Impact on glycaemic outcomes of transitioning from in-person to virtual FreeStyle Libre onboarding during the Covid-19 pandemic

Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.

Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Managing diabetes remotely: Evaluation of the uptake and efficacy of the DBm app

Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.

Optimal continuous glucose monitoring metrics for identification of women with gestational diabetes

Aims: Continuous glucose monitoring (CGM) is widely used in pregnant women with pre-gestational diabetes, but optimal targets have not been defined in gestational diabetes. Previous work identified mild hyperglycaemia in pregnant women without gestational diabetes, but with risk factors such as obesity. We aimed to examine CGM metrics and patterns of glycaemia in women with gestational diabetes compared to healthy pregnant women with comparable risk factors. Method(s): We recruited 73 healthy women with >1 risk factor (gestational diabetes excluded using Covid-19 criteria, OGTT) and 200 women with gestational diabetes (NICE and interim-Covid- 19 criteria) from antenatal clinics at 28 weeks' gestation. A Dexcom G6 CGM device was cited on the non-dominant upper arm. Result(s): Women with gestational diabetes had significantly higher weight (mean +/- SEM 95.7 kg +/- 1.3 Vs 85.4 kg +/- 2.2) and BMI (36.0 +/- 0.5 Vs 31.3 +/- 0.7) compared to healthy pregnant women (p < 0.01). Women with gestational diabetes had significantly higher mean CGM-glucose (mean +/- SEM 5.6 +/- 0.01 Vs 5.4 +/- 0.01mmol/l;p < 0.01), significantly altered time-below- range (median(IQR);1.0% (0.2-2.9) vs 2.5% (0.7-5.5);p < 0.05) and time-in- range (95.0% (91.1-97.9) vs 94.5% (87.9-96.2);p < 0.05) but comparable time-above- range to healthy women with risk factors. Diurnal glucose profiles in women with gestational diabetes were comparable to healthy women between 14:00 and 18:00, but demonstrated significant increases in glucose at all other time points during the 24-h cycle (p < 0.01). Conclusion(s): Mean CGM glucose is the most reliable CGM metric to distinguish women with gestational diabetes from healthy pregnant women with risk factors.

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

Change in glycaemic control immediately after Covid-19 vaccination in people with type 1 diabetes: What happens after the most recent vaccination?

Aims: We have shown previously in 93 individuals with type 1 diabetes using the FreeStyle Libre flash glucose monitor that the week after their first Covid-19 vaccination, the percent 'time in target range 3.9-10mmol/ l' (%TTR) average went from 55.2%-> 52.4% (effect size -5.1%) with 58% of people recording a fall. 47 (50%) people with HbA1c < 56mmol/mol %TTR went from 69.3-> 63.5 (-8.3%) and 24 (25%) people using insulin+oral treatment 56.7%-> 50.7% (-10.1%). We have now repeated the exercise after the most recent Covid-19 vaccination. Method(s): FreeStyle Libre data and medical records of the same patients from the previous study were examined for the week before and week after their most recent Covid-19 vaccination. () in the results section show change in %TTR as % of the prior value to show effect size. TTR% results from 2 weeks before and after were also considered. Result(s): Median time between vaccines was 38 weeks IQR (37-40). After the latest vaccination average %TTR average went from 51.1%-> 49.8% (-2.5%) with a reduction found in 54% of patients. Impact on the 39 patients with HbA1c < 56mmol/mol -% TTR from 66.2%-> 61.8% (-6.5%) and the 20 (25%) patients using insulin+oral %TTR from 48.2%-> 47.1% (-2.2%). 65% of the patients whose %TTR fell previously, fell again after this vaccination. Fortnight average %TTR 53.5%-> 52.1% (-2.7%) whereas in the previous study across fortnight %TTR 55.4%-> 54.0% (-2.4%). Conclusion(s): The perturbation effect on blood glucose with 1st Covid-19 vaccination was seen again in the latest vaccination but reduced in magnitude, confirming that a significant group of type 1 diabetes individuals' glycaemic control is still being impacted by the Covid-19 vaccination.

Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.

Automated Contactless Continuous Temperature Monitoring System for Pandemic Disease Controlling Infrastructures

People are being thermally screened in hospitals and in such facilities, all the data collected must be stored and displayed. The person responsible for keeping track of people's body temperatures must put in more time and effort. This approach is a tedious task, especially during times of dealing with the pandemic diseases like Covid-19. Hence, in this paper, an automated contactless continuous temperature monitoring system is designed to eliminate this time-consuming process. If a person's temperature is too high, that is, higher than the usual temperature range, the system records it and monitors it continuously via a mobile application. In this paper, we present the development of an Automated contactless continuous body temperature monitoring system using a Raspberry Pi camera and mobile application. © 2023 IEEE.

My diabetes My Way: Intelligent delivery of diabetes education and support for self-management

Background: My Diabetes My Way (MDMW) is NHS Scotland's interactive website, offering education, structured eLearning and online records access for people living with diabetes. We aimed to analyse user activity during the last 12 months. Method(s): Data were collected during the period from November 2021 to October 2022. Registration and user audit logs were analysed, observing activity across all website content and features. Result(s): An average of 62,853 pages were accessed on the public website each month. Significant activity increases were observed in December 2021 (n = 81,237). There were increased views in September 2022 (n = 76,502) and October (n = 73,039) The top five pages accessed were;Coronavirus: advice for people living with diabetes (n = 12,478), FreeStyle Libre (n = 4325), Emergency advice (n = 1576), Blood pressure-reducing your risks of complications (n = 1559) and Blood glucose monitoring and HbA1c targets (n = 1485). eLearning: During this period, 382 individuals completed one of 11 QISMET-accredited structured eLearning courses. eLearning course usage increased in relation to patient awareness activity. Social Media: There are currently 3919 Facebook and 3600 Twitter followers. Records Access: 67,655 patients had registered to access their data and 35,157 had actively accessed their records by the end of October 2022. Patient feedback remains highly positive. Conclusion(s): MDMW is a consistent and reliable resource for people with diabetes and their families to access at any time online. User statistics continue to rise, while latest development plans include the addition of new Patient Reported Outcome Measures, risk prediction features, and enhanced sharing of data with the healthcare team.

Digitalization of Professional Regulation Commission (PRC) Process of Accreditation of Nutritionist and Dietetics

With the recent global COVID-19 pandemic and lockdowns, accreditation delays have become inevitable in lieu of the strict travel restrictions. The usual accreditation inspection process conducted face-To-face was affected. Organizations are shifting to a reliance on technology to adapt to the national emergency. The study aims to bridge the gap by digitalization Professional Regulation Commission's (PRC) monitoring and accreditation system to conduct a virtual inspection and monitoring. With all of these said, the specific objectives of the researchers and developers are to develop an efficient digitized system that captures the original one. In developing the proposed accreditation and monitoring system and document management system (website) for PRC, the group will adapt and take inspiration from the Agile Development Lifecycle methodology, which will help the modification and other functionality of the system by using the iterative style in the development of the system. The proposed digital monitoring system undergoes a cross-browser test, and performance test, i.e., Requirements Traceability Matrix (RTM). These tests show that the proposed system passed the compatibility for commonly used browsers like Chrome, Edge, Mozilla, and many more. The Final Test in Performance Testing showed that the system RTM functions had passed all final testing. © 2023 IEEE.

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

Real-time Cheating Detection System Based on Sight Detection

Due to the COVID-19 pandemic, many exams, written tests and interviews are conducted online and remotely, which raises a series of questions such as how to prevent cheating. In this project, the methods commonly used in the existing cheating monitoring system are fully investigated and their shortcomings are improved one by one. Finally, a line of sight detection algorithm based on computer vision technology is designed, and a prototype of auxiliary cheating detection system that can get good results only with a small number of samples is developed. © 2022 SPIE.

Remotely Controlled Real-Time Health Surveillance System of COVID-19 Patients

This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.

An Efficient IoT e-Environment System to Monitor and Control Pollution and Maintain Hygienics in Educational Institutions

The Covid-19 Pandemic (C19P) situation of the entire world now affects all fields in terms of Excellencies and let to suffer drastically from normal functioning. The whole world is now concentrating on the protection from the C19 virus in the form of vaccination (C19V) and social distancing (SD). There is a kind enough need arises to maintain the hygiene environment during and after the post C19P situations, and this IoT e-Environment Pollution Monitoring and Controlling System (IEE-PMCS) with 3 parameters (air, water, sound) resolves and addresses the issues in the hygiene maintenance of various environments as common. In the IEE-PMCS proposed work, the 3 measuring parameters and their real-time and current values are percept with the appropriate sensors of IoT elements, and the data are collected and stored on a cloud and are verified with the predefined threshold values of pollution measures with included tolerance values of permissible values to indicate if there is any cause of the pollution on the real-time perceptions. The verification and decision-making of the system is reliable on the new algorithms proposed in this work. This work is based on system modeling and providing an efficient architecture to the maximum extent of the intended purpose, with a detailed description of the flow of operations and with the algorithmic level. © 2022 IEEE.

The Optimal Method to Predict Hemoglobin A1c from Flash Glucose Monitor Data

In situations where it is difficult for patients to visit hospitals, such as the coronavirus disease pandemic, it is important to more detailly predict hemoglobin A1C (HbA1c) from flash glucose monitor (FGM) data. CGM data over 14 days can be obtained from a FGM sensor;therefore, there are many options for extracting the duration from which glucose levels are derived. Thus, the extracted durations were closely studied to determine which mean glucose levels can predict HbA1c more accurately. Seventy-three outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a FGM (FreeStyle Libre Pro), and did not change diabetic treatments, on a hospital visit. FGM data over 24 h 13 days (from 00:00 on day 2 to 24:00 on day 14 [FGM attachment: day 1]) were analyzed. The mean glucose levels were calculated corresponding to the following durations: 1 day: day 2 ~ day 14 (n=13), 2 days: days 2-3 ~ days 13-14 (n=12) 12 days: days 2-13 ~ days 3-14 (n=2), 13 days: days 2-14 (n=1) [total 91 durations] (extracted mean glucose levels). Data were analyzed in all patients (n=73), in patients with hypoglycemia in the 13 days (Hypo) group (n=40), and in patients without hypoglycemia in the 13 days (Nonhypo) group (n=33). In all patients, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.76-0.86, p<0.001). HbA1c was the most significantly correlated to the mean glucose levels over 13 days (days 2-14). "Correlation coefficients between HbA1c and extracted mean glucose levels" ("r, HbA1c, EMGL") were also correlated to number of extracted days for the extracted mean glucose levels (r=0.80, p<0.001 [n=91]). In the Hypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.55-0.73, p<0.001). The mean glucose levels over 13 days (days 2-14) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.68, p<0.001;Fig. 2). In the Nonhypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.73-0.87, p<0.001). The mean glucose levels over 12 days (days 2-13) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.61, p<0.001). The results of the present study are consistent with that of a previous study reporting that the minimum duration needed to estimate time in range over 90 days is 14 days. In the prediction of HbA1c using data from one FGM sensor, prolonged measurement can make the glucose management indicator more accurate. Especially for patients with hypoglycemia, the importance of prolonged measurement may be applicable.

A Smart Social Distance Monitoring System

The entire world is affected by Covid-19 pandemic. One of the major reasons is that it is contagious and a minimum distance should be maintained to stay safe. Social distancing might be a difficult task to implement effectively. Social distancing plays a pivotal role in curbing diseases that are contagious like Covid-19.Now that situations are returning to normal, the risk of getting infected is still high. Governments are deciding to ease lockdown regulations, as part of the unlocking public places, workspaces and educational institutions started to resume their activities. Considering the current scenario, the public has to strictly follow all the necessary Covid-19 protocols to reduce the spike in the number of Covid cases. This project aims to develop a prototype device that helps in implementing social distancing using Ultra-Wide Band (UWB) wireless technology based solution. Prototype issues an alert signal when the distance between individuals is less than the prescribed threshold distance. If the protocol is breached, the user is alarmed through an LED. UWB is known for its advantages as it has greater signal strength compared to Bluetooth. The design of the prototype enables implementation as wearable such as an ID card. © 2022 IEEE.

Smart Embedded Framework of Real-Time Pollution Monitoring and Alert System

The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. © 2022 IEEE.

YOLO KeepSafe: Utilization of YOLOv4-Based Algorithm to Employ a Real-time People Detection and Social Distance Monitoring System

This paper outlines the process used to create the social distance detection system - YOLO KeepSafe. The researchers discuss several calibration procedures, investigate the difficulties that arise when used in real-time situations, and offer potential solutions. © 2022 IEEE.

Internet of Things System Wearable Healthcare for Monitoring the Challenges of the COVID-19 Pandemic

During the COVID-19 situation, various application-based work has to be studied and deployed to enable an IoT-based health framework. This work-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. Therefore, it identifies various technologies of IoT-based systems for monitoring pandemic situations. The mechanisms included in IoT like actuators, sensors, and the cloud-based network serves to help people from home rather than visiting the hospital occasionally. It uses optimizers to train the "noise” and "cough” target classes. Mel Frequency Cepstral Coefficients (MFCCs) were initially employed in several speech processing approaches, but as the discipline of Music Information Retrieval (MIR) advanced alongside machine learning, it was discovered that MFCCs could accurately capture timbre. Overall, the study finds different IoT applications for the medical area during the pandemic situation with detailed descriptions. In this present condition, advanced methodologies have given way to innovation in day-to-day life. The IoT-based model provides an enhancement of 98.8% with a minimum training loss of 0.15. The framework depicts the excellent working of the proposed framework, and a true positive value of around 96.6% is shown in the confusion matrix and a true negative rate of around 97% was illustrated using this model. By making it possible for the cost-effective fabrication of wearable sensors through printing on a variety of flexible polymeric substrates, the rapid advancements in solution-based nanomaterials presented a hopeful viewpoint to the field of wearable sensors. This review focuses on the most recent significant advancements in the field of wearable sensors, including novel nanomaterials, manufacturing techniques, substrates, sensor types, sensing mechanisms, and readout circuits. It concludes with difficulties in the subject's future application. © 2021 Journal of Robotics and Control (JRC). All rights reserved.