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There are still outbreaks of COVID-19 across the world. Ships increase the risk of worldwide transmission of the virus. Close contact tracing remains as an effective method of reducing the risk of virus transmission. Therefore, close contact tracing in ship environments becomes a research topic. Exposure Notifications API (Application Programming Interface) can be used to determine the encountered location points of close contacts on ships. Location points of close contact are estimated by the encountered location points. Risky areas in ships can be calculated based on the encountered location points. The tracking of close contacts is possible with Bluetooth technology without the Internet. The Bluetooth signal can be used to judge the proximity among detecting devices by using the feature that Bluetooth has a strong signal at close range. This Bluetooth feature makes it possible to trace close contacts in ship environments. In this paper, we propose a method for close contact tracing and showing the risky area in a ship environment by combining beacon and Exposure Notification API using Bluetooth technology. This method does not require an Internet connection for tracing close contacts and can protect the personal information of close contacts. Copyright © 2023 KSII.
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As COVID-19 is still spreading globally, the narrow ship space makes COVID-19 easier for the virus to infect ship passengers. Tracking close contacts remains an effective way to reduce the risk of virus transmission. Therefore, indoor positioning technology should be developed for ship environments. Today, almost all smart devices are equipped with Bluetooth. The Angle of Arrival (AoA) using Bluetooth 5.1 indoor positioning technology is well suited for ship environments. But the narrow ship space and steel walls make the multipath effect more pronounced in ship environments. This also means that more noises are included in the signal. In the Uniform Rectangular Array (URA) type receiving antenna array, elevation and azimuth angles are two important parameters for the AoA indoor positioning technology. Elevation and azimuth angles are unstable because of the influence of noise. In this paper, a Self-Learning Mean Optimization Filter (SLMOF) is proposed. The goal of SLMOF is to find the optimal elevation and azimuth angles as a way to improve the Bluetooth 5.1 AoA indoor positioning accuracy. The experimental results show that the Root Mean Square Error (RMSE) of SLMOF is 0.44 m, which improves the accuracy by 72% compared to Kalman Filter (KF). This method can be applied to find the optimal average in every dataset.
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Introduction: Burnout in academia is an issue of growing concern. The COVID-19 pandemic has increased the risk of burnout;however, the data are limited for pharmacy faculty and the associating factors have not been identified. Research Question or Hypothesis: To evaluate the factors contributing to burnout among pharmacy faculty during the COVID-19 pandemic. Study Design: Quantitative survey study Methods: Pharmacy faculty in the U.S. and Canada were invited to take part in a web-based survey. The survey collected demographic information, responses from the standardized Maslach Burnout Inventory Educators Survey (MBI-ES) and questions assessing how the pandemic has affected the respondent's personal and professional life. MBI-ES specifically measured emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Responses to each of the three MBI-ES subcategories were converted to a sum score. Factors contributing to burnout were identified by comparing the mean sum scores of the MBI-ES versus demographics, primary responsibilities, academic ranks, and other variables. Single-factor analysis of variance and the post-hoc Tukey HSD-Kramer test were used to determine statistical significance between groups. Result(s): We received 128 responses during a 10-week period. The mean sum scores for EE, DP, and PA for the entire cohort were 25.9, 6.1, and 33.6 respectively. These scores are higher than those from studies conducted before the pandemic, suggesting higher burnout in general. Junior faculty experienced higher burnout. Faculty with primarily research responsibilities experienced the greatest EE;those with primarily patient-care responsibilities experienced the most DP;and those with primarily didactic teaching responsibilities experienced the lowest PA. Factors having the most impact on burnout included increased workhours, having school-age children, and not having a consistent work location. Conclusion(s): Pharmacy faculty experienced a heightened level of burnout during the pandemic. Interventions should be targeted towards junior faculty, those with significant research responsibilities, and those with school-age children.
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The purpose of this study is to verify the mediating effect of individual factors and friend factors in the process of influencing youths' emotional factors on school maladjustment in the COVID-19 pandemic situation. To this end, a survey was conducted on 495 youths living in South Korea. The data were analyzed with SPSS 22.0 and PROCESS macro ver. 3.5 programs. The research results were derived as follows. First, it was found that there was a significant correlation between emotional risk factors, school maladjustment, personal protection factors, and friend protection factors of adolescents. Second, emotional risk factors, personal protection factors, and friend protection. Phosphorus was found to have a significant effect on school maladjustment. Third, it was found that personal protection factors and friend protection factors were multi-mediated in the process of adolescents' emotional risk affecting school maladjustment. Through these research results, the significance and limitations of this study were presented, and discussions for subsequent studies were presented © 2022 IEEE.
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Currently, the most effective way to reduce transmission of COVID-19 is to differentiate between close contacts. Location points of close contact are essential for differentiation. As a major mode of transportation, ships provide a vehicle for virus transmission. Timely detection location of close contacts inside a ship can prevent the spread of viruses. Location-based services can be provided for ship passengers. Bluetooth is widely available in many wearable devices. The Bluetooth 5.1 angle of arrival (AoA) indoor positioning algorithms can provide a certain indoor positioning accuracy for ship passengers. The two most essential parameters in Bluetooth 5.1 AoA indoor positioning are elevation angle and azimuth angle. Elevation and azimuth are often not accurate enough due to noise, which increases indoor positioning errors. As a result, this paper proposes a mean optimization filter for ship environments, which combines the box plot method to improve Bluetooth 5.1 AoA indoor positioning accuracy, with an RMSE of 0.34 m. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Aims Background Child and young person (CYP) mental health is a public health priority with 75% of life long mental health problems starting before the age of 25.1 Prevalence of all mental health disorders among children aged 5-15 years is 11.2%.2 Aims * To highlight current in-patient numbers and pathway, from initial presentation to discharge. * Identify any changes that can be made to improve care foryoung people admitted with mental health presentations Methods Patients were identified from daily handover sheets from January 2019 to April 2021. Information on each patient was gathered using electronic patient record. Information gathered included: 1. Number of CAMHS inpatients broken down by age, gender and diagnosis. 2. Patient pathway including time spent waiting in A&E 3. Use of Registered mental health nurse (RMN) 4. Treatments provided on ward 5. Daily review by peadiatric and CAMHS team 6. Length of stay 7. Place of discharge Results 231 CYP admitted under CAMHS between January 2019 and April 2021. See figure 1. Of these admissions 86% were female. 27 patients accounted for a fifth of admissions. Average age at admission 14 years (8-16 years). IntentionalOverdose was the most common reason for admission. See figure 2. Due to changes in computer systems, complete information regarding patient stay was available for patients between April 2020 to April 2021. 72% of patients beached four hour ED wait of which 80% due to waiting for RMN to become available. 86% of patients required an RMN. 13% of patients were under section at some point during admission. Fifth of patients were discharged to tier 4 services. Average time from admission to discharge was 10 days. The longest admission was 264 days. 97% of patients had daily paediatric reviews and 80% of patients had a daily CAMHS reviews. 62% of patientsengaged in alternative therapy during their stay. Eg art therapy or youth support worker. Conclusion Admission rates dropped during the first COVID 19 lockdown and rates tend to be lower during summer months. There tends to be increased mental health admission following return to school in September. Eating disorder patients tend to have more prolonged inpatients stay due to waiting for Tier 4 bed availability. Lack of RMNs lead to prolonged wait time in ED prior to admission. A mental health awareness teaching week was delivered to the department to encourage the team to carry out HEADSSS assessment for all young people admitted and tailor services around the YP to help improve care of patients with mental health issues. (Figure Presented).
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COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health. © 2022 IEEE.
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Introduction: Coronavirus Disease 2019 (COVID-19) is an ongoing public health crisis that has sickened or precipitated death in millions. The etiologic agent of COVID-19, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), infects the intestinal epithelium and can persist long after the respiratory infection has cleared. We previously observed that intestinal SARS-CoV-2 infection levels varied by individual donors and did not correlate positively with ACE2, the cognate SARS-CoV-2 receptor. Therefore we aimed to delineate host factors that influence viral infection in the intestine. Methods: Published dataset GSE75214 was downloaded and expression levels of select genes were querried. Primary human ileal spheroids (enteroids), derived from healthy donors and patients with Crohn's disease (CD), were grown on 2D transwells until confluent. Cells were differentiated for 3d before infection with a modified vesicular stomatitis virus expressing the SARS-CoV-2 spike protein (VSV-SARS-CoV-2) and GFP for 1h at a multiplicity of infection of ~0.5. Cells were harvested pre-infection and 24h after infection and expression of select genes was performed by qRT-PCR. Expression data were fit to a linear regression model to predict viral RNA levels. Results: Small intestine biopsy samples from CD patients demonstrated a reduction in ACE and an increase in CTSB and CTSL expression during active inflammation compared to healthy controls. Viral RNA expression did not correlate with ACE2 expression in CD enteroids. A subset of CD enteroids exhibited enhanced protease expression (TMPRSS2, TMPRSS4, CTSL), each of which correlated with higher viral RNA levels (P=0.04, P=0.002, P=0.006, respectively). Expression of these proteases was higher in the pre-infection for the sample subset. Principle component analysis of uninfected expression data demonstrated these samples clustered separately from the others, with the difference driven by TMPRSS2, TMPRSS4, and CTSL. Modeling viral RNA levels based on gene expression revealed expression levels of these proteases are a predictive expression signature. Conclusions: Host protease expression can predict SARS-CoV-2 infection and represent potential therapeutic targets for COVID-19. This is consistent with the recent report showing that cathepsin inhibition reduces SARS-CoV-2 spike-mediated syncytia formation. High expression of these proteases in the intestine may also be a novel biomarker for the risk of intestinal complications associated with COVID-19.(Figure Presented)RNA data from dataset GSE75214 demonstrating reduced ACE2 and increased CTSB and CTSL in patients with Crohn's disease during active inflammation compared to healthy controls. (Figure Presented) Enteroids from healthy control donors and patients with Crohn's disease were grown in 2D transwells and expression of indicated genes was assessed in pre-infection (A) and after infection with VSV-SARS-CoV-2 (B)
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Concern about the health of people who traveled on board was raised in the COVID-19 outbreak on the Diamond Princess cruise ship. The ship narrow space offers an environment conducive to the virus spread. Close contact isolation remains one of the most important current measures to stop the rapid spread of the virus. Contacts can be identified efficiently by detecting smart devices nearby. The smartphone Bluetooth RSSI signal is significant data for positioning. The traditional indoor positioning algorithm cannot be directly applied in the mobile ship environment. It is necessary to study the indoor positioning algorithm which applies to the mobile ship environment. In this paper, we propose an in-ship localization algorithm, which can achieve indoor positioning without the fingerprint map, with an RMSE of 1.63 m. © 2022 Little Lion Scientific
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Because of the COVID-19 pandemic, the number of bicycle users has increased, raising concerns regarding bicycle safety. Although various small electronic devices have been used to ensure bicycle safety, such devices require an external battery, which introduces certain limitations such as recharging requirements. Several researchers have investigated methods to sustainably harvest energy from bicycles. Triboelectric-generator-based solutions, which can utilize the mechanical motion of a rolling tire can serve as the auxiliary power source of small electronics or self-powered sensors. However, research on practical and reliable bicycle-related triboelectric nanogenerators is limited. In this study, a triboelectric bicycle tire (TBT) was developed, considering the actual material/structure of commercial bicycle tires, and the novel electricity-generation mechanism was clarified. As the TBT system had a fully inserted (packaged) structure, it could generate extremely stable electrical output for 120,000 cycles. The electrical performance was quantitatively analyzed depending on the design parameters and riding situation. The findings demonstrated that the TBT system can be effectively used to enhance bicycle safety;according to the peak magnitude and waveform data, the TBT system can function as a self-powered bicycle pressure sensor. Second, the freestanding-mode TBT system can be utilized as a self-powered bicycle safety light in real time, demonstrated by its ability to power LEDs. © 2021
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Due to the recent spread and long-term continuation of COVID-19, numerous non-contact body temperature measuring equipment is being introduced in public places to prevent cross-infection. Therefore, extensive research on the non-contact body temperature measurement method, using a thermal imaging camera, is being conducted globally. The existing method of measuring the body temperature using a thermal imaging camera has several limitations including a severe temperature deviation while measuring the body temperature, blurring of the location of the set measuring point, or not obtaining an accurate measurement due to obstacles. To overcome these limitations, we used deep learning to detect faces in thermal images, and measure the body temperature using a multipoint-based image processing with histograms of the detected areas. The proposed deep-learning-based method exhibited several advantages such as higher accuracy, wide area coverage, and efficient detection irrespective of obstacles. Compared to the conventional body temperature measurement, that proposed in this paper resulted in an average body temperature measurement error rate of less than 2%. © 2021, Institute of Control, Robotics and Systems. All rights reserved.
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Staying healthy while following social distancing protocols is of great importance to older adults due to increased risk of serious complications from COVID-19. Mild to moderate physical activity improves immune system responses to viral respiratory infections. Additionally, social engagement has cumulative health protective benefits across the lifespan. At present, active and social recreation opportunities have been drastically reduced or disbanded due to group size limitations, stay-at-home orders, and reductions in services and facilities. As a result, community dwelling older adults are homebound and need alternative exercise and social opportunities to maintain their health during this time. Leisure professionals can promote physical activity and social well-being among older adults by increasing home-based opportunities, including offering additional online leisure services, opportunities for volunteerism, and social interactions.
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Background: New York City (NYC)-based Rogosin Institute has provided a specialty case management to patients via its Program for Education in Advanced Kidney Disease (PEAK) since 2015. PEAK aims to educate patients about options for renal replacement therapy and encourages adoption of home dialysis modalities and transplantation. The global pandemic due to the novel coronavirus SARS-CoV-2 (COVID-19) hit NYC at the beginning of March 2020 and has resulted in over 40,000 hospital admissions and claimed the lives of 15,000 NYC residents. Methods: A city-wide requirement to adopt social distancing caused us to adapt our approach so we could continue our patient management and delay dialysis starts where possible. Our multidisciplinary team continued to provide regular consults via a telehealth platform. pulseData built a query to look at the standard deviation of lab results for creatinine and potassium over the prior six months leading to decreased need for patients to leave their homes for venipuncture. We also built a query to identify those patients at high risk of poor COVID-19 outcomes (those over age 65, with COPD or other respiratory conditions, and other comorbid risk factors). These high risk patients were discussed in a weekly care plan meeting. Results: The Rogosin team delivered 481 telehealth appointments between January 1 and May 1 2020. We identified 17 of 189 patients seen in PEAK over the last 12 months as high risk for poor COVID-19 outcomes. The standard deviation (SD) of the last six months of serum creatinine measurements for PEAK patients was 0.24 (IQR 0.16-0.44), patients with an SD in the top quintile were considered to be in need of repeat lab tests and home-based venipuncture was used where possible. Only 12 PEAK patients began dialysis treatment between January 1 and May 1 2020 compared with 28 patients in the same period in 2019. Most of these patients (67%) began dialysis optimally (as an outpatient and with venous access) in Jan-May 2020 vs. only 43% in the same period 2019 (a 56% increase). Conclusions: Responding to the challenges of the COVID-19 crisis were acutely felt in NYC and represented a major disruption to our ability to deliver nephrology care. Adopting a coordinated, data driven approach we were able to continue to deliver multidisciplinary care to patients and improve renal replacement therapy outcomes.
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The colostrum contains diverse and numerous immunoglobulins. The neonatal calf obtains majority of passive immunity from colostrum, which especially has huge effects on calf health. Historically, the amount of immunoglobulins in colostrum has been predicted through the density of the colostrum, but this correlation has not yet been clearly determined. In this study, we determined the correlation between the density and the immunoglobulin concentration, the difference between Holstein and Jersey cattle, and the impact by parity number. Densities were measured with Cruise densimeter. The concentration of immunoglobulins was assayed by ELISA. The concentration of total immunoglobulin G (IgG) and antibody against foot-and-mouth disease virus (FMDV) tend to increase following the density of the colostrum. However, the level of antibody against rota-corona virus was not positively correlated with the density. Regarding species and parity effects, interestingly, Holstein cows had significantly higher values in total IgG and FMDV antibody than Jersey cows. Multiparous dairy cows produced colostrums with higher concentrations of total IgG, FMDV antibody than primiparous dairy cows.