Analysis Model Research Articles

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

North Atlantic and the Barents Sea variability contribute to the 2023 extreme fire season in Canada

In the late spring to summer season of 2023, Canada witnessed unprecedented wildfires, with an extensive burning area and smoke spreading as far as the East Coast of the United States and Europe. Here, using multisource data analysis and climate model simulations, we show that an abnormally warm North Atlantic, as well as an abnormally low Barents Sea ice concentration (SIC), are likely key climate drivers of this Canadian fire season, contributing to ~80% of the fire weather anomaly over Canada from June to August 2023. Specifically, the warm North Atlantic forms an anomalous regional zonal cell with ascending air over the Atlantic and descending air encircling Canada, creating hot and dry local conditions. Meanwhile, reduced Barents SIC leads to a high-pressure center and reinforces the dry northern winds in Canada through Rossby wave dynamics. These exacerbated dry and hot conditions create a favorable environment for the ignition and spread of fires, thus contributing to the prolonged and extreme fire season in Canada. These teleconnections can extend to decadal scales and have important implications for understanding and predicting decadal fire activity in Canada and the surrounding regions.

Weight Restriction Approach in a Two-stage Network Structure: A DEA-based Approach

The standard data envelopment analysis model aimed to maximize the efficiency scores using the most favourable input/output weights. In two-stage network structures, the presence of intermediate undesirable measures leads to a distinct domain of weight restriction study. In the presence of undesirable intermediate measures and equipped with non-cooperative game theory, this study developes a joint weight restriction approach to derive a common set of weights. Concurrently, the overall efficiency of the system can be evaluated. Numerical instances of 34 OECD countries in 2012 have been presented to elucidate the details and applicability of the proposed method.

Optimal Design of Water Distribution System Using Improved Life Cycle Energy Analysis: Development of Optimal Improvement Period and Unit Energy Formula

Water distribution systems (WDSs) are crucial for providing clean drinking water, requiring an efficient design to minimize costs and energy usage. This study introduces an enhanced life cycle energy analysis (LCEA) model for an optimal WDS design, incorporating novel criteria for pipe maintenance and a new resilience index based on nodal pressure. The improved LCEA model features a revised unit energy formula and sets standards for pipe rehabilitation and replacement based on regional regulations. Applied to South Korea’s Goyang network, the model reduces energy expenditure by approximately 35% compared to the cost-based design. Unlike the cost-based design, the energy-based design achieves results that can relatively reduce energy when designing water distribution networks by considering recovered energy. This allows designers to propose designs that consume relatively less energy. Analysis using the new resilience index shows that the energy-based design outperforms the cost-based design in terms of pressure and service under most pipe failure scenarios. The implementation of the improved LCEA in real-world pipe networks, including Goyang, promises a practical life cycle-based optimal design.

A loaded tooth contact analysis (LTCA) model of profile modified gears

A loaded tooth contact analysis (LTCA) model of profile modified gears

Biomechanics of Parkinson’s Disease with Systems Based on Expert Knowledge and Machine Learning: A Scoping Review

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models.

Understanding the effect of experimental variables on the recovery of 231Pa from siliceous cake using factorial design

AbstractFour-factor (oxalic acid concentration, contact time, temperature, ultra-sound), two-level experimental design was applied during the recovery of 231Pa from siliceous cake—a solid radioactive-waste, produced in monazite processing. Oxalic acid (0.5 to 1.1 M) had the highest positive (F = 1018.6) and ultrasound had the least (F = 33.0) impact on recovery. All inter-factor interactions were significant, as Fcalculated < Fcritical and P < 0.05. Enthalpy of protactinium-oxalate complexation was found to be − 24.8 kJ mol−1. The regression coefficient (0.9853), randomness and magnitude (< ± 2) of fitted residuals explicated the suitability of present statistical model for predictive analysis.

Exploring the Path of Balanced Development: The Decoupling Evaluation of Agricultural Production Efficiency and Land Carrying Capacity

ABSTRACTHigh‐quality development in agriculture is crucial for maintaining the harmonious balance between human society and the natural environment, and promoting this development model is one of the key measures to alleviate land degradation issues. This study, grounded in the PRED theory (Population, Resources, Environment, and Development theory) framework, establishes an evaluation system for high‐quality agricultural development by selecting 128 cities within the Yangtze River Economic Belt as its samples. It quantifies land carrying capacity, utilizes the Stochastic Frontier Analysis (SFA) model to assess agricultural production efficiency, and applies the Tapio decoupling model to analyze the interplay between these two factors. The results reveal that the land resource carrying index has risen from 1.245 to 1.70, indicating an escalating tension between population and food resources. Furthermore, agricultural production efficiency has seen a 16.56% increase, reflecting positive advancements in agricultural production across the region. Spatial distribution analysis shows that the standard deviation ellipse is concentrated in the mid and lower reaches, centered in Changde, Hunan, and expanding westward, with a broader coverage area and perimeter. Additionally, the decoupling relationship between land carrying capacity and agricultural production efficiency primarily manifests in three forms: strong negative decoupling, weak decoupling, and expansive negative decoupling. This research offers significant insights for effectively mitigating the strain between population growth and resource‐environmental carrying capacity.

Design and multidimensional analysis of dental orthopedic prosthetics quality

The purpose of the research is a multidimensional analysis of the "quality of life" of patients before and after dental prosthetics. It is necessary to assess the quality of life of patients after prosthetics, depending on the type of prostheses, gender, and age of patients and give recommendations on the choice of prosthetics method. Methods. Dental prosthetics was performed at the clinic of the Moscow State Medical University named after A. I. Evdokimov, four types of prostheses were tested - the classic traditional prosthesis (TR) and three types of digital prostheses (D1, D2, D3). Based on the developed OHIP-20 DG questionnaire, the quality of life of patients was assessed before prosthetics, immediately after prosthetics and six months later. The quality of life of patients after prosthetics was determined within the framework of the theory of latent variables. Using a four-factor analysis of variance, the impact on the quality of life of the type of prostheses, the duration of adaptation after prosthetics, gender and age of patients was assessed. Results. Within the framework of the theory of latent variables, it is shown that the developed questionnaire can be used as a measuring tool to analyze the influence of the studied factors on the quality of life of patients after prosthetics. Due to the use of four-factor analysis of variance, the accuracy of statistical analysis has been improved and the statistical significance of four sources of variance has been revealed. Based on the results of statistical analysis, recommendations on the choice of prosthetics method have been developed. Conclusion. A multidimensional analysis of the studied factors affecting the quality of life of patients after dental prosthetics was carried out. The quality of life of patients increases statistically significantly after prosthetics. Due to the use of a four-factor model of variance analysis, the precision of assessments of the quality of life of patients has been improved. The prosthetics method (D1) is recommended for women, the prosthetics method (D3) is recommended for men. The obtained results are recommended to be used in practice.

Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages.

Effects of structural parameters on the load distribution unevenness in CFRP hybrid bonded‐bolted joint

AbstractCarbon Fiber Reinforced Polymer (CFRP) Hybrid Bonded/Bolted (HBB) joint structures are distinguished for their superior jointing performance among current mechanical joint systems, making them a favored option for mechanical joints. These structures are characterized by many structural parameters, with the relationship between these parameters and jointing performance being notably complex. Additionally, the laminate's brittleness and the uneven distribution of bolt loads in multi‐bolt joint structures impair the overall jointing performance. To investigate the impact of structural parameters on the uneven load distribution within CFRP HBB joint structures and improve their jointing performance, a finite element analysis (FEA) model grounded in 3D Hashin failure criterion is developed. Validation of the model with experimental data confirmed the uneven load distribution among bolts in multi‐bolt joints. The study elucidated the influence of changes in structural parameters (overlap length, bolt‐hole spacing, and clearance fit) on the uneven load distribution and the connection strength of CFRP HBB joint structures. A negative correlation is found between the unevenness of load distribution and connection strength, offering insights for enhancing and researching connection strength in CFRP HBB joint structures.Highlights Developed a FEA model based on the 3D Hashin failure criterion for CFRP Bonded‐Bolted joints. Identified key factors affecting HBB joint load distribution and jointing performance. Evaluated the impact of overlap length, bolt‐hole spacing, and clearance fit on CFRP joints. Suggested design optimizations for enhancing the performance of HBB joints.

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

Enhancing performance factor analysis through skill profile and item similarity integration via an attention mechanism of artificial intelligence

IntroductionFrequent formative assessment is essential for accurately evaluating student learning, enhancing engagement, and providing personalized feedback. In STEM education, understanding the relationship between skills that students have internalized (mastered) and those they are developing (emergent) is crucial. Traditional models, including item response and cognitive diagnosis models, primarily focus on emergent skills, often overlooking internalized skills. Moreover, new tools like large language models lack a complete approach for tracking knowledge and capturing complex skill relationships.MethodsThis study incorporates artificial intelligence, specifically attention mechanisms, into educational assessment to evaluate both emergent and internalized skills. We propose a modified version of Performance Factor Analysis (PFA), which assesses student abilities by analyzing past responses and comparing them with peer performance on the same items, using parameters from a sigmoid function. This model leverages attention mechanisms to capture item order-based similarity and decay principles, providing a nuanced view of student skill profiles.ResultsThe Modified Performance Factor Analysis model significantly improved discriminative power, accuracy, precision, recall, and F1 scores across various skill areas compared to traditional PFA models.DiscussionThese results indicate that the Modified Performance Factor Analysis model allows for a more accurate and comprehensive evaluation of student performance, effectively identifying both emergent and internalized skills. By integrating AI into assessment, educators gain deeper insights, enabling them to refine teaching strategies and better support students' mastery of both types of skills.

Teatro: mirada, distancia y conciencia. Hacia un modelo de análisis (imposible) para el teatro contemporáneo

The article aims to return to previous reflections on the “contemporary theatre” category, advancing, on this occasion, towards a methodological dimension of the approach and proposing a theoretical discussion on various concepts that configure a thinking and analysis model for the plays that this “contemporary theatre” produces. Under the recognition of the impossibility of such a model, various dimensions of attention are proposed such as the gaze-distance-consciousness triad, the discussion of the theatre category and a possible situated field, the development of a properly theatrical theatricality, the triad resources-procedures-strategies, and the production of liminality. All these considerations are not intended to consolidate an analysis model for contemporary theatre works as an exception or opposition to works configured under the dramatic paradigm of representation, but rather, the (im)possibility of a model that allows the understanding of contemporary theatre as a field in which all these manifestations, practices and, finally, plays, could coincide.

Phase angle is a useful predicting indicator for protein-energy wasting and cardiovascular risk among maintenance hemodialysis patients.

Protein-energy wasting (PEW) is a major contributor to the high mortality among maintenance hemodialysis (MHD) patients. Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in MHD patients. Previous studies have confirmed that PA may be a good objective indicator for determining the nutritional status and prognosis of MHD patients. Our study aimed to determine the predictive value of phase angle (PA) as detected by bioelectrical impedance analysis (BIA) on PEW and cardiovascular (CV) risk among MHD patients. Our retrospective observational study involved 161 adult patients with HD. The Cardiovascular risk score is a risk model based on the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS). We established LASSO logistic regression analysis model to identify key parameters related to body composition that can predict PEW in MHD patients. The area under the curve (AUC) values for PA, appendicular skeletal muscle mass index (ASMI), body cell mass (BCM), and mid-arm circumference (MAC) in predicting PEW in male MHD patients were relatively large, with 0.708, 0.674, 0.663, and 0.735, respectively. The predicted PEW values of these parameters were slightly lower in female patients than in men. We incorporated PA, ASMI, BCM, and MAC into a model that predicted the incidence of PEW in maintenance hemodialysis patients using LASSO technology. We discovered that the model predicted a greater AUC of PEW occurrence than any single factor, 0.877 for men and 0.76 for women. The results of the univariate logistic regression analysis showed that the low PA tertile array group had a greater incidence of PEW than the high PA group (P < 0.001). Additionally, we also found that lower PA was associated with higher CV risk scores. The PA detected by bioelectrical impedance analysis could predict the risk of PEW and cardiovascular events among patients with MHD. When used in conjunction, PA, ASMI, BCM, and MAC have a high diagnostic efficacy for PEW in patients on maintenance hemodialysis.

Saudi Arabia's Foreign Policy Under MBS: Transition from Defensive Realism to Offensive Realism (2016-2023)

This study seeks to elucidate the underlying factors driving Saudi Arabia’s shift from defense realism to offensive realism, focusing on MBS’s authoritarian rule and structural reforms aimed at minimizing bureaucratic hurdles in security and defense decision-making. The research examines the kingdom’s transition from conservative foreign policies to a more activist stance, illustrated by its support for Palestine and its adoption of non-conventional approaches to address emerging challenges. Adopting the John Scott document analysis model and utilizing secondary data, this study delves into the nuances of Saudi Arabia’s foreign policy shift under MBS’s leadership. It examines how his leadership style, combined with Vision 2030 goals and objectives, reflects a post-ideologically motivated transition that seeks to assert power and influence in the Middle East. The research explores the potential implications of this shift on regional security, including the Yemen civil war, balance of power realignment, and Saudi Arabia’s relationships with Western partners.

Determining Factors of Liquidation of Regional Public Companies: Case Study of Perumda Banongan, Situbondo Regency

The research entitled Determining Determinants of Regional Public Companies Liquidation: Case Study of Perumda Banongan, Situbondo Regency using qualitative methods. Miles and Huberman's interactive analysis model. This research was conducted with the aim of analyzing the determining factors for the liquidation of regional public companies at Perumda Banongan, Situbondo Regency. The data source for this research uses primary and secondary data from the Banongan Regional Public Company, Situbondo Regency. The results of the research show that the determining factor for liquidation lies in unhealthy financial conditions and problems with the Governance of Perumda Banongan. So it can be concluded that the Regional Government as the Capital Holding Authority must immediately improve management governance, human resources, and implementation supervision over the performance of Perumda and Perseroda so that they can run more effectively.

COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS MODELS ON BANKING INVESTMENT IMPACT BY MACHINE LEARNING ALGORITHM

COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS MODELS ON BANKING INVESTMENT IMPACT BY MACHINE LEARNING ALGORITHM

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&amp;D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.