Structural Modeling Research Articles

NONLINEAR MODELING AND ANALYSIS OF DAMAGED REINFORCED CONCRETE BEAMS USING ANSYS AND LIRA-SAPR SOFTWARE PACKAGES

This article presents a comparative analysis of the nonlinear behavior of a reinforced concrete beam with damaged reinforcement in the tension zone under a gradually increasing load until failure. The experimental beam, measuring 2100 mm × 200 mm × 100 mm, consists of a 20 mm diameter rebar in the tension zone, two 6 mm diameter rebars in the compression zone, and 6 mm diameter stirrups spaced 75 mm apart for transverse reinforcement. Nonlinear calculations were performed using ANSYS and LIRA-SAPR, with identical initial conditions applied to both models for accurate comparison. The study focuses on key aspects such as result accuracy, ease of use, and time required for nonlinear calculations, including material and geometric nonlinearity. By highlighting the strengths and weaknesses of each software, the research offers insights for engineers and researchers working on complex structural modeling of reinforced concrete.

Improving halalness and food safety management systems in the Indonesian broiler supply chain: an interpretive structural modeling and Bayesian network approach

Purpose By exploring the halalness and food safety risks from the perspective of technology and the relationship among them, this study aims to make quantitative predictions of such risks in the broiler supply chain to determine the critical control points (CCPs) in Hazard Analysis Critical Control Point (HACCP). Design/methodology/approach This study integrates Interpretive Structural Modeling (ISM) and Bayesian Network (BN) to achieve the objectives. Data were collected from focus group discussions (FGDs) with experts and direct observations at the broiler supply chain. Findings This paper identified 19 risks in the Indonesian broiler supply chain. The risk for halalness and food safety reached 30.92%, indicating that assuring halalness and food safety remains improbable or unlikely. The two CCPs of halalness and food safety are the knife’s sharpness and the vehicle’s storage temperature. Research limitations/implications This study quantifies the halalness and food safety risks in the Indonesian broiler supply chain, but it only involves one step forward and one step backward in the slaughterhouse’s chain. Practical implications The findings can provide insights for stakeholders, such as business owners, employees, management system auditors and consumers, regarding the critical control points of halalness and food safety in the broiler supply chain to improve the halalness and food safety management systems. Originality/value This study’s novelty lies in the examination of halalness and food safety risks using a risk prediction model to determine CCPs for the HACCP plan in the broiler supply chain in Indonesia.

ОСОБЛИВОСТІ ПСИХОЛОГІЧНОГО ІМУНІТЕТУ УКРАЇНСЬКИХ КОМБАТАНТІВ ТА ЧЛЕНІВ ЇХНІХ РОДИН: ТЕОРЕТИЧНІ АСПЕКТИ

The article is devoted to the analysis of the problem of psychological immunity of active Ukrainian military personnel and their families. The psychological immune system is a multidimensional but integrated unit of personal resources of resilience and adaptive capacities, also called "psychological antibodies", which provide immunity against stress and traumatic events. Under martial law, the Ukrainian military is the most important asset in countering armed aggression. Their psychological immunity can ensure effective stress management and protect them from possible psychopathology. Promotion of resources (psychological antibodies) will help active military personnel, as well as their family members, to use and overcome their traumatic experiences, stress and negative influences in a useful and productive way, including creating support networks, learning new skills, and achieving combatant goals during combat missions. It is justified that the return of a vital sense of security for successful recovery and functioning depends on the presence of psychological immunity not only among servicemen, but also among their families as a center of rehabilitation potential. The three-component structure of psychological immunity according to A.Olakh is described, which includes: the approach-conviction subsystem, the monitoring-creation-execution subsystem and the self-regulation subsystem. These subsystems make it possible to recognize one's environment, search for and activate the necessary resources for constructive interaction with it, and maintain emotional equanimity. Based on the experience of communicating with active military personnel, veterans, and members of their families, the authors propose to add a fourth component to the existing structural model - the spiritual and value subsystem. The allocation of this component is due to the peculiarities of Ukrainian identity and the specifics of the Russian-Ukrainian war. This component includes the spiritual self, the values of patriotism, moral qualities and religiosity (as an internal need to believe).

Driving Digital Transformation: The Impact of SME Digitalization on Technology Acceptance and Usage in Small and Medium Enterprises of Yemen

This research investigates the relationships between digitalization, perceived usefulness, attitudes toward using technology, and actual system use of technologies within the context of small and medium-sized enterprises (SMEs) in Yemen. Using a two-step structural equation modelling approach, the research investigates both direct and mediating effects. Data were gathered from 392 SMEs in Yemen by employing a survey research design. The measurement model was estimated to evaluate the constructs' accuracy, while the structural model was analyzed to investigate the proposed relationships. Findings reveal that SME digitalization significantly enhances perceived usefulness, which positively influences users' attitudes toward technology adoption and subsequently drives actual system use. Specifically, the analysis shows that improvements in digitalization practices lead to heightened perceptions of usefulness and favorable attitudes, ultimately resulting in increased actual system use. Moreover, the results indicate that perceived usefulness and attitudes serve as significant mediators between digitalization and system use. This research contributes to understanding how enhancing perceived usefulness and fostering positive attitudes can facilitate technology adoption in SMEs, providing important implications for practitioners, policymakers, and researchers focused on digital transformation. Overall, the findings emphasize the importance of strategic digitalization efforts to improve operational efficiency and promote successful technology acceptance in small and medium enterprises.

Exploring the Influence Factors of Digital Entrepreneurship in Improving the Digitization of Small and Medium Enterprises in Yemen

This research provides valuable insights into the impact of digital entrepreneurship on the digitalization of small and medium-sized enterprises (SMEs) in Yemen. By integrating the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, the research examines the role of various factors-technological, organizational, environmental, and individual, affecting technology adoption and utilization in SMEs. The study primarily explores the direct and indirect influences of these factors on actual system use, with a particular focus on the mediating roles of perceived usefulness and perceived ease of use in shaping attitudes toward technology adoption. Data were collected through a survey of 392 respondents, and the results were analyzed using partial least squares structural equation modelling (PLS-SEM). The measurement model was validated for reliability and accuracy, followed by an evaluation of the structural model to test the proposed hypotheses. The findings highlight the critical role of information and communication technology (ICT) adoption in enhancing both perceived ease of use and perceived usefulness. Additionally, the knowledge of IT entrepreneurs and government support were found to uniquely influence perceived ease of use and perceived usefulness, respectively. Both of these perceptions, in turn, positively impacted attitudes toward and the actual use of digital tools within SMEs. To address the challenges faced by Yemeni SMEs, it is recommended to enhance digitalization through ICT adoption and promote a pro-technology culture. Policymakers should create a supportive environment via targeted policies, subsidies, and public-private partnerships to foster sustainable growth and competitiveness.

Impact of electronic word of mouth (e-WOM) on purchasing decisions: an empirical study

PurposeIn today’s fast-paced and interconnected market, companies must adapt to the evolving demands of their customers. Therefore, it is essential to examine the impact of online reviews on potential customers' intent to purchase. This study seeks to identify the characteristics of electronic word-of-mouth (eWOM) that influence a buyer’s intention to purchase goods and services.Design/methodology/approachWe used the snowball sampling method to collect data using a pre-tested survey instrument with a five-point Likert scale. We received 696 usable responses. We conducted assumption tests to ensure that we could use covariance-based structural equation modelling (CB-SEM) for data analysis. The collected data were analysed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to compute the latent variables. We then tested our research hypotheses using CB-SEM.FindingsEight latent constructs – perceived persuasion, perceived information, image aesthetics, ease_of_experience, eWOM_credibility, eWOM_usefulness and eWOM_adoption – have been identified, which determine the influence of eWOM on purchase intent (PI) for both tangible and experiential products. Though the structural model emerged relatively similar, the constructs had differential impacts on PI for commodities and services. The perceived information quality and source trustworthiness had a greater impact on eWOM credibility in the case of services than in the case of commodities, while image aesthetics played a more crucial role in determining the eWOM credibility for commodities than services. In both cases, credible eWOM was found useful, but a persuasive eWOM influenced its perceived usefulness more in the case of commodities. The likelihood of adopting a useful eWOM and converting it to positive PI is present in the case of both services and commodities, but the impact is much higher in the case of services.Research limitations/implicationsThe study has examined the interplay of three theoretical consumer behaviour models: elaboration likelihood model (ELM), stimulus-organism-response model (SOR), and information adoption model (IAM). Thus, it adds to the existing literature on the characteristics of eWOM that influence the PI of online buyers.Practical implicationsThis study’s findings demonstrate how eWOM influences consumers' perceptions of the utility of goods and services, impacting their intention to purchase. It also provides valuable insights into marketing and consumer behaviour in the Indian market. Thus, this study assists marketers in adjusting their digital marketing strategies to ensure the effective use of eWOM characteristics to positively influence the PI of the target audience in the Indian market.Originality/valueThis research study examines the relationship between eWOM characteristics and PI for both goods and services sectors. Most existing literature is skewed towards specific service sectors, such as hospitality and health care. A generalised comparative study is what makes this research work unique.

Toxic small alarmone synthetase FaRel2 inhibits translation by pyrophosphorylating tRNAGly and tRNAThr.

Translation-targeting toxic small alarmone synthetases (toxSAS) are effectors of bacterial toxin-antitoxin systems that pyrophosphorylate the 3'-CCA end of transfer RNA (tRNA) to prevent aminoacylation. toxSAS are implicated in antiphage immunity: Phage detection triggers the toxSAS activity to shut down viral production. We show that the toxSAS FaRel2 inspects the tRNA acceptor stem to specifically select tRNAGly and tRNAThr. The first, second, fourth, and fifth base pairs of the stem act as the specificity determinants. We show that the toxSASs PhRel2 and CapRelSJ46 differ in tRNA specificity from FaRel2 and rationalize this through structural modeling: While the universal 3'-CCA end slots into a highly conserved CCA recognition groove, the acceptor stem recognition region is variable across toxSAS diversity. As phages use tRNA isoacceptors to overcome tRNA-targeting defenses, we hypothesize that highly evolvable modular tRNA recognition allows for the escape of viral countermeasures through tRNA substrate specificity switching.

Physiological cost of antibiotic resistance: Insights from a ribosome variant in bacteria.

Antibiotic-resistant ribosome variants arise spontaneously in bacterial populations; however, their impact on the overall bacterial physiology remains unclear. We studied the naturally arising antibiotic-resistant L22* ribosome variant of Bacillus subtilis and identified a Mg2+-dependent physiological cost. Coculture competition experiments show that Mg2+ limitation hinders the growth of the L22* variant more than the wild type (WT), even under antibiotic pressure. This growth disadvantage of L22* cells is not due to lower ribosome abundance but rather due to reduced intracellular Mg2+ levels. Coarse-grained elastic-network modeling of ribosome conformational dynamics suggests that L22* ribosomes associate more tightly with Mg2+ when compared to WT. We combined the structural modeling and experimental measurements in a steady-state model to predict cellular adenosine 5'-triphosphate (ATP) levels, which also depend on Mg2+. Experiments confirmed a predicted ATP drop in L22* cells under Mg2+ limitation, while WT cells were less affected. Intracellular competition for a finite Mg2+ pool can thus suppress the establishment of an antibiotic-resistant ribosome variant.

Risk assessment of abnormal pressure zones with a focus on shallow gas pockets based on surface gas logging data and statistical relationships

The assessment of alterations in hydrocarbon components preserved in reservoirs is important for oil field management and drilling procedures. Drilling fluid analysis yields valuable insights into the reservoir fluid. This study focuses on identifying and quantifying shallow gas basins in the Azadegan oil field. Risk assessment of abnormal pressure zones with a focus on shallow gas pockets based on surface gas logging data and statistical relationships is also a key aspect of this research. Shifts in the C1 gas ratio to other gas components signify a notable peak in the Asmari Formation. Consistency among various wetness (Wh), balance (Bh), and character (Ch) indices suggests that the Aghajari and Gachsaran formations potentially harbor gas and contain some heavy or residual oil with limited production potential. However, the Asmari Formation shows the potential for natural gas production, while the Gurpi Formation has the potential for wet gas or condensate. Pixler parameters were computed and plotted to affirm the results of other parameters. The investigation of stratigraphic columns for component changes and lithology reveals that lithology plays a pivotal role in preserving valuable fluids. The upper section of the Aghajari Formation, primarily composed of shale-evaporite horizons, exhibits lower frequency compared to its lower part. The Gachsaran Formation displays an exceptional ability to preserve components in anhydrite sections. Although Asmari has a high accretion ratio, the Chilean calcareous sandstone horizons are more prominent in the upper part of the Gurpi Formation. The structural model includes humpback microstructures as controls for shallow gas pockets and component accumulation. Evaporite and shale layers significantly influence the movement of these components across different horizons. The 3D model illustrates migration from the Gurpi Formation to the Aghajari Formation, as both formations serve as sources due to the propagation paths leading to the Pabdeh and Gurpi Formations.

Study on intelligent recognition of urban road subgrade defect based on deep learning.

China's operational highway subgrades exhibit a trend of diversifying types and an increasing number of defects, leading to more frequent urban road safety incidents. This paper starts from the non-destructive testing of urban road subgrade defects using geological radar, aiming to achieve intelligent identification of subgrade pathologies with geological radar. The GprMax forward simulation software is used to establish multi-layer composite structural models of the subgrade, studying the characteristics of geological radar images for different types of subgrade diseases. Based on the forward simulation images of geological radar for subgrade defects and field measurement data, a geological radar subgrade defect image database is established. The Faster R-CNN deep learning algorithm is applied to achieve target detection, recognition, and classification of subgrade defect images. By comparing the loss value, total number of identified regions, and recognition accuracy as metrics, the study compares four improved versions of the Faster R-CNN algorithm. The results indicate that the faster_rcnn_inception_v2 version is more suitable for the intelligent identification of non-destructive testing of urban road subgrade defects.

Decoding determinants of pro-environmental behaviors of higher education students: insights for sustainable future

Purpose The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students. Design/methodology/approach This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model. Findings The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth. Research limitations/implications Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data. Practical implications The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs. Originality/value First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling.

Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Previous methods mainly focused on protein sequence features, overlooking the semantic meaning of protein labels. We introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation. The online demo is at: https://huggingface.co/spaces/wenkai/FAPM_demo. Supplementary data are available at Bioinformatics online.

ENTREPRENEURIAL ORIENTATION AND SME SUCCESS: UNVEILING THE MEDIATING IMPACT OF AMBIDEXTERITY

This research seeks to assess how ambidexterity in exploration and exploitation mediates the relationship between entrepreneurial orientation and the performance of small and medium-sized enterprises (SMEs). Specifically, this study investigates the indirect influence of entrepreneurial orientation on business performance through the mediating roles of ambidexterity exploration and exploitation. A quantitative approach using structural equation modeling (SEM) is employed to examine the relationships among these variables. The research sample comprises 200 SMEs operating primarily in East Java, Indonesia. The findings demonstrate a significant mediating effect (p<0.05) of ambidexterity exploration and exploitation, which enhances the influence of entrepreneurial orientation on business performance. This indirect effect proves to be stronger than the direct impact of entrepreneurial orientation itself. The structural model shows a high R-square value of 68.6%, indicating a strong predictive capability. Additionally, the goodness-of-fit index reflects high model suitability (0.543 > 0.36), and the predictive power of the PLS model is considered medium. The study’s results underscore the importance of fostering both ambidexterity in exploration and exploitation alongside entrepreneurial orientation to optimize SME performance.

THE EFFECT OF HALAL AWARENESS IN ENHANCING MUSLIM TRAVELLERS’ EXPERIENCE AND SATISFACTION THROUGH RELIGIOUS RESPECT

This study focuses on halal awareness provided by halal destination providers to enhance Muslim travellers’ experience and satisfaction through religious respect. A survey involves Muslim travelers who visited halal destinations in Indonesia in 2024 with a total sample of 364. The analysis employs covariance-based structural equation modelling (CB-SEM), incorporating confirmatory factor analysis (CFA) and structural model evaluation to validate the hypotheses. The results show the effective role of religious respect in the relationship between halal awareness, Muslim travellers’ experience and satisfaction when they visit halal destinations. The framework significantly advances the theory of destination image emphasizing the crucial aspect of halal awareness in shaping the experience and satisfaction of Muslim travellers particularly the role of religious respect as a key factor in mediating these relationships.

The effects of conflicting economic and environmental goals on local governments’ responses to citizens’ complaints: evidence from China

PurposeThe paper examines how local governments’ responses to citizens’ complaints about environment issues are affected when the complaints involve conflicting goals, particularly economic versus environmental goals. This study focuses on the responsiveness of provincial governments to citizen environmental complaints on the Local Leader Message Board (LLMB) in China.Design/methodology/approachThis study collected 125,364 environment-related complaints lodged by citizens on the LLMB from 2013 to 2021 and identified complaints embodying conflicting goals through a Structural Topic Model (STM). Advanced supervised machine learning (ML) algorithms were employed to enhance the robustness of the findings.FindingsThe results indicate that provincial governments prioritize citizens’ complaints across different types of issues. However, complaints embodying conflicting goals (related to environmental issues) are less likely to get a response. This relationship is moderated by an inverted U-shaped effect of economic dependence on industries. This suggests that the impact of conflicting goals on government responsiveness is dynamic, with the likelihood of provincial governments responding to conflicting complaints initially increasing and then decreasing as economic dependence on industries rises.Originality/valueThe findings enrich the understanding of the consequences of conflicting goals by highlighting their potential as a mechanism to explain the strategic reactions of governments to citizens.

Modelling of critical success factors to improve the supply chain resilience for sustainable construction sector

PurposeThe construction industry has an intricate and constantly evolving supply chain (SC) that includes multiple stakeholders and processes. Enhancing SC resilience (SCR) is essential for promoting industry expansion and competitiveness. The SCR has attracted considerable interest because of the increasing worldwide challenges and threats facing SCs. This study introduces a conceptual framework for recognizing and classifying resilient supply chains’ critical success factors (CSFs) in sustainable construction.Design/methodology/approachThis research employs a sequential mixed-methods approach, combining qualitative and quantitative analyses. Initially, an extensive literature review and expert consultations were conducted to identify potential CSFs. The fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was then applied to classify these CSFs into cause-and-effect groups. To further analyse the interrelationships among the CSFs, interpretive structural modelling (ISM) was utilised, establishing a hierarchical framework. This integrated methodology provides a comprehensive understanding of the CSFs’ relative importance and their driving and dependence power, facilitating targeted interventions for enhancing supply chain resilience in the construction sector.FindingsThis integrated methodology establishes a hierarchical ranking of CSFs across five levels by revealing their causal interrelationships. The study addresses crucial SCR issues and provides actionable insights. The 17 finalized CSFs have 12 causal and five effect factors. The “safety measures and guidelines” have the highest driving potential, whereas the “availability of standardized materials/equipment” and “skilled human resources” depend highly on causal CSFs. The findings will offer a detailed perspective to narrow the gap between unsustainability and sustainability by enhancing the resilience of CSC.Practical implicationsIt offers a pragmatic framework for allocating resources and efforts to enhance the resilience of construction supply chains (CSCs). Policymakers can also use this study to create a more resilient SC for sustainable construction.Originality/valueThis study uniquely identifies and evaluates CSFs for SCR in the construction industry, providing a novel analysis of their driving power, dependence power and ranking. No previous studies have demonstrated such a comprehensive evaluation.

Thermomechanical Nonlinear Vibration of Axially Loaded Functionally Graded Material Cylindrical Panels Including Porosity

For the first time, the combined influences of axial compressive load, porosity, geometric imperfection, elastic edge restraint, and elevated temperature on the nonlinear free vibration of functionally graded material (FGM) cylindrical panels are investigated in this paper. Unlike previous studies, the structural model considered in this paper includes practical situations and conditions arising in the design process and application of FGM cylindrical panels. The properties of material constituents are assumed to be temperature-dependent, and the effective properties of porous FGM are determined using a modified rule of mixture. Governing equations in terms of deflection and stress function are established based on first-order shear deformation theory, taking into account von Kármán–Donnell nonlinearity and initial geometric imperfection. Analytical solutions are assumed to satisfy simply supported boundary conditions, and the Galerkin method is applied to derive a time differential equation containing both quadratic and cubic nonlinear terms. This differential equation is numerically integrated employing the fourth-order Runge–Kutta scheme to determine the frequencies of nonlinear free vibration. Parametric studies are carried out to assess numerous effects on both linear and nonlinear frequencies of porous FGM cylindrical panels. The study reveals that natural frequencies are strongly decreased and that frequency nonlinearity is more significant due to axial compressive loads.

From awareness to action: unraveling the interplay of employee AI awareness and change leadership in fostering knowledge hiding

PurposeThis paper investigates the mediating role of employee awareness of artificial intelligence (AI) in the relationship between technological turbulence and knowledge hiding, with a focus on the moderating impact of change leadership.Design/methodology/approachThe survey study adopted a quantitative approach to propose and test a model based on predictors of knowledge hidings. The survey approach received 320 respondent firms in the hotel management sector. The structural and measurement model was calculated using SmartPLS.FindingsEmployee AI awareness mediates the relationship between technological turbulence and knowledge hiding. Change leadership significantly moderates this relationship, reducing knowledge hiding by promoting innovative discussions and collaboration. High employee AI awareness can lead to knowledge hiding due to perceived threats to job security, but effective leadership mitigates this by fostering a collaborative environment.Originality/valueThe study highlights the importance of effective leadership in reducing knowledge hiding and emphasizes the need for a collaborative environment where employees view external partnerships as opportunities for learning and acquiring AI knowledge.

Mesoscopic Tensile Deformation of Steel Cord with Diverse Layered Nonuniformity Based on Microcomputed Tomography Optimizing Simulation Model

The finite‐element analysis plays a crucial role in enhancing the quality and predicting the safety of steel cord. The conventional parametric model for finite‐element analysis often overlooks the inhomogeneity of steel cord, making it difficult to achieve precise simulation. This article utilizes microcomputed tomography to track the mesoscopic deformation of real steel cord during stretching, reconstructing 3D digital images and quantitatively analyzing the corresponding rule for an optimized finite‐element modeling method. In comparison to the parametric model's experimental deviation of 28.37% in strain, the actual structural tensile model aligns with the tensile test with a deviation of only 2.88%, indicating high processing quality. In addition, when simulating the impact of layered nonuniformity on the fracture strain of cords, it is observed that a small degree of nonuniformity leads to an increase in compression between the cord filaments and subsequently enhances the fracture strain. This study provides a practical and dependable quantitative analysis on a mesoscopic scale to optimize the design and production of cords.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.