Multidimensional Model Research Articles

Dimensional assessment of self-reported musculoskeletal symptoms by workers: Amulti-case study.

Work-related musculoskeletal disorders (WMSD) encompass a range of conditions affecting muscles, tendons, and nerves. Visual diagrams are widely used to identify symptoms and to generate musculoskeletal discomfort metrics. However, there is no consensus on the number of discomfort dimensions that can originate from self-reported musculoskeletal symptoms by individuals. This study aimed to test the fit of WMSD symptom models from workers in two samples of different sizes. A combination of Full-Information Item Factor Analysis (FIFA) and Item Response Theory (IRT) was utilized to analyze and test the models. The study was conducted in two samples of workers (n1 = 6944 and n2 = 420) who had their symptoms identified with the aid of a human body diagram. An analysis was conducted considering each sample's unidimensional and three multidimensional models. The unidimensional model (general musculoskeletal discomfort), bi-dimensional model (discomfort in upper and lower body), and tridimensional model (discomfort in the upper limbs, lower limbs, and trunk) showed good values of factor loading and communalities, along with satisfactory item discrimination ability. Regardless of sample size, parameter estimation for IRT and FIFA proceeded without issues, presenting suitable fit parameters. Three models were valid and reliable for more extensive and smaller samples. However, the tridimensional model was best for generating discomfort scores in body regions. Companies and safety professionals can use these findings to devise strategies to mitigate musculoskeletal pains based on perceived symptom locations.

Building College Education Management in the New Era: Theoretical Discussion and Model Construction Based on New Quality Productivity

This study analyses the use of new quality productivity theory in higher education management and develops a framework called the "multi-dimensional collaborative innovation management model" to tackle the challenges of globalisation and information technology. This concept combines the three dimensions of scientific and technological innovation, management innovation, and system innovation to promote effective resource allocation, improve educational quality and efficiency, and increase the education system's adaptability. This study uses mixed-methods research to verify the model's effectiveness in practical educational management as well as to present fresh theoretical perspectives and practical tactics for cultivating high-qualified talents who will satisfy the needs of future society.

Tritium management in ITER test blanket systems port cell for maintenance operations

Four Test Blanket Systems (TBS) will be tested in the International Thermonuclear Experimental Reactor equatorial ports #16 and #18 to verify tritium breeding and heat extraction technology. A significant quantity of tritium would be produced in TBM, and partly released into the port cell from the pipework of TBS or other high-temperature components due to its strong mobility and high permeation. The port cell should be accessible during equipment maintenance and human intervention. This work built a multi-dimensional geometric model to characterize HTO transport in the port cell, absorption/desorption, and diffusion in walls and discussed the effect of paint thickness, ventilation rate, source term, and epoxy properties on detritiation efficiency. The results suggest that a 0.1–0.16 mm paint with the lowest HTO solubility is optimal from the compromise between quick cleanup and tritiated waste decommission. A higher ventilation rate could accelerate detritiation while minimizing the radioactive source by a tritium-resisting layer is the most direct method. The optimized design options for reducing the time required to reach 1 DAC in 12 h still need further discussion because of the delayed HTO source from epoxy paint and dead zone of the flow field.

Multivariate regularly varying insurance and financial risks in multidimensional risk models

Abstract Multivariate regular variation is a key concept that has been applied in finance, insurance, and risk management. This paper proposes a new dependence assumption via a framework of multivariate regular variation. Under the condition that financial and insurance risks satisfy our assumption, we conduct asymptotic analyses for multidimensional ruin probabilities in the discrete-time and continuous-time cases. Also, we present a two-dimensional numerical example satisfying our assumption, through which we show the accuracy of the asymptotic result for the discrete-time multidimensional insurance risk model.

Similarity Measures between Buying and Selling Prices of Various Exchange Rates with the inclusion of Multidimensionality and Multicollinearity

This paper introduces a new statistical model, the multidimensional measurement error model with multicollinearity, to study the relationship between buying and selling prices of foreign exchange rates of various currencies. Such a model is needed due to the possible rise in multidimensionality and multicollinearity issues that may occur due to the movement of the financial markets towards stock market integration of various countries since the occurrences of the financial crisis as well as the part result of globalization. As this integration involves the participation of various countries, it will affect the foreign exchange rate. Hence, the analyses are performed on seven currencies against the Malaysian Ringgit where four models’ performances are compared. From this research, it can be concluded that the proposed model comparatively performs better than the other models in representing the relationship of the stationary prices and performs as well as existing models towards non-stationary prices. It can also be seen that the Japanese Yen is the currency that has the strongest influence and closer similar trends towards other currencies while the Great British Pound showed otherwise.

Development and external validation of a multidimensional deep learning model to dynamically predict kidney outcomes in IgA nephropathy.

Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. This study aimed at establishing a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. In this retrospective cohort study of 2,056 IgA nephropathy patients at 18 kidney centers, a total of 28,317 data points were collected by the sliding window method. Among them, 15,462 windows in a single center were randomly assigned to training (80%) and validation (20%) sets while 8797 windows in 18 kidney centers were assigned to an independently test set. Interpretable Multi-Variable Long Short-Term Memory (IMV-LSTM), a deep learning model, was implemented to predict kidney outcomes (kidney failure or 50% decline in kidney function) based on time-invariant variables measured at biopsy and time-variant variables measured during follow-up. Risk performance was evaluated using Kaplan-Meier analysis and the C statistic. Trajectory analysis was performed to assess the various trends of clinical variables during follow-up. The model achieved a higher C statistic (0.93; 95% CI, 0.92-0.95) on the test set than the XGBoost prediction model that we developed in a previous study using only baseline information (C statistic, 0.84; 95% CI, 0.80-0.88). Kaplan-Meier analysis showed that groups with lower predicted risks from the full model survived longer than groups with higher risks. Time-variant variables demonstrated higher importance scores than time-invariant variables. Within time-variant variables, more recent measurements showed higher importance scores. Further interpretation showed that certain trajectory groups of time-variant variables such as serum creatinine and urine protein were associated with elevated risks of adverse outcomes. In IgA nephropathy, a deep learning model can be used to accurately and dynamically predict kidney prognosis based on longitudinal data, and time-variant variables show strong ability to predict kidney outcome.

Novel occupancy detection method based on convolutional neural network model using PIR sensor and smart meter data

Occupancy information, especially the number of occupants, can effectively guide the operation of building systems. Capturing real-time and accurate occupancy information is critical. Passive infrared (PIR) sensors and smart meters have been deployed extensively. To improve the occupancy detection accuracy, this study proposes an innovative occupant number detection approach involving a temporal sequential analysis of occupancy-related data using data fusion from PIR sensors and smart meters with a convolutional neural network (CNN) model. A university office was investigated as a case study. PIR sensors and smart meters were adopted to collect data. And CNN model was established with both various data types and historical data as multi-dimensional model inputs. The model outperformed than traditional ANN models with an improved accuracy by 26.8%. The proposed occupancy detection model was applied to optimize the control strategy of an outdoor air system, which helped to provide comfortable built environment and realize energy savings.

The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis

Background The etiology of ischemic stroke is multifactorial. Several gene mutations have been identified as leading causes of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary disease that causes stroke and other neurological symptoms. Objective We aimed to identify the variants of NOTCH3 and thrombophilia genes, and their complex interactions with other factors. Methods We conducted a hierarchical cluster analysis (HCA) on the data of 100 patients diagnosed with ischemic stroke. The variants of NOTCH3 and thrombophilia genes were identified by polymerase chain reaction with confronting 2-pair primers and real-time polymerase chain reaction. The overall preclinical characteristics, cumulative cutpoint values, and factors associated with these somatic mutations were analyzed in unidimensional and multidimensional scaling models. Results We identified the following optimal cutpoints: creatinine, 83.67 (SD 9.19) µmol/L; age, 54 (SD 5) years; prothrombin (PT) time, 13.25 (SD 0.17) seconds; and international normalized ratio (INR), 1.02 (SD 0.03). Using the Nagelkerke method, cutpoint 50% values of the Glasgow Coma Scale score; modified Rankin scale score; and National Institutes of Health Stroke Scale scores at admission, after 24 hours, and at discharge were 12.77, 2.86 (SD 1.21), 9.83 (SD 2.85), 7.29 (SD 2.04), and 6.85 (SD 2.90), respectively. Conclusions The variants of MTHFR (C677T and A1298C) and NOTCH3 p.R544C may influence the stroke severity under specific conditions of PT, creatinine, INR, and BMI, with risk ratios of 4.8 (95% CI 1.53-15.04) and 3.13 (95% CI 1.60-6.11), respectively (Pfisher<.05). It is interesting that although there are many genes linked to increased atrial fibrillation risk, not all of them are associated with ischemic stroke risk. With the detection of stroke risk loci, more information can be gained on their impacts and interconnections, especially in young patients.

Stepwise Multidimensional Climate Envelop Modeling of Pitch Pine (Pinus rigida)

Understanding the intricate relationships between climate and vegetation remains a fundamental challenge in contemporary ecology. The ability to anticipate the specific climatic factors affecting different tree species and understand how they respond is crucial for mitigating the impacts of climate change on forested ecosystems. Additionally, quantitatively assessing habitat loss resulting from anthropogenic activities is essential for informed conservation efforts. Our objective is to evaluate the potential distribution of pitch pine (Pinus rigida) in North America and assess the associated habitat loss. To achieve this, we employ a stepwise multidimensional climate envelope modeling approach, comparing two data-intensive models—the Variable Interaction Model (VIM) and the Variable Non-Interaction Model (VNM). These models discern the influence of diverse combinations of climatic characteristics on the distribution of the species. Both VNM and VIM employ Shapley values for factor ranking during construction. VNM assumes independent effects, resulting in a hyperrectangle-shaped climate envelope, while VIM considers interactions, yielding a complex, data-driven multidimensional envelope. Data integration involves mining the US Forest Inventories and climatic data, encompassing 19 parameters. The results unequivocally highlight the superior predictive accuracy of VIM compared to the Variable Non-Interaction Model, VNM. The modeling approach developed in this study has the potential to enhance species distribution models for various tree species in the context of evolving climatic conditions.

A systematic review of multidimensional relevance estimation in information retrieval

AbstractIn information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct relevance aspects employed for estimating multidimensional relevance. Moreover, we highlight the approaches used to aggregate scores related to these factors and rank information items. Our insights underline the importance of concise definitions and unified methods for estimating relevance factors within and across domains. Finally, we identify benchmark collections for evaluations based on multiple relevance aspects while underscoring the necessity for new ones. Our findings suggest that large language models hold considerable promise for shaping future research in this field, mainly due to their relevance labeling abilities.This article is categorized under: Application Areas > Science and Technology Technologies > Computational Intelligence

Direct mineral content prediction from drill core images via transfer learning

Deep subsurface exploration is important for mining, oil and gas industries, as well as in the assessment of geological units for the disposal of chemical or nuclear waste, or the viability of geothermal energy systems. Typically, detailed examinations of subsurface formations or units are performed on cuttings or core materials extracted during drilling campaigns, as well as on geophysical borehole data, which provide detailed information about the petrophysical properties of the rocks. Depending on the volume of rock samples and the analytical program, the laboratory analysis and diagnostics can be very time-consuming. This study investigates the potential of utilizing machine learning, specifically convolutional neural networks (CNN), to assess the lithology and mineral content solely from analysis of drill core images, aiming to support and expedite the subsurface geological exploration. The paper outlines a comprehensive methodology, encompassing data preprocessing, machine learning methods, and transfer learning techniques. The outcome reveals a remarkable 96.7% accuracy in the classification of drill core segments into distinct formation classes. Furthermore, a CNN model was trained for the evaluation of mineral content using a learning data set from multidimensional log analysis data (silicate, total clay, carbonate). When benchmarked against laboratory XRD measurements on samples from the cores, both the advanced multidimensional log analysis model and the neural network approach developed here provide equally good performance. This work demonstrates that deep learning and particularly transfer learning can support extracting petrophysical properties, including mineral content and formation classification, from drill core images, thus offering a road map for enhancing model performance and data set quality in image-based analysis of drill cores.

Modeling and evaluating the impact of variable bus lane on isolated signal intersection performance

Dedicated Bus Lane (DBL) is a typical strategy to ensure priority for public transportation. However, when the frequency of bus departures is low, the DBL often remains vacant, reducing the efficiency of traffic utilization. Variable bus lane (VBL) strategy allows for the provision of service to social vehicles in specific scenarios, aiming to alleviate traffic congestion and improve emission efficiency at intersections. To analyze the impact of this strategy on dynamic queuing behavior and traffic efficiency at an isolated intersection, this paper proposes a multidimensional stochastic queuing model. The model incorporates various factors such as traffic demand, signal timing, bus arrival time, and the length of the variable area at intersections. By examining the steady-state distribution of queue length and calculating performance indicators, the effectiveness of VBL in improving intersection performance is evaluated. Numerical simulations have demonstrated that the proposed model accurately describes the vehicle queuing process at intersections under different conditions. Theoretical analysis confirms that the implementation of VBL strategy can effectively improve traffic efficiency at intersections and minimize the queue length of vehicles.

Role of muscle ultrasound in frailty assessment in older adults with type 2 diabetes mellitus

BackgroundFrailty is a geriatric syndrome that is characterized by increased vulnerability to intrinsic and extrinsic stressors due to decreased biologic reserves. Muscle ultrasound (US) is a valid and reliable method for assessing muscle quantity in older adults. The study aims to examine the relationship between frailty definitions and US-derived muscle parameters.MethodsWe conducted a cross-sectional study with type 2 diabetes mellitus outpatients in a tertiary hospital, and all participants underwent a comprehensive geriatric assessment. For frailty assessment, the Fried Frailty Phenotype (FFP), the Clinical Frailty Scale (CFS), and the Edmonton Frailty Scale (EFS) were performed. Muscle US measurements included Gastrocnemius Medialis (GM) muscle thickness, GM fascicle length, GM pennation angle, Rectus Femoris (RF) muscle thickness, Rectus Femoris cross-sectional area (RFCSA), Rectus Abdominis (RA) muscle thickness, External Oblique (EO) muscle thickness, Internal Oblique (IO) muscle thickness, and Transverse Abdominis (TA) muscle thickness.ResultsIn all, 373 participants were included in the study. The median age of participants was 72.7 ± 5.9 years, and 64.6% of them were female. According to the FFP, 18.2% of the participants were living with frailty, 56% of them were pre-frail; 57.4% of them were living with frailty according to the CFS; 25.2% of them were living with frailty, and 20.6% of them were pre-frail according to the EFS. The FFP, CFS, and EFS scores were related to muscle thickness of GM, RF, and RA, fascicle length of GM, and pennation angle of GM and RFCSA. Particularly, GM pennation angle, RF muscle thickness, and RFCSA were associated with an increased risk of frailty. Besides muscle thickness of GM, RF, and RA, fascicle length of GM, pennation angle of GM, and RFCSA were significant for predicting the presence of frailty.ConclusionsUS-derived regional muscle measurements are associated with frailty definitions (in both physical, cumulative deficit, and multidimensional models) in a diabetic geriatric population.

Trophic interactions between common minke whales (Balaenoptera acutorostrata) and their prey during summer in the northern Barents Sea

Global warming is causing rapid change in marine food webs, particularly at northern latitudes where temperatures are increasing most rapidly. In this study, the diet of common minke whales Balaenoptera acutorostrata was assessed both in terms of short-term (morphological analyses of digestive tract contents) and longer-term (tissue chemical markers: fatty acids and stable isotopes) prey use in the northern Barents Sea to see if they are prey shifting. Samples (blubber cores, muscle, and stomach contents) were obtained from 158 common minke whales taken during Norwegian commercial whaling operations during summer over the period 2016–2020. Two prey items, capelin Mallotus villosus and krill (primarily Thysanoessa sp.), dominated the stomach contents in the entire period of investigation, which included sampling both in June and in August, similar to findings from earlier studies. A few gadoids were also observed in the whale stomachs. Lower blubber fatty acid (FA) contents in 2016/2017 as compared with 2018/2019 were observed. This is most likely explained by differences in sampling time (June in 2016/2017 vs August in 2018/2019, i.e., after a longer feeding period during the summer in the latter case). This explanation also fits with the fact that FA profiles of the 2018/2019 whales were more similar to the FA profiles of the potential prey, presumably reflecting the two months longer assimilation time for these whales. Multidimensional mixing models based on carbon and nitrogen isotope composition of the most likely prey groups suggested that the whales ate mostly krill in four of the five sampling years. In 2018 there were indications of a higher proportion of gadoid fish, showing some dietary flexibility. The trophic level of the whales’ feeding, as interpreted from the nitrogen isotope values, was positively correlated with blubber thickness suggesting that fish-eaters tended to assimilate more energy than whales that focused more exclusively on lower trophic prey. The variation suggested by different dietary analyses methods − stomach contents, fatty acids, and stable isotopes – most likely reflects different turnover times, with muscle stable isotopes likely representing several months of dietary integration, while lipid stores are more dynamic and may represent weeks, and stomach contents represent feeding events during the last few hours. The change in diet of minke whales from small pelagic fishes (in the past) to a greater quantity of krill and demersal fish (seen in this study) suggests that the whales are responding to the ongoing borealization of the Barents Sea ecosystem.

Sustainable management of land use patterns and water allocation for coordinated multidimensional development

The sustainable and integrated management of resources is a crucial approach to achieving sustainable regional development in the face of limited land and increasingly scarce water resources. This paper proposes a multidimensional optimization model to jointly optimize land use patterns and water resource allocation under the conditions of maximizing economic benefits and minimizing social differences, ecological loads, environmental pollution, and carbon emissions. The optimization model is solved using the affiliation function method and then combined with random forest (RF) and cellular automata (CA) models to further obtain the optimal spatial layout of land use. This paper takes the Mid-Yangtze River City Cluster as an example and optimizes the land use pattern and water allocation in the region in 2020 and 2030. The results of the study show that the optimized water and land resource allocation scheme performs better than the status quo in all dimensions. The optimized economic benefits, social effects, ecological effects, environmental effects, and overall sustainability in 2020 are increased by 45.97%, 22.89%, 10.07%, 24.65%, and 42.73%, respectively. By 2030, overall sustainability reaches 0.69. The developed modeling framework helps to elucidate the internal linkages between land use and water allocation, and it provides decision-makers with diversified approaches to land and water resource management and adjustment for different cities in different periods, which promotes the sustainable development of urban agglomerations.

Validation of the translated Negative Physical Self Scale in a sample of Asian women living in Canada.

Body dissatisfaction is often linked to the internalization of Western beauty standards. Existing measures of body dissatisfaction, developed in Western societies, may fail to capture complex variations across ethnicities and cultures. The Negative Physical Self Scale (NPSS) assesses cognitive, affective, perceptual, and behavioural facets of body dissatisfaction. While unique in its consideration of Chinese ideals of body image, the NPSS has recently been translated and validated in a North American sample. The English-translated version of the NPSS has the potential to be an appropriate body dissatisfaction assessment tool for Asian women living in North America. The current study aims to validate the NPSS in an Asian female population living in Canada. A sample of 899 undergraduate women residing in Canada with self-identified Asian ethnicity completed an online survey consisting of the NPSS and other measures of body dissatisfaction. An initial confirmatory factor analysis indicated that the four-factor structure of the NPSS, previously suggested in a primarily non-Asian North American sample, was a poor fit for the data. A second-order multidimensional model, based on a model proposed during the original development of the NPSS in a Chinese sample, indicated good fit once items were removed due to loadings < .60. High internal consistency between subscales and strong convergent validity with other measures were demonstrated. Notably, the NPSS Body Concern subscale demonstrated high convergence with other popular measures of body dissatisfaction and has the potential for use as a brief measure of body dissatisfaction among North American Asian females in clinical and research settings. The NPSS provides a valid assessment of body dissatisfaction among a sample of Asian women living in Canada, a specific subpopulation that has not been previously investigated. The findings highlight the importance of developing culturally sensitive measures of body dissatisfaction for differing ethnic and cultural groups.

Numerical study on thermal runaway in a cell and battery pack at critical heating conditions with variation in heating powers

The thermal stability of lithium-ion cell is still a major concern in electric vehicle and energy storage applications affecting the cell to its chemistry level. The thermal abuse is the most common type of thermal runaway which is triggered either by excess heat accumulation or localized heating in the battery pack. Analysis of thermal runaway in the perspective of various critical heating positions with temperature distribution, heat generation still lacks. Thus, the present work is focused on this issue. A three-dimensional homogeneous Multi-Scale, Multi-Dimensional model with a finite volume method solver ANSYS FLUENT has been utilised to assess the effect of heating positions, and variation in spacings on thermal runaway for a 18650 cell and battery pack in a detailed way. It is observed that the direction of flow is the typical deciding factor of the thermal runaway spreading pattern in the cell. The negative solvent is responsible for the generation of maximum heat in the thermal runaway of a 18650 cell. The results show that cell heated at the bottom end (10 mm width) is fast prone to thermal runaway, vertical (5 mm width) being slower in disturbing the separator stability. The NCA cathode material has a safety hierarchy that goes as follows with regard to positions: vertical 10 mm > bottom end > bottom 5 mm > centre 10 mm > bottom 10 mm. The effect of spacing on thermal runaway is varying non-uniformly with the location of the heating position in the 3 × 3 battery pack. The highest temperature attained by the 7000 W/m2 heating power battery pack is discovered to be greater than the 10,000 W/m2 by 10 K. With a power increase from 7000 W/m2 to 10,000 W/m2, there is a reduction in time delay of 17 %.

Chemical stability of anion exchange membranes for alkaline fuel cells and water electrolysers: Experiment, multi-dimensional modeling and hybrid simulations

The unstable nature of anion exchange membranes (AEMs) has hindered the progress of alkaline fuel cells and water electrolyzers. Density functional theory (DFT) can explore the correlation between the structure and properties of model compounds. However, theoretical explorations of AEMs remain immensely insufficient owing to the complexity of modeling the realistic solution system which caused by multi-dimensional parameters such as polymer chain entanglement, steric hindrance, and migration of ions. In this study, molecular dynamic simulation was utilized to obtain the multi-dimensional parameters of polyelectrolytes, which were considered when calculating the degradation energy barrier by DFT, thus leading to more accurate results and optimizing the conventional DFT calculations. Combined with the computational works, pyridinium functionalized polyolefin and polyether sulfone were synthesized to verify the feasibility of the simulation scheme. This optimized scheme fills the gap in the DFT calculations scale and is easy to extend to other types of AEMs, which help design the chemically stable AEMs and promote the development of alkaline fuel cells and water electrolysers.

Development of the Imagination-Creativity Process Scale in Design

In this study, the undergraduate students’ Imagination–Creativity Process Scale (ICPS) in design was developed with reference to the Imagination–Creativity Process in design (Wang & Ho, 2021), and the scale reliability and validity were tested. Two samples of undergraduate students participated in test development, one for item revision and the other for validation. Sample 1 and 2 consisted of 285 and 488 students, respectively. Five to ten items were created for each scale dimension, resulting in a total of 31 items scored using a five-point Likert scale. A multidimensional Rasch partial-credit model was employed to verify the content and structural validity, generalizability, substantive validity, interpretive validity, and external validity of the scale. 30 items were retained for the final version of ICPS in design. The person separation reliabilities were .84, .83, .86, and .83, respectively for the four dimensions of the scale: (1) ideation and analysis, (2) generation and revision, (3) implementation and evaluation, and (4) communication and optimization. The results indicated that the ICPS had good model–data fit and was suitable for measuring undergraduate students’ Imagination–Creativity Process in design. Recommendations for future studies were proposed regarding the development and application of the ICPS in design.

Toward a multidimensional model of legitimacy: Validation of the Traffic Rules Perceived Legitimacy scale.

Toward a multidimensional model of legitimacy: Validation of the Traffic Rules Perceived Legitimacy scale.