Centralized Location Research Articles

Development of Learning Management System with Gamification Approach for Project-Based Learning

This study focuses on the development of the Learning Management System for Innovation in Graphic and Visual Media with a gamification approach in the context of teaching the Graphic and Visual Media Innovation course. Currently, learning management utilizes the SIPEJAR Learning Management System (LMS) and the Progress Report Card (PRC), but their separate existence poses challenges to efficiency and effectiveness. Students are required to monitor their own learning resources and track their learning performance through the PRC. The optimal learning environment would be one in which all learning resources and progress are accessible in a single, integrated platform, allowing for convenient access from any location at any time. The objective of this research is to develop a Learning Management System (LMS) that incorporates gamification elements into project-based learning. Research design employed is Research and Development (RnD), adopting the Web-Based Instructional Design model. The sample for this research is composed of 134 students. The research was conducted between October and December. The data collected for this study is quantitative data obtained using the Technology Acceptance Model questionnaire and the level of student engagement in the learning process. The result is that students perceive the IMGV LMS as a beneficial addition to their learning experience. The LMS enables students to monitor their performance in the final course project and access assignment materials in a centralized location. Additionally, the LMS facilitates knowledge acquisition through the incorporation of quizzes, which are designed to engage students in a competitive learning environment.

Neurodegeneration Focused Biorepositories: NCRAD, BioSEND, MJFF

AbstractBackgroundBiorepositories play an integral role in the advancement of our understanding of neurodegenerative diseases and improving human health outcomes. Research efforts are accelerated when access to high‐quality clinical specimens is made available from a large, diverse participant group. Indiana University is home to three important neurodegenerative disease‐focused biorepositories including the NIA‐funded National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), the NINDS‐funded Biospecimen Exchange for Neurological Disorders (BioSEND), and the Michael J. Fox Foundation (MJFF) biorepository. Having all three repositories in one location presents a unique opportunity to leverage common protocols and shared resources to provide researchers with access to a wide breadth of specimens, facilitating broad specimen sharing and standardization across studies and repositories.MethodAll three repositories provide exceptional data and study coordination, data management, scientific support, and technical resources. Uniform best practice standard operating procedures (SOPs) and materials for collection and processing of samples yield consistently high‐quality specimens across studies from all repositories. Banked specimen types include DNA, RNA, whole blood, plasma, serum, cerebrospinal fluid, urine, stool, brain tissue, peripheral blood mononuclear cells (PBMCs), fibroblasts, and induced pluripotent stem cells (iPSCs). Online catalogs are available for specimen selection along with project management that can assist in the selection and approval of specimens across studies and repositories.ResultAmong the three repositories, over 4 million specimen aliquots from more than 150 studies have been banked. This collection represents over 142,000 unique participants across a diverse array of neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Lewy Body Dementia, Concussion, Traumatic Brain Injury, Huntington’s Disease, Spinocerebellar Ataxia and other neurodegenerative proteinopathies. More than 600,000 specimen aliquots have been distributed to over 370 investigators, resulting in almost 1,000 publications.ConclusionNCRAD, BioSEND, and MJFF serve as crucial resources for investigators requiring high‐quality clinical specimens collected and processed in accordance with best practice SOPs. The centralized location at Indiana University provides the opportunity to synergize and align common study objectives to provide shared resources with broader sample diversity, increased statistical power, improved reproducibility, and enhanced opportunities for broad specimen sharing, thereby fostering collaborative research initiatives.

A place of encounter: Health and social care workers’ perspectives on public gathering spaces for people using drugs

Background: The use of drugs is an escalating concern, with significant implications for the health and daily functionality for individuals. It has proven challenging to provide accessible health and social services. In the context of addressing substance use issues in urban areas, public gathering places for individuals who use drugs play a multifaceted role, serving as sites of belonging, support and potential illicit activity. This qualitative study explores the experiences of healthcare and social workers regarding a public gathering place, referred to as “the Bench”, in a medium-sized Norwegian city. Methods: A qualitative research design consisting of semi-structured focus group interviews and individual interviews with 13 participants representing various support agencies. Results: The analysis resulted in four thematic areas: (1) belonging and a gateway to support and services; (2) balancing user autonomy at the Bench and systems support; (3) the role of a centralised location; and (4) the preventive role of support workers at the Bench. Conclusion: While the Bench fosters a sense of community and provides access to vital services, concerns about illicit activities persist due to its visibility. Nevertheless, its central location facilitates outreach efforts and ensures easy access to support services. The study underscores the preventive role of healthcare and social workers in stabilising the environment and mitigating conflicts. Furthermore, it highlights the importance of central locations for public gathering places, emphasising the proximity to low-threshold services. The Bench represents a crucial venue for establishing relationships and providing assistance to individuals who use drugs, contributing to harm reduction efforts in the community.

Getting mammograms shouldn’t be so bumpy: Improving mammogram workflow in VA community care.

345 Background: The interplay between VA-based care and Community Care (CC) has long been important for continuity of patient care, though transitioning care between the two is often fraught with problems. Mammograms ordered at VA San Diego (VASD) are often deferred to Community Care; while this process should be smooth and seamless, it is instead challenging to navigate, with inconsistent rates of completion and results often not relayed to patients leading to delayed/missed diagnoses. We applied extensive QI methodology towards overhauling the VASD Community Care Mammogram workflow to improve rates of CC mammogram completion and results released to patients in a timely manner. Methods: We evaluated the CC mammogram workflow and implemented interventions at VASD from 01/2022-04/2023. An A3 was used for tracking process improvement. Using Lean Six Sigma process improvement strategies, we identified several problematic areas within the CC Mammogram workflow that are prone to errors and can lead to patient safety issues. Weekly meetings/interviews were set up to identify pain points with the primary users to allow for continuous process evaluation and improvement. Primary outcome was the frequency with which a CC mammogram order had mammogram completed and result released to patient within the expected timeframe by VISN 22 metrics (BIRADS 0,3,4,5,6 <7 days and BIRADS 1,2 <14 days to notification). Results: Process mapping of the VASD CC mammogram process revealed significant waste and failure modes. Notably among these was the splintered tracking systems used across multiple users and lack of automated follow-up reminders. Multiple PDSAs involved standardizing workflow by developing an SOP that focused on standardization of tracking orders/results, creating automated task reminders, while also adding backup systems and double-checks. To centralize and standardize the patient tracking process, we repurposed the Coordinated Care Tracking System (CCTS), a VA-based tool that is used for tracking patients in other departments and creating reminders for follow up, while storing patient data in a centralized location. We also redesigned and streamlined the mammogram ordering menu within CPRS. Implementation of the above interventions increased monthly rates of mammograms completed and reported to patients in a timely manner from a median of 67.5% to 100% in all BIRADS groups by 10/2022, and sustained at 100% through 04/2023. Conclusions: Standardization of workflow and incorporation of CCTS software into the CC Mammogram process led to an appreciable improvement in rates of CC mammogram scheduling and timely result notifications. Improved tracking of CC mammogram patients has the potential to prevent unnecessary patient harm. It is worth considering whether CCTS and a similar standardized process should be incorporated into other CC programs, as these issues are not unique to VASD mammograms.

Remote Healthcare: Development of a Centralized Patient Monitoring System for Ward Patients.

Patient monitoring is an essential practice in healthcare settings to assess well-being and medical conditions. This study proposes a central patient monitoring system enabling healthcare personnel to have a comprehensive perspective of multiple patients' health state from a central location. The proposed prototype enables healthcare providers to detect and respond to changes in patients' condition efficiently. The project contains multiple patient monitoring units with each measuring temperature, ECG, SpO2, and heart rate along with a display on a centralized screen. The hardware of monitors is placed at each patient bed connected with all the patients and the result of each patient can be seen from the general ward counter. Different sensors are used to design monitoring units and are connected with node MCU through different pin connections. Node MCU sends the information gathered from sensors to the Raspberry pi unit using Wi-Fi. The real-time data of patients are being displayed and saved using server “thing speak”. There are different options on the server window to switch between different patients’ real time results to monitor them. The proposed prototype uses computer technology to monitor the vital signs of multiple patients on a single monitor from a centralized location using internet which may eventually reduce the time and workload of medical staff leading to improved patient care.

Creation of an Automated and Comprehensive Resident Progress System for Residents and to Save Hours of Faculty Time: Mixed Methods Study.

It is vital for residents to have a longitudinal view of their educational progression, and it is crucial for the medical education team to have a clear way to track resident progress over time. Current tools for aggregating resident data are difficult to use and do not provide a comprehensive way to evaluate and display resident educational advancement. This study aims to describe the creation and assessment of a system designed to improve the longitudinal presentation, quality, and synthesis of educational progress for trainees. We created a new system for residency progress management with 3 goals in mind, that are (1) a long-term and centralized location for residency education data, (2) a clear and intuitive interface that is easy to access for both the residents and faculty involved in medical education, and (3) automated data input, transformation, and analysis. We present evaluations regarding whether residents find the system useful, and whether faculty like the system and perceive that it helps them save time with administrative duties. The system was created using a suite of Google Workspace tools including Forms, Sheets, Gmail, and a collection of Apps Scripts triggered at various times and events. To assess whether the system had an effect on the residents, we surveyed and asked them to self-report on how often they accessed the system and interviewed them as to whether they found it useful. To understand what the faculty thought of the system, we conducted a 14-person focus group and asked the faculty to self-report their time spent preparing for residency progress meetings before and after the system debut. The system went live in February 2022 as a quality improvement project, evolving through multiple iterations of feedback. The authors found that the system was accessed differently by different postgraduate years (PGY), with the most usage reported in the PGY1 class (weekly), and the least amount of usage in the PGY3 class (once or twice). However, all of the residents reported finding the system useful, specifically for aggregating all of their evaluations in the same place. Faculty members felt that the system enabled a more high-quality biannual clinical competency committee meeting and they reported a combined time savings of 8 hours in preparation for each clinical competency committee as a result of reviewing resident data through the system. Our study reports on the creation of an automated, instantaneous, and comprehensive resident progress management system. The system has been shown to be well-liked by both residents and faculty. Younger PGY classes reported more frequent system usage than older PGY classes. Faculty reported that it helped facilitate more meaningful discussion of training progression and reduced the administrative burden by 8 hours per biannual session.

Eco-FL: Enhancing Federated Learning sustainability in edge computing through energy-efficient client selection

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking control necessitates a deeper understanding of flow-level dynamics for comprehensively monitoring interconnection statuses across multiple sites. In this paper, we first propose an IO Visor-enabled tracing solution for Linux-based boxes to efficiently enable the comprehensive collection of network packet flows across interconnected sites. Next, we apply IP prefix-based flow-level analysis at a centralized location to support the intent-based networking control application. This flow-level analysis involves generating policy-based specific action (i.e., redirect) via SDN controllers for specific source IP prefixes, which are causing unknown or potentially vulnerable flows. Furthermore, we employ an open-source ONOS SDN controller to tackle the challenge of managing the hybrid SDN-IP interconnections. By leveraging intent-based networking control, we effectively apply ONOS intents based on IP routing information and generated a set of forwarding action. We evaluate our proposed solution in an experimental SDN-cloud testbed, demonstrating its effectiveness in real-world scenarios. Overall, through the seamless integration of these monitoring and control approaches, we manage to enhance the adaptability and security of the interconnected cloud sites of the testbed.

Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems

The majority of current research on the application of artificial intelligence (AI) and machine learning (ML) in science, technology, engineering, and mathematics (STEM) education relies on centralized model training architectures. Typically, this involves pooling data at a centralized location alongside an ML model training module, such as a cloud server. However, this approach necessitates transferring student data across the network, leading to privacy concerns. In this paper, we explore the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem. We highlight the potential benefits FL offers to students, classrooms, and institutions. Also, we identify a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector. Finally, we discuss a series of open research directions, focusing on nuanced aspects of FL implementation in educational contexts. These directions aim to explore and address the complexities of applying FL in varied educational settings, ensuring its deployment is technologically sound, beneficial, and equitable for all stakeholders involved.

Future proofing core facilities with a seven-pillar model.

Centralised core facilities have evolved into vital components of life science research, transitioning from a primary focus on centralising equipment to ensuring access to technology experts across all facets of an experimental workflow. Herein, we put forward a seven-pillar model to define what a core facility needs to meet its overarching goal of facilitating research. The seven equally weighted pillars are Technology, Core Facility Team, Training, Career Tracks, Technical Support, Community and Transparency. These seven pillars stand on a solid foundation of cultural, operational and framework policies including the elements of transparent and stable funding strategies, modern human resources support, progressive facility leadership and management as well as clear institute strategies and policies. This foundation, among other things, ensures a tight alignment of the core facilities to the vision and mission of the institute. To future-proof core facilities, it is crucial to foster all seven of these pillars, particularly focusing on newly identified pillars such as career tracks, thus enabling core facilities to continue supporting research and catalysing scientific advancement. Lay abstract: In research, there is a growing trend to bring advanced, high-performance equipment together into a centralised location. This is done to streamline how the equipment purchase is financed, how the equipment is maintained, and to enable an easier approach for research scientists to access these tools in a location that is supported by a team of technology experts who can help scientists use the equipment. These centralised equipment centres are called Core Facilities. The core facility model is relatively new in science and it requires an adapted approach to how core facilities are built and managed. In this paper, we put forward a seven-pillar model of the important supporting elements of core facilities. These supporting elements are: Technology (the instruments themselves), Core Facility Team (the technology experts who operate the instruments), Training (of the staff and research community), Career Tracks (for the core facility staff), Technical Support (the process of providing help to apply the technology to a scientific question), Community (of research scientist, technology experts and developers) and Transparency (of how the core facility works and the costs associated with using the service). These pillars stand on the bigger foundation of clear policies, guidelines, and leadership approaches at the institutional level. With a focus on these elements, the authors feel core facilities will be well positioned to support scientific discovery in the future.

Immediate lymphatic reconstruction: Lessons learned over eight years

Immediate lymphatic reconstruction (ILR) is recognized as a surgical approach used to reduce the risk of developing secondary lymphedema, and evidence demonstrating the efficacy of ILR is favorable. Our Lymphatic Center has become a centralized location offering ILR for the risk-reduction in breast cancer-related lymphedema (BCRL) in New England. Over the course of our experience, we made several modifications and adapted our approach to enhance the operative success of this procedure. These include advancements in our use of indocyanine green (ICG) imaging to identify baseline lymphatic anatomical variation, utilization of fluorescein isothiocyanate for lymphatic vessel visualization, application of the lymphosome concept to guide arm injection sites, verification of anastomotic patency (using ICG), localization of reconstruction to guide radiation therapy, incorporation of intraoperative tools to facilitate better anatomic visualization of the axilla, and addition of a lower extremity vein graft to mitigate venous-related complications. Collecting information from each surgery in a standardized manner, including intraoperative lymphatic channel measurements, and deploying clips for possible future radiation exposure, enables future studies on ILR patient outcomes. In this contribution, we aimed to share our institutional modifications with the surgical community to facilitate further adoption, conversation, and advancement of ILR for the risk-reduction in BCRL.

Organization of spontaneous spatial behaviors under dark conditions is unaffected in adult male and female long-Evans rats after moderate prenatal alcohol exposure.

Prenatal alcohol exposure can produce disruptions in a wide range of cognitive functions, but it is especially detrimental to spatial navigation. In open environments, rodents organize their spatial behaviors around centralized locations, termed home bases, from which they make circuitous and slow locomotor trips (progressions) into the rest of the environment. Open-field behaviors are organized even under darkened test conditions, suggesting a role for self-motion cues (vestibular, motor, etc.). The impact of moderate prenatal alcohol exposure (mPAE) on the organization of spontaneous open-field behaviors under darkened conditions has not been investigated. Here we tested adult female and male rats with mPAE or saccharin control exposure in a circular open field for 30 min in a testing room that was made completely dark. While general locomotion, as measured by reductions in travel distance and increased stop duration, decreased across the test session, the organization of these behaviors, as measured by stop duration, home base establishment, home base stability, progression accuracy, and scaling of peak speeds with progression length, did not differ between mPAE and saccharin control rats. Together, the findings strongly suggest that spontaneous movement organization in relation to self-motion cues remains intact in adult mPAE rats. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Data Management System - Water Supply and Channeling with GIS Mapping

GIS mapping enables visualizing spatial data related to water sources, distribution networks, infrastructure assets, and other pertinent information crucial for effective management. Asset management functionalities enable tracking the condition, maintenance schedules, and lifecycle of water infrastructure assets, ensuring optimal utilization and performance. This paper presents the design and implementation of an integrated data management system tailored for water supply and channeling, augmented with Geographic Information System (GIS) mapping capabilities. The system aims to streamline the management of water resources, optimize distribution networks, and enhance decision making processes for water utility providers and stakeholders. The system architecture comprises several key components: GIS mapping, data collection mechanisms, database management, sensor integration, network analysis tools, asset management functionalities, decision support systems, remote monitoring and control capabilities, and user interfaces. Data collection mechanisms encompass various sources such as sensors, surveys, satellite imagery, and existing databases to gather comprehensive datasets on water sources, quality parameters, infrastructure condition, and demographic factors. A robust database management system is employed to efficiently store, manage, and secure the collected data, facilitating easy retrieval and analysis. Real-time sensor data from water meters, flow meters, and pressure sensors are integrated into the system to provide insights into water usage patterns, network performance, and system health. Network analysis tools utilize GIS capabilities to optimize the layout of distribution networks, identify areas for expansion, and improve operational efficiency Remote monitoring and control capabilities empower operators to monitor and manage water systems from a centralized location, enabling proactive maintenance, leak detection, and emergency response. Security measures are implemented to safeguard sensitive water infrastructure data against unauthorized access, cyber threats, and data breaches, ensuring the integrity and confidentiality of the system. Overall, the integrated data management system presented herein offers a comprehensive solution for optimizing water supply and channeling operations, enhancing efficiency, reliability, and sustainability while minimizing costs and environmental impact

Sustainable food chains designed for optimised resource use: Optimising downscaled food chains for sustainable resource use: A comprehensive case study on tomato juice

As consumers increasingly prefer locally sourced food, there is a growing movement towards optimising resource use and reducing emissions in supply chains. While Short Food Supply Chains (SFSCs) traditionally emphasise social and institutional proximity, this study strategically addresses the environmental impact by implementing innovative technologies on a smaller scale. This article explores the possibility of implementing a mobile processing unit (FOX unit) that utilises two innovative technologies, one for gentle juice extraction (spiral filter) and the other for preservation of freshly pressed products (pulsed electric fields - PEF). The study investigates a solution that could streamline the supply chain between producers and consumers by offering a decentralised and modular approach to processing. Using Life Cycle Assessment (LCA), tomato juice production is compared under traditional thermal pasteurisation (Scenario 1) and the FOX unit (Scenario 2). This study also assesses the impacts of centralising production and shipping raw materials between countries (Scenario 3), considering the FOX unit as part of the evaluation. Further analysis includes relocating this unit to the countries of raw material origin (Scenario 4). Data retrieved from literature, databases, and practical trials, revealed that tomato juice production with the FOX unit led to a 15% reduction in environmental impact across all categories compared to traditional thermal pasteurisation. When relocating the unit, the overall environmental impact decreased as the volume of processed raw materials increased, reaching levels comparable to the results obtained from shipping 1 ton of raw material to a centralised location in various studied countries (with variations in midpoint categories depending on the location, such as 200 tons in France, 15 tons in Italy, and 45 tons in Spain), albeit with variation in the categories. This study highlights the potential environmental benefits of integrating the FOX unit in SFSCs, offering valuable insights for sustainable food supply chain practices.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

A Systematic Literature Survey on Electronic Health Record System Using Blockchain

Abstract-Compared with traditional paper-based medical records , Electronic health records (EHRs) are widely used because of their efficiency, secu- rity, and reducing data redundancy.The Electronic Health Record (EHR) system is a major technolog- ical development in healthcare that has completely changed how patient data is collected, stored, and used. Through the use of cutting-edge digital tech- nologies, the EHR system offers a safe, centralized location for all patient data. Healthcare personnel may easily access, edit, and share patient informa- tion thanks to its user-friendly interfaces, which helps them make more informed clinical decisions and provide better treatment.The system developed using the Ethereum network using Ganache. The measured approach is to store patients data and execute functions in a decentralized system using blockchain smart contracts. To further protect pa- tient privacy and adhere to privacy laws, the system places a high priority on data security and uses strong authentication and encryption mechanisms. Keywords-Blockchain, Decentralized, Ethereum, Healthcare, Smartcontacts,

Proposal of Digital Twin-Enabled New Product Design Framework

Digital twin (DT) has provided numerous opportunities for product development businesses to leverage data acquired from the physical model. Ideally, many companies are improving their use cases by using the collected physical data in a virtual model. Thanks to emerging communication technologies, 5th generation mobile network (5G), the internet of things (IoT), industrial internet of things (IIoT) sensors, etc., which help to collect data from the physical product and send data to cloud computing, called big data. With the gap in the non-availability of standard architecture, many companies are using big data and building their digital models. This paper proposes a novel DT-enabled new product design framework that has built an interaction between the DT and product design environments. This framework enables the data to be captured from various physical entities, stored in a centralized location, and pushed into the digital entity, which processes the data and converts the raw data into information used for decision-making. In the digital entity, multiple technologies are used to process data into information, such as artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. The processed data that emerges from these models are called synthetic or DT data; this data is later used for control / monitoring and visualization / interaction purposes. In this paper, the data classified as visualization and interaction is integrated with the new product design framework in all four design stages, viz., concept design, detailed design, design verification, and redesign, with various data contributions to the design. A case study was discussed by applying the DT data processed using the AI / ML model in the concept design phase. Using synthetic data, the medium-duty truck frame assembly�s design concept was validated using ten load cases iteratively, and the design was finalized. The results are encouraging that the overall weight of the frame assembly was optimized by 9% without impacting the strength and factor of safety of the frame assembly.

An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare.

Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able to work for multi-modal data. This paper presents a new high-order multi-modal learning approach using multimodal deep learning and the Tucker decomposition (F- HoFCM). Furthermore, we propose an edge-cloud-aided private scheme to facilitate the clustering efficiency for its embedding in edge resources. Specifically, the computationally intensive tasks, such as parameter updating with high-order back propagation algorithm and clustering through high-order fuzzy c-means, are processed in a centralized location with cloud computing. The other tasks such as multi-modal data fusion and Tucker decomposition are performed at the edge resources. Since the feature fusion and Tucker decomposition are nonlinear operations, the cloud cannot obtain the raw data, thus protecting the privacy. Experimental results state that the presented approach produces significantly more accurate results than the existing high-order fuzzy c-means (HOFCM) on multi-modal healthcare datasets and furthermore the clustering efficiency are significantly improved by the developed edge-cloud-aided private healthcare system.

Gravitational wave alert generation infrastructure on your laptop

Gravitational wave alert generation infrastructure on your laptop

A federated learning aided system for classifying cervical cancer using PAP-SMEAR images

Cervical cancer is a significant contributor to female mortality on a global scale, especially in low-income countries where effective screening programs for the detection and treatment of precancerous conditions are lacking. Classification of pap-smear test cervical cell images is crucial as it gives essential information for the diagnosis of malignant or precancerous lesions and thus helps in providing a proper diagnosis. Most of the existing methods require accumulating pap-smear test images of all patients in a centralized location for classification purposes. However, this procedure may hamper the privacy of patient data and create data ownership issues. In this study, different convolutional neural network-based federated learning architectures are introduced to achieve both the objectives of accurate image classification and data privacy in three different experimental settings. In the proposed system, the updates of the locally trained models get aggregated with an initially untrained global model in order to increase its performance. In traditional ML-based systems, the more the train data, the more efficiently the model performs, but in the proposed system, clients can participate remotely to train a robust model even with the disadvantage of possessing a small dataset. The proposed CNN-based FL architecture showed test accuracy of 94.36% and 78.4% in an IID (Independent and Identically Distributed) and a non-IID setting respectively. Thus multiple hospitals across different countries can use the proposed system to train their local models with their private dataset without sharing it centrally, which eventually will help to build the central model of federated learning architecture with diverse datasets.