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Purpose: By reviewing the available literature this paper made a solid foundation to the current scenario ofold age homes in geriatric care as a social necessity and the satisfaction of the inmates is a factor which measures the administrative efficiency of the old age homes. Administrative inefficiency and inadequacy of governance may contribute to poor treatment and poor treatment of members in old age homes. Medical care provided to the inmates in the old age homes is a major factor which contributes to the satisfaction level of the inmates along with several other factors. This study was taken place to examine the level of satisfaction of the inmates on the infrastructural factors such as atmosphere, facilities, environment, and behavior of staff toward the elderly inmates. Objective: The study tries to examine the administrative efficiency of old age homes for the elderly and the inmate’s level of satisfaction. Design/Methodology/Approach: The codes are taken from the literature and used in the review of literature. This research was explanatory research that was conducted through an exploratory approach. On the internet's platform, desktop research was carried out. Almost 100 research articles were reviewed in depth and studied to identify the research gap. Findings/Result: The review of the articles indicates elderly are quite satisfied with the efficiency of old age homes and they are happy to be there in the old age homes other than being isolated at home and also, they are comfortable with their embers in the caretaking homes. In this study it can be found that the level of satisfaction of old age home inmates is different from each other as there are different types of paid and non-paid old age homes. Originality/Value: This review presents key findings from studies that show the level of satisfaction of the inmates in the old age home. This article was unique in its way of being a review article and compiling a satisfactory report of elderly inmates of old age home in the past 15 years. In all these studies the researcher can find that the efficiency and satisfaction of the old age home are different from each other. Paper Type: It was a review-based study which is also known as an integrative review where many articles from the past 15 years were selected from different search engines such as Google scholar, Research gate, and others.

Functional timing analysis (FTA) is a renowned method of finding the true critical delay for the design under interest. By constructing conjunctive normal form (CNF) clauses based on temporal and function constraints, false paths can be identified through the satisfiability (SAT) solving. As a result, the critical delay estimated by FTA is more accurate than that by conventional static timing analysis (STA). However, FTA suffers from the extremely long formulation and computation time, as the number of the clauses in CNF grows exponentially with the increasing size of the design. Due to the reconvergent effect, thousands of clauses can be redundantly formulated for one pin. Even worse, most of them are found useless but seriously lengthen the computation time. Therefore, to avoid ineffective computation in FTA, three novel techniques are proposed: 1) encoding duplication removal (EDR) for removing duplicated functional literals; 2) redundant state propagation (RSP) for propagating temporal states to identify redundant clauses; and 3) temporal footprint identification (TFI) for combining clauses that represent constraints with the same behavior. The experiments show that under a given timing constraint, 94% clauses and 95% literals can be pruned averagely (99% clauses and 99% literals under the best case), resulting in $15.3 \times $ speedup ( $72.99 \times $ under the best case) for formulation and SAT solving. As a result, the proposed techniques (EDR, RSP, and TFI) are proven effective to reduce useless computation and improve the overall performance of FTA.

Control of an electric vehicle (EV) is a laborious task because of the varying nature of operation parameters and the road conditions. In the middle of these uncertainties, achieving a fast response to track the desired drive cycle is a hurdle to the vehicle. A perfect control mechanism is required to achieve this target. The efficiency of drive cycle tracking mainly depends on the effectiveness of the control technique. There are several control methods available in the literature to accomplish this intention. Proportional Integral (PI) controllers are widely used for the majority of control purposes in the industry. But they are inefficient to handle the disturbance rejection. In such a condition, robust control techniques are needed. Sliding mode control (SMC) is one among the best methods for a system with nonlinearity and perturbations. In this paper, a comparative study of the PI controller, conventional SMC, and higher order SMC (HOSMC) for drive cycle tracking of an EV has been performed. The evaluation is done in Matlab/Simulink. Compared to the conventional SMC method, introduction of HOSMC improved the settling time and reduced almost 83% of the chattering in the driving torque. Result confirms the superior performance of HOSMC in the drive cycle tracking of an EV.

The five great trans-Pacific tsunamis of the 20th century that occurred in 1946, 1952, 1957, 1960 and 1964 were accurately recorded by analogue tide gauges on the coast of British Columbia. All available pen-and-paper records of these events were collected, digitised, de-tided and analysed. The 1946 Aleutian Islands event was recorded at two stations, Tofino and Victoria, where maximum trough-to-crest tsunami wave heights were 55 and 27 cm, respectively. These two gauged stations, as well as Prince Rupert, Alert Bay and Kitimat, also recorded the 1952 Kamchatka tsunami, which generated a maximum wave height of 77 cm at Tofino. The 1957 Andreanof Islands tsunami was recorded at six primary stations and the 1960 Chile tsunami by 17 primary and temporary tide gauges. For both of these events, the maximum tsunami wave heights also occurred at Tofino: 48 cm (1957) and 132 cm (1960). The 1964 Alaska tsunami remains the strongest tsunami yet instrumentally recorded on the coast of British Columbia. Our examination of 16 records from this event shows that maximum wave heights at eight stations were higher than 1 m, including Port Alberni (770 cm), Ocean Falls (376 cm), Tofino (237 cm) and Alert Bay (222 cm). We also find that the maximum wave at all stations for this event was among the first three waves. Subsequent wave heights rapidly attenuated following this group of waves. Frequency-time (f–t) analyses of the tsunami waveforms reveal that, for each station, the dominant frequencies of the waves and their evolution with time were very similar for different tsunamis, but differed considerably among sites for a particular tsunami, indicating the strong influence of local/regional topography on the incoming waves. From the latter point of view, the 1964 tsunami was exceptional. The epicentre of the 1964 Alaska earthquake was located much closer to the BC coast than for the other events and, therefore, the influence of the source was much stronger. The “ringing” of this tsunami was substantially shorter (

Background: Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. Objective: The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. Methods: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. Results: We found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%. Conclusions: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions. (JMIR Med Inform 2021;9(10):e29584) doi: 10.2196/29584

Objective: To understand the predominant topics of discussion, stance and associated language used on social media platforms relating to maternal vaccines in 15 countries over a six-month period. Background: In 2019, the World Health Organisation prioritised vaccine hesitancy as a top ten global health threat and recognized the role of viral misinformation on social media as propagating vaccine hesitancy. Maternal vaccination offers the potential to improve maternal and child health, and to reduce the risk of severe morbidity and mortality in pregnancy. Understanding the topics of discussion, stance and language used around maternal vaccines on social media can inform public health bodies on how to combat vaccine misinformation and vaccine hesitancy. Methods: Social media data was extracted (Twitter, forums, blogs and comments) for six months from 15 countries (Australia, Brazil, Canada, France, Germany, India, Italy, Korea, Mexico, Panama, South Africa, Spain, United Kingdom and United States). We used stance, discourse and topic analysis to provide insight into the most frequent and weighted keywords, hashtags and themes of conversation within and across countries. Results: We exported a total of 19,192 social media posts in 16 languages obtained between 1st November 2018 and 30th April 2019. After screening all posts, 16,000 were included in analyses, while excluding retweets, 2,722 were annotated for sentiment. Main topics of discussion were the safety of the maternal influenza and pertussis vaccines. Discouraging posts were most common in Italy (44.9%), and the USA (30.8%). Conclusion: The content and stance of maternal vaccination posts from November 2018 to April 2019 differed across countries, however specific topics of discussion were not limited to geographical location. These discussions included the promotion of vaccination, involvement of pregnant women in vaccine research, and the trust and transparency of institutions. Future research should examine the relationship between stance (promotional, neutral, ambiguous, discouraging) online and maternal vaccination uptake in the respective regions. Crown Copyright (C) 2020 Published by Elsevier Ltd. All rights reserved.