With First Year Residency Performance

The main consideration during residency recruitment is identifying applicants who will succeed during residency. However, few studies have identified applicant characteristics that are associated with competency development during residency, such as the Accreditation Council for Graduate Medical Education milestones. As mini multiple interviews (MMIs) can be used to assess various competencies, we aimed to determine if simulated surgical skills MMI scores during a general surgery residency interview were associated with Accreditation Council for Graduate Medical Education milestone ratings at the conclusion of intern year. Retrospective cohort study. Interns' Step 1 and 2 clinical knowledge (CK) scores, interview day simulated surgical skills MMI overall score, traditional faculty interview scores, average overall milestone ratings in the spring of residency, and intern American Board of Surgery In-Training Examination (ABSITE) percentile scores were gathered. Two multiple linear regression were performed analyzing the association between Step 1, Step 2 CK, MMI, and traditional faculty interview scores with (1) average overall milestone rating and (2) ABSITE percentile scores, controlling for categorical/preliminary intern classification. One academic medical center PARTICIPANTS: General surgery interns matriculating in 2020-2021 RESULTS: Nineteen interns were included. Multiple linear regression revealed that higher overall simulated surgical skills MMI score was associated with higher average milestone ratings (β = .45, p = 0.03) and higher ABSITE score (β = .43, p = 0.02) while neither Step 1, Step 2 CK, nor faculty interview scores were significantly associated with average milestone ratings. Surgical residency programs invest a tremendous amount of effort into training residents, thus metrics for predicting applicants that will succeed are needed. Higher scores on a simulated surgical skills MMIs are associated with higher milestone ratings 1 year into residency and higher intern ABSITE percentiles. These results indicate a noteworthy method, simulated surgical skills MMIs, as an additional metric that may select residents that will have early success in residency.