Blockchain-based federated learning methodologies in smart environments

Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.

     

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.     

Privacy preserving personalized blockchain reliability prediction via federated learning in IoT environments

Cluster Computing - The integration of blockchain and the Internet of Things (IoT) is seen as having significant potential. In IoT Environments, Blockchain builds a trusted environment for IoT...     

AI applications to medical images: From machine learning to deep learning

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.     

Searching for a Role of Nursing Personnel in Developing Landscape of Ehealth: Factors Determining Attitudes toward Key Patient Empowering Applications

IntroductionNurses may play an important role in the delivery of medical services based on the use of ehealth tools. Nevertheless, their taking an active role in an ehealth environment depends on their possessing the appropriate skills and mindset. The main objective of this paper was to assess nurses’ opinions and to analyze the predictors of their acceptance of ehealth features relevant to patient empowerment with a strong focus on chronic care.MethodsA survey was conducted among nurses from hospital centers of south-eastern Poland based on a questionnaire designed to assess their attitudes toward the ehealth domain. The predictors of the nurses’ acceptance of ehealth usage within specific contexts were assessed with uni- and multivariate logistic regression.ResultsAn analysis was performed on data from 648 questionnaires retained after a quality check. The duration of Internet use was consistently related to higher acceptance of ehealth applications and more certainty regarding the reliability of health-related information available on the Internet. Nurses from urban medical centers were more skeptical about the use of specific ehealth solutions.ConclusionPrevious experience in using information technologies is the main factor influencing the acceptance of specific ehealth solutions relevant for care provided to patients suffering from chronic conditions.     

Natural Leaf Made Triboelectric Nanogenerator for Harvesting Environmental Mechanical Energy

Distributed environmental mechanical energy is rarely collected due to its fluctuating amplitudes and low frequency, which is usually attributed as “random” energy. Considering the rapid development of the Internet of things (IoT), there is a great need for a large number of distributed and sustainable power sources. Here, a natural leaf assembled triboelectric nanogenerator (Leaf‐TENG) is designed by utilizing the green leaf as an electrification layer and electrode to effectively harvest environmental mechanical energy. The Leaf‐TENG with good adaptability has the maximum output power of ≈45 mW m^?2, which is capable of driving advertising LEDs and commercial electronic temperature sensors. Besides, a tree‐shaped energy harvester is integrated with natural Leaf‐TENG to harvest large‐area environmental mechanical energy. This work provides a new prospect for distributed and environmental‐friendly power sources and has potential applications in the IoT and self‐powered systems.     

Potential for industrial ecology to support healthcare sustainability: Scoping review of a fragmented literature and conceptual framework for future research

Healthcare is a critical service sector with a sizable environmental footprint from both direct activities and the indirect emissions of related products and infrastructure. As in all other sectors, the “inside‐out” environmental impacts of healthcare (e.g., from greenhouse gas emissions, smog‐forming emissions, and acidifying emissions) are harmful to public health. The environmental footprint of healthcare is subject to upward pressure from several factors, including the expansion of healthcare services in developing economies, global population growth, and aging demographics. These factors are compounded by the deployment of increasingly sophisticated medical procedures, equipment, and technologies that are energy‐ and resource‐intensive. From an “outside‐in” perspective, on the other hand, healthcare systems are increasingly susceptible to the effects of climate change, limited resource access, and other external influences. We conducted a comprehensive scoping review of the existing literature on environmental issues and other sustainability aspects in healthcare, based on a representative sample from over 1,700 articles published between 1987 and 2017. To guide our review of this fragmented literature, and to build a conceptual foundation for future research, we developed an industrial ecology framework for healthcare sustainability. Our framework conceptualizes the healthcare sector as comprising “foreground systems” of healthcare service delivery that are dependent on “background product systems.” By mapping the existing literature onto our framework, we highlight largely untapped opportunities for the industrial ecology community to use “top‐down” and “bottom‐up” approaches to build an evidence base for healthcare sustainability.     

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.

     

The utility of metabolomics in natural product and biomarker characterization

BackgroundMetabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms.Scope of reviewWe review here some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers.Major conclusionsThis review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states.General significanceThis review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers.     

DNA-mounted self-assembly: new approaches for genomic analysis and SNP detection

This article presents an overview of new emerging approaches for nucleic acid detection via hybridization techniques that can potentially be applied to genomic analysis and SNP identification in clinical diagnostics. Despite the availability of a diverse variety of SNP genotyping technologies on the diagnostic market, none has truly succeeded in dominating its competitors thus far. Having been designed for specific diagnostic purposes or clinical applications, each of the existing bio-assay systems (briefly outlined here) is usually limited to a relatively narrow aspect or format of nucleic acid detection, and thus cannot entirely satisfy all the varieties of commercial requirements and clinical demands. This drives the diagnostic sector to pursue novel, cost-effective approaches to ensure rapid and reliable identification of pathogenic or hereditary human diseases. Hence, the purpose of this review is to highlight some new strategic directions in DNA detection technologies in order to inspire development of novel molecular diagnostic tools and bio-assay systems with superior reliability, reproducibility, robustness, accuracy and sensitivity at lower assay cost. One approach to improving the sensitivity of an assay to confidently discriminate between single point mutations is based on the use of target assembled, split-probe systems, which constitutes the main focus of this review.     

Nanoproteomics comes of age

ABSTRACT

Introduction: Nanoproteomics, which is defined as quantitative proteome profiling of small populations of cells (<5000 cells), can reveal critical information related to rare cell populations, hard-to-obtain clinical specimens, and the cellular heterogeneity of pathological tissues.

Areas covered: We present a brief review of the recent technological advances in nanoproteomics. These advances include new technologies or approaches covering major areas of proteomics workflow ranging from sample isolation, sample processing, high-resolution separations, to MS instrumentation.

Expert commentary: We comment on the current state of nanoproteomics and discuss perspectives on both future technological directions and potential enabling applications.     

A fuzzy approach for optimal placement of IoT applications in fog-cloud computing

Fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of Things (IoT) applications. IoT applications should meet different characteristics such as deadline, frequency rate, and input file size. Fog nodes are heterogeneous, resource-limited devices and cannot accommodate all the IoT applications. Due to these difficulties, designing an efficient algorithm to deploy a set of IoT applications in a fog-cloud environment is very important. In this paper, a fuzzy approach is developed to classify applications based on their characteristics then an efficient heuristic algorithm is proposed to place applications on the virtualized computing resources. The proposed policy aims to provide a high quality of service for IoT users while the profit of fog service providers is maximized by minimizing resource wastage. Extensive simulation experiments are conducted to evaluate the performance of the proposed policy. Results show that the proposed policy outperforms other approaches by improving the average response time up to 13%, the percentage of deadline satisfied requests up to 12%, and the resource wastage up to 26%.

     

Mutual authentication scheme of IoT devices in fog computing environment

The radical shift in the technology with the advent of connected things has led to the significant proliferation in demand for IoT devices, commonly called ‘smart devices’. These devices are capable of data collection, which can help in umpteen applications, particularly in healthcare. With the tremendous growth in these resource-constrained end devices, there has been a substantial increase in the number of attack varieties. Since these end devices deal with the sensitive data that might cause severe damage if not handled properly. Hence, defending its integrity, preserving its privacy, and maintaining its confidentiality as well as availability is of utmost importance. However, there are many protocols, models, architecture tools, etc. proposed to provide security. Nevertheless, almost every solution propound so far is not fully resilient and lacks in giving full protection to the system in some way or the other. So here, we have proposed a lightweight anonymous mutual authentication scheme for end devices and fog nodes.

     

Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare

Transmitting electronic medical records (EMR) and other communication in modern Internet of Things (IoT) healthcare ecosystem is both delay and integrity-sensitive. Transmitting and computing volumes of EMR data on traditional clouds away from healthcare facilities is a main source of trust-deficit using IoT-enabled applications. Reliable IoT-enabled healthcare (IoTH) applications demand careful deployment of computing and communication infrastructure (CnCI). This paper presents a FOG-assisted CnCI model for reliable healthcare facilities. Planning a secure and reliable CnCI for IoTH networks is a challenging optimization task. We proposed a novel mathematical model (i.e., integer programming) to plan FOG-assisted CnCI for IoTH networks. It considers wireless link interfacing gateways as a virtual machine (VM). An IoTH network contains three wirelessly communicating nodes: VMs, reduced computing power gateways (RCPG), and full computing power gateways (FCPG). The objective is to minimize the weighted sum of infrastructure and operational costs of the IoTH network planning. Swarm intelligence-based evolutionary approach is used to solve IoTH networks planning for superior quality solutions in a reasonable time. The discrete fireworks algorithm with three local search methods (DFWA-3-LSM) outperformed other experimented algorithms in terms of average planning cost for all experimented problem instances. The DFWA-3-LSM lowered the average planning cost by 17.31%, 17.23%, and 18.28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively. Statistical analysis demonstrates that the performance of DFWA-3-LSM is better than other experimented algorithms. The proposed mathematical model is envisioned for secure, reliable and cost-effective EMR data manipulation and other communication in healthcare.

     

Visualising health risks with medical imaging for changing recipients’ health behaviours and risk factors: Systematic review with meta-analysis

BackgroundThere is ongoing clinical and research interest in determining whether providing personalised risk information could motivate risk-reducing health behaviours. We aimed to assess the impact on behaviours and risk factors of feeding back to individuals’ images of their bodies generated via medical imaging technologies in assessing their current disease status or risk.Methods and findingsA systematic review with meta-analysis was conducted using Cochrane methods. MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched up to July 28, 2021, with backward and forward citation searches up to July 29, 2021. Eligible studies were randomised controlled trials including adults who underwent medical imaging procedures assessing current health status or risk of disease, for which personal risk may be reduced by modifying behaviour. Trials included an intervention group that received the imaging procedure plus feedback of visualised results and assessed subsequent risk-reducing health behaviour. We examined 12,620 abstracts and included 21 studies, involving 9,248 randomised participants. Studies reported on 10 risk-reducing behaviours, with most data for smoking (8 studies; n = 4,308), medication use (6 studies; n = 4,539), and physical activity (4 studies; n = 1,877). Meta-analysis revealed beneficial effects of feedback of visualised medical imaging results on reduced smoking (risk ratio 1.11, 95% confidence interval [CI] 1.01 to 1.23, p = 0.04), healthier diet (standardised mean difference [SMD] 0.30, 95% CI 0.11 to 0.50, p = 0.003), increased physical activity (SMD 0.11, 95% CI 0.003 to 0.21, p = 0.04), and increased oral hygiene behaviours (SMD 0.35, 95% CI 0.13 to 0.57, p = 0.002). In addition, single studies reported increased skin self-examination and increased foot care. For other behavioural outcomes (medication use, sun protection, tanning booth use, and blood glucose testing) estimates favoured the intervention but were not statistically significant. Regarding secondary risk factor outcomes, there was clear evidence for reduced systolic blood pressure, waist circumference, and improved oral health, and some indication of reduced Framingham risk score. There was no evidence of any adverse effects, including anxiety, depression, or stress, although these were rarely assessed. A key limitation is that there were some concerns about risk of bias for all studies, with evidence for most outcomes being of low certainty. In particular, valid and precise measures of behaviour were rarely used, and there were few instances of preregistered protocols and analysis plans, increasing the likelihood of selective outcome reporting.ConclusionsIn this study, we observed that feedback of medical images to individuals has the potential to motivate risk-reducing behaviours and reduce risk factors. Should this promise be corroborated through further adequately powered trials that better mitigate against risk of bias, such interventions could usefully capitalise upon the widespread and growing use of medical imaging technologies in healthcare.

In a systematic review and meta-analysis, Gareth Hollands and colleagues study the relationship between receipt of visual feedback of results following medical imaging procedures and risk-reducing health-related behaviors.     

Doctors' use of electronic medical records systems in hospitals: cross sectional survey

ObjectivesTo compare the use of three electronic medical records systems by doctors in Norwegian hospitals for general clinical tasks.DesignCross sectional questionnaire survey. Semistructured telephone interviews with key staff in information technology in each hospital for details of local implementation of the systems.Setting32 hospital units in 19 Norwegian hospitals with electronic medical records systems.Participants227 (72%) of 314 hospital doctors responded, equally distributed between the three electronic medical records systems.ResultsMost tasks listed in the questionnaire (15/23) were generally covered with implemented functions in the electronic medical records systems. However, the systems were used for only 2-7 of the tasks, mainly associated with reading patient data. Respondents showed significant differences in frequency of use of the different systems for four tasks for which the systems offered equivalent functionality. The respondents scored highly in computer literacy (72.2/100), and computer use showed no correlation with respondents'' age, sex, or work position. User satisfaction scores were generally positive (67.2/100), with some difference between the systems.ConclusionsDoctors used electronic medical records systems for far fewer tasks than the systems supported.

What is already known on this topic

Electronic information systems in health care have not undergone systematic evaluation, and few comparisons between electronic medical records systems have been madeGiven the information intensive nature of clinical work, electronic medical records systems should be of help to doctors for most clinical tasks

What this study adds

Doctors in Norwegian hospitals reported a low level of use of all electronic medical records systemsThe systems were mainly used for reading patient data, and doctors used the systems for less than half of the tasks for which the systems were functionalAnalyses of actual use of electronic medical records provide more information than user satisfaction or functionality of such records systems     

Indoor Thin‐Film Photovoltaics: Progress and Challenges

Energy generation and consumption have always been an important component of social development. Interests in this field are beginning to shift to indoor photovoltaics (IPV) which can serve as power sources under low light conditions to meet the energy needs of rapidly growing fields, such as intelligence gathering and information processing which usually operate via the Internet‐of‐things (IoT). Since the power requirements for this purpose continue to decrease, IPV systems under low light may facilitate the realization of self‐powered high‐tech electronic devices connected through the IoT. This review discusses and compares the characteristics of different types of IPV devices such as those based on silicon, dye, III‐V semiconductors, organic compounds, and halide perovskites. Among them, specific attention is paid to perovskite photovoltaics which may potentially become a high performing IPV system due to the fascinating photophysics of the halide perovskite active layer. The limitations of such indoor application as they relate to the toxicity, stability, and electronic structure of halide perovskites are also discussed. Finally, strategies which could produce highly functional, nontoxic, and stable perovskite photovoltaics devices for indoor applications are proposed.     

Calculation of the Average Cost per Case of Dengue Fever in Mexico Using a Micro-Costing Approach

IntroductionThe increasing burden of dengue fever (DF) in the Americas, and the current epidemic in previously unaffected countries, generate major costs for national healthcare systems. There is a need to quantify the average cost per DF case. In Mexico, few data are available on costs, despite DF being endemic in some areas. Extrapolations from studies in other countries may prove unreliable and are complicated by the two main Mexican healthcare systems (the Secretariat of Health [SS] and the Mexican Social Security Institute [IMSS]). The present study aimed to generate specific average DF cost-per-case data for Mexico using a micro-costing approach.MethodsExpected medical costs associated with an ideal management protocol for DF (denoted ´ideal costs´) were compared with the medical costs of current treatment practice (denoted ´real costs´) in 2012. Real cost data were derived from chart review of DF cases and interviews with patients and key personnel from 64 selected hospitals and ambulatory care units in 16 states for IMSS and SS. In both institutions, ideal and real costs were estimated using the program, actions, activities, tasks, inputs (PAATI) approach, a micro-costing technique developed by us.ResultsClinical pathways were obtained for 1,168 patients following review of 1,293 charts. Ideal and real costs for SS patients were US$165.72 and US$32.60, respectively, in the outpatient setting, and US$587.77 and US$490.93, respectively, in the hospital setting. For IMSS patients, ideal and real costs were US$337.50 and US$92.03, respectively, in the outpatient setting, and US$2,042.54 and US$1,644.69 in the hospital setting.ConclusionsThe markedly higher ideal versus real costs may indicate deficiencies in the actual care of patients with DF. It may be necessary to derive better estimates with micro-costing techniques and compare the ideal protocol with current practice when calculating these costs, as patients do not always receive optimal care.     

Vulnerability assessment of industrial systems using Shodan

Cluster Computing - The Internet, and many of the related things, hence the term Internet of Things, IoT, continue to expand and take more roles in human lives. Indeed, this enables us to be...     

Quantification Methodology of “Health Human Resources” Related to “Health Technologies” Based on Indicators “RhSan”, “TechSan” and “RhTech”: Application to Senegal

ObjectivesAfter a century of spectacular advances, healthcare systems are facing unprecedented crisis, linked to shortage of health human resources and health technologies. In fact, availability of care depends on both technological and human resources of health. The objective of this study is to develop indicators that can measure qualitatively human resources and technologies of health in healthcare facilities, in order to assess availability of care in sub-Saharan African countries.Materials and MethodsRegarding “health technology” related to “medical devices”, an indicator called “TechSan” for “Technologies de Santé” was previously developed and published (Ndione FB et al. (2019) [6]). To address the deficiencies in usual indicators related to health human resources, a second indicator called “RhSan” for “Ressources humaines de santé” in French is proposed. This indicator assigns a weight to each health worker taking into account his specific “level of medical knowledge” and “experience”. In order to correlate “RhSan” with “TechSan”, a third indicator called “RhTech” is also developed to assess matches between “health technologies” and “health human resources” and establish realistic availability of care. These indicators have the advantage to be consolidated by specialty such as laboratory, imaging, surgery, and “mother and child care”.ResultsThe application of TechSan, RhSan and RhTech to data collected in Senegal in 2016, enabled to assess the distribution of “health technology” and “health human resources” in this country. They also permit the mapping of care availability per specialty in Senegal. The results show a strong oversupply of Dakar in terms of both human resources and technologies of health compared to other Senegalese regions. Oppositely, Sedhiou, Kaffrine, Matam and Kédougou are poorly endowed showing limits of the Senegalese health pyramid system.ConclusionTechSan, RhSan and RhTech can provide reliable decision-making tools in order to elaborate health policies in sub-Saharan African countries on more rigorous basis.