Components Of Model Research Articles

Contaminated Gibbs-Type Priors

Gibbs-type priors are combinatorial processes widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems and mixture modeling. We introduce a new family of processes which extends the Gibbs-type one, by including a contaminant component in the model to account for an excess of observations with frequency one. We first investigate the induced random partition, the associated predictive distribution, the asymptotic behavior of the total number of blocks and the number of blocks with a given frequency: all the results we obtain are in closed form and easily interpretable. A remarkable aspect of contaminated Gibbs-type priors relies on their predictive structure, compared to the one of the standard Gibbs-type family: it depends on the additional sampling information on the number of observations with frequency one out of the observed sample. As a noteworthy example we focus on the contaminated version of the Pitman-Yor process, which turns out to be analytically tractable and computationally feasible. Finally we pinpoint the advantage of our construction in different applications: we show how it helps to improve predictive inference in a species-related dataset exhibiting a high number of species with frequency one; we also discuss the use of the proposed construction in mixture models to perform density estimation and outlier detection.

Effects of Surface Turbulence Flux Parameterizations on the MJO: The Role of Ocean Surface Waves

Abstract This study investigates the sensitivity of the Madden–Julian oscillation (MJO) to changes to the bulk flux parameterization and the role of ocean surface waves in air–sea coupling using a fully coupled ocean–atmosphere–wave model. The atmospheric and ocean model components of the Energy Exascale Earth System Model (E3SM) are coupled to a spectral wave model, WAVEWATCH III (WW3). Two experiments with wind speed–dependent bulk algorithms (NCAR and COARE3.0a) and one experiment with wave-state-dependent flux (COR3.0a-WAV) were conducted. We modify COARE3.0a to include surface roughness calculated within WW3 and also account for the buffering effect of waves on the relative difference between air-side and ocean-side momentum flux. Differences in surface fluxes, primarily caused by discrepancies in drag coefficients, result in significant differences in MJO’s properties. While COARE3.0a has better convection–circulation coupling than NCAR, it exhibits anomalous MJO convection east of the date line. The wave-state-dependent flux (COR3.0-WAV) improves the MJO representation over the default COARE3.0 algorithm. Strong easterlies over the Pacific Ocean in COARE3.0a enhance the latent heat flux (LHFLX). This is responsible for the anomalous MJO propagation after the date line. In COR3.0a-WAV, waves reduce the anomalous easterlies, leading to a decrease in LHFLX and MJO dissipation after the date line. These findings highlight the role of surface fluxes in MJO simulation fidelity. Most importantly, we show that the proper treatment of wave-induced effects in bulk flux parameterization improves the simulation of coupled climate variability.

Lightweight two-stage transformer for low-light image enhancement and object detection

In low-light conditions, due to the loss of image details the visual tasks are challenging. To achieve real-time image enhancement and improve the accuracy of object detection task, we propose a lightweight two-stage Transformer. First, we use dynamic convolution to improve the adaptability of the network to different samples and preliminarily enhance the image through predicting the multiplicative and additive maps of the least squares method. In the second stage, we propose a FFT-Guidance Block (FGB) to obtain frequency components for explicit modeling, guiding the recovery of image potential information. In addition, joint our model with YOLOv3 to build a dark object detection framework, and we only use normal detection loss to simplify the training process. On the LOLv2 dataset, our model achieves advanced results. The enhanced model maintains good performance while the parameters are only 0.050M, and increase the accuracy of the downstream object detection task. The detection framework reaches 77.9% and 60.2 in mAP and FPS respectively on ExDark dataset, which can be better robust in dark conditions.

LogGT: Cross-system log anomaly detection via heterogeneous graph feature and transfer learning

Automated system log anomaly detection plays a crucial role in ensuring service reliability. Existing methods incompletely utilize structured log entries, resulting in the loss of key information such as components and time. Besides, due to the limitations of labeled data, models trained by a single system are difficult to apply to other systems. Therefore, we propose a cross-system log anomaly detection method named LogGT, which simultaneously models log events, components and time, leveraging labeled system knowledge to achieve anomaly detection in unlabeled systems. Specifically, we firstly design a heterogeneous graph to accurately represent the interactions between different events and components in the log sequence. Then, in order to avoid noise interference and conduct cross-system semantic analysis, we employ BERT to extract log sentence vectors, and Normalizing Flow is used to optimize them for smoother node embedding. A Graph Transformer Architecture with Time Intervals (GTAT) is proposed to model heterogeneous graphs by integrating time feature, allowing for a comprehensive analyze of execution order and time anomalies. Additionally, we design a semantic weighting method and utilize a novel domain-adapted transfer learning technology to effectively transfer the heterogeneous graph features of the source system to the target system. Experimental results demonstrate that LogGT outperforms five log anomaly detection methods, achieving an average anomaly detection F1-score higher than 0.95. Moreover, the AUC value of GTAT exceeds the sequence model by more than 2.3%.

Generalized Weakly Supervised Object Localization.

With the goal of learning to localize specific object semantics using the low-cost image-level annotation, weakly supervised object localization (WSOL) has been receiving increasing attention in recent years. Although existing literatures have studied a number of major issues in this field, one important yet challenging scenario, where the test object semantics may appear in the training phase (seen categories) or never been observed before (unseen categories), is still beyond the exploration of the existing works. We define this scenario as the generalized WSOL (GWSOL) and make a pioneering effort to study it in this article. By leveraging attribute vectors to associate seen and unseen categories, we involve threefold modeling components, i.e., the class-sensitive modeling, semantic-agnostic modeling, and content-aware modeling, into a unified end-to-end learning framework. Such design enables our model to recognize and localize unconstrained object semantics, learn compact and discriminative features that could represent the potential unseen categories, and customize content-aware attribute weights to avoid localizing on misleading attribute elements. To advance this research direction, we contribute the bounding-box manual annotations to the widely used AwA2 dataset and benchmark the GWSOL methods. Comprehensive experiments demonstrate the effectiveness of our proposed learning framework and each of the considered modeling components.

Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait.

As metabolic cost is a primary factor influencing humans' gait, we want to deepen our understanding of metabolic energy expenditure models. Therefore, this paper identifies the parameters and input variables, such as muscle or joint states, that contribute to accurate metabolic cost estimations. We explored the parameters of four metabolic energy expenditure models in a Monte Carlo sensitivity analysis. Then, we analysed the model parameters by their calculated sensitivity indices, physiological context, and the resulting metabolic rates during the gait cycle. The parameter combination with the highest accuracy in the Monte Carlo simulations represented a quasi-optimized model. In the second step, we investigated the importance of input parameters and variables by analysing the accuracy of neural networks trained with different input features. Power-related parameters were most influential in the sensitivity analysis and the neural network-based feature selection. We observed that the quasi-optimized models produced negative metabolic rates, contradicting muscle physiology. Neural network-based models showed promising abilities but have been unable to match the accuracy of traditional metabolic energy expenditure models. We showed that power-related metabolic energy expenditure model parameters and inputs are most influential during gait. Furthermore, our results suggest that neural network-based metabolic energy expenditure models are viable. However, bigger datasets are required to achieve better accuracy. As there is a need for more accurate metabolic energy expenditure models, we explored which musculoskeletal parameters are essential when developing a model to estimate metabolic energy.

Seasonality as Factor of Logistic Costs Formation

The seasonality of demand for goods and services creates vital issues both for the organization of their production, and for implementation of logistic processes. Seasonal fluctuations, as a rule, negatively affect work of the enterprises that is shown in equipment downtimes, incomplete use production and warehouse, in uneven use of human resources, violation of rhythm of activity, to the unreasonable growth of costs. In logistic activity the seasonality influences a set of its components, including creation and maintenance of stocks, existence of warehouse spaces, number and capacities of sales channels and supply. This influence in much defines fluctuations of values of logistic costs. The article proposes an approach to identifying the presence of seasonality, quantitative characteristics in various periods of the annual cycle, the separation of seasonal and trend components of seasonality dynamics, mathematical and statistical modeling of seasonality in the formation of such costs. A forecast assessment of the future dynamics of costs is carried out. Emphasis is made on the highlighting the seasonal component in the indicators of logistics costs that allows to analyze their dynamics more deeply, improve the quality of management decisions associated with them.

A Staged Defragmented Simultaneous Debriefing Model As Integrated Micro-debriefing Components Inside Online Simulation for Competencies Formation.

Background Currently, there are no separate debriefing models for online simulation training, and existing models simply imitate the traditional models used in on-site simulation training (the physical presence of individuals, such as students or trainees, in a simulation center). This involves hands-on, in-person training within a simulated environment to enhance practical skills and knowledge in a controlled setting. This scenario does not fully meet the requirements and capabilities of distance learning. Objective To develop a staged defragmented debriefing model as integrated micro-debriefing components inside an online simulation to support the development of clinical decision-making andcompetencies formation within medical education and offer recommendations to support the use of this debriefing model as a teaching strategy. Methods This descriptive study was conducted from August 2020 to September 2023. To build a staged defragmented debriefing model as integrated micro-debriefing components inside an online simulation for competencies formation the traditional debriefing model's components for on-site simulation training, simulation type, and structure, modern concepts of e-learning, and classification of the seriousness of medication errors were used. The main focus of this study was on providing a detailed account of the debriefing components for online simulation training, features, and implementation of this new teaching model.A total of 38 participants, healthcare professionals, were recruited for this study. The participants were randomly assigned to two groups: one experiencing the staged defragmented debriefing model (n = 20) and the other control group, which received traditional debriefing following simulation training (n = 18). Results The results allowed us tosuccessfully develop a staged defragmented debriefing model inside the simulation that integrates micro-debriefing components located at different points of the simulation scenarios. This teaching approach was successfully implemented in online clinical case scenarios in the "ClinCaseQuest" Simulation Training Platform for continuous medical education.Additionally, an internal validation experiment comparing the effectiveness of the staged defragmented debriefing model with the traditional debriefing method demonstrated superior learning outcomes and participant satisfaction in the staged debriefing group. Conclusions The staged defragmented debriefing model, when integrated into online simulations, represents a promising strategy for advancing clinical decision-making skills and competencies formation in medical education. Implementation of this debriefing model as a teaching strategy holds promise for enhancing learning outcomes in medical education settings. Further research, validation, and implementation are recommended to maximize the model's potential impact on medical education and training.

Phyto-pharmacological Potential of Aegle marmelos (L.) for Neurological Disorders: Progress and Prospects.

Aegle marmelos, an Indian plant, has been extensively utilized by the people of the Indian subcontinent over about 5000 years. The leaves, bark, roots, and fruits, including seeds, are widely used to cure a variety of diseases in the Indian traditional system of medicine, Ayurveda, along with numerous folk medicines. By revealing the existence of significant bioactive chemicals, modern research has effectively substantiated the therapeutic effects of bael. The objective of this study was to review the literature regarding A. marmelos geographical distribution, morphology, therapeutic benefits, and phytochemicals found in the bael leaves, fruits, and other parts of the plant that offer a wide range of pharmacological applications in neurological disorders. A thorough literature search was conducted using five computerized databases, such as PubMed, Google Scholar, ScienceDirect, Elsevier, and Wiley Online Library (WOL), by using standard keywords "A. marmelos," "Geographical distribution," "Morphological description," "Ethnobotanical Uses," "Phytoconstituents" and "Neuroprotective activities" for review papers published between 1975 and 2023. A small number of earlier review articles focused on phyto-pharmacological potential of Aegle marmelos (L.) for neurological disorders. According to some research, Aegle marmelos extracts potentially have neuroprotective benefits. This is due to its capacity to alter cellular mechanisms that cause neuronal damage. Neurodegenerative illnesses usually induce permanent neuronal network loss overall the brain along with the spinal cord (CNS), resulting in chronic functional impairments. The review summarizes the multiple aspects and processes of A. marmelos extract and its components in several models of neurodegenerative diseases such as anxiety, epilepsy, depression, Parkinson's disease, Alzheimer's disease, and others. MDA, nitrite, TNF-, and IL-6 levels were dramatically elevated, whereas glutathione levels were significantly lowered in the hippocampus of STZ-treated rats. Furthermore, STZ-treated rats showed a substantial drop in catalase activity and an increase in AChE activity, indicating cholinergic hypofunction and neuronal injury. The neuroprotective ability of A. marmelos against STZ-induced oxidative stress and cognitive loss in rats suggests that it has therapeutic relevance in Alzheimer's disease (AD).

Modeling and Control of a Novel Variable Stiffness Three DoFs Wrist

This study introduces an innovative design for a Variable Stiffness 3 Degrees of Freedom actuated wrist capable of actively and continuously adjusting its overall stiffness by modulating the active length of non-linear elastic elements. This modulation is akin to human muscular cocontraction and is achieved using only four motors. The mechanical configuration employed results in a compact and lightweight device with anthropomorphic characteristics, making it potentially suitable for applications such as prosthetics and humanoid robotics. This design aims to enhance performance in dynamic tasks, improve task adaptability, and ensure safety during interactions with both people and objects. The paper details the first hardware implementation of the proposed design, providing insights into the theoretical model, mechanical and electronic components, as well as the control architecture. System performance is assessed using a motion capture system. The results demonstrate that the prototype offers a broad range of motion ([55, −45]° for flexion/extension, ±48° for radial/ulnar deviation, and ±180° for pronation/supination) while having the capability to triple its stiffness. Furthermore, following proper calibration, the wrist posture can be reconstructed through multivariate linear regression using rotational encoders and the forward kinematic model. This reconstruction achieves an average Root Mean Square Error of 6.6°, with an R2 value of 0.93.

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

Community-embedded follow-up management intervention for geriatric primary care: a mixed-methods study of an integrated health services model

BackgroundTo propose a community-embedded follow-up management model to provide health services for elderly patients with osteoporosis who live alone.MethodsResearchers randomly selected 396 people with osteoporosis living alone from five communities in Nantong, China, for the study. These participants were randomly assigned to control and intervention groups. Twenty-four community physicians in five communities provided professional support based on a community-embedded follow-up management model. Participants completed quantitative questionnaires at baseline and after the 6-month follow-up intervention, and some participants underwent semi-structured face-to-face interviews. The primary outcome is the effectiveness of the community-embedded follow-up management model in improving the quality of life of elderly patients with osteoporosis living alone. Based on an objective quantitative assessment, the qualitative study explains and adds essential components of this community-based follow-up management model.ResultsThe quantitative study showed that scores in physical functioning, ability to perform daily activities, self-efficacy, and mental status were significantly improved in the intervention group compared to the control group (p < 0.05). The most significant improvements were found in “mental status” (p = 0.012) and “self-care skills” (p = 0.003). The qualitative study reported the essential elements of a community healthcare model for older people living alone with osteoporosis, including professional support, personalized services, social support, and empowerment.ConclusionsCommunity-embedded follow-up management meets the need for elderly patients with osteoporosis living alone. It helps to improve health perception, promote physical and mental health, and optimize the quality of life in this population. Personalized services and professional support are two major contributing factors to effective embedded follow-up management in the community.

RESTORATION OF RESOURCES FOR TEACHERS AND STUDENTS OF BORYS GRINCHENKO KYIV UNIVERSITY DURING THE FIRST YEAR OF THE WAR IN UKRAINE

The article is aimed at highlighting the problems of restoring the resources of teachers and students during the first year of hostilities in Kyiv, Ukraine. Modern scientific views on the concepts of “resources”, “human resources” in the context of the impact of the war on the condition of teachers and students are analyzed. The research is based on the “BASIC Ph” resource model developed by Mouli Laad and Ofra Ayalon (Israel). The focus is not on the negative consequences of the war for the participants of the survey, but on the ability to recover, to open up new opportunities for the ability to live on, to live a full life, as much as possible in the changed circumstances during the war. The article presents the results of the study of the content and features of the restoration of the resources of teachers and students of Borys Grinchenko Kyiv University during the first year of the war in Ukraine. The authors present an analysis of the features and significance of each component of the resource model for teachers and students, compare the results and draw conclusions. This article can be useful for everyone who is interested in the consequences of military conflicts on civilians, on their ability to overcome crisis situations and rebuild a new life.

Improved Fault Detection Method based on HSMM

This paper proposes a fault detection method for multivariate statistical process control. The proposed method combines the Forward-Backward Hidden Semi-Markov Model (HSMM) and Principal Component Analysis (PCA). A stochastic automaton was used for multi-mode detection with many observation sequences. We used agglomerative clusters to find the initial parameters of HSMM. We allocated an adaptive threshold and a fixed threshold in each mode for fault detection with PCA, including Hotelling T2 statistic and squared predictive error (Q statistic). We simulated this method on the Tennessee Eastman Process (TEP). Some faults were designed with various runs and times of occurrence. The experimental results were compared with the Mixture Bayesian PCA, Hidden Markov Model (HMM), and HSMM methods. The results are robust with an efficient detection rate. This activity recommends ways to find action plans for multi-mode process monitoring in chemical plants.

Interspecific niche overlap and climatic associations of native Quercus species in the Zagros forests of Iran

In the arid regions surrounding southwestern Asia, oak trees play an important role in the environment by conserving water and preserving soil and supporting wildlife. There are concerns for the health and continued productivity of these forests, and there is a widespread oak decline. In this study, ecological niche modeling (ENM) and multivariate principal component analysis (PCA) were used to determine the environmental variations among Quercus brantii, Q. infectoria, and Q. libani in the Zagros forests of western Iran. Based on the area under the curve (AUC > 0.9) and the true skill statistic (TSS > 0.8), three algorithms, including random forest (RF), maximum entropy (MaxEnt), and maximum picking neural net (MAXnet), were used in an ensemble approach to explore the most important environmental variables within the projected suitable habitat of each species. Environmental variables related to climate, including annual precipitation (bio12) and annual mean temperature (bio1), were among the most important drivers of habitat suitability for oak species. The results illustrated that suitable areas for Q. infectoria and Q. libani often occur in the northern parts of the Zagros, which are humid and cooler than the southern areas. The results suggested that Q. brantii has a higher tolerance for harsh conditions in Zagros and therefore inhabits almost all areas of Zagros, including the southern parts. The climatic overlap between pairs of species was found to be significant based on equivalency and similarity tests, suggesting that the environmental conditions of the two species compared are more similar than would be expected by chance. The gap analysis for the three oak species revealed that only a few protected areas currently exist within their suitable habitats, with most of the highly suitable habitats located on non-protected lands, particularly for Q. libani and Q. infectoria. In addition, deforestation is known to sometimes occur within the boundaries of protected areas. Improving the level of protection by upgrading the current protected areas to national parks can help with conservation.

Parameterizing Mesoscale Eddy Buoyancy Transport Over Sloping Topography

AbstractMost of the ocean's kinetic energy is contained within the mesoscale eddy field. Models that do not resolve these eddies tend to parameterize their impacts such that the parameterized transport of buoyancy and tracers reduces the large‐scale available potential energy and spreads tracers. However, the parameterizations used in the ocean components of current generation Earth System Models rely on an assumption of a flat ocean floor even though observations and high‐resolution modeling show that eddy transport is sensitive to the potential vorticity gradients associated with a sloping seafloor. We show that buoyancy transport coefficient diagnosed from idealized eddy‐resolving simulations is indeed reduced over both prograde and retrograde bottom slopes (topographic wave propagation along or against the mean flow, respectively) and that the reduction can be skilfully captured by a mixing length parameterization by introducing the topographic Rhines scale as a length scale. This modified “GM” parameterization enhances the strength of thermal wind currents over the slopes in coarse‐resolution, non‐eddying, simulations. We find that in realistic global coarse‐resolution simulations the impact of topography is most pronounced at high latitudes, enhancing the mean flow strength and reducing temperature and salinity biases. Reducing the buoyancy transport coefficient further with a mean‐flow dependent eddy efficiency factor, has notable effects also at lower latitudes and leads to reduction of global mean biases.

Graph Time-series Modeling in Deep Learning: A Survey

Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across timesteps, while for graph modeling, graph neural networks model the inter-relations between nodes. Recent research in deep learning requires simultaneous modeling for time-series and graphs when both representations are present. For example, both types of modeling are necessary for time-series classification, regression, and anomaly detection in graphs. This article aims to provide a comprehensive summary of these models, which we call graph time-series models. To the best of our knowledge, this is the first survey article that provides a picture of related models from the perspective of deep graph time-series modeling to address a range of time-series tasks, including regression, classification, and anomaly detection. Graph time-series models are split into two categories: (a) graph recurrent/convolutional neural networks and (b) graph attention neural networks. Under each category, we further categorize models based on their properties. Additionally, we compare representative models and discuss how distinctive model characteristics are utilized with respect to various model components and data challenges. Pointers to commonly used datasets and code are included to facilitate access for further research. In the end, we discuss potential directions for future research.

Estimation of Infiltration Parameters: The Role of Pedotransfer Functions and Initial Moisture Conditions

Infiltration is one of the key components of rainfall-runoff processes at small catchment scale and is therefore a vital component of hydrological models. However, small catchments typically lack direct infiltration measurements and associated soil hydraulic characteristics, which are being substituted with soil databases and pedotransfer functions built upon these databases. We utilized two sets of pedotransfer functions Rosetta 3 and EUPTFv2 derived from two well known soil databases: UNSODA, based on US and global soil data, and EU-HYDI, based on EU soil data. Our study evaluates the impact of choosing one of these pedotransfer functions on modeled infiltration in relation to variable initial moisture content- It investigates the behavior of the simplified infiltration models of Green-Ampt and Philip under different initial conditions, in comparison with Richards’ complex solution, which is used as a benchmark. Our evaluation was conducted using soil property data available in the Czech Republic, covering ten USDA soil textures that are prevalent within the European Union. The applied initial moisture conditions were derived for specific suction pressure values that correspond to hydrological initial conditions ranging from near saturation to near the permanent wilting point. Variation in the derived parameters for the Green-Ampt and Philip models across the pedotransfer function sources results in considerable variation in the modeled infiltration. Our results imply different sensitivities of the simplified infiltration models to the initial moisture for different pedotransfer functions. The Green-Ampt and Philip infiltration models demonstrate better alignment with Richards’ complex solution with a low and consistent RMSE when using the Rosetta 3 pedotransfer function. By contrast, the EUPTFv2 model scenarios, show a clear trend of increasing RMSE towards dry initial conditions. As the European soil database is not an open data source, we only hypothesize that the distributions of soil characteristics in the European soil database differ systematically from those in UNSODA, and that this would cause different results of pedotransfer functions for different initial moisture conditions. The findings of this study are important for selecting appropriate infiltration models and, appropriate pedotransfer functions for estimating model parameters, and for assessing uncertainties connected with varying initial conditions.

Information system quality and organizational performance: The effect of information system integration in Tabriz metropolitan municipality, Iran

Due to the multiplicity of administration in urban management in Iran and with the aim of feasibility of implementing the integrated urban management, this study evaluates the impact of information system (IS) quality and IS integration on performance of Tabriz metropolitan municipality, Iran, as the trustee of urban affairs. The quantitative method was applied using survey questionnaires that have been conducted based on the relevant literature. SPSS software was used to perform regression and covariance analyses to analyze the collected data (80 valid responses) as well as the relationships among the components of the conceptual research model. The quantitative data for this research was limited to the Tabriz municipality's ICT experts (all experts of Statistics and Information Technology organization). The results prove positive relationships among IS quality, IS integration and the organizational performance. Additionally, the findings reveal that IS integration fully mediates the relationship between IS quality toward organizational performance. The originality of this study lies in examining the integrated urban management in Iran from the perspective of urban information infrastructures (urban information systems) for the first time.

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.