Traditional Assessment Methods Research Articles

Vehicle Damage Analysis

Abstract: Vehicle damage, resulting from accidents, environmental factors, and everyday use, presents a ubiquitous challenge to vehicle owners and the broader automotive industry. An efficient and accurate vehicle damage assessment is essential for insurance claims, resale valuations, and overall vehicle safety. However, the traditional damage assessment methods often involve human subjectivity and can be time-consuming, leading to inefficiencies and customer dissatisfaction. This abstract explores the need for modernizing and automating the vehicle damage assessment process. Vehicle damage analysis plays a crucial role in accident reconstruction, insurance claims, safety assessment, and legal proceedings. This abstract provides an overview of the key aspects of vehicle damage analysis, including the methods and tools used, as well as its broader implications. Vehicle damage analysis involves the systematic examination and assessment of damage sustained by vehicles involved in accidents, incidents, or natural disasters. This process is essential for determining the cause and severity of the damage, estimating repair costs, and establishing liability in legal cases. This abstract highlights the multifaceted nature of the problem, encompassing the need for accurate and efficient damage detection, cost estimation, integration with insurance systems, and improved customer experiences. Addressing these challenges requires the application of advanced technologies such as computer vision, artificial intelligence, and machine learning, as well as a commitment to data security, privacy, and regulatory compliance.

Text Message-Based Assessment of 90-Day Modified Rankin Scale After Stroke.

The modified Rankin Scale (mRS) is commonly used to measure disability after stroke, traditionally assessed through telephone or in-person evaluation. Here, we investigated the validity of mRS assessment through an automated text messaging system based on the simplified mRS questionnaire as an alternative method to traditional methods of assessment. A total of 250 patients admitted to 3 hospitals within the University of Pennsylvania Health System with ischemic or hemorrhagic stroke were enrolled. Participants received automated text messages sent 48 hours before their outpatient appointment at about 90 days after stroke. The mRS scores were assigned on the basis of participant responses to 2 to 4 text questions eliciting yes/no responses. The mRS was then evaluated in person or by telephone interview for comparison. Responses were compared with κ. A total of 142 patients (57%) completed the study. The spontaneous response rate to text messages was 46.5% and up to 72% with an additional direct in-person or phone call reminder. Agreement was substantial (quadratic-weighted κ=0.87 [95% CI, 0.83-0.89]) between responses derived from the automated text messaging and traditional interviews. Agreement for distinguishing functional independence (mRS 0-1) from dependence (mRS 2-5) was substantial (unweighted κ=0.79 [95% CI, 0.69-0.90]). An automated text messaging system is a feasible method for remotely obtaining the mRS after stroke and a potential alternative to traditional in-person or telephone assessment. Further studies are needed to evaluate the generalizability of text message-based approaches to stroke outcome measurement.

Scholar- Study Material Application

Scholar- Study Material Application

A Comparative Analysis of Traditional and Modern Approaches to Assessment and Evaluation in Education

For tracking student progress and designing educational programs, assessment and evaluation are critical components of the process. Assessment is data collection to monitor students' development, while evaluation analyzes this data to make informed educational choices. To enhance educational results, practical and effective assessment and evaluation techniques must be applied. These strategies enable instructors to discover and customize instruction by identifying each student's strengths and limitations. This article compares classic and modern assessment and evaluation methodologies to draw implications for educational policies and practices. The research employs a systematic literature review to analyze data gathered from diverse fields. Efficiency, appropriateness, scalability, inclusivity, technology integration, and stakeholder acceptability are critical factors in comparing assessment and evaluation approaches. These parameters were used to analyze the benefits and drawbacks of each strategy. Traditional approaches, for example, have been proven insufficient in expressing individual characteristics since they are ubiquitous and uniform. Modern approaches stand out for their ability to meet student demands and adapt to diverse circumstances. The broad use of these modern procedures, however, is much more complicated than the simply adaptable traditional methods. According to the study's results, current and traditional assessment and evaluation methods have common themes such as purpose, quality assurance, involvement, ethics, and continual development. Traditional approaches, on the other hand, remain more summative, but novel methods concentrate more on student growth via formative strategies and ongoing feedback. Furthermore, novel methods provide flexibility, technology integration, and inclusion, while conventional methods may be restricted to a specific format. Modern methodologies reflect technology's growing importance in education and provide substantial opportunities to build engaging and attractive learning environments. Traditional techniques, on the other hand, emphasize academic knowledge and memory abilities while ignoring practical applications. Assessments that involve problem and project-based learning, as well as real-world situations, are prioritized in modern techniques. Consequently, integrating traditional and contemporary assessment and evaluation methodologies is recommended and will result in a more effective and inclusive educational assessment system. This research on the applicability and efficacy of assessment and evaluation methodologies provides for the exploration of new educational approaches and techniques, as well as the chance to assess current practices and make suggestions for implementation.

Effectiveness of E-Rapor Implementation: Literature Review

Implementing E-Rapor (Electronic Report) in education has become an exciting research topic that has captured the attention of numerous researchers. Based on the findings of a literature review, E-Rapor aims to bring benefits and positive impacts to the field of education, promoting its growth in the current digital era. However, researchers have also noted several limitations in using the E-Rapor-based information system, including difficulties in its application for information processing, competence planning, and data management. This research seeks to analyze gaps in the learning outcomes achieved by students when the E-Report application system is implemented compared to traditional or manual assessment methods. The data collection method employed in this study is a literature review, which involves gathering secondary data from various reputable sources. As for the research instrument used, the researchers have formulated five research questions that serve as valuable guides throughout the data collection process. The results of the discussion show that E-Reports are useful for regularly assessing student abilities by educators and school employees, allowing quick intervention when necessary, and increasing overall educational efficiency. Due to this, the author recommends further research to understand the impact of using E-Reports on learning outcomes and educational management to be comprehensive.

Deep Learning for Predictive Toxicology Assessment Early Detection of Adverse Drug Reactions.

Predicting adverse drug reactions (ADRs) early in the drug development process is crucial for ensuring drug safety and reducing costly late-stage failures. Traditional methods for toxicity assessment rely heavily on animal testing and empirical observations, which are often time-consuming, expensive, and ethically questionable. In recent years, deep learning techniques have emerged as powerful tools for predictive toxicology, offering the potential to accelerate the identification of potential ADRs while reducing reliance on animal models.This paper reviews the current state-of-the-art deep learning approaches for predictive toxicology assessment, focusing on their applications in the early detection of ADRs. [1],[2] We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), and their utilization in analyzing diverse data types such as chemical structures, omics data, and adverse event reports.Furthermore, we examine the challenges and limitations associated with deep learning-based predictive toxicology, including data availability, model interpretability, and regulatory acceptance. We also explore ongoing efforts to address these challenges, such as the development of standardized datasets, explainable AI techniques, and collaborations between academia, industry, and regulatory agencies.Overall, this paper highlights the potential of deep learning for early detection of ADRs in drug development and underscores the need for continued research and collaboration to realize the full benefits of these techniques in ensuring drug safety and improving public health.

E058 Disease activity measurement in rheumatoid arthritis - simpler in not always better!

Abstract Background/Aims Rheumatoid arthritis is one of the commonest inflammatory arthritis (1). The traditional method of disease activity assessment would incorporate activity measures like DAS 28, CDAI and SDAI which have been validated in clinical research (2). All these indices use 28 joint assessment method. These measures use different aspects of disease activity including swollen joint count, tender joint count, patient global scores and physician global assessment scores and inflammatory markers are included in some of the measures. We conducted a small study to compare these outcome measures in patients with moderate RA (per DAS criteria) who were recruited for disease activity assessment through MSK ultrasound. Methods Of the 30 biologic naive patients recruited, 27 were included in this study. Patients with moderate disease activity (per DAS criteria) were recruited for MSK ultrasound evaluation by a single rheumatologist. Patients with a diagnosis of fibromyalgia were excluded. These patients had been reviewed by two assessors prior to USS assessment and disease activity measures updated. Patient data was collected through electronic patient records. Data was analysed through MS Excel. Results The median age was 63. There were 66% females and 34% male patients. Majority were sero positive for either RhF, Anti CCP or both (77%). Mean DAS-28 score was 4.4 and corresponding mean CDAI and SDAI scores were 22.8 and 23.5 respectively. Mean HAQ score 1.08 and median CRP was 8.6. The median composite EULAR OMERACT synovitis score was 9.18. The average physician global assessment score for disease activity before USS was 4.9/10 and it correlated well with decision to escalate treatment in 41% patients as a result of USS assessment. Conclusion With a small sample size, this project shows that simplified disease activity measures do not necessarily provide clear evidence of disease activity and are heavily influenced by patient reported outcome measures (tender joint count and global pain scores). CDAI scores can be slightly higher compared to DAS and SDAI scores for the similar patients. Physician assessment and ultrasound can provide robust evaluation of disease activity and help in decision-making. A multicentric study with a large simple size to evaluate these findings would be helpful. Judicious use of clinical evaluation and imaging can help in tailoring treatment according to patient’s clinical condition and can also save money. Disclosure H. Crawshaw: None. E. Gates: None. C. Acebes: None. M. Jamal: None.

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A user-friendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the sustainable management of concrete infrastructures and emphasizing the integration of advanced machine learning techniques with metaheuristic optimization for environmental and structural engineering advancements.

Real-time safety risk assessment of floating offshore platforms based on on-site monitoring

Real-time safety risk assessment of in-service marine engineering structures is one of the key components for the intelligent development of marine resources. Floating platforms typically have weak modes and time-varying characteristics, and hence, traditional structural damage identification and safety assessment methods cannot be directly applied to floating platforms. The objective of this study was to achieve the real-time safety risk assessment of in-service floating platforms. First, a structural damage identification method for floating platforms was proposed based on virtual loads, and the physical parameters of the platforms were obtained through inversion of measurable data. The identification result was verified based on the lengths and stiffnesses of polyester cables, which affected the dynamic response of the floating platform. The results showed that the proposed method effectively detected the parameter changes of the platform during long-term service. Then, the neural network was used to construct a load–stress surrogate model at key positions of the floating offshore platform. This model could calculate the stresses at key positions in real time, thereby evaluating the safety risks of the platform. Lastly, a real-time safety risk assessment method for floating offshore platforms was proposed, which provides a technical solution for ensuring the safety of offshore platforms.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

Integrated Performance Assessments: Providing Equitable Instruction and Assessment for ELLs/MLLs

AbstractIn this Teaching Issues article, we make the case for using Integrated Performance Assessments (IPAs) to get a better picture of ELLs/MLLs learning progress and language development and thus, contributing to more equitable learning environments in both instruction and assessment practices. An Integrated Performance Assessment allows learners to use the three modes of communication—interpretive, interpersonal, and presentational—to communicate their learning and demonstrate language development in an integrated way. We show how IPAs can be used as assessments for, of, and as learning; they promote more authentic teaching and assessment methods, in both formative and summative ways. To illustrate the potential opportunities IPAs can offer over traditional teaching and assessment methods, we share a classroom‐ready example designed by a teacher who participated in a 15‐month professional development project. We posit that IPAs value the resources ELLs/MLLs bring to the classroom and allow them to use these resources in their learning, as well as promote authentic language use and provide a window into ELLs’/MLLs’ progress in ways that advance their learning and language development through collaboration and interaction.

Beyond inundation: a comprehensive assessment of sea level rise impact on coastal cultural heritage in China

The rise in sea levels, driven by global climate change, poses a significant threat to cultural heritage in coastal regions. Traditional risk assessment methods, focusing on direct inundation, often fail to consider the crucial impact of socio-economic factors, which are significantly vulnerable to sea level rise. To bridge this gap, this study introduces an innovative Sea Level Rise Cultural Heritage Impact Assessment Model (SLR-CHIA Model), a novel approach that integrates both land inundation and socio-economic aspects. This comprehensive model evaluates potential risks to various types of cultural heritage in coastal China, including intangible cultural heritage, relics, and traditional villages. The study’s findings are striking: (1) About 7.79% of coastal villages, 53.94% of relics, and 2.53% of intangible cultural heritage are potentially at high risk in a 100-year sea level rise event; (2) Relics in the Eastern coast and villages in the Southern coast are most vulnerable; (3) Different types of cultural heritage rely on diverse principal factors for protection, resulting in varied risk levels under sea level rise conditions. The SLR-CHIA Model provides a vital methodological framework for evaluating cultural heritage risks in other global regions.

Assessing Residents in the Department of Surgery at a Tertiary Care Centre Using Mini-Clinical Evaluation Exercise (Mini-CEX).

This study aimed to introduce, sensitize, and train our postgraduate students and faculty of the department of general surgery with the use of mini-Clinical Evaluation Exercise (mini‑CEX) and to assess the perception of students and faculty towards it. A cross‑sectional observational study was conducted over a period of four months. Ten surgery residents in the department were asked to volunteer to participate and five professors conducted the session. Five sessions of mini‑CEX(nine points) were conducted for each resident in different settings of the out‑patient department (OPD) and in‑patient department (IPD). A total of five skills were tested. Feedback from faculty and residents regarding the perception of mini‑CEX was alsotaken. A statistically significant difference in mean scores of all domains was observed comparing the first and last assessment (p<0.05). Hundred percent of the residents scored superior category (7-9) in the final assessment in all domains, whereas the maximum was in a satisfactory scoring grade in 1st assessment. The time taken for the assessment significantly reduced from 1st assessment to the last assessment in OPD and IPD settings (p=0.001). The mini-CEX assessment tool got 100% feedback from faculty in terms of skill improvement, method, attitude of residents, and ability to identify gaps in knowledge. However, one assessor thought that "time given for assessment" was inadequate and more effort was required than the usual traditional assessment methods. The most identified problem faced by residents was that the "time given during assessment" was less (50%); however, overall residents also found it valid, effective, and helpful in identifying knowledge gaps and improving clinical and communication skills. Mini‑CEX improves the learning environment in residency and also leads to improvement in medical interviewing skills, physical examination skills, humanistic qualities/professionalism, and counseling skills. So, it can be used for residency training in clinical departments.

Resilience in Focus: Rethinking the Risk Matrix

This research presents a three-dimensional risk matrix model designed for the analysis and prioritisation of critical risks in the context of resilience. Traditional risk assessment methods prevalent in information security, which typically juxtapose the likelihood and consequences of risks, are inadequate for fully capturing the intricacies of critical risks. The proposed three-dimensional model addresses these shortcomings by cohesively integrating the dimensions of likelihood, impact and cost of risk management. This integration provides a holistic tool for resilient risk analysis that goes beyond the capabilities of traditional models. A key feature of this model is its ability to address the complexities associated with critical risks, which are often not adequately addressed by traditional risk matrices due to their stochastic nature and significant potential impact on organisational resilience. By incorporating budgetary constraints into the risk assessment process, the model enables a more objective and quantifiable approach to managing critical risks. It shifts the evaluative focus from a purely probabilistic perspective to a cost-value based assessment, emphasising the balance between potential benefits and mitigation expenditure. This approach not only refines the accuracy of critical risk assessment, but also enhances existing risk management practices, providing a more robust and strategic tool for managing organisational risk.

Construction of street tree risk assessment system and empirical analysis based on non-destructive testing technologies

The traditional visual tree assessment method is subjective in evaluating tree risks and therefore not effective in precisely detecting internal decay in tree trunk and root systems. To improve the accuracy of street tree risk assessment, a new nondestructive testing method was proposed. This new tree risk assessment method combines different non-destructive testing technologies, such as sonic tomography and ground-penetrating radar, which could significantly increase the accuracy of risk assessment in tree trunks and roots. The method was applied to evaluate the risk of 1,001 street trees in Shanghai’s historical feature protection area. The results revealed that despite most street trees having low branch and trunk risk levels, more than one-third had high root risk levels. The risk factors of street trees were mainly in the trunk and root system, with a significant correlation between the street tree risk level and tree cavities, diseases, and insect pests, as well as the depth and range of the root distribution, leaning, and internal decay in trunks. With the help of non-destructive testing and risk assessment analysis, as well as targeted prevention measures, the possibility of street risk damage was largely reduced, including street trees tilting and collapsing during typhoons, etc.

Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Automated Artificial Intelligence–Based Thai Food Dietary Assessment System: Development and Validation

BackgroundDietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. ObjectivesTo address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence–based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. MethodsINMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. ResultsThis combination significantly enhances the system’s ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. ConclusionsINMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.

Creating authentic assessment in mathematics

Assessment of students’ mathematics knowledge within higher education (HE) has normally taken a very traditional approach. Closed-book assessments have long been the favoured mode of assessment (Iannone Simpson, 2011) which often requires students to recall facts, formulae, and methods. One could argue that this type of assessment is limited in its ability to effectively assess how well a student’s ability to authentically use mathematics has developed. Due to the recent pandemic, many institutions were forced to rethink their assessment methods so that they could be delivered online and remotely. As such, there has been a renewed sense of need for more ‘authentic’ assessments for mathematics-based programmes.In this paper, we will discuss our journey of creating more authentic assessments for apprentices enrolled on a new Data Science Degree Apprenticeship, particularly in mathematics/statistics. We will compare two years of delivery of the course; the first year of delivery which comprised of traditional assessment methods (coursework/exam) and the second year of delivery that used more authentic assessment methods. We will discuss the pros and cons of each model by reflecting on our practice and drawing on apprentices’ feedback.

A comparison of traditional road safety assessment methods and the newly developed 'road safety deserts' approach

Road fatalities were labelled a pandemic as early as 1973 (BMJ 1973). The number of road fatalities reached 1.35 million in 2016. Currently over 3 500 people perish every day on the world's roads. South Africa has one of the highest road fatality rates in the world, with a fatality rate of 25.9 deaths per 100 000 population (WHO 2018). In order to understand and improve the road safety situation of a region, effective road safety assessments must be carried out. This paper presents a comparison of four different road safety assessment approaches, both traditional and novel, and serves as a proof of concept for the 'road safety desert' methodology, a new technique adapted from the 'transit desert' concept. This new approach to road safety assessment explores the possibility of geo-coded supply and demand comparisons to identify 'road safety deserts' - areas that have a comparatively higher road safety risk. This paper shows that there are several unique and effective ways to assess road safety, and that each approach incorporates different characteristics within their methodologies. It is recommended that road safety analysis is conducted using a multitude of methods, so as to improve understanding and intervention selection.

Multi-uncertainty river health assessment in the Loess Plateau based on a novel comprehensive similarity cloud model

With the development of related research on river health assessment, the traditional assessment methods cannot solve the uncertainty problems caused by diversified indicators and ambiguous weights. In order to comprehensively understand the river health status in the Loess Plateau and solve the uncertainty of assessment results, this study takes Wuding River as the research object, and constructs an assessment indicator system incorporating the ecological environment and social functions of the river. The genetic algorithm-BP neural network was used to establish a weight learning mechanism for assessment indicators, and the system weight was calculated by combining various weighting preferences. Based on the cloud model theory, a sample data cloud and a health level cloud were constructed, and a comprehensive similarity model considering the shape and distance of the cloud diagram was developed to identify the health level of Wuding River. The spatial and temporal variability of the river health status and the sources of pressure causing unhealthy problems were analyzed. It is shown that Wuding River is in basic health status, which gradually decreases from the upstream reaches to the downstream reaches. The health status of social service functions is better than that of the ecological environment attributes such as water resources and water quality. The sources of health pressure in the upstream and downstream reaches are different. In the upstream reaches, the pressure mainly comes from natural attributes such as successive declines in water production, slow governance of water and soil erosion, and the poor stability of the river. The health pressure in the midstream and dowmstream reaches comes from the interference of human economic activities, which include deterioration of water quality caused by the discharge of fossil energy industry, destruction of aquatic habitats, and flood safety hazards caused by insufficient standards for flood control project design. In addition, SDSCM provides river managers with accurate and comprehensive health risk information to help them manage rivers more effectively.