A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management.

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.

Alignment of port policy to the context of the Physical Internet.

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy.

Application of geospatial technology on land suitability analysis for wheat and maize farming: In a case of Guder sub-watershed North West Oromia, Ethiopia.

Cereal crops like wheat and maize are crucial to providing food security in rural areas of Ethiopia. However, due to population growth, agricultural practices for these cereal crops have been expanded to vulnerable areas, and increasing land degradation. Geospatial technologies are essential for decision-making to reduce land degradation and ensure sustainable agriculture activities. In the Guder sub-watershed, Oromia regional state of Ethiopia, where land degradation has been a persistent issue, agricultural suitability study is crucial. This study is focused on the Guder sub-watershed, which aimed to analyze the land suitability based on ten controlling parameters, including elevation, slope, soil texture, soil depth, soil PH, soil drainage, proximity to the road, temperature, rainfall and land use/land cover, for the two most significant cereal crops (wheat and maize). All of these factors were weighted in accordance with the relative importance of each component for the appropriateness of wheat and maize land suitability using MCDA and AHP method, based on the recommendations of numerous writers and expert opinions. The findings of the study showed that 6 %, 50.58 %, 23.26 %, and 20.26 % of the total study area were highly, moderately, marginally and not suitable for wheat cultivation, respectively, whereas 5.1 %, 57.3 %, 17.3 %, and 20.3 % of the study area were highly, moderately, marginally and not suitable for cultivating maize crop respectively. This result support decision makers to develop land use planning thereby improve productivity and minimize land degradation.

Unraveling urban hydro-environmental response to climate change and MCDA-based area prioritization in a data-scarce developing city.

Climate change leads to more frequent and intense heavy rainfall events, posing significant challenges for urban stormwater management, particularly in rapidly urbanizing cities of developing countries with constrained infrastructure. However, the quantitative assessment of urban stormwater, encompassing both its volume and quality, in these regions is impeded due to the scarcity of observational data and resulting limited understanding of drainage system dynamics. This study aims to elucidate the present and projected states of urban flooding, with a specific emphasis on fecal and organic contamination caused by combined sewer overflow (CSO). Leveraging a hydrological model incorporating physical and biochemical processes validated against invaluable observational data, we undertake simulations to estimate discharge, flood volume, and concentrations of suspended solids (SS), Escherichia coli (E. coli), and chemical oxygen demand (COD) within the drainage channel network of Phnom Penh City, Cambodia. Alterations in flood volumes, and pollutant concentrations and loads in overflow under two representative concentration pathways (RCPs 4.5 and 8.5) for extreme rainfall events are projected. Furthermore, we employ a multi-criteria decision analysis (MCDA) framework to evaluate flood risk, incorporating diverse indicators encompassing physical, social, and ecological dimensions. Our results demonstrate the exacerbating effects of climate change on flood volumes, expansion of flooded areas, prolonged durations of inundation, elevated vulnerability index, and heightened susceptibility to pollutant contamination under both scenarios, underscoring increased risks of flooding and fecal contamination. Spatial analysis identifies specific zones exhibiting heightened vulnerability to flooding and climate change, suggesting priority zones for investment in flood mitigation measures. These findings provide crucial insights for urban planning and stormwater management in regions with limited drainage infrastructure, offering essential guidance for decision-making in locales facing similar challenges.

Assessment of bioenergy plant locations using a GIS-MCDA approach based on spatio-temporal stability maps of agricultural and livestock byproducts: A case study.

Addressing the global challenge of energy sustainability and global directives on farming emissions, the United Nations, the European Union, and China have led with strict targets for clean energy, renewable share growth, and carbon neutrality, highlighting a commitment to collective sustainability. This work is situated within the ambit of the Sustainable Development Goals (SDGs), advocating for a transition towards renewable energy sources. With substantial and accessible bioenergy resources, notably in Hubei Province, China, biogas technology has emerged as an emission-cutting solution. This research, focused on the Jianghan Plain, employs an integrated approach combining spatial analyses with machine learning tools to evaluate crop yield stability over two decades, with the aim of maximising the biogas yield from agricultural byproducts, i.e., crop straw and livestock manure. Using Multi-Criteria Decision Analysis (MCDA), which is informed by grey-based DEMATEL, 9 constraints and 13 environmental, social, and economic criteria were assessed to identify optimal sites for biogas facilities. The findings underscore the significant bioenergy potential of agricultural byproducts from the plain of 6.3 × 1012 kJ/year at an 11.4 kJ/m2 density. Stability analyses revealed consistent biomass availability, with rice in Gongan and Shayang and wheat in Jiangling being the primary contributors. Through the MCDA, 45-66 optimal biogas plants were identified across 4 critical counties (Zhongxiang, Shangyang, Jingshan, and Yichen), balancing the energy supply and demand under various stable scenarios. Furthermore, this study demonstrated the criticality of moderate biomass stability for stakeholder consensus and identified areas of high stability essential for energy demand fulfilment. Theoretically, this study offers a practical model for bioenergy resource exploitation that aligns with global sustainability and carbon neutrality goals to address the urgent need for renewable energy solutions amidst the global energy crisis. Practically, this study sets a precedent for policy and planning in environmental, agricultural, and renewable sectors, signifying a step forwards in achieving environmental sustainability and an energy-efficient future.

Mapping flood susceptibility with PROMETHEE multi-criteria analysis method.

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00-19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

Determining value in the treatment of activated PI3Kδ syndrome in Spain: a multicriteria decision analysis from the perspective of key stakeholders.

Introduction: Activated phosphoinositide 3-kinase (PI3K)δ syndrome (APDS) is an ultra-rare inborn error of immunity (IEI) combining immunodeficiency and immune dysregulation. This study determined what represents value in APDS in Spain from a multidisciplinary perspective applying multicriteria decision analysis (MCDA) methodology. Methods: A multidisciplinary committee of nine experts scored the evidence matrix. A specific framework for orphan drug evaluation in Spain and the weights assigned by a panel of 98 evaluators and decision-makers was used. Re-evaluation of scores was performed. Results: APDS is considered a very severe disease with important unmet needs, including misdiagnosis and diagnostic delay. Current management is limited to treatment of symptoms with off-label use of therapies supported by limited evidence. Therapeutic benefit is partial, resulting in limited disease control. Haematopoietic stem cell transplantation (HSCT), the only potential curative alternative, is restricted to a reduced patient population and without evidence of long-term efficacy or safety. All options present a limited safety profile. Data on patients' quality of life are lacking. APDS is associated with high pharmacological, medical and indirect costs. Conclusions: APDS is considered a severe disease, with limited understanding by key stakeholders of how treatment success is assessed in clinical practice, the serious impact that has on patients and the associated high economic burden. This study brings to light how MCDA methodology could represent a useful tool to complement current clinical and decision-making methods used by APDS experts and evaluators.

Setting a shared development agenda: prioritizing the sustainable development goals in the Dominican Republic with fuzzy-LMAW.

The sustainable development goals (SDGs) were established by the United Nations as an international call to eradicate poverty, safeguard the environment, and guarantee that everyone lives in peace and prosperity by 2030. The SDGs aim to balance growth and sustainability in three dimensions: social, economic and environmental. However, in the post-pandemic era, when resources for public development policies are scarce, nations face the problem of prioritizing which SDGs to pursue. A lack of agreement is one of the determinants of low performance levels of the SDGs, and multicriteria decision analysis tools can help in this task, which is especially relevant in developing countries that are falling behind in achieving the SDGs. To test the feasibility and appropriateness of one of these tools, the Fuzzy Logarithm Methodology of Additive Weights, we apply it to prioritize the SDGs in the Dominican Republic, to see if the priorities established are consistent. Seventeen experts were surveyed, and the main result was that Decent work and economic growth was the most important goal for the country. Our findings, consistent with the literature, show the path to similar applications in other developing countries to enhance performance levels in the achievement of the SDGs.

Value contribution of blood-based neurofilament light chain as a biomarker in multiple sclerosis using multi-criteria decision analysis.

Introduction: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease that represents a leading cause of non-traumatic disability among young and middle-aged adults. MS is characterized by neurodegeneration caused by axonal injury. Current clinical and radiological markers often lack the sensitivity and specificity required to detect inflammatory activity and neurodegeneration, highlighting the need for better approaches. After neuronal injury, neurofilament light chains (NfL) are released into the cerebrospinal fluid, and eventually into blood. Thus, blood-based NfL could be used as a potential biomarker for inflammatory activity, neurodegeneration, and treatment response in MS. The objective of this study was to determine the value contribution of blood-based NfL as a biomarker in MS in Spain using the Multi-Criteria Decision Analysis (MCDA) methodology. Materials and methods: A literature review was performed, and the results were synthesized in the evidence matrix following the criteria included in the MCDA framework. The study was conducted by a multidisciplinary group of six experts. Participants were trained in MCDA and scored the evidence matrix. Results were analyzed and discussed in a group meeting through reflective MCDA discussion methodology. Results: MS was considered a severe condition as it is associated with significant disability. There are unmet needs in MS as a disease, but also in terms of biomarkers since no blood biomarker is available in clinical practice to determine disease activity, prognostic assessment, and response to treatment. The results of the present study suggest that quantification of blood-based NfL may represent a safe option to determine inflammation, neurodegeneration, and response to treatments in clinical practice, as well as to complement data to improve the sensitivity of the diagnosis. Participants considered that blood-based NfL could result in a lower use of expensive tests such as magnetic resonance imaging scans and could provide cost-savings by avoiding ineffective treatments. Lower indirect costs could also be expected due to a lower impact of disability consequences. Overall, blood-based NfL measurement is supported by high-quality evidence. Conclusion: Based on MCDA methodology and the experience of a multidisciplinary group of six stakeholders, blood-based NfL measurement might represent a high-value-option for the management of MS in Spain.

Multi-criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples.

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment.

Wastewater management decision-making: A literature review and synthesis.

Wastewater management decision-making is complicated because of: (1) a complex regulatory structure, (2) the wide variety of conflicting expectations by stakeholders external and internal to the responsible utility, and (2) constrains including regulatory requirements, available technologies and practices, and customer willingness to pay. This review synthesizes the results from over 200 papers published since 2000 and presents a decision-making structure and process which is (1) science and fact-based, (2) reflects sustainability, (3) clear and transparent, (4) inclusive, (5) produces an objective-oriented decision, (6) scalable, (7) repeatable, and (8) efficient. Tools supporting the decision-making process are reviewed, including Multi-Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). Ultimately it was determined that engagement of decision-makers and relevant stakeholders to assess their values and preferences, coupled with supporting data and analyses, is necessary to reach a decision that, critically, has the support needed for it to be implemented. The results demonstrate that an understanding of the components of the decision process, coupled with an orderly process, enables good wastewater management decision-making. PRACTITIONER POINTS: A decision-making structure and process leading to the selection of implementable solutions is presented. The process possesses the following attributes: (1) science and fact-based, (2) reflect sustainability, (3) clear and transparent, (4) inclusive, (5) produce an objective-oriented decision, (6) scalable, (7) repeatable, and (8) efficient An extensive summary and analysis of tools supporting the decision process are provided, including Multi-Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). The critical role of internal and external stakeholders and differentiating their involvement relative to decision-makers is emphasized.

Development of the Emirates Multi-Criteria Decision Analysis Tool for Orphan Drugs.

Background The number of orphan drug approvals is currently increasing globally. This creates a significant burden on payers and healthcare systems. This study aimed to create a multi-criteria decision analysis (MCDA) tool for evaluating orphan drugs within the United Arab Emirates (UAE). The intended result of the tool is to provide evidence-based guidance to decision-makers in reimbursement and procurement decisions. Methods We conducted a literature search and local expert interviews to identify relevant preliminary criteria for the MCDA tool. Then we conducted a structured consensus-building session for healthcare experts and decision-makers in the UAE to develop the Emirati MCDA tool for orphan drugs. The experts voted for the criteria to be included in the tool and their ranking according to importance, as well as the weight of each criterion and its scoring function. To improve understanding and facilitate the voting process, experts were provided with a brief illustration of similar tools conducted in other countries before the voting sessions. Finally, the tool was developed in a Microsoft Excel sheet (Microsoft Corporation, Redmond, Washington, United States), and it was validated and tested based on real case studies, then it was fine-tuned accordingly based on the experts' discussions. The final tool was provided to the attendees to guide their decisions in the reimbursement and procurement of orphan drugs. Results The created tool provides a score for each analyzed orphan drug based on its value. Ten criteria were included in the final MCDA tool. These were cost-effectiveness (25.1% of the weight), magnitude of health gain (20.1%), availability of therapeutic alternative (14.3%), disease severity (11%), budget impact (7.9%), disease rarity (5.6%), strength of clinical evidence (5.6%), burden on households (4.5%), indication uniqueness (3.2%), and patients' age (2.6%). Conclusions Implementation of evidence-based healthcare necessitates assessing the fair value of each health technology. Addressing the high unmet medical needs and improving healthcare for patients with rare diseases are priorities within the UAE. The created Emirates MCDA tool for orphan drugs has the potential to help decision-makers implement value-based and evidence-based reimbursement decisions for orphan drugs.

National Immunization Program Decision Making Using the CAPACITI Decision-Support Tool: User Feedback from Indonesia and Ethiopia.

To ensure that limited domestic resources are invested in the most effective interventions, immunization programs in low- and middle-income countries (LMICs) must prioritize a growing number of new vaccines while considering opportunities to optimize the vaccine portfolio, as well as other components of the health system. There is a strong impetus for immunization decision-making to engage and coordinate various stakeholders across the health system in prioritization. To address this, national immunization program decision-makers in LMICs collaborated with WHO to structure deliberation among stakeholders and document an evidence-based, context-specific, and transparent process for prioritization or selection among multiple vaccination products, services, or strategies. The output of this effort is the Country-led Assessment for Prioritization on Immunization (CAPACITI) decision-support tool, which supports using multiple criteria and stakeholder perspectives to evaluate trade-offs affecting health interventions, taking into account variable data quality. Here, we describe the user feedback from Indonesia and Ethiopia, two initial countries that piloted the CAPACITI decision-support tool, highlighting enabling and constraining factors. Potential immunization program benefits and lessons learned are also summarized for consideration in other settings.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Pregnancy and obstetric outcomes of dichorionic triamniotic triplet pregnancies with selective foetal reduction after assisted reproductive technology.

BACKGROUND: It is generally beneficial and recommended that dichorionic triamniotic (DCTA) triplet pregnancies be reduced to monochorionic (MC) twin or singleton pregnancies after assisted reproductive technology (ART). However, some infertile couples still have a firm desire to retain twins. For this reason, the best foetal reduction strategies need to be available for infertile couples and clinicians. Given that data on the elective reduction of DCTA triplet pregnancies to twin pregnancies are scarce, we investigated the outcomes of elective reduction of DCTA triplet pregnancies through the retrospective analysis of previous data. METHOD: Patients with DCTA triplet pregnancies who underwent elective foetal reduction between January 2012 and June 2020 were recruited. A total of 67 eligible patients with DCTA triplet pregnancies were divided into two groups: a DCTA-to-dichorionic diamniotic (DCDA) twin group (n = 38) and a DCTA-to-monochorionic diamniotic (MCDA) twin group (n = 29); the basic clinical data of the two groups were collected for comparison. RESULTS: Compared with the DCDA-to-MCDA twin group, the DCTA-to-DCDA twin group had lower rates of complete miscarriage (7.89% versus 31.03%, p = 0.014), early complete miscarriage (5.26% versus 24.14%, p = 0.034), late preterm birth (25.71% versus 65.00%, p = 0.009) and very low birth weight (0 versus 11.11%, p = 0.025). In addition, the DCTA-to-DCDA twin group had higher rates of full-term delivery (65.71% versus 25.00%, p = 0.005), survival (92.11% versus 68.97%, p = 0.023), and taking the babies home (92.11% versus 68.97%, p = 0.023) than did the DCTA-to-MCDA twin group. In terms of neonatal outcomes, a significantly greater gestational age (38.06 ± 2.39 versus 36.28 ± 2.30, p = 0.009), average birth weight (3020.77 ± 497.33 versus 2401.39 ± 570.48, p < 0.001), weight of twins (2746.47 ± 339.64 versus 2251.56 ± 391.26, p < 0.001), weight of the larger neonate (2832.94 ± 320.58 versus 2376.25 ± 349.95, p < 0.001) and weight of the smaller neonate (2660.00 ± 345.34 versus 2126.88 ± 400.93, p < 0.001) was observed in the DCTA-to-DCDA twin group compared to the DCTA-to-MCDA twin group. CONCLUSION: The DCTA-to-DCDA twin group had better pregnancy and neonatal outcomes than the DCTA-to-MCDA twin group. This reduction approach may be beneficial for patients with dichorionic triamniotic triplet pregnancies who have a strong desire to have DCDA twins.

Biosensor-based multiple cross displacement amplification platform for visual and rapid identification of hepatitis C virus.

Hepatitis C remains a global health problem, especially in poverty-stricken areas. A rapid and sensitive point-of-care (POC) diagnostic tool is critical for the early detection and timely treatment of hepatitis C virus (HCV) infection. Here, for the first time, we reported a novel molecular diagnostic assay, termed reverse transcription multiple cross displacement amplification integrated with a gold-nanoparticle-based lateral flow biosensor (RT-MCDA-AuNPs-LFB), which was developed for rapid, sensitive, specific, and visual identification of HCV. HCV-RT-MCDA induced rapid isothermal amplification through a specific primer set targeting the 5'untranslated region gene from the major HCV genotypes 1b, 2a, 3b, 6a, and 3a that are prevalent in China. The optimal reaction temperature and time for RT-MCDA-AuNPs-LFB were 68°C and 25 min, respectively. The limit of detection of the assay was 10 copies per test, and the specificity was 100% for the experimental strains. The whole detection procedure, including crude nucleic acid isolation (~5 min), RT-MCDA (68°C, 25 min), and visual AuNPs-LFB result confirmation (less than 2 min), was performed within 35 min. The preliminary results indicated that the HCV-RT-MCDA-AuNPs-LFB assay could be a valuable tool for sensitive, specific, visual, cost-saving, and rapid detection of HCV and has potential as a POC diagnostic platform for field screening and early clinical detection of HCV infection.

Assessing evidence-based practice competencies of undergraduate nursing students using a developed multi-criteria decision-analysis model.

AIM: The developed multi-criteria decision analysis model was used to identify the inter-influence relationships and key factors affecting the EBP competencies of UNSs, to assess the EBP competencies of UNSs and based on these results, to formulate an improvement strategy to enhance the EBP competencies of UNSs. BACKGROUND: EBP is considered a core competency in international nursing practice. However, few studies have developed EBP evaluation models and applied them to assessing and improving the EBP competencies of UNSs. DESIGN: This is a quantitative study with multi-criteria decision-analysis model. METHODS: Firstly, the questionnaire was designed based on the characteristics of the DEMATEL and VIKOR-AS methods, which was completed by 17 nursing experts from a case hospital in Zhejiang Province, China. Subsequently, the DEMATEL method was used to analyze the inter-influence relationships among various criteria to determine their respective weights. Finally, the VIKOR method is utilized to integrate multiple criteria and their relative weights to assign comprehensive scores to each UNSs. RESULTS: The use of the DEMATEL method reveals that "Knowledge (C1)", "Mastering the basic scientific research methods during the study of the undergraduate courses (C11)", "Being able to consult clinical experts appropriately when encountering problems in clinical practice (C23)" and "Understanding the importance of reading journals related to the nursing profession regularly (C34)" were critical influencing factors. "Skill (C2)," "Being able to explain the essential roles of the best research evidence in determining clinical practice (C15)," "Being able to apply the collected research evidence to the individual case in nursing care (C25)" and "Paying attention to using the evidence-based nursing practice concept to determine the best clinical practice (C35)" were the most influential factors. According to the VIKOR method, the performance of the UNSs in the case hospitals in terms of EBP competencies from highest to lowest was Student C, Student B and Student A. However, all of these students suffered from deficiencies at the knowledge level. CONCLUSIONS: The application of the DEMATEL and VIKOR methods provides a systematic and comprehensive approach to the assessment of EBP competencies of UNSs. The lack of EBP competencies of UNSs in case hospitals is mainly reflected in knowledge level. To improve UNSs' EBP competencies, medical schools and hospital educators should propose short- and long-term strategies to improve knowledge.

A comparative analysis of methods and tools for low impact development (LID) site selection.

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods.