A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

Value contribution of etranacogene dezaparvovec gene therapy in moderately severe and severe haemophilia B through multi-criteria decision analysis.

INTRODUCTION: The value of gene therapies for haemophilia needs to be assessed holistically. AIM: To determine the value of etranacogene dezaparvovec (ED) compared to current extended half-life (EHL) recombinant factors (rFIX), using multi-criteria decision analysis (MCDA). METHOD: MCDA EVIDEM methodology adapted to orphan drugs was used, with nine quantitative criteria and four contextual criteria. The MCDA framework was rated by 28 multidisciplinary experts. Descriptive statistics were performed for quantitative and qualitative criteria. RESULTS: Haemophilia B (HB) was considered a severe disease (mean ± SD: 4.3 ± 0.7) with some unmet needs (mean ± SD 3.3 ± 0.9). Experts found ED more effective (mean ± SD 2.0 ± 2.3) and provide better quality of life (QoL) (mean ± SD: 1.8 ± 1.5) than the comparative HB treatments but with safety uncertainties (mean ± SD -1.2 ± 1.8). ED could lead to medical cost and non-medical cost savings over time (mean ± SD: 1.6 ± 2.0 and 2.0 ± 1.5, respectively). The quality of the evidence was high (mean ± SD: 3.9 ± 0.9). ED was considered aligned with the priorities of the National Health System (NHS) and the specific interests of patients. ED's value contribution was 0.45 (+1 = highest value). CONCLUSIONS: ED brings added value in the treatment of moderately severe and severe HB (sHB) compared to current EHL rFIX, addressing the severity of the disease and increasing efficacy and patients' QoL especially related to the single dose and low bleeding rate. Concerns about long-term safety need to be addressed.

GIS-based multi-criteria decision analysis model for utility water demand: The case of Lodwar Municipality, Turkana County, Kenya.

Water scarcity is a global issue resulting from rapid urbanization, increasing population growth, industrial development and expansion of human activities over time and space. Water shortage affects every continent and is listed as one of the largest global risks hence the need for proper management of water resources. Municipalities and cities worldwide are struggling to maintain a steady supply of water to meet the increasing water demand. The study used Geographic Information System (GIS) techniques and Multi-Criteria Decision Analysis (MCDA) to develop a decision support model that can be applied to improve the utility water demand management for the Lodwar Municipality in Turkana Kenya. The data comprised remotely sensed data, population density, spatial plans, utility infrastructure maps and metered water connections data. The AHP pairwise comparison matrix was applied to assign weights for the 8 criteria influencing water demand in the area. The population density, proximity to water network facilities and land use criteria were equivalent to 30 %, 25 %, and 23 % respectively whereas 22 % of other criteria were dependent on each other. The analysis of satellite images showed the expansion of built-up areas and emerging human activities in regions towards the South and Western of Lodwar Town. The resulting model outcome identified the potential demand priority sites within the region of which some are underserved. The model efficiency was assessed through the application of statistical indicators as well as graphical and map presentations. Consequently, the addition of more input variables affecting demand is likely to improve the results over changing aspects within the zones. Municipality water utility managers and decision-makers can therefore employ the model information to highlight suitable areas for network expansion as well as infrastructure management planning within the municipality. This method offers an alternative hybrid technique for mapping potential utility water demand in rural municipalities with inadequate data.

Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model.

Purpose: To identify the main variables affecting the academic adaptability of hospital nursing interns and key areas for improvement in preparing for future unpredictable epidemics. Methods: The importance of academic resilience-related variables for all nursing interns was analyzed using the random forest method, and key variables were further identified. An importance-performance analysis was used to identify the key improvement gaps regarding the academic resilience of nursing interns in the case hospital. Results: The random forest showed that five items related to cooperation, motivation, confidence, communication, and difficulty with coping were the main variables impacting the academic resilience of nursing interns. Moreover, the importance-performance analysis revealed that three items regarding options examination, communication, and confidence were the key improvement areas for participating nursing interns in the case hospital. Conclusions: For the prevention and control of future unpredictable pandemics, hospital nursing departments can strengthen the link between interns, nurses, and physicians and promote their cooperation and communication during clinical practice. At the same time, an application can be created considering the results of this study and combined with machine learning methods for more in-depth research. These will improve the academic resilience of nursing interns during the routine management of pandemics within hospitals.

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions.

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks.

Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA.

The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time increases. Thus, determining the reliability of drought forecasting for specific months ahead presents a challenging task. This study introduces a robust approach that utilizes the Beluga Whale Optimization (BWO) algorithm to train and optimize the parameters of the Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models. The applied models are validated against a KNN benchmark model for forecasting drought from one- to six-month ahead across four hydrological stations distributed over the Colorado River. The achieved results demonstrate that RELM-BWO outperforms RF-BWO and KNN models, achieving the lowest root-mean square error (0.2795), uncertainty (U95 = 0.1077), mean absolute error (0.2104), and highest correlation coefficient (0.9135). Also, the current study uses Global Multi-Criteria Decision Analysis (GMCDA) as an evaluation metric to assess the reliability of the forecasting. The GMCDA results indicate that RELM-BWO provides reliable forecasts up to four months ahead. Overall, the research methodology is valuable for drought assessment and forecasting, enabling advanced early warning systems and effective drought countermeasures.

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.

A multi-criteria decision analysis approach for ranking the performance of CMIP6 models in reproducing precipitation patterns over Abaya-Chamo sub-basin, Ethiopia.

The most suitable multi-model ensemble set of general circulation models is used to reduce the uncertainty associated with GCM selection and improve the accuracy of the model simulations. This study evaluated the performance of 20 global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing precipitation patterns over the Abaya-Chamo Sub-basin, Ethiopia. For the validation and selection of the models' capabilities, datasets from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) were used after comparing them with ground observational datasets. The objective was to identify the most suitable multi-model ensemble (MME) of a subset of CMIP6 GCMs to capture the rainfall for the 1981-2014 period over the region. Climate Data Operators (CDOs) were used in climate data processing and extraction, and the Mann-Kendall test and Theil-Sen slope estimator methods were utilized to analyze the trends of the CMIP6 simulations. Four statistical metrics (Nash-Sutcliffe coefficient, percent bias, normalized root mean square error, and Kling-Gupta efficiency) were used to further assess the performance of the models. A multi-criteria decision analysis approach, namely, the technique for order preferences by similarity to an ideal solution (TOPSIS) method, was used to obtain the overall ranks of CMIP6 models and to select the best-performing CMIP6 model in the region. The results indicated that CHIRPS and most of the CMIP6 simulations generally reproduced bimodal precipitation patterns over the region. The CESM2-WACCM, NorESM2-MM, NorESM2-LM, and NorESM2-LM models performed better than the other models in reproducing seasonal patterns for the winter, spring, summer, and autumn seasons, respectively. On the other hand, FGOALS-f3-L revealed the trends of the reference datasets for all seasons. In terms of the NSE, PB, NRMSE, and KGE metrics, EC-Earth3-C, EC-Earth3, EC-Earth3-C, and EC-Earth-C, respectively, were considered good at representing the observed features of precipitation over the region. EC-Earth3-C,EC-Earth3, EC-Earth3-Veg-LR, ACCESS-CM2, MPI-ESM1-2-HR, and CNRM-CM6-1-HR exhibited the best performances in the Abaya-Chamo Sub-basin.

[Benefit-risk assessment of three oral Chinese patent medicines for activating blood and eliminating mass in treating primary liver cancer based on network Meta-analysis and multi-criteria decision analysis].

Network Meta-analysis and multi-criteria decision analysis(MCDA) model were performed to evaluate the benefit-risk of Compound Cantharis Capsules, Huisheng Oral Solution, and Jinlong Capsules in the adjuvant treatment of primary liver cancer(PLC). The randomized controlled trial(RCT) of Compound Cantharis Capsules, Huisheng Oral Solution, and Jinlong Capsules in treating PLC were retrieved from CNKI, Wanfang, VIP, Web of Science, PubMed, and Cochrane Library. R 4.2 was employed to conduct a network Meta-analysis, on the basis of which the effect values of the three medicines were obtained by indirect comparison. MCDA was performed to establish the value tree based on the benefit-risk indexes. Hiview 3.2 was used to calculate the benefit values, risk values, and benefit-risk values of the three medicines in treating PLC, and a sensitivity analysis was carried out to evaluate the robustness of the results. Oracle Crystal Ball 11.1 was employed to optimize the evaluation results by Monte Carlo simulation. A total of 39 RCTs were included. The results showed that Compound Cantharis Capsules, Huisheng Oral Solution, and Jinlong Capsules combined with transcatheter arterial chemoembolization(TACE) had the benefit values of 45, 51 and 45, the risk values of 59, 47, and 41, and the benefit-risk values of 52, 49, and 43, respectively. The benefit-risk differences and [95%CI] of Compound Cantharis Capsules vs Huisheng Oral Solution, Compound Cantharis Capsules vs Jinlong Capsules, and Huisheng Oral Solution vs Jinlong Capsules were 3.00[-13.09, 21.82], 9.00[-4.39, 24.62], and 6.00[-8.84, 20.28], respectively. Based on the results of MCDA, Huisheng Oral Solution, Jinlong Capsules, and Compound Cantharis Capsules combined with TACE had the greatest benefit, the greatest risk, and the best overall benefit, respectively. Considering the efficacy and safety, the priority of the three oral Chinese patent medicines combined with TACE for treating PLC followed the trend of Compound Cantharis Capsules, Huisheng Oral Solution, and Jinlong Capsules.

Uncertainty management for In Silico screening of reversed-phase liquid chromatography methods for small compounds.

The process of developing new reversed-phase liquid chromatography methods can be both time-consuming and challenging. To meet this challenge, statistics-based strategies have emerged as cost-effective, efficient and flexible solutions. In the present study, we use a Bayesian response surface methodology, which takes advantage of the knowledge of the pKa values of the compounds present in the analyzed sample to model their retention behavior. A multi-criteria decision analysis (MCDA) was then developed to exploit the uncertainty information inherent in the model distributions. This strategic approach is designed to integrate seamlessly with quantitative structure retention relationship (QSRR) models, forming an initial in-silico screening phase. Of the two methods presented for MCDA, one showed promising results. The method development process was carried out with the optimization phase, generating a design space that corroborates the results of the selection phase.