Modeling Perspective Research Articles

Performance Appraisal's Role in Employee Development: A Structured Equation Modeling Perspective at Etico Life Science Pvt Ltd

Performance Appraisal's Role in Employee Development: A Structured Equation Modeling Perspective at Etico Life Science Pvt Ltd

Investigation of the Bending Behavior in Silicon Nanowires: A Nanomechanical Modeling Perspective

Nanowires (NWs) play a crucial role across a wide range of disciplines such as nanoelectromechanical systems, nanoelectronics and energy applications. As NWs continue to reduce in dimensions, their mechanical properties are increasingly affected by surface attributes. This study conducts a comprehensive examination of nanomechanical models utilized for interpreting large deformations in the bending response of silicon NWs. Specifically, the Heidelberg, Hudson, Zhan, SimpZP and ExtZP nanomechanical models are explored regarding their capability to predict the elastic properties of silicon NWs with varying critical dimensions and crystal orientations. Molecular dynamics simulations are employed to model silicon NWs with unreconstructed surface states. The calculation of intrinsic stresses and the methodology for quantifying surface properties, including surface stresses and surface elasticity constants, are carried out using atomistic modeling. The findings reveal significant disparities of up to 100 GPa among nanomechanical models in interpreting a singular force-deflection response obtained for a silicon NW. Inadequate consideration of surface and intrinsic effects in nanomechanical modeling of NWs leads to substantial variability in their mechanical properties. This investigation yields valuable insights into the surface characteristics of silicon NWs, thereby enhancing our understanding of the essential role played by nanomechanical models in the intricate interpretation of mechanical properties at the nanoscale.

Correction to: Traumatic Stress-Enhanced Alcohol Drinking: Sex Differences and Animal Model Perspectives

Correction to: Traumatic Stress-Enhanced Alcohol Drinking: Sex Differences and Animal Model Perspectives

THE SCALE SIMULATION APPLICATION IN THE MILITARY VEHICLES ARMORING

The armoring of the civilian vehicles during martial law requires the search for new parameters selecting methods that provide the required indicators of dynamic properties. The additional armored sheets installation on ordinary civilian vehicles leads to the weight increase, the center of mass position change and dynamic properties deterioration. Therefore, the ordinary civilian vehicles armoring can also be considered from the perspective of large-scale modeling. One of the ways to ensure the necessary dynamic indicators of the ordinary civilian vehicles when armoring is to use the theory of large-scale modeling due to the use of the mass scale as the main scale. In the study, using the theory of similarity, a system of scale coefficients was developed for choosing the main design parameters of armored vehicles. The dependences of engine power, wheel diameter and rigid suspension and tires, engine and transmission torques on the car reservation coefficient are obtained. The value of the indicator of the relevant parameters of the vehicle degree is given. The higher the degree indicator, the higher the sensitivity of the vehicle parameter to increase when armoring. It was determined that different physical (mechanical) quantities have different sensitivity to the increase in the weight of the vehicle. The greatest sensitivity is the value of the engine torque, and the least is the linear dimension. These dependencies can be used in the process of designing a car armoring. In addition, it was determined that there is a relationship between the maximum permissible parameter measurement error during dynamic tests of full-scale models and the maximum permissible parameter measurement error during full-scale machine testing. Іmproved requirements for the dynamic parameters measurement accuracy during armored vehicles tests are considered. During armored vehicles tests, the maximum permissible error of dynamic parameters measuring increases, which makes it possible not to use more complex measuring and recording equipment.

A comprehensive review of community detection in graphs

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based methods, spectral clustering, probabilistic modeling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.

The relationship between feminist collective action and social media engagement

Purpose This study aims to investigate the impact of online collective feminist actions on social media participation and the perceived value of social media as part of the social identity model of collective action (SIMCA). Design/methodology/approach A mixed methodology is used. Within the SIMCA model in the context of feminist collective actions, social identity, group-efficacy and fear predicted the intention of online collective action participation in the first part (quantitative) of the study. Contrary to predictions, the influence of anger on the intention to participate in collective action was negative. In-depth interviews are conducted in the study’s second (qualitative) part to investigate why individuals do not participate in collective actions despite their anger at violence against women. Findings The concept of online feminist collective action, from the perspective of the SIMCA model, deals with violence against women in the context of social marketing, revealing the importance of online collective actions as an antecedent of social media participation and the perceived value of social media. The possible causes of the negative impact of anger, which is an important emotion within the framework of the SIMCA model, on online feminist collective action were determined, and a contribution was made to the social marketing literature in the context of women’s rights. Originality/value The study makes three major contributions to the literature. First, women’s rights are addressed in the context of online collective action, an issue that has received little attention in social marketing. Second, in the context of the social identity theory of collective action, online collective feminist action and its predecessors are addressed. Finally, the Turkish case is used to highlight the probable causes of anger’s negative impact on collective action.

Aeronautical composite/metal bolted joint and its mechanical properties: a review

PurposeBolted joint is the most important connection method in aircraft composite/metal stacked connections due to its large load transfer capacity and high manufacturing reliability. Aircraft components are subjected to complex hybrid variable loads during service, and the mechanical properties of composite/metal bolted joint directly affect the overall safety of aircraft structures. Research on composite/metal bolted joint and their mechanical properties has also become a topic of general interests. This article reviews the current research status of aeronautical composite/metal bolted joint and its mechanical properties and looks forward to future research directions.Design/methodology/approachThis article reviews the research progress on static strength failure and fatigue failure of composite/metal bolted joint, focusing on exploring failure analysis and prediction methods from the perspective of the theoretical models. At the same time, the influence and correlation mechanism of hole-making quality and assembly accuracy on the mechanical properties of their connections are summarized from the hole-making processes and damage of composite/metal stacked structures.FindingsThe progressive damage analysis method can accurately analyze and predict the static strength failure of composite/metal stacked bolted joint structures by establishing a stress analysis model combined with composite material performance degradation schemes and failure criteria. The use of mature metal material fatigue cumulative damage models and composite material fatigue progressive damage analysis methods can effectively predict the fatigue of composite/metal bolted joints. The geometric errors such as aperture accuracy and holes perpendicularity have the most significant impact on the connection performance, and their mechanical responses mainly include ultimate strength, bearing stiffness, secondary bending effect and fatigue life.Research limitations/implicationsCurrent research on the theoretical prediction of the mechanical properties of composite/metal bolted joints is mainly based on ideal fits with no gaps or uniform gaps in the thickness direction, without considering the hole shape characteristics generated by stacked drilling. At the same time, the service performance evaluation of composite/metal stacked bolted joints structures is currently limited to static strength and fatigue failure tests of the sample-level components and needs to be improved and verified in higher complexity structures. At the same time, it also needs to be extended to the mechanical performance research under more complex forms of the external loads in more environments.Originality/valueThe mechanical performance of the connection structure directly affects the overall structural safety of the aircraft. Many scholars actively explore the theoretical prediction methods for static strength and fatigue failure of composite/metal bolted joints as well as the impact of hole-making accuracy on their mechanical properties. This article provides an original overview of the current research status of aeronautical composite/metal bolted joint and its mechanical properties, with a focus on exploring the failure analysis and prediction methods from the perspective of theoretical models for static strength and fatigue failure of composite/metal bolt joints and looks forward to future research directions.

Training of teachers who teach Mathematics with DICT in the Elementary School: a systematic review of the SBEM publications

This bibliographic survey identifies teachers training models that address the teachers training for assignments in mathematics in the Elementary School with DICT. We use, as a theoretical foundation, the training models of José Contreras, and the documentary research methodology of the chronicle type. We have investigated a total of ten articles which were published between 2013 and 2022, in the SBEM journals, and in articles from the ENEM annals. The analysis showed the implicit predominance of the reflective training model for teachers. Thus, we point out the need for research that theoretically explains the training models being used and that addresses the use of DICT from the perspective of the critical intellectual model of mathematics teachers training.

Turning peril into opportunity: Construction and validation of a resilience assessment system for China's coastal megacities

Enhancing urban resilience and strengthening risk resistance capacities have emerged as pivotal concerns in the construction and governance of contemporary urban landscapes globally. Given the uniqueness of coastal megacities in terms of geography and population, a more targeted and effective resilience assessment is urgently needed. This investigation develops a comprehensive, multidimensional index system to assess the resilience life cycle of China's coastal megacities. First, we creatively introduce the process perspective of the 4R model of crisis management into urban resilience assessment, revealing the preventive, defensive, recovery, and transformative resilience of China's coastal megacities. Second, we propose a spatio-temporal assessment matrix that integrates four processes of urban resilience and six representative subsystems in coastal megacities: social, economic, ecological, infrastructural, technological, and institutional. Third, we select 59 indicators to construct a three-level assessment index system for China's coastal megacities. This index system is strictly screened and optimized through qualitative and quantitative methods. Fourth, we validate the assessment system using Qingdao's data by calculating an urban resilience index and analyzing trends. We find the most important improvement goals and resilience enhancement suggestions for Qingdao by analyzing the indicator sensitivity. This study contributes to the development of urban resilience theories and identifies weaknesses in different dimensions to strengthen and improve the resilience of coastal megacities.

Exploring ICT-based learning adoption in higher education: An extended perspective of the technology acceptance model

This article analyses the self-perception of digital technology learning competence among engineering and science students. The use of information and communication technologies (ICT) is now considered highly essential in higher education. Today's higher education institutions (HEIs) seldom employ traditional methods of instruction; instead, sophisticated ICT is starting to emerge as a feasible paradigm for fundamental change. To this end, Pakistan's Higher Education Commission (HEC) has heavily invested in technology in the education sector and initiated several programs. The ramifications of ICT utilization on the environment, student academic performance, and capabilities remain ambiguous. In delving into how students embrace and utilize ICT systems for learning in HEIs, researchers felt the need to grasp the factors that influence students' acceptance and utilization of ICT skills through the lens of the Extended Technology Acceptance Model (TAM). This study develops an exclusive conceptual model by incorporating TAM with a set of latent variables identified in the available literature: interest, ICT self-efficacy, economic cost, and performance expectancy. The adapted TAM model developed in this study considers the influence of these variables on students’ ICT acceptance and its impact on academic performance in emerging countries. As part of this study, reports on the development of instruments and validation in the research field were managed using a cross-sectional survey method and SPSS-22 and Smart PLS-4 software. In addition, association rule mining applied in demographic data. A sample of 69 students was randomly selected from three universities representing engineering, medical, and general HEIs in the Sindh province. Though the sample size was small, it showcases that the reliability of the scales is within an acceptable range and can be used to test the main study hypotheses. A conceptual framework model is introduced to offer a comprehensive framework derived from the amalgamation of various acceptance and usage models of technology. The results demonstrate that the survey items are appropriate and suitable for further research.

Effects of Career Aspirations of New Generation Employees on Proactive Socialization Behavior

Organizational socialization of the new generation of employees has become a hot topic affecting the sustainable development of organizations. From the perspective of a proactive motivation model, the mechanism of career aspirations on new generation employees’ proactive socialization behaviors was explored. Three hundred three responses were gathered from an online survey. The results show that career aspirations were positively associated with proactive socialization behavior. Sense of hope played a mediating role in the relationship between career aspirations and proactive socialization behavior. Perceived organizational support moderated the relationship between career aspirations and a sense of hope. As employees’ perception of organizational support increases, the positive effect of career aspirations on a sense of hope is weakened. Our findings help to understand why new generation employees exhibit proactive socialization behavior and provide management insights for organizations to motivate new generation employees to take proactive socialization behaviors.

Multi-perspective feature collaborative perception learning network for non-destructive detection of pavement defects

Pavement defects detection has made significant progress with the development of convolutional neural networks. Due to the topological complexity of pavement defects regions, most existing detection methods are limited to a limited number of types of defects, which results in unsatisfactory detection results for multi-classified defects. Several studies have used enhanced data to address this issue, however, few have considered the inefficiency caused by imbalanced differences between classes from the perspective of the network model itself. In light of this issue, our goal is to fill this gap by better exploring the fusion of different feature information to improve the performance of the network. As a result, we propose a multi-perspective feature collaborative perception learning network (MFCPLNet), which uses the model's contextual information to guide high-quality multi-classified defects detection and that can be adapted to various pavement conditions. Specifically, for adaptive feature extraction, an irregular perceptual extraction network (IPENet) with enhanced edge information is first designed to draw upon hybrid deformable convolution for efficient network block hybridization. Additionally, a multi-balanced collaborative learning mechanism (MCLM) consisting of auxiliary axial information and 3D feature guidance is constructed to enhance deep semantic features, improve the localization of defects in defective regions, and compensate for weak category supervision. Lastly, in the detection head, an iterative soft aggregation module has been designed to enhance scalable spatial interactions. The results of extensive testing were based on publicly available pavement defects datasets. Based on the proposed network, the mAP index reaches 29.6% and 33.8%, respectively, an increase of 8.3% and 4.6% over the baseline, which is qualitatively and quantitatively superior to mainstream methods.

A pore-network modeling perspective on the dynamics of residual trapping in geological carbon storage

This study presents the application of an efficient and robust Dynamic Pore-Network Modeling (DPNM) framework for accurate prediction of carbon dioxide (CO2) trapping behavior under the dynamic flow conditions prevalent in subsurface applications. Residual trapping of CO2 is crucial in the context of geological sequestration, serving as a constitutive relationship that controls relative permeability hysteresis and thereby determining the magnitude of CO2 trapping within formations over varying timescales. Utilizing our DPNM framework, we study the complexities of residual trapping in miniature-core-sized digital replicate of a sandstone. We systematically conduct series of two-phase flow simulations to examine the relationships between total residual CO2 amount, trapping efficiency, and initial saturations across a spectrum of capillary numbers and wettability states. Dynamic simulation results lead to a simple power-law scaling equation correlating CO2 trapping efficiency with initial saturation across various capillary numbers. Further analysis explores the morphological and topological characteristics of fluids during primary drainage and various imbibition displacements within the pore network. This work contributes an essential tool and deepens our understanding of CO2 trapping dynamics, driving progress towards more effective and safer strategies for carbon capture and storage.

Pursuing sustainable performance in healthcare organizations: a sustainable business model perspective

PurposeThis paper explores the use of Dynamic Business Modeling for Sustainability (DBMfS) in healthcare management settings as a holistic approach to integrating economic, social, and environmental dimensions into the business formula of these complex organizations.Design/methodology/approachAfter framing sustainable value in healthcare in terms of organizational complexity, wicked problems, and emerging managerial challenges, the paper proposes and illustrates the DBMfS approach that adopts a systemic view of sustainable value in healthcare. According to a qualitative perspective, a single-case study of a maternity-related healthcare treatment is also described to explore the implications and limitations of using this approach to sustainable business modeling in healthcare organizations.FindingsFindings show that, by embracing sustainability through DBMfS, healthcare organizations can improve operational efficiency, reduce waste (like energy, water, and medical supplies), and enhance cost-effectiveness, thus contributing significantly to societal well-being.Originality/valueIn recent years, the healthcare sector has faced numerous challenges and wicked problems, including escalating costs, resource constraints, and growing environmental concerns. As a result, the concept of sustainability has gained significant attention, thus calling scholars and practitioners to develop methods and tools able to integrate it into strategic management systems of healthcare organizations. By proposing the adoption of DBMfS in the healthcare sector, this paper contributes to feeding up the current debate.

M-XAF: Medical explainable diagnosis system of atrial fibrillation based on medical knowledge and semantic representation fusion

BackgroundElectrocardiogram (ECG) is a critical diagnostic tool used for screening atrial fibrillation (AF). In recent years, several deep learning-based ECG automatic diagnosis methods have been proposed and have yielded satisfactory results. However, the lack of interpretability in these systems limits their clinical usage. MethodsTo address this issue, we propose a medical explainable AF diagnosis system (M-XAF) that not only predicts AF diagnostic results but also generates an analysis report of the ECG medical features that influenced the classification model's decision. Using a novel ECG signal features occlusion analysis method, M-XAF establishes a connection between the medical knowledge space and the representation space from the convolutional neural network (CNN) by extracting understandable semantic sensitive channels (SSCs) via gradient-weighted channel sensitivity. Through the visualization and statistical modeling of SSCs, M-XAF provides multimodal explanations, including ECG feature descriptions, regions of interest (ROI), and class activation mapping (CAM), for AF diagnostic results. ConclusionThe experimental results demonstrate that the M-XAF has achieved satisfactory results in the AF identification task, with accuracy, specificity, PPV, F1, and MCC reaching over 0.9715, 0.9896, 0.9778, 0.9567, and 0.9365 on two public ECG datasets. In addition, M-XAF generates quantitative explainable reports and CAM for each predicted sample from the perspective of classification models. Furthermore, the explainable reports provided by M-XAF can address the high-confidence problem in classification models. Ultimately, the explainable framework in M-XAF can be widely applied in AI-ECG classification scenarios.

A study of uncertain 4D transportation problems with rough interval parameters and additional real-life factors

In developing countries like India and Nepal, a vehicle document verification system is implicated at the checkpoints and border crossings that allows users to authenticate and verify the validity of vehicle related documents such as registration certificates, driving licenses, pollution under control certificates, and insurance policies. This results in the formation of long queues of the vehicles and hence a significant time wastage. These vehicles waiting in the long queues, continue to emit carbon dioxide and other greenhouse gases into the atmosphere. To address these issues, the government has introduced the Automated Number Plate Recognition (ANPR) technology which plays a significant role in the efficient and accurate document verification by identifying and validating vehicle registration details. It also automates toll collection by capturing the vehicle’s number plate, reducing waiting times and congestion at toll barriers. To analyze all these benefits, in this study, for the first time, ANPR technology has been introduced in the multi-objective model of transportation problem and its impact on various objectives has been observed. Also, real-life factors: fixed-charge cost, carbon emission, multiple routes have been considered in the proposed model and the problem is named as multi-objective fixed-charge four dimensional green transportation problem. Rough set theory has been employed as a tool for effectively addressing the indeterminacy and facilitating the modeling of real-life transportation problems. Two methods: the expected value method and the rough chance constrained programming method, are proposed to transform the suggested model of multi-objective transportation problem into a deterministic form. To solve these transformed models, a new multi-objective optimization approach based on the neutrosophic theory, named as neutrosophic goal programming has been introduced. To further demonstrate the practicality of the proposed study, an application for the apple fruit transportation has been solved. Detailed comparisons between obtained results are done on the basis of different modeling perspectives, different solution techniques and also with/and without ANPR technology. Based on the outcomes, the suggested neutrosophic goal programming technique proved to be superior to the existing techniques in the literature.

The use of an imperfect vaccination and awareness campaign in the control of antibiotic resistant gonorrhoea infection: A mathematical modelling perspective

The multi drug-resistant Neisseria gonorrhoeae has been classified by the World Health Organisation (WHO) as a high-priority global public health problem. This underlines the need for better understanding of the transmission dynamics and proposing an optimal intervention strategy to control the disease. In this article, a deterministic mathematical model for the transmission dynamics of gonorrhoea as an antibiotic resistant disease in a population with an imperfect vaccination is proposed and analysed. The model incorporates the classes of vaccinated individuals and individuals equipped with self protection interventions to reduce antibiotic resistant cases. The threshold parameter R0, the basic reproduction number, for the analysis of the model is calculated. In the given setting, the model exhibits a backward bifurcation for R0<1. However, if the efficacy of the vaccine is 100% without a waning effect, the model is shown to be without a backward bifurcation and the disease-free equilibrium is globally asymptotically stable whenever R0<1. The global sensitivity analysis of the model to variations in parameter values is also performed to determine the most influential parameters on the disease transmission. Moreover, the optimal control analysis of the full model is presented and the optimal intervention strategies are proposed. The proposed intervention strategies are shown to be able to control the disease within a relatively shorter period of time. Finally, numerical experiments are presented to support the theoretical analysis of the model.

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

To investigate the relationship between histological properties and thermal conductivities of binary self-assembled/self-organized fractal materials fabricated by the mixed diffusion of filler particles, the particle group distribution of the filler particles was analyzed. Unlike conventional research on particle size distribution, we performed an analysis that focuses on interactions and connectivity between particles from the perspective of the Barabasi-Albert model. In three complex network systems of β-Si3N4 (SN)/austenitic stainless steel (SUS), SN/piezoelectric polyvinylidene fluoride (PVDF), and BaTiO3 (BT)/PVDF, the size distribution of secondary particles was a power-law distribution until the particle group grew relatively large. In the SN/SUS and SN/PVDF systems, a crossover phenomenon was observed in which the order distribution exhibited a power-law in the region where the particle group area was small, and as the particle group became larger, the order distribution became exponential distribution. On the other hand, the BT/PVDF system deviated from the power law distribution as the particle group area increased but did not exhibit an exponential distribution. The characteristics of the particle group distribution of these filler particles were closely related to thermal conductivity, indicating that the connection of particle groups was important. Furthermore, the relationship between the multifractal capacitance dimension, which is strongly correlated with the distance between the particle groups, and the total number of filler particle groups suggests that the texture of these composite material systems may have both small-world and scale-free properties. We also focused on the relationship between the multifractal and complex network properties of the three particle groups. As a result, a correlation was found between the slope γ, which reflects the characteristics of the power distribution, and the thermodynamic parameter α (corresponding to internal energy change) obtained from multifractals. In particular, the smaller the change in γ relative to the change in α, the more pronounced the bonding of particles and particle groups, while the larger change in γ suggested the dispersion of particle groups.

How do academic smart city centres operate in complex environments? A business model perspective

In recent years, an increasing number of academic smart city centres have emerged globally. These centres play a crucial role in conducting cutting-edge research and proposing innovative solutions for cities; they often operate in a complex multi-purpose, multi-disciplinary, and multi-stakeholder environment, even as they struggle to secure enough resources for their core activities. Despite their important role in smart city projects and initiatives, we lack scientific understandings of how smart city centres generate and deliver value to city stakeholders. As a first step in addressing this gap, we studied seven smart city centres across Europe and North America through in-depth interviews. To complement those interviews, we conducted an online survey and collected responses from a larger sample of centres from several regions around the world. Consequently, we provide a fresh understanding of how these centres orchestrate their operations and capabilities to create and deliver value to smart city stakeholders. Inspired by recent research on business model innovation for non-profit organizations, we argue that academic centres can benefit from using business models to align their operations with their strategies to achieve their goals. Our study contributes to smart city and business model research while providing practical implications for smart city centres.

Quantifying the Impacts of an Urban Area on Clouds and Precipitation Patterns: A Modeling Perspective

AbstractOver the past century, rapid urbanization has greatly altered landscapes and affected atmospheric conditions causing impacts across a wide range of sectors including human welfare, infrastructure, and ecosystems. As a result, there is a growing imperative to improve understanding of urbanization impacts on local and regional weather events and hydroclimates. This study investigates how urbanization affects precipitation and cloud fraction (CF) in the vicinity of Indianapolis. We employ multi‐month simulations using the Weather Research and Forecasting model to: (a) assess the impact of an urban area on precipitation and CF, (b) quantify how this impact varies with urban growth, and (c) examine the main mechanisms through which the city alters the local hydroclimate. Specifically, two perturbed simulations, where the urban land cover is either replaced by croplands or is increased in size, are also performed for the two rainiest months. Comparisons of the control run with no city against the perturbed runs indicate statistically significant impacts of the urban area in enhancing precipitation amounts, frequency and low‐level CF, particularly within the first 100 km radius downwind of the city boarder. The urban environment is found to increase precipitation efficiency over the city and in downwind regions. Temperature at 2 m height, planetary boundary layer height, turbulent kinetic energy, and convective available potential energy, are also enhanced in the perturbed runs and drive changes in vertical mixing, downwind precipitation amounts, frequency and low‐level height CF. All these changes appear to be a non‐linear function of the city size.