Transformation Framework Research Articles

Exploring the Lived Experiences of ELT Teachers: Nepali Teachers' Voices

This study explores the experiences of Nepali English language teachers teaching English in Nepal, with a special focus on the challenges and transformations they face as learners and educators. Drawing on interviews with four high school teachers from institutional schools in the Kathmandu Valley, the research employs a transformational theoretical framework to transcribe, code, and thematically analyze data. The findings highlight persistent traditional teacher roles and reveal critical challenges, including discriminatory policies, multilingual classroom dynamics, and inadequate pedagogical training. The study also emphasizes the shifting status of English from a foreign to a second language and the influence of pedagogical advancements and technological innovations, particularly during the COVID-19 pandemic. These changes have redefined teachers' roles, transforming them from knowledge dispensers to guides, mentors, and facilitators.

보행공간 경계요소에 있어서 디지털 매체를 통한 정서적 변용에 관한 연구

The boundaries of modern pedestrian spaces have evolved into multi-layered environments that integrate physical demarcation, psychological stability, information provision, and aesthetic experiences. However, these changes often fail to fully address pedestrians’ emotional needs. Boundary spaces combined with digital media tend to provide excessive information and focus on commercial elements, making it difficult for pedestrians to experience consistent emotional stability while limiting opportunities for social bonding. This study aims to redefine boundary elements beyond their physical functions by emphasizing their role in enhancing emotional stability and social connectedness. Through the integration of digital media, it explores the possibility of offering pedestrians meaningful and emotionally supportive experiences. To achieve this, the study examines the fundamental meaning of boundary elements, categorizes them by their attributes, and analyzes their physical and psychological impacts. Additionally, it investigates case studies on digital media applications to identify how boundary elements can create emotionally engaging spatial experiences. The results demonstrate that when physical boundaries are transformed into media-based elements, pedestrians can achieve psychological stability and enriched emotional experiences. This transformation supports public, artistic, and social interactions, emphasizing the potential of boundary elements as multi-dimensional mediators. The study highlights the necessity of applying this transformation framework in real-world pedestrian environments and suggests future empirical research for validation.

Society 5.0 For lean-driven sustainable construction: A conceptual framework

Nowadays, the construction industry is significantly driven by the objective of achieving sustainable development. It creates a necessity to implement lean principles within the construction industry, as lean construction greatly contributes to sustainability. Despite lean construction's potential to achieve sustainable development, numerous barriers hinder its implementation. Consequently, the concept of Society 5.0 can be utilised as a transformative framework that integrates advanced technologies with a human-centric approach to overcome these issues. The aim of this research is to investigate how lean construction can be utilised along with society 5.0 to drive sustainable development in construction. To address this objective, a comprehensive literature review was conducted, and the content was analysed to align with the research objective. The findings of this research indicated that lean construction principles can be an effective application in any part of the construction process to prioritise sustainable outcomes. Moreover, the research revealed 95 barriers for lean integrated sustainable construction, categorised under political/government barriers, economic/financial barriers, technical barriers, social barriers, and other barriers. The research highlighted the necessity to integrate lean construction and Society 5.0 to overcome the barriers and drive sustainable development in construction. These research findings are paving the way towards conducting further research on the area of Society 5.0-enabled lean construction to drive sustainable development in construction.

A Comprehensive and Innovative Environmental PSR Model for Biodiversity Priority Conservation Areas

Biodiversity is essential for ecosystem resilience and human well-being, yet it faces accelerating threats from habitat loss, climate change, and human activities. Conservation models often inadequately address the intertwined ecological and socio-economic drivers of biodiversity loss, leaving a gap between theoretical frameworks and real-world implementation. This study introduces an advanced Pressure-State-Response (PSR) model, developed through extensive fieldwork and leveraging Geographic Information Systems (GIS) and remote sensing technologies. The model integrates ecological indicators with socio-economic factors, including stakeholder engagement, education, and local economic conditions, creating a dynamic, context-specific approach to conservation. By adopting a Multi-Criteria Decision Analysis (MCDA) framework, specifically the Analytic Hierarchy Process (AHP), the enhanced PSR model prioritizes biodiversity hotspots based on ecological urgency and socio-economic resilience. It overcomes limitations of traditional models by incorporating customizable criteria and fostering equitable conservation strategies. The approach optimizes resource allocation, ensuring interventions target areas of highest biodiversity value while balancing local development needs. This study provides a replicable and adaptable methodology for conservation planning, addressing 21st-century challenges of biodiversity loss and socio-ecological complexity. By aligning conservation priorities with sustainable development goals, the model advances a transformative framework that bridges science, policy, and practice, offering global applicability for safeguarding biodiversity and ecosystem services.

HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.

Structural variation (SV) is an important component of the diversity of the human genome. Many studies have shown that SV has a significant impact on human disease and is strongly associated with the development of cancer. In recent years, the Hi-C sequencing technique has been shown to be useful for detecting large-scale SVs, and several methods have been proposed for identifying SVs from Hi-C data. However, due to the complexity of the 3D genome structure, accurate identifying SVs from the Hi-C contact matrix remains a challenging task. Here, we present HiSVision, a method for identifying large-scale SVs from Hi-C data using a detection transformer framework. Inspired by object detection network, we transform the Hi-C contact matrix into images, then identify candidate SV regions on the image by detection transformer, and finally filter SVs based on features around the breakpoints. Experimental results show that HiSVision outperforms existing methods in terms of precision and F1 score on cancer cell lines and simulated datasets. The source code and data are available from https://github.com/dcy99/HiSVision .

“I Was Trusted for Once”: Imagining More Humane Income Supports Through the Ontario Basic Income Pilot

Current social assistance programmes in Canada and beyond have been criticised for normalising the dehumanisation of recipients through policy design and implementation. In this article we look at how exposure to a form of basic income through the Ontario Basic Income Pilot (OBIP) allowed recipients to imagine a different kind of support. We report on the findings from a study in OBIP from Hamilton, Canada, thematically analysing a subset of interviews with forty OBIP participants. We find that the higher levels of support, fewer behavioural conditions compared to social assistance, and reduced surveillance under OBIP-nurtured feelings of trust and confidence. Participants felt rehumanised as full members of society in reciprocal relationships with community and government that had been strained under previous forms of social assistance. We consider how the OBIP model provided a transformative framework for participants’ expectations for income support programmes and discuss implications for future research.

A DURAÇÃO RAZOÁVEL DOS PROCESSOS CIVIS NO BRASIL: INOVAÇÕES DO CPC/2015 E DESAFIOS PARA A CELERIDADE E SEGURANÇA JURÍDICA

The study examines the evolution of the 2015 Brazilian Code of Civil Procedure (CPC/15), emphasizing its aim to balance efficiency and legal certainty within the judicial system. The research combines normative analysis, doctrinal contributions, and case law to explore the transformations and challenges introduced by the new code. The CPC/15 upholds traditional principles, such as the separation between cognition and enforcement, while incorporating innovations like provisional enforcement of judgments, interlocutory decisions as enforceable titles, and the use of consensual methods for conflict resolution. These changes seek to meet societal demands for efficiency and justice but require caution to safeguard fundamental rights. The inclusion of mediators and conciliators represents progress in peaceful dispute resolution, while strict criteria ensure protection against abuse in enforcement. By blending tradition with innovation, CPC/15 emerges as a transformative framework, modernizing civil procedure to address the dynamic needs of a constantly evolving society.

Style Transfer of Chinese Wuhu Iron Paintings Using Hierarchical Visual Transformer.

Within the domain of traditional art, Chinese Wuhu Iron Painting distinguishes itself through its distinctive craftsmanship, aesthetic expressiveness, and choice of materials, presenting a formidable challenge in the arena of stylistic transformation. This paper introduces an innovative Hierarchical Visual Transformer (HVT) framework aimed at achieving effectiveness and precision in the style transfer of Wuhu Iron Paintings. The study begins with an in-depth analysis of the artistic style of Wuhu Iron Paintings, extracting key stylistic elements that meet technical requirements for style conversion. Furthermore, in response to the unique artistic characteristics of Wuhu Iron Paintings, this research constructs a multi-layered network structure capable of effectively capturing and parsing style and content features. Building on this, we have designed an Efficient Local Attention Decoder (ELA-Decoder) that adaptively decodes the style and content features through correlation, significantly enhancing the length dependency of local and global information. Additionally, this paper proposes a Content Correction Module (CCM) to eliminate redundant features generated during the style transfer process, further optimizing the migration results. In light of the scarcity of existing datasets for Wuhu Iron Paintings, this study also collects and constructs a dedicated dataset for the style transfer of Wuhu Iron Paintings. Our method achieves optimal performance in terms of loss metrics, with a reduction of at least 4% in style loss and 5% in content loss compared to other advanced methods. Moreover, expert evaluations were conducted to validate the effectiveness of our approach, and the results show that our method received the highest number of votes, further demonstrating its superiority.

Revolutionizing Neuroimmunology: Unraveling Immune Dynamics and Therapeutic Innovations in CNS Disorders.

Neuroimmunology is reshaping the understanding of the central nervous system (CNS), revealing it as an active immune organ rather than an isolated structure. This review delves into the unprecedented discoveries transforming the field, including the emerging roles of microglia, astrocytes, and the blood-brain barrier (BBB) in orchestrating neuroimmune dynamics. Highlighting their dual roles in both repair and disease progression, we uncover how these elements contribute to the intricate pathophysiology of neurodegenerative diseases, cerebrovascular conditions, and CNS tumors. Novel insights into microglial priming, astrocytic cytokine networks, and meningeal lymphatics challenge the conventional paradigms of immune privilege, offering fresh perspectives on disease mechanisms. This work introduces groundbreaking therapeutic innovations, from precision immunotherapies to the controlled modulation of the BBB using nanotechnology and focused ultrasound. Moreover, we explore the fusion of immune modulation with neuromodulatory technologies, underscoring new frontiers for personalized medicine in previously intractable diseases. By synthesizing these advancements, we propose a transformative framework that integrates cutting-edge research with clinical translation, charting a bold path toward redefining CNS disease management in the era of precision neuroimmunology.

Green Pan-Africanism: Normative power and the making of a regional sustainability order

Abstract This article examines the role of normative power in shaping the global sustainability order. It challenges the prevailing focus on hegemonic leadership and norm diffusion from dominant states, arguing that less powerful states have contributed significantly to the global order by creating regional initiatives tailored to their unique contexts. The article adopts an alternative theoretical framework of norm-governed change, comprising norm-building, institutionalisation, and transformation. Using an illustrative case study of Africa’s regional economic institutions, it employs process-tracing and archival analysis of key policy documents. The study demonstrates how African states have proactively embedded environmental norms within their regional initiatives, while contributing to the global sustainability agenda, exemplifying a form of normative power referred to as ‘Green Pan-Africanism’. This approach broadens the understanding of global sustainability governance, positioning less economically powerful actors as active participants in world-making. The findings highlight the critical role of normative power in advancing global sustainability governance, particularly in addressing complex global challenges such as climate change.

Intelligent Enterprise Integration: An AI Framework for Dynamic Data Transformation and Process Optimization

Intelligent Enterprise Integration: An AI Framework for Dynamic Data Transformation and Process Optimization

Porosity identification using residual PPTransformer network

Precisely estimating the carbonate’s porosity is essential for subsurface reservoir characterization. However, conventional methods for obtaining porosity using either core measurements or logging interpretation are expensive and inefficient. Considering the sequence data feature of logging curves and the booming development of intelligent networks in geoscience, this study proposes a reliable and low-cost intelligent Porosity Prediction Transformer (PPTransformer) framework for reservoir porosity prediction using logging curves as inputs. PPTransformer network not only extracts global features through convolutional layers but also captures local features using Encoders and self-attention mechanisms. This proposed network is a data-driven supervised learning framework with a superior accuracy and robustness. The testing results demonstrate that compared to the Transformer network, Long Short-Term time series network, and support vector machine method, the PPTransformer framework exhibits the highest average correlation coefficient and determination coefficient indicators and the lowest root mean square error and absolute error indicators. Moreover, adding stratigraphic lithology as geological constraints to the PPTransformer framework further improves the prediction performance. This indicates that geological constraints will enhance network performance.

TransSMPL: Efficient Human Pose Estimation with Pruned and Quantized Transformer Networks

Existing Transformers for 3D human pose and shape estimation models often struggle with computational complexity, particularly when handling high-resolution feature maps. These challenges limit their ability to efficiently utilize fine-grained features, leading to suboptimal performance in accurate body reconstruction. In this work, we propose TransSMPL, a novel Transformer framework built upon the SMPL model, specifically designed to address the challenges of computational complexity and inefficient utilization of high-resolution feature maps in 3D human pose and shape estimation. By replacing HRNet with MobileNetV3 for lightweight feature extraction, applying pruning and quantization techniques, and incorporating an early exit mechanism, TransSMPL significantly reduces both computational cost and memory usage. TransSMPL introduces two key innovations: (1) a multi-scale attention mechanism, reduced from four scales to two, allowing for more efficient global and local feature integration, and (2) a confidence-based early exit strategy, which enables the model to halt further computations when high-confidence predictions are achieved, further enhancing efficiency. Extensive pruning and dynamic quantization are also applied to reduce the model size while maintaining competitive performance. Quantitative and qualitative experiments on the Human3.6M dataset demonstrate the efficacy of TransSMPL. Our model achieves an MPJPE (Mean Per Joint Position Error) of 48.5 mm, reducing the model size by over 16% compared to existing methods while maintaining a similar level of accuracy.

From Traditional to Digital: Transforming Local Administrative Organization Workflows in Thailand Through Social Listening Tools

Digital transformation offers transformative potential for public service delivery, yet many local administrative organizations (LAOs) in Thailand struggle with integrating digital tools effectively into their workflows. This study investigates the integration of social listening tools (SLTs) to enhance the efficiency and responsiveness of public service delivery in Thailand’s LAOs. The primary goal is to redesign traditional, manual workflows through the development of a digital-by-design framework, addressing inefficiencies in public engagement and service provision. Employing a mixed-method approach, this research combines interviews and focus groups with municipal staff from four municipalities in Northeast Thailand to identify challenges and co-create solutions. The redesigned workflow integrates digital practices into existing organizational structures and achieves a significant 282% improvement in efficiency, measured in transactions per manpower-hour. Additionally, the new process enhances operational speed, responsiveness, and public engagement. To ensure sustainability, this study recommends a phased implementation strategy and consistent staff training. This research contributes to the public administration literature by providing a practical, scalable framework for digital transformation in local governance. It underscores the potential of SLTs to modernize public sector workflows, enabling more dynamic, responsive, and citizen-centric interactions between LAOs and the communities they serve.

RT-DETR-Tea: A Multi-Species Tea Bud Detection Model for Unstructured Environments

Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production.

Real-Time Data Transformation in Connected Vehicles: A Systematic Analysis of Architectures, Methods, and Applications

The emerging landscape of connected vehicles has introduced unprecedented challenges in processing and utilizing vast streams of real-time data. This article presents a comprehensive framework for real-time data transformation in connected vehicle environments, addressing the critical aspects of data processing architectures, analytical methodologies, and practical implementations. The article examines the integration of edge computing, cloud-based solutions, and hybrid architectures to optimize data transformation workflows while minimizing latency and bandwidth constraints. The article analyzes various data types generated by connected vehicles, including telemetry, diagnostics, and user-generated content, and explores their transformation requirements for enabling advanced functionalities such as predictive maintenance, traffic optimization, and enhanced driver assistance systems. Through multiple industry case studies, the article demonstrates the practical application of proposed frameworks and their impact on operational efficiency, safety metrics, and overall vehicle performance. Our findings highlight the significance of balanced architectural choices, the role of machine learning in data transformation processes, and the importance of addressing security and scalability challenges. This article contributes to the growing body of knowledge in connected vehicle technologies while providing practical insights for automotive industry practitioners implementing real-time data transformation solutions.

A transformative theory-based framework for intermediate phase pre-service language teachers’ application of transformative assessment practices

Limited studies illustrate the significance of a transformative perspective on pre-service teachers’ assessment practices during their study in higher education institutions (HEIs). As such, the need to prepare pre-service teachers for the transformative realities of the workplace has been widely acknowledged as a crucial element of contemporary teacher education. Therefore, this conceptual article aims to provide a transformative theory-based elucidation on assessment practices and their application by intermediate-phase pre-service language teachers (IPPLTs). Having applied transformative learning theory (TLT) and a narrative literature review (NLR), this article proposes a transformative theory-based framework (TTbF) that HEIs can apply to equip IPPLTs with transformative assessment practices (TAPs) during their training and teaching practice sessions. The findings reveal that transformability, transformative-targeted interventions, and adaptive capacity can be valuable takeaways for IPPLTs’ professional development in applying TAPs. The study recommends that IPPLTs consider applying the TTbF to strengthen their ability to implement TAPs in schools.

Real-Time Data Analytics in Customer Experience Management: A Framework for Digital Transformation and Business Intelligence

Real-time data analytics has emerged as a transformative force in customer experience management, enabling organizations to process and respond to customer interactions instantaneously. This article presents a comprehensive framework for implementing real-time analytics in customer-centric business operations, examining both theoretical foundations and practical applications across multiple industries. Through systematic analysis of implementation strategies, technical architectures, and organizational challenges, this article illuminates the critical role of real-time analytics in enabling personalized customer experiences, dynamic pricing optimization, and predictive service delivery. The article synthesizes current industry practices while identifying key success factors and potential barriers in real-time analytics adoption. Findings indicate that organizations leveraging real-time analytics capabilities demonstrate enhanced customer satisfaction, improved operational efficiency, and strengthened competitive positioning. This article contributes to both theoretical understanding and practical implementation of real-time analytics in customer experience management, providing actionable insights for practitioners while identifying promising directions for future research. The article concludes with a structured framework for organizations seeking to implement or enhance their real-time analytics capabilities in pursuit of superior customer experience.

MULTILEVEL STRATEGIC PLANNING FOR SUSTAINABLE DEVELOPMENT IN THE GLOBAL ECONOMY

The article explores multilayer strategic planning challenges for sustainable development within the framework of continuous global economic transformation. The increasing focus on environmental deterioration, disparities in social status, and unstable economies presents obstacles for national governments as well as multinational enterprises seeking to adopt sustainable development strategies. Among the many challenges of globalization, this article explores key aspects of multi-level strategic planning at the global, national and local levels. The study aimed to examine diverse methods of multi-level strategic planning within the framework of globalization. Consequently, a multi-level strategic planning model was constructed that considers the impact of environmental, social, and economic factors in addition to the well-known ones to accomplish sustainable development objectives. The paper highlights the value of the systemic approach by demonstrating how efficient multi-level planning supports the financial stability of economic actors in the context of globalization. The paper also offers insightful suggestions for businesses, governments, and international organizations.

Transforming public services

Abstract The public service works to provide frontline and vital services to meet the needs of the people of Ireland. Working on behalf of the government in a diverse range of organisations, public servants play an established role in the economic and social life of this country. The public service has grown and evolved to take advantage of opportunities, address new challenges, and meet the changing needs and expectations of the people of Ireland. Since 2011, the Department of Public Expenditure, NDP Delivery and Reform, has led a dedicated programme for reform and transformation, used its role and functions to enhance governance, build capacity and delivery effectively, working with the full range of public service bodies to build a more unified and agile public service. In 2023, the department published Better Public Services, a strategy that lays out the current framework and programme of transformation up to 2030, which is delivering tangible outcomes in improved public services, living standards and infrastructure in Ireland.