Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects.

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning\'\' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
CONCLUSIONS:

Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for IT and healthcare industries over traditional methods. However, a lack of trust between CSUs and CSPs is hindering the widespread adoption of cloud computing across industries. Since cloud computing offers a wide range of trust models and strategies, it is essential to analyze the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggests an accurate, fuzzy logic-based trust evaluation model for evaluating the trustworthiness of a cloud service provider. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using Quality of Service (QoS) characteristics like security, privacy, dynamicity, data integrity, and performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting.

BACKGROUND: Our case report provides the first clinical evaluation of autopsy practices for a patient death that occurs on the cloud. We question how autopsy practices may require adaptation for a death that presents via the \'Internet of Things\', examining how existing guidelines capture data related to death which is no longer confined to the patient\'s body.
METHODS: The patient was a British man in his 50s, who came to the attention of the medical team via an alert on the cloud-based platform that monitored his implanted cardioverter defibrillator (ICD). The patient had a background of congenital heart disease, with previous ventricular fibrillation cardiac arrest, for which the ICD had been implanted two years earlier. Retrospective analysis of the cloud data demonstrated a gradually decreasing nocturnal heart rate over the previous three months, falling to a final transmission of 24 beats per minute (bpm). In the patient post-mortem the ICD was treated as medical waste, structural tissue changes precluded the effective evaluation of device hardware, potential issues related to device software were not investigated and the cause of death was assigned to underlying heart failure. The documentation from the attending law enforcement officials did not consider possible digital causes of harm and relevant technology was not collected from the scene of death.
CONCLUSIONS: Through this patient case we explore novel challenges associated with digital deaths including; (1) device hardware issues (difficult extraction processes, impact of pathological tissue changes), (2) software and data limitations (impact of negative body temperatures and mortuary radio-imaging on devices, lack of retrospective cloud data analysis), (3) guideline limitations (missing digital components in autopsy instruction and death certification), and (4) changes to clinical management (emotional impact of communicating deaths occurring over the internet to members of family). We consider the implications of our findings for public health services, the security and intelligence community, and patients and their families. In sharing this report we seek to raise awareness of digital medical cases, to draw attention to how the nature of dying is changing through technology, and to motivate the development of digitally appropriate clinical practice.

The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user\'s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a \"privacy preservation model in the cloud environment\". The four stages of recommended security preservation methodology are \"identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration\". Initially, owner\'s data enters the Sensitive data identification process. The sensitive information in the input (owner\'s data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

This paper examines the use of quality control indicator audits in healthcare services to identify potential risk issues and improve the quality of medical care. A multi-stage approach was adopted to establish audit tools. This involved creating a cloud-based assessment form using Microsoft Form, developing Power BI dashboards connected to databases, and creating a mobile application for on-the-go auditing and result retrieval. Mobile application has replaced the need to search for and print audit forms, reducing paper usage. Automated audit results are linked to Power BI dashboards, significantly reducing the time spent on manual documentation and chart analysis, saving an average of 40 minutes per audit indicator. The paper proposes that the dashboard\'s real-time and automated features assist managers in promptly identifying potential issues and taking necessary intervention and guidance measures. These findings emphasise the significance of quality control indicator audits in enhancing the quality and safety of healthcare services.

People\'s requirements for the quality of medical services have increased. It has become a trend to introduce digital technology into nursing work, which can improve the quality of nursing care, and patient satisfaction at discharge is an important indicator of quality control. In the past, nurses in our unit would distribute paper questionnaires on discharge satisfaction to discharged patients every day and collect and archive the data. They had to work overtime for up to 4.06 hours every month to process 150 questionnaires. This article establishes an electronic discharge satisfaction questionnaire system and puts forward the concept of cloud-based service integration in line with the characteristics of Chinese people. The concept integrates existing software platforms and services and can be reused for other target applications. It can effectively realize the function of rapid management. This convenient model improves the satisfaction of both nurses and patients.