Analysis And Investigation Of A Novel Microwave Sensor With High Q-Factor For Liquid Characterization

In this paper, a new design of microwave sensor with high Q-factor for liquid characterization is analyzed and investigated. The new microwave sensor is based on a gap waveguide cavity resonator (GWCR). The GWCR consists of upper plate, lower plate and array of pins on the lower plate. The liquid under test (LUT) is characterized by placing it inside the GWCR where the electric field concentrates using a quartz capillary that is passing through microfluidic channels. The results show that the proposed sensor has a high Q-factor of 4832. Moreover, the proposed sensor has the ability to characterize different typesof liquids such as oils, ethanol, methanol and distilled water. The polynomial fitting method is used to extract the equation of the unknown permittivity of the LUT. The results show that the evaluated permittivity using the proposed sensor has a good agreement with the reference permittivity. Therefore, the proposed sensor is a good candidate for food and pharmaceutical application

Enhanced symmetrical split ring resonator for metallic surface crack detection

An enhanced sensor based on symmetrical split ring resonator (SSRR) functioning at microwave frequencies has been proposed in order to detect and characterize the metal crack of the materials. This sensor is based on perturbation theory, in which the dielectric properties of the material affect the quality factor and resonance frequency of the microwave resonator. Conventionally, coaxial cavity, waveguide, dielectric resonator techniques have been used for characterizing materials. However, these techniques are often large, and expensive to build, which restricts their use in many important applications. Thus, the enhanced bio-sensing technique presents advantages such as high measurement sensitivity with the capability of suppressing undesired harmonic spurious and permits potentially metal crack material detection. Hence, using a High Frequency Structure Simulator (HFSS) software, the enhanced sensor is modeled and the reflection S11 is performed for testing the aluminum metal with crack and without crack at the frequency range of 100 MHz to 3GHz. Variation of crack width and depth has been investigated and the most obvious finding emerged from this study is that the ability of detecting a minimum of sub-millimeter crack width and depth which is a round 10 m width or depth where the minimum shift of reflected frequency is recorded at 6.2 MHz and 3 MHz for crack width and depth respectively. The enhanced SSRR provides high capability of detecting small crack defection by utilizing the interaction between coupled gap resonators and it is useful for various applications such as aircraft fuselages, nuclear power plant steam generator tubing, and steel bridges and for others that can be compromised by metal fatigue

Analysis and investigation of a novel microwave sensor with high Q-factor for liquid characterization

In this paper, a new design of microwave sensor with high Q-factor for liquid characterization is analyzed and investigated. The new microwave sensor is based on a gap waveguide cavity resonator (GWCR). The GWCR consists of upper plate, lower plate and array of pins on the lower plate. The liquid under test (LUT) is characterized by placing it inside the GWCR where the electric field concentrates using a quartz capillary that is passing through microfluidic channels. The results show that the proposed sensor has a high Q-factor of 4832. Moreover, the proposed sensor has the ability to characterize different types of liquids such as oils, ethanol, methanol and distilled water. The polynomial fitting method is used to extract the equation of the unknown permittivity of the LUT. The results show that the evaluated permittivity using the proposed sensor has a good agreement with the reference permittivity. Therefore, the proposed sensor is a good candidate for food and pharmaceutical applications

Tribological behavior of shape-specific microplate-enriched synovial fluids on a linear two-axis tribometer

Nano- and micro-particles are being increasingly used to tune interfacial frictional properties in diverse applications, from friction modifiers in industrial lubrication to enhanced biological fluids in human osteoarthritic joints. Here, we assessed the tribological properties of a simulated synovial fluid enriched with non-spherical, poly lactic-co-glycolic acid (PLGA) microparticles (μPL) that have been previously demonstrated for the pharmacological management of osteoarthritis (OA). Three different μPL configurations were fabricated presenting a 20 μm 20 μm square base and a thickness of 5 μm (thin, 5H μPL), 10 μm (10H μPL), and 20 μm (cubical, 20H μPL). After extensive morphological and physicochemical characterizations, the apparent Young’s modulus of the μPL was quantified under compressive loading returning an average value of 6 kPa, independently of the particle morphology. Then, using a linear two-axis tribometer, the static (μs) and dynamic (μd) friction coefficients of the μPL-enriched simulated synovial fluid were determined in terms of particle configuration and concentration, varying from 0 (fluid only) to 6105 μPL/mL. The particle morphology had a modest influence on friction, possibly because the μPL were fully squeezed between two mating surfaces by a 5.8 N normal load realizing boundary-like lubrication conditions. Differently, friction was observed to depend on the dimensionless parameter , defined as the ratio between the total volume of the μPL enriching the simulated synovial fluid and the volume of the fluid itself. Both coefficients of friction were documented to grow with reaching a plateau of μs 0.4 and μd 0.15, already at  210-3. Future investigations will have to systematically analyze the effect of sliding velocity, normal load, and rigidity of the mating surfaces to elucidate in full the tribological behavior of μPL in the context of osteoarthritis

Recent advances of data compression in wireless sensor network

Wireless Sensor Networks (WSNs) have emerged as one of the most promising wireless communication systems supporting wide variety of applications ranging from military tasks, healthcare, disaster prediction and indoor positioning. The low complexity and cost of the nodes result in constraints such as computational power, communication bandwidth and battery power. Energy consumption is one of the most critical to WSN. In WSN communication, data transmission is considered the largest contributor to total energy exhaustion and apparently, it is influenced by the size of the data. Favorably, data compression can be used to reduce the amount of data that requires to be transmitted and hence prolongs sensor's lifetime. In this study, we survey various approaches, issues and challenges to WSN efficiency related to data compression discuss the effect of the data size on the sensor efficiency and how data compression algorithms can be used to address small size data transmission. Finally, recent approaches are reviewed with highlighting of advantages and disadvantages of each solution

An update on the impact of SARS-CoV-2 pandemic public awareness on cancer patients' COVID-19 vaccine compliance: Outcomes and recommendations

Background:Aside from the pandemic's negative health effects, the world was confronted with public confusion since proper communication and favorable decisions became an ongoing challenge. As a result, the public's perceptions were influenced by what they knew, the many sources of COVID-19 information, and how they interpreted it. With cancer patients continuing to oppose COVID-19 vaccines, we sought to investigate the COVID-19 pandemic and vaccine sources of this information in adult cancer patients, which either helped or prevented them from taking the vaccine. We also assessed the relevance and impact of their oncologists' recommendations in encouraging them to take the vaccine.MethodsFrom June to October 2021, an online survey was conducted at King Hussein Cancer Center. A total of 441 adult cancer patients took part in the study. Patients who had granted their consent were requested to complete an online questionnaire, which was collected using the SurveyMonkey questionnaire online platform. Descriptive analysis was done for all variables. The association between categorical and continuous variables was assessed using the Pearson Chi-square and Fisher Exact.ResultsOur results showed that 75% of the patients registered for the COVID-19 vaccine, while 12% refused vaccination. The majority of participants acquired their information from news and television shows, whereas (138/441) got their information through World Health Organization websites. Because the SARS-CoV-2 vaccines were made in such a short period, 54.7 % assumed the vaccines were unsafe. Only 49% of the patients said their oncologists had informed them about the benefits of SARS-CoV-2 vaccines.ConclusionsWe found that SARS-CoV-2 vaccine hesitancy in cancer patients might be related to misinformation obtained from social media despite the availability of supportive scientific information on the vaccine's benefits from the physicians. To combat misleading and unreliable social media news, we recommend that physicians use telehealth technology to reach out to their patients in addition to their face-to-face consultation, which delivers comprehensive, clear, and high-quality digital services that guide and help patients to better understand the advantages of COVID-19 vaccines

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of stateof-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation