Enrichment Evaluation Research Articles

Towards sustainable energy selection for ships in response to maritime regulation

Ship owners and operators are continuously seeking financially viable ways to comply with the Sulphur emission limits set out by the IMO, which calls for a reduction of sulphur content in ship fuel to less than 0.5% in 2020. The prudent decision in managing required energy sources by shipowners/operators will lead to an efficient ship operating decision, subsequently resulting in long-term financial return. The study aims to identify the factors influencing ship owners and operators to select the energy types in operating their vessels. Furthermore, the research explores the most desired energy sources to address the IMO Sulphur Cap Regulations 2020. One operation research method, namely the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and one econometrics tool, namely the conditional logit model, have been used to explore the research objectives. The PROMETHEE analysis suggests that clean fuel (VLSFO) is the best alternative to respond IMO 2020, followed by scrubber installation. The conditional logit analysis suggests almost the same, indicating clean fuel as the only alternative for responding to regulation. The decision is driven by the vessel's age negatively and the size of the vessel positively. The study will enormously impact the ship’s energy procurement practices in Bangladesh.

Enhancing information fusion and feature selection efficiency via the PROMETHEE method for multi-source dynamic decision data sets

With the surge in big data, the complexity of synthesizing information from multiple sources has become a critical challenge for feature selection methodologies. Feature selection is the process of reducing the number of attributes in data. Traditional single-source centric approaches are inefficient, requiring extensive preprocessing for multi-source data consolidation prior to feature selection. At the same time, an information fusion method is needed to transform the multi-source information system with selected features into a single-source information system. This paper introduces a novel multi-source information fusion and feature selection approach that seamlessly integrates the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) with a dynamic adaptation mechanism. This method is adept at addressing the complexities introduced by the evolving nature of feature and information source dimensions. The Attribute Evaluation Matrix (AEM) and the Attribute Preference Degree Matrix (APDM) are proposed to systematically assess and rank the significance of attributes within a static decision-making framework. Following this, an information fusion method using the source center is proposed. The dynamic feature selection and information fusion methods are proposed to deal with the condition when number of attributes and samples change. Extensive experimental validation confirms that this method not only reduces the computational overhead associated with multi-source feature selection but also significantly enhances the efficiency as the volume and variety of data sources increase.

Analysis of the Optimum Performance for Polymer and Polymer-Nanocomposite-modified Asphalt by Using Multicriteria Decision Analysis.

The influence of Acrylate Styrene Acrylonitrile (ASA) and ASA/nanosilica (ASA/Si) additives was investigated by using a dynamic shear rheometer (DSR). Firstly, an ASA polymer was blended with the virgin asphalt binder at two different concentrations (3% ASA and 5% ASA). After observing that 5% ASA was the optimum concentration for modification, nanosilica particles were further incorporated into the 5% ASA-modified asphalt binder with two different percentages (5% ASA 3%Si; 5% ASA 5%Si). Frequency sweep tests were conducted across various frequencies at elevated temperatures. The experimental outcomes were analyzed using master curves, rutting, and fatigue resistance parameter plots. Additionally, to provide a more holistic analysis, two different multicriteria decision analysis (MCDA) techniques, namely the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and the Technique for the Order of Preference by a Similarity to Ideal Solution (TOPSIS), were conducted to identify the best-performing asphalt binder by considering three different parameters: workability, performance under different conditions, and cost. The frequency sweep tests showed that the 5% ASA 5%Si asphalt worked best in terms of resistance to rutting. On the other hand, the virgin binder performed better than all modified binders when it failed to resist fatigue. On the other hand, the PROMETHEE analysis identified the 5% ASA-modified asphalt binder as the optimal choice, while the TOPSIS analysis determined that the 5% ASA 3%Si-modified binder provided the best performance. The differences between the experimental results and the MCDA were due to using more than one evaluation parameter and looking at how well the asphalt binder worked at different temperature ranges at the same time.

STATISTICAL INTEGRATION OF QGIS AND MULTI-CRITERIA DECISION ANALYSIS FOR SUSTAINABLE RURAL DEVELOPMENT IN BELSAR DEVELOPMENT BLOCK

The definition of “rural areas” varies significantly across regions, making the determination of rurality levels crucial for sustainable development and effective policy design. This study introduces a comprehensive rurality index to rank and categorize villages within the Belsar Development Block of Gonda District, India. Utilizing Multiple-criteria Decision-making Methods (MCDMs) such as Fuzzy Analytic Hierarchy Process (F-AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Product Model (WPM), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), we developed a robust methodology for this assessment. A thorough sensitivity analysis was also conducted to ensure the reliability of the results. The study identified fourteen critical factors, grouped into five domains: (1) socioeconomic status, (2) education, (3) land use, (4) employment, and (5) healthcare. Based on these factors, villages were ranked and classified into four categories according to their rurality levels, and a detailed rurality map was created with QGIS. The study revealed that the modified F-AHP emerged as the most effective MCDM method for this rurality assessment. Given the systematic execution of this assessment, it can serve as a model for comparative studies in other regions of India and globally, promoting sustainable rural development through informed decision-making.

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

A two-stage approach for modeling, performance evaluation, and decision support of disassembly processes

ABSTRACT This paper tackles an issue of disassembly lot-sizing within the framework of stochastic batch demand for multi-products featuring a two-level structure. The suggested methodology relies on a batch deterministic and stochastic Petri net as a modeling tool to address such problems in the context of stochastic processes. The primary objective is to determine the optimal sequencing of disassembly for each product type to meet stochastic batch demands of root items across multiple criteria. A novel mathematical model is introduced to minimize the cumulative costs associated with disassembly operations and extra costs, as well as holding costs for diverse products. In a subsequent stage, a decision support tool, namely the preference ranking organization method for enrichment evaluation method, is put forward for prioritizing different scenarios. For assessing the efficiency of the suggested approach, a real case study from the manufacturing industry is simulated. The simulation results prove the suggested approach’s capability to adapt to dynamic demands for leaf items throughout the planning horizon.

Implementation of multiobjective decision-making algorithms and image analysis in HPTLC-guided extraction optimization of natural products

A new, efficient, and low-cost approach for monitoring extraction optimization was proposed based on high-performance thin-layer chromatography (HPTLC) coupled with digital image analysis. Since HPTLC produces rich chromatographic signals corresponding to various analytes which may be differently affected by extraction conditions, four multicriteria decision-making (MCDM) techniques were compared for their ability to aggregate multiple chromatographic responses: Derringer's desirability approach, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-2), and the Sum of ranking differences (SRD). Ultrasound-assisted extraction (UAE) of green tea leaves with ethanol-water mixtures was used as a model system. The amount of ethanol and extraction time were varied according to the central composite design. Ranking eleven extracts by Derringer's desirability approach, TOPSIS, and PROMETHEE-2 showed the same results. SRD analysis yielded slightly different results from previous methods. Response surface models (RSM) based on the previous three MCDM approaches demonstrated that extraction conditions with moderate amounts of ethanol (73%) and extraction times (46 min) lead to optimal chromatographic profiles. RSM optimization performed on individual peaks, tentatively corresponding to rutin, chlorophyll, and gallic acid, led to different results, which justified the use of MCDM algorithms for aggregation of multiple responses. Aside from natural products, the proposed approach has the potential to be implemented in various extraction optimizations.

A novel multicriteria web‐based decision support system to enhance resource allocation in energy companies: a portfolio selection with c‐optimal PROMETHEE

AbstractIn an uncertain world, decision support systems (DSSs) are essential for effective portfolio management. Electricity companies often face challenges in allocating limited resources among competing projects, influenced by sustainability and regulatory requirements. To address these challenges, multicriteria approaches have been developed, focusing on complex issues involving outranking methods. This paper introduces a methodological advance with a novel algorithm that enhances the c‐optimal PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach, resolving scaling issues inherent in the PROMETHEE V method. Our key contribution is a refined procedure for efficiently identifying and comparing c‐optimal solutions while preserving the method's axiomatic integrity, ensuring the selection of the “true best” portfolio. Additionally, we present a web‐based DSS designed to implement this methodology, featuring interactive visualization and sensitivity analysis tools. Freely available online, both the algorithm and DSS were validated through a case study with a Brazilian electricity company, demonstrating their practical benefits and applicability across various business contexts.

A systematic framework for the design and material selection of composite for tennis racket upon impact

The present study explores the comprehensive composite material selection methodology of a tennis racket involving coupled Multi-Criteria Decision-Making (MCDM) and numerical analysis. Initially all the possible material alternatives from the composite family were shortlisted using CES Selector 2016. A design space was developed incorporating MCDM technique naming VICKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank the most suitable composite material based on critical mechanical properties to identify the most suitable composite materials for applications requiring optimal combinations of rigidity, strength, and structural performance. Using finite element analysis, the structural integrity of each of the materials was evaluated on static and dynamic stress analysis modules of ANSYS. Out of all material alternatives, T300 carbon fiber epoxy was considered as the most suitable for tennis rackets. Further, a comparison between the selected material was also made with conventional materials and were found in good agreement with selected material.

Rapid assessment implementation in the development of biocriteria and organic enrichment evaluation in the Citarum River, Indonesia

ABSTRACT Socio-economic activities along the Citarum River basin in Indonesia can induce ecological disturbances in the structure of the benthic macroinvertebrate community. This research aims to (1) identify the environmental factors responsible (2) describe the ecological disturbances using biological measurements; (3) and to develop local biocriteria using a multi-metric conceptual approach. An inventory of benthic macroinvertebrates was carried out at eight stations over a sampling period of 3 months, along with water quality monitoring. The results confirm the change in the structure of benthic macroinvertebrate communities, explained by seven environmental parameters: dissolved oxygen (DO), ambient habitat quality, turbidity, nutrient enrichment, conductivity, temperature, and embeddedness percentage. The changes were then assessed using six biological measurements. Only four have been shown to be capable as alternatives to existing biocriteria. Their implementation enabled the development of local biocriteria compatible with the multimetric concept known as the cumulative biotic index (CBI).

A Pangenomic Analysis of the Diversity and Biological Functioning of the Genus Azotobacter

Azotobacter is a diverse genus of free-living soil bacteria that fixes atmospheric nitrogen and act as a biocontrol agent for different phytopathogens. This study utilized the genomic sequences from four different Azotobacter species, comprising thirty strains, to construct the genus pangenome. Through comparative genomics and pangenomics analysis, we elucidated the genomic diversity and functional relationship between and within the species of the Azotobacter genus. The selected Azotobacter strains exhibited an ANI of ≥ 85%, with core genes constituting up to 9% and a large proportion of 55% of unique genes. This indicated the sharing of a highly open pangenome of the Azotobacter genus. The COG analysis revealed that core, unique, and accessory genes play a significant role in the Azotobacter’s metabolic and information storage processes. The KEGG enrichment evaluation showed general functioning, amino acid transport and metabolism, as the major biological processes of the Azotobacter’s genomic content. Furthermore, the predominant BGCs present in the genomic structure of Azotobacter genus exhibited a 27% participation in the production of NRP-metallophores, 17% for the NI-siderophores and 17% for type III polyketide synthase (T3PKS). The genomic content analysis of the core genome revealed various genes that encoded for acid and universal stress proteins, playing a role in Azotobacters adaptation. For the biological functioning attributes, it was found that alginate production, riboflavin transporters, nitrogen-responsive regulatory flavoprotein, nitrogen-related phosphotransferase, and molybdenum cofactor genes were involved in its core genome for the major functioning of its nitrogen fixation.

Analysis of the Application the Promethee Method in Determining Work Partners at the Central Statistics Agency of Central Aceh Regency

The selection of partners at the Central Statistics Agency of Central Aceh Regency faces challenges because manual selection methods are considered inefficient and inaccurate. Therefore, this study aims to improve the accuracy and efficiency of the selection process by applying the Promethee (Preference Ranking Organization Method for Enrichment Evaluations) method. This method is used as a multi-criteria decision-making technique and is implemented using the Python programming language for automatic and real-time data processing. The data used comes from the 2024 partner recruitment process at the Central Statistics Agency of Central Aceh Regency, consisting of candidates who have met the administrative requirements. The validity of the promethee method is tested through the calculation of an error percentage that reaches 10%, demonstrating the method's ability to filter partners according to the established criteria. In conclusion, the application of the promethee method has been proven to improve the efficiency and accuracy of the partner selection process, where the Python-based system developed can reduce the time and effort required in decision-making while producing more valid and reliable partner recommendations.

Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review

Multi-criteria decision-making (MCDM) methods provide a framework for addressing sustainable forest management challenges, especially under climate change. This study offers a systematic review of MCDM applications in forest management from January 2010 to March 2024. Descriptive statistics were employed to analyze trends in MCDM use and geographic distribution. Thematic content analysis investigated the appearance of MCDM indicators supplemented by Natural Language Processing (NLP). Factorial Correspondence Analysis (FCA) explored correlations between models and publication outlets. We systematically searched Web of Science (WoS), Scopus, Google Scholar, Semantic Scholar, CrossRef, and OpenAlex using terms such as ‘MCDM’, ‘forest management’, and ‘decision support’. We found that the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the most commonly used methods, followed by the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the Analytic Network Process (ANP), GIS, and Goal Programming (GP). Adoption varied across regions, with advanced models such as AHP and GIS less frequently used in developing countries due to technological constraints. These findings highlight emerging trends and gaps in MCDM application, particularly for argan forests, emphasizing the need for context-specific frameworks to support sustainable management in the face of climate change.

Multicriteria Analysis of Innovation Ecosystems and the Impact of Human Capital and Investments on Brazilian Industries

Innovation is one of the main factors associated with industrial growth, as it contributes to increased productivity, sustainability, and international competitiveness. There is a certain degree of complexity in measuring innovation, since there are various metrics for this purpose, and each sector of the economy handles it differently. In Brazil, the Industrial Survey of Technological Innovation (PINTEC) was developed to construct sectorial indicators on innovation activities. Based on these data, this paper investigates how ten sectors of the economy performed during two historical series (2011 to 2014 and 2014 to 2017) considering five criteria linked to the innovation process and the impact of qualified human capital. The problem is analyzed in light of multicriteria decision analysis (MCDA), using preference ranking organization methods for enrichment evaluation (PROMETHEE II) to provide a ranking of Brazilian industrial sectors. The results show how the sectors have changed over the years, highlighting that innovation expenditure proved crucial in measuring companies’ commitment to innovation, but also show that a highly qualified workforce emerged as a leading factor. Furthermore, the research presents which criteria have contributed positively and negatively to each sector, which can serve as drivers for policy formulation to strengthen the Brazilian industry.

Assessment of soil environmental capacity for heavy metals in Shantou City, Guangdong Province, China: source analysis and enrichment evaluation.

Most studies assessing soil environmental capacity (EC) often overlook the impact of heavy metal sources. Analyzing the sources of heavy metals (HMs) provides a better understanding of regional environmental capacity characteristics and their dynamic changes. The current study focuses on the surface soil of Shantou, using 511 soil samples to assess the soil environmental capacity. Results indicate that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in Shantou's surface soil are notable, with lead moderately enriched and other metals lightly enriched. The principal component analysis (PCA) identifies five primary sources of heavy metals: mixed natural and agricultural sources, mixed agricultural and industrial sources, industrial sources, mining sources, and quarrying sources. The primary source contributing significantly to soil HM concentrations in Shantou City is a complex interplay between natural geological processes and extensive agricultural practices. In terms of static environmental capacity, Zn, Cr, Ni, Pb, Cu, As, Hg, and Cd are ranked in descending order. The overall environmental capacity for heavy metals in the soil is at a medium level, influenced by geological backgrounds. However, regions such as Yanhong Town, Guiyu Town, and Chendian Town face lower environmental capacities due to comprehensive human activities, posing certain risks. This study provides a scientific reference for forecasting, controlling soil heavy metal pollution, and improving soil quality and environmental capacity in Shantou City.

Geochemical evaluation of fluoride enrichment in the complexe terminal aquifer and its implications for human health in Oued Souf, Algerian Sahara

Geochemical evaluation of fluoride enrichment in the complexe terminal aquifer and its implications for human health in Oued Souf, Algerian Sahara

Yixin-Fumai granules modulate autophagy through the PI3K/AKT/FOXO pathway and lead to amelioration of aging mice with sick sinus syndrome

ObjectiveBy employing network pharmacology alongside molecular docking techniques, we can delve into the intricate workings of Yixin-Fumai granules (YXFMs) and their impact on sick sinus syndrome (SSS) within wrinkles mice. Specifically, we aim to understand how YXFMs enhance autophagy through the PI3K/AKT/FOXO path.MethodsThe active ingredients and medicinal uses of Ginseng, ligusticum wallichii, Ophiopogon, Schisandra, salvia, and astragalus were compiled using the BATMAN-TCM database. We also used Genecards, OMIM, and Disgenet files to identify the disease goals. A hierarchical diagram of “disease-drug-key targets” was generated using the Cytoscape programs. In addition, we established a target protein interaction (PPI) network using the STRING database. Then, the Cluster Profiler R package was used to conduct GO functional enrichment evaluation and KEGG pathway enrichment analyses of the targets. Based on the PPI system, we chose the top communicating targets and substances over molecular docking. In vivo studies were performed to validate these selections further. The mouse model was induced to study the damaged sinoatrial node (SAN) in mice with lower heart rates due to age-related changes. Electrocardiogram and Masson staining assessments were performed to obtain the results. The transmission electron microscope was used to assess the autophagy level of SAN cells. Western blot was employed to analyze the impact of YXFMs on protein expression in the PI3K/AKT/FOXO signaling process throughout SSS therapy in aging mice.ResultsOne hundred forty-two active ingredients, 1858 targets, 1226 disease targets, and 266 intersection targets were obtained. The key targets of the PPI network encompassed TP53, AKT1, CTNNB1, INS, and TNF, among others. According to GO functional analysis, the mechanism underlying YXFMs in SSS treatment may primarily be associated with the control of ion transport across membranes, cardiac contraction, regulation of blood circulation, and other biological processes. Based on the results of KEGG pathway enrichment analysis, it was determined that they were mainly enriched in multiple pathways of signaling such as the PI3K-Akt signaling route, MAPK signaling process, AGE-RAGE signaling path, FOXO signaling path, HIF-1 signaling process, and several other paths. Molecular docking demonstrated that five compounds had excellent binding to the key candidate target proteins AKT1 and INS. Through the in vivo studies, we noticed notable effects when administering YXFMs. These effects included the suppression of aging-induced SSS, a decrease in the R-R interval, a rise in heart rate, a reduction in fibrosis, a boost in the autophagy process level, and a spike in the levels of expression of key protein molecules in the PI3K/AKT/FOXO signaling path.ConclusionThis research has made preliminary predictions about the potential of YXFMs in treating SSS. It suggests that YXFMs may have the ability to target key proteins and critical paths associated with the condition. Further testing has been conducted to discover new findings and evidence of ideas for tackling SSS triggered by aging.

Three-tier integrated demand-supply energy management for optimized energy usage in institutional building

Sustainable Energy Plan is an indispensable field of study in the context of Building Energy Management (BEM) which yearns for a cutting-edge technique to raise consumers’ level of contentment with energy usage. This paper introduces a three-tier strategic approach for demand cum supply side management at the prosumer end named TID-SEM (Three-tier Integrated Demand cum Supply Energy Management) framework. The framework commences with data collection, machine learning, and Random Forest classifier-based Demand side management to reduce peak demand. Followingly, a feasibility study on available sources (Solar PV + Battery + Diesel Generator + Grid) was done using the HOMER simulator. The quality techno-enviro-economic shows distinguished optimal one under each criterion. Ultimately to overcome, an ideal computative multicriteria decision-making approaches Fuzzy Analytic Hierarchy Process, the Fuzzy Preference Ranking Organization Method for Enrichment Evaluations, and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution employed to highlight the most optimal configuration. The study focuses on a department building within a higher education institution in the Madurai district, Tamil Nadu, India. On implementing the TID-SEM, ‘PV + Grid’ after DSM configuration was ranked as the most optimum. Lastly, a sensitivity analysis was conducted to evaluate the techno-enviro-economic impact of uncertain parameters on the optimal configuration, ensuring the robustness of the proposed framework.

Recent Progress of Multifunctional Molecular Probes for Triple-Negative Breast Cancer Theranostics.

Breast cancer (BC) poses a significant threat to women's health, with triple-negative breast cancer (TNBC) representing one of the most challenging and aggressive subtypes due to the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. Traditional TNBC treatments often encounter issues such as low drug efficiency, limited tumor enrichment, and substantial side effects. Therefore, it is crucial to explore novel diagnostic and treatment systems for TNBC. Multifunctional molecular probes (MMPs), which integrate target recognition as well as diagnostic and therapeutic functions, introduce advanced molecular tools for TNBC theranostics. Using an MMP system, molecular drugs can be precisely delivered to the tumor site through a targeted ligand. Real-time dynamic monitoring of drug release achieved using imaging technology allows for the evaluation of drug enrichment at the tumor site. This approach enables accurate drug release, thereby improving the therapeutic effect. Therefore, this review summarizes the recent advancements in MMPs for TNBC theranostics, encompassing the design and synthesis of MMPs as well as their applications in the field of TNBC theranostics.

Assessment of brain-derived extracellular vesicle enrichment for blood biomarker analysis in age-related neurodegenerative diseases: An international overview.

Brain-derived extracellular vesicles (BEVs) in blood allows for minimally-invasive investigations of central nervous system (CNS) -specific markers of age-related neurodegenerative diseases (NDDs). Polymer-based EV- and immunoprecipitation (IP)-based BEV-enrichment protocols from blood have gained popularity. We systematically investigated protocol consistency across studies, and determined CNS-specificity of proteins associated with these protocols. NDD articles investigating BEVs in blood using polymer-based and/or IP-based BEV enrichment protocols were systematically identified, and protocols compared. Proteins used for BEV-enrichment and/or post-enrichment were assessed for CNS- and brain-cell-type-specificity, extracellular domains (ECD+), and presence in EV-databases. A total of 82.1% of studies used polymer-based (ExoQuick) EV-enrichment, and 92.3% used L1CAM for IP-based BEV-enrichment. Centrifugation times differed across studies. A total of 26.8% of 82 proteins systematically identified were CNS-specific: 50% ECD+, 77.3% were listed in EV-databases. We identified protocol steps requiring standardization, and recommend additional CNS-specific proteins that can be used for BEV-enrichment or as BEV-biomarkers. Across NDDs, we identified protocols commonly used for EV/BEV enrichment from blood. We identified protocol steps showing variability that require harmonization. We assessed CNS-specificity of proteins used for BEV-enrichment or found in BEV cargo. CNS-specific EV proteins with ECD+ or without were identified. We recommend evaluation of blood-BEV enrichment using these additional ECD+ proteins.