Prioritization Approach Research Articles

A novel comprehensive approach to soil and water conservation: integrating morphometric analysis, WSA, PCA, and CoDA-PCA in the Naama sub-basins case study, Southwest of Algeria.

This research paper presents a detailed investigation into the morphometric characteristics of sub-basins within the Naama region of Algeria, aiming to prioritize areas vulnerable to soil erosion and runoff risks. Focusing on five key sub-basins that collectively represent 75% of the Wilaya of Naama, the study employs a comprehensive methodological framework, integrating morphometric analysis (MA), weighted sum analysis (WSA), principal component analysis (PCA), and the novel approach of compositional data analysis (CoDA). Through the rigorous evaluation of sixteen distinct morphometric parameters selected based on their relevance to hydrological and geomorphological processes that influence erosion and runoff, this research provides a nuanced understanding of the factors influencing erosion susceptibility within each sub-basin. The analysis reveals a clear hierarchy of sub-basins based on their calculated compound parameters, effectively classifying them into high, moderate, and low priority categories for targeted intervention and resource allocation. The results highlight the Ain Sefra and Wadi Er Rosafa sub-basins as the highest priority areas, collectively encompassing 31.51% of the wilaya and posing the most significant threats of runoff and soil erosion. This identification allows for the prioritization of conservation efforts and the implementation of tailored management strategies in these critical areas. Furthermore, the integration of multiple prioritization approaches, including the innovative application of CoDA, ensures a robust and comprehensive assessment of the sub-basin landscapes. This multi-faceted approach provides a more nuanced understanding of the complex interplay between various morphometric parameters and their influence on erosion and runoff potential. The findings of this research have significant implications for sustainable land and water resource management within the Naama region. By identifying and prioritizing vulnerable sub-basins, the study provides a crucial foundation for informed decision-making, enabling stakeholders to implement targeted interventions and mitigate the detrimental impacts of soil erosion and excessive runoff. Moreover, the methodological framework presented in this research paper offers a valuable blueprint for similar studies in other regions facing comparable challenges. The cost-effective and time-efficient nature of the approach makes it a practical tool for prioritizing erosion and runoff risks in arid and semi-arid environments worldwide, contributing to the broader goals of environmental sustainability and land degradation neutrality.

Redefining crowdsourced test report prioritization: An innovative approach with large language model

Context:Crowdsourced testing has gained popularity in software testing, especially for mobile app testing, due to its ability to bring diversity and tackle fragmentation issues. However, the openness of crowdsourced testing presents challenges, particularly in the manual review of numerous test reports, which is time-consuming and labor-intensive. Objective:The primary goal of this research is to improve the efficiency of review processes in crowdsourced testing. Traditional approaches to test report prioritization lack a deep understanding of semantic information in textual descriptions of these reports. This paper introduces LLMPrior, a novel approach for prioritizing crowdsourced test reports using large language models (LLMs). Method:LLMPrior leverages LLMs for the analysis and clustering of crowdsourced test reports based on the types of bugs revealed in their textual descriptions. This involves using prompt engineering techniques to enhance the performance of LLMs. Following the clustering, a recurrent selection algorithm is applied to prioritize the reports. Results:Empirical experiments are conducted to evaluate the effectiveness of LLMPrior. The findings indicate that LLMPrior not only surpasses current state-of-the-art approaches in terms of performance but also proves to be more feasible, efficient, and reliable. This success is attributed to the use of prompt engineering techniques and the cluster-based prioritization strategy. Conclusion:LLMPrior represents a significant advancement in crowdsourced test report prioritization. By effectively utilizing large language models and a cluster-based strategy, it addresses the challenges in traditional prioritization approaches, offering a more efficient and reliable solution for app developers dealing with crowdsourced test reports.

Towards a unified approach to prioritization of regenerative agricultural practices across cropping systems

Prioritizing field practices according to their contribution to regenerative efforts, in a structured and standardized manner, can enable efficient resource allocation and higher adoption rates. However, it is remarkably hampered by the diversity of production environments and their respective best management practices. This paper proposes prioritizing regenerative practices using a multicriteria assessment method informed by expert knowledge. To develop it, we mined and synthesized knowledge from eleven experts for three diverse cropping systems, showing that the prioritization of practices varies greatly across production environments. By integrating the multicriteria approach with a multi-indicator sustainability framework, we demonstrate how practice weights can be used to monitor sustainability efforts and outcomes of thirty vineyard farms in Maharashtra, India. The suggested prioritization approach can be used to standardize the assessment of the importance of regenerative practices in different cropping systems and be adopted in sustainable sourcing schemes of the agro-food industry.

Prior3D: An R package for three-dimensional conservation prioritization

Systematic conservation planning (SCP) is essential for meeting global conservation goals and mitigating anthropogenic impacts on biodiversity. Effective conservation planning should incorporate the three-dimensional nature of ecosystems, including species distribution by depth. Recent advancements, like the 3D prioritization approach, address this by considering multiple depth zones. The R package prior3D is an open-source tool designed for prioritizing conservation efforts across different depth zones in a unified framework. Starting from the deepest zone and moving upwards, it optimizes conservation priorities and allows flexible allocation of protection levels per depth. This approach strategically prioritizes areas with higher species gains, while ensuring minimum representation of all depth zones in the final prioritization solution. While conceived for marine conservation, prior3D is applicable to any 3D ecosystem making it a critical tool for multi-realm conservation efforts.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model

Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 251 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log Kow), water solubility, biodegradation half-life, and soil adsorption coefficient (Koc)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R2 = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.

Enhancing gas drainage and ventilation efficiency in underground coal mines: A hybrid expert decision approach for booster fan prioritization

Expanding mining operations in goaf zones heightens gas production potential, posing challenges in maintaining adequate ventilation within development panels, consequently impacting coal production. Various strategies have been explored to enhance mine ventilation and gas drainage effectiveness. However, deficiencies persist in the proposed ventilation system for the Okaba underground coal mine, prompting this study’s necessity. Addressing these concerns, the study evaluates the feasibility of employing booster fans to mitigate the identified drawbacks. Prioritizing booster fans for airflow distribution in underground mines is a complex decision-making process, requiring an advanced expert system approach. To address this, the study proposes an intuitionistic-based fuzzy TOPSIS (IFT) method for booster fan prioritization in the Okaba mine. Results indicate that booster fan 4 (BF4) ranks highest, followed by booster fan 3 (BF3), consistent with fuzzy TOPSIS findings. Sensitivity analysis supports the predicted importance order, affirming the efficacy of the hybrid expert decision method in selecting a booster fan capable of enhancing the overall efficiency of gas drainage and ventilation systems in underground mines. This study introduces a Hybrid Expert Decision Approach that integrates IFT and traditional fuzzy TOPSIS methodologies. This hybrid approach is particularly novel because it combines the strengths of both methods to prioritize booster fans in underground coal mines.

A methodology for prioritizing sustainable development goals targets at the local level: The case of Barcelona City Council

AbstractThe implementation of the 2030 Agenda relies heavily on the active involvement of local governments. The concept of “Localization”—the process of adapting and customizing the SDGs and translating them into local development plans and strategies—has gained prominence as governments address emerging challenges, necessitating innovative approaches. Practical insights from local governments are essential for guiding others in this process. A key debate focuses on “prioritization,” emphasizing the need to focus on a specific set of SDG targets based on available resources, the local government's experience with sustainable policies or the normative framework in which it operates. Prioritization is crucial to prevent fragmentation of efforts that could hinder the local government's contribution to the 2030 Agenda. This paper aims to introduce a methodology for prioritizing SDG targets within local governments. Using a multicriteria analysis tool, the paper shows the process undertaken by the Barcelona City Council to cluster the SDG targets around four priority groups based on four criteria: (i) distance to the goal, (ii) strategic coverage, (iii) normative competence, and (iv) budgetary effort. The prioritization approach contributes to a better monitoring and evaluation process to assess progress towards the SDGs and enhance collaborative efforts across municipal policy and management levels. The findings provide valuable insights and serve as a reference for other local governments aiming at contributing to the 2030 Agenda. The approach is adaptable and can be applied to different subnational governments by tailoring to their specific scope and requirements of the context of application.

Exome variant prioritization in a large cohort of hearing-impaired individuals indicates IKZF2 to be associated with non-syndromic hearing loss and guides future research of unsolved cases

Although more than 140 genes have been associated with non-syndromic hereditary hearing loss (HL), at least half of the cases remain unexplained in medical genetic testing. One reason is that pathogenic variants are located in ‘novel’ deafness genes. A variant prioritization approach was used to identify novel (candidate) genes for HL. Exome-wide sequencing data were assessed for subjects with presumed hereditary HL that remained unexplained in medical genetic testing by gene-panel analysis. Cases in group AD had presumed autosomal dominantly inherited HL (n = 124), and in group AR, presumed autosomal recessive HL (n = 337). Variants in known and candidate deafness genes were prioritized based on allele frequencies and predicted effects. Selected variants were tested for their co-segregation with HL. Two cases were solved by variants in recently identified deafness genes (ABHD12, TRRAP). Variant prioritization also revealed potentially causative variants in candidate genes associated with recessive and X-linked HL. Importantly, missense variants in IKZF2 were found to co-segregate with dominantly inherited non-syndromic HL in three families. These variants specifically affected Zn2+-coordinating cysteine or histidine residues of the zinc finger motifs 2 and 3 of the encoded protein Helios. This finding indicates a complex genotype–phenotype correlation for IKZF2 defects, as this gene was previously associated with non-syndromic dysfunction of the immune system and ICHAD syndrome, including HL. The designed strategy for variant prioritization revealed that IKZF2 variants can underlie non-syndromic HL. The large number of candidate genes for HL and variants therein stress the importance of inclusion of family members for variant prioritization.

Inbox message prioritization and management approaches in primary care.

Patient messaging to clinicians has dramatically increased since the pandemic, leading to informatics efforts to categorize incoming messages. We examined how message prioritization (as distinct from categorization) occurs in primary care, and how primary care clinicians managed their inbox workflows. Semi-structured interviews and inbox work observations with 11 primary care clinicians at MedStar Health. We analyzed interview and observation transcripts and identified themes and subthemes related to prioritization and inbox workflows. Clinicians widely reported that they did not prioritize messages due to time constraints and the necessity of attending to all messages, which made any prioritization purely additive to overall inbox time. We identified 6 themes and 14 subthemes related to managing inbox workloads. The top themes were (1) establishing workflow norms with different teams, primarily medical assistants (MAs); (2) boundary-setting with patients, other clinicians, and with themselves; and (3) message classification heuristics that allowed clinicians to mentally categorize messages that required follow-up, messages that could be quickly deleted or acknowledged, and purely informational messages that ranged in clinical utility from tedious to valuable for care coordination. Time constraints in primary care prevent clinicians from prioritizing their inbox messages for increased efficiency. Involvement of MAs and co-located staff was successful; however, standardization was needed for messaging workflows that involved centralized resources like call centers. Organizations should consider ways in which they can support the establishment and maintenance of boundaries, to avoid this responsibility falling entirely on clinicians. Clinicians generally lack the time to prioritize patient messages. Future research should explore the efficacy of collaborative inbox workflows for time-savings and management of patient messages.

Energy signature approach for retrofit prioritization: A proposal for building identification methodology

Amidst the urgent call for carbon reduction, retrofitting existing buildings for energy efficiency has become imperative. Nonetheless, the challenge of selecting suitable buildings for retrofitting persists. This paper proposes a method for identifying retrofit target buildings using the heating Energy Signature (ES) approach to achieve effective carbon reduction through retrofitting.The method involves comparing the actual ES, derived from monthly heating energy usage in existing buildings, with the optimal ES based on the building's physical performance to assess the need for retrofitting or behavioral improvements. It takes into account factors such as building physical characteristics, occupants’ heating behavior, and outdoor environment, integrating ES parameters like balance-point temperature (BPT) and heating sensitivity.In practical application, the proposed method involves four steps. Step 1 collects five essential building data inputs from the building user. Step 2 supplements this information using open-source platforms to calculate the optimal ES parameters. Step 3 computes both the actual and optimal ES, and Step 4 compares these to identify energy inefficiencies. The results are then presented in a user-friendly format for intuitive understanding.Offering novelty, this method accurately identifies the causes of building energy consumption, streamlines calculation processes, requires minimal input data, and yields intuitive results. It furnishes valuable insights for stakeholders engaged in retrofitting projects and holds potential for the effective implementation of building retrofit policies on a larger scale.

Solid-Phase Reactivity-Directed Extraction (SPREx): An Alternative Approach for Simultaneous Extraction, Identification, and Prioritization of Toxic Electrophiles Produced in Water Treatment Applications.

Current strategies to assess water quality are ineffective at prioritizing the most toxic chemicals within a treated water sample. Although it is well known that oxidation byproducts (OBPs) from water treatment processes (e.g., chlorination and ozonation) are linked to adverse health outcomes such as skin diseases, reproductive toxicity, and various cancers, we are still unable to account for a large fraction of the toxicity drivers. Previous approaches utilize in vitro or in vivo assays to assess OBPs on an individual basis, which is too time- and resource-intensive considering the countless number of transformation byproducts of unknown toxicities that exist in treated waters. In vitro assays have also been developed to analyze the toxicity of OBPs in environmental mixtures, but these approaches do not provide identification information about the responsible toxicants. Furthermore, an additional challenge for OBP detection arises during the extraction and detection stages of analysis, as certain OBPs are typically lost using traditional extraction methods or are not detectable via liquid-chromatography-high-resolution mass spectrometry (LC-HRMS) without derivatization. To address these issues, we have developed the analytical assay Solid-Phase Reactivity-directed Extraction (SPREx), which aims to provide an all-in-one evaluation for (i) in chemico toxicity screening, (ii) extraction, (iii) detection, and (iv) identification via LC-HRMS. The performance of SPREx was evaluated by testing different nucleophile probes for the capture and detection of 24 different carbonyl compounds, which serve as model electrophiles and are known OBPs that provide unique extraction and detection challenges. SPREx provided distinct advantages for extraction recoveries and was an effective screening tool for carbonyl detection and quantification in complex water matrices such as drinking water and wastewater.

Comprehensive monitoring and prioritizing for contaminants of emerging concern in the Upper Yangtze River, China: An integrated approach

Contaminants of emerging concern (CECs) in aquatic environments can adversely impact ecosystems and human health even at low concentrations. This study assessed the risk of 162 CECs, including neonicotinoid pesticides, triazine pesticides, carbamate pesticides, psychoactive substances, organophosphate esters, antidepressants, per- and polyfluoroalkyl substances, and antibiotics in 10 drinking water sources and two tributaries (Jialing and Wujiang Rivers) of the Upper Yangtze River in Chongqing, China. Target screening detected 156 CECs at 0.01–2218.2 ng/L, while suspect screening via LC-QTOF-MS identified 64 CECs, with 13 pesticides, 29 pharmaceuticals and personal care products, and 2 industrial chemicals reported for the first time in the Yangtze River Basin. Risk quotient-based ecological risk assessment revealed that 48 CECs posed medium to high risks (RQ > 0.1) to aquatic life, with antibiotics (n = 20) as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoids, triazines, antidepressants, and antibiotics were concerning. A multi-criteria prioritization approach integrating occurrence, physico-chemical properties, and toxicological data ranked 26 CECs as high priority. This study underscores the importance of comprehensive CEC screening in rivers and provides insights for future monitoring and management strategies.

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Characterizing PFAS hazards and risks: a human population-based in vitro cardiotoxicity assessment strategy

Per- and poly-fluoroalkyl substances (PFAS) are emerging contaminants of concern because of their wide use, persistence, and potential to be hazardous to both humans and the environment. Several PFAS have been designated as substances of concern; however, most PFAS in commerce lack toxicology and exposure data to evaluate their potential hazards and risks. Cardiotoxicity has been identified as a likely human health concern, and cell-based assays are the most sensible approach for screening and prioritization of PFAS. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a widely used method to test for cardiotoxicity, and recent studies showed that many PFAS affect these cells. Because iPSC-derived cardiomyocytes are available from different donors, they also can be used to quantify human variability in responses to PFAS. The primary objective of this study was to characterize potential human cardiotoxic hazard, risk, and inter-individual variability in responses to PFAS. A total of 56 PFAS from different subclasses were tested in concentration-response using human iPSC-derived cardiomyocytes from 16 donors without known heart disease. Kinetic calcium flux and high-content imaging were used to evaluate biologically-relevant phenotypes such as beat frequency, repolarization, and cytotoxicity. Of the tested PFAS, 46 showed concentration-response effects in at least one phenotype and donor; however, a wide range of sensitivities were observed across donors. Inter-individual variability in the effects could be quantified for 19 PFAS, and risk characterization could be performed for 20 PFAS based on available exposure information. For most tested PFAS, toxicodynamic variability was within a factor of 10 and the margins of exposure were above 100. This study identified PFAS that may pose cardiotoxicity risk and have high inter-individual variability. It also demonstrated the feasibility of using a population-based human in vitro method to quantify population variability and identify cardiotoxicity risks of emerging contaminants.

Development and application of a rapid research prioritisation process for identifying health research priorities in low- and middle-income countries: the RAPID-RP stakeholder analysis

Background Health research should be locally prioritised by key stakeholders to ensure the best use of available resources, maximum likelihood of research leading to uptake into policy and practice and relevance to the needs of the potential beneficiaries. Established approaches are complex and time-consuming, therefore not feasible in settings where prioritisation is uncommon and time is limited. Objectives We developed and applied a simple, rapid research prioritisation approach to elicit the views of stakeholders. Setting and participants This was a collaboration between a multidisciplinary group of United Kingdom academics, the International Primary Care Respiratory Group and primary care respiratory researchers from four low- and middle-income countries: Brazil, China, Georgia and North Macedonia. We identified 10 topics for research prioritisation through workshops involving 26 topic, methods and local context experts, and considering the programme remit, to develop, adapt and evaluate culturally appropriate community and behavioural approaches for the prevention, early identification and management of chronic obstructive pulmonary disease in primary care. Design and methods In each setting, local research teams convened stakeholder groups of patients, clinicians, managers/policymakers and researchers. Each group briefly discussed the 10 potential research study topics. Discussions were facilitated using short vignettes for each topic, and participants were encouraged to consider importance, feasibility and equity. Individual participants rated each study on a three-point traffic light scale, then ranked them following a facilitated discussion on what drove the ratings. The research team rated then ranked each study considering three further criteria (international novelty, potential for future funding and capacity building on a five-point scale). Within each group, ranks were summed to create a final rank order which guided our research programme and provided insight for future projects. Results In each country, four to eight members attended each stakeholder group. The engagement process was completed in less than 4 hours and feedback was very positive, especially from patients who valued the opportunity to contribute to research decision-making about their own condition. Ranking varied to some extent between groups and settings, but there was consistency around topics that were prioritised among the top five in all groups (identifying efficient chronic obstructive pulmonary disease screening test strategies, evaluations of lung age to aid smoking cessation, feasibility of locally adapted pulmonary rehabilitation, clinical education for primary care staff) and study topics that consistently ranked low (use of e-cigarettes for smoking cessation, weight management to improve chronic obstructive pulmonary disease symptoms and handwashing to reduce infections). Limitations Despite attempts to maximise inclusivity and diversity, stakeholders were mainly limited geographically to the centres where researchers were based, potentially limiting generalisability of views across the countries. Facilitator styles varied and may have influenced some of the discussions and potentially the ranking. Conclusions Despite some limitations, we demonstrated the feasibility and acceptability of the rapid research prioritisation approach stakeholder analysis for identifying locally relevant research priorities in low- and middle-income country settings. Future work Further validation is needed for aspects of the process. We have identified some useful lessons from our evaluation of the process, to facilitate future use of this approach. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 16/137/95.

Land use and environmental degradation in the island state of Mauritius: Governance and problem conceptions

Biodiversity and natural ecosystems underpin human life and wellbeing, yet they are systematically degraded by anthropic activity, with land use change being the direct driver most impacting terrestrial ecosystems. Since scholars argue that solving the biodiversity crisis hinges on the problem conceptions that steer governance, and since land use is governed predominantly at the national level, we seek to identify the problem conceptions expressed through the governance of the land use – environment nexus in a country where ecosystem degradation persists despite tenure security: Mauritius. Through interviews with key informants, corroborated by documentation review, we investigate the current governance systems across legislative, policy, and institutional frameworks to elicit the main governance issues that may reveal underlying problem conceptions. Our results point to weak commons and compromise approaches, which systematically enable land use changes for financial maximization at the expense of natural ecosystems. Economic optimization and ecosystem prioritization approaches were not observed despite recognition of nature’s role in the wellbeing of citizens. In this context, which may be similar in many developing countries, the introduction of biophysical and monetary natural capital accounting is recommended to provide commensurate data on the benefits derived from ecosystems and those derived from economic activities, in order to better inform compromise and prioritization.

Spatiotemporal variations and priority ranking of emerging contaminants in nanwan reservoir: A case study from the agricultural region in huaihe river basin in China

The presence of emerging contaminants (ECs) in drinking water sources is an increasing concern, yet limited data exists on their occurrence and risk in the upper Huaihe River Basin, an important agricultural region in Central China. This study investigated 70 ECs, including pesticide and antibiotics in surface water from drinking water source areas in Nanwan Reservoir along the upper reaches of the Huaihe River Basin to prioritize the ECs based on ecological risk and health risk assessment. A total of 66 ECs were detected in the surface water at least once at the selected 38 sampling sites, with concentrations ranging from 0.04 to 2508 ng/L. Ecological risk assessment using the risk quotient (RQ) method revealed high risks (RQ > 1) from 7 ECs in the dry season and 15 ECs in the wet season, with triazine pesticides as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoid and carbamate pesticides and macrolide antibiotics were concerning for teenagers. Ciprofloxacin exhibited a high level of resistance risk during the wet season. A multi-indicator prioritization approach integrating occurrence, risk, and chemical property data ranked 6 pesticides and 3 antibiotics as priority pollutants. The results highlight EC contamination of drinking water sources in this agriculturally-intensive region and the need for targeted monitoring and management to protect water quality.

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States.

Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

Unveiling barriers to the integration of blockchain‐based circular economy and Industry 5.0 in manufacturing industries: A strategic prioritization approach

AbstractAlthough Industry 4.0 plays the role of a catalyst in the transition from a linear economy to a circular economy (CE), some sustainability goals are neglected by prioritizing machines over humans. Industry 5.0 insists on combining technology with human values to achieve sustainable development goals. Therefore, the integration of CE and Industry 5.0 leads to sustainable synergy in various industries, especially in manufacturing industries. However, their integration is always associated with challenges and barriers. This study identifies a set of these barriers in the electric vehicle battery production industry from the financial, technical, supply chain, cultural, and regulatory and processes perspectives through a deep literature review. The fuzzy weighted influence non‐linear gauge system method is used to analyze the relationship between identified barriers and their prioritization. Finally, we leverage the knowledge of four experts from an electric vehicle battery manufacturing company in Southeast Asia to validate the proposed approach and to rank the barriers. The results demonstrate that immature technology providers are the biggest obstacle in the integration of blockchain‐based CE and Industry 5.0 in the electric vehicle battery manufacturing industry.