Highly efficient uranium uptake by the eco-designed cocamidopropyl betaine-decorated Na-P1 coal fly-ash zeolite.

In some locations around the globe, the U concentrations may exceed WHO standards by 2-folds therefore, effective yet environmentally wise solutions to purify radioactive waters are of significant importance. Here, the optimized and fully controlled coal-fly-ash based Na-P1 zeolite functionalization by employing novel, biodegradable biosurfactant molecule - cocamidopropyl betaine (CAPB) is showcased. The zeolite's surface decoration renders three composites with varying amounts of introduced CAPB molecule (Na-P1 @ CAPB), with 0.44, 0.88, and 1.59-times External Cation Exchange Capacity (ECEC). Wet-chemistry experiments revealed extremely high U adsorption capacity (qmax = 137.1 mg U/g) unveiling preferential interactions of uranyl dimers with CAPB molecules coupled with ion-exchange between Na+ ions. Multimodal spectroscopic analyses, including Fourier-Transformed Infra-Red (FT-IR), X-ray Photoelectron (XPS), and X-ray Absorption Fine Structure (XAFS), showed the hexavalent oxidation state of U, and no secondary release of the CAPB molecule from the composite. The EXAFS signals fingerprint changes in the interatomic distances of adsorbed U, showing the impact of the O and N, heteroatoms present in the CAPB molecule on U binding mechanism. The presented research outcomes showcase the easy, scalable, optimized, and environmentally friendly synthesis of biofunctional zeolite effectively purifying the real-life U-bearing wastewaters from the vicinity of the Pribram deposit (Czech Republic).

Elucidating uranium interactions with synthetic Na-P1 zeolite/Ca2+-substituted alginate composite granules through batch and spectroscopic studies: Emphasizing the significance of ion exchange and complexation.

Uranium, a key member of the actinides series, is radioactive and may cause severe environmental hazards once discharged into the water due to high toxicity. Removal of uranium via adsorption by applying tailored, functional adsorbents is at the forefront of tackling such pollution. Here, we report the optimized functionalization of the powder coal fly-ash (CFA) derived Na-P1 synthetic zeolite to the form of granules by employing the biodegradable polymer-calcium alginate (CA) and their application to remove aqueous U. The optimized synthesis showed that granules are formed at the CA concentration equals to 0.5 % wt., and that application of 1% wt. solution renders the most effective U scavengers. The maximum U adsorption capacity (qmax) increases significantly after CA modification from 44.48 mgU/g for native, powder Na-P1 zeolite to 62.53 mg U/g and 76.70 mg U/g for 0.5 % wt. and 1 % wt. CA respectively. The U adsorption follows the Radlich-Peterson isotherm model, being the highest at acidic pH (pHeq∼4). The U adsorption kinetics reveals swift U uptake, reaching equilibrium after 2h for 1 % ZACB and 3 h for 0.5 % wt. ZACB following the pseudo-second-order (PSO) kinetic model. SEM-EDXS investigation elucidates that adsorbed U occurs onto materials as an inhomogenous, well-dispersed, and micrometer-scale aggregate. Further, XPS and µ-XRF spectroscopies complementarily confirmed the hexavalent oxidation state of adsorbed U and its altered distribution on ZACBs with varying CA concentrations. U distribution was probed "in-situ" onto materials while correlations between the major elements (Al, Si, Ca, U) contributing to U scavenging were calculated and compared. Finally, a real-life coal mine wastewater (CMW) polluted by 238U and 228,226Ra was successfully purified, satisfying WHO guidelines after treatment using ZACBs. These findings offer new insights on successful yet optimized Na-P1 zeolite modification using biodegradable polymer (Ca2+-exchanged alginate) aimed at efficient U removal, displaying a near-zero environmental impact.

Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity.

Health is gold, and good health is a matter of survival for humanity. The development of the healthcare industry aligns with the development of humans throughout history. Nowadays, along with the strong growth of science and technology, the medical domain in general and the healthcare industry have achieved many breakthroughs, such as remote medical examination and treatment applications, pandemic prediction, and remote patient health monitoring. The advent of 5th generation communication networks in the early 2020s led to the Internet of Things concept. Moreover, the 6th generation communication networks (so-called 6G) expected to launch in 2030 will be the next revolution of the IoT era, and will include autonomous IoT systems and form a series of endogenous intelligent applications that serve humanity. One of the domains that receives the most attention is smart healthcare. In this study, we conduct a comprehensive survey of IoT-based technologies and solutions in the medical field. Then, we propose an all-in-one computing architecture for real-time IoHT applications and present possible solutions to achieving the proposed architecture. Finally, we discuss challenges, open issues, and future research directions. We hope that the results of this study will serve as essential guidelines for further research in the human healthcare domain.

Blockchained Federated Learning for Privacy and Security Preservation: Practical Example of Diagnosing Cerebellar Ataxia.

Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor.

Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.

Diagnosis Cerebellar Ataxia using Deep Learning with Time Series Transformed Image.

Cerebellar ataxia (CA) is defined by disrupted coordination of movement suffering from disease of the cerebellum. It reflects fragmented movements of the eyes, vocal, upper limbs, balance, gait, and lower limbs. This study aims to use a motion sensor to form a simple yet effective CA quantitative assessment framework. We suggest a pendant device to use a single kinematic sensor attached to the wearer's chest to investigate the balance capability. Via a standard neurological test (Romberg's standing), the device may reveal an early symptom of Cerebellar Ataxia tailoring toward rehabilitation or therapeutic program. We adopt a transformed-image based approach to leverage the advantage of state-of-the-art deep learning models into diagnosis CA. Three transform techniques are employed including recurrence plot, melspectrogram, and Poincaré plot. Experiment results show that melspectrogram transform technique performs best in implementation with MobileNetV2 to diagnose CA with an average validation accuracy of 89.99%.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

A Mobile Cloud based IoMT Framework for Automated Health Assessment and Management.

In recent years, there has been growing interest in the use of mobile cloud and Internet of Medical Things (IoMT) in automated diagnosis and health monitoring. These applications play a significant role in providing smart medical services in modern healthcare systems. In this paper, we deploy a mobile cloud-based IoMT scheme to monitor the progression of a neurological disorder using a test of motor coordination. The computing and storage capabilities of cloud server is employed to facilitate the estimation of the severity levels given by an established quantitative assessment. An Android application is used for data acquisition and communication with the cloud. Further, we integrate the proposed system with a data sharing framework in a blockchain network as an innovative solution that allows reliable data exchange among healthcare users. The experimental results show the feasibility of implementing the proposed system in a wide range of healthcare applications.