Multidimensional Model Research Articles

Predictive evaluation of city sustainability based on benchmarking method - A case study of 34 cities in northeastern China

Sustainability is a recurring theme in city development, aiming to foster an environmentally friendly, livable, and prosperous future. Sustainable development, as a long-term vision for cities, requires an interdisciplinary method and a systems-orientated perspective on evaluating and solving city problems. The multi-criteria decision making (MCDM) method is highly applicable given the excellent multidimensional analytical and model building capabilities. As a widely used decision-making tool, the classical paradigm of MCDM is to make summary measurements of the system based on historical data to facilitate the best conclusions. Nevertheless, from the perspective of long-term sustainability evaluation, the currently available MCDM models are gradually failing to meet the predicting needs highlighted by policy makers in urban planning. Therefore, this paper proposes an innovative predictive evaluation method of city sustainability by integrating benchmarking and stochastic simulation under the framework of MCDM, which enables cities to develop visionary scenarios to accelerate the achievement of sustainability goals. This method was used to predict the future sustainability of 34 cities in northeastern China, which is a good demonstration of its utility and validity in sustainable cities and related fields.

Attraction by pairwise coherence explains the emergence of ideological sorting.

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only adopt more extreme views but also become correlated across issues that are, a priori, seemingly unrelated. This phenomenon, known as "ideological sorting", has been receiving greater attention in recent years but the micro-level mechanisms underlying its emergence remain poorly understood. Here, we study the conditions under which a social dynamic system is expected to become ideologically sorted as a function of the mechanisms of interaction between its individuals. To this end, we developed and analyzed a multidimensional agent-based model that incorporates two mechanisms: homophily (where people tend to interact with those holding similar opinions) and pairwise-coherence favoritism (where people tend to interact with ingroups holding politically coherent opinions). We numerically integrated the model's master equations that perfectly describe the system's dynamics and found that ideological sorting only emerges in models that include pairwise-coherence favoritism. We then compared the model's outcomes with empirical data from 24,035 opinions across 67 topics and found that pairwise-coherence favoritism is significantly present in datasets that measure political attitudes but absent across topics not considered related to politics. Overall, this work combines theoretical approaches from system dynamics with model-based analyses of empirical data to uncover a potential mechanism underlying the pervasiveness of ideological sorting.

Adaptive multidimensional trust-based recommendation model for peer to peer applications

Abstract In today’s world, the widespread utilization of services such as Nearby Share, Near Field Communication (NFC), and Wi-Fi Direct for deploying various applications has significantly bolstered the demand for reliable and secure distributed ad-hoc peer-to-peer networks. Yet, ensuring the trustworthiness of participating nodes remains a significant challenge. Trust among nodes plays a pivotal role in collaborative network applications, especially in environments like Mobile Ad-hoc Networks and VANET (Vehicular Ad-hoc Networks). Evaluating the trustworthiness of nodes is essential for promptly identifying misleading entities, thereby preemptively preventing their involvement in ongoing transactions. Attributes or characteristics exhibited by nodes, such as honesty, selfishness, or malicious behavior, serve as key factors in trust computation. The effectiveness of trust evaluation directly influences the encouragement of honest nodes and the deterrence of malicious ones, thereby nurturing a healthy and competitive network ecosystem. Recognizing the dynamic nature of network environments, trust computation methods must be adaptable and diverse. The adaptive multidimensional trust (AMT) model introduced in this article goes beyond simple reputation assessment. It offers three distinct methods such as Direct Trust ( Direct Trust ) \left({{\rm{Direct}}}_{{\rm{Trust}}}) , multiple security parameters, identification of qualified recommenders, which got selected dynamically as per change in trust ratings of peers. AMT advocates for an incentive-driven approach to identify legitimate peers, monitoring gradual increases in their performance ratings, whereas, spikes in performance alert to potential colluding peers or nodes displaying erratic behavior. This article evaluates the effectiveness of the AMT through a case study focused on an E-commerce application. It scrutinizes the model’s performance across different percentages of malicious nodes within the network, providing a thorough analysis and discussion of the results based on the trust value of malicious and benign peers and efficiency by selecting genuine service for transaction.

Assessment of the Aerodynamic and Aeroelastic Performance of Long-Span Twin-Box Bridges Based upon Multidimensional Surrogate Models

Twin-box decks are being extensively used in the design of long-span bridges due to their superior flutter performance. Although the significant role played by the gap distance has been previously addressed in the frame of experimental studies, there is still a lack of understanding about the complex interplay between box geometry, gap distance and the aerodynamic force coefficients and flutter derivatives. In the present work, firstly, a surrogate model is developed, considering three design inputs for the geometry of the deck along with the angle of attack, providing the force coefficients as output. Afterwards, the work is then extended by developing another surrogate model, considering as inputs the reduced velocity and the same geometric variables of the deck, with the outputs being the flutter derivatives. The methodology, comprising the selection of the design domain, the definition of the samples, the CFD-based evaluation of the samples' response and the construction of the surrogate through the application of the neural network-based radial basis method, is reviewed. The surrogate models enable a quantitative description of the impact caused in the force coefficients and the flutter derivatives by modifications in the geometry of the twin-box deck. It has been found that flutter derivatives H1*, H2* and A2* are strongly dependent on the gap distance.

Multidimensional binary layered model for census prediction using machine learning techniques

Multidimensional binary layered model for census prediction using machine learning techniques

Factors Influencing Adherence to Treatment in Adult Patients with Hypertension

Introduction Adherence to treatment including medication and lifestyle modification is crucial for improving health outcomes in patients with hypertension. However, there is limited research investigating the factors influencing adherence to medication and lifestyle modification, particularly among adult patients diagnosed with hypertension. Objective To investigate adherence to treatment and the predictive power of work status, comorbidity, complexity of medication regimens, perceived severity and risk of hypertension complications, and quality of care affecting adherence to treatment in adult patients with hypertension. Design Cross-sectional correlational predictive research using the multidimensional adherence model as the conceptual framework. Methodology The sample included 156 adult patients with essential hypertension aged 18 to 59 years old who had received antihypertensive medication and lifestyle modification treatment for at least six months at a super-tertiary hospital’s hypertension clinic. Data were collected between April and July 2023. The research instruments consisted of a general profile questionnaire, a treatment adherence questionnaire for patients with hypertension, a perceived severity and risk of hypertension complications questionnaire, a patient assessment of chronic illness care, the Charlson Comorbidity Index, a medication regimen complexity index, and an illness record form. These instruments were examined for content validity by experts and tested for reliability prior to use in data collection. The reliability ranged from .77-1.00. The data were analyzed using descriptive statistics and multiple linear regression. Results The sample had an average age of 46.50 years (SD = 9.65); 63.46% were female. The average score for adherence to treatment was 87.97 (SD = 9.02), with the highest score for average medication adherence of 34.37 (SD = 2.52) and the lowest score for average exercise adherence of 4.33 (SD = 1.89). Additionally, 14.74% were overweight, and 71.80% were obese. The majority were employed (83.33%), without any comorbidities (62.82%), and had a low level of medication regimen complexity (68.59%). Furthermore, they demonstrated a high level of perceived severity and risk of hypertension complications (96.15%) and reported a high level of quality of care (61.54%). In the Multiple linear regression, 30.9% of the variance in adherence to treatment was explained (Adjusted R2 = .309 F(5,150) = 14.858, p < .001). Work status (β = -.262, p < .001), comorbidity (β = -.184, p = .020), perceived severity and risk of hypertension complications (β = .466, p < .001), and quality of care (β = .165, p = .017) together were significant predictors of adherence to treatment in adult patients with hypertension. Recommendation To promote adherence to treatment in adult patients with hypertension, nurses should develop strategy to improve quality of care within hypertension clinics and enhance the perceived severity and risk of hypertension complications as well as enhance lifestyle modifications particularly regarding diet and exercise for overweight and obese adults with hypertension.

Delineating Landscape Features Perception in Tourism-Based Traditional Villages: A Case Study of Xijiang Thousand Households Miao Village, Guizhou

This study is a feature perception study of traditional village landscapes, refines the research method of traditional village landscape features using tourists online comment data, combs the landscape perception features of Xijiang Thousand Households Miao Village from tourists perspectives, digs out the managers’ problems in landscape planning, protection, utilisation, and puts forward the corresponding improvement strategies. In this paper, the Octopus data collector was used to collect online evaluation data from nine target platforms, and ROST CM6 was used to analyse the data for word frequency analysis, semantic network analysis, and sentiment analysis. Finally, using Nvivo12, the data was coded and a multidimensional model of landscape perception was constructed: natural landscapes, cultural and social landscapes, perceived landscapes, seasonal landscapes, and services and infrastructures. The results show that in the landscape perception model, the cultural and social landscape modules are the core factors affecting tourists’ psychological perceptions, with most tourists holding positive emotions towards the scenic spots and a few negative emotions mainly focusing on the relatively weak natural landscape, the unsatisfactory road system and charging system, and the over-commercialised development. In response to these problems, this study proposes corresponding planning recommendations. The results of the study reflect the trend of deep integration of culture and tourism in traditional villages in China, respond to the policy guidance of China’s rural revitalisation policy, and provide useful references and insights for the landscape construction and development of the Xijiang Thousand Households Miao Village and even similar tourist-oriented traditional villages.

Data monetization capability: from the systematic literature review to the proposition of a multidimensional model

Data monetization capability: from the systematic literature review to the proposition of a multidimensional model

Longitudinal Patterns of Systolic Blood Pressure, Diastolic Blood Pressure, Cardiorespiratory Fitness, and Their Association With Dementia Risk: The HUNT Study.

High blood pressure and poor cardiorespiratory fitness are independent risk factors for dementia. However, few studies have examined if combined longitudinal patterns of these modifiable risk factors are associated with dementia risk. In this prospective cohort study, we used data from the population-based Trøndelag Health (HUNT) Study, Norway. We applied group-based multidimensional trajectory modeling to identify age-specific multidimensional trajectories of SBP, DBP, and estimated cardiorespiratory fitness across 3 surveys (HUNT1, 1984-1986 to HUNT3, 2006-2008). Dementia was diagnosed in the HUNT4 70+ substudy in 2017-2019. We used multivariate logistic regression to estimate odds ratios (ORs) and risk differences (RDs) of dementia. In total, 7594 participants (54.9% women) were included, with a mean age of 44.7 (SD 6.3) years at HUNT1. Dementia was diagnosed in 1062 (14.0%) participants. We identified 2 multidimensional trajectories throughout adulthood within 3 age groups: one with higher systolic blood pressure (SBP) and diastolic blood pressure (DBP), and lower estimated cardiorespiratory fitness (the poorer group), and one with lower SBP and DBP, and higher cardiorespiratory fitness (the better group). After adjustment for sex, apolipoprotein E ε4 status, education, marital status, and diabetes, the better group had consistently lower risk of dementia in all age groups with the lowest OR in the middle-aged group of 0.63 (95% confidence intervals [95% CI]: 0.51, 0.78) with corresponding RD of -0.07 (95% CI: -0.10, -0.04). Having a beneficial multidimensional trajectory of SBP, DBP, and cardiorespiratory fitness in adulthood was associated with reduced dementia risk. Aiming for optimal SBP, DBP, and estimated cardiorespiratory fitness throughout adulthood may reduce dementia risk.

A multidimensional probabilistic model based evolutionary algorithm for the energy-efficient distributed flexible job-shop scheduling problem

With escalating environmental effects, the spotlight on low-carbon manufacturing has garnered significant attention. The rise of distributed production has emerged as a prominent trend in response to the imperatives of economic globalization. This article focuses on addressing the energy-efficient distributed flexible job-shop scheduling problem (EE_DFJSP), with the aim of minimizing both makespan and total energy consumption (TEC) simultaneously. The production process contains four pivotal phases: 1) job assignment in distributed factories; 2) machine selection within factories; 3) operation allocation on flexible machines; and 4) machine speed adjustment for processing. Given the problem's multi-phase and strong coupling characteristics, it is imperative to develop a promising evolutionary algorithm (EA) for EE_DFJSP. To tackle this challenge, we propose a multidimensional probabilistic model-based EA (MPMEA) paradigm. First, problem-specific encoding and decoding schemes are developed based on the solution features of EE_DFJSP. Second, a hybrid initialization strategy incorporating four heuristic rules is devised to yield an initial population with diversity. Third, an effective union probabilistic model (UPM) is formulated to learn promising patterns from superior solutions, and an efficient sampling strategy is designed to produce high-quality offspring individuals. To achieve a balance between global exploration and local exploitation, problem-specific multiple neighborhood operators are proposed to perform an in-depth local search. Furthermore, a two-stage energy-saving speed adjustment strategy is designed for the superior solutions obtained through local search. Finally, computational comparisons and simulation studies are conducted to validate the effectiveness and superiority of the MPMEA in effectively addressing EE_DFJSP.

Goldfit Soccer: A Multidimensional Model for Talent Identification of Young Soccer Players

ABSTRACT Studies have provided empirical evidence on the prognostic relevance of test batteries and the “coach’s eye” for talent identification. The aims were 1) to compare objective and subjective assessments as well as a combined soccer-specific potential index between future selected and non-selected players and 2) to evaluate the prognostic validity of a multidimensional model as a tool for talent identification in soccer. The sample was composed by 132 male players (14,5 ± 1,4 years; regional competitive level) from U13 to U17 age groups of a Brazilian soccer talent development program. Athletes completed a multidimensional test battery and were subjectively rated by their coaches for sporting potential. Players’ success was evaluated five years later and was operationalized by achieving under-20 category of the Brazilian Championship or adult professional level (success rate, 15.9%). Confirming univariate prognostic validity, future selected outperformed non-selected players regarding 20-m sprint (p = .009), agility (p = .04), countermovement jump (p = .04), sit-and-reach (p = .001), Yo-Yo IR1 (p = .001), dribbling (p < .001), perceived competence (p = .007), peaking under pressure (p = .01), confidence/motivation (p = .03), coping skills (p = .03), intangibles (p < .001) and player potential (p < .001). A combined index (objective tests, athlete’s assessments and coach’s eye) named Gold Score Soccer (GSS) showed high prognostic validity (p < .001). A binary logistic regression estimated the probability of success (yes/not) with GSS, ambidextrous and predicted age at peak height velocity as predictors. This multidimensional model named GoldFit Soccer showed high prognostic validity (sensitivity = 85.7%; specificity = 83.8%; accuracy = 84.1%; area under the ROC curve = .93 [.87–.98]). Thus, GoldFit Soccer is a valid multidimensional scientific model for talent identification in soccer.

Multi-Dimensional Iterative Constitutive Model of Concrete under Complex Stress Conditions in Composite Structures

In composite structures or complex concrete members, some concrete bears multiple forces, called core concrete. The properties of the core concrete are variable under complex stress conditions, which will influence the structure performance analysis. Therefore, it is necessary to establish an accurate and theoretical constitutive model of concrete under complex stress conditions. The elastic–plastic properties of concrete in complex stress conditions were analyzed first. Then, the failure criterion of concrete in complex stress conditions was discussed to identify the key parameters. And the relationship between the stress–strain curve and failure criterion was analyzed through mathematical derivation. Finally, the multi-dimensional iterative constitutive model of concrete under complex stress conditions was established and verified. Based on the analysis results, the concrete under multi-axial stress conditions shows a spindle-shape stress envelope diagram. The failure criterion should be established by the analysis of concrete under high multi-axial compression conditions, tension–compression conditions, and shear–compression conditions. The plastic modulus is the key to reflecting the plastic strain development trend and the stress–strain relationship.

Modelling developments in consciousness within a multidimensional framework.

A recent advancement in consciousness science has been the introduction of a multidimensional framework of consciousness. This framework has been applied to global states of consciousness, including psychedelic states and disorders of consciousness, and the consciousness of non-human animals. The multidimensional framework enables a finer parsing of both various states of consciousness and forms of animal consciousness, paving the way for new scientific investigations into consciousness. In this paper, the multidimensional model is expanded by constructing temporal profiles. This expansion allows for the modelling of changes in consciousness across the life cycles of organisms and the progression over time of disorders of consciousness. The result of this expansion is 2-fold: (i) it enables new modes of comparison, both across stages of development and across species; (ii) it proposes that more attention be given to the various types of fluctuations that occur in patients who are suffering from disorders of consciousness.

Continuity and Change in Substance Use Patterns During the Transition from Adolescence to Young Adulthood: Examining Changes in Social Roles

AbstractThis study offers a model for using multidimensional growth mixture models to identify polysubstance use trajectories by examining transitions among conjoint substance use trajectories from adolescence to young adulthood and exploring potential moderators that may facilitate transitions towards healthier substance use trajectories in young adulthood. Longitudinal mixture modeling was used to examine six waves of data collected during adolescence (ages 13, 14, 15) and young adulthood (ages 25, 29, 31) in Seattle, Washington. Data were drawn from the International Youth Development Study, a longitudinal, cross-national study examining the life course patterns of substance use and development among youth. Participants (N = 961) completed questionnaires on six occasions that assessed demographics (sex, race, highest parent education), suspension and expulsion, individual substance use, partner substance use, and social role transitions (education, marriage, childbearing, employment). Four substance use classes were identified in adolescence and included low use (n = 572, 59.6%), alcohol dominant (n = 177, 18.4%), increasing use (n = 103, 10.7%), and poly-use (n = 109, 11.3%). Five substance use classes were identified in young adulthood and included low use (n = 134, 15.3%), alcohol only (n = 349, 39.8%), alcohol and tobacco (n = 97, 11.0%), alcohol and cannabis (n = 162, 18.5%), and poly-use (n = 135, 15.4%). The transition from adolescence to young adulthood showed the strongest continuity in the poly-use class and the weakest in the low use class, with a general trend toward adding substances rather than reducing them. College graduation moderated the transition in substance use patterns from adolescence to young adulthood for low use and alcohol dominant adolescent classes but not for the poly-use class. Delays in adult role assumptions were not consistently associated with substance use classes during this transition. However, where significant, delayed marriage and parenthood acted as protective factors against the progression of substance use leading into young adulthood. The findings underscore the need for early detection and tailored prevention efforts among adolescents. By identifying pivotal periods and specific substance use patterns, these findings inform the timing and focus of targeted interventions designed to reduce the escalation of substance use leading into young adulthood.

Geometrically nonlinear transient analyses of rotating structures through high-fidelity models

This work presents geometrically nonlinear transient analyses of various rotating blades. The structures are discretized through refined beams or multi-dimensional finite element models, which are generated using the Carrera Unified Formulation (CUF). The CUF offers a procedure to develop low- and high-fidelity one-dimensional, two-dimensional, and three-dimensional finite element models hierarchically and automatically. Various beam models were developed using different kinematics models based on Taylor or Lagrange expansion functions. Multi-dimensional models were obtained by merging beam and solid elements, exploiting the unique feature of Lagrange polynomials to have only pure displacements as unknowns. This property allows beam and solid elements to be coupled at the node level without requiring complicated mathematical formulations. By utilizing the Finite Element Method in conjunction with the CUF, the governing equations are written by including all rotation effects, namely the Coriolis term, spin-softening, and geometrical stiffening. In a total Lagrangian scenario, the Hilbert-Hughes-Taylor-α method and the iterative Newton-Raphson scheme are employed to solve the equations of motion. The proposed methodology has been applied to evaluate different blade configurations, comparing the solution obtained using linear, linearized, and nonlinear approaches. The results have been verified and validated by comparing them with existing solutions present in the literature.

The multi-dimensional coagulation-fragmentation model with unbounded kernel

The multi-dimensional coagulation-fragmentation model with unbounded kernel

Research on a Multi-Dimensional Indicator Assessment Model for Evaluating Landslide Risk near Large Alpine Reservoirs

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas.

Measuring teacher entrepreneurial behavior: a scale development and validation study

PurposeThis study aims to develop and validate a scale to measure Teacher Entrepreneurial Behavior (TEB), which encapsulates the behaviors teachers employ to identify and amplify innovation in schools. TEB are catalysts for innovation, navigating their peers through risks and building trust, which empowers the collective to transcend structural constraints and pioneer new educational initiatives. Despite the importance of TEB, there is a notable absence of a well-validated measurement instrument.Design/methodology/approachDrawing on existing empirical TEB studies, this study conducts four interconnected studies following scale-development procedures. The content validity, construct validity, internal consistency, and external validity of the proposed scale were assessed using exploratory factor analysis, confirmatory factor analysis, invariance analysis, and regression analysis.FindingsThe result is a multidimensional TEB model featuring 15 items with a good model fit. The TEB scale comprises four factors: Advocating Innovation, Seeking Resources, Cultivating Cohesiveness, and Mitigating Risk.Originality/valueThis study represents a rigorous attempt to develop and validate a reliable instrument for measuring TEB. It provides a validated tool for future research aimed at understanding the nature of TEB as an independent construct and associated dynamics. Accurate measurement is important for the robustness and replicability of research. Furthermore, the insights gained on TEB scale can significantly inform both the preparation and evaluation of teacher leaders by emphasizing the importance of entrepreneurial behaviors in promoting teachers’ collaboration and actualizing innovative initiative.

The General Psychopathology 'p' Factor in Adolescence: Multi-Informant Assessment and Computerized Adaptive Testing.

Accumulating evidence supports the presence of a general psychopathology dimension, the p factor ('p'). Despite growing interest in the p factor, questions remain about how p is assessed. Although multi-informant assessment of psychopathology is commonplace in clinical research and practice with children and adolescents, almost no research has taken a multi-informant approach to studying youth p or has examined the degree of concordance between parent and youth reports. Further, estimating p requires assessment of a large number of symptoms, resulting in high reporter burden that may not be feasible in many clinical and research settings. In the present study, we used bifactor multidimensional item response theory models to estimate parent- and adolescent-reported p in a large community sample of youth (11-17 years) and parents (N = 5,060 dyads). We examined agreement between parent and youth p scores and associations with assessor-rated youth global functioning. We also applied computerized adaptive testing (CAT) simulations to parent and youth reports to determine whether adaptive testing substantially alters agreement on p or associations with youth global functioning. Parent-youth agreement on p was moderate (r =.44) and both reports were negatively associated with youth global functioning. Notably, 7 out of 10 of the highest loading items were common across reporters. CAT reduced the average number of items administered by 57%. Agreement between CAT-derived p scores was similar to the full form (r =.40) and CAT scores were negatively correlated with youth functioning. These novel results highlight the promise and potential clinical utility of a multi-informant p factor approach.

A low-carbon route optimization method for cold chain logistics considering traffic status in China

In the paper of cold chain logistics, it is necessary to consider both traffic status and carbon reduction simultaneously. In this paper, an improved BPR (Bureau of Public Roads) road impedance function is designed, which reflects the traffic status in China under congested status more accurately. A low-carbon cold chain logistics route optimization model considering the traffic status in China is constructed, considering the multiple impacts of traffic status on total delivery cost, freshness of fresh products, and carbon emissions. To address the challenges posed by this multidimensional and discrete model, an improved discrete firefly algorithm (IDFA) is proposed. The numerical result demonstrates that IDFA has higher solving efficiency and better quality of the obtained solutions. Moreover, this paper also analyzes the impact of traffic status, vehicle types, and carbon trading prices on cold chain logistics delivery, as well as the comprehensive impact of traffic status and carbon trading prices. The proposed model is suitable for cold chain logistics enterprises with a single delivery center, active emission reduction willingness, and mostly delivering in congested road sections with high traffic volume. It offers valuable guidance for decision making and management in low-carbon cold chain logistics delivery.