Results Of Evaluation Research Articles

Development and user evaluation of an immersive light field system for space exploration

This paper presents the developmental work and user evaluation results of an immersive light field system built for the European Space Agency’s (ESA) project called “Light field-enhanced immersive teleoperation system for space station and ground control.” The main aim of the project is to evaluate the usefulness and feasibility of light fields in space exploration, and compare it to other types of immersive content, such as 360° photos and point clouds. In the course of the project, light field data were captured with a robotically controlled camera and processed into a suitable format. The light field authoring process was performed, and a light field renderer capable of displaying immersive panoramic or planar light fields on modern virtual reality hardware was developed. The planetary surface points of interest (POIs) were modeled in the laboratory environment, and three distinct test use cases utilizing them were developed. The user evaluation was held in the European Astronaut Centre (EAC) in the summer of 2023, involving prospective end-users of various backgrounds. During the evaluation, questionnaires, interviews, and observation were used for data collection. At the end of the paper, the evaluation results, as well as a discussion about lessons learned and possible improvements to the light field system, are presented.

A high-efficiency and low-carbon strategy for selective lithium recovery from spent lithium-ion batteries: Combining mechanochemical activation with biomass reduction roasting

Biomass reduction roasting has attracted considerable attention as an emerging strategy for selectively recovering lithium from spent lithium-ion batteries (LIBs). However, the utilization of excessive roasting conditions in current practices results in significant energy consumption and C emissions, thereby hindering further development. Herein, we propose a novel recycling strategy that combines mechanochemical activation (MCA) with biomass reduction roasting. The optimized roasting conditions with MCA pretreatment achieve a 100 °C reduction in roasting temperature, a halving of biomass ratio and roasting time, compared to the case without activation. By analyzing the changes in physicochemical properties of mixed samples (cathode materials and biomass) under different activation conditions and their impacts, the action mechanism of MCA on biomass reduction roasting is revealed. The introduction of MCA pretreatment achieves milder roasting conditions by promoting homogeneous mixing and disrupting NCM crystal structures through the “pre-oxygen elements removal” process. Furthermore, mechanical treatment was applied to enhance the carbonated water leaching process. The results of sustainability analysis and economic evaluation demonstrate that the proposed recycling strategy not only reduces CO2 emissions and energy consumption but also generates considerable benefits. Overall, this high-efficiency and low-carbon recycling strategy provides a promising direction for the development of spent LIBs recycling technology.

Feasibility and uptake of a digital mental health intervention for depression among Lebanese and Syrian displaced people in Lebanon: a qualitative study.

Digital interventions are increasingly regarded as a potential solution for the inaccessibility of mental health treatment across low-and-middle-income settings, especially for common mental disorders. Step-by-Step (SbS) is a digital, guided self-help intervention for depression found effective in two Randomized Controlled Trials (RCTs) in Lebanon. For research implementation and further scale-up, this paper reports the results of a qualitative evaluation of SbS among the Lebanese and others and displaced Syrians in Lebanon. Thirty-four Key Informant Interviews (KIIs) were executed with participants of the RCTs, SbS staff members, and external stakeholders. Questions garnered feedback about the feasibility, acceptability, enabling factors, and barriers to adhering to the research, implementation, and the SbS intervention. A thematic analysis was conducted using NVivo, and key themes, topics, and recommendations, on research methods and the intervention itself, were generated and reported. Results showed a high level of acceptability of SbS among Lebanese and Syrians and identified sub-groups for whom acceptance or use might be lower, such as older adults and people with limited access to the internet or smartphones. Furthermore, interviews identified the main enabling factors and barriers to adherence related to the research design, content, and delivery approach. Barriers related to feasibility included lengthy assessments as part of the RCTs, and mistrust related to delays in study compensations. Other common challenges were forgetting login credentials, poor internet connection, being busy and competing needs. Enabling factors and best practices included motivating participants to use the intervention through the weekly support provided by helpers, setting an oral contract for commitment, and dividing the compensations into several installments as part of the RCTs. Recommendations regarding sustainability were given. The findings show that overall, SbS is feasible, acceptable, and much needed in Lebanon among the Lebanese and Syrians. This assessment identifies reasons for low adherence to the research and the intervention and presents improvement solutions. Recommendations generated in this paper inform the upscale of SbS and the planning, design, and implementation of future digital interventions in research and service provision settings in the mental health field.

Benefits evaluation of ecological restoration projects based on value realization of ecological products

Ecological restoration project is the premise to realize the value of ecological products. The current ecological product value accounting and ecological restoration project benefit evaluation methods are interlinked. The benefit evaluation of ecological restoration projects can not only analyze the completion of ecological restoration projects, but also quantify the impact of externalities on regional development. This study proposes a hypothesis that the benefit evaluation results of ecological restoration projects can affect the realization of urban ecological product value. In order to verify the above hypothesis, this study designed a benefit evaluation framework to evaluate the economic benefits, ecological benefits and project costs of ecological restoration projects. In this study, the benefit evaluation results are divided into three cases to calculate the benefit-cost ratio under different conditions. The three cases are only considering the direct economic benefits (DEB), considering the indirect economic benefits (TEB) and considering the comprehensive benefits (AEB). Taking Jiangyin City of Jiangsu Province as an example, the benefit evaluation of water source protection project in Jiangyin City is carried out. The results show that the benefit-cost ratios in the three cases are 6.68,7.60 and 9.36, respectively. The results show that the benefit-cost ratio is different in different benefit situations. Therefore, when discussing the realization path of ecological product value in ecological restoration areas, it is also necessary to discuss the situation. The evaluation system can provide reference for the benefit evaluation of ecological restoration projects, and also provide data and theoretical support for the realization of regional ecological product value.

Exploring Dense Retrieval for Dialogue Response Selection

Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. However, in real-world scenarios, the high computation cost forces existing dialogue response selection models to rank only a small number of candidates, recalled by a coarse-grained model, precluding many high-quality candidates. To overcome this problem, we present a novel and efficient response selection model and a set of tailor-designed learning strategies to train it effectively. The proposed model consists of a dense retrieval module and an interaction layer, which could directly select the proper response from a large corpus. We conduct re-rank and full-rank evaluations on widely used benchmarks to evaluate our proposed model. Extensive experimental results demonstrate that our proposed model notably outperforms the state-of-the-art baselines on both re-rank and full-rank evaluations. Moreover, human evaluation results show that the response quality could be improved further by enlarging the candidate pool with nonparallel corpora. In addition, we also release high-quality benchmarks that are carefully annotated for more accurate dialogue response selection evaluation. All source codes, datasets, model parameters, and other related resources have been publicly available. 1

Optimized Design of Scoring Criteria for Large-Scale Innovation Competitions

The reasonable and reliable evaluation score calculation scheme wins the top priority in large-scale innovation-competitions currently. Due to the disparity among individual experts, and the existing standard score calculation model merely based on the experts' own circumstance, it cannot fully reflect the comprehensive level of players, resulting in a certain degree of error in the evaluation results. From the perspective of individual and decision-making of a group, this paper improves existing models and introduces the concept of modified scores. To verify the feasibility of the model, four schemes were designed based on the analysis of data distribution. Consistency and difference factors were applied to compare. Come to a conclusion: scheme adopted the new standard score calculation model ranked the top, indicating that the new calculation model is more reliable. Next, considering that the volatile correction factor affected by data, there is a possibility of some poor actual results with high correction scores. To rectify this case, a power exponent is added to the correction factor and a sign function is adopted to specify the positive or negative of correction scores. To verify the feasibility of the model, four sets of controlled experiments were designed with the introduction of two factors: power exponent and reconsideration bonus, as well as the ranking consistency test based on the condition that the award order of the expert agreement was consistent. In the end, it was found that the scheme that introduced both power exponent and reconsideration bonus points had a sorting consistency rate of 74%, which increased by 30% compared to the original model, indicating the rationality of the modification. The expert evaluation standard score calculation model established in this paper comprehensively considers individual and group decision-making, and provides reasonable correction for the differential scoring between experts. At the same time, this scheme provides a reference basis for further optimizing large-scale innovation competition plans in the future.

GraphMoCo: A graph momentum contrast model for large-scale binary function representation learning

In the field of cybersecurity, the ability to compute similarity scores at the function level for binary code is of utmost importance. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale Binary Code Similarity Detection (BCSD). To overcome the aforementioned problems, we propose GraphMoCo: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. We adopt an unsupervised learning strategy. Our approach eliminates the need for manual labeling. By leveraging the intrinsic structural information at multiple levels of the binary code, our model could achieve higher accuracy with a simple CNN-based model. By introducing the preshuffle mechanism, the issue of information leakage in graph momentum update algorithm is mitigated. The evaluation results indicate that GraphMoCo exhibits superior performance compared to SOTA approaches in the function pair search task, showing an average improvement of 7% on AUC, and 10% on MRR and Recall@1. Furthermore, GraphMoCo achieves a MAP of 0.93 on the more challenging dataset 2, which comprises a larger function pool. In a real-world scenario, specifically in known vulnerability searching, GraphMoCo achieves a MRR that surpasses existing SOTA models by 5%.

Optimizing aspect-based sentiment analysis using sentence embedding transformer, bayesian search clustering, and sparse attention mechanism

The aspect-based sentiment analysis of hotel reviews represents a critical yet underexplored area of research. Such reviews encompass a range of expressions that extend beyond simple positive, negative, or neutral sentiments, delving into specific aspects discussed within the review. For instance, a statement like 'the location is comfortable, the food is good, but unfortunately, the service is bad' encapsulates multiple aspects, including location, food, and service. Existing research in Aspect-Based Sentiment Analysis (ABSA) frequently employs word embedding techniques for feature extraction, utilizing both Term and Transformer models. This study introduces an innovative approach by transitioning from word to sentence embedding, employing the SBERT transformer model for this transformation. This study further innovates label creation and topic modeling by integrating Bayesian search clustering with Inverse Document Frequency (IDF) calculations. This approach enables the identification of relevant topics, categorizing IDF scores based on occurrence frequencies. Scores ranging between 4–6 are marked as having low value but high informative potential, making them suitable candidates for label-specific datasets, particularly those without pre-existing labels. Additionally, this study incorporates the BigBird model to enhance this classification methodology. Applying this comprehensive framework to hotel review datasets, the result of the ABSA evaluation achieved an impressive accuracy score of 99%, indicating the efficacy of the proposed methodology in this nuanced field.

THE INFLUENCE OF SAFETY INDUCTION DESIGN AND MANUFACTURE AT PT. KUNANGO JANTAN PEKANBARU

Based on the company's data percentage of work accident rate is 37.14% and medical expenses incurred Rp.89.637.490,00 for it required a policy and organizational commitment that mutually influential and related to each other so that the system is running well and optimal One satunys by designing and making safety induction to inform the company condition in general and special by using The Hazard Operability Study (HAZOP) method for hazard identification method in the area and using morphology diagram method for the best alternative and concept in the design used in this research. Based on the results of evaluation of the results by distributing questionnaires to the respondents ie visitors and employees each amounted to 15 pieces, obtained a conclusion 98.67% of visitors and 97.33% of employees said the safety induction is feasible and already meet the requirements to use.

A general framework for qualitative analysis of Raman spectroscopy based on deep learning

Deep learning has become the prevailing method for qualitative analysis of Raman spectroscopy. However, for researchers and engineers without a background in computer science or artificial intelligence, the knowledge required can be challenging. This includes the underlying principles of deep learning and the features of frameworks such as PyTorch, TensorFlow, and Keras. To address this challenge, we summarize the Raman spectroscopy analysis process and design a suitable deep learning framework, including dataset creation, data preprocessing, data augmentation, deep learning model development, model training, model evaluation, model visualization analysis, and model deployment. We use four open-source Raman spectroscopy datasets, including binary classification and multi-classification examples, to explain the framework’s components and operation and provide comprehensive evaluation results. This framework has the potential to improve work efficiency and accelerate the research process.

The Effect of PEG 4000 in Co-processed Excipient using Melt Granulation Method on the Physical Characteristics of Chitosan Tablets in Mangrove Crab Shells (Scylla serrata)

Mangrove Crab (Scylla serrata) is an animal that likes to be consumed but its shell waste is underutilized. The shell contains chitosan which has many benefits, one of which can lower cholesterol levels. The combination of 2 or more excipients is called co-processed excipients which are then continued into tablets by direct compression. The aim of the study was to find out whether chitosan could be formalized into coprocessed tablets (lactose, primogel, and PEG 4000 7.5% & 15%) by direct compression method and the effect of differences in the characteristics of coprocessed and chitosan tablets with a ratio of PEG 4000 7.5% and 15 % as binder. The evaluation results showed that chitosan tablets fulfilled the requirements for the uniformity of weight, hardness, disintegration time (F1) tests, while they did not meet the requirements for the uniformity of tablet size, friability and disintegration times (F2) tests and the results of the coprocessing evaluation met specifications both F1 and F2, namely: particle size distribution, moisture content, flow rate, angle of repose (very good), compressibility index (adequate) and Hausner's ratio (very good). The evaluation results were then processed statistically using the SPSS application using the independent t-test method which showed that there was no significant difference in the effect of PEG 4000 7.5% and 15% as a binder on the characteristics of co-processed and chitosan tablets. Chitosan tablets from mangrove crab shells (Scylla serrata) cannot be formalized by direct compression with co-processing.

Usability, Appeal, and Relevance of Music and Wellbeing Training for Carers of People with Dementia: A Think-Aloud Study.

Personalized music listening can be highly effective in supporting people experiencing changes in mood and behavior due to Alzheimer's disease and other conditions causing dementia. However, a lack of staff education can be a barrier to effective use of music in residential aged care. This study investigates the usability, appeal and relevance of an online training course designed to help care staff develop personalized music programs for people living with dementia in their care. A mixed methods approach was taken in which 13 participants took part in a think-aloud session while using the training. Qualitative data from the think-aloud session and interviews were triangulated with results of a survey evaluation of the training. Themes relating to Engagement, Usability and Appeal, Pedagogical Design and Content, and Transfer, Impact and Barriers to Uptake were discussed. Results indicated that participants regarded the virtual environment and pedagogical design as of high quality, although some minor issues in navigability were identified. Participants also found the training to be highly relevant to their roles as caregivers and reported ways they would incorporate key concepts into care practices. However, ongoing barriers to implementation of such training were identified including systemic issues that contribute to a culture of focus on physical care. Consistency of navigation and clear instructions for interacting with content are important in training of time-poor care staff. Linking training to industry standards can provide important motivation for implementation of training, although systemic barriers can impede real-world change.

Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic sets

With the development of the internet economy, e-commerce has rapidly risen, and a large number of small and micro e-commerce enterprises have emerged. However, these enterprises have low financial information transparency, small scale, and high development uncertainty. Therefore, combining the characteristics of the internet economy, it is of great significance to dynamically evaluate credit risk. This not only helps to enhance the quality and rationality of credit risk evaluation results, but also helps to improve financing efficiency and reduce financing risks. The credit evaluation for small and micro enterprises is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and TOPSIS method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as an effective tool for characterizing uncertain information during the credit evaluation for small and micro enterprises. In this paper, the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is inaugurated to solve the MAGDM under 2TLNSs. Finally, a numerical case study for credit evaluation for small and micro enterprises is inaugurated to confirm the proposed method. The prime contribution of this paper are outlined: (1) The information entropy based on score function and accuracy function are built on the 2TLNSs to obtain weight information; (2) an integrated the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is established to cope with MAGDM; (3) An illustrative example for credit evaluation for small and micro enterprises has accomplished to illustrate the 2TLNN-TODIM-TOPSIS; (4) some comparative analysis are employed to verify the 2TLNN-TODIM-TOPSIS method.

Virtual and Interprofessional Objective Structured Clinical Examination in Dentistry and Dental Technology: Development and User Evaluations.

Interprofessional education (IPE) facilitates interprofessional collaborative practice (IPCP) to encourage teamwork among dental care professionals and is increasingly becoming a part of training programs for dental and dental technology students. However, the focus of previous IPE and IPCP studies has largely been on subjective student and instructor perceptions without including objective assessments of collaborative practice as an outcome measure. The purposes of this study were to develop the framework for a novel virtual and interprofessional objective structured clinical examination (viOSCE) applicable to dental and dental technology students, to assess the effectiveness of the framework as a tool for measuring the outcomes of IPE, and to promote IPCP among dental and dental technology students. The framework of the proposed novel viOSCE was developed using the modified Delphi method and then piloted. The lead researcher and a group of experts determined the content and scoring system. Subjective data were collected using the Readiness for Interprofessional Learning Scale and a self-made scale, and objective data were collected using examiner ratings. Data were analyzed using nonparametric tests. We successfully developed a viOSCE framework applicable to dental and dental technology students. Of 50 students, 32 (64%) participated in the pilot study and completed the questionnaires. On the basis of the Readiness for Interprofessional Learning Scale, the subjective evaluation indicated that teamwork skills were improved, and the only statistically significant difference in participant motivation between the 2 professional groups was in the mutual evaluation scale (P=.004). For the viOSCE evaluation scale, the difference between the professional groups in removable prosthodontics was statistically significant, and a trend for negative correlation between subjective and objective scores was noted, but it was not statistically significant. The results confirm that viOSCE can be used as an objective evaluation tool to assess the outcomes of IPE and IPCP. This study also revealed an interesting relationship between mutual evaluation and IPCP results, further demonstrating that the IPE and IPCP results urgently need to be supplemented with objective evaluation tools. Therefore, the implementation of viOSCE as part of a large and more complete objective structured clinical examination to test the ability of students to meet undergraduate graduation requirements will be the focus of our future studies.

Theoretical and experimental study on a novel evaluation criteria of heat exchangers applied in aero engines

For the application of heat exchangers in the aeronautic field, an effective evaluation method is required to support the satisfaction of the multi-aspect design restrictions including the thermal and hydraulic performance, occupied volume, total weight, etc. To weigh the benefit and cost for both heat transfer units and heat exchangers, this study thus proposes three evaluation indexes Rp, RV, and Rm defined by the product of heat transfer coefficient and heat transfer area of unit windward area times pressure drop (hA/(AfrontΔp)), that of unit volume (hA/V) and that of unit weight (hA/m). Considering the factors h and A comprehensively and excluding the influence of mean differential temperature, the three indexes can reflect the low flow resistance, compactness, and lightness characteristics under fixed operating conditions and heat transfer duty requirement accordingly. A validation experiment is carried out and the deviation between the evaluation and experiment results is less than 10.88% for the three indexes. To show the applicability, a detailed evaluation of six different heat transfer units and five types of heat exchangers is conducted. A heat transfer unit selection method based on the new indexes is provided finally for heat exchanger design.

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises.

In conventional and robotic rehabilitation, the patient's active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients' therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients' progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients' improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients' participation during the rehabilitation exercises.

Coupling Monte Carlo simulation with CRITIC-enhanced water quality assessment for the Weishui Reservoir

Abstract Traditional methods for water quality assessment often overlook the uncertainty of water quality data during the sample collection process, leading to limitations in their application. Therefore, this study combines the comprehensive water quality index (CWQI) method and the improved CWQI method based on CRITIC with the Monte Carlo method to evaluate the water quality in the Weishui Reservoir watershed. The results indicate that (1) there is a noticeable difference in water quality between the Shaxiping and Dayanzui sampling points. The water quality at the Shaxiping sampling point is excellent, with a water quality classification of Class I. In contrast, the water quality at the Dayanzui sampling point is comparatively poorer, with an average water quality classification of Class III. (2) Sensitivity analysis shows that TN, NH4+-N, and TP are more sensitive than other indicators, suggesting that they are the primary factors influencing the evaluation results. (3) Compared to the traditional CWQI method, combining the CRITIC-based improved CWQI method with the Monte Carlo method is more scientifically rigorous. It considers the variety of evaluation indicators, allocates weights rationally, and provides evaluation results that align better with seasonal variations, resulting in higher discriminative power.

Evaluation of the effect of calcium concentration in the wool of sires on se- men quality characteristics

Chemical elements, as structural components of the body, influence physiological processes and functions of the organism, growth, productive and reproductive traits, resistance and others. In this regard, the present study aimed to evaluate the effect of different calcium concentrations in the wool of Holstein sires on the semen quality characteristics. The studies were conducted in conditions of Leningrad and Vologda regions on Holstein sires, 3–5 years old (n = 40). Calcium concentration in wool was determined by atomic emission and mass spectrometry (AES–ICP and MS–ICP). According to the results of this analysis and comparison with previously developed reference intervals, we formed four groups of sires: I – level of Ca concentration less than 25th percentile, II and IV – within 25–75th percentile (physiological norm: 1597–2926 mg/kg) and III – more than 75th percentile. As a result of this distribution, the Ca concentration in the wool of animals of Group II was higher by 75.9 % compared to Group I; in turn, individuals of Group IV exceeded Group III – by 27.9 %. According to the results of semen quality characteristics evaluation, it was found that in the group of sires with Ca level within the previously developed reference intervals, the concentration of spermatozoa was higher by 15.3 % and their activity – by 0.17 points in fresh semen in comparison with the group with its reduced level (below the 25th percentile). High Ca content (above the 75th percentile) in wool contributed to a 9.0 % decrease in the concentration of spermatozoa compared to its average values. Thus, the calcium concentration, assessed by its level in the fur, affects the concentration and activity of sperm. It is necessary to evaluate it to improve the quality characteristics of sperm systematically.

Remote tutoring with low-tech means to accelerate learning: Evidence for El Salvador

This paper presents the results of an impact evaluation with an experimental design, that estimates the effect of a low-tech low-cost remote tutoring intervention applied during the pandemic for remedial education purposes on girls and boys aged 9-14 years in three departments of El Salvador. Our main contributions are a) the provision of strong experimental evidence that the intervention can improve student math learning in developing countries for closing education gaps; and b) the measurement of student anxiety levels before and after the treatment, which allows verifying whether student-tutor interactions mitigated some of the negative socioemotional effects of student confinement. The program is found to have had a positive and significant effect of 0.24 standard deviations on math learning, which is equivalent to a 33.8 percent acceleration as compared to the control group. However, no significant effects were observed on student anxiety levels, which suggests that the academic gains were not mediated by these types of socioemotional factors. The results provide valuable information for the design of tutor training and for the development of tutoring protocols, among other aspects, for future similar programs.

Evaluation and optimization of ecological spatial resilience of Yanhe River Basin based on complex network theory

The loess hilly and gully areas have broken terrain, vertical and horizontal ravines and fragile ecological environments. Improving the resilience of the regional ecological space is conducive to improving the quality of the local ecological environment. With the ecological space of the Yanhe River Basin selected as the research object, this paper constructs a research framework of "network identification topology-resilience evaluation-spatial optimization" and uses morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance model (MCR) to identify ecological spatial networks. Based on circuit theory, the ecological pinch point is identified, the ecological spatial network is optimized, and scenario simulation is performed. Through complex network theory and related indicators, the ecological spatial resilience of the basin is evaluated, and the hierarchical optimization strategy of the ecological space is confirmed. According to the ecological function of the source area and the results of the resilience evaluation, the boundaries of the protected control area, guidance development area, remediation area, and maintenance and improvement area of the basin are delineated. The importance of ecological source and corridor protection is classified, and corresponding protection strategies are proposed. The research results can provide theoretical support and practical guidance for the territorial spatial planning and ecological space construction of the Yanhe River Basin and provide a reference for the ecological restoration, resource development and environmental governance of the Yanhe River Basin.