Framework Potential Research Articles

A Framework for Decentralized Identity and Credential Management Leveraging Blockchain Technology

Abstract: As internet technology rapidly advances and the infrastructure continues to evolve, web applications have become an integral part of our daily lives, with electronic identities (e-identities) playing a crucial role. Today, e-identities are vital for verifying personal identity in numerous online activities, and as our dependence on them grows, so does the concern for their security. This increasing importance of e-identity security raises various challenges, highlighting the need for robust solutions. In response to these challenges, this article introduces a novel solution: a blockchain-based decentralized identity and credential management framework. This approach not only enhances security but also offers greater control and flexibility for users. The article delves into the intricacies of the framework, detailing its design and operational mechanism. Additionally, it explores a range of practical applications, demonstrating the framework's versatility and effectiveness through diverse case studies. This comprehensive examination underscores the framework's potential to revolutionize e-identity management and addresses the pressing need for secure and efficient online identity verification in our increasingly digital world.

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization Using Natural Language Expressions.

This study addresses challenges in surgical education, particularly in neuroendoscopy, where the demand for optimized workflow conflicts with the need for trainees' active participation in surgeries. To overcome these challenges, we propose a framework that accurately identifies anatomical structures within images guided by language descriptions, facilitating authentic and interactive learning experiences in neuroendoscopy. Utilizing the encoder-decoder architecture of a conventional transformer, our framework processes multimodal inputs (images and language descriptions) to identify and localize features in neuroendoscopic images. We curate a dataset from recorded endoscopic third ventriculostomy (ETV) procedures for training and evaluation. Utilizing evaluation metrics, including "R@n," "IoU= θ," "mIoU," and top-1 accuracy, we systematically benchmark our framework against state-of-the-art methodologies. The framework demonstrates excellent generalization, surpassing the compared methods with 93.67 % accuracy and 76.08 % mIoU on unseen data. It also exhibits better computational speed compared with other methods. Qualitative results affirms the framework's effectiveness in precise localization of referred anatomical features within neuroendoscopic images. The framework's adeptness at localizing anatomical features using language descriptions positions it as a valuable tool for integration into future interactive clinical learning systems, enhancing surgical training in neuroendoscopy. The exemplary performance reinforces the framework's potential in enhancing surgical education, leading to improved skills and outcomes for trainees in neuroendoscopy.

Mission Impact Levers (MIL) Framework: A Value Lens Approach to Leadership and Resource Management

This study evaluates the effectiveness of the Mission Impact Levers (MIL) Framework in aligning institutional actions and resource allocations with core missions within higher education institutions (HEIs). By employing a dataset that includes retention and graduation status, and integrating machine learning techniques, particularly the Random Forest model, the research quantitatively analyzes the framework’s impact on key institutional metrics. The MIL Framework is designed to foster strategic coherence and long-term sustainability by ensuring mission-centric decision-making. Preliminary findings indicate that institutions implementing the MIL Framework experience enhanced institutional effectiveness, demonstrating notable improvements in student retention and graduation rates. This data-driven approach not only confirms the framework's potential to significantly benefit HEIs in a competitive educational environment but also enhances strategic decision-making. This validation study aims to equip institutional leaders with robust tools to foster resilience and success, thereby positioning their institutions to thrive amidst increasing competition and dynamic educational demands.

Linkage Disequilibrium-Informed Deep Learning Framework to Identify Genetic Loci for Alzheimer's Disease Using Whole Genome Sequencing Data.

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD). First, 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving interactions between SNPs in performing the dimensionality reduction, which can potentially introduce bias or lead to information loss. The Deep-Block approach identified both known and novel genetic variation, enhancing our understanding of the genetic architecture of and demonstrating the framework's potential for application in large-scale sequencing studies.

Association between cardiovascular health assessed by life's essential 8 and hyperuricemia in U.S. adults: the NHANES 2009-2020.

This study presented the new Life's Essential 8 (LE8) framework for examining cardiovascular health (CVH) to analyze the potential relationship between the latter and hyperuricemia (HUA) in the U.S. population. Data on individuals aged at least 20 years were collected from the National Health and Nutrition Examination Survey (NHANES) 2009-2020. Smoothed curve fitting and multivariate logistic regression analyses were then performed on a sample of 25,681 adults to explore the association between LE8 and HUA. A sensitivity analysis was conducted to examine the robustness of the research findings. The study found a strong negative association between LE8 and HUA, with an odds ratio (OR) of 0.71 and a 95% confidence interval (CI) from 0.69 to 0.73 after adjusting for multiple confounding factors. The sensitivity analysis further validated the robustness of this association. This analysis consistently showed negative associations across different genders, ages, races, and education levels (p < 0.05), but there were no significant relationships with marital status. The association between uric acid levels and LE8 displayed an inverted L-shaped curve, with an inflection point around 41.43. The findings indicate a strong negative relationship between LE8 and HUA among the U.S. population, suggesting that higher scores on the LE8, which assesses CVH, were associated with reduced uric acid levels. The consistent negative association underscores the LE8 framework's potential as a valuable tool for understanding and managing HUA in CVH.

FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting.

This study presents the Fast Fruit 3D Detector (FF3D), a novel framework that contains a 3D neural network for fruit detection and an anisotropic Gaussian-based next-best view estimator. The proposed one-stage 3D detector, which utilizes an end-to-end 3D detection network, shows superior accuracy and robustness compared to traditional 2D methods. The core of the FF3D is a 3D object detection network based on a 3D convolutional neural network (3D CNN) followed by an anisotropic Gaussian-based next-best view estimation module. The innovative architecture combines point cloud feature extraction and object detection tasks, achieving accurate real-time fruit localization. The model is trained on a large-scale 3D fruit dataset and contains data collected from an apple orchard. Additionally, the proposed next-best view estimator improves accuracy and lowers the collision risk for grasping. Thorough assessments on the test set and in a simulated environment validate the efficacy of our FF3D. The experimental results show an AP of 76.3%, an AR of 92.3%, and an average Euclidean distance error of less than 6.2 mm, highlighting the framework's potential to overcome challenges in orchard environments.

Strategic project management for digital transformations in public sector education systems

This review paper presents a strategic framework for project management in digital transformations within public sector education systems. The framework emphasizes key components such as needs assessment, stakeholder engagement, project planning, change management, resource allocation, risk management, project execution, and outcome evaluation. Practical examples, including implementing a learning management system and digital inclusion initiatives, demonstrate the framework's application in real-world scenarios. The research contributes to the field by providing a comprehensive guide for decision-making and project planning, emphasizing the importance of maximizing impact and efficiency in digital transformation projects. The implications of the research findings for practitioners and policymakers are discussed, highlighting the framework's potential to drive successful digital transformation initiatives. Recommendations for future research include long-term impact assessment, change management strategies, scalability and sustainability, ethical and privacy considerations, hybrid learning environments, and teacher professional development. Keywords: Strategic Framework, Project Management, Digital Transformations.

Landscape-Scale Biodiversity Impacts Analysis of Côte d'Ivoire's Cocoa Cultivation along Export Supply Chains.

Agricultural land use for export commodities leads to significant biodiversity impacts. A spatially detailed assessment of these impacts is crucial for implementing effective mitigation policies. Using cocoa cultivation and exports in Côte d'Ivoire as an example, we present a novel framework that combines earth observations, enhanced landscape-scale biodiversity models, and subnational export supply chain data sets to track the tele-connected potential biodiversity impacts of export groups and importing countries. We found that cocoa cultivation accounts for ∼44% of the biodiversity impacts in Côte d'Ivoire's cocoa cultivation areas, with >90% attributable to cocoa exports. The top 10 importing countries account for ∼84% of these impacts. Our method offers improved spatial detail compared to the existing approaches, facilitating the identification of biodiversity impact hotspots. Additionally, the biodiversity impacts of agroforestry cocoa are not always lower compared to full-sun cocoa, especially when agroforestry systems are established in regions of high biodiversity importance. Our transferable framework provides a comprehensive understanding of biodiversity footprint and promotes informed decision-making for sustainable agricultural production, processing, and trade. Our framework's application is currently constrained by the scarcity of detailed supply chain data sets; we underscore the urgent need for improved supply chain transparency to fully unlock the framework's potential.

Malware Detection Using Machine Learning

In this work, we present a flexible system that makes use of various machine learning methods to efficiently distinguish between malware and clean files while purposefully reducing false positives. In the field of cybersecurity, our strong framework is both flexible and strong, working along with different machine learning algorithms. Our study unfolds with an exploration of basic principles using the Random Model, K Nearest Neighbouring Classifier (KNN), and Logistic Regression as foundational parts, emphasizing the differentiation between malware and benign files. Extensive experiments on mediumsized datasets that include malware and clean files verify the effectiveness of our methodology. The system then goes through a painstaking scaling-up process that guarantees smooth operation with big datasets containing both malware and clean files. Our methodology is validated by analysing three important algorithms: Random Model, KNN, and Logistic Regression, each of which adds unique advantages to the malware detection system. The evaluation, which is carried out on several datasets, aims to minimize false positives while striking a compromise between precision and recall. Finally, our flexible system, implemented and evaluated on many datasets, demonstrates its efficacy in distinguishing malware from clean files. The framework's flexibility and scalability make it an invaluable tool in the everevolving field of cybersecurity, providing a sophisticated method of malware detection. The proposed algorithms emphasize the framework's potential as a supplementary tool to current cybersecurity measures while also adding to its reliability. Keywords— ML, KNN, RM, LR

A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption

The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.

Simulation of Human Movement in Zero Gravity.

In the era of expanding manned space missions, understanding the biomechanical impacts of zero gravity on human movement is pivotal. This study introduces a novel and cost-effective framework that demonstrates the application of Microsoft's Azure Kinect body tracking technology as a motion input generator for subsequent OpenSim simulations in weightlessness. Testing rotations, locomotion, coordination, and martial arts movements, we validate the results' realism under the constraints of angular and linear momentum conservation. While complex, full-body coordination tasks face limitations in a zero gravity environment, our findings suggest possible approaches to device-free exercise routines for astronauts and reveal insights into the feasibility of hand-to-hand combat in space. However, some challenges remain in distinguishing zero gravity effects in the simulations from discrepancies in the captured motion input or forward dynamics calculations, making a comprehensive validation difficult. The paper concludes by highlighting the framework's practical potential for the future of space mission planning and related research endeavors, while also providing recommendations for further refinement.

Becoming the change we want to see: Aspirations and initial progress with diversity, equity, access, and inclusion practices to create welcoming environments and center community in informal science institutions

AbstractThis study focuses on the diversity, equity, access, and inclusion (DEAI) practices of informal science institutions (ISI) that are part of a statewide grants program. Data were collected to understand how ISIs interpret and implement DEAI in thought and action in their efforts to create more welcoming spaces for members of communities that are often underrepresented or marginalized in informal learning spaces. Modeled after the Cultural Competence Learning Institute's (CCLI) Framework, survey data were collected to understand DEAI practices being used to create welcoming environments. Interview data were collected 2 years later to understand how ISIs collaborate with others to center communities in their work. Results indicated that while DEAI was considered a high priority, strategies were limited. A positive relationship was found between the number of strategies used and perceived success. ISIs' stories of collaboration focused most often on transactional relationships with organizational partners. Those working with communities directly collaborated in needs‐based or reciprocal ways. Results are interpreted in relation to the CCLI Framework's potential to provide benchmarks for both individual institutions and groups like our statewide grants program to use as comparison points for their own DEAI practice.

Psychometric properties of the TACT framework-Determining rigor in qualitative research.

The credibility of qualitative research has long been debated, with critics emphasizing the lack of rigor and the challenges of demonstrating it. In qualitative research, rigor encompasses explicit, detailed descriptions of various research stages, including problem framing, study design, data collection, analysis, and reporting. The diversity inherent in qualitative research, originating from various beliefs and paradigms, challenges establishing universal guidelines for determining its rigor. Additionally, researchers' often unrecorded thought processes in qualitative studies further complicate the assessment of research quality. To address these concerns, this article builds on the TACT framework, which was developed to teach postgraduate students and those new to qualitative research to identify and apply rigorous principles and indicators in qualitative research. The research reported in this article focuses on creating a scale designed to evaluate the psychometric properties of the TACT framework. This involves analyzing the stability of its dimensions and understanding its effectiveness as a tool for teaching and research. The study's findings indicate that the TACT framework, when assessed through the newly developed scale, exhibits stable dimensions consistent with rigorous qualitative research principles. The framework effectively guides postgraduate students and new researchers in assessing the rigor of qualitative research processes and outcomes. The application of the TACT framework and its evaluation scale reveals several insights. Firstly, it demonstrates the framework's utility in bridging the gap in pedagogical tools for teaching rigor in qualitative research methods. Secondly, it highlights the framework's potential in providing a structured approach to undertaking qualitative research, which is essential given this field's diverse methodologies and paradigms. However, the TACT framework remains a guide to enhancing rigor in qualitative research throughout all the various phases but by no means a measure of rigor. In conclusion, the TACT framework and its accompanying evaluative scale represent significant steps toward standardizing and enhancing the rigor of qualitative research, particularly for postgraduate students and early career researchers. While it does not solve all challenges associated with obtaining and demonstrating rigor in qualitative research, it provides a valuable tool for assessing and ensuring research quality, thereby addressing some of the longstanding criticisms of the quality of research obtained through qualitative methods.

Leveraging the OECD Framework to Create Policies That Enable Eco-Innovation: A Case Study of Neom Part 2

This research explores the adaptability and efficacy of the OECD eco-innovation framework in guiding sustainable urban development within emerging urban centres, mainly through Neom, Saudi Arabia. It seeks to elucidate the framework's potential in facilitating eco-innovation in urban planning, contributing to the broader discourse on sustainable urban development. Employing a qualitative research design, the study engages with a cohort of experts through structured interviews, complemented by a thematic analysis of these discussions. This approach allows for an in-depth exploration of the application and impact of the OECD eco-innovation framework on Neom’s urban planning and development strategies. The study also incorporates a systematic review of relevant documents to understand further the integration of eco-innovative practices in Neom's planning processes. The findings highlight the critical role of the OECD eco-innovation framework in driving eco-innovation within Neom’s urban development. Key insights include the importance of financial incentives, regulatory frameworks, market mechanisms, and knowledge exchange in fostering urban eco-innovation. Additionally, the research emphasises the need for multisectoral collaborations and data-driven strategies to effectively integrate eco-innovation into urban development, underscoring the transformative potential of Neom as a model for sustainable urban planning. This study contributes original insights into utilising the OECD eco-innovation framework in emerging urban developments, explicitly focusing on Neom, Saudi Arabia. It enriches the sustainable urban development literature by demonstrating the framework's adaptability and relevance, offering valuable implications for policymakers, urban planners, and researchers. The research underscores the necessity of tailoring international frameworks to local contexts, enhancing their practical applicability and impact in promoting sustainable urban ecosystems.

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

AbstractRecently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC‐Juniata‐hydroDL2) that mimics the classical Muskingum‐Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach‐scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real‐world data, the trained differentiable routing model produced more accurate long‐term routing results for both the training gage and untrained inner gages for larger subbasins (&gt;2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national‐scale hydrologic flood simulations.

Few-Shot Classification with Meta-Learning for Urban Infrastructure Monitoring Using Distributed Acoustic Sensing.

This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processing methods for DAS data and (ii) the implementation of a neural network model based on meta-learning to learn a representation of the processed data. In the context of urban infrastructure monitoring, we develop a few-shot classification framework that classifies query samples with only a limited number of support samples. The model consists of an embedding network trained on a meta dataset for feature extraction and is followed by a classifier for performing few-shot classification. This research thoroughly explores three types of data pre-processing, that is, decomposed phase, power spectral density, and frequency energy band, as inputs to the neural network. Experimental results show the efficient learning capabilities of the embedding model when working with various pre-processed data, offering a range of pre-processing options. Furthermore, the results demonstrate outstanding few-shot classification performance across a large number of event classes, highlighting the framework's potential for urban infrastructure monitoring applications.

Exploring nurses' experiences in applying AIDET framework to improve communication skills in the emergency department: A qualitative study.

Recognizing the significance of effective communication in raising emergency department service standards and preventing misinterpretation of patient needs among registered nurses is crucial for improving patient satisfaction. The utilization of the AIDET (Acknowledge, Introduce, Duration, Explanation, and Thank you) communication framework is acknowledged to enhance communication among registered nurses, patients, family members, and other healthcare professionals. This study aimed to explore Thai nurses' experiences in applying the AIDET framework for communications with patients and their relatives in the emergency department. A qualitative descriptive research design was employed. Semi-structured interviews with 15 emergency nurses were conducted between September and March 2022 in a regional hospital in Thailand. Data were analyzed using content analysis. Three themes were generated: 1) Ability to follow the AIDET framework, 2) Recognizing the value of using AIDET steps for positive communication, and 3) Establishing a foundation for successful communication. The study's findings highlighted the AIDET framework's potential to enhance communication skills between nurses, healthcare professionals, and patients. The results also emphasized the importance of providing training and mentorship to junior nurses, integrating AIDET into daily routines and institutional policies, and facilitating continuous education for registered nurses. These efforts contribute to improving care quality and aligning with patient and family needs.

Evaluating the Environmental Footprint: BPE Framework for Sustainable and Energy-Efficient Residential Buildings in India

In the context of enhancing building performance assessment, this study introduces the BPE (Building Performance Evaluation) framework and explores its application through a residential complex in India. An expert evaluation of the questionnaire is carried out to investigate the main element and obstacles to the execution of BPE. The framework, designed to scrutinize five parameters, initially assesses design aspects, including building form, orientation, and aesthetics. Findings reveal that the building design lacks efficient circulation, storage facilities, and satisfactory spatial allocation. Building energy monitoring, essential for comprehensive analysis, faces limitations due to insufficient data availability, emphasizing the need for thorough planning. Thermal comfort analysis, based on temperature and humidity measurements, unveils significant fluctuations beyond comfort thresholds. Expert surveys and occupant feedback further expose reduced utilization of natural ventilation, high air conditioner adoption rates, and adaptive behaviours. The framework's insights prompt opportunities for improvement, yet validation requires broader application across diverse buildings. The study's academic survey emphasizes the importance of integrating BPE in industries with government policies. Field observations highlight challenges in space utilization, material selection, and occupant engagement. This study's findings underscore the BPE-RBPI framework's potential to refine performance assessment, sustainable and energy efficient to foster industry confidence, and drive holistic improvements in India's building sector.

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy.

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.