Prioritization Metrics Research Articles

Maximizing opportunities for co-implementing fuel break networks and restoration projects

Increasing impacts from wildfires are reshaping fire policies worldwide, with expanded investments in a wide range of fuel reduction strategies. In many fire prone regions, especially in the Mediterranean basin, fuel management programs have relied on fuel break networks for decades to facilitate fire suppression and reduce area burned and damage. By contrast, on the fire prone federal forests in the western United States, fuel management is guided primarily by landscape restoration goals, including improving fire resiliency such that wildfires can be managed for ecological benefit, and suppression is used more as a tool to shape burn patterns and less to extinguish fires. New policies in both fire systems are now calling for hybrid approaches that rely on both types of investments and efficient allocation of alternative spatial treatment patterns: linear networks versus patches across the landscape. However, studies that combine these strategies and examine alternative co-prioritization outcomes and potential synergies are largely non-existent. Here, we analyzed scenarios for implementing both types of treatments in concert while varying the prioritization metrics for one type or the other on a western United States national forest. We measured the response related to both treatment strategies including fire intersection rate, improvement in forest resiliency, and net revenue. We found that projects with benefits to both strategies can be identified and mapped independently of the implementation scenario and restoration objective. However, scenarios prioritized for fuel breaks preceding restoration resulted in the identification of more projects that met the criteria for providing dual benefits. The study is a rare example of optimizing hybrid fuel treatment projects that serve both restoration and fire protection goals with different spatial treatment designs.

Pharmacist Metrics in the Pediatric Intensive Care Unit: an Exploration of the Medication Regimen Complexity-Intensive Care Unit (MRC-ICU) Score.

The medication regimen complexity-intensive care unit (MRC-ICU) score has been developed and validated as an objective predictive metric for patient outcomes and pharmacist workload in the adult critically ill population. The purpose of this study was to explore the MRC-ICU and other workload metrics in the pediatric ICU (PICU). This study was a retrospective cohort of pediatric ICU patients admitted to a single institution -between February 2, 2022 - August 2, 2022. Two scores were calculated, including the MRC-ICU and the pediatric Daily Monitoring System (pDMS). Data were extracted from the electronic health record. The primary outcome was the correlation of the MRC-ICU to mortality, as measured by Pearson -correlation -coefficient. Additionally, the correlation of MRC-ICU to number of orders was evaluated. Secondary -analyses explored the correlation of the MRC-ICU with pDMS and with hospital and ICU length of stay. A total of 2,232 patients were included comprising 2,405 encounters. The average age was 6.9 years (standard deviation [SD] 6.3 years). The average MRC-ICU score was 3.0 (SD 3.8). For the primary outcome, MRC-ICU was significantly positively correlated to mortality (0.22 95% confidence interval [CI 0.18 - 0.26]), p<0.05. Additionally, MRC-ICU was significantly positively correlated to ICU length of stay (0.38 [CI 0.34 - 0.41]), p<0.05. The correlation between the MRC-ICU and pDMS was (0.72 [CI 0.70 - 0.73]), p<0.05. In this pilot study, MRC-ICU demonstrated an association with existing prioritization metrics and with mortality and length of ICU stay in PICU population. Further, larger scale studies are required.

Smart Cities—A Structured Literature Review

Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques for analysis, and prioritization metrics. In addition, our study analyzes the implementation of smart city solutions in international contexts and proposes strategies to overcome implementation challenges. The integration of technology and data-driven solutions in smart cities has the potential to revolutionize urban living by providing citizens with personalized and accessible services. However, the implementation also presents challenges, including data privacy concerns, unequal access to technology, and the need for collaboration across private, public, and government sectors. This study provides insights into the current state and future prospects of smart cities and presents an analysis of the challenges and opportunities they present. In addition, we propose a concise definition for smart cities: “Smart cities use digital technologies, communication technologies, and data analytics to create an efficient and effective service environment that improves urban quality of life and promotes sustainability”. Smart cities represent a promising avenue for urban development. As cities continue to grow and face increasingly complex challenges, the integration of advanced technologies and data-driven solutions can help to create more sustainable communities.

The Value of Better Vehicle Occupancy Estimates for Project Prioritization

Transportation projects are increasingly evaluated based on how they improve person throughput, which requires knowing the passenger car occupancy. Yet obtaining occupancies is labor intensive, leading agencies to rely on statewide values. How useful are locality-specific vehicle occupancies? A case study of 38 candidate Virginia highway investments showed an occupancy change of .15 randomly applied to one-half of the projects affects 24% to 42% of project rankings if locality-specific occupancies are used. Occupancy is not the only data element affecting project prioritization, but it is an underlooked one. The study showed that an occupancy uncertainty of .15 is equivalent to an uncertainty of 3.90% in the discount rate or 13.2% in assigned traffic volume. As these investments had a mean value of $17 M, this study demonstrated that when project prioritization metrics are based on person throughput, city-or county-specific occupancies are an integral component of a data-driven prioritization process.

Oil and gas development does not reduce duck pair abundance in the Prairie Pothole Region

AbstractConservation partners are concerned that oil and gas development in the Prairie Pothole Region may reduce the abundance of breeding duck pairs using associated wetland habitat. We conducted wetland-based surveys for breeding pairs of 5 species of dabbling ducks in the Bakken oil field during 2015–2017 across a gradient of oil and gas development intensity to test the hypothesis that the abundance of breeding duck pairs on survey wetlands would decrease as the development of oil and gas resources increased. We included covariates traditionally used to predict breeding duck pairs (i.e. wetland size and class) and a spatiotemporal index of disturbance when developing zero-inflated Poisson models relating pair abundance to environmental predictors. Similar to past analyses, pair abundance was strongly associated with wetland size. Our results were mixed and suggested that the abundance of early and late nesting species was positively and negatively related, respectively, to an index of disturbance that was largely driven by oil and gas development. Regardless of the direction of the relationship, effect sizes were small and not considered biologically significant. Our findings indicate that in our study area, strategies to conserve wetland resources for breeding duck pairs should not deviate from previous prioritization metrics within the range of oil and gas development we observed. We believe that our findings may have implications to similar landscapes within the Bakken.

Use of occupancy modelling to identify sample sizes and waterbodies with fish exceeding mercury consumption advisory thresholds.

Identifying waterbodies where fish methylmercury concentrations are elevated is critical for development of consumption guidelines. However, mercury concentrations vary among waterbodies and fishes due to a suite of environmental conditions and detection of elevated mercury concentrations is imperfect, resulting in inaccurate consumption guidelines. Occupancy models may be a useful approach for addressing these issues but have not been used for these purposes. Our objectives were to use occupancy modeling to (1) estimate number of samples needed to detect mercury levels surpassing >0.30 mg/kg wet weight in fish at a waterbody (2) identify individual fish-level factors associated with detection probability, and (3) identify environmental-level factors linked to elevated mercury levels in fish at a waterbody. Mercury concentrations were estimated from >500 largemouth bass Micropterus salmoides and walleye Sander vitreus from 30 waterbodies throughout Iowa, USA to identify individuals with concentrations > or <0.30 mg/kg. Probability of detecting mercury concentrations >0.30 mg/kg varied between species and increased with fish length; consequently, more samples were needed to detect elevated mercury concentrations in small versus large fish. The probability of a waterbody having fish with elevated mercury levels increased with the percent grassland and declined with percent agriculture in the watershed, providing prioritization metrics for mercury surveillance programs. Our results demonstrate that occupancy models can be a valuable tool for mercury surveillance due to their ability to estimate necessary sample sizes and identify fish sizes and waterbodies with elevated mercury concentrations while accounting for imperfect detection probabilities.

What type of value information is most valuable to stakeholders? Multi-sector perspectives on the utility and relevance of water valuation information

As the volume of ecosystem services modeling and valuation studies grows, decision-makers have access to more information than ever before on how human activities affect the provision and value of ecosystem services. However, few studies aim to understand how value information in different formats is interpreted and used by decision makers. Motivated by a desire to better understand how private, non-profit, and government sectors perceive and use valuation information in their work, we conducted over 40 semi-structured interviews with water resource professionals in the U.S. state of Minnesota. Our goal was to understand the factors that determine the utility, relevance, and influence of ecosystem services valuation information as it relates to water management. We presented interviewees with monetary return on investment statistics, spatial prioritization metrics, surveys on water values, and narrative stories and asked respondents to describe how they might use the results in their own work. The most common stated application of valuation information was for “raising awareness”, rather than the design of policies or incentive programs. Across sectors, respondents characterized monetary values as less influential than qualitative representations of values in the form of stories. Consistent with previous research, we found accessibility, ease of use, and credibility of information enhanced respondents stated utility of valuation information. Respondents expressed a desire for increased collaboration between ecosystem services researchers and decision-makers, creation of finer scale information, and improved communication about the methods and assumptions behind valuation studies.

Dynamic prioritization of surveillance video data in real-time automated detection systems

Automated object detection systems are a key component of modern surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data recorded by surveillance cameras. Due to the security and safety implications of these systems, this visual data st be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing little or no useful information to the object detection system. Sub-sampling surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Consequently, several works have explored dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. Few works, however, have combined and evaluated different modalities of information together for real-time prioritization of visual surveillance data. This work evaluates several individual and combined prioritization metrics derived from different modalities of information for use with a modern deep learning-based object detection algorithm. Both processing time and object detection rate are measured and used to rank the prioritization metrics. A novel approach that uses the historical detection confidences created by the object detection algorithm was demonstrated to be the best standalone prioritization metric. Additionally, a novel ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics to create a dynamic prioritization method is presented. This ensemble approach is shown to increase the object detection rate by up to 60% as compared to a static sub-sampling baseline as demonstrated using three publicly available datasets. The increased object detection rate was achieved while meeting the real-time constraints of the automated object detection system.

The DisGeNET knowledge platform for disease genomics: 2019 update.

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

Putting people on the map improves the prioritization of ecosystem services

With limited resources to improve the provision of multiple ecosystem services, conservation programs often rely on spatial tools to identify the best locations for restoration or protection. However, most programs still base prioritization on biophysical metrics that do not fully predict where interventions will deliver the greatest benefits to people. We argue that prioritization metrics need to consider not only biophysical data on the supply of ecosystem services but also socioeconomic data that highlight the value of those services. We use the example of prioritizing best management practices (BMPs) in an agricultural watershed in the US state of Iowa to evaluate how considering the location and preferences of beneficiaries affects the spatial prioritization of investments. We show that adopting metrics that incorporate endpoints of interest and value to people changes the spatial prioritization of BMPs, especially for air quality, lake recreation, and drinking water. Our work demonstrates how publicly available biophysical and social data can be integrated into spatial ecosystem services metrics to enhance the delivered benefits of conservation activities.

Introducing a computational method to estimate and prioritize systemic body exposure of organic chemicals in humans using their physicochemical properties

This report describes a computational method developed to predict systemic exposure (s-exposure), chemical disposition {(CD) intestinal absorption, transport, membrane permeability, distribution, sequestration, phospholipidosis and toxicokinetics} of organic chemicals in humans. The method qualitatively and quantitatively estimates a chemical's CD activity profile based upon computed molecular descriptor properties (descriptors), and it facilitates in silico signal-detection of data-gaps, prioritization, risk-ranking, read-across, and re-assessments (if mandated) of large sets of chemicals in a safety evaluation setting. The investigation used a reference set of 2372 marketed human pharmaceuticals to define decision rules for an optimal chemical space (OCS) in which chemicals have high s-exposure, good CD, and a potential for chemical toxicity (CT); conversely, chemicals outside the OCS have low s-exposure, poor CD into the body, and low potential for CT. The method requires computation of 29 descriptors, identification of OCS molecular descriptor property violations (descriptor_violations), and alignment of descriptor_violations with specific decision rules for individual CD endpoint activities. The investigation predicted the CD activities of food and cosmetic preservatives, ingredients in GRAS (generally recognized as safe). Notices submitted to the FDA, reference pharmaceuticals, and it provides prioritization metrics and indices that facilitate prioritization of chemical in silico computed CD activities.

Environmental cost of using poor decision metrics to prioritize environmental projects.

Conservation decision makers commonly use project-scoring metrics that are inconsistent with theory on optimal ranking of projects. As a result, there may often be a loss of environmental benefits. We estimated the magnitudes of these losses for various metrics that deviate from theory in ways that are common in practice. These metrics included cases where relevant variables were omitted from the benefits metric, project costs were omitted, and benefits were calculated using a faulty functional form. We estimated distributions of parameters from 129 environmental projects from Australia, New Zealand, and Italy for which detailed analyses had been completed previously. The cost of using poor prioritization metrics (in terms of lost environmental values) was often high--up to 80% in the scenarios we examined. The cost in percentage terms was greater when the budget was smaller. The most costly errors were omitting information about environmental values (up to 31% loss of environmental values), omitting project costs (up to 35% loss), omitting the effectiveness of management actions (up to 9% loss), and using a weighted-additive decision metric for variables that should be multiplied (up to 23% loss). The latter 3 are errors that occur commonly in real-world decision metrics, in combination often reducing potential benefits from conservation investments by 30-50%. Uncertainty about parameter values also reduced the benefits from investments in conservation projects but often not by as much as faulty prioritization metrics.

Virtualizing on-chip distributed ScratchPad memories for low power and trusted application execution

Emerging multicore platforms are increasingly deploying distributed scratchpad memories to achieve lower energy and area together with higher predictability; but this requires transparent and efficient software management of these critical resources. In this paper, we introduce the concept of ScratchPad Memory virtualization, a hardware/software run-time layer (called SPMVisor) that virtualizes the scratchpad memory space in order to facilitate the use of distributed SPMs in an efficient, transparent and secure manner. We introduce the notion of virtual scratchpad memories (vSPMs), which can be dynamically created and managed as regular SPMs. The SPMVisor exploits policy-driven allocation strategies based on application privilege levels and data level prioritization metrics (e.g., utilization) to efficiently manage the on-chip memory real-estate. Our experimental results on Mediabench/CHStone benchmarks running on various Chip-Multiprocessor configurations and software stacks (RTOS, virtualization, secure execution) showed that SPMVisor enhances performance by 71 % on average and reduces power consumption by 79 % on average with respect to traditional context switching schemes. We showed the benefits of using vSPMs in a various environments (a RTOS multi-tasking environment, a virtualization environment, and a trusted execution environment). Furthermore, we explored the effects of mapping instructions and data onto vSPMs, and showed that sharing on-chip space reduces both execution time and energy by an average 16 % and 12 % respectively. We then compared our priority-driven memory allocation scheme with traditional dynamic allocation and showed an average 54 % execution time reduction and 65 % energy savings. Finally, to further validate the SPMVisor’s benefits, we modified the initial bus-based architecture to include a mesh-based CMP with up to 4×4 nodes. We were able to observe that SPMVisor’s priority-driven allocator was able to reduce execution time by an average 17 % with respect to competing allocation policies, while saving an average 65 % across various architectural configurations. We were also able to observe that SPMVisor reduces execution time by an average 12.6 % with respect to competing allocation policies, while saving an average 63.5 % in total energy for various architectural configuration running 1024 jobs.

Optimised whole-landscape ecological metrics for effective delivery of connectivity-focused conservation incentive payments

Market-based instruments provide incentives for conservation on private lands, combining the economic efficiency of an auction with ecological site prioritization to select the best sites from those offered by landholders. However, landscape-scale goals such as increased habitat connectivity are difficult to deliver with site-based prioritization metrics. Assessing alternative ways to re-connect landscapes is a complex task, requiring knowledge of how biodiversity will respond over time to alternative conservation actions, such as replanting, managing areas of natural regrowth or protecting existing habitat. It also presents computational challenges since projects must be assessed as combinations rather than individually. We investigated practical aspects of ecological metric design to achieve desired spatial configurations. Realistic, mock bids were submitted by members of the local community in a simulated tender exercise for enhanced cassowary habitat near Mission Beach, Australia. Optimization heuristics helped solve the problem within a reasonable time. Our results demonstrate that integrating whole-landscape assessment models with market-based instruments can feasibly address the inherent complexity when pursuing whole-landscape ecological benefits through cost-efficient and innovative means. Our methodology redresses asymmetries in knowledge about biodiversity in delivering conservation incentive payments, and is justified when policy goals demand a high level of rigor.

Intact Faunal Assemblages in the Modern Era

In light of limited conservation funding, global conservation initiatives are increasingly focused on regions of the planet that have been identified as valuable on the basis of their species diversity, the vulnerability of resident species to extinction, or the perceived pristine nature of their ecosystems. Regions that have been resilient to high rates of extinction have not yet been systematically considered in conservation efforts. We used published range maps for 392 vertebrate species to compare historical and current species ranges. We used the results of the comparison to identify regions of the globe in which no known vertebrate species has been extirpated in the past 200 years. In 17 regions, no detectable vertebrate extinctions occurred in the past 200 years. In 6 other regions, reintroductions of species restored the full historic complement of vertebrate species. The effects of humans on a landscape, as measured by the human-footprint index, although useful, was not a singularly good predictor of faunal intactness because more than 20% of intact land area was in heavily affected areas (50% of Earth's land area), and several regions where humans have had very little effect did not have intact faunas. Only 22% of intact land area was within protected-area networks. High-latitude areas were particularly underrepresented; they made up 3 of the 4 least-protected areas in our analyses. Our results indicate that although protected areas are in some cases associated with the prevention of extinctions, there are many regions in which human activity coexists with intact vertebrate assemblages. In addition, our new approach for assessing the value of global regions for conservation identifies several regions that are not represented in other prioritization metrics.

Ecoregion Prioritization Suggests an Armoury Not a Silver Bullet for Conservation Planning

In the face of accelerating species extinctions, map-based prioritization systems are increasingly useful to decide where to pursue conservation action most effectively. However, a number of seemingly inconsistent schemes have emerged, mostly focussing on endemism. Here we use global vertebrate distributions in terrestrial ecoregions to evaluate how continuous and categorical ranking schemes target and accumulate endangered taxa within the IUCN Red List, Alliance for Zero Extinction (AZE), and EDGE of Existence programme. We employed total, endemic and threatened species richness and an estimator for richness-adjusted endemism as metrics in continuous prioritization, and WWF's Global200 and Conservation International's (CI) Hotspots in categorical prioritization. Our results demonstrate that all metrics target endangerment more efficiently than by chance, but each selects unique sets of top-ranking ecoregions, which overlap only partially, and include different sets of threatened species. Using the top 100 ecoregions as defined by continuous prioritization metrics, we develop an inclusive map for global vertebrate conservation that incorporates important areas for endemism, richness, and threat. Finally, we assess human footprint and protection levels within these areas to reveal that endemism sites are more impacted but have more protection, in contrast to high richness and threat ones. Given such contrasts, major efforts to protect global biodiversity must involve complementary conservation approaches in areas of unique species as well as those with highest diversity and threat.