Transformation Model Research Articles

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

AbstractTitan aluminium alloys belong to the group of $$\alpha $$ α –$$\beta $$ β -alloys, which are used for many applications in industry due to their advantageous mechanical properties, e.g. for laser powder bed fusion (PBF-LB) processes. However, the composition of the crystal structure and the respective magnitude of the solid fraction highly influences the material properties of titan aluminium alloys. Specifically, the thermal history, i.e. the cooling rate, determines the phase composition and microstructure for example during heat treatment and PBF-LB processes. For that reason, the present work introduces a phase transformation framework based, amongst others, on energy densities and thermodynamically consistent evolution equations, which is able to capture the different material compositions resulting from cooling and heating rates. The evolution of the underlying phases is governed by a specifically designed dissipation function, the coefficients of which are determined by a parameter identification process based on available continuous cooling temperature (CCT) diagrams. In order to calibrate the model and its preparation for further applications such as the simulation of additive manufacturing processes, these CCT diagrams are computationally reconstructed. In contrast to empirical formulations, the developed thermodynamically consistent and physically sound model can straightforwardly be extended to further phase fractions and different materials. With this formulation, it is possible to predict not only the microstructure evolution during processes with high temperature gradients, as occurring in e.g. PBF-LB processes, but also the evolving strains during and at the end of the process.

Transforming educational leadership in higher education with innovative administrative strategies

In developing nations particularly in Africa, innovative administrative strategies are essential for educational leaders to navigate inherent challenges in education. This study aims to explore how educational leaders in higher education can be equipped with innovative administrative strategies to cause a positive change in education delivery. A systematic literature review was conducted to achieve this aim, drawing on the transformational leadership model as its theoretical underpinning. The search encompassed scholarly publications from major repositories like Scopus, Web of Science, and International Bibliography of the Social Sciences and Textbooks from 1967 to 2023. The search process went a series of exclusive and inclusive processes and yielded 43 research articles. The synthesized findings were analyzed using a thematic approach. The findings indicate that transformational leadership has the potential to incorporate innovation into higher educational administrative practices. It was also found that innovative administrative practices in higher education can be achieved through staff motivation, technology integration, adoption of agile methodologies, collaborative research, interdisciplinary collaboration, seminars, workshops, and positive organizational culture. Based on the findings, professional development programs for managers and administrators in higher education were highly recommended. These programs would help enhance the knowledge and skills of employees in educational administration.

The sociopolitical liberalization of young adults: transforming a dominated habitus

Since the 1940s research has suggested that higher education has a liberalizing influence on sociopolitical orientation, although none has demonstrated how this might occur. In the U.S. there is an increasing trend among conservative politicians and media to depreciate the value of a college education, by alleging that college turns people into liberals. To examine the charge that ‘college turns people into liberals’, I conducted biographical narrative interviews with 24 American college-educated young adults who changed their sociopolitical orientation from conservative to liberal. Utilizing Bourdieu’s concepts of habitus and habitus clivé, and grounded theory methods of analysis, I developed a processual model of such transformation. Findings from this study refute narratives of professorial or institutional indoctrination. Instead, the model indicates a more complex process of habitus transformation, that in most cases begins before college students arrive on campus. This study is important because a successful democracy requires an educated populace.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand. Our model's performance, assessed through Pearson's correlation coefficient (PCC), root mean square error (RMSE), and R-squared (R2) metrics, was compared with temporal convolutional network (TCN), long short-term memory network (LSTM), and bidirectional encoder representation from transformers model (BERT). Additionally, we also calculate the training time and the inference time of the models. The results show that FIT is the most performant, with excellent estimation accuracy and low computational cost. Our model contributes to the development of HCI technology and has significant practical value.

Transformational Leadership of Pesantren as a Role Model for Contemporary Islamic Education Institutions

This study aims to find out about the transformational leadership of Islamic boarding schools as role models in contemporary Islamic educational institutions dan and knowing the characteristics of Islamic boarding school transformational leadership. This research method is library research. Library research is research in which the data or materials needed to complete research come from the library in books, encyclopedias, dictionaries, journals, magazines and so on. The results of this study indicate that the transformational leadership model of pesantren as a role model for contemporary Islamic educational institutions must have four dimensions: charismatic leadership, a leader who can make his followers admire, respect, and trust him. Inspirational motivation leadership, leaders who can articulate clear expectations from all members. Intellectual stimulation leadership is a leader who can develop new ideas from his subordinates. And individualized consideration leadership is a leader who always listens to the aspirations and inputs of others. This research provides practical implications and policy recommendations for Islamic boarding schools and other Islamic educational institutions that wish to adopt transformational leadership strategies. With a better understanding of the role of transformational leadership in Islamic boarding schools, it is hoped that Islamic boarding schools can continue to develop as models of contemporary Islamic education that are relevant and competitive in today's digital era

Bayesian Back Analysis of Axially Loaded Pile Response Using Fibre Optic Strain Data

The load-transfer method has been extensively used to evaluate the deformation behavior of foundation piles, with the nonlinear load-settlement behavior of a pile-soil system modeled using load-transfer curves. Although load-transfer models have a considerable impact on the pile response, the model parameters are usually estimated empirically from soil parameters using transformation models, and the associated uncertainties are often neglected. Fiber optic sensing technology provides detailed axial strain information along the entire length of a pile; however, the full potential of such utilization has not yet been realized. This paper presents a Bayesian back analysis method for statistical descriptions of load-transfer curves and pile responses based on fiber optic measurement. A pile test case study is used to illustrate the proposed method, and several candidate model configurations are compared. The results show that the predicted pile response generally matches the measured data, and the quantified uncertainty allows for a better understanding of the possible ranges of the pile response, in addition to the single best-estimate result.

Evaluation of variability of shear wave velocity in India

The shear wave velocity is a significant component in seismic hazard studies. This involves standard practice in seismic hazard analysis through ground motion prediction equations (GMPEs), ground response analyses (GRAs), or the computation of amplification factors. In practice, the shear wave velocity may be obtained through invasive methods, noninvasive methods, transformation models, or correlation-based methods. Currently, the uncertainty component of shear wave velocity has either been overlooked or oversimplified. The aim was to quantify the range of variations in shear wave velocity across India. The database included data of reported shear wave velocities in 20 states collected from various project reports and publications. A brief review of the methods used to obtain the data is given, along with a systematic highlight of the range of uncertainties encountered and their statistics. Base-case profiles for response analyses are recommended, and their statistics are examined. These observations aid computation of amplification factors. The transformation uncertainties of the shear wave velocity and the standard penetration test (SPT) were examined. The outcomes of the research will guide reliable seismic hazard studies across countries. Furthermore, this study paves the way for future development toward addressing uncertainties in shear wave velocity and facilitates the use of reliability-based design procedures.

A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review.

Leukemia is a rare but fatal cancer of the blood. This cancer arises from abnormal bone marrow cellsand requires prompt diagnosis for effective treatment and positive patient prognosis. Traditional diagnostic methods (e.g., microscopy, flow cytometry, and biopsy) pose challenges in both accuracy and time, demanding an inquisition on the development and use of deep learning (DL) models, such as convolutional neural networks (CNN), which could allow for a faster and more exact diagnosis. Using specific, objective criteria, DL might hold promise as a tool for physicians to diagnose leukemia. The purpose of this review was to report the relevant available published literature on using DL to diagnose leukemia. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles published between 2010 and 2023 were searched using Embase, Ovid MEDLINE, and Web of Science, searching the terms "leukemia" AND "deep learning" or "artificial neural network" OR "neural network" AND "diagnosis" OR "detection." After screening retrieved articles using pre-determined eligibility criteria, 20 articles were included in the final review and reported chronologically due to the nascent nature of the phenomenon.The initial studies laid the groundwork for subsequent innovations, illustrating the transition from specialized methods to more generalized approaches capitalizing on DL technologies for leukemia detection. This summary of recent DL models revealed a paradigm shift towardintegrated architectures, resulting in notable enhancements in accuracy and efficiency.The continuous refinement of models and techniques, coupled with an emphasis on simplicity and efficiency, positions DL as a promising tool for leukemia detection. With the help of these neural networks, leukemia detection could be hastened, allowing for an improved long-term outlook and prognosis. Further research is warranted using real-life scenarios to confirm the suggested transformative effects DL models could have on leukemia diagnosis.

Age- and sex-related changes in vertebral trabecular bone architecture in Neolithic and Mediaeval populations from Poland.

This paper investigates trabecular bone ontogenetic changes in two different Polish populations, one prehistoric and the other historical. The studied populations are from the Brześć Kujawski region in Kujawy (north-central Poland), one from the Neolithic Period (4500-4000 BC) and one from the Middle Ages (twelfth-sixteenth centuries AD), in total 62 vertebral specimens (32 males, 30 females). Eight morphometric parameters acquired from microCT scan images were analysed. Two-way ANOVA after Box-Cox transformation and multifactorial regression model were calculated. A significant decrease in percentage bone volume fraction (BV/TV; [%]) with age at death was observed in the studied sample; Tb.N (trabecular number) was also significantly decreased with age; trabecular separation (Tb.Sp) increased with advancing age; connectivity density (Conn.D) was negatively correlated with biological age and higher in the Neolithic population. These data are found to be compatible with data from the current biomedical literature, while no loss of horizontal trabeculae was recorded as would be expected based on modern osteoporosis.

On the Vector Model of Transformation of the Kinetic Energy Operator of Multiparticle Systems to the Description of Internal Motions

On the Vector Model of Transformation of the Kinetic Energy Operator of Multiparticle Systems to the Description of Internal Motions

Shadow Detection in Remote Sensing Images Based on Spectral Radiance Separability Enhancement.

Shadow detection is a basic task of remote sensing image analysis, but it is often seriously disturbed by vegetation, water bodies, and black objects. It is observed that vegetation and dark objects often show a dark look in visible bands but brighter in the near-infrared (NIR), and is also noticed that the reflection of inland water bodies in the green band is stronger than that in the blue band. Taking advantage of these physical properties and combining them with the bluish and dark appearance of shadows, we propose a simple but effective shadow detection method for multispectral remote sensing images. These physical properties are used to create transformation models that suppress features such as vegetation, water bodies, etc., but at the same time enhance shadows. Then, we transform the shadow representation into a color space to generate candidate shadows using dominant color components. To separate shadows from the others, we propose two indexes, the normalized Color Difference Composite Index (CDCI) and Color Purity Index (CPI), and fuse them to achieve shadows and their confidence. The experimental results indicate that the proposed method can effectively detect the shadows in multispectral images and outperforms the state-of-the-art approaches.

A Developed Graphical User Interface-Based on Different Generative Pre-trained Transformers Models

Objective: The article investigates the integration of advanced Generative Pretrained Transformers (GPT) models into a user-friendly Graphical User Interface (GUI). The primary objective of this work is to simplify access to complex Natural Language Processing (NLP) tasks for a diverse range of users, including those with limited technical background. Method: The development process of the GUI was comprehensive and systematic: Needs Assessment: This stage involved understanding the requirements and expectations of potential users to ensure the GUI effectively addresses their needs. Preliminary Design and Development: The initial designs were created and developed into a functional GUI, emphasizing the integration of features supporting various NLP tasks like text summarization, translation, and question-answering. Iterative Refinement: Continuous improvements were made based on user feedback, focusing on enhancing user experience, ease of navigation, and customization capabilities. Results: The developed GUI successfully integrated GPT models, including GPT-4 Turbo and GPT-3.5, resulting in an intuitive and adaptable interface. It demonstrated efficiency in performing various NLP tasks, thereby making these advanced language processing tools accessible to a broader audience. The GUI's design, emphasizing user-friendliness and adaptability, was particularly noted for its ability to cater to both technical and non-technical users. Conclusion: In conclusion, the article illustrates the significant impact of combining advanced GPT models with a Graphical User Interface to democratize the use of NLP tools. This integration not only makes complex language processing more accessible but also marks a pivotal step in the inclusive application of AI technology across various domains. The successful implementation of the GUI highlights the potential of AI in enhancing user interaction and broadening the scope of technology usage in everyday tasks.

Digital transformation as a catalyst for business model innovation: A critical review of impact and implementation strategies

In today's fast-paced and highly competitive business environment, digital transformation has emerged as a crucial driver of organizational success. This paper presents a comprehensive review of the intersection between digital transformation and business model innovation, examining the impact and implementation strategies associated with this transformative process. The study begins by elucidating the concept of digital transformation and its significance in reshaping traditional business models. It explores how advancements in digital technologies, such as artificial intelligence, big data analytics, and the Internet of Things, have revolutionized various aspects of business operations, including customer engagement, operational efficiency, and revenue generation. Furthermore, the paper critically evaluates the impact of digital transformation on business model innovation, emphasizing the need for organizations to adapt and evolve in response to changing market dynamics and consumer preferences. Through a systematic analysis of relevant literature and case studies, the study highlights the key drivers, challenges, and opportunities inherent in leveraging digital technologies to drive business model innovation. Moreover, the paper examines different implementation strategies adopted by organizations to effectively integrate digital transformation initiatives into their existing business models. It discusses the importance of strategic planning, organizational culture, and leadership commitment in driving successful digital transformation initiatives. Overall, this critical review contributes to a deeper understanding of the role of digital transformation as a catalyst for business model innovation. It provides valuable insights for practitioners, policymakers, and researchers seeking to navigate the complex landscape of digital disruption and capitalize on emerging opportunities for organizational growth and competitiveness.

Comparative analysis and implementation of a transformative business and supply chain model for the FMCG sector in Africa and the USA

This paper conducts a comparative analysis of business and supply chain models in the Fast Moving Consumer Goods (FMCG) sector between Africa and the USA, aiming to identify transformative strategies for enhancing efficiency, sustainability, and market penetration. The FMCG sector serves as a critical component of both economies, contributing significantly to GDP and employment. However, stark differences in infrastructure, consumer behavior, and regulatory environments between Africa and the USA necessitate tailored approaches to business and supply chain management. Drawing on empirical data and case studies, this study examines key elements of successful FMCG models in each region, including distribution networks, inventory management, technology adoption, and market dynamics. It explores how factors such as urbanization, income levels, and cultural preferences influence consumer demand and supply chain strategies. Moreover, the analysis delves into the impact of emerging trends such as e-commerce, sustainability, and digitalization on FMCG operations in both regions. The findings reveal opportunities for mutual learning and adaptation between Africa and the USA. While the USA boasts mature infrastructure and advanced technological capabilities, Africa offers potential for innovative solutions tailored to its unique challenges and opportunities. Through cross-regional knowledge exchange and strategic partnerships, FMCG companies can develop hybrid models that combine the best practices from both regions to drive growth, reduce costs, and enhance resilience. The study concludes by proposing actionable recommendations for FMCG companies seeking to implement transformative business and supply chain models in Africa and the USA, emphasizing the importance of flexibility, agility, and stakeholder collaboration in navigating diverse market landscapes and driving sustainable growth.

Bengali fake reviews: A benchmark dataset and detection system

The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.

Imperative Strategy of Digital Transformation: A Case Study of Surabaya City's Digital ID Card Identity

Digitalization is an innovation based on technological advances that can make governance more effective and efficient. This makes human activities carry out digital transformation in both the public and private sectors. One of the implementations of digital transformation in the public sector is the implementation of Digital ID cards. Digital transformation in the context of digital ID cards must be balanced with the readiness of both implementers and the community as policy actors. This research identifies digital transformation in the context of Digital KTP in Surabaya City using the imperative strategy theory of digital transformation model Verhoef which has 4 indicators, namely: Digital Resources, Organizational Structure, Improvement Strategy, and Measures and Goals. Data is obtained through analysis of observation results, documentation, and interviews with implementing agencies and the community. The results of the study found that there was a 25% target non-achievement from the Ministry of Home Affairs of the Republic of Indonesia for the activation of Digital KTP in Surabaya, this was due to a less massive improvement strategy and a lack of public awareness regarding this innovation. This study concludes that there are still indicators that have obstacles such as digital resources, which in its implementation there are still obstacles in the IKD application and growth strategies that still need strategies to reach the elderly and add features to increase the value of innovation to increase public awareness of digital transformation.

The Digital Transformation Model for Innovative Marketing Maturity in the Oil Companies

Purpose: The manufacturing industry has been at the cutting-edge of technology advancements, which has Objectives: The objective of this research is to examine digital transformation within the oil and gas industry, focusing on the transition to renewable energy sources and the role of innovative technologies. It aims to explore how digital technology transforms operational strategies and the importance of data utilization, brand recognition, and ethical data practices in this transformation process. Methods: This study employs a qualitative investigation methodology to delve into digital transformation in the oil and gas sector. Through interviews with industry decision-makers, the research gathers insights into the challenges and opportunities faced by firms adopting digital solutions and strategies. Results: The findings of the research highlight the transformative nature of digital technology within the oil and gas industry. They underscore the significance of managing internal and external boundaries during the transformation process. Key factors such as data utilization, brand recognition, and ethical data practices emerge as crucial for successful digital transformation. The study provides empirically grounded directions for developing frameworks to guide digital transformation practices in the industry. Conclusion: In conclusion, this research contributes to the understanding of digital transformation in the oil sector. By shedding light on the challenges and opportunities inherent in adopting digital solutions, the study aims to facilitate sustainable development and enhance innovation potential in the energy landscape. The findings offer valuable insights for firms navigating the complexities of digital transformation and pave the way for future research in this area.

Innovation Transformation and Industry-Education Integration: A Study on the Integration Path of Guangxi Zhuang’s Intangible Cultural Heritage and Master of Arts Education in China

This study focuses on the master of arts education in higher education institutions in Guangxi Zhuang Autonomous Region of China, explores the path of integrating Guangxi Zhuang’s intangible cultural heritage with the teaching of master of arts, and puts forward the teaching mode of “thinking guidance-autonomous judgement-program construction.” A theoretical model of innovative transformation of intangible cultural heritage is also summarized. Through the development of this study, it is expected to further enrich the practical teaching mechanism of master of arts education in Chinese universities and form a master of arts teaching model with strong local cultural characteristics. At the same time, the teaching reform based on the integration of Guangxi Zhuang’s intangible cultural heritage and master of arts education also has strong practical significance for promoting the inheritance and innovation of Chinese intangible cultural heritage, promoting the development of cultural and creative industries, and serving the economic and social development of Guangxi.

The phases of self in transformative experiences: Insights from astronauts, econauts and psychonauts

In the transformation age, individuals pursue experiences that offer awe-inducing moments and long-lasting transformation. Grounded in a psychological and phenomenological approach, this research aims to map out the holistic process a person undergoes in transformative tourism experiences by analysing some of the world's most transformational settings, namely, outer space, natural environments, and psychedelic journeys. The experiences of astronauts, econauts, and psychonauts are explored and synthesised into a multi-phasic transformative experience process model from initiation to integration - through the stages of the varying self. This study contributes novel theoretical and practical insights to tourism on how to design transformative offers and guide experiences to integration for a long-lasting positive impact on an individual's life.

Research on the mechanism of chlorine corrosion resistance of graphite tailings modified recycled coarse aggregate concrete: Corrosion product transformation and multi-scale mathematical characterization model

This research investigated the impact of substituting sand with graphite tailings (GT) on the chloride ion erosion resistance of recycled aggregate concrete (RAC). The role of GT is to significantly inhibit the penetration of chloride ions into the interior and slow down the corrosion process. However, the durability of graphite tailings recycled aggregate concrete (GTRAC) still depends on the replacement rate of recycled aggregate. At the same time, a multi-scale dynamic model of physical adsorption-chemical binding-free transport and erosion of chloride ions in GTRAC were established based on unsteady viscous fluid force and molecular dynamics.