Health literacy has been recognized as a significant social determinant of health, defined as the ability to access, understand, appraise, and apply health-related information across healthcare, disease prevention, and health promotion. This systematic review aims to understand the relationship between health literacy, blood pressure, and dietary salt intake. A web-based search of PubMed, Web of Science, CINAHL, ProQuest, Scopus, Cochrane Library, and Prospero was performed using specified search/MESH terms and keywords. Two reviewers independently performed the data extraction and analysis, cross-checked, reviewed, and resolved any discrepancies by the third reviewer. Twenty out of twenty-two studies met the inclusion criteria and were rated as good quality papers and used in the final analysis. Higher health literacy had shown to have better blood pressure or hypertension knowledge. However, the relationship between health literacy with dietary salt intake has shown mixed and inconsistent findings. Studies looking into the main four domains of health literacy are still limited. More research exploring the links between health literacy, blood pressure, and dietary salt intake in the community is warranted. Using appropriate and consistent health literacy tools to evaluate the effectiveness of salt reduction as health promotion programs is required.
Malaysia rolled out a diverse portfolio of predominantly three COVID-19 vaccines (AZD1222, BNT162b2, and CoronaVac) beginning 24 February 2021. We evaluated vaccine effectiveness with two methods, covering 1 April to 15 September 2021: (1) the screening method for COVID-19 (SARS-CoV-2) infection and symptomatic COVID-19; and (2) a retrospective cohort of confirmed COVID-19 cases for COVID-19 related ICU admission and death using logistic regression. The screening method estimated partial vaccination to be 48.8% effective (95% CI: 46.8, 50.7) against COVID-19 infection and 33.5% effective (95% CI: 31.6, 35.5) against symptomatic COVID-19. Full vaccination is estimated at 87.8% effective (95% CI: 85.8, 89.7) against COVID-19 infection and 85.4% effective (95% CI: 83.4, 87.3) against symptomatic COVID-19. Among the cohort of confirmed COVID-19 cases, partial vaccination with any of the three vaccines is estimated at 31.3% effective (95% CI: 28.5, 34.1) in preventing ICU admission, and 45.1% effective (95% CI: 42.6, 47.5) in preventing death. Full vaccination with any of the three vaccines is estimated at 79.1% effective (95% CI: 77.7, 80.4) in preventing ICU admission and 86.7% effective (95% CI: 85.7, 87.6) in preventing deaths. Our findings suggest that full vaccination with any of the three predominant vaccines (AZD1222, BNT162b2, and CoronaVac) in Malaysia has been highly effective in preventing COVID-19 infection, symptomatic COVID-19, COVID-19-related ICU admission, and death.
COVID-19 vaccines are indispensable, with the number of cases and mortality still rising, and currently no medicines are routinely available for reducing morbidity and mortality, apart from dexamethasone, although others are being trialed and launched. To date, only a limited number of vaccines have been given emergency use authorization by the US Food and Drug Administration and the European Medicines Agency. There is a need to systematically review the existing vaccine candidates and investigate their safety, efficacy, immunogenicity, unwanted events, and limitations. The review was undertaken by searching online databases, i.e., Google Scholar, PubMed, and ScienceDirect, with finally 59 studies selected. Our findings showed several types of vaccine candidates with different strategies against SARS-CoV-2, including inactivated, mRNA-based, recombinant, and nanoparticle-based vaccines, are being developed and launched. We have compared these vaccines in terms of their efficacy, side effects, and seroconversion based on data reported in the literature. We found mRNA vaccines appeared to have better efficacy, and inactivated ones had fewer side effects and similar seroconversion in all types of vaccines. Overall, global variant surveillance and systematic tweaking of vaccines, coupled with the evaluation and administering vaccines with the same or different technology in successive doses along with homologous and heterologous prime-booster strategy, have become essential to impede the pandemic. Their effectiveness appreciably outweighs any concerns with any adverse events.
Pneumonia is one of the common complications of SARS-CoV-2 infection where most patients have moderate to severe symptoms that pose a higher risk for death. This study aims to evaluate the treatment outcome of COVID-19-associated Pneumonia among patients with/without comorbidity in a public hospital in Indonesia. This is a retrospective cohort study involving unvaccinated confirmed COVID-19 patients admitted to the hospital between March and December 2020. All confirmed COVID-19 patients with Pneumonia (n = 1522) treated at the hospital were included. The majority of patients (99%) had mild COVID-19 symptoms while the remaining had moderate symptoms. The median age was about 32 years old and the average treatment duration was 6.25 ± 1.83 days. Most patients (88.8%) received a combination of azithromycin and oseltamivir. There was a very significant relationship (p < 0.001) between comorbidities with treatment and duration of treatment of Pneumonia in COVID-19 patients. Although most patients had Pneumonia and comorbidities, they were successfully treated with azithromycin and oseltamivir combination following approximately five days of treatment.
Vaccine willingness among the mass populace, as well as their proper knowledge and perception regarding vaccines and the vaccination process, may contribute extensively towards attaining their anticipated vaccination rates. The current study endeavored to ascertain the Bangladeshi population's knowledge, perception, and willingness towards COVID-19 vaccination. Relevant information was collected from 1201 adults aged 18 years or older by employing an online-based survey from 1 to 30 July 2021. Descriptive statistics, the chi-square (χ2) test, and a binary logistic regression analysis were applied in order to compare the extent of knowledge and perception prevalent among different demographic groups and correlate such prevalence with respective vaccine willingness. The participants expressed mean (± standard deviation) knowledge and perception scores of 6.48 ± 1.13 out of 8 and 5.37 ± 1.22 out of 7, respectively. A multivariate analysis confirmed the significant association (p < 0.05) of gender, age, and family income with the knowledge score, whereas age and knowledge level significantly influenced perception. Current living area, family income, and age were considerable contributors to COVID-19 vaccine willingness. Overall vaccine willingness was found to be significantly curtailed by inadequate knowledge (AOR 0.514, CI 95% 0.401-0.658, p < 0.001) and perception (AOR 0.710, CI 95% 0.548-0.920, p = 0.010) among the participants. All of the concerned authorities' efforts are warranted in order to improve public understanding, perception, and inclination towards vaccination.
Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
Communications between nodes in Vehicular Ad-Hoc Networks (VANETs) are inherently vulnerable to security attacks, which may mean disruption to the system. Therefore, the security and privacy issues in VANETs are entitled to be the most important. To address these issues, the existing Conditional Privacy-Preserving Authentication (CPPA) schemes based on either public key infrastructure, group signature, or identity have been proposed. However, an attacker could impersonate an authenticated node in these schemes for broadcasting fake messages. Besides, none of these schemes have satisfactorily addressed the performance efficiency related to signing and verifying safety traffic-related messages. For resisting impersonation attacks and achieving better performance efficiency, a Secure and Efficient Conditional Privacy-Preserving Authentication (SE-CPPA) scheme is proposed in this paper. The proposed SE-CPPA scheme is based on the cryptographic hash function and bilinear pair cryptography for the signing and verifying of messages. Through security analysis and comparison, the proposed SE-CPPA scheme can accomplish security goals in terms of formal and informal analysis. More precisely, to resist impersonation attacks, the true identity of the vehicle stored in the tamper-proof device (TPD) is frequently updated, having a short period of validity. Since the MapToPoint hash function and a large number of cryptography operations are not employed, simulation results show that the proposed SE-CPPA scheme outperforms the existing schemes in terms of computation and communication costs. Finally, the proposed SE-CPPA scheme reduces the computation costs of signing the message and verifying the message by 99.95% and 35.93%, respectively. Meanwhile, the proposed SE-CPPA scheme reduces the communication costs of the message size by 27.3%.
Schmidt rebound hammer test was employed in this study as a nondestructive test. This test method has been universally utilized due to its non-destructiveness for quick and easy assessment of material strength properties and quality of concrete of an existing structure. Industrial waste materials (air-dried alum sludge, treated alum sludge, limestone dust and quarry dust) were employed as replacement material for fine aggregates in this study. A normal strength concrete was designed to achieve 35 MPa at 28 days, with industrial waste materials replacing fine aggregate at different percentages (0%, 5%, 10% and 15%), and then cured for 7, 28 and 180 days. The compressive strength values and rebound numbers for all the mixes obtained were correlated, and a regression equation was established between compressive strength and Schmidt rebound number. The correlation result showed an excellent relationship between rebound number and compressive strength of concrete produced in this study at all curing ages, with correlation coefficients of R2 = 0.98, R2 = 0.99 and R2 = 0.98. The predicted equation showed a strong relationship with the experimental compressive strength. Therefore, it can be used for the prediction of compressive strength of concrete with industrial waste as a replacement for fine aggregate.
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
The recent development of the smartphone Global Navigation Satellite System (GNSS) chipsets, such as Broadcom BCM47755 and Qualcomm Snapdragon 855 embedded, makes instantaneous and cm level real-time kinematic (RTK) positioning possible with Android-based smartphones. In this contribution we investigate the instantaneous single-baseline RTK performance of Samsung Galaxy S20 and Google Pixel 4 (GP4) smartphones with such chipsets, while making use of dual-frequency L1 + L5 Global Positioning System (GPS), E1 + E5a Galileo, L1 + L5 Quasi-Zenith Satellite System (QZSS) and B1 BeiDou Navigation Satellite System (BDS) code and phase observations in Dunedin, New Zealand. The effects of locating the smartphones in an upright and lying down position were evaluated, and we show that the choice of smartphone configuration can affect the positioning performance even in a zero-baseline setup. In particular, we found non-zero mean and linear trends in the double-differenced carrier-phase residuals for one of the smartphone models when lying down, which become absent when in an upright position. This implies that the two assessed smartphones have different antenna gain pattern and antenna sensitivity to interferences. Finally, we demonstrate, for the first time, a near hundred percent (98.7% to 99.9%) instantaneous RTK integer least-squares success rate for one of the smartphone models and cm level positioning precision while using short-baseline experiments with internal and external antennas, respectively.
Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.
MeSH terms: Humans; Biosensing Techniques*; Human Body
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.
Cloud ERP is a type of enterprise resource planning (ERP) system that runs on the vendor's cloud platform instead of an on-premises network, enabling companies to connect through the Internet. The goal of this study was to rank and prioritise the factors driving cloud ERP adoption by organisations and to identify the critical issues in terms of security, usability, and vendors that impact adoption of cloud ERP systems. The assessment of critical success factors (CSFs) in on-premises ERP adoption and implementation has been well documented; however, no previous research has been carried out on CSFs in cloud ERP adoption. Therefore, the contribution of this research is to provide research and practice with the identification and analysis of 16 CSFs through a systematic literature review, where 73 publications on cloud ERP adoption were assessed from a range of different conferences and journals, using inclusion and exclusion criteria. Drawing from the literature, we found security, usability, and vendors were the top three most widely cited critical issues for the adoption of cloud-based ERP; hence, the second contribution of this study was an integrative model constructed with 12 drivers based on the security, usability, and vendor characteristics that may have greater influence as the top critical issues in the adoption of cloud ERP systems. We also identified critical gaps in current research, such as the inconclusiveness of findings related to security critical issues, usability critical issues, and vendor critical issues, by highlighting the most important drivers influencing those issues in cloud ERP adoption and the lack of discussion on the nature of the criticality of those CSFs. This research will aid in the development of new strategies or the revision of existing strategies and polices aimed at effectively integrating cloud ERP into cloud computing infrastructure. It will also allow cloud ERP suppliers to determine organisations' and business owners' expectations and implement appropriate tactics. A better understanding of the CSFs will narrow the field of failure and assist practitioners and managers in increasing their chances of success.
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
MeSH terms: Machine Learning*; Algorithms*; Electricity; Fraud; Time Factors
Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
MeSH terms: Machine Learning; Electroencephalography; Humans; Signal Processing, Computer-Assisted
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
Positive-strand RNA virus evolution is partly attributed to the process of recombination. Although common between closely genetically related viruses, such as within species of the Enterovirus genus of the Picornaviridae family, inter-species recombination is rarely observed in nature. Recent studies have shown recombination is a ubiquitous process, resulting in a wide range of recombinant genomes and progeny viruses. While not all recombinant genomes yield infectious progeny virus, their existence and continued evolution during replication have critical implications for the evolution of the virus population. In this study, we utilised an in vitro recombination assay to demonstrate inter-species recombination events between viruses from four enterovirus species, A-D. We show that inter-species recombinant genomes are generated in vitro with polymerase template-switching events occurring within the virus polyprotein coding region. However, these genomes did not yield infectious progeny virus. Analysis and attempted recovery of a constructed recombinant cDNA revealed a restriction in positive-strand but not negative-strand RNA synthesis, indicating a significant block in replication. This study demonstrates the propensity for inter-species recombination at the genome level but suggests that significant sequence plasticity would be required in order to overcome blocks in the virus life cycle and allow for the production of infectious viruses.
Among the main bio-based polymer for food packaging materials, whey protein isolate (WPI) is one of the biopolymers that have excellent film-forming properties and are environmentally friendly. This study was performed to analyse the effect of various concentrations of bio-based nanocrystalline cellulose (NCC) extracted from pineapple crown leaf (PCL) on the properties of whey protein isolate (WPI) films using the solution casting technique. Six WPI films were fabricated with different loadings of NCC from 0 to 10 % w/v. The resulting films were characterised based on their mechanical, physical, chemical, and thermal properties. The results show that NCC loadings increased the thickness of the resulting films. The transparency of the films decreased at higher NCC loadings. The moisture content and moisture absorption of the films decreased with the presence of the NCC, being lower at higher NCC loadings. The water solubility of the films decreased from 92.2% for the pure WPI to 65.5% for the one containing 10 % w/v of NCC. The tensile strength of the films peaked at 7% NCC loading with the value of 5.1 MPa. Conversely, the trend of the elongation at break data was the opposite of the tensile strength. Moreover, the addition of NCC produced a slight effect of NCC in FTIR spectra of the WPI films using principal component analysis. NCC loading enhanced the thermal stability of the WPI films, as shown by an increase in the glass transition temperature at higher NCC loadings. Moreover, the morphology of the films turned rougher and more heterogeneous with small particle aggregates in the presence of the NCC. Overall, the addition of NCC enhanced the water barrier and mechanical properties of the WPI films by incorporating the PCL-based NCC as the filler.
Asian countries have abundant resources of natural fibers, but unfortunately, they have not been optimally utilized. The facts showed that from 2014 to 2020, there was a shortfall in meeting national demand of over USD 2.75 million per year. Therefore, in order to develop the utilization and improve the economic potential as well as the sustainability of natural fibers, a comprehensive review is required. The study aimed to demonstrate the availability, technological processing, and socio-economical aspects of natural fibers. Although many studies have been conducted on this material, it is necessary to revisit their potential from those perspectives to maximize their use. The renewability and biodegradability of natural fiber are part of the fascinating properties that lead to their prospective use in automotive, aerospace industries, structural and building constructions, bio packaging, textiles, biomedical applications, and military vehicles. To increase the range of applications, relevant technologies in conjunction with social approaches are very important. Hence, in the future, the utilization can be expanded in many fields by considering the basic characteristics and appropriate technologies of the natural fibers. Selecting the most prospective natural fiber for creating national products can be assisted by providing an integrated management system from a digitalized information on potential and related technological approaches. To make it happens, collaborations between stakeholders from the national R&D agency, the government as policy maker, and academic institutions to develop national bioproducts based on domestic innovation in order to move the circular economy forward are essential.
This work was conducted to investigate the effect of carbon nanotube (CNT) on the mechanical-physico properties of the electron beam irradiated polyvinyl alcohol (PVOH) blends. The increasing of CNT amount up to 1.5 part per hundred resin (phr) has gradually improved tensile strength and Young's modulus of PVOH/CNT nanocomposites due to effective interlocking effect of CNT particles in PVOH matrix, as evident in SEM observation. However, further increments of CNT, amounting up to 2 phr, has significantly decreased the tensile strength and Young's modulus of PVOH/CNT nanocomposits due to the CNT agglomeration at higher loading level. Irradiation was found to effectively improve the tensile strength of PVOH/CNT nanocomposites by inducing the interfacial adhesion effect between CNT particles and PVOH matrix. This was further verified by the decrement values of d-spacing of the deflection peak. The increasing of CNT amounts from 0.5 phr to 1 phr has marginally induced the wavenumber of O-H stretching, which indicates the weakening of hydrogen bonding in PVOH matrix. However, further increase in CNT amounts up to 2 phr was observed to reduce the wavenumber of O-H stretching due to poor interaction effect between CNT and PVOH matrix. Electron beam irradiation was found to induce the melting temperature of all PVOH/CNT nanocomposite by inducing the crosslinked networks.