Reasonable Model Research Articles

ANALYSIS FACTORS AFFECTING COVID-19 MORTALITY USING COUNT REGRESSION

The ”2019 novel coronavirus” known as “ the 2019-nCoV” or simply“COVID-19” has been declared by the World Health organization (WHO), in first quarter of 2020, as a world pandemic and a public health emergency of international concern. Alas, many details related to the COVID-19 have remained unsolved completely. The success of government strategies in fighting the COVID-19 relays mainly on the results from epidemiological or statistical studies. Statistical models play a major role in providing reliable results based on appropriate analyses. Traditional (one-part) models, mixture models and mixed-effects models for counts are used to investigate effects of the WHO-regions and Cumulated COVID-19 cases on the outcome variable COVID-19 new deaths tolls. Overall result reveals there is a strong association between number of new deaths COVID-19 with predictors including the WHO regions and cumulated cases.Besides, models that account for the overdispersion feature have smallest AICs and have reasonable regression model fits.

Evaluation and Use of Natural Language Processing (NLP) Reasoning and Classification Models to Support Clinical Trial Patient Identification and Enrollment in the Community Oncology Setting

Clinical trial research in oncology relies heavily on clinical documentation within the electronic medical record (EMR) to ascertain patient eligibility in clinical trials based on inclusion and exclusion criteria. The structured data elements within the EMR serve as the primary information source for defining patient cohorts, with clinical cancer stage and performance status being two pivotal criteria determining trial eligibility. The challenge arises from the inconsistent availability of clinical stage and performance status data within the structured fields of the EMR despite their consistent presence in clinical notes. Additionally, there is a deficiency of standardization of this data that exists in the unstructured field. Hence, due to lack of structured data and standardization of said data, there are limitations in developing artificial intelligence (AI) models. To increase the comprehensiveness of clinical records, a clinical research team at a community oncology practice was consulted to identify requirements and extract essential clinical features from de-identified data. The methods outlined in this paper focused on eliminating false positives to allow future development of Large Language Models (LLM) using the outputted structured fields which resulted in an increase in patient record completeness with high accuracy. The accuracy ranged from 97.5-97.75% for the models that were developed. Out of the 60,000+ patients, the numerical staging, TNM (tumor, node, metastasis) staging, and Karnofsky performance score models added a structured field for 29.62%, 21.01%, and 40.64% patients respectively. Additionally, a semi-supervised NLP algorithm was applied on the performance status algorithm which achieved a mean absolute error (MAE) of 1.57. This work demonstrates the use case of natural language processing (NLP) in optimizing the clinical research enrollment process by providing an efficient and accurate method to detect key clinical values in unstructured patient data. Similar methodology with more advanced algorithms such as LLM can be employed to detect additional patient elements such as molecular biomarkers, imaging reports, postoperative surgical outcomes (i.e., clear margins etc.) and patient treatment outcomes using the extracted structured fields.

Validity and reliability of the Japanese version of the Substance Use Stigma Mechanism Scale.

Stigma against people who have alcohol and drug problems severely affects their health and well-being. An instrument based on stigma theory assessing individual-level stigma is essential for a comprehensive understanding of their stigma. We evaluated the validity and reliability of the Japanese version of the Substance Use Stigma Mechanism Scale (SU-SMS-J) among a population who had alcohol or drug use problems. Adults with experience in substance use disorders from psychiatry outpatient departments and rehabilitation facilities participated in the self-administered questionnaire survey. Confirmatory factor analysis was conducted to test the structural validity of the 5-factor model proposed in other language versions, and factor loadings and correlation between the subscales were confirmed. The correlations between the SU-SMS-J and psychometric properties related to substance use (e.g., severity of substance use, motivation to change) were investigated to assess concurrent validity. Internal consistency was assessed using Cronbach's alpha coefficients. Data from 126 participants were analyzed. The 5-factor model was acceptable with good or reasonable model fit indices. The correlations between subscales were weak to moderate, and this result suggested the SU-SMS-J assessed different but related components of stigma: enacted, anticipated, and internalized stigma from different stigma sources (family and healthcare workers). The SU-SMS-J and subscales showed moderate concurrent validity. Internal consistency was mostly sufficient, with Cronbach's alpha coefficients of 0.86 for all items and 0.66-0.93 for subscales. The SU-SMS-J is valid and reliable for use among populations with substance use problems in various settings in Japan.

Hardness results for decoding the surface code with Pauli noise

Real quantum computers will be subject to complicated, qubit-dependent noise, instead of simple noise such as depolarizing noise with the same strength for all qubits. We can do quantum error correction more effectively if our decoding algorithms take into account this prior information about the specific noise present. This motivates us to consider the complexity of surface code decoding where the input to the decoding problem is not only the syndrome-measurement results, but also a noise model in the form of probabilities of single-qubit Pauli errors for every qubit.In this setting, we show that quantum maximum likelihood decoding (QMLD) and degenerate quantum maximum likelihood decoding (DQMLD) for the surface code are NP-hard and #P-hard, respectively. We reduce directly from SAT for QMLD, and from #SAT for DQMLD, by showing how to transform a boolean formula into a qubit-dependent Pauli noise model and set of syndromes that encode the satisfiability properties of the formula. We also give hardness of approximation results for QMLD and DQMLD. These are worst-case hardness results that do not contradict the empirical fact that many efficient surface code decoders are correct in the average case (i.e., for most sets of syndromes and for most reasonable noise models). These hardness results are nicely analogous with the known hardness results for QMLD and DQMLD for arbitrary stabilizer codes with independent X and Z noise.

Implementing community policing practices to manage conflict resolution in Galeshewe, South Africa

Community policing has emerged as a vital instrument for combatting crime and enhancing public safety in South Africa. As a result, it has the capacity to go beyond traditional law enforcement functions as a mediator in disputes, fostering improved relationships between the police and the communities where they work. This paper analyses the implementation of community policing strategies by the South African police with the purpose of resolving conflicts. This study aims to address social crime prevention-related concerns through community policing methods in the Galeshewe police area within the Francis Baard policing regions of the Sol Plaatje Municipality, South Africa. The paper examines the tactics that community police employ to enforce the law, avoid social issues, and manage conflict resolution in the communities. A qualitative method and descriptive design were employed. Comprehensive document analysis, semi-structured interviews, and observations were employed as data collection strategies. An inductive reasoning model was used to analysis data. The findings of the study demonstrated that community policing plays an important role in optimizing problem mapping and it increases public knowledge of the importance of upholding security and order in the different police operations that support the community policing program.

A Polyvinyl Alcohol (PVA)-Based Phantom for Prostate Cancer Detection Using Multiparametric Ultrasound: A Validation Study

Multiparametric ultrasound (mpUS) enhances prostate cancer (PCa) diagnosis by using multiple imaging modalities. Tissue-mimicking materials (TMM) phantoms, favoured over animal models for ethical and consistency reasons, were created using polyvinyl alcohol (PVA) with varying molecular weights (Mw). Methods: Four PVA samples, varying in Mw with constant concertation, were mixed with glycerol, silicon carbide (SiC), and aluminium oxide (Al2O3). Phantoms with varying depth and inclusion sizes were created and tested using shear-wave elastography (SWE). An mpUS phantom was developed to mimic prostate tissue, including isoechoic and hypoechoic inclusions and vessels. The phantom was scanned using supersonic ultrasound, strain elastography, and Doppler ultrasound. Validation was performed using radical prostatectomy data and shear-wave elastography. Results: The acoustic properties varied with enhancers like glycerol and Al2O3. Low Mw PVA samples had a speed of sound ranging from 1547.50 ± 2 to 1553.70 ± 2.2 m/s and attenuation of 0.61 ± 0.062 to 0.63 ± 0.05 dB/cm/MHz. High Mw PVA samples ranged from 1555 ± 2.82 to 1566 ± 4.5 m/s and 0.71 ± 0.02 to 0.73 ± 0.046 dB/cm/MHz. Young’s modulus ranged from 11 ± 2 to 82.3 ± 0.5 kPa across 1 to 10 freeze-thaw cycles. Inclusion size, depth, and interaction statistically affect the SWE measurements with p-value = 0.056327, p-value = 8.0039 × 10−8, and p-value = 0.057089, respectively. SWE showed isoechoic inclusions, prostate tissue, and surrounding tissue only. The Doppler velocity was measured in three different inner diameters. Conclusion: PVA mixed with enhancer materials creates an mpUS phantom with properties that mimic normal and abnormal prostate tissue, blood vessels, and soft tissue, facilitating advanced diagnostic training and validation.

A comparative study of the established methods and evaluation of rat trauma models.

Scientific animal models are indispensable for studying trauma repair. This work aimed at establishing a more scientific rat trauma model by studying different rat trauma models caused by different trauma numbers, locations, and trauma attachment tension unloaders and rat age. A four-trauma self-upper, lower, left and right control model; a two-trauma self-trauma bare and ring control model; and a young and old rat trauma model were created to evaluate the condition of these traumas. In the four-trauma self-control model, the healing status of the upper proximal cephalic trauma was better than that of the lower proximal caudal trauma, whereas there was no significant difference between the left and right trauma. The healing rate and postwound condition of the trauma with a ring control in the two-trauma model were better than those of the bare side. The healing speed of the old rats was slower, and the amount of extracellular matrix in the subcutaneous tissue after healing was significantly lower than that of the young rats. The double trauma with a ring is a more scientific and reasonable experimental model. There is a significant difference between young and old rats in the wound healing process. Therefore, the appropriate age of the rats should be selected according to the main age range of the patients with similar conditions in the clinical setting being mimicked.

A quantitative classification criterion for beam-to-column rigid connections in steel braced frames

As the key part of the force-transfer path in a steel frame, a reasonable modeling method of beam-to-column connections in the structural analysis model is crucial. From the designer’s perspective, a rational and accurate classification of beam-to-column connections is an invaluable basis for improving modeling efficiency and design accuracy. Although some investigations led by Eurocode 3 (EC3) have provided a systematic and quantitative approach for classifying beam-to-column connections, it is not sufficiently developed for general steel braced frames. In this paper, based on a modified analytical model of the beam-to-column connection substructure that accounts for the effect of braces, the rotational stiffness demand of the beam-to-column rigid connection is derived from the perspectives of structural lateral stiffness and stability. The quantitative relationship is established between the rotational stiffness demand and the relative stiffness of the braces. The accuracy and reasonability of the proposed analytical model and stiffness demand are verified using finite element models (FEMs) at both the story and structure levels. Particularly, the effect of the non-linear behavior of beam-to-column connections on the structural response under high-intensity earthquakes is further comparatively analyzed in the context of seismic design. The results demonstrate that, a beam-to-column connection can be modeled as a continuous rigid connection in non-linear analysis only if it simultaneously satisfies the following requirements: the rotational stiffness meets the demand proposed in this study, and the bending capacity is designed to be overstrength compared to the beam cross-section.

Perceptions about dementia clinical trials among underrepresented populations: a nationally representative survey of U.S. dementia caregivers

BackgroundThe research community has historically failed to enroll diverse groups of participants in dementia clinical trials. A unique aspect of dementia care research is the requirement of a study partner, who can attest to the care recipient’s clinical and functional capacity. The aim of this study is to assess racial and ethnic differences and the importance of various trial considerations among dementia caregivers, in their decision to participate in clinical research as study partners.MethodWe embedded a vignette about a hypothetical dementia clinical trial in a nationally representative survey of U.S. dementia caregivers, oversampling non-Hispanic Black and Hispanic caregivers. Dementia caregivers were asked about their willingness to participate in the trial with their care recipient and rated the importance of nine considerations in hypothetical decisions to participate. Caregiver demographic characteristics were analyzed as predictors of trial participation in a base demographic model. In a second reasons model caregiver demographic characteristics and the rated importance of the nine considerations were separately analyzed as predictors; both models used survey-weighted logistic regression.ResultThe sample consisted of 610 dementia caregivers, including 156 non-Hispanic Black and 122 Hispanic caregiver participants. In the base demographic model, hypothetical trial participation was negatively associated with older caregiver age (OR (odds ratio) = 0.72, p = < 0.001). In the reasons model, the rated importance of a social responsibility to help others by participating in research was significantly associated with participation (OR = 1.56, p = 0.049), while the importance of the possibility of the care recipient experiencing serious side effects was negatively associated with participation (OR = 0.51, p = 0.003). In both models there was no significant difference in hypothetical participation between non-Hispanic Black and non-Hispanic White caregivers, or between Hispanic and non-Hispanic White caregivers.ConclusionHispanic and non-Hispanic Black dementia caregivers were not less likely than non-Hispanic White dementia caregivers to participate in a hypothetical dementia clinical trial. Our study suggests that failures to recruit diverse populations in dementia clinical research are not attributable to less willingness among members of underrepresented groups but may instead reflect structural barriers and historic exclusion from trial participation.

Smooth Dispersion Is Physically Appropriate: Assessing and Amending the D4 Dispersion Model.

The addition of dispersion corrections to density functionals is essential for accurate energy and geometry predictions. Among them, the D4 scheme is popular due to its low computational cost and high accuracy. However, due to its design, the D4 correction can occasionally lead to anomalies, such as unphysical curvature and bumps in the potential energy surface. We find these anomalies are common in the D4 model, although observable consequences are rarer than in the D3 model for reasons we explain. Nevertheless, we uncover instances of unphysical local minima and stationary points with the D4 scheme and propose two solutions that yield smoother dispersion energy as a function of nuclear position. One is trivial to implement, based on a smoother reparametrization of Gaussian weighting (D4S) to find the effective coordination number. The other replaces Gaussian weighting with soft linear interpolation (D4SL). These new approaches usually remove artificial extremum points, while maintaining accuracy.

Ecological Compensation Based on the Ecosystem Service Value: A Case Study of the Xin’an River Basin in China

To establish a sound ecological compensation (EC) mechanism in the Xin’an River Basin, this study suggested utilizing ecosystem service valuation to determine the compensation amount. In this study, the first step was to establish a reasonable watershed EC model using the ecological compensation supply coefficient (ECSC) based on the value spillover theory (VST) of the ecosystem services and the ecological compensation demand coefficient (ECDC). The second step was to classify the ecosystem services of the Xin’an River Basin into three categories, including supply service, regulating service, and cultural service, with 14 specific functions to determine the ecological compensation standard accounting scope in these services. Then, a case study on the Xin’an River Basin for EC standards was presented. The total ecosystem service value (ESV) in the Xin’an River Basin was estimated to be CNY 70.271 billion, with supply service accounting for 22.7%, regulating service accounting for 24.6%, and cultural service accounting for 52.7%. Based on the compensation scope, the ecosystem service values for the upper and lower limits of the EC were calculated as CNY 57.779 billion and CNY 17.292 billion. Combined with the results of the ECSC and ECDC, the upper and lower limits of the EC standard in the Xin’an River Basin were computed to be CNY 4.085 billion and CNY 1.438 billion, respectively. Therefore, the ESV-based EC model for the Xin’an River Basin can effectively address the challenge of inadequate EC in the watershed. It also facilitates balanced regional development and serves as a theoretical foundation and empirical evidence for the government to establish a unified national policy on cross-border river basin ecological compensation.

Predicting chemotherapy-induced thrombotoxicity by NARX neural networks and transfer learning

BackgroundThrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual’s risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.MethodsWe aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin’s lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model.ResultsOf the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances.ConclusionNARX networks can be utilized to predict an individual’s thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future.

A new modeling strategy for the predictive model of chub mackerel (Scomber japonicus) central fishing grounds in the Northwest Pacific Ocean based on machine learning and operational characteristics of the light fishing vessels

The chub mackerel (Scomber japonicus) is one of the most influential small pelagic fish in the Northwest Pacific Ocean, and accurate modeling approaches and model selection are critical points in predicting the Scomber japonicus fishing grounds. This study investigated the changes in catches and fishing days on no moonlight and bright moonlight days (2014-2022) and compared the differences in predictive performance between the LightGBM and RF models on three datasets under the two modeling approaches [those based on the light fishing vessels operational characteristics (Approach one) and those not (Approach Two)]. The results were as follows: 1) Stronger moonlight intensity (e.g., full moon) can limit the fishing efficiency of light fishing vessels, with most years showing a trend of a higher percentage of fishing days on bright moonlight days than catches percentage, i.e., no moonlight days resulted in higher catches with lower fishing days; 2) Compared to Modeling Approach Two, under Modeling Approach one, RF model achieved better predictive performance on dataset B, while the LightGBM model achieved better predictive performance on both datasets A and B; 3) Overall, the Approach One achieved more satisfactory prediction performance, with the optimal prediction performance on the complete dataset C improved from 65.02% (F1-score of the RF model, Approach Two) to 66.52% (F1-score of the LightGBM model, Approach Two); 4) Under the optimal modeling approach (Approach One) and the optimal model (LightGBM model), the differences in the importance of the variables on dataset A (no moonlight days) and dataset B (bright moonlight days) were mainly centered on the environmental variables, with CV, SLA, and SSS being the most important in dataset A, and CV, DO, and SLA being the most important in dataset B. This study provides a more scientific and reasonable modeling undertaking for the research of light purse seine fishing vessels, which is conducive to guiding fishermen to select the operating area and operating time of the Scomber japonicus fishery more accurately and comprehensively and realizing the balanced development of fisheries in terms of ecology and economy.

Comparison of item characteristic analysis models of reading literacy test with polytomous Item Response Theory

This study aims to compare the analysis model of the characteristics of reading literacy items with the polytomous item response theory, which uses the Graded Response Model (GRM), Partial Credit Model (PCM), Generalized Partial Credit Model (GPCM), and Nominal Reasons Model (NRM). This research is quantitative research in nature, and secondary data were used from about 1000 test takers’ responses to reading literacy items in the 2018 reading literacy study analyzed with the R program. This model comparison was carried out so that the analysis results obtained were more accurate in representing the level of reading literacy skills in Indonesia. The results show that the GPCM model is the fit model with an AIC value of 23753.89 and a BIC value of 24042.45, and the number of suitable testlets is 7 out of a total of 7 testlets. Based on the relationship between information function scores and SEM, reading literacy items provide higher information when participants’ abilities range between -2.3 and +2.

LLM-enhanced Composed Image Retrieval: An Intent Uncertainty-aware Linguistic-Visual Dual Channel Matching Model

Composed image retrieval (CoIR) involves a multi-modal query of the reference image and modification text describing the desired changes, allowing users to express image retrieval intents flexibly and effectively. The key of CoIR lies in how to properly reason the search intent from the multi-modal query. Existing work either aligns the composite embedding of the multi-modal query and the target image embedding in the visual domain through late-fusion or converts all images into text descriptions and leverage large language models (LLM) for text semantic reasoning. However, this single-modality reasoning approach fails to comprehensively and interpretably capture the users’ ambiguous and uncertain intents in the multi-modal queries, incurring the inconsistency between retrieved results and ground truth. Besides, the expensive manually-annotated datasets limit the further performance improvement of CoIR. To this end, this paper proposes an LLM-enhanced Intent Uncertainty-aware Linguistic-Visual Dual Channel Matching Model (IUDC), which combines the strengths of multi-modal late-fusion and LLMs for composed image retrieval. We first construct an LLM-based triplet augmentation strategy to generate more synthetic training triplets. Based on this, the core of IUDC consists of two matching channels: the semantic matching channel is responsible for intent reasoning on the aspect-level attributes extracted by an LLM, and the visual matching channel accounts for the fine-grained visual matching between multi-modal fusion embedding and target images. Considering the intent uncertainty presented in the multi-modal queries, we introduce Probability Distribution Encoder (PDE) to project the intents as probabilistic distributions in the two matching channels. Consequently, a mutually enhanced module is designed to share knowledge between the visual and semantic representations for better representation learning. Finally, the matching scores of two channels are added to retrieve the target image. Extensive experiments conducted on two real datasets demonstrate the effectiveness and superiority of our model. Notably, with the help of the proposed LLM-based triplet augmentation strategy, our model achieves a new record of state-of-the-art performance among all datasets.

Models of Variability in Probabilistic Causal Judgments

AbstractMost theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating ‘non-reasoning’ processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.

Music education training for kindergarten teachers: a workshop integrating Shulman’s pedagogical content knowledge and pedagogical reasoning and action model

ABSTRACT Kindergarten teachers often express concerns about the insufficient preparation they receive in their training programs to effectively teach music. This lack of confidence in their music teaching and musical ability can impact their effectiveness of teaching, which in turn can affect children’s music learning. To address this deficiency in music training for preservice teachers, this study consists of a workshop that integrates Shulman’s Pedagogical Content Knowledge (PCK) through the Pedagogical Reasoning and Action Model (PRA). The workshop takes an experiential approach, moving away from the traditional separation of content knowledge and pedagogy. Nine participants took part in the workshop and were interviewed to address two research questions: 1. How do preservice teachers perceive the significance of their musical competence and music pedagogy skills when conducting a music lesson? 2. In what ways does the workshop contribute to the professional growth and effectiveness of preservice teachers in music teaching? The integrated approach employed in the workshop proved effective in enhancing the preservice teachers’ comprehension of PCK through PRA model. The study emphasizes the significance of designing training programs that encompass the breadth and depth of Shulman’s theory of teaching, equipping teachers to deliver music education.

SONAR: An Adaptive Control Architecture for Social Norm Aware Robots

AbstractRecent advances in robotics and artificial intelligence have made it necessary or desired for humans to get involved in interactions with social robots. A key factor for the human acceptance of these robots is their awareness of environmental and social norms. In this paper, we introduce SONAR (for SOcial Norm Aware Robots), a novel robot-agnostic control architecture aimed at enabling social agents to autonomously recognize, act upon, and learn over time social norms during interactions with humans. SONAR integrates several state-of-the-art theories and technologies, including the belief-desire-intention (BDI) model of reasoning and decision making for rational agents, fuzzy logic theory, and large language models, to support adaptive and norm-aware autonomous decision making. We demonstrate the feasibility and applicability of SONAR via real-life experiments involving human-robot interactions (HRI) using a Nao robot for scenarios of casual conversations between the robot and each participant. The results of our experiments show that our SONAR implementation can effectively and efficiently be used in HRI to provide the robot with environmental and social and norm awareness. Compared to a robot with no explicit social and norm awareness, introducing social and norm awareness via SONAR results in interactions that are perceived as more positive and enjoyable by humans, as well as in higher perceived trust in the social robot. Moreover, we investigate, via computer-based simulations, the extent to which SONAR can be used to learn and adapt to the social norms of different societies. The results of these simulations illustrate that SONAR can successfully learn adequate behaviors in a society from a relatively small amount of data. We publicly release the source code of SONAR, along with data and experiments logs.

Spatio-temporal interactive reasoning model for multi-group activity recognition

Spatio-temporal interactive reasoning model for multi-group activity recognition

Formation of GW230529 from Isolated Binary Evolution

In this paper, we explore the formation of the mass-gap black hole-neutron star (mgBHNS) merger detected in gravitational wave (GW) event, i.e., GW230529, from the isolated binary evolution channel, and study potential signatures of its electromagnetic counterparts. By adopting the “delayed” supernova prescription and reasonable model realizations, our population synthesis simulation results can simultaneously match the rate densities of mgBHNS and total BHNS mergers inferred from the population analyses, along with the population distribution of the BH mass in BHNS mergers reported by the LIGO–Virgo–KAGRA Collaboration. Because GW230529 contributes significantly to the inferred mgBHNS rate densities, we suggest that GW230529 can be explained through the isolated binary evolution channel. Considering the AP4 (DD2) equation of state, the probability that GW230529 can make tidal disruption is 12.8% (63.2%). If GW230529 is a disrupted event, its kilonova peak apparent magnitude is predicted ∼23–24 mag, and hence, can be detected by the present survey projects and Large Synoptic Survey Telescope. Since GW230529 could be an off-axis event inferred from the GW observation, its associated gamma-ray burst (GRB) might be too dim to be observed by γ-ray detectors, interpreting the lack of GRB observations. Our study suggests the existence of mgBHNS mergers formed through the isolated binary evolution channel due to the discovery of GW230529, indicating that BHNS mergers are still likely to be multimessenger sources that emit GWs, GRBs, and kilonovae. Although mgBHNS mergers account for ∼50% of the cosmological BHNS population, we find that ≳90% of disrupted BHNS mergers are expected to originate from mgBHNS mergers.