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An atypical presentation of orthostatic hypotension and falls in an older adult
Get PDFIntroduction: Falls are a significant cause of morbidity and mortality in older adults. Orthostatic hypotension (OH) is very common in this cohort of patients and is a significant risk for falls and associated injuries. We present the case of an 89-year-old female who fell at home, witnessed by her husband. OH was identified during the clinical assessment and considered to be the predominant contributing factor, although the clinical presentation was not associated with classical symptoms.
Case presentation: The patient lost balance while turning away from the kitchen sink; she noted some instability due to a complaint of generalised weakness in both of her legs. No acute medical illness or traumatic injury was identified. A comprehensive history was obtained that identified multiple intrinsic and extrinsic risk factors for falling. The cardiovascular examination was unremarkable except for OH, with a pronounced reduction in systolic blood pressure of 34 mmHg at the three-minute interval and which reproduced some generalised weaknesses in the patient’s legs and slight instability. Although classical OH symptoms were not identified, this was considered to be the predominant factor contributing to the fall. A series of recommendations was made to primary and community based care teams based upon a rapid holistic review; this included a recommendation to review the patient’s dual antihypertensive therapy.
Conclusion: It is widely known that OH is a significant risk factor for falls, but asymptomatic or atypical presentations can make diagnosis challenging. Using the correct technique to measure a lying and standing blood pressure, as defined by the Royal College of Physicians, is crucial for accurate diagnosis and subsequent management. Ambulance clinicians are ideally placed to undertake this quick and non-invasive assessment to identify OH in patients that have fallen
Predicting the risk of heart failure based on clinical data
Get PDFThe disorder that directly impacts the heart and the blood vessels inside the body is cardiovascular disease
(CVD). According to World Health Organization (WHO) reports, CVDs are the leading cause of mortality
worldwide, claiming the human life of nearly 23.6 million people annually. The categorization of diseases in
CVD includes Coronary Heart Disease, Strokes and Transient Ischaemic Attacks (TIA), Peripheral Arterial
Disease, Aortic Disease. Most CVD fatalities are caused by strokes and heart attacks, with an estimated one third of these deaths currently happening before 60. The standard medical organization "New York Heart
Association" (NYHA) categorize the various stages of heart failure as Class I: with no symptoms, Class II:
mild symptoms, Class III: comfortable only when in resting position, Class IV: severe condition or Patient is
bed-bound, and Class V: unable to determine the class. Machine Learning-based methods play an essential
role in clinical data analysis. This research presents the importance of various essential attributes related to
heart disease based on a hybrid machine learning model. The proposed hybrid model SVM-GA is based on a
Support vector machine (SVM) and the Genetic Algorithm (GA). This research analyzed an online dataset
obtainable at the UCI machine learning repository with the medical data of 299 patients who suffered from
heart failures and are classified as Class III or IV as per the standard NYHA. This dataset was collected
through patients' available follow-up and checkup duration and involved thirteen clinical characteristics. The
proposed Machine Learning models were used to calculate feature importance in this research. The proposed
model and existing well-known machine learning based-models, i.e., Bayesian Generalized Linear Model,
ANN, Bagged CART, Bag Earth, and SVM, are implemented using python and various performance
measuring parameters, i.e.,,, Accuracy, processing time, precision, recall, f-measures are calculated.
Experimental analysis shows the proposed SVM-GA model strengthens in terms of better Accuracy,
processing time, precision, recall, f-measures over existing methods
A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for Twitter sentiment analysis
Get PDFConcept-based sentiment analysis (CBSA) methods have gained prominence in natural language
processing in recent years. These methods consider the underlying semantic meanings of text to
perform different tasks such as Twitter sentiment analysis (assigning positive, negative, or neutral
sentiment to Tweets). CBSA is superior to traditional statistical methods for accurately discovering
sentiment labels. Due to a limited knowledge base, these methods are unable to identify the sentiment
polarity of all kinds of text. Therefore, supervised learning techniques are mostly ensembled with
CBSA methods to classify whole text. These techniques require labeled data. It is a tedious and time consuming task due to the manually labeling of large datasets (Such as Twitter datasets). Therefore,
an unsupervised learning mechanism can be a better alternative to solve this problem. In this paper,
a novel unsupervised learning framework based on Concept-based and hierarchical clustering is
proposed for Twitter sentiment analysis. Popular hierarchical clustering methods including single
linkage, complete linkage, and average linkage algorithms are ensembled serially. Two different
feature representation methods including Boolean and Term frequency-inverse document frequency
(TF-IDF) are investigated. We have also experimented with Well-known classifiers (Naïve Bayes,
Neural Network) for a fair comparison. Accuracy measure (proportion of correct predictions) is used
to evaluate the performance of understudied techniques. It is empirically shown that the performance
of unsupervised learning techniques is comparable with supervised learning techniques
Using mentimeter to enhance learning and teaching in a large class
Get PDFMentimeter is a web-based Clicker, Audience Response System (ARS) or Student Response System (SRS) which allows students to answer digital questions using a mobile device. It has the potential to transform the classroom environment into a more interactive, engaging and inclusive one. In this paper, a brief literature review has been provided which addresses the benefits of using ARS including Mentimeter in a large class. Additionally, the mechanics behind how the workings of the Mentimeter, its features and applications have been evaluated in order to offer the instructors with the insights about using Mentimeter for their own practice. Finally, a case study has been explained where Mentimeter was used for the formative assessment. In the present study, a Mentimeter formative assessment model has been developed which can be implemented as a good practice in Higher Education (HE). A survey on perception of students about using Mentimeter has been assessed and from the results, it is evident that using Mentimeter has a positive impact on students’ attitude and performance, learning environment and technical aspects. These results will be further discussed by linking pedagogical theories and its benefits
Assessment of dynamic swarm heterogeneous clustering in cognitive radio sensor networks
Get PDFPlethora of optimization algorithms have been created to determine the most energy efficient transmission mode, allowing for lower power consumption during transmission over
shorter distances while minimising interference from Primary Users (PUs). According to the
Improved Cooperative Clustering Algorithm (ICCA), it performs superior spectrum sensing across
groups of multi-users when compared to any other method currently available in terms of sensing
inaccuracy, power savings and convergence time than any other method currently available. The
proposed ICCA algorithm is employed in this research study to find the optimal number of clusters
based on their connectivity, as well as the most energy-efficient distributed cluster-based sensing
technique available. In this research, a large number of randomly chosen Secondary Users (SUs)
and Primary Users (PUs) are investigated for potential implementation opportunities. Therefore, as
compared to the present optimization strategies, the proposed ICCA algorithm enhanced the
convergence speed by integrating the multi-user clustered communication into a single
communication channel. Experimental results revealed that the new ICCA algorithm reduced node
power by 9.646 percent as compared to traditional ways when comparing the novel algorithm to
conventional approaches.
In a similar vein, as compared to the prior methodologies, the ICCA algorithm reduced the average
node power of SUs by 24.23 percent on average. When the Signal-to-Noise Ratio (SNR) is
decreased to values below 2dB, the likelihood of detection improves dramatically, as seen in Figure
1. ICCA has a low false alarm rate when compared to other existing optimization algorithms for
direct detection, and the proposed method outperforms them all. In accordance with the findings of
the simulations, the proposed ICCA technique effectively addresses multimodal optimization
difficulties and optimises network capacity performance in wireless networks. A detailed discussion
of SS applications for the Internet of Things and Wireless Sensor Networks, both of which are based
on CR, is provided. There is also a thorough discussion of the most recent advancements in
Spectrum Sensing as a Service, in which the Internet of Things or Wireless Sensor Networks may
play an important part in feeding the CR network with spectrum sensing data, as well as the future
of spectrum sensing. The use of CR for the Fifth Generation and beyond, as well as its potential
application in frequency allocation, are also discussed.. In order to stay up with the advancement of
communication technology, SS should give additional features, such as the capacity to investigate
different available channels and accessible space for transmission, in order to remain competitive.
On the basis of current and prospective techniques in wireless communications, we highlight crucial
future research paths and difficulty spots in signal processing for cognitive radio, as well as potential
solutions (SS-CR)
On the utility of thermogravimetric analysis for exploring the kinetics of thermal degradation of lignins
Get PDFThe kinetics of pyrolysis of organosolv (TcA) and hydroxypropyl-modified (TcC) lignins have been investigated using thermogravimetric analysis (TGA). Three isothermal models (single first order, Guggenheim and Avrami-Erofeev) and one non-isothermal model (Kissinger) were used to analyse the mass-loss data. Sensible derived kinetic parameters, i.e., activation energy and pre-exponential factor, were obtained only for the initial stages of pyrolysis where the kinetics were approximately first order. Models that analysed TGA data beyond the initial stage gave inconsistent results, indicating the complexity of subsequent decomposition steps occurring at higher temperatures and/or longer times. The kinetics of the initial stage are important for designing routes to lignin's valorisation into useful products, such as carbon fibres, activated carbons, polymer additives, etc. TcC had a higher activation energy (41.5 kJ/mol) for initial decomposition than TcA (39 kJ/mol), consistent with its greater thermal stability observed previously during conversion of lignin-based fibres into carbon fibres
A novel approach to cure depression using Bi-directional LSTM and Global Vector
Get PDFIn today's world, there are many people suffering from mental health
problems such as depression and anxiety. If these conditions are not identified and
treated early, they can get worse quickly and have far-reaching negative effects.
Unfortunately, many people suffering from these conditions, especially depression
and hypertension, are unaware of their existence until the conditions become
chronic. Thus, this paper proposes a novel approach using Bi-directional Long
Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm
for the prediction and treatment of these conditions. Smartwatches and fitness
bands can be equipped with these algorithms which can share data with a variety
of IoT devices and smart systems to better understand and analyze the user’s
condition. We compared the accuracy and loss of the training dataset and the
validation dataset of the two models namely, Bi-LSTM without a global vector
layer and with a global vector layer. It was observed that the model of Bi-LSTM
without a global vector layer had an accuracy of 83%, while Bi-LSTM with a
global vector layer had an accuracy of 86% with a precision of 86.4%, and an F1
score of 0.861. In addition to providing basic therapies for the treatment of
identified cases, our model also helps prevent the deterioration of associated
conditions, making our method a real-world solution
A novel framework for abnormal risk classification over fetal nuchal translucency using Adaptive Stochastic Gradient Descent Algorithm
Get PDFIn most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More
fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy. Birth defects and congenital abnormalities are certain fetal abnormalities. Fetal abnormalities have become common in several industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to
evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters like accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through suggested technique is of 98.642.%
European university students' mental health during Covid-19: Exploring attitudes towards Covid-19 and governmental response
Get PDFThe effects of Covid-19 have been felt worldwide and one population that are of increasing concern are university students. University students have endured unique and drastic changes to their everyday and academic lives. It is important to understand how university students in different parts of the world have been affected by the Covid-19 pandemic and how it has affected their mental health? A cross-sectional study was conducted during the first wave of Covid-19, in May 2020 with 2,006 university students from the UK, Italy, Germany and Spain. Participants were recruited online and were asked to complete a series of standardised measures of psychological distress, anxiety, flourishing and wellbeing. Attitudes towards Covid-19 were measured using a new scale. The factor structure and reliability of this new scale was confirmed using this European sample. Results indicated that all university students were suffering from poor mental health, considerably below pre-pandemic norms. There were many geographical differences in the way that university students perceived the Covid19 pandemic, in terms of their fears, anxieties, loneliness and positivity. There were also significant mental health comparisons between students from the UK, Italy, Germany and Spain. Student beliefs that their government had provided effective leadership during the Covid-19 pandemic were strongly related to numerous mental health outcomes. A picture of university students' mental health is provided and discussed. Geographical comparisons are discussed, as are the implications for practice and future directions.Supplementary informationThe online version contains supplementary material available at 10.1007/s12144-022-02854-0
Analysis on security-related concerns of unmanned aerial vehicle: attacks, limitations, and recommendations
Get PDFWith time the use of UAVs (unmanned aerial vehicles)/drones is increasing across several
civil and military application domains. Such domains include real-time monitoring, remote sensing,
wireless coverage in a disaster area, search and rescue, delivery of goods, surveillance, security,
agriculture, civil infrastructure inspection, and the list goes on. This rapid growth is opening doors to
numerous opportunities and conveniences in everyday life. On the other hand, security and privacy
concerns for unmanned aerial vehicles/drones are progressively increasing. With limited
standardization and regulation of unmanned aerial vehicles/drones, security and privacy concerns are
quite frightful. This paper presents a brief analysis of unmanned aerial vehicle's/drones security and
privacy-related concerns. The paper also presents countermeasures and recommendations to address
such concerns. To give a brief knowledge about unmanned aerial vehicles/drones, the paper also
provide readers with up-to-date information on existing regulations, classification, architecture, and
communication methods, application areas, vulnerabilities, existing countermeasures against different
attacks, and their limitations. In the end, the paper concludes with a discussion on open research areas
and recommendations on how the security and privacy of unmanned aerial vehicles can be improved