Adaptive Framework Research Articles

Feasibility of a culturally adapted technology-delivered, family-based childhood obesity intervention for Latino/Hispanic families in rural Nebraska: the Hispanic Family Connections study protocol

IntroductionAddressing childhood obesity risk factors like home environment, parental roles, excess weight, physical activity and healthy eating among Latino/Hispanic (L/H) families living in rural communities is an important priority. However,...

An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting

An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting

Is climate change hindering the economic progress of Nigerian economy? Insights from dynamic models

This study investigates the link between climate change and economic growth in Nigeria from 1961 to 2022. To provide a robust analysis that facilitates a nuanced examination of this dynamic relationship, this study employs state-of-the-art econometric approaches, including autoregressive distributed lag (ARDL), fully modified least squares (FMOLS), novel quantile autoregressive distributed lag (QARDL), and time-varying causality. The empirical results of this study are as follows: (1) the impact of climate change on economic growth is not felt in the short run. However, climate change negatively influences economic growth in Nigeria in the long run, (2) the elasticity of climate change increases across the conditional quantile economic growth, (3) unidirectional causality from climate change to economic growth across different time dimensions. These empirical outcomes advocate for a proactive and adaptive policy framework, emphasising the need for the Nigerian government to adopt climate-smart policies.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Optimizing Serverless Function Orchestration for Complex Workflows in Cloud Platforms

Purpose: This paper aims to investigate ways of increasing the efficiency of various workloads of serverless function orchestration in cloud systems while decreasing latency and optimizing resources. Materials and Methods: The report suggests adaptive learning systems and a feedback loop framework for managing the orchestrating serverless environment. Findings: It has also been affirmed that case studies help validate the framework rather than presenting the practical implications of the proposed techniques. Implications to Theory, Practice and Policy: The paper recommends focusing on managing performance, dependencies, and expenses in serverless function orchestration by employing adaptive learning systems and feedback loop frameworks.

Scheduling multi-skill technicians and reassignable tasks in a cloud computing company

We investigate a multi-skill technician and reassignable task scheduling problem in a cloud computing company. In the problem, multi-skill technicians are assigned to process a large number of tasks from customer requests in a certain scheduling horizon. The tasks are allowed to be reassigned to another technician multiple times, and one technician can process multiple tasks in parallel. The company not only focuses on processing efficiency, but also expects to improve customers’ experience and technicians’ satisfaction. We characterize the feasible solutions and introduce a weighted objective with three metrics: processing efficiency, response delay, and workload balance. An effective two-stage hierarchical optimization method embedded in a greedy randomized adaptive search procedure framework is proposed. In the first stage, initial solutions are generated by a greedy randomized construction procedure, and then improved by local search with an ejection chain operator to optimize processing efficiency. In the second stage, two local search procedures with five operators for improving response delay or workload balance are designed. Computational experiments are conducted to evaluate the effectiveness of our algorithm. The results show that the proposed algorithm is competent in fast computing a schedule of high quality. It also reveals that reassignments are helpful in reducing response delay and balancing workloads in the scheduling.

Acoustic Camera-Based Adaptive Mosaicking Framework for Underwater Structures Inspection in Complex Marine Environments

Acoustic Camera-Based Adaptive Mosaicking Framework for Underwater Structures Inspection in Complex Marine Environments

Reducing semantic ambiguity in domain adaptive semantic segmentation via probabilistic prototypical pixel contrast

Domain adaptation aims to reduce the model degradation on the target domain caused by the domain shift between the source and target domains. Although encouraging performance has been achieved by combining contrastive learning with the self-training paradigm, they suffer from ambiguous scenarios caused by scale, illumination, or overlapping when deploying deterministic embedding. To address these issues, we propose probabilistic prototypical pixel contrast (PPPC), a universal adaptation framework that models each pixel embedding as a probability via multivariate Gaussian distribution to fully exploit the uncertainty within them, eventually improving the representation quality of the model. In addition, we derive prototypes from probability estimation posterior probability estimation which helps to push the decision boundary away from the ambiguity points. Moreover, we employ an efficient method to compute similarity between distributions, eliminating the need for sampling and reparameterization, thereby significantly reducing computational overhead. Further, we dynamically select the ambiguous crops at the image level to enlarge the number of boundary points involved in contrastive learning, which benefits the establishment of precise distributions for each category. Extensive experimentation demonstrates that PPPC not only helps to address ambiguity at the pixel level, yielding discriminative representations but also achieves significant improvements in both synthetic-to-real and day-to-night adaptation tasks. It surpasses the previous state-of-the-art (SOTA) by +5.2% mIoU in the most challenging daytime-to-nighttime adaptation scenario, exhibiting stronger generalization on other unseen datasets. The code and models are available at https://github.com/DarlingInTheSV/Probabilistic-Prototypical-Pixel-Contrast.

SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification

SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification

Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis

Varying components and operating conditions in industrial machines lead to different distribution characteristics and fault states of monitoring data for critical rotating machinery. Adequate and accurate labelling of monitoring data is challenging to achieve. Existing data-driven unsupervised domain adaptation methods exhibit poor interpretability and low confidence in the target domain’s pseudo-labelling. Therefore, a physics-informed unsupervised domain adaptation framework is proposed. Data and physical information are combined to enhance the reliability and interpretability of the model while ensuring its generalization capability. First, physical information is extracted by merging the knowledge of the mechanical component fault characteristics with indicators like the signal spectrum’s energy value. Subsequently, the physical label and loss are created. The physical loss is then embedded in the loss function of the source domain model. Next, by aligning the physical labels and the clustering results of the spectral clustering, the target domain samples’ hard pseudo-labels are obtained. The probability values of the sample belonging to each category as determined by the Wasserstein criterion comprise the soft pseudo-label of the sample. Furthermore, the confidence of hard pseudo-labels is continually increased by iteratively updating the probability values of soft pseudo-labels using the multi-label as a guide and the inverse improved sigmoid function as a gradient. Finally, unsupervised cross-machine diagnostic models with high robustness can be obtained. Three datasets are employed in the experimental section, and five sub-datasets are created and crossed for cross-machine experiments to confirm the suggested approach’s efficacy and superiority.

Adaptable Framework for K-12 STEM Outreach Activities: The Case of a Civil Engineering Workshop on Sustainable and Hazard-Resistant Earth Masonry

Adaptable Framework for K-12 STEM Outreach Activities: The Case of a Civil Engineering Workshop on Sustainable and Hazard-Resistant Earth Masonry

Farm adaptive business strategies in crisis management: COVID-19

The global COVID-19 pandemic significantly impacted businesses worldwide, with rural businesses being no exception. This study examines the effects of the global crisis on upland farmers in England. It builds on the under-researched area of constrained rural entrepreneurship. Institutional theory is applied as a theoretical lens to understand how crisis events shape the constrained institutional contexts in which farm businesses strategically navigate. Using a qualitative, multiple case study approach, we explore how different farmers, traditionalist and entrepreneurial, have strategically responded to the aftermath of crisis-influenced constraints. Our findings highlight the importance of business strategy—particularly farm diversification—in helping farmers navigate these challenges. We provide a conceptual framework, the Farm Crisis Adaptation Framework, (FCAF) that theorizes farmer adaptation strategies in response to crisis situations. Our findings indicate a greater needed for rural policymakers to help develop the strategic entrepreneurship skillsets of farmers—helping farmers to future-proof their businesses against uncertainties. Moreover, investment is needed in the local and regional economy, establishing a conducive environment supportive of farm development strategies.

Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments.

Ethical Considerations in Clinical Trials: Ethical Dilemmas in Patient Consent and Animal Testing

Clinical trials are indispensable to the advancement of medical science, providing a framework for evaluating the safety and efficacy of new treatments, interventions, and technologies. However, they present profound ethical challenges, particularly in relation to patient consent and the use of animals in preclinical testing. Informed consent is central to safeguarding patient autonomy, yet practical difficulties arise from issues such as patient comprehension, vulnerability, and cultural differences. Animal testing, while traditionally viewed as a necessary component of preclinical research, prompts serious ethical concerns regarding animal welfare and the broader moral implications of causing harm to animals for human benefit. This review examines these ethical dilemmas, tracing their historical evolution, analysing current regulatory frameworks, and evaluating potential alternatives to both patient consent challenges and animal testing. Through the examination of case studies like the Tuskegee Syphilis Study and the Henrietta Lacks case, as well as debates surrounding animal rights, this article emphasizes the need for continuous ethical vigilance and adaptive frameworks that can balance scientific progress with moral responsibility.

Generative AI for Intelligence Augmentation: Categorization and Evaluation Frameworks for Large Language Model Adaptation

Generative AI for Intelligence Augmentation: Categorization and Evaluation Frameworks for Large Language Model Adaptation

명예퇴직 여성 교원이 경험하는 삶의 적응 과정에 관한 근거이론적 접근

Purpose: The purpose of this study was to explore and explain the experiences that honorably retired female teachers have after retirement and the process by which they adapt to life. Accordingly, the research questions are: First, what is the life experienced by honorably retired female teachers, and what are the difficulties? Second, what are the adaptation processes and types of adaptation experienced by honorably retired female teachers? Methods: To this end, we used the grounded theory methodology of Strauss and Corbin (1998), selected research participants through theoretical sampling, conducted semi-structured questionnaires and in- depth interviews with eleven female teachers who had experienced honorable retirement within the past three years, and analyzed the raw data. Results: Open coding resulted in 483 concepts, 118 subcategories, and 21 categories. As a result, the causal condition was 'experiencing the limits of maintaining a teaching career', the central phenomenon was 'living as an ordinary person without any affiliation', and the contextual condition was 'accepting and transitioning to life as a retiree', action/interaction strategy was 'entering the process of self- integration', and the result was 'gradually satisfying the need for self-actualization'. The core categories derived from selective coding are 'entering a stable old age through acceptance of changes in life and self-reflection and then advancing to the stage of mature self-realization'. It was found to go through an adaptation stage of 'introduction period', 'confusion period', 'change period', and 'actualization period'. Conclusion: By presenting a theoretical framework for the post-retirement adaptation of honorably retired female teachers, it is expected to be used as a useful tool in active research on honorably retired female teachers and in the development of retirement adaptation programs.

A digital service standardization model for Nigeria: The role of NITDA in regulatory compliance

The digital transformation in Nigeria has accelerated the need for effective standards and regulations to ensure quality and interoperability of digital services. The National Information Technology Development Agency (NITDA) plays a crucial role in this context by overseeing regulatory compliance and fostering a conducive environment for digital innovation. This review presents a comprehensive model for digital service standardization tailored to Nigeria's unique socio-economic and technological landscape. The proposed model emphasizes the importance of regulatory frameworks established by NITDA in promoting consistency, reliability, and security across various digital platforms and services. The model outlines key components necessary for effective standardization, including policy formulation, compliance monitoring, and stakeholder engagement. It integrates best practices from international standards while adapting them to local needs and challenges. Central to the model is the role of NITDA in developing and enforcing standards that align with global benchmarks, thereby ensuring that Nigerian digital services are competitive and secure. A significant aspect of the model is its focus on regulatory compliance, which is vital for maintaining the integrity of digital services and protecting user data. The review discusses how NITDA's regulatory initiatives address common issues such as data privacy, cybersecurity, and service quality. By examining case studies and industry feedback, the review highlights the impact of NITDA's efforts on improving the overall digital service ecosystem in Nigeria. The model also addresses the need for continuous evaluation and adaptation of standards in response to technological advancements and emerging threats. It proposes mechanisms for regular updates and revisions to standards, ensuring that they remain relevant and effective. Additionally, the review explores the role of collaborative partnerships between NITDA, industry stakeholders, and academic institutions in advancing digital service standardization. The proposed digital service standardization model provides a structured approach to enhancing regulatory compliance and fostering a robust digital environment in Nigeria. It underscores the pivotal role of NITDA in driving these efforts and offers a framework for ongoing improvement and adaptation to the dynamic digital landscape.

Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering

Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regarding F1 score, accuracy, and recall, indicating its strong potential for precise problem localization. The study highlights the necessity of continuously adapting models to tackle idea drift in dynamic datasets. Our research suggests a path for future endeavours involving improving feature engineering and integrating real-time online learning to sustain high performance in bug localization activities.

Setting new benchmarks for combating financial crimes and ensuring the safety and security of America's digital financial landscape

The increasing digitalization of financial services in the United States has introduced significant challenges in combating financial crimes, including fraud, money laundering, and cybercrimes. As digital transactions become more pervasive, so too do the opportunities for illicit activities that exploit the vulnerabilities of these systems. This research seeks to establish new benchmarks for combating financial crimes and ensuring the security of America's digital financial landscape. By leveraging advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and blockchain, this study explores innovative approaches to enhancing fraud detection, improving transparency, and reducing the risk of money laundering. A mixed-methods approach was employed, combining qualitative analysis of existing regulatory frameworks with quantitative case studies to assess the effectiveness of these technologies in real-world applications. The results indicate that AI and ML significantly enhance financial institutions' ability to detect fraudulent activities in real-time, reducing false positives and allowing for more efficient resource allocation. Furthermore, blockchain technology improved financial transactions' traceability, enhancing financial systems' transparency and accountability. The study concludes with recommendations for policy and practice, emphasizing the need for adaptive regulatory frameworks, increased investment in advanced technologies, and more vital collaboration between the public and private sectors. These findings suggest that a proactive approach, integrating technological innovation with strategic policy measures, is crucial for safeguarding America's financial infrastructure against emerging threats in the digital era.

NAVIGATION IN E-GOVERNMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE FORMATION OF THE LEGAL FRAMEWORK FOR THE PROTECTION OF INTELLECTUAL PROPERTY RIGHTS

The integration of artificial intelligence (AI) into electronic government (e-government) systems is revolutionizing public administration by enhancing efficiency and improving service delivery. However, the adoption of AI technologies in this context also raises complex legal challenges, particularly concerning intellectual property (IP) rights. Traditional IP laws, which were developed with human authorship in mind, struggle to accommodate the unique characteristics of AI-generated content. This article examines how AI is reshaping the legal framework for IP protection within e-government systems, highlighting the implications and challenges that arise from this technological shift. The primary aim of this study is to explore the role of AI in the formation of IP law frameworks within e-government, focusing on how current laws address—or fail to address—the challenges of AI-generated content. The methodology includes a comprehensive literature review, analysis of legislative documents, case studies, and a benchmarking analysis to compare approaches across jurisdictions. Additionally, expert interviews provide insights into practical considerations and emerging trends in the field. The results indicate that while some jurisdictions, such as the European Union, are actively adapting their IP laws to address AI's impact, most existing frameworks remain inadequate for protecting AI-generated works. Divergent approaches across countries reveal a lack of international harmonization, which complicates cross-border collaboration and legal enforcement. The analysis also highlights the importance of public-private partnerships and sector-specific IP protections, which can address the unique needs of different e-government applications. From a forward-looking perspective, the study underscores the need for flexible, AI-specific IP protections that promote innovation while safeguarding IP rights. International cooperation will be essential for establishing consistent standards, facilitating global e-government initiatives, and supporting the responsible use of AI in public services. By fostering a balanced and adaptive IP framework, policymakers and stakeholders can help build a resilient digital ecosystem that accommodates future advancements in AI technology.