Prioritization Of Actions Research Articles

Multi-criteria approach for weights definition of sustainability indicators in the swine supply chain

Goal: This study presents a four-steps methodology for the definition of the weights of sustainable indicators in the Swine Supply Chain (SSC) considering the Triple Bottom-Line (TBL) perspective. Design/Methodology/Approach: The methodology proposed was based on the Bellagio principles that guide the definition of importance levels for TBL criteria. It was applied to the swine supply chain using Saaty’s scale and information obtained from managers of two agribusiness companies, adjusted by the AHP method. Results: The results showed the relevance of an appropriate definition of the preferences of decision-makers to decide on the best actions towards environmental, economic, and social sustainability. Limitations: The limitations of the proposed model are mainly its subjective nature. Despite being necessary in multi-criteria methods, it could be a limitation if more quantitative indicators are relevant or demanded. Nevertheless, the proposed methodology also provides a quantitative perspective, as a result of using the Saaty scale. Practical implications: The definition and application of importance levels within a TBL-based model can help managers to prioritize environmental, social, and/or economic dimensions under a large diversity of alternatives toward more sustainable scenarios. The AHP method used in this work is a hierarchy method, particularly appropriate for defining weights that are essentially compensatory by nature. Originality/value: The correct definition and weight of the different dimensions, sub-dimensions, and respective indicators is currently one relevant gap in the literature, limiting the design and prioritization of corrective actions in each stage of the SSC and in the different dimensions of the TBL.

Improving Patient Wait Times on the First Day of Radiotherapy Treatment

Long wait times on starting day of radiotherapy (day 1) can cause dissatisfaction among both patients and healthcare providers. Reducing these wait times will decrease stress and decongest hospital facilities especially in current coronavirus disease 2019 times. A multidisciplinary core team was formed during the Stanford-India Collaborative Quality Improvement training to reduce the median wait times on day 1 of treatment from 6 to 4.5 hours (a 25% reduction). Several factors were identified on the fishbone diagram, and key causes were identified using a Pareto chart and action prioritization matrix. The Plan-Do-Study-Act Cycle strategy was undertaken for the identified interventions. The outcome measure was time from arrival at the hospital to entry into a treatment room. Data were obtained from time charts at various stations and electronic records. The secondary measures were visual analog scale (VAS) scores, 80th percentile wait times, and the day-2 delay percentage. The balancing measure was “new errors” due to interventions. The interventions included the completion of all administrative tasks not needing patients' presence on the day before day 1. Baseline data from 198 patients and postintervention data from 160 patients were compared and analyzed. The median wait time at baseline, which was 6 hours, was reduced to 4.2 hours. The VAS score showed 70.4, 67.7, and 71.9% satisfaction for the resident physician, therapists, and patients, respectively. The 80th percentile wait times reduced from 8 to 5.7 hours; and the day 2 starting rate decreased from 22.5 to 2.04%, with no new errors reported. Radiotherapy day 1 wait times can be safely decreased, leading to improved satisfaction among patients and healthcare providers, by utilizing classic quality improvement methods and tools.

Anuran occupancy varies with stream characteristics and flow across Arizona wilderness areas

Abstract Riparian ecosystems comprise less than 2% of the landscape in the arid western United States, yet they provide habitat and resources to over half of arid‐land wildlife species, including a broad diversity of anurans (frogs and toads). Despite this, relatively few studies describe riparian herpetofaunal communities in the Southwest, and remote wilderness areas are particularly undersurveyed. We employed passive acoustic monitoring to capture anuran advertisement calls in wilderness area tributaries of the Verde River, Arizona, USA. In the spring and summer of 2021 and 2022, 13–29 autonomous recording units (ARUs) were deployed along perennial, intermittent and ephemeral tributary stream reaches. We characterized sites based on the per cent of pool, riffle, run and side channel habitat within 100 m of each ARU and quantified substrate, wetted channel width and canopy cover. To relate anuran occupancy to environmental and hydrological variables, we evaluated acoustic detection data using single‐species, single‐season occupancy models. Four species were detected in this study: canyon treefrog (Hyla arenicolor), red‐spotted toad (Anaxyrus punctatus), Woodhouse's toad (Anaxyrus woodhousii) and non‐native American bullfrog (Lithobates catesbeianus), with canyon treefrog being the most ubiquitous species observed. Vocal activity of canyon treefrog was higher at perennial and intermittent sites compared to ephemeral sites, and the presence of pools was most strongly associated with canyon treefrog occupancy. Persistent, slow‐moving water best predicted the presence of breeding canyon treefrogs. Notably, this study did not detect several species with historical records in the middle Verde River catchment, including Arizona toad (Anaxyrus microscaphus) and northern leopard frog (Lithobates pipiens). Given climate change‐related flow declines and intensifying demands for water in arid lands globally, maintaining stream flows that provide consistent and suitable hydroregimes for anuran breeding and larval development is of increasing importance. Determining habitat use and flow regimes necessary to support anuran populations can aid in prioritization of conservation actions related to water management and predict how changes in water availability may impact stream‐breeding anurans.

A decision support tool for habitat connectivity in Australia

Context Species connectivity mapping is a technically challenging task for conservation practitioners and nongovernment organisations to undertake as it requires experience in geographic information systems and often some computer programming. Aims We developed a decision support tool to provide spatial information and data on potential habitat connectivity and optimum connectivity pathways for a selection of forest-dependent vertebrate fauna in eastern and south-western Australia. Methods We systematically searched spatial data repositories for Australian spatial datasets for modelling connectivity. A least cost paths and patch connectivity approach was used to map potential habitat connectivity for (1) a single species – the glossy black cockatoo (Calyptorhynchus lathami) of South East Queensland, and (2) four species guilds – rainforest pigeons, gliding possums, the black cockatoos of south-western Western Australia and a landscape level forest connectivity. Optimum connectivity pathways were assessed for protection status. Key results In total 71 spatial datasets useful for habitat connectivity mapping were identified. Species and guild modelling found that the protection status for optimum connectivity pathways varied between 24.7% and 53.3%. A decision support mapping tool was then created to enable users to interactively explore the connectivity data and download the spatial datasets for further analysis. Conclusions The development of a decision support tool for mapping habitat connectivity in eastern and south-western Australia represents a useful platform for conservation practitioners as it provides valuable spatial information on potential connectivity pathways for forest-dependent vertebrate fauna. Implications The tool can aid in the prioritisation of conservation actions aimed at enhancing habitat connectivity and mitigating the impacts of habitat fragmentation on biodiversity in the two regions.

Disentangling drivers of annual grass invasion: Abiotic susceptibility vs. fire-induced conversion to cheatgrass dominance in the sagebrush biome

Invasive annual grasses are often facilitated by fire, yet they can become ecologically dominant in susceptible locations even in the absence of fire. We used an extensive vegetation plot database to model susceptibility to the invasive annual grass cheatgrass (Bromus tectorum L.) in the sagebrush biome as a function of climate and soil water availability variables. We built random forest models predicting cheatgrass presence or dominance (>15 % relative cover) under unburned (37,219 plots) and burned conditions (6340 plots). We mapped predicted probability of cheatgrass presence and dominance, conditional on burning. We combined predicted susceptibility with burn probability to quantify the 10-year total risk of cheatgrass dominance. Finally, we identified portions of the landscape (1) at risk of fire-induced conversion to cheatgrass dominance, (2) consistently susceptible to cheatgrass dominance, or (3) consistently resistant to cheatgrass dominance. At the scale of the sagebrush biome, we found that abiotic susceptibility to cheatgrass dominance drives total risk, regardless of fire. At local scales (i.e., individual 30 m pixels), burning increased the probability of cheatgrass dominance by a median of 14 %. Threshold-based analyses indicate that 10–31 % of the sagebrush biome was at risk of fire-induced dominance, with 55 % exhibiting abiotic resistance and 5 % exhibiting abiotic susceptibility to dominance regardless of fire. Burn probability was higher in areas predicted to be susceptible to dominance, illustrating how cheatgrass invasion can cause ecosystem conversions that are then sustained by grass-fire cycles. Disentangling the influence of abiotic conditions and fire contributes to our understanding of the mechanisms driving invasion dynamics, and modeling the probability of dominance can help anticipate where ecological transformations are at risk of occurring. Our approach can facilitate the prioritization of management actions in the sagebrush biome and be used as a framework for modeling invasion risk in other disturbance-prone ecosystems.

Prioritizing management actions for invasive non-native plants through expert-based knowledge and species distribution models

Given the high number of non-native plants that are being introduced worldwide and the time required to process formal pest risk analyses, a framework for the prioritization of management actions is urgently required. We therefore propose a framework for a replicable and standardized prioritization for management actions (eradication, control and monitoring) of invasive non-native plants, combining expert knowledge, current and future climatic suitability estimated by species distribution models (SDMs), clustering and ordination techniques. Based on expert consultation and using Italy as case study, invasive non-native plant species were selected and three categories of management actions were identified: eradication, control and containment, and monitoring. Finally, two further classes of priorities were proposed for each of the management actions: “high” and “low” priority. Overall, SDMs highlighted a high and very high suitability for Continental and Mediterranean bioregions for most invasive plants. Cluster analysis revealed three distinct clusters with varying levels of suitability for the Italian bioregions. Cluster 1 exhibited a higher suitability across all Italian bioregions, whereas non-native plants grouped in Cluster 2 predominantly featured high suitability in Mediterranean areas. Finally, Cluster 3 showed the lowest suitability values. Two ordination analysis highlighted the variability in bioclimatic suitability for each non-native plant within each cluster, as well as their current distribution pattern. Lastly, a third ordination, integrating bioclimatic suitability and spatial patterns, has allowed the differentiation of management actions for each non-native plant at both national and bioregional scales. Specifically, seven non-native plants were earmarked for eradication action, six for monitoring action, while the remaining species were deemed suitable for control and containment. Our results and the methodology proposed meet the demand for replicable new early warning tools; that is to predict the location of new outbreaks, to establish priorities for eradication, control and containment, and to monitor invasive non-native species.

Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial

AbstractRecent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.

Identifying priority double-duty actions to tackle the double burden of malnutrition in infants and young children in Peru: Assessment and prioritisation of government actions by national experts.

Multiple forms of malnutrition coexist in infants and young children (IYC) in Peru. The World Health Organization has proposed double-duty actions (DDAs) to simultaneously address undernutrition and overweight/obesity. We assessed current implementation of- and priority for- government-level actions to tackle multiple forms of malnutrition in IYC in Peru. Mapping of current policy activity was undertaken against 47 indicators of good practice for five DDAs (exclusive breastfeeding, complementary feeding, food marketing, maternal nutrition, preschool nutrition; assessed by 27 indicators) and for the enabling policy environment, i.e., 'infrastructure support' (health in all policies, platforms for interactions, financing, monitoring, governance, leadership; assessed by 20 indicators). Interviews with 16 national experts explored views on the level of and barriers to implementation of DDAs and infrastructure support, as well as their prioritisation based on likely impact and feasibility. The level of implementation of actions was categorised into two groups (agenda setting/formulation vs. implementation/evaluation). Mean scores were generated for prioritisation of DDAs and infrastructure support. Deductive qualitative analysis was undertaken to identify barriers that influence policy implementation. Only 5/27 DDA indicators were reported as fully implemented by all national experts (international code that regulates the marketing of breastmilk substitutes, iron supplementation for IYC, micronutrient powders in IYC, iron/folic acid supplementation in pregnant women, paid maternity leave). Only 1/20 infrastructure support indicator (access to nutrition information) was rated as fully implemented by all experts. Barriers to implementing DDAs and infrastructure support included: legal feasibility or lack of regulations, inadequate monitoring/evaluation to ensure enforcement, commercial influences on policymakers, insufficient resources, shifting public health priorities with the COVID-19 pandemic and political instability. The experts prioritised 12 indicators across all five DDAs and eight infrastructure support indicators. Experts highlighted the need to improve implementation of all DDAs and identified ways to strengthen the enabling policy environment.

Well-Candidate-Recognition Solution Offers Time Savings, Production Enhancement

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213329, “Data-Driven Well-Candidate-Recognition Solution: Case Study in a Digital Oil Field in Abu Dhabi,” by Erismar Rubio, SPE, Nagaraju Reddicharla, SPE, and Mayada Ali Sultan Ali, ADNOC, et al. The paper has not been peer reviewed. _ A well-candidate-recognition (WCR) data-analytics solution was developed to expedite the process of identifying unhealthy wells that may require rig or rigless interventions based on data integration, automation, and advanced data-driven models. The solution expedites the well-performance-review process to pinpoint candidates for stimulation, nitrogen lift, gas lift conversion, and water or gas shutoff, providing a flexible visualization platform to highlight hidden well-performance insight. Introduction Mature oil fields will face challenges in terms of increasing water cut, lack of pressure support, and the requirement of artificial lift. The critical concern is prioritization of remedial actions that are economically attractive with high returns and low risk. In the past, the asset was prioritizing the rig and rigless intervention candidates on a manual basis with no technical framework, mostly relying on engineers’ backgrounds and experience. Lack of understanding and failure to apply lessons learned from previous jobs were frequent outcomes because no single, comprehensive database or documentation existed that effectively captured well-intervention history. Every year, the operator requested a well-performance-review report for every reservoir containing hundreds of diagnostic plots that required massive manual updates, although the subject asset is a digital oil field fully instrumented with real-time data streaming. However, no tools were available to integrate periodic and nonperiodic required data. The complete paper addresses these challenges of integrating huge amounts of data and model frameworks and developing a systematic workflow to identify opportunities for production enhancement. Data-driven models, combined with the integration of static and dynamic data, enable proactive surveillance routines that allow engineers to focus on problematic wells and opportunity generation in a timely manner. Objective The WCR data-analytics solution was developed as part of the operator’s digital transformation strategy to streamline the process of improving diagnostics, identifying unhealthy wells, and reviewing value gain. The solution aims to help achieve the following objectives: - Facilitate well-performance review to pinpoint candidates for rig and rigless intervention - Provide a flexible visualization platform to highlight hidden well performance insight - Integration of real-time data and official databases - Create a collaborative environment for improved decision-making - Improve rigless success factor for the most-expensive activities - Optimize and prioritize the reservoir-monitoring plan (RMP)

Quantifying the effects of fragmentation of connectivity networks of deep‐sea vulnerable marine ecosystems

AbstractAimProtection of vulnerable marine ecosystems (VMEs) in the high seas has focussed on identifying concentrations of indicator species and prohibiting the operation of bottom‐contact fishing gears where those occur in significant concentrations. Most such species have planktonic larvae and depend on dispersal networks for inter‐generational persistence. Yet, connectivity amongst patches of VME has seldom been considered when spatial management measures are introduced. Here, the relative importance of individual patches for the maintenance of their connectivity networks is evaluated, and a prioritization scheme for management action is proposed. Effective conservation measures should maintain approximately natural network configurations whenever possible.LocationGrand Bank and Flemish Cap, Northwest Atlantic Ocean.Methods3‐D Lagrangian particle tracking was used to model larval dispersal connections between known patches of each of seven groups of benthic invertebrate taxa, previously recognized as indicators of VME. Connectivity networks were constructed and the effects of habitat loss simulated by systematic removal of whole patches, to determine the importance of each patch to connectivity within its respective network.ResultsThe various patches differed widely in their contributions to network connectivity. Each taxon group had both some patches that, if removed from the network, would result in a major decline in connectedness but also several which could be lost with negligible consequences for the remainder.Main ConclusionsWhile protecting each patch of VME has conservation value, the wide variation in connectedness shows that some patches are much more critical than others to the long‐term persistence of the taxa, providing a foundation for prioritization of conservation actions.

Air pollutant emissions and sources in Lao People’s Democratic Republic: a provincial scale analysis for years 2013-2019

Recent rapid economic development in Lao People’s Democratic Republic (PDR) has increased national fuel consumption, vehicle fleet, industrial output, waste generation, and agricultural production. This has contributed to national average ambient fine particulate matter (PM2.5) air pollutant levels that are four times higher than World Health Organisation guidelines. Emission inventories are a key tool in understanding the major sources to these air pollution levels, and provide a starting point to identify where mitigation action can be targeted. A national air pollutant emission inventory has not been developed in Lao PDR and, combined with a limited air quality monitoring network means there is limited capacity to develop and track the effectiveness of mitigation actions. This study describes the first air pollutant emission inventory at the national and provincial scale for Lao PDR, covering 2013–2019. Emissions of nine air pollutants, and two greenhouse gases, were quantified using national statistics and international default emission factors. In 2019, national total PM2.5, Nitrogen Oxides (NOx), Black Carbon (BC), Sulphur Dioxide (SO2), Non-Methane Volatile Organic Carbons (NMVOCs), and Ammonia (NH3) were 125, 83, 9.7, 26, 219, and 99 thousand tonnes respectively. Key source sectors include forest fires, residential cooking, agriculture, electricity generation, and transport. However, the contribution of different sources varies across provinces. Forest fires are the primary source determining the spatial trend of particulate air pollution while residential and agricultural emissions contribute more significantly to rural provinces such as Savannakhet. Key sectors in major urban provinces (Vientiane Capital and Xayaboury) are industry, transport and electricity generation. These sectors are also significant sources of greenhouse gases (CO2 and CH4), demonstrating the potential for identification, evaluation and prioritisation of actions that simultaneously improve air quality and achieve Lao PDR’s international climate change commitments.

Assessing consumer knowledge, attitudes, and adoption of circular economy practices in Colombia

Consumers play a pivotal role in the successful implementation of the circular economy (CE) model. In Colombia, despite measures designed to support CE, there is a paucity of empirical evidence regarding consumer engagement. This study aimed to assess consumer knowledge, attitudes, and adoption of CE in Colombia. A survey was conducted in three cities known for their strong interest in CE and their significant contributions to national development: Medellin, Pereira, and Barranquilla. The data gathered from 1184 responses were analyzed using descriptive and non-parametric statistical methods. The results revealed that only 26 % of consumer responses denote adequate knowledge of CE, highlighting a substantial lack of awareness regarding the CE concept and both government and private sector initiatives. Consumer attitudes towards CE were notably positive, with 77 % of responses expressing a favorable stance. Concerning the adoption of circular strategies in their daily lives, 53 % of consumer responses involved CE adoption, while 23 % exhibited resistance. Medellin and Pereira's consumers exhibited similar and encouraging circular behavior, while those in Barranquilla faced more barriers. Furthermore, this study reports that the city of residence, educational level, and socioeconomic stratum influence how consumers perceive, value, and adopt CE. These findings emphasize the urgent need to enhance consumer engagement and performance to support Colombia's CE plans. The implications outlined herein will benefit the identification and prioritization of synergistic actions to transform the role of the consumer and turn it into a catalyst for CE.

Rural Depopulation in Spain: A Delphi Analysis on the Need for the Reorientation of Public Policies

In recent years, various plans have been implemented by the central government with the aim of promoting more coordinated policies to address depopulation. The severity of this challenge in Spain, which has continued to intensify, underscores the need for more decisive action. The information presented in this research is derived from a survey conducted in two rounds with 35 experts in the field, following the Delphi methodology. The general objective was to assess the opinions of an expert panel on relevant aspects concerning policies to combat depopulation in Spain. Firstly, confirming the significance of the depopulation challenge; secondly, evaluating whether the actions taken so far have been insufficient and poorly coordinated, necessitating a reconsideration; and finally, establishing a prioritization of actions that should be implemented without further delay, encompassing various areas (financing, taxation, coordination, etc.), are among the many measures advocated by the Spanish Federation of Municipalities and Provinces (FEMP) and proposed in the recent diagnoses by highly authoritative institutions such as the Bank of Spain or the Economic and Social Council of Spain (CES).

Citizen scientists filling knowledge gaps of phosphate pollution dynamics in rural areas

In situ monitoring is fundamental to manage eutrophication in rivers and streams. However, in recent decades, the frequency and spatial coverage of regulatory monitoring have often been reduced due to funding and infrastructure limitations. This reduction has made it impossible to provide adequate coverage for most water bodies. In this study, trained citizen scientists filled spatial and temporal gaps in agency monitoring across a major catchment in rural England. By integrating data from citizen scientists, regulatory agencies, and the local water company, it was possible to demonstrate the opportunities for hypothesis-based citizen scientist monitoring to identify continuous and event-driven sources of phosphate pollution. Local citizen scientists effectively covered important spatial gaps, investigating river conditions both upstream and downstream of suspected pollution point sources, improving the identification of their temporal dynamics. When combined with long-term monitoring data from regulatory agencies, it became possible to identify areas within the catchment that exhibited increased phosphate concentrations during periods of low river discharge (summer). Inter-annual trends and anomaly detection suggested that continuous pollution sources dominated over event-driven sources in many sub-basins, allowing for the prioritisation of mitigation actions. This study highlights the opportunity for citizen scientists to fill gaps in regulatory monitoring efforts and contribute to the improved management of eutrophication in rural catchments.

The Radical Welcome Engagement Restoration Model and Assessment Tool for Community-Engaged Partnerships.

People experiencing addiction, houselessness, or who have a history of incarceration have worse health outcomes compared with the general population. This is due, in part, to practices and policies of historically White institutions that exclude the voices, perspectives, and contributions of communities of color in leadership, socio-economic development, and decision-making that matters for their wellbeing. Community-based participatory research (CBPR) approaches hold promise for addressing health inequities. However, full engagement of people harmed by systemic injustices in CBPR partnerships is challenging due to inequities in power and access to resources. We describe how an Allentown-based CBPR partnership-the Health Equity Activation Research Team of clinicians, researchers, and persons with histories of incarceration, addiction, and houselessness-uses the Radical Welcome Engagement Restoration Model (RWERM) to facilitate full engagement by all partners. Data were collected through participatory ethnography, focus groups, and individual interviews. Analyses were performed using deductive coding in a series of iterative meaning-making processes that involved all partners. Findings highlighted six defining phases of the radical welcome framework: (a) passionate invitation, (b) radical welcome, (c) authentic sense of belonging, (d) co-creation of roles, (e) prioritization of issues, and (f) individual and collective action. A guide to assessing progression across these phases, as well as a 32-item radical welcome instrument to help CBPR partners anticipate and overcome challenges to engagement are introduced and discussed.

Filling knowledge gaps in insect conservation by leveraging genetic data from public archives.

Insect decline has become a growing concern in recent years, with studies showing alarming declines in populations of several taxa. Our knowledge about genetic spatial patterns and evolutionary history of insects still exhibits significant gaps hindering our ability to effectively conserve and manage insect populations and species. Genetic data may provide valuable insights into the diversity and the evolutionary relationships of insects' species and populations. Public repositories, such as GenBank and BOLD, containing vast archives of genetic data with associated metadata, offer an irreplaceable resource for researchers contributing to our understanding of species diversity, population structure and evolutionary relationships. However, there are some issues in using these data, as they are often scattered and may lack accuracy due to inconsistent sampling protocols and incomplete information. In this paper we describe a curated georeferenced database of genetic data collected in GenBank and BOLD, for insects listed in the International Union for Conservation of Nature (IUCN) Italian Red Lists (dragonflies, bees, saproxylic beetles and butterflies). After querying these repositories, we performed quality control and data standardization steps. We created a dataset containing approximately 33 000 mitochondrial sequences and associated metadata about taxonomy, collection localities, geographic coordinates and IUCN Red List status for 1466 species across the four insect lists. We describe the current state of geographical metadata in queried repositories for species listed under different conservation status in the Italian Red Lists to quantify data gaps posing barriers to prioritization of conservation actions. Our curated dataset is available for data repurposing and analysis, enabling researchers to conduct comparative studies. We emphasize the importance of filling knowledge gaps in insect diversity and distribution and highlight the potential of this dataset for promoting other research fields like phylogeography, macrogenetics and conservation strategies. Our database can be downloaded through the Zenodo repository in SQL format. Database URL: https://zenodo.org/records/8375181.

TunGPR: Enhancing data-driven maintenance for tunnel linings through synthetic datasets, deep learning and BIM

Non-destructive Testing (NDT) techniques and data-driven technologies are increasingly applied in underground infrastructure maintenance, which can facilitate predictive monitoring for informed decision-making. Ground Penetrating Radar (GPR) is extensively utilised in rapid condition assessment of tunnel linings, particularly for detecting defects that lead to unforeseen changes of dielectric properties of materials. In this paper, a prototyped framework is proposed, namely TunGPR, for GPR-based tunnel lining assessment by incorporating Building Information Modelling (BIM), synthetic database and deep learning-enabled interpretation. The first module integrates laser-scanned point clouds and GPR Scan-to-BIM of tunnel lining with geological model. Subsequently, interoperability is achieved between the geo-integrated BIM and GPR simulation software. From the dielectric model retrieved from the BIM model, a database is established, considering a variety of condition combinations (i.e., voids, cavities, delamination, and water intrusion) leveraging domain randomisation and Finite-Difference Time-Domain (FDTD) modelling, as well as monitored field data. The dataset is then fed into the diagnostic module underpinned by a dual-rotational Convolutional Neural Network (CNN) that is customised to enhance accuracy and automation of hyperbola detection. Lastly, a preliminary risk assessment matrix is implemented into the BIM model for data management and action prioritisation. These efforts serve as an initial step to validate the feasibility and effectiveness of the GPR-enabled data-driven maintenance for tunnel linings in a BIM-centred framework.

Identifying priority mitigation areas for human–osprey conflict

AbstractRaptor nests on human‐built structures represent a significant source of conflict, as they can result in bird mortality, fires, or power outages due to falling nest materials or animals connecting with energized conductors. Power companies typically try to mitigate these conflicts to avoid service disruptions. Performing mitigation measures across all potentially problematic power infrastructure is generally not a practical solution given logistical, time, or budgetary constraints. Therefore, there is a need for quantitative, landscape‐scale tools to identify conflict risk and prioritize mitigation. We examined the influence of habitat and transmission infrastructure distribution on the potential risk of osprey (Pandion haliaetus) nesting‐infrastructure conflict in the Tennessee Valley Authority (TVA) power service area within Tennessee, Alabama, Georgia, Kentucky, Mississippi, North Carolina, and Virginia, USA, using osprey nesting and conflict records from 1990–2020. We modeled risk using a 3‐stage approach, which intersected a nesting habitat suitability model and density of infrastructure to evaluate the potential risk of conflict. Habitat suitability was greater on or near open water and closer to developed areas. Transmission line density was low (0.14 ± 0.29 lines/km2) and heterogenous across the service area with more lines within urban centers and near power facilities. Integrating habitat suitability and transmission infrastructure information revealed that very low and low‐risk areas comprised 99% of the service area. Less than 1% comprised 1,113.7 km2 of moderate and 82.2 km2 of high or very high risk, mostly concentrated along major rivers and lakes, and around urban centers. Risk was more prevalent in the northeastern portion of the service area. This work presents a top‐down approach to mitigating osprey–power infrastructure conflict, which allows for the prioritization of mitigation actions and can facilitate long‐term coexistence with this protected species.

Estimating landscape spread of a low‐prevalence disease using multiple surveillance methods

Abstract Diseases occurring at low prevalence in free‐ranging wildlife can be difficult to study and predict at a landscape scale. This problem is compounded by imperfect pathogen detection. We used data from multiple surveillance methods to determine factors influencing spread of a low‐prevalence disease, bovine tuberculosis (bTB), among white‐tailed deer (Odocoileus virginianus) in a bTB endemic area over a 10‐year period. We fit dynamic occupancy models to predict spatial spread of bTB through a county in Michigan, USA, within the bTB endemic area, while accounting for imperfect detection and a suite of ecological processes potentially driving bTB spread. Detection by all surveillance methods was low overall (posterior mean detection probabilities 0.01–0.03). Probability of bTB occurrence was 1.46 times as likely in the historical centre of the outbreak compared to outside. Deer density had a weakly positive effect on bTB spread. The probability of a site being newly infected with bTB annually was low (posterior mean colonization probabilities 0.08–0.27), while the probability of persistence after initial infection was high (posterior mean persistence probability 0.67). Synthesis and applications: We used three surveillance methods to estimate probability of presence and spread of a low‐prevalence pathogen at a landscape scale under incomplete sampling coverage. Our methods are relevant for determining spatial risk of any wildlife disease that is monitored using multiple methods of surveillance, which is important for prioritization of management actions.

Wildlife vaccination strategies for eliminating bovine tuberculosis in white-tailed deer populations.

Many pathogens of humans and livestock also infect wildlife that can act as a reservoir and challenge disease control or elimination. Efficient and effective prioritization of research and management actions requires an understanding of the potential for new tools to improve elimination probability with feasible deployment strategies that can be implemented at scale. Wildlife vaccination is gaining interest as a tool for managing several wildlife diseases. To evaluate the effect of vaccinating white-tailed deer (Odocoileus virginianus), in combination with harvest, in reducing and eliminating bovine tuberculosis from deer populations in Michigan, we developed a mechanistic age-structured disease transmission model for bovine tuberculosis with integrated disease management. We evaluated the impact of pulse vaccination across a range of vaccine properties. Pulse vaccination was effective for reducing disease prevalence rapidly with even low (30%) to moderate (60%) vaccine coverage of the susceptible and exposed deer population and was further improved when combined with increased harvest. The impact of increased harvest depended on the relative strength of transmission modes, i.e., direct vs indirect transmission. Vaccine coverage and efficacy were the most important vaccine properties for reducing and eliminating disease from the local population. By fitting the model to the core endemic area of bovine tuberculosis in Michigan, USA, we identified feasible integrated management strategies involving vaccination and increased harvest that reduced disease prevalence in free-ranging deer. Few scenarios led to disease elimination due to the chronic nature of bovine tuberculosis. A long-term commitment to regular vaccination campaigns, and further research on increasing vaccines efficacy and uptake rate in free-ranging deer are important for disease management.