Computer-assisted Methods Research Articles

Safety and Accuracy of Guided Interradicular Miniscrew Insertion: A Systematic Review and Meta-Analysis

Background: Achieving ideal anchorage is crucial in orthodontics for controlled tooth movement. Miniscrews (MSs) have improved skeletal anchorage, but freehand placement poses risks like root damage and limited precision. Guided techniques, including radiographic guides and computer-assisted methods (static [sCAS] and dynamic [dCAS]), were developed to enhance accuracy and safety. Objective: This systematic review and meta-analysis aimed to evaluate the safety and accuracy of MS placement using different guidance approaches. Materials: A systematic search up to March 2024 identified studies on guided MS insertion, assessing safety (root contact/damage) and accuracy (angular, coronal, and apical deviations) of guided vs. freehand placement. Two reviewers assessed the risk of bias and study quality using RoB 2 for RCTs, NOS for cohort studies, and an adapted tool for pre-clinical studies. Random-effects meta-analysis was performed for studies with common parameters, and safety outcomes were pooled using logit-transformed proportions. Heterogeneity was evaluated with I² and χ² tests. Results: Eleven studies (652 MSs) were included, though no dCAS studies were analyzed. The only RCT had “some concerns” regarding risk of bias, cohort studies ranged from medium to low quality, and most pre-clinical studies had high bias risk. sCAS significantly reduced root damage compared to freehand methods (OR = 0.11; 95% CI: 0.04–0.36; p < 0.001; I² = 1%) and reduced angular and linear deviations. Due to heterogeneity, no quantitative synthesis of accuracy outcomes was performed. Conclusions: sCAS improves the safety and accuracy of MS insertion compared to freehand and radiographic guide methods. These results highlight the clinical benefits of sCAS in orthodontics. Future studies should refine protocols and explore dCAS for further accuracy improvements.

Wireless capsule endoscopy anomaly classification via dynamic multi-task learning

Wireless capsule endoscopy (WCE) provides a painless, non-invasive means for early gastrointestinal disease detection and cancer prevention. However, clinicians must diagnose only about 5% of lesion images from tens of thousands of frames, highlighting the need for computer-assisted diagnostic methods to enhance efficiency and reduce the elevated misdiagnosis rates attributed to visual fatigue. Previous research heavily relied on module design, an effective yet highly coupled method with the baseline and incurring additional computational costs. This paper proposes a dynamic multi-task learning method that combines triplet loss and weighted cross-entropy loss to respectively guide the model in learning compact fine-grained representations and establishing less biased decision boundaries, without incurring additional computational costs. Our method outperforms previous advanced methods on two publicly available datasets, achieving an F1 score of 96.47% on Kvasir-Capsule and an F1 score of 96.75% with an accuracy of 96.72% on CAD-CAP. Visualization of the representations and heatmaps confirms the model’s precision in focusing on the lesion area. The prediction model has been uploaded to https://github.com/xli122/WCE_MTL.

Research on Teaching Methods Based on Mathematical Modeling and Their Applications

Mathematical modeling serves as a crucial bridge between mathematical theory and real-world problems, playing an increasingly important role in education. This paper aims to explore the current state, applications, and future prospects of mathematical modeling teaching methods. Firstly, the article provides an overview of the basic concepts and theoretical foundations of mathematical modeling, introducing the classifications and characteristics of traditional teaching methods and modern teaching methods, including problem-based modeling teaching methods, project-based teaching methods, and computer-assisted modeling teaching methods. Secondly, by analyzing the effectiveness, technical support, and innovations in the application of mathematical modeling teaching methods, the paper reveals the importance of teaching effectiveness, technology application, and innovation. Finally, the paper looks ahead to the challenges faced by mathematical modeling teaching, future development trends, and policy recommendations, offering references for further improving the quality of mathematical modeling education and student capabilities. The research presented in this paper is significant for promoting the innovation and optimization of mathematical modeling education.

Role of residual mandibular teeth after computer-assisted mandibular reconstruction using a fibular flap

Computer-assisted mandibular reconstruction after mandibulectomy enables accurate reconstruction of the occlusal positions between the maxilla and mandible. Understanding the remaining teeth condition is essential for sensory mastication in patients with numerous tooth loss. However, no studies have examined the dental status of the remaining mandible after computer-assisted mandibular reconstruction using a fibular flap. This study evaluated the role of residual teeth and other factors in effective acquisition of oral intake after computer-assisted mandibular reconstruction using a fibular flap. Postoperative oral intake and associated factors were retrospectively examined in 57 consecutive patients. Oral intake was assessed using the Functional Oral Intake Scale. Multivariate analysis was performed to evaluate the remaining teeth arrangement (Eichner's classification), mandibular dentures, extent of resection (Brown's classification), age, performance of glossectomy, history of radiation therapy, and computer-assisted methods. Multivariate analysis revealed that Eichner's classification had a positive (p<0.001) and radiation therapy had a negative (p<0.05) impact on oral intake. The patients with dentures anchored to the remaining teeth in the occlusal support area (i.e., premolar and molar) had higher Functional Oral Intake score than those with dentures anchored to the remaining teeth in the non-occlusal support area (6.78±0.03 vs. 6.10±0.07, p<0.005). In computer-assisted mandibular reconstruction with accurate occlusion, the residual teeth in the occlusal support area are essential for good postoperative oral intake with dentures. During mandibulectomy, if oncologically acceptable, these teeth should be preserved, and selective placement of dental implants in the occlusal support area should be considered.

Weaving narrative threads with social psychological processes: Narrative modulations in online consumer reviews of a medical memoir

With the growing prevalence of health and illness narratives on digital platforms, research examining the social psychological processes involved in these storytelling environments remains scarce. This paper addresses this research gap by conducting a mixed-methods study of digital storytelling within the UK’s healthcare context, focusing on online consumer reviews of the medical memoir, Do no harm: stories of life, death and brain surgery (Marsh, 2014). Utilising computer-assisted text analysis methods of LIWC-22 and the Sketch Engine, linguistic cues for cognitive, affective, social and perceptual processes are identified in a corpus of online consumer reviews. A subsequent qualitative analysis, based on ‘narrative modulation’ (Huang, 2024, 2020), investigates the role of these processes in constructing and developing storylines across the user reviews. Finally, the study explores how consumer reviews in the form of ‘small stories’ challenge canonical narratives in the UK’s healthcare services. This research advances the field of narrative studies by emphasising the role of social psychological processes (Chung and Pennebaker, 2019) in modulating emerging, evolving and counter narratives in digital storytelling. The findings reveal an instrumental role of social psychological processes, as signalled by linguistic cues, in shaping narrative threads in online user reviews. This study not only develops narrative modulation as a valuable concept for narrative analysis, but also underscores its effectiveness when combined with computer-assisted text analysis tools for in-depth examinations of narrative data. Furthermore, it provides critical insights into digital storytelling in healthcare contexts, promoting knowledge transfer across narrative studies, stylistics, social psychology and medical humanities.

Multi-stage automatic and rapid ablation and needle trajectory planning method for CT-guided percutaneous liver tumor ablation.

Computer-assisted planning methods have increasingly contributed to preoperative ablation planning; however, these methods cannot automatically obtain the final optimal solution within a short time and are rarely validated in practice, greatly limiting their clinical applicability. We aimed to propose a full-automatic multi-stage ablation and needle trajectory planning method for CT-guided percutaneous liver ablation to attain the final optimal plans under multiple clinical constraints rapidly. Our proposed method integrates the ablation zone planning fulfilling complete tumor coverage and critical structure avoidance while reaching a trade-off between ablation number and healthy tissue damage, and needle trajectory planning under multiple clinical constraints. Our needle trajectory planning determines feasible skin entry regions based on hard constraints, where the multi-objective optimization (MOO) considering soft constraints is performed using the Pareto Optimality and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the final optimal solution. The performance of our proposed method was evaluated on 30 tumors of various characteristics from 23 patients and clinically validated in five clinical cases. Our proposed method achieved 99.8% treatment zone coverage and 40.5% ablation efficiency without involving critical structures, and completely satisfied multiple clinical constraints in all needle trajectory planning results. The average planning time was 23.6 s for tumors of different sizes. All the plans were considered clinically acceptable by the doctors' evaluation. Our method achieved complete tumor coverage without complications in clinical case validation. Our proposed planning method can generate a final optimal plan satisfying multiple clinical constraints within a short time, potentially facilitating preoperative planning for hepatic tumor ablation.

Machine Learning Accelerates the Discovery of Epitope-based Dual-bioactive Peptides Against Skin Infections

Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold significant potential, particularly those possessing antimicrobial and anti-inflammatory properties. However, obtaining these peptides solely through traditional wet laboratory experiments is costly and time-consuming, and peptides identified by current computer-assisted predictive models largely lack validation of their effects via wet laboratory experiments. Consequently, this study aimed to integrate computer-assisted methods and traditional wet laboratory experiments to identify anti-inflammatory and antimicrobial peptides. We developed a computer-assisted mining pipeline to screen potential peptides from the epitopes of the major histocompatibility complex class II (MHC-II). The peptide AIMP1 was identified, with the ability to physically damage Escherichia coli by increasing bacterial cell membrane permeability and with the ability to inhibit inflammation by binding to endotoxin-lipopolysaccharide. Additionally, in an LPS-induced inflammation animal model, AIMP1 slightly increased levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and in a skin wound infection animal model, AIMP1 effectively accelerated healing, reduced levels of these pro-inflammatory cytokines, and showed no acute hepatotoxicity or nephrotoxicity. In conclusion, this study not only developed a computer-assisted mining pipeline for identifying anti-inflammatory and antimicrobial peptides but also successfully pinpointed the peptide AIMP1, demonstrating its therapeutic potential for skin injury treatment.

Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.

Computerized Penetrometry Methodology Assisted by Advanced Algorithms Applied in the Multi-Dimensional Analysis of the Rheology of Mold-Ripened Cheeses

The main objective of this study is to understand the rheological behavior of various types of cheeses with mold subjected to multiple stresses during processing/handling, transport, or storage, aiming to maintain or even improve product quality, using computer-assisted penetration methods and advanced regression algorithms. Uniaxial penetration tests with a cone at a constant speed were conducted using a universal Hounsfield testing equipment connected to a computer to analyze the texture behavior (tangential stress, flow index, apparent viscosity) of four of the most common types of cheese with mold depending on the cone’s tip angle (9, 19, and 90 degrees) and penetration speed (12, 30, and 60 mm/min). From the results obtained for the four categories of mold cheeses (Brie, Camembert, Dorblue, and Roquefort), the amplitude of the speed and angle of penetration were considered as the main influencing factors for the shear deformation (τc), flow index (Kf), and apparent viscosity (ηa). The moisture and firmness of mold cheeses are closely linked and depend on the type of mold, the maturation process, or storage conditions. Careful control of these factors is essential to achieve the desired texture and taste characteristics in cheeses with mold.

Supplementation of Rooster Semen Extender with Aqueous Extract of Urtica dioicafor a Long Time Preservation by Low Temperature.

The peroxidation of spermatozoa membrane phospholipids is a primary cause of irreversible changes in the preservation of avian semen. To address this issue, the objective of the present study was to assess the potential of Urtica dioica extracts in protecting avian spermatozoa during prolonged storage. Gas chromatography-mass spectroscopic techniques were employed to evaluate the bioactive compounds present in the aqueous and ethanolic extracts obtained from the aerial parts and roots of U. dioica. Semen samples were collected from 16 roosters twice a week and were diluted in Lake's extender containing different concentrations (0, 0.5, and 1 mg/100 mL) of the various extracts. Subsequently, the extended semen samples were cooled and stored at 5°C, and the sperm quality parameters were assessed at 0, 12, 24, and 36 hours of storage. The data from this experiment clearly demonstrate that the addition of nettle root aqueous extracts to the semen diluent, especially at a concentration of 0.5 mg/100 mL, resulted in a significant improvement in various sperm quality parameters. Notably, there were enhancements in total and progressive sperm motilities, viability, fertility, membrane integrity, acrosomal membrane integrity, and a reduction in malondialdehyde production in rooster semen stored in vitro for up to 36 hours. Interestingly, the present study reveals that the beneficial effects of the aqueous extracts from different parts of the nettle were supported not only by the conventional manual method but also by the computer-assisted sperm analysis system. This dual confirmation further emphasizes the positive impact of the aqueous extract on various sperm traits during cooled semen preservation. In conclusion, this study highlights the potential of U. dioica extracts, particularly the aqueous extract from nettle roots at a concentration of 0.5 mg/100 mL, in safeguarding avian spermatozoa during prolonged storage. The significant improvements in various sperm quality parameters and the validation of results through both manual and computer-assisted analysis methods provide strong evidence for the application of U. dioica extracts in avian breeding programs and artificial insemination practices.

The Quantification of Myocardial Fibrosis on Human Histopathology Images by a Semi-Automatic Algorithm

(1) Background: Considering the increasing workload of pathologists, computer-assisted methods have the potential to come to their aid. Considering the prognostic role of myocardial fibrosis, its precise quantification is essential. Currently, the evaluation is performed semi-quantitatively by the pathologist, a method exposed to the issues of subjectivity. The present research proposes validating a semi-automatic algorithm that aims to quantify myocardial fibrosis on microscopic images. (2) Methods: Forty digital images were selected from the slide collection of The Iowa Virtual Slidebox, from which the collagen volume fraction (CVF) was calculated using two semi-automatic methods: CIELAB-MATLAB® and CIELAB-Python. These involve the use of color difference analysis, using Delta E, in a rectangular region for CIELAB-Python and a region with a random geometric shape, determined by the user’s cursor movement, for CIELAB-MATLAB®. The comparison was made between the stereological evaluation and ImageJ. (3) Results: A total of 36 images were included in the study (n = 36), demonstrating a high, statistically significant correlation between stereology and ImageJ on the one hand, and the proposed methods on the other (p &lt; 0.001). The mean CVF determined by the two methods shows a mean bias of 1.5% compared with stereology and 0.9% compared with ImageJ. Conclusions: The combined algorithm has a superior performance compared to the proposed methods, considered individually. Despite the relatively small mean bias, the limits of agreement are quite wide, reflecting the variability of the images included in the study.

Hybridization of Acoustic and Visual Features of Polish Sibilants Produced by Children for Computer Speech Diagnosis.

Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)-the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results of assessing the usefulness of hybrid feature vectors extracted based on multimodal (video and audio) data for the place of articulation assessment in sibilants /s/ and /ʂ/. We used acoustic features and, new in this field, visual parameters describing selected articulators' texture and shape. Analysis using statistical tests indicated the differences between various sibilant realizations in the context of the articulation pattern assessment using hybrid feature vectors. In sound /s/, 35 variables differentiated dental and interdental pronunciation, and 24 were visual (textural and shape). For sibilant /ʂ/, we found 49 statistically significant variables whose distributions differed between speaker groups (alveolar, dental, and postalveolar articulation), and the dominant feature type was noise-band acoustic. Our study suggests hybridizing the acoustic description with video processing provides richer diagnostic information.

Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing

In the digital age, China’s economic development is transitioning from high speed to high quality. Through the application of digital technology, China’s manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China’s intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications.

Machine learning Techniques for Spondylolisthesis Diagnosis: a review

Spondylolisthesis, a condition marked by vertebral slippage, presents a challenge in medical diagnosis and grading. This study examines previous research on image processing for spondylolisthesis severity evaluation. Methodologies, sample sizes, algorithms, and measurement accuracy are the main topics of interest. The study shows the potential of computer-assisted methods for diagnosing spondylolisthesis, particularly in situations where qualified medical personnel are scarce. Machine learning techniques and deep learning models, including convolutional neural networks (CNNs), are utilized to accurately detect and assess spondylolisthesis. Notably, these findings address a gap in previous research by measuring spondylolisthesis severity and distinguishing between normal and abnormal spines. The analysis emphasizes the significance of selecting the appropriate modality and data quality, with X-rays predominating as the preferred imaging technique. This review highlights how deep learning and machine learning models can improve spondylolisthesis diagnosis, enabling enhanced diagnosis and treatment methods.

Short-Term Outcomes of Total Knee Arthroplasty Using a Conventional, Computer-Assisted, and Robotic Technique: A Pilot Clinical Trial.

Background: Total Knee Arthroplasty (TKA) is a prevalent surgical procedure for treating severe knee arthritis, aiming to alleviate pain and restore function. Recent advancements have introduced computer-assisted (CAS) and robot-assisted (RA-TKA) surgical techniques as alternatives to conventional methods, promising improved accuracy and patient outcomes. However, comprehensive comparative studies evaluating the short-term outcomes and prostheses survivorship among these techniques are limited. We hypothesized that the outcome of RA-TKA and/or CAS- TKA is advantageous in function and prosthesis survivorship compared to manually implanted prostheses. Methods: This prospective controlled study compared the short-term outcomes and prostheses survivorship following TKA using conventional, CAS, and RA-TKA techniques. One hundred seventy-eight patients requiring TKA were randomly assigned to one of the three surgical groups. The primary outcomes were knee function (KSS knee score) and functional recovery (KSS function score), which were assessed before surgery three years postoperatively. Secondary outcomes included prosthesis alignment, knee range of movements, and complication rates. Survivorship analysis was conducted using Kaplan-Meier curves, with revision surgery as the endpoint. Results: While all three groups showed significant improvements in knee function postoperatively (p < 0.001), the CAS and RA-TKA groups demonstrated superior prosthetic alignment and higher survivorship rates than the conventional group (100%, 97%, and 96%, respectively). However, although the RA-TKA group had a maximal 100% survivorship rate, its knee score was significantly lower than following CAS and conventional techniques (mean 91 ± 3SD vs. mean 93 ± 3SD, p = 0.011). Conclusion: The RA-TKA technique offers advantages over conventional and CAS methods regarding alignment accuracy and short-term survivorship of TKA prostheses. Since short-term prosthesis survivorship indicates the foreseen rates of mid- and long-term survivorship, the current data have a promising indication of the improved TKA prosthesis's long-term survivorship by implementing RA-TKA. According to the presented data, although the survival rates were 100%, 97%, and 96% in the three study groups, no clinical difference in the functional outcome was found despite the better mechanical alignment and higher survivorship in the group of patients treated by the RA-TKA.

Morphology and Composition of Lumbar Intervertebral Discs: Comparative Analyses of Manual Measurement and Computer-Assisted Algorithms.

The morphology and internal composition, particularly the nucleus-to-cross sectional area (NP-to-CSA) ratio of the lumbar intervertebral discs (IVDs), is important information for finite element models (FEMs) of spinal loadings and biomechanical behaviors, and, yet, this has not been well investigated and reported. Anonymized MRI scans were retrieved from a previously established database, including a total of 400 lumbar IVDs from 123 subjects (58 F and 65 M). Measurements were conducted manually by a spine surgeon and using two computer-assisted segmentation algorithms, i.e., fuzzy C-means (FCM) and region growing (RG). The respective results were compared. The influence of gender and spinal level was also investigated. Ratios derived from manual measurements and the two computer-assisted algorithms (FCM and RG) were 46%, 39%, and 38%, respectively. Ratios derived manually were significantly larger. Computer-assisted methods provide reliable outcomes that are traditionally difficult for the manual measurement of internal composition. FEMs should consider the variability of NP-to-CSA ratios when studying the biomechanical behavior of the spine.

The Impact of Removing the 10-Minute Bout Requirement and of Different Survey Administration Methods on National Physical Activity Estimates in Austria.

Monitoring survey methods, as well as movement recommendations, evolves over time. These changes can make trend observations over time difficult. The aim of this study was to examine the differences between 2 computer-assisted survey administration methods and the effect of the omission of the 10-minute minimum bout requirement in physical activity (PA) questions on PA outcomes. We used data from the second Austrian PA Surveillance System for 2998 adults (18-64y), applying computer-assisted personal interviewing and computer-assisted web interviewing. Within the computer-assisted web interviewing sample only, we added PA questions without the 10-minute requirement. Quantile and logistic regressions were applied. Between computer-assisted web interviewing and computer-assisted personal interviewing, within the computer-assisted personal interviewing sample, we found lower PA estimates in the leisure domain and work and household domain, but not in the travel domain, and no significant difference in the proportion of people meeting the PA recommendations. In all 3 PA domains, the median minutes did not differ when assessed with or without the 10-minute requirement. However, the percentage participation in the travel domain and work and household domain performing >0minutes per week PA was higher when there was no 10-minute requirement. The proportion of people meeting the Austrian aerobic recommendation for adults when computed with or without the 10-minute requirement did not differ. Our findings suggest that the omission of the 10-minute requirement does not seem to result in marked differences in PA estimates or the proportion of adults meeting the recommendations.

FULL FACTORIAL EXPERIMENTAL DESIGN AND COMPUTER SIMULATION APPLIED TO SHELL AND TUBE HEAT EXCHANGER SETUP

In the operation of heat exchangers there are some variables to be controlled, making it difficult to found optimized parameters. The aim of this study was to compare the experimental and simulated values of the outlet temperatures, as well as to understand the influence of operating variables for the equipment. It was used a shell and tube heat exchanger didactic module, with a constant cold fluid flow rate equal to 1.4 L.min 1. The experiments were carried out on the basis of a 2² full factorial experimental design with central points, as well as computer simulations in the steady state and transient regime. The higher values for heat exchange overall heat transfer coefficient determined was around 250 W.m 2.K 1. Thus, the flow regime affects the evaluated response. In addition, the computer simulation in the permanent regime presented less relative deviation. Therefore, it can be seen that although the simulations show results close to the experimental ones, there are still associated errors that should be studied and minimized, since factors such as bubble formation were not considered in the simulations. Thus, it was found that computer simulations can be used to understand the operation of heat exchangers, but they are limited to real phenomena that are not considered in theoretical mathematical models. Therefore, this study elucidates the application of statistical and computer-assisted methods as a tool to comprehend heat exchangers behavior for industrial and didactic purposes.

Construction of a Computer-Aided Dual-Parameter Landslide Prediction Method

This study focuses on the typical geological region of granitic weathered soil in the hilly areas of southern China, integrating computer-assisted statistical methods and intelligent computing techniques to analyze geological disaster data and hourly precipitation data from 2016 to 2022. The research indicates that in the study area, geological disasters triggered by precipitation generally occur within 48 hours following the precipitation event. For 90% of the events, the average rainfall intensity is concentrated between 1.2 to 27.7 mm/h, with 72% having an average rainfall intensity of less than 10 mm/h, and only 13% exceeding 20 mm/h. The influence of prior precipitation mainly depends on the cumulative effect of effective rainfall over the previous 11 days. Based on these findings, this paper constructs a meteorological risk prediction model for geological disasters using two parameters: the average rainfall intensity (I) and the duration of rainfall (D). After comparing the prediction results with 62 test samples from 2022, it is noted that the method's TS score is superior to other prediction methods, significantly reducing the false alarm and missed alarm rates for meteorological forecasts in the granitic weathered soil geological region. Moreover, the computer implementation of this model demonstrates its efficiency and reliability in handling large-scale geological data, providing strong computational support for geological disaster risk assessment.

Advances in Research on the Activity Evaluation, Mechanism and Structure-Activity Relationships of Natural Antioxidant Peptides.

Antioxidant peptides are a class of biologically active peptides with low molecular weights and stable antioxidant properties that are isolated from proteins. In this review, the progress in research on the activity evaluation, action mechanism, and structure-activity relationships of natural antioxidant peptides are summarized. The methods used to evaluate antioxidant activity are mainly classified into three categories: in vitro chemical, in vitro cellular, and in vivo animal methods. Also, the biological effects produced by these three methods are listed: the scavenging of free radicals, chelation of metal ions, inhibition of lipid peroxidation, inhibition of oxidative enzyme activities, and activation of antioxidant enzymes and non-enzymatic systems. The antioxidant effects of natural peptides primarily consist of the regulation of redox signaling pathways, which includes activation of the Nrf2 pathway and the inhibition of the NF-κB pathway. The structure-activity relationships of the antioxidant peptides are investigated, including the effects of peptide molecular weight, amino acid composition and sequence, and secondary structure on antioxidant activity. In addition, four computer-assisted methods (molecular docking, molecular dynamics simulation, quantum chemical calculations, and the determination of quantitative structure-activity relationships) for analyzing the structure-activity effects of natural peptides are summarized. Thus, this review lays a theoretical foundation for the development of new antioxidants, nutraceuticals, and cosmetics.