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The Automated Meteorology—Ice—Geophysics Observation System 3 (AMIGOS-3) is a multi-sensor on-ice ocean mooring and weather, camera and precision GPS measurement station, controlled by a Python script. The station is designed to be deployed on floating ice in the polar regions and operate unattended for up to several years. Ocean mooring sensors (SeaBird MicroCAT and Nortek Aquadopp) record conductivity, temperature and depth (reported at 10 min intervals), and current velocity (hourly intervals). A Silixa XT fiber-optic distributed temperature sensing system provides a temperature profile time-series through the ice and ocean column with a cadence of 6 d−1 to 1 week−1 depending on available station power. A subset of the station data is telemetered by Iridium modem. Two-way communication, using both single-burst data and file transfer protocols, facilitates station data collection changes and power management. Power is supplied by solar panels and a sealed lead-acid battery system. Two AMIGOS-3 systems were installed on the Thwaites Eastern Ice Shelf in January 2020, providing data well into 2022. We discuss the components of the system and present several of the data sets, summarizing observed climate, ice and ocean conditions.
As the presentation of anxiety may differ between younger and older adults, it is important to select measures that accurately capture anxiety symptoms for the intended population. The 21-item Beck Anxiety Inventory (BAI) is widely used; however, its high reliance on somatic symptoms may result in artificial inflation of anxiety ratings among older adults, particularly those with medical conditions. The 30-item Geriatric Anxiety Scale (GAS) was specifically developed for older adults and has shown strong psychometric properties in community-dwelling and long-term care samples. The reliability and validity of the GAS in a memory clinic setting is unknown. The present study aimed to compare the psychometric properties of the GAS and the BAI in a memory disorder clinic sample.
Participants and Methods:
Participants included 35 older adults (age=73.3±5.0 years; edu=15.3±2.8 years; 42% female; 89% non-Hispanic white) referred for a neuropsychological evaluation in a memory disorders clinic. In addition to the GAS and BAI, the Geriatric Depression Scale (GDS) and Montreal Cognitive Assessment (MoCA) were included. Cutoffs for clinically significant anxiety were based on published data for each measure. A dichotomous anxiety rating (yes/no) was created to examine inter-measure agreement; minimal anxiety was classified as “no” and mild, moderate and severe anxiety were classified as “yes.” Internal scale reliability was examined using Cronbach’s alpha. Convergent and discriminant validity were examined using Spearman rank correlation coefficients. Frequency distributions determined the proportion of yes/no anxiety ratings, and a McNemar test compared the proportion of anxiety classifications between the two measures.
Results:
Both measures had excellent internal consistency (BAI: a=.88; GAS: a=.94). The BAI and GAS were highly correlated with each other (r=.79, p<.001) and positively correlated with a depression measure (BAI-GDS: r=.51, p=.002; GAS-GDS: r=.53, p=.001). Discriminant validity was supported by lower correlations between the anxiety measures and cognition (BAI-MoCA: r=.38, p=.061; GAS-MoCA: r=.34, p=.098). The BAI classified 14 participants as having anxiety (40%) and 21 participants as not having anxiety (60%), whereas the GAS classified 21 participants as having anxiety (60%) and 14 participants as not having anxiety (40%). The proportion of anxiety classifications were significantly different between the two measures (p =.016). For 28 participants (80%), there was agreement between the anxiety ratings. Seven participants (20%) were classified as having anxiety by the GAS, but not by the BAI; GAS items related to worry about being judged or embarrassed may contribute to discrepancies, as they were frequently endorsed by these participants and are unique to the GAS.
Conclusions:
Results support that both anxiety measures have adequate psychometric properties in a clinical sample of older adult patients with memory concerns. It was expected that the BAI would result in higher classification of anxiety due to reliance on somatic symptoms; however, the GAS rated more participants as having anxiety. The GAS may be more sensitive to detecting anxiety in our sample, but formal anxiety diagnoses were not available in the current dataset. Future research should examine the diagnostic accuracy of the GAS in this population. Overall, preliminary results support consideration of the GAS in memory disorder evaluations.
As COVID-19 was declared a health emergency in March 2020, there was immense demand for information about the novel pathogen. This paper examines the clinician-reported impact of Project ECHO COVID-19 Clinical Rounds on clinician learning. Primary sources of study data were Continuing Medical Education (CME) Surveys for each session from the dates of March 24, 2020 to July 30, 2020 and impact surveys conducted in November 2020, which sought to understand participants’ overall assessment of sessions. Quantitative analyses included descriptive statistics and Mann-Whitney testing. Qualitative data were analyzed through inductive thematic analysis. Clinicians rated their knowledge after each session as significantly higher than before that session. 75.8% of clinicians reported they would ‘definitely’ or ‘probably’ use content gleaned from each attended session and clinicians reported specific clinical and operational changes made as a direct result of sessions. 94.6% of respondents reported that COVID-19 Clinical Rounds helped them provide better care to patients. 89% of respondents indicated they ‘strongly agree’ that they would join ECHO calls again.COVID-19 Clinical Rounds offers a promising model for the establishment of dynamic peer-to-peer tele-mentoring communities for low or no-notice response where scientifically tested or clinically verified practice evidence is limited.
We estimate spatial gradients in the ionosphere using the Global Positioning System and GLONASS (Russian global navigation system) observations, utilising data from multiple Global Positioning System stations in the vicinity of Murchison Radio-astronomy Observatory. In previous work, the ionosphere was characterised using a single-station to model the ionosphere as a single layer of fixed height and this was compared with ionospheric data derived from radio astronomy observations obtained from the Murchison Widefield Array. Having made improvements to our data quality (via cycle slip detection and repair) and incorporating data from the GLONASS system, we now present a multi-station approach. These two developments significantly improve our modelling of the ionosphere. We also explore the effects of a variable-height model. We conclude that modelling the small-scale features in the ionosphere that have been observed with the MWA will require a much denser network of Global Navigation Satellite System stations than is currently available at the Murchison Radio-astronomy Observatory.
We compare first-order (refractive) ionospheric effects seen by the MWA with the ionosphere as inferred from GPS data. The first-order ionosphere manifests itself as a bulk position shift of the observed sources across an MWA field of view. These effects can be computed from global ionosphere maps provided by GPS analysis centres, namely the CODE. However, for precision radio astronomy applications, data from local GPS networks needs to be incorporated into ionospheric modelling. For GPS observations, the ionospheric parameters are biased by GPS receiver instrument delays, among other effects, also known as receiver DCBs. The receiver DCBs need to be estimated for any non-CODE GPS station used for ionosphere modelling. In this work, single GPS station-based ionospheric modelling is performed at a time resolution of 10 min. Also the receiver DCBs are estimated for selected Geoscience Australia GPS receivers, located at Murchison Radio Observatory, Yarragadee, Mount Magnet and Wiluna. The ionospheric gradients estimated from GPS are compared with that inferred from MWA. The ionospheric gradients at all the GPS stations show a correlation with the gradients observed with the MWA. The ionosphere estimates obtained using GPS measurements show promise in terms of providing calibration information for the MWA.
An 8-year-old girl with supraventricular tachycardia and an implanted vagus nerve stimulator underwent radiofrequency ablation of her supraventricular tachycardia substrate. No known literature exists addressing the potential interaction of these two technologies, although there are reported cases of interaction between radiofrequency and other implanted stimulating devices such as pacemakers. The procedure was performed successfully without observed interaction, and the patient’s family reported no significant change in frequency of seizure control.
This text provides an up-to-date description of the photovoltaic (PV) components and systems. It contains detailed information on several carefully planned experiments on solar PV cells and modules. The book is divided into two sections: User Manual and Experiments. The experiments are related to the characterization and simulation of solar cells to allow the users to measure various kinds of data on solar cells, modules and PV systems. The simulation experiments would enable the users to simulate solar cells and circuits containing solar cells. The Manual provides an intuitive grasp of PV system components and their behaviour in the field through a discussion of the underlying objectives, expected outcome, theory, equipment used, measurement methodology and results. The Manual will help users in understanding and execution of various experiments related to solar PV.
This book would be an extremely useful reference manual not only for the technicians and system installers working in the PV field, but also for the students and researchers interested in understanding the fundamental aspects of PV system components and their interconnection.
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
India has launched the Jawaharlal Nehru National Solar Mission (JNNSM) in 2009 with the ambitious target of installing 20,000 MW of solar power in the country by the year 2022. In order to achieve such a target, there is a need for a large number of trained people in the area of Solar Photovoltaics.
It is our pleasure to present this PV lab training manual as part of the ‘Teach a 1000 Teachers’ training programme on Photovoltaics. This initiative is part of the National Centre for Photovoltaic Research and Education (NCPRE, www.ncpre.iitb.ac.in), established at IIT Bombay by the Ministry of New and Renewable Energy (MNRE). We hope that this training initiative taken by NCPRE will be useful in fulfilling the manpower needs of JNNSM.
The manual contains detailed information on several carefully planned experiments on solar PV cells and modules. The planned experiments are in the areas of ‘characterization’ and ‘simulation’. The characterization experiments are planned to allow you to measure various data on solar cells, modules and PV systems. The simulation experiments enable you to simulate solar cells and circuits containing solar cells. For each experiment the details, such as its objective, expected outcome, theory, the equipments used, measurement methodology, results and discussion, are given.
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India
Chetan S. Solanki, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Brij M. Arora, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Juzer Vasi, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India,Mahesh B. Patil, Professor, Department of Electrical Engineering, Indian Institute of Technology Bombay, India