Model Assessment Research Articles

Responding to AI

The influence of artificial intelligence “has enormous implications for higher education” (Bearman, Ryan & Ajjawi, 2023, p. 369). Those of us teaching in the higher education sector have felt this profoundly: after the rapid uplift in digital teaching and learning practices in response to COVID 19 we are required, yet again, to respond to a new set of challenges brought about by the impact of AI. As an example, chatbot technology is calling into question the reliability of the traditional essay as a valid assessment tool (Stokel-Walker, 2022), requiring significant critical reflection and reevaluation of assessments from teaching academics to ensure course learning outcomes are met. This presentation outlines how a teaching specialist academic working in an initial teacher education (ITE) programme at a South Australian university carried out one such redesign. The focus of the re-design was a ‘unit/year plan’ assessment task for a final year curriculum specialization topic. The redesign aligned with the two guiding principles from TEQSA’s Assessment reform for the age of artificial intelligence (Lodge et al., 2023). The first of these guiding principles: ‘Assessment and learning experiences equip students to participate in a world where AI is ubiquitous’ (p. 2) points to the importance of educating students around AI use relevant to their learning. The second guiding principle: ‘Forming trustworthy judgements about student learning in a time of AI requires multiple, inclusive and contextualised approaches to assessment’ (p. 3) underscores the ongoing necessity for universities to have effective means by which to measure student learning outcomes, especially in accredited courses. The redesign of the assessment task both acknowledged the potential of AI for use in the professional space, and simultaneously sought to ensure assessment validity through the inclusion of an interview with the pre-service teachers. Overall, the redesign of the assessment task incorporated three components. Firstly, a brief introductory module about discipline-specific uses of AI was incorporated into the online course content. Secondly, pre-service teachers were ‘permitted’ to utilise available AI platforms in the creation of their planning task. Thirdly, the assessment process was adapted to include an oral presentation (Gulikers, Bastiaens & Kirschner, 2004) conducted as a panel interview in which each pre-service teacher could provide evidence of the pedagogical perspectives underpinning their plan and subsequently demonstrate the successful achievement of topic learning outcomes. The purpose of the panel interview was to assist assessors in forming a judgement about pre-service teachers’ pedagogical understanding and application, in line with relevant criteria from Matheis and John’s Comprehensive Authentic Assessment Model (CAAM) (Matheis & John, 2024). Autoethnographic reflections on the first delivery of the redesigned assessment are provided, including alternative approaches for future iterations. The learnings from the teaching initiative outlined in this presentation contribute to the growing discussion about how we can develop the capacity of our pre-service teachers as skilled and critical practitioners who can utilize AI as a valuable tool for their ongoing professional practice.

An assessment of large language models for OpenMP-based code parallelization: a user perspective

Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

Risk-Prediction Model of Restenosis after Endovascular Treatment for Peripheral Arterial Disease: A Systematic Review and Meta-analysis

Background: Peripheral artery disease (PAD) patients after endovascular treatment (EVT) have a relatively high restenosis rate. However, this risk can be mitigated through precise risk assessment and individualized self-management intervention plans. Moreover, the number of predictive models for restenosis risk in PAD patients after EVT is gradually increasing, yet these results of study exhibit certain discrepancies, raising uncertainties regarding the quality and applicability in clinical practice and future research. Objective: The objective of this study was to systematically evaluate risk-predictive models for restenosis in patients with PAD after EVT. Methods: A systematic review and meta-analysis of predictive model construction and validation using observational studies was undertaken. The China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched from inception to January 1, 2024. Two researchers independently conducted literature screening and data extraction, encompassing study design, data sources, outcome definition, sample size, predictive factors, model development, and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for risk of bias and applicability assessment of the models. Results: A total of 4275 studies were retrieved, ultimately resulting in the inclusion of 7 articles comprising 7 predictive models for restenosis in PAD patients after EVT, with a restenosis incidence ranging from 21.8% to 39.7%. The total sample size of the included models ranged from 137 to 1578 cases, with logistic regression analysis being the most commonly used modeling method. All models were built using R software. Only 2 models underwent external validation, and the reported area under the curve ranged from 0.728 to 0.864. The summary area-under-the-curve statistic was 0.80 (95% confidence interval [CI], 0.74-0.86), with an approximate prediction interval of 0.80 (95% CI, 0.62-0.91) . The number of included predictive factors ranged from 3 to 10, with the most common factors being age, Trans-Atlantic Inter-Society Consensus Ⅱ classification, hypertension, diabetes, high-sensitivity C-reactive protein, and surgical approach. All studies exhibited high risk of bias, primarily attributed to inappropriate sources of data and poor reporting of the analysis domain. Conclusion: Predictive models for restenosis after EVT in PAD patients demonstrate overall good predictive performance but are still in the developmental stage with higher risk of bias. Future studies should follow the TRIPOD statement, focusing on the development of new models with larger samples, rigorous study designs, and multicenter external validation. Clinical Impact This systematic review adheres to the PRISMA 2020 statement, offering the most recent systematic assessment of risk prediction models for restenosis following endovascular treatment in peripheral arterial disease.The newly developed PROBAST tool was employed to assess the risk of bias and the applicability of the existing evidence.This review emphasizes the practical utility, limitations of the current evidence, and recommendations for future research, with the goal of providing valuable information for clinicians and patients in their decision-making process, while also supporting the advancement of future research endeavors.

Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

AbstractTo have effective water resource management, the distributed hydrological models are commonly applied for supporting the decision‐making processes. Among different inputs, the spatial distributed rainfall plays significant role in those model simulations. Many interpolation methods have been developed for generating distributed rainfall based on measurement samples. However, depending on the catchment characteristics and data availability, the suitable interpolation algorithm is case‐dependent. This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment (Var catchment, France). Based on the daily rainfall data (2008–2014) collected from 16 stations in the Var catchment, six different interpolation approaches including: inverse distance weight (IDW), spline, kriging with linear and spherical semi‐variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested. Integrated the results of statistical and modeling assessments, the 400 m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment. Moreover, the strategy described in the article also shows promising acceptability for other catchments.

Customizing a mass appraisal model for residential property tax assessment: a hedonic regression approach in Bahir Dar city, Ethiopia

PurposeThe paper aims to estimate the house rental values for the purpose of customizing mass appraisals in Bahir Dar City, Ethiopia. It seeks to identify the critical factors affecting the rental values of residential properties and customize a mass appraisal model for such properties. The study focuses on identifying attributes that significantly affect house rental values.Design/methodology/approachThe paper adopted a survey research design, utilizing a survey questionnaire, expert group discussion and document analysis. The data were analyzed using thematic, descriptive and inferential statistical analysis, including correlation and hedonic regression analysis.FindingsAmong the variables included in the model, the number of rooms, availability of schools, land value grading, type of nearest road, housing typology, built-up area, plot area, walling material, traveling cost and fencing materials were the most significant factors for predicting the annual rental value of residential properties in the city.Research limitations/implicationsThe findings of this study will provide valuable insights to tax assessors, property owners and local government authorities, including municipalities, concerning the key determinants of the rental values of residential properties. Besides, these findings will serve as a useful tool for valuers and researchers in the field of property value modeling.Originality/valueThis study represents the first attempt to develop a framework for mass appraisal of residential properties using annual rental values in the Ethiopian context.

Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison

Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison

Towards Computer-Supported Functional Modelling in Engineering Design Education

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool’s lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions.

Airspace Safety Assessment and Risk Estimation based on the Fuzzy Petri Net Model with Multi-Risk Factor Analysis

In this paper an airspace safety assessment and risk estimation method based on the fuzzy Petri net (FPN) model is presented by analyzing 44 related multi-risk factors. In the proposed method, two kinds of real air accident data from 2010 until the present are used to statistically deduce the causes of the corresponding air accidents, namely accidents between manned aircraft, as well as the accidents between manned and unmanned aircraft. All of the data needed are drawn from the authoritative aviation safety network (ASN). These 44 related multi-risk factors are derived from four main aspects that include man, machine, medium, and management. From this, a multi-level evaluation index system for airspace safety is built. Furthermore, the determination of the risk index is based on the official reports which could make the proposed method more objective and accurate. Correspondingly, the airspace safety assessment and risk estimation model based on FPN can be generated by defining multi-risk factors and calculating the weight coefficients. To overcome the deficiency of many network nodes, a risk threshold is introduced to simplify the risk estimation model. The Zhuhai terminal area is taken as an example to assess and estimate its safety. The effectiveness and accuracy of the proposed method are also verified. Results indicate that management is a key factor in airspace safety, with a lack of training being a significant cause of a 126.59% rise in air risk.

Implementation of the Junior Mobile Programmer Professional Competency Certification Assessment Model

Implementation of the Junior Mobile Programmer Professional Competency Certification Assessment Model

Prospectively predicting 6-month risk for non-suicidal self-injury among adolescents after psychiatric hospitalization based on a predictive model

BackgroundIt is challenging to predict the occurrence of non-suicidal self-injury (NSSI) among adolescents over short periods. Moreover, the predictive value of indices for NSSI remains elusive. Thus, this study aimed to identify predictors of NSSI within 6 months among adolescents after psychiatric hospitalization by establishing a risk assessment model.MethodsA total of 632 high-risk participants were included in this study. The distribution characteristics of adolescent NSSI were initially assessed through a cross-sectional survey, following which risk factors were identified using logistic regression analysis. The risk score method was then used to construct a 6-month risk assessment model for NSSI. Lastly, the predictive effect of the model was evaluated by indicators such as the area under the receiver operating characteristic (ROC) curve and the positive predictive value.ResultsAfter 6 months, 412 cases of NSSI were identified. According to the logistic regression model, the frequency of relapses, medication status, and NSSI history were identified as influencing factors. Higher scores on the Impulsive Behavior Scale and Pittsburgh Sleep Quality Index were associated with a higher risk of NSSI. Conversely, higher scores on the Pain and Belief Perception Scale were correlated with a lower risk of NSSI. Moreover, the area under the ROC curve for the predictive model was 0.9989, with a 95% confidence interval of (0.9979, 0.9999), highlighting its high predictive ability and accuracy. The predictive model was validated using 78 patients, yielding an area under the ROC curve of 0.9703 and a 95% confidence interval of (0.9167, 0.9999), demonstrating outstanding predictability.ConclusionThese results collectively showed that the predictive model could accurately predict adolescent NSSI. Thus, the model’s primary variables may be applied to predict the risk of NSSI in the clinical setting.

Integrated Risk Assessment Model Based on Unstable Fuzzy Linguistic Terms in Occupational Health and Safety; Application in Machinery Sector in Turkey

Integrated Risk Assessment Model Based on Unstable Fuzzy Linguistic Terms in Occupational Health and Safety; Application in Machinery Sector in Turkey

Study on the correlation between the level of insulin resistance and changes in the degree of cerebral atherosclerosis in non-diabetic patients

BACKGROUND: Cerebral infarction is a health problem of global concern and brings a particular burden to medical treatment. OBJECTIVE: To analyze the correlation between insulin resistance (IR) levels and changes in cerebral atherosclerosis (AS) degree in non-diabetic patients with cerebral infarction. METHODS: A total of 134 non-diabetic patients with cerebral infarction who visited the Department of Neurology of our hospital from May 2019 to October 2020 were selected and underwent MRA/CTA (Magnetic resonance angiography/Computed tomography angiography) of cerebral arteries to refine the cerebrovascular imaging data, and according to the results of cerebral AS load, the patients were divided into mild AS group and severe AS group, and the insulin resistance index was calculated with HOMA-IR (homeostasis model assessment of insulin resistance) to evaluate the IR level and HOMA-IR was compared between the two groups. Spearman correlation was used to analyze the correlation between the levels of IR in patients and the changes in cerebral AS load. RESULTS: 54 individuals had severe AS and 80 patients had mild AS, according to an MRA/CTA of the cerebral arteries. There was a significant difference (P< 0.05) in HOMA-IR between the difficult and gentle AS groups. A significant link between HOMA-IR and the severity of cerebral AS in patients was found using Spearman correlation analysis (r= 0.850, P< 0.05). CONCLUSION: The IR phenomenon was prevalent in non-diabetic patients with cerebral infarction, and the level of IR was closely related to the severity of cerebral AS.

Metabolic stress in patients with acute severe ulcerative colitis - a single-center cohort study

Background and aimsAcute severe ulcerative colitis (ASUC) is characterized by systemic inflammation, which may initiate an acute-phase response leading to hypercatabolism. Patients with ASUC are usually treated with high-dose steroids that may further accelerate the metabolic response and lead to hyperglycemia and insulin resistance. Nevertheless, the degree of synergy between inflammation and steroid treatment and their influence on the insulin resistance remains unknown. We aimed to measure the degree of metabolic stress including insulin resistance in patients with ASUC during admission and three weeks after discharge.MethodsThis single-center cohort study was conducted in adult patients with ASUC, defined and assessed by Truelove and Witt’s criteria. Indirect calorimetry, bioelectrical impedance analysis, and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) were applied at baseline and at follow-up three weeks after discharge.ResultsAmong the 22 patients admitted during the project period, 15 provided consent for participation in the study. Median C-reactive protein at inclusion was 37.6 [4; 154.7]. Both median HOMA-IR and fasting plasma glucose were markedly increased at inclusion (median 8.6 [3.8; 14.1] and 7.1 [6; 8.7], respectively), and both had decreased significantly three weeks after discharge (p=0.0036 and p=0.0039, respectively). No significant differences were observed in resting energy expenditure or anthropometric measurements from baseline to follow-up.ConclusionPatients with ASUC presented with marked insulin resistance, indicating that the days following admission and high-dose steroid treatment are particularly vulnerable. Despite improvement at three-week follow-up, patients still exhibited insulin resistance compared with relevant control groups.Clinical trial registrationClinicalTrials.gov, identifier NCT0527183.

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography–angiography

The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV risk factors. The value of swept-source retinal optical coherence tomography–angiography (SS OCT-A) imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features. We report on the interest of using machine-learning (ML) and deep-learning (DL) models for CV assessment from SS OCT-A microvasculature imaging. We assessed the accuracy of ML and DL algorithms in predicting the CHA2DS2-VASc neurocardiovascular score based on SS OCT-A retinal images of patients from the open-source RASTA dataset. The ML and DL models were trained on data from 491 patients. The ML models tested here achieved good performance with area under the curve (AUC) values ranging from 0.71 to 0.96. According to a classification into two neurocardiovascular risk groups, the EfficientNetV2-B3, a well suited DL model for retinal OCT-A images, predicted risk correctly in 68% of cases, with a mean absolute error (MAE) of approximately 0.697. Our models enable a confident prediction of the CHA2DS2-VASc score from SS OCT-A imaging, which could be a useful tool contributing to the assessment of neurocardiovascular profiles in the future.

Apple Pomace Integrated Biorefinery for Biofuels Production: A Techno-Economic and Environmental Sustainability Analysis

The combination of techno-economic process modelling and life cycle assessment is an integrated methodology that addresses quantitative operational data, and evaluates the emissions associated with any process under development. In particular, the valorisation of waste streams within the context of the circular economy could be considered a valid and promising approach, especially regarding techno-economic and environmental indicators. This manuscript aims to evaluate the integral valorisation of apple pomace from the processing industry into bioethanol, and vinasses (a byproduct of the distillation process) into biogas and digestate as biofertiliser. In addition to biogas production, lagooning and composting were considered as strategies for vinasse management. After the conceptual design of the process options was completed, the environmental profile of bioethanol production was estimated across different scenarios. When biogas production was integrated to reduce the biorefinery’s energy demand, the carbon footprint was 1.13 kg CO2eq∙kg−1. This footprint increased to values around four when lagooning and composting were used as vinasse management strategies. Although the economic dimension posed a significant limitation due to high investment costs, the eco-efficiency analysis showed that the scenario of the co-production of bioethanol and biogas is the best alternative. Despite the promising results, further research is needed to explore the recovery of additional co-products to develop a high-potential strategy for apple pomace.

Intensity Model and Traffic Quality Assessment of the Selected Section of the D1 Highway

Intensity Model and Traffic Quality Assessment of the Selected Section of the D1 Highway

Associations of physiologic subtypes based on HOMA2 indices of β-cell function and insulin sensitivity with the risk of kidney function decline, cardiovascular disease, and all-cause mortality from the 4C study

BackgroundPrevious studies have been limited by their inability to differentiate between the effects of insulin sensitivity and β-cell function on the risk of kidney function decline, cardiovascular disease (CVD), and all-cause mortality. To address this knowledge gap, we aimed to investigate whether the physiological subtypes based on homeostasis model assessment-2 (HOMA2) indices of β-cell function (HOMA2-B) and insulin sensitivity (HOMA2-S) could be used to identify individuals with subsequently high or low of clinical outcome risk.MethodsThis retrospective cohort study included 7,317 participants with a follow-up of up to 5 years. Based on HOMA2 indices, participants were categorized into four physiologic subtypes: the normal phenotype (high insulin sensitivity and high β-cell function), the insulinopenic phenotype (high insulin sensitivity and low β-cell function), the hyperinsulinaemic phenotype (low insulin sensitivity and high β-cell function), and the classical phenotype (low insulin sensitivity and low β-cell function). The outcomes included kidney function decline, CVD events (fatal and nonfatal), and all-cause mortality. Cox regression models were used to calculate hazard ratios (HRs) for outcomes, and spline models were used to examine the dose-dependent associations of HOMA2-B and HOMA2-S with outcomes.ResultsA total of 1,488 (20.3%) were classified as normal, 2,179 (29.8%) as insulinopenic, 2,173 (29.7%) as hyperinsulinemic, and 1,477 (20.2%) as classical subtypes. Compared with other physiological subtypes, the classical subtype presented the highest risk of kidney function decline (classical vs. normal HR 11.50, 95% CI 4.31–30.67). The hyperinsulinemic subtype had the highest risk of CVD and all-cause mortality (hyperinsulinemic vs. normal: fatal CVD, HR 6.56, 95% CI 3.09–13.92; all-cause mortality, HR 4.56, 95% CI 2.97–7.00). Spline analyses indicated the dose-dependent associations of HOMA2-B and HOMA2-S with outcomes.ConclusionsThe classical subtype had the strongest correlation with the risk of kidney function decline, and the hyperinsulinemic subtype had the highest risk of CVD and all-cause mortality, which should be considered for interventions with precision medicine.Graphical abstract

A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.

Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft

In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.

Resource supply and demand model of military settlements in the cold weapon era: case of Zhenbao Town, Ming Great Wall

As an important military defense project in Chinese history, the Ming Dynasty Great Wall and its associated military settlements played a crucial role in maintaining border security. A systematic analysis of how military settlements allocated resources based on external threats and geo-strategic needs is essential to understanding their settlements defense systems. However, this aspect has been relatively under-explored in existing research. Therefore, a resource allocation assessment model for Great Wall military settlements during the Cold Weapon era is established to facilitate a more scientific analysis and quantitative assessment of Military Defense Capabilities(MDC) and Military Requirements(MR). This study employs a combined Analytical Hierarchy Process (AHP) and Principal Component Analysis (PCA) approach to determine the weight of each indicator for both military defense capability and military requirement. Spatial clustering using K-means is conducted to visualize the distribution of these strengths and weaknesses. An Ordinary Least Squares (OLS) regression model links military requirements with defense capabilities, assessing the relationship between settlement locations and historical battle sites. Residual analysis is utilized to identify areas of resource over-allocation or under-allocation. The study reveals that the regional differences in military defense capabilities and requirements within the Zhenbao Town area during the Ming Dynasty correspond closely with the locations of frequent conflicts. This finding suggests that the distribution of defensive capabilities were strengthened in areas with challenging terrain, while the effectiveness and allocation of defense capabilities were determined by logistical support and transportation conditions. This study enhances our understanding of historical military strategies from a historical geography perspective and offers innovative insights for analyzing Great Wall settlement, moving beyond mere sensory intuition and historical experience.