Transformation Model Research Articles

Tourism digital transformation and future supply chain competition: an integrated perspective on real options theory and digital competencies

PurposeThis study analyzes how digital technologies collaboration, and technological capabilities affect tourism products' advantage and supply chain resilience via virtual integration and customer service capabilities.Design/methodology/approachTo achieve the goals of this study, a digital transformation model was formulated based on the real option theory (ROT) and digital competencies perspective. Data were collected from travel agencies in Taiwan. This study uses the partial least square structural equation modeling (PLS-SEM) technique to analyze the research model, and 384 samples were collected from travel agencies for analysis.FindingsThe research results point out that digital technology collaboration and technical capabilities affect virtual integration and customer service capabilities; customer service capabilities should also be regarded as key influencing variables to improve tourism product advantages and supply chain flexibility.Originality/valueThis study shares a unique perspective on the digital transformation model, which includes antecedents, mediators and moderators, to construct the critical effects for analyzing the tourism products' advantage and supply chain resilience.

CAIMP: Cross-Architecture IoT Malware Detection and Prediction Based On Static Feature

Abstract IoT malware and cross-platform malware are currently the top threats to information systems. This paper proposes a robust cross-architecture IoT malware detection and prediction model based on machine learning and opcode features using a novel approach. In our method, a feature opcode transformation model between chip architecture platforms is proposed to facilitate the process of building a detection model for cross-architecture malware on IoT devices. The feature transformation model is capable of converting opcodes between different architecture platforms using an unsupervised machine learning approach. In our approach, a machine learning model is used for the detection of cross-platform malware based on the proposed opcode features. Experiments have demonstrated that our method is effective in detecting and predicting cross-platform malware with an accuracy of up to 99.4% and an F1-score of 99.3%. The method is capable of learning on one architecture platform and detecting malware on a different architecture platform. Therefore, the method can be used to develop cross-architecture detection and zero-day malware prediction solutions on IoT devices.

Microstructure Evolution of Polyacrylonitrile-Based Fibers during Thermal Pre-Oxidation

In this work, pre-oxidized polyacrylonitrile fibers are treated with ultrasonic etching and solution etching to produce ultra-thin sections. The evolution of the fibers’ microstructure in the pre-oxidation process is observed, and the transformation model of the microstructure of the pre-oxidized fibers is proposed. Scanning electron microscopy and high-resolution transmission electron microscopy were used to observe the microstructure changes of the fibers. Fourier transform infrared spectroscopy and X-ray diffraction were used to observe the chemical structure transformation and crystallization degree of the fibers in the pre-oxidation process. The results revealed that pre-oxidized fibers exhibited a smooth surface, while their interior consisted of fibrils. The longitudinal microfibrils were connected by the transverse microfibrils and amorphous regions. The fracture morphology of the fibers shifted from ductile to brittle, and the cross-section gradually became smoother. The linear molecular chain of PAN transformed into a ring structure as pre-oxidation progressed, subsequently leading to the cross-linking of this ring structure into an orderly trapezoidal configuration. The connection between the fibrils was enhanced, and the fiber structure became more compact and stable.

A Novel Multi-camera Fusion Approach at Plant Scale: From 2D to 3D

Non-invasive crop phenotyping is essential for crop modeling, which relies on image processing techniques. This research presents a plant-scale vision system that can acquire multispectral plant data in agricultural fields. This paper proposes a sensory fusion method that uses three cameras, Two multispectral and a RGB depth camera. The sensory fusion method applies pattern recognition and statistical optimization to produce a single multispectral 3D image that combines thermal and near-infrared (NIR) images from crops. A multi-camera sensory fusion method incorporates five multispectral bands: three from the visible range and two from the non-visible range, namely NIR and mid-infrared. The object recognition method examines about 7000 features in each image and runs only once during calibration. The outcome of the sensory fusion process is a homographic transformation model that integrates multispectral and RGB data into a coherent 3D representation. This approach can handle occlusions, allowing an accurate extraction of crop features. The result is a 3D point cloud that contains thermal and NIR multispectral data that were initially obtained separately in 2D.

MODELS OF PROFESSIONAL DEVELOPMENT FOR BIOLOGY TEACHERS IN THE CONTEXT OF SOCIAL CHANGES

This article aims to explore the impact of social changes on the professional development of biology teachers. The purpose of the study is the analysis of modern challenges in the field of education and the development of a model of professional growth of a biology teacher. The following methods are employed in the study: analysis and synthesis of literature on the research topic to determine the current state of the investigated problem, systematic analysis, and synthesis of obtained information to identify the main models of professional development for biology teachers. The regulatory framework ensuring conditions for the professional development of teachers has been analyzed. Modern economic and technological changes, socio-cultural and political transformations, dynamics of interethnic and cultural relations in Ukraine, high population mobility, and migration processes affect the education system and the professional development of teachers. In the scientific discourse, among effective models of professional development for teachers, scholars distinguish: concepts of teaching and development of teachers' professional abilities based on knowledge (knowledge for practice, knowledge in practice, personal knowledge); models of professional development for educators (training model, award-bearing model, deficit model, cascading model, standards-based model, coaching/mentoring model, community of practice model, action research model, transformational model); competency, cognitive, human-centered models; models of applied science, craftsmanship, reflective, apprentice-expert model, rationalist model, case studies model, cascading. The author proposes an original model of professional development, which involves six stages: analysis of contemporary challenges, professional training based on teachers' needs, creation of an individual development plan, integration of innovative technologies into professional activities, self-evaluation, and the establishment and support of professional communities. The main research results indicate the need to reconsider traditional approaches to teaching biology and emphasize the use of modern technologies in the professional development of teachers for successful implementation of changes in the educational environment.

UMPL- VINS: Generalized SLAM for multi-scene metaverse applications

The use of line features to improve the localization accuracy of point-based visual inertial SLAM (VINS) is of increasing interest because of the additional constraints they can provide on the scene structure. It is found that although the introduction of line features improves the accuracy of relative position estimation in some scenes due to the additional constraints, the two constraints only achieve an equalization for the estimated relative position. Therefore, in some environments two constraints can reduce the accuracy of the single-feature constraint algorithm and also make the real-time performance of the system more challenging. To address such issues, in this paper, we design a generalized SLAM system with point-line features that can be applied to multiple metaverse scenarios. We first enhance the image frames used for feature extraction by eliminating motion blur frames through the fuzzy metric method and mutation modeling of velocity and rotation. Then we improve the traditional line detection model by short line fusion, uniform distribution of line features, and refinement of edge features to obtain high-quality line features for building SLAM. Finally, based on the application of point features and line features for different scenes, a point and line feature separation-union model is proposed. In addition, we design a transformation model for line-point features to enhance the processing of point-line features. Experimental results on EuRoc, TUM_VI, KITTI, PennCOSYVIO and our own recorded dataset prove that the method proposed in this paper realizes a generalized SLAM with point and line features applied to multi-scenes with good advantages.

Digital transformation model for universities: A preliminary proposal

This article presents the results of a research project aimed at designing a theoretical model for digital transformation at the Autonomous University of Chile. The research methodology adopts a case study approach with a mixed method combining quantitative and qualitative techniques. This approach provides a nuanced and comprehensive understanding of the dimensions and factors shaping the digital transformation model, incorporating insights from 97 students and eight experts. The model comprises three phases, nine dimensions, and 54 indicators, emphasizing the integral connection between digital maturity and value creation for students. Key success determinants in the university's digital transformation processes are outlined, including the strategic approach, organizational culture, utilization of technological infrastructure, and technology management capabilities. Practical implications underscore the significance of acknowledging the impact of power relations during the digital transformation journey, influencing internal processes, IT governance coordination, roles of personnel in technical and managerial positions, teachers’ performance, and students' trust and loyalty to the institution. The article concludes by identifying factors that influence the institution's capacity to implement technological changes aligned with its strategic, technical, and logistical requirements. Future studies could redefine the roles of administrative, teaching, and student staff, fostering increased digital maturity, continuous innovation in services and processes, and the creation of new digital learning spaces centered around student needs.

Fostering product development efficiency through cross-functional team leadership: Insights and strategies from industry experts

This study investigates the impact of leadership models on the efficiency and innovation outcomes of cross-functional teams (CFTs) in product development contexts. With the aim of identifying effective leadership practices, the research focuses on how transformational, shared, and situational leadership models influence team dynamics, communication, collaboration, and ultimately, product development efficiency. Employing a systematic literature review combined with content analysis, the study analyzes peer-reviewed articles and scholarly books published from 2013 to 2024. This methodology facilitates a comprehensive examination of current leadership models and their applicability to CFTs. Key findings reveal that transformational leadership inspires and motivates team members, shared leadership enhances ownership and accountability, and situational leadership adapts to the dynamic needs of product development projects. These leadership models significantly contribute to fostering an environment conducive to open communication and collaboration, driving innovation and efficiency in product development processes. Based on these insights, strategic recommendations for industry leaders include adopting flexible leadership styles, promoting open communication, encouraging shared leadership, and investing in leadership development. These strategies are pivotal for leveraging the diverse expertise within CFTs and enhancing product development outcomes. The study underscores the critical role of leadership in maximizing the potential of CFTs for innovation and efficiency. It highlights the need for leaders to adapt their strategies to the unique challenges of managing diverse teams, offering a path forward for effective team leadership in product development. Keywords: Cross-functional teams, Leadership models, Product development efficiency, Innovation outcomes.

Moral Transformation as Shifting (Im)Possibilities

AbstractThe phenomenon of moral transformation, though important, has received little attention in virtue ethics. In this paper we propose a virtue-ethical model of moral transformation as character transformation by tracking the development of new identity-defining (‘core’) character traits, their expressions, and their priority structure, through the change in what appears as possible or impossible to the moral agent. We propose that character transformation culminates when what previously appeared as morally possible to the agent now appears impossible, i.e. unconceived and unthinkable, moving through stages of transformation where some possibilities gradually disappear while others open up. While we show an example of moral transformation towards virtue, we allow that such transformation can occur in the opposite direction, hence we make claims about ‘character traits’ rather than virtues of vices. Through the example of former slave-trader Rodrigo’s transformation in the film The Mission, we follow the parallel development of new objects of value and ways of valuing (with respect to a group of indigenous people of South America) with the closing down of the possibility of disrespecting and harming them, to the end-point of transformation, where allowing their capture is for Rodrigo both unconceived and, when conceived, unthinkable.

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

Leading with well-being: Introducing a model for well-being informed leadership.

Higher education brings a catalog of peaks and valleys for students, staff, and faculty. These are heightened by global crises, challenging legislation, and exclusionary practices. These kinds of adversities influence how we show up in higher education spaces and impact both our leadership and well-being. As leadership reciprocally affects, and is affected by, one's well-being, the responsibility to cultivate both within higher education continuously increases. To consistently support and uplift our students and understand the intricate challenges higher education continues to face, we introduce the well-being & leadership transformation (WBLT) model. Informed by leadership and well-being frameworks, the WBLT model integrates leadership and well-being in an intentional and holistic way. This piece establishes the elements of the model and demonstrates the WBLT model in action through various examples.

Frameworks and Models for Digital Transformation in Engineering Education: A Literature Review Using a Systematic Approach

In response to the opportunities and challenges posed by rapid technological advancement, digital transformation (DT) has recently emerged as a key concept in higher engineering education. DT involves using digital technologies to transform educational and pedagogical practices to enhance the learning and teaching experiences, prepare students for the needs of industry, and foster innovation. Despite a growing number of small-scale empirical studies concentrating on digitalization at lower single-activity and classroom levels, the practices of traditional education largely remain. There is a need for more systematic and holistic frameworks to facilitate and guide DT in engineering education. This study reviews 13 studies, using a systematic approach to identify and analyze the literature on frameworks for DT of engineering education. Several characteristics are identified, including types of DT frameworks and models; drivers of DT; and digital learning tools and types. In addition, various anticipated outcomes of DT reported in the included studies are described at the micro, mezzo, and macro levels. Recommendations for future practices for engineering students, educators, and institutions and future research directions for engineering educational researchers are also proposed to support the further development of digital education.

Quantile-based cumulative Kullback-Leibler divergence in past lifetime: Some properties and applications

This article introduces a quantile-based cumulative Kullback-Leibler (KL) divergence measure for past lifetimes (QPCKL divergence) and studies its various properties. Unlike the cumulative KL divergence using the distribution function, the proposed measure offers an alternative method for computing the divergence between two random variables, especially when one lacks a closed-form distribution function but possesses a closed-form quantile function. A characterization property of a distribution is derived using the constant QPCKL divergence model. Subsequently, a comparison of some important reliability characteristics using the proposed measure is discussed. The QPCKL divergence for a general family of transformation models is derived. We also propose a non parametric estimator for the measure, and simulation studies are carried out for validation. Finally, the importance of the estimator is also illustrated through real data sets useful in survival studies.

Corrosion segmentation method of concrete drainage pipes based on point transformer

Deep learning based Closed Circuit Television (CCTV) image detection has achieved remarkable accuracy in identifying and segmenting concrete pipeline damages. However, the lack of in-depth information and requirement for large amounts of training data are two major drawbacks. Therefore, this study proposes a point cloud data segmentation method that incorporates depth information to address the issue of insufficient training data for depth models, enabling efficient and accurate segmentation. Firstly, 3D simulation technology is employed to construct a simulation dataset. Secondly, an optimized point transformation model is established to identify and segment the point cloud data. The proposed method achieved a test accuracy of 94.36 % by optimizing the network structure and the training strategy. The Mean Intersection over Union (MIoU) increased by 3.62 % and 10.3 %, respectively, compared with the classic PointNet++ and before adding simulation data, reaching 86.31 %. These results demonstrate that the proposed method exhibits high precision defect detection capability on three-dimensional point clouds of drainage pipelines.

Simulating non-stationary and non-Gaussian cross-correlated fields using multivariate Karhunen–Loève expansion and L-moments-based Hermite polynomial model

In practical engineering, cross-correlated random fields are often used to model structural materials or random loads containing multiple correlations. Effective and accurate simulation of these cross-correlation fields is an important prerequisite for subsequent reliability analysis and uncertainty quantification of complex systems. Therefore, this paper proposes a new simulation method for non-stationary non-Gaussian cross-correlated random fields to satisfy realistic engineering requirements. In this new method, L-moments-based Hermite polynomials model (LHPM) is extended to non-stationary non-Gaussian cross-correlated fields. Then, a transformation model from non-stationary non-Gaussian correlation matrix function (CMF) to the underlying Gaussian CMF is explicitly given. Multivariate K-L expansion is further used to approximate the underlying Gaussian cross-correlated fields, where the eigenvalue and eigenfunctions are estimated via the Nyström method with uniform weights. The approximation permits the application of few random variables to characterize the entire Gaussian cross-correlated fields. Ultimately, the simulated underlying Gaussian cross-correlated fields are mapped back to the target non-stationary non-Gaussian cross-correlated fields based on LHPM. Three typical examples, including exponential kernel, Wiener process kernel and spatially varying non-Gaussian and nonstationary seismic ground motions, are used to validate the effectiveness of the proposed method. The source code is readily available at: https://github.com/zhaozhao23/Simulation-of-non-stationary-and-non-Gaussian-cross-correlated-fields.

Molecular Composition of Humic Acids of Different Aged Fallow Lands and Soils of Different Types of Use in Northwest of Russia

Post-agrogenic transformation of fallow soils leads to changes in soil carbon content, the molecular composition of humic substances, and rates of organic matter stabilization, which can affect climate change on the planet. In this regard, we analyzed the molecular composition of humic acids isolated from natural and fallow soils in the southern Taiga zone of northwest Russia. Different-aged soils on fallow lands represent a model of soil transformation in time, and data on the transformation of soil humic acid molecular composition make a significant contribution to the understanding of soil organic matter stabilization aspect issues. In this case, the molecular structure of humic acids isolated from natural and fallow soils in northwest Russia was analyzed. To study the molecular composition of HAs, the elemental composition was analyzed, and 13C (CP/MAS) NMR spectroscopy of HAs isolated from different aged abandoned soils and soils of different types of use was carried out. The obtained data showed that with the increasing age of soils in the fallow state, there is an increase in the carbon content of humic acids as well as a decrease in nitrogen content. As a result of the increasing age of soils in the fallow state, there are dynamics in the content of aromatic structural fragments in humic acids: 34% for 40 years old, 28% for 80 years old, and 31% for 120 years old. This is due to changes in the precursors of humification and the further transformation of plant residues in the soil. Re-involved fallow land soils lead to an increase in the content of aromatic structural fragments in the composition of HA in relation to HA extracted from mature soils. The lowest content of aromatic structural fragments was observed in the humic acids of 130-year-old agricultural soil, which is associated with the long-term application of organic fertilizers.

Integrating Open Knowledge and Administrative Management in the Digital Transformation Model of Education Institutions: An Effective Approach

This paper explores the integration of Open Knowledge and Administrative Management into the Digital Transformation Model within educational institutions, focusing on the National Academy of Education Management and three secondary schools in Hanoi. Through a comprehensive methodology involving data collection, analysis, and system development, the study examines the effectiveness of this integrated approach in enhancing educational outcomes. The findings reveal significant improvements in knowledge dissemination, resource allocation, and administrative efficiency following the implementation of the integrated model. By harnessing open knowledge resources and streamlining administrative processes, educational institutions have been able to optimize teaching and learning practices, facilitate collaboration among stakeholders, and promote innovation in education. Furthermore, the study highlights the importance of ongoing research, evaluation, and professional development initiatives to sustain the momentum of this integrated approach. By investing in capacity building, promoting collaboration, and embracing emerging technologies, educational institutions can adapt to the evolving needs of learners and prepare them for success in the digital age. Overall, the integration of Open Knowledge and Administrative Management represents a promising pathway towards educational excellence and equity. Through collective efforts and a commitment to continuous improvement, educational stakeholders can drive meaningful change and empower learners to thrive in an increasingly complex and interconnected world.

An approach for fatigue damage estimation under broadband non-Gaussian random loadings based on the Johnson transformation model

Estimating fatigue damage of marine structures using spectral methods under broadband random loadings is a complex and challenging task. In the presence of non-Gaussian loading, the spectral method typically employs the Hermite polynomial model (HPM) to address the non-Gaussianity. However, the applicability of HPM is limited in certain non-Gaussian cases due to the requirement of monotonicity. This paper adopts the Johnson transformation model (JTM) instead of HPM and integrates it with the non-Gaussian TB method (Ding and Chen, 2015) to overcome the constraints in broadband non-Gaussian fatigue estimation. To verify the performance of the new approach, this paper analyses the transformation accuracy of the two models. Additionally, taking the time-domain fatigue damage obtained by the rainflow counting method and the linear damage accumulation rule as a benchmark, this paper compares the fatigue damage estimates obtained from the spectral methods. The comparative assessment encompasses various spectra, S-N curves, and non-Gaussian characteristics. The results indicate the performance of JTM within the non-Gaussian TB method is comparable to HPM in most cases and even surpasses it in hardening non-Gaussian cases. Given the broader applicability of JTM across ranges of skewness and kurtosis, it emerges a superior option for the non-Gaussian TB method in estimating broadband non-Gaussian fatigue damage. Furthermore, the limitations of the proposed approach are discussed.

Persinggungan Kepemimpinan Transformational Dengan Kepemimpinan Visioner Dan Situasional

The purpose of writing this article is to determine the intersection of transformational leadership with visionary and situational leadership. This research uses a type of library research. Library research is research carried out by reviewing various journals and related reference books. The results of his research show that the transformative leadership model is a leadership model that can be an alternative to be applied in the current organizational system with various organizational dynamics and changes in line with the increasingly high demands of society. This is in line with the need for leaders who are able to bring about change. Transformative leadership is a leadership model that is different from traditional leadership models which view the relationship between leaders and employees (leadership and followership) as based solely on reward and punishment. A visionary leader is also understood as a leader who has a clear vision and mission in the organization. He has the ability to predict future events and express them in a clear and measurable vision and mission. Visionary leaders are able to raise the enthusiasm of their members with their motivation and imagination, to make the organization more alive and growing. Another situational leadership model is known as the Situational Contingency leadership model which revises the behavioral approach which is considered unable to explain ideal leadership. This approach illustrates that the style developed depends on the leader himself, the support of his followers, and a conducive situation. By analyzing the basic motivations of their subordinates, leaders can place them in appropriate situations. Transformative, visionary and situational leadership models have several elements in common, both in terms of systems, processes and objectives of Islamic Education Management. From the system side, there are elements of education unit leaders, educators, education staff, and various other elements; It is hoped that they can synergize in a process that is conducive to achieving goals.

Confinement and magnetic-field effect on chiral ferroelectric nematic liquid crystals in Grandjean-Cano wedge cells.

We explore the structure and magnetic-field response of edge dislocations in Grandjean-Cano wedge cells filled with chiral mixtures of the ferroelectric nematic mesogen DIO. Upon cooling, the ordering changes from paraelectric in the cholesteric phase N^{*} to antiferroelectric in the smectic SmZ_{A}^{*} and to ferroelectric in the cholesteric N_{F}^{*}. Dislocations of the Burgers vector b equal to the helicoidal pitch P are stable in all three phases, while dislocations with b=P/2 exist only in the N^{*} and SmZ_{A}^{*}. The b=P/2 dislocations split into pairs of τ^{-1/2}λ^{+1/2} disclinations, while the thick dislocations b=P are pairs of nonsingular λ^{-1/2}λ^{+1/2} disclinations. The polar order makes the τ^{-1/2} disclinations unstable in the N_{F}^{*} phase, as they should be connected to singular walls in the polarization field. We propose a model of transformation of the composite τ^{-1/2} line-wall defect into a nonsingular λ^{-1/2} disclination, which is paired up with a λ^{+1/2} line to form a b=P dislocation. The SmZ_{A}^{*} behavior in the in-plane magnetic field is different from that of the N_{F}^{*} and N^{*}: the dislocations show no zigzag instability, and the pitch remains unchanged in the magnetic fields up to 1 T. The behavior is associated with the finite compressibility of smectic layers.