Key Evaluation Indicators Research Articles

Effect and mechanism of different exogenous biomolecules on the thermal-induced gel properties of surimi: A review.

Surimi products are favored by domestic and foreign consumers due to their distinctive gelatinous texture, rich nutrition, and convenient consumption. Gel properties are key evaluation indicators for the quality of surimi products, which was mainly determined by the gel-forming ability of the myofibrillar protein (MP). In recent years, the surimi processing industry has faced challenges in product quality that limits the further development, and how to effectively improve the gel properties of surimi products has become one of the key scientific problems to be solved in surimi processing industry. A viable strategy for improving the product quality involves combining surimi with exogenous additives, such as proteins, polysaccharides, and lipids, to enhance the gel-forming ability of MP. At present, there is limited literature review to systematically investigate the role of these exogenous additives in interacting with MPs in surimi gel system and their effect on the gel properties of heat-induced surimi. Therefore, in this review, we systematically discussed the formation mechanism and influencing factors of surimi gel, the interactions of exogenous biomolecules (proteins, polysaccharides, and lipids) with surimi protein, as well as their effects on the gel properties of surimi product. The aim of this review was to help us with a better understanding for the intrinsic action mechanisms of complex surimi system and provide some theoretical guidance for the improvement of gel quality and development of surimi products.

An Assessment of Water Quality and Pollution Sources in a Source Region of Northwest China

China prioritizes ensuring drinking water safety, particularly in the water-scarce northwest region. This study, utilizing water quality data from 52 village and town water sources since August 2022, assesses water quality, with a specific focus on key indicators related to organic pollution sources. This study provides a scientific foundation for enhancing water quality in these sources. Employing category factor analysis for classification and grading, principal component analysis for qualitative analysis of key evaluation indicators, and the absolute principal component linear regression equation for quantitative calculation of pollution sources, this study reveals that all 52 water sources meet quality standards. Principal component analysis categorizes pollution sources as diverse types of organic compounds in surface water. Source analysis calculations highlight decay-type organic substances as major contributors to increased water color and permanganate index, with pollution contribution rates of 54.78% and 31.31%, respectively. Fecal-type organic substances dominate the increase in dissolved total solids and total coliforms, with pollution contribution rates of 56.65% and 40.16%, respectively. Additionally, high-molecular-weight organic substances exhibit lower concentrations in the water. This article presents a systematic water quality assessment methodology, which is used for the first time to qualitatively assess the types of water sources and to quantitatively trace specific sources of organic pollution in source water in northwest China. This systematic study’s results, involving initial assessment followed by traceability, recommend the adoption of a simple contact filtration and disinfection process to enhance water quality in the region.

A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques

Climate change poses a threat to the global ecosystem. Many countries adopt various approaches, including ecosystem-based adaptation (EbA), to address this problem. However, the assessment of the effectiveness of the EbA interventions is conducted manually, is resource-intensive, and is focused on short-term outputs. These limitations underscore a critical gap: the need for a comprehensive, automated system that enables long-term monitoring and predictive analysis. This study aimed to address this gap by developing an innovative framework that integrates Internet of Things (IoT) devices and machine learning (ML) algorithms to continuously monitor weather, hydrological, environmental, and other variables. We conducted a thorough analysis to design an appropriate framework. In addition, to obtain the relevant information and data, we conducted interviews with the local community and collected secondary data from various sources. The proposed framework consists of five layers: (i) EbA interventions; (ii) IoT-based key performance indicators (KPI) for monitoring and evaluation (M&E); (iii) primary data collection; (iv) data storage; and (v) application. As a proof of concept, we developed a system that supports early flood and drought alerts while simultaneously providing long-term evaluations of the effectiveness of EbA strategies. The developed system consists of IoT devices and a web application integrated with machine learning (ML). We set up and tested the IoT devices before deploying them in the study area. The devices capture data for two primary purposes: (1) short-term: flood detection and alerting, and (2) long-term: drought prediction and evaluation of EbA effectiveness through continuous data analysis. This research represents a significant advancement in the automation and long-term assessment of climate adaptation measures, offering a scalable and effective solution to disaster risk reduction.

Development of an evaluation system for rational drug use in patients with chronic kidney disease using the Delphi method.

Chronic kidney disease (CKD) stages 3-4 present a significant clinical challenge due to the absence of a systematic approach to managing associated medication-related problems (MRPs). This lack of a structured framework hinders the timely identification and effective intervention for these complications, potentially compromising patient safety and prognosis. This study aims to leverage the Delphi method to establish an evaluation index for a rational drug use evaluation system dedicated to CKD patients in stages 3-4. This system will function as a platform for the continuous identification and management of MRPs, ultimately contributing to improved medication safety and patient outcomes. This research uses the modified Delphi technique to develop an evaluation system for rational drug use in patients with chronic kidney disease. The initial questionnaire was developed by literature review for patients with chronic kidney disease. Twenty-six senior experts formed a panel in order to evaluate items across two Delphi rounds. Consensus was defined as at least 95% agreement (first round) and 85% agreement (second round), agreeing with an average score of at least 4.5 (first round) and 4.0 (second round). Items that fulfill the stipulated criteria are eligible for inclusion in the consensus list. All experts participated in both rounds (100% response rate). Consensus was achieved on three patient-related items in the first round of 34 items. Based on expert feedback, 18 revised items were included in the second round after refining, restructuring, and removing some elements. Following two rounds of consultation, 20 items achieved consensus, encompassing aspects such as drug selection, dosage assessment, treatment duration, prescription and dispensing practices, patient-related factors, and other relevant considerations. This study has successfully identified 20 key evaluation indicators for a rational drug use evaluation system specifically designed for CKD patients in stages 3 and 4. This system will serve as a tool for continuous MRP identification and timely intervention, ultimately enhancing medication safety and patient prognosis.

Energy efficiency evaluation of wind turbines based on entropy weight method and stacked autoencoder

Abstract To solve the problems of complex operating conditions of wind turbines and difficult to measure and evaluate energy efficiency, a quantitative evaluation method of energy efficiency of wind turbines based on the entropy weight method (EWM) and stacked automatic encoder (SAE) was proposed. Firstly, based on the Supervisory Control And Data Acquisition (SCADA) data of wind turbines, key evaluation indicators are extracted through preprocessing and feature extraction of wind turbine data. The EWM is used to determine the weights, and deep learning is combined with SAE to comprehensively obtain the energy efficiency value of wind turbines. Subsequently, the quartile method was used to determine the discrimination threshold for inefficient units, and the effectiveness of this method on SCADA data of a 5.5MW offshore wind farm was verified through numerical examples. The results show that this method can evaluate the energy efficiency level of wind turbines in real time and achieve 100% accuracy in identifying inefficient units, providing a reference for the maintenance and technical improvement of wind turbine energy efficiency performance.

Quality evaluation of chicken soup based on entropy weight method and grey correlation degree method

This study aimed to develop an assessment framework for evaluating the quality of different chicken soup variants. Three types of chicken soup, traditional chicken soup (TCS), concentrated chicken soup (CCS), and blended chicken soup (BCS), were prepared and analyzed for various physicochemical parameters, including gross protein content, crude fat content, pH level, solid content, viscosity, and chromatic aberration value. Sensory evaluation was also conducted to assess overall quality. Correlation analysis helped identify three key evaluation indicators: gross protein content, L* value (lightness), and b* value (chromatic aberration). The weight assigned to gross protein content was the highest using the entropy weight method (EWM). Moreover, the grey correlation degree method was comprehensively applied to evaluate the chicken soup's quality. This analysis identified TCS and CCS as varieties with superior overall quality, showing a positive correlation with sensory evaluation, consistent with the results of nuclear magnetic resonance (NMR) used in this paper. These results provide theoretical support for assessing comprehensive quality and selecting chicken soup varieties.

Molecular Photoelectrochemical Energy Storage Materials for Coupled Solar Batteries.

ConspectusSolar-to-electrochemical energy storage is one of the essential solar energy utilization pathways alongside solar-to-electricity and solar-to-chemical conversion. A coupled solar battery enables direct solar-to-electrochemical energy storage via photocoupled ion transfer using photoelectrochemical materials with light absorption/charge transfer and redox capabilities. Common photoelectrochemical materials face challenges due to insufficient solar spectrum utilization, which restricts their redox potential window and constrains energy conversion efficiency. In contrast, molecular photoelectrochemical energy storage materials are promising for their mechanism of exciton-involved redox reaction that allows for extra energy utilization from hot excitons generated by superbandgap excitation and localized heat after absorption of sub-bandgap photons. This enables more efficient redox reactions with a less restricted redox potentials window and, thus, better utilization of the full solar spectrum. Despite these advantages, practical application remains elusive due to the mismatch between the short lifetime of the charge separation state (<ns) and the slower redox reaction rate (>μs). This mismatch results in a significant portion of the photogenerated charges recombining before participating in desired electrochemical energy storage reactions, leading to diminished overall efficiency. It is therefore highly important to develop molecular materials with intrinsic prolonged charge separation state and extrinsic effective mass-electron transfer to enable efficient coupled solar batteries for practical applications.In this Account, we begin with an introduction of the general solar-to-electrochemical energy storage concept based on molecular photoelectrochemical energy storage materials, highlighting the advantages of periodic oxidative donor-reductive acceptor porous aggregate structures that have synergistic implications on charge separation state lifetime extension and mass-electron transfer. We then present our earliest trial on the design and application of molecular photoelectrochemical energy storage materials, which stimulated our subsequent studies on tuning electron donor and acceptor structures for enhanced charge separation and diverse photoelectrochemical redox reactions. Moreover, we introduce the best practices in the design and assembly of various coupled solar battery devices, along with our literature contributions and progresses in solar-to-electrochemical energy storage efficiency (ηSES) over nearly the past decade. Finally, we conclude by highlighting the universality of our strategies as essential design principles, spanning from regulating long-lived charge separation states and photocoupled ion transfer processes in molecular materials to the construction of efficient coupled solar batteries. We offer perspectives on the synergy between photovoltage and redox potentials and the practical significance of 3D printing, providing key evaluation indicators for large-scale application. This Account provides molecular level insights for the construction of high-efficiency photoelectrochemical energy storage materials and guidance for practical solar-to-electrochemical energy storage applications.

Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model.

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of "Temporal Information Entropy (TIE)", integrating it into the Node2Vec algorithm's random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value.

Detection of thermotolerant coliforms and SARS-CoV-2 RNA in sewage and recreational waters in the Ecuadorian coast: A call for improving water quality regulation.

Wastewater surveillance represents an alternative approach to regulating contamination and the early detection of infectious agents and outbreaks of diseases of public health importance. This study evaluated domestic wastewater effects on recreational waters in estuarine and seawater bodies in Guayas and Santa Elena provinces in Ecuador, South America. Fecal indicator bacteria (thermotolerant coliforms) served as key indicators for evaluation. Physical, chemical, and microbiological quality markers following the Ecuadorian environmental quality standard and the discharge of effluents to the water resource were analyzed. Samples were collected from 44 coastal sites and 2 oxidation lagoons during the dry and rainy seasons of 2020 and 2021, respectively. SARS-CoV-2 RNA was detected in samples with higher E. coli concentrations using reverse transcription quantitative PCR to detect the genes N and ORF1ab. All samples analyzed for SARS-CoV-2 showed Ct ˂ 40 for at least one gene. Four samples showed at least 20 genome copies of gene N per reaction. These were at an artisanal fishing port, an estuarine area (Palmar), a recreational bay, and an oxidation lagoon. A moderate correlation was found between SARS-CoV-2 RNA, thermotolerant coliform and E. coli (p-value ≤ 0.0037), and a strong and positive correlation between thermotolerant coliform and E. coli. (p-value ≤ 0.00001), highlighting the utility of these established parameters as a proxy of the virus. Significant differences were found in the concentrations of thermotolerant coliforms between seasons (p-value = 0.016) and sites (p-value = 0.005). The highest levels of coliforms were found in the dry season (63000 MPN/100 mL) in Anconcito and during the rainy season (14000 MPN/100 mL) at Esterillo in Playas County. It is recommended that the decentralized autonomous governments of the surveyed provinces in Ecuador implement urgent corrective actions and establish medium-term mechanisms to minimize a potential contamination route. Additional parameters must be included in the monitoring, such as Enterococcus and intestinal parasites, due to their public health implications. In the oxidation lagoons, maintenance actions must be carried out, including the dissolution of sediments, an increase in water retention times, and in situ treatment of the sludge, to improve the system's performance.

An adaptive weighted ensemble learning network for diabetic retinopathy classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.

A Fast Optimizing Method of Clutch–Clutch Shift Calibration Parameters

Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V” development process. A controlled-object simulation model of a six-speed automatic transmission (6AT) is established, and the shift calibration parameters and shift quality evaluation system are studied. Therefore, a model-in-the-loop (MIL) calibration platform for key calibration parameters and evaluation indicators in the shift process is constructed. By setting the optimization target of evaluation indicators, the initial calibration parameters’ ranges are reduced. Finally, the TCU is flashed with the optimized calibration parameters, and a hardware-in-the-loop (HIL) test system is built to verify the shift quality. The same shift quality verification test is carried out in the vehicle. The test results show that the application of the MIL simulation platform can enable the batch simulation analysis of eight key shift calibration parameters, gradually refine the initial rough interval, and ensure shift quality. The shift calibration parameters are cross-validated in multiple development links, and the dependence on vehicle calibration is greatly reduced. The development cycle is effectively shortened to less than 6 months.

Ventilation benefit assessment of space organization design for 'L' type inner corridor buildings

The insufficiency of fresh air throughout deep inner corridor plans has long been highlighted, while the introduction of combined spaces can often result in obvious benefits of natural ventilation. Considering the abundant inner corridor buildings in northern China and the emerging context of global warming, it is therefore important to understand the hidden correlation between space combinations and ventilation performance in inner corridor buildings. Through this study, baseline prototype of L-shape inner corridor together with its representative optimization prototypes of combined space design (multi-bay, through penetrating and duplex) were modelled and examined based on case studies and literature reviews. Employing the computational fluid dynamics (CFD) tool, and key evaluation indicators of air velocity and age of air, this work investigated the benefit potentials of natural ventilation through space organization measures among inner corridor buildings from a qualitative and quantitative perspective, respectively. As a result, ventilation enhancement effects have been validated for three combined spaces, with the through penetrating space achieving 15% more the compliance rate of AGE than the multi-bay space. In particular, the duplex space is preferred, not only it shows the best ventilation improvement at the corner, but also it can effectively improve uneven ventilation conditions within and between layers by redistributing air flow (the average wind speed on the leeward side of the inner corridor space can be increased by up to 0.12 m/s).

The index system for the implementation effect evaluation of water-saving renovation of key medium-sized irrigation districts: A case study.

Irrigation district plays an important role in China's agricultural production. In recent years, China conducted many water-saving renovation construction projects of irrigation districts. However, the current implementation effect of irrigation district water-saving renovation has not been well-characterized. Comprehensive and systematic evaluation of the implementation effect of water-saving renovation in irrigation districts can provide scientific basis for further construction and management of irrigation districts. This study screened preliminary index system in four dimensions: preliminary work, completion of construction tasks, completion of planned investment, management and reform. Based on the statistical results of the questionnaire surveys and subsequently analyzed, fourteen key evaluation indicators were finally identified. Percentile system and expert evaluation method were then used to determine the assigned score of every indicator. Based on the evaluation criteria, the evaluation method of the implementation effect was formulated. Ten key medium-sized irrigation districts in southeastern China were taken as a research example in this study, with the implementation effect of water-saving renovation of 10 irrigation districts being comprehensively evaluated. The results show that these irrigation districts have a relatively high implementation effect of water-saving renovation. The data results from the scores of four dimensions and fourteen indicators show that the index system is reasonable and practicable, and the evaluation results are basically in line with actual situation. These findings have a good practical reference value for making decisions about how to instruct the modernization development of water-saving renovation of irrigation districts in China.

Key indicators for the credit risk evaluation of clients and their changing characteristics

Key indicators for the credit risk evaluation of clients and their changing characteristics

Sustainability evaluation of green building construction based on a combination method of weighting and improved matter-element extension

&lt;p&gt;In light of the pressing global challenges related to greenhouse gas emissions from the construction industry, current evaluation systems for green building construction sustainability remain limited, often overlooking sustainability domains. This study innovatively established an evaluation framework by exploring five critical domains: environmental sustainability, economic benefits, socio-cultural impacts, technological innovation, and health and well-being. Sixteen key evaluation indicators were identified using the Delphi method, with the novel inclusion of a carbon emission reduction target achievement indicator, thereby promoting the goal of carbon neutrality in green buildings. To determine a more reasonable weight distribution, this paper combined the fuzzy analytic hierarchy process (fuzzy AHP) with the entropy weight method. Additionally, the study employed a fuzzy matter-element method enhanced by genetic algorithms for precise evaluation of green building construction sustainability. The feasibility and effectiveness of the proposed model were validated through an empirical analysis of a green building project in Beijing. The results of this research provide innovative theoretical references and practical guidelines for green building construction sustainability evaluation.&lt;/p&gt;

Analyzing the Impact of Comprehensive Risk Control Nursing Intervention on Postoperative Pain and Recovery in Elderly Orthopedic Surgery Patients

Objective: To provide insights into how a proactive, risk-focused nursing intervention model canenhance recovery outcomes and quality of life of vulnerable elderly population. Methods: Keyevaluation indicators included Time to First Ambulation, Pain Levels (measured using the Visual AnalogScale and Numerical Rating Scale); Complications; and Daily Living Ability. Results: The observationgroup demonstrated superior recovery outcomes compared to the control group, achieving earlierambulation, quicker suture removals, and shorter hospital stays. Notably, primary wound healingrates were significantly higher in the observation group. Post-intervention, both groups experienceda significant reduction in pain scores measured by VAS and NRS, with the observation group showinga more substantial decrease. Additionally, improvements were observed in both the Barthel Index (BI)and SF-36 scores; however, the observation group achieved notably better results. The complicationrate in the observation group was significantly lower at 6.67%, compared to 17.33% in the control group(all, P&lt;0.05). Conclusion: Integrating comprehensive risk management nursing into the postoperativecare of elderly orthopaedic patients enhances recovery, optimizes pain management, improves dailyfunction and quality of life and reduces complication rates compared to traditional nursing methods.

NUMERICAL INVESTIGATION OF THE VALVE MODEL FOR PRIMARY SUTURELESS IMPLANTATION

HighlightsThe presented numerical model of the bioprosthesis support frame for sutureless fixation needs to be optimized to reduce the amplitude of the von Mises stresses.The presented setup of numerical modeling has been validated using the study of commercial analogs. AbstractAim. To assess the stress-strain state of three computer models of TAVI prosthesis support frames during crimping and shaping to the delivery configuration.Methods. The study included three models of stent-like support frames for balloon-expandable devices, which are intended to serve as a foundation for the subsequent development of a domestic aortic valve prosthesis: two models of commercial bioprostheses and one experimental. The objects were evaluated numerically under the conditions of stress-strain state that arises in the stent support frames, simulating two loads: crimping and shaping to the delivery configuration. The study was conducted using the numerical modeling complex Abaqus/CAE (“Dassault Systemes”, France). The key indicators for evaluation were the von Mises stress, as a strength criterion, and its distribution over the frame; the presence and proportion of elastic recoil.Results. It was shown that all samples are capable of reaching the required diameter without excessive material. A quantitative investigation of the Von Mises stress showed that commercial models display amplitudes below the material`s strength limit (892.4 and 916.8 MPa), whereas the proprietary model exceeds this limit, reaching 991.4 MPa, requiring geometry optimization. Shaping to the delivery configuration indicates that all models provide safe expansion up to 26 mm, with a Von Mises stress level in the range of 882.4–914.1 MPa, which is below the strength limit of the cobalt-chrome alloy. Moreover, we have noted heterogeneous stress distribution, with concentration in the lamella junction areas.Conclusion. Thus, it has been demonstrated that numerical modeling and the finite element method can be effectively applied to assess the stress-strain state of sutureless prostheses. Geometry optimization and further development of this project – the development of a Russian minimally invasive aortic valve prosthetic system, may contribute towards increasing the accessibility of this treatment method.

Experimental investigation of a thermochemical energy storage system based on MgSO4-silica gel for building heating: adsorption/desorption performance testing and system optimization

To address the gap between the thermochemical energy storage (TCES) performance of MgSO4-porous matrix composites in small-scale prototypes and their practical application, a TCES system with an output power of 50 kW was designed and constructed to investigate the feasibility of using MgSO4-silica gel composites in large-scale storage systems for long-term heating applications. Thirty consecutive sets of experiments were performed on both the reactor and the system. Aiming at energy storage system for space heating and hot water supply, four parameters (energy consumption, energy storage density, energy storage efficiency, and specific energy storage capacity) were investigated as the key performance evaluation indicators. The experiment results indicated the energy consumption of the heater and humidifier account for approximately 80 % of total energy consumption. The findings also demonstrated the potential to increase the ESD from 0.47 GJ/m3 to 0.76 GJ/m3 by exploiting the heat generated from the additional circulation water in the reactor’s shell. The optimized system achieved an efficiency increase of ∼ 11 %, reaching over 60 %, and the specific energy storage capacity can reach up to 198.15 Wh/kg. In addition, the supply water temperature can stay above 50 °C for roughly 4 h, with a maximum value from 70.1 °C to 82.4 °C, and the water temperature lift was found to as high as 57.4 °C. Additionally, the adsorption heat accounted for approximately 44 % of the total energy storage.

Assessing the sustainability of the homestay industry for the East Coast of Malaysia using the Delphi approach

Homestay ecotourism in Malaysia has been extensively examined in terms of its concepts, approaches, activities, and community engagement. However, a comprehensive assessment of the sustainability factors pertaining to host families remains a critical area awaiting exploration. This is paramount for ensuring the long-term viability of homestays and fostering economic benefits within rural communities. The present study seeks to establish direct subjective measurements for evaluating the interplay between local communities, tourism, and resources in safeguarding sustainable homestays. Utilizing the Delphi approach, this research conducted interviews with 51 experts who were actively involved in six homestays located on the East Coast of Peninsular Malaysia. The objective was to identify key evaluation indicators pertinent to the homestay industry. The findings underscored the pivotal roles played by community resources and tourism in the sustainability of homestays. Additionally, environmental, economic, and social factors emerged as crucial components for maintaining the industry's sustainability. This innovative assessment methodology offers a valuable instrument for enhancing the sustainability of the homestay sector, especially in the wake of the COVID-19 pandemic. By embracing this approach, homestay operators can fortify their sustainable management practices and prepare themselves for future pandemics. This study represents a significant contribution to the field of homestay ecotourism, emphasizing the imperative for continued research in this dynamic domain.

Roadmap for implementation of genomics in healthcare: towards equity in access to genomics

Abstract Issue Implementing genomic medicine in healthcare can bring us closer to making personalised medicine a reality, with notable socioeconomic benefits. Description of the problem The 1+Million Genomes Initiative is promoting a reflection in European countries regarding their national strategies for genomic medicine, and how these contribute to research and improve clinical practice. Implementation of genomic medicine is complex and requires investment and adjustments in the organisation and practices of healthcare services, including technical infrastructure for genomic data generation and management, a trained healthcare workforce, and appropriate ethical and legal frameworks. Methods To promote the implementation of genomic medicine in healthcare systems, and improve its societal impact, the Beyond 1+MG (B1MG) project organised three country exchange visits for capacity building, developed and validated in real world settings a comprehensive Maturity Level Model (B1MG MLM) for genomic medicine, and organized workshops on health economics models for genomics. Results The products resulting from these activities provide policy recommendations for adoption of genomic medicine, an innovative MLM Toolkit for health systems to self-assess their current maturity status regarding genomic medicine implementation, and plan the path for improvement, as well as several brief papers highlighting main challenges and key indicators for health economic evaluation of genomic tests. Overall, this set of B1MG products provide a Playbook to support the development of national or regional roadmaps for the implementation of genomic medicine in healthcare systems. Lessons Country healthcare systems have specific practices, barriers and constraints, so the actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system. Key messages • The B1MG Roadmap will support the definition of strategies for adoption of genomics in healthcare systems, towards equity in the access to genomic medicine for all European citizens. • Country healthcare systems actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system.