bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digitalization in Dental Prosthesis and Comparative Analysis of IOPA and RVG techniques for Tooth Measurements
Authors: Vikas Seema Chawla 
Published: June 2021

AbstractThe tooth height is mostly measured during root canal procedure. It is the most common reason for taking intra oral radiography during any procedure performance. Dental radiograph is also captured to find underlying cause of dental problems like peri-apical lesions, carries, bone defects, post procedure assessment of surgery or procedure done by dentist, etc. Intra oral radiograph also helps in gathering essential information of morphological bone status that helps to determine prognosis of treatment. Dental radiographs help in selecting the treatment plan, and determining the outcomes of different therapies. The study was aimed at evaluating the parametric measurements as got through radiovisiography (RVG) and conventional (intraoral periapical [IOPA] radiographs) for the determination of height of tooth, crownlength, root length, mesiodistal diameter at crown and mesio-distal diameter at tooth root. Radiographs of12 subjects (healthy patients) were taken by conventional methods as well as digital method, 5 standard points were selected on each tooth and dimensions of 3 tooth of each subjects were recorded in mm. Total 180 sites were recorded and result was compared with previous studies done on dogs and other animals. The results didn’t show any significant statistical parametric variations in measured values between conventional and digital radiographic methods. Hence, both IOPA radiograph as well as RVG can be used, but RVG provides good images with lots of colour changes in software in lesser time with good storage facility in computers as compared to conventional methods.
PRE-DETECTION OF FOOT ULCER FOR DIABETIC PATIENT USING THERMAL IMAGER
Authors: Priya Vishnu , Pamela D. , Geard Joe Nigel K. , Michael Prawin Angel 
Published: June 2021

Abstract
As per the statistics taken in the year 2020, a measurable audit shows that over 35% of the Indian population is influenced with foot ulcer. Foot ulcer is an issue looked by the greater part of the patient who have gone through diabetes mellitus (DM). Around 20% of the diabetes patients is influenced by foot ulcer, another 20% is affected by diabetic neuropathy, 30% patient are influenced with both the conditions. Generally the foot ulcer can be inspected with x-beams, bone output, MRI, CT, Bacterial culture of the ulcer, and even with blood tests. These methods are inspected by obtrusive method of estimations that will hurt the patient more. In order to reduce the discomfort during the interaction and furthermore for the early identification of this condition, this arrangement has been created. This pre-recognition measure is a non-intrusive method for estimation with the thermal imager. The temperature scope of ordinary foot is under 30 degree Celsius. It very well may be fluctuated (expanded) least of 2 degree Celsius for ulcered foot. These ulcered foot pictures has been procured with the assistance of thermal imager (AMG8833) and handled utilizing MATLAB and result is shown in a histogram. This histogram helps us to differentiate the typical and ulcered foot. The classification is done using neural network.
Classification of lung disorders based on lung sounds using deep learning
Authors: Joy Reshma P , Haider Nishi Shahnaj 
Published: June 2021

Abstract
Treatment of lung sicknesses, that are the 0.33 maximum not unusual place reason of demise within side the world, is of high-quality significance within side the clinical field. Many research the use of lung sounds recorded with stethoscope had been carried out within side the literature so as to diagnose the lung sicknesses with synthetic intelligence-well suited gadgets and to help the professionals of their diagnosis [5]. Lung sounds carry applicable records associated to pulmonary problems, and to assess sufferers with pulmonary conditions, the medical doctor or the medical doctor makes use of the conventional auscultation method. How-ever, this method suffers from limitations. For example, if the medical doctor is not properly trained, this will result in a incorrect diagnosis. Moreover, lung sounds are non-stationary, complicating the duties of analysis, popularity, and distinction. This is why growing computerized popularity structures can assist to address those limitations [1].Classification of lung sounds on the earliest may be very important to customize their treatment. In this project, the lung problems are categorized primarily based totally on the time, frequency in addition to photograph capabilities extracted from auscultatory datasets and categorized the use of deep gaining knowledge of method. The set of rules used is a Convolutional Neural Network the use of Mel Frequency Cepstral Coefficient feature extraction.
Rheumatoid arthritis detection using image processing
Authors: Verghese Jenny Ann , Pamela D. , Michael Prawin Angel , Meenal R. 
Published: June 2021

Abstract
Rheumatoid Arthritis (RA) may be a general disease characterized by inflammation, discomfort, and tenderness of the joints and might involve additional body part organs in severe cases. Leading to increased vascular disorder in the zone of inflammatory tissue, joint autoimmune lesions are associated with elevated fever. The detection of RA usually involves blood sample tests. This thesis proposes a novel methodology of detection by processing the Xray images. This automated system requires clear Xray images, which after preprocessing and segmentation using Support Vector Machine implemented via MATLAB gives a clear classification about the abnormal and normal images. Different output parameters were used to assess separation tasks. The accuracy of the model section has improved to the use of an optimized SVM network. The proposed model was effective in accurately separating the samples.
Literature Review on Sleep APNEA Analysis by Machine Learning Algorithms Using ECG Signals
Authors: Ankitha V. , Manimegalai P. , Jose P. Subha Hency , Raji P. 
Published: June 2021

Abstract
Obstructive Sleep Apnea is a respiratory disorder that impairs sleep quality by causing respiratory arrest. An irregular breath delay or decrease of airflow during sleep is the hallmark of the apnea syndrome. According to the literature, approximately 2% of middle-aged women and 4% of middle-aged men are affected. The disease is diagnosed by the physician in two steps. In the first stage, the physician reviews the medical records obtained using the polysomnography system. The disease is diagnosed in two stages by the physician, who examines the patient records taken with the polysomnography system in the first stage. New diagnostic processes and equipment are required as a result of the negative aspects of this procedure. The heart rate variable (HRV) and electrocardiography (ECG) signals are used, and ECG records from the patient and control groups are obtained. The optical filter was used to clean ECG signals and heart rate variables (HRV) from patient and control classes. After that, the ECG signal was used to calculate the HRV parameter. The HRV and ECG signals were then used to extract functionality. Reduced machine learning techniques, such as random forest, SVM, and the kNN feature selection process, were used to classify the extracted features. To evaluate the classifiers’ efficiency, the sensitivity and specificity values, as well as the accuracy rates for each class in the test set, were computed, and a receiver operating characteristic curve was developed. The method can be realized with Random forest, Support Vector Machine, and KNN, which have the best accuracy of 82.5 percent, 97 percent, and 89 percent, respectively, using 11 ECG and HRV features, according to the results. The system will work with these success rates. It is possible to implement a realistic sleep/awake detection method when all of these factors are taken into account. This means that using machine learning and signal processing methods, the ECG signal can be used to diagnose obstructive sleep apnea.
Optimization of Preparation Conditions for Nano Porous Catalytic Carbon From Post Consumer Poly Ethylene Terphthalate Bottles Using Response Surface Methodology
Authors: Abira Bright B , Suresh Kumar A , Vijayakumar Poornima , Princy J. , Livingston R.Jone 
Published: June 2021

Abstract
Using response surface techniques, this research proposes the best conditions for producing Nano porous catalytic carbon from post-consumer polyethylene terephthalate (PET). The carbon yield and surface area of post-consumer PET-based AC is evaluated. The PET-based AC was rendered using a zinc chloride-based chemical activation technique (ZnCl2). The results of three preparation variables, including activation temperature, activation time, and chemical impregnation (ZnCl2:char) ratio, on both surface area and yield, were studied. To assess the impact of planning conditions on responses, the central composite design (CCD) method was used Quadratic models for surface area and yield were constructed using the CCD. The study of variance revealed the effects of the three main parameters, the properties of activated carbon, and the important factors on each experimental design reaction (ANOVA). Using a 5% impregnation ratio, a 1000°C activation temperature, and the best conditions for PET-based AC preparation were found to be at 60-minute activation time, yielding 1102.62 m2/g of surface area and a 16.218 % yield.
Predicting the effect of fiber orientations and boundary conditions on the optimal placement of PZT sensor on the composite structures
Authors: Samyal Rahul , Bagha Ashok Kumar , Bedi Raman , Bahl Shashi , Saxena Kuldeep K , Sehgal Shankar 
Published: June 2021

Abstract
In this paper, the modal-model of the composite structure is predicted and viewed to decide the optimal position of the PZT sensors on the composite structures. The novelty of this work is to systematically study the effect of fiber orientations and boundary conditions on the modal-model and the optimal location of the PZT sensors on the composite structures. The glass fibers are reinforced in a polyester matrix at different fiber orientations such as 0°, 30°, 45°, 60° and 90°. It is used for various engineering applications, especially in the aerospace and automobile sector, and it is very important to measure its dynamical response. The PZT patches can be embedded on the composite structures to measure their vibrational response. In this paper, ABAQUS software is used to build the finite element model of the PZT-composite structure. The composite structure is modeled with different boundary conditions. It is observed that the orientation of the fibers as well as the boundary condition directly put their effect on the modal-model of the composite structure and also on the selection of the optimal position of the PZT patches. It is found that the optimal position of the PZT directly depends upon the fiber orientation.
Wafer-scale, epitaxial growth of single layer hexagonal boron nitride on Pt(111)
Authors: Hemmi Adrian , Cun Huanyao , Brems Steven , Huyghebaert Cedric , Greber Thomas 
Published: June 2021

Abstract
Single-layer hexagonal boron nitride is produced on 2 inch Pt(111)/sapphire wafers. The growth with borazine vapor deposition at process temperatures between 1000 and 1300 K is in situ investigated by photoelectron yield measurements. The growth kinetics is slower at higher temperatures and follows a tanh2 law which better fits for higher temperatures. The crystal-quality of hexagonal boron nitride (h-BN)/Pt(111) is inferred from scanning low energy electron diffraction (x-y LEED). The data indicate a strong dependence of the epitaxy on the growth temperature. The dominant structure is an aligned coincidence lattice with 10 h-BN on 9 Pt(1 × 1) unit cells and follows the substrate twinning at the millimeter scale.
On a class of hypergeometric diagonals
Authors: Bostan Alin , Yurkevich Sergey 
Published: June 2021

We prove that the diagonal of any finite product of algebraic functions of the form

(
1
−

x
1

−
⋯
−

x
n

)
R

,

R
∈

Q

,

\begin{align*} {(1-x_1- \dots -x_n)^R}, \qquad R\in \mathbb {Q}, \end{align*}

is a generalized hypergeometric function, and we provide an explicit description of its parameters. The particular case

(
1
−
x
−
y

)
R

/

(
1
−
x
−
y
−
z
)

(1-x-y)^R/(1-x-y-z)

corresponds to the main identity of Abdelaziz, Koutschan and Maillard in [J. Phys. A 53 (2020), 205201, 16 pp., §3.2]. Our result is useful in both directions: on the one hand it shows that Christol’s conjecture holds true for a large class of hypergeometric functions, on the other hand it allows for a very explicit and general viewpoint on the diagonals of algebraic functions of the type above. Finally, in contrast to the approach of Abdelaziz, Koutschan and Maillard, our proof is completely elementary and does not require any algorithmic help.
Barriers and potential solutions in the recruitment and retention of older patients in clinical trials—lessons learned from six large multicentre randomized controlled trials
Authors: Buttgereit Thomas , Palmowski Andriko , Forsat Noah , Boers Maarten , Witham Miles D , Rodondi Nicolas , Moutzouri Elisavet , Navidad Antonio Jesus Quesada , van’t Hof Arnoud W J , van der Worp Bart , Coll-Planas Laura , Voshaar Marieke , de Wit Maarten , da Silva José , Stegemann Sven , Bijlsma Johannes W , Koeller Marcus , Mooijaart Simon , Kearney Patricia M , Buttgereit Frank 
Published: June 2021

AbstractBackgroundolder people remain underrepresented in clinical trials, and evidence generated in younger populations cannot always be generalized to older patients.Objectiveto identify key barriers and to discuss solutions to specific issues affecting recruitment and retention of older participants in clinical trials based on experience gained from six current European randomised controlled trials (RCTs) focusing on older people.Methodsa multidisciplinary group of experts including representatives of the six RCTs held two networking conferences and compiled lists of potential barriers and solutions. Every item was subsequently allocated points by each study team according to how important it was perceived to be for their RCTs.Resultsthe six RCTs enrolled 7,612 older patients. Key barriers to recruitment were impaired health status, comorbidities and diverse health beliefs including priorities within different cultural systems. All trials had to increase the number of recruitment sites. Other measures felt to be effective included the provision of extra time, communication training for the study staff and a re-design of patient information. Key barriers for retention included the presence of severe comorbidities and the occurrence of adverse events. Long study duration, frequent study visits and difficulties accessing the study site were also mentioned. Solutions felt to be effective included spending more time maintaining close contact with the participants, appropriate measures to show appreciation and reimbursement of travel arrangements.Conclusionrecruitment and retention of older patients in trials requires special recognition and a targeted approach. Our results provide scientifically-based practical recommendations for optimizing future studies in this population.
Long-term effect of a 24-week multicomponent intervention on physical performance and frailty in community-dwelling older adults
Authors: Oh Gahee , Lee Heayon , Park Chan Mi , Jung Hee-Won , Lee Eunju , Jang Il-Young , Guralnik Jack M , Kim Dae Hyun 
Published: June 2021

Abstract

Introduction
Multicomponent interventions improve physical function and frailty in older adults, but their long-term benefit remains uncertain.

Methods
This prospective non-randomised study was conducted in 383 older Koreans (mean age, 76.8 years; female 72.3%) who were living alone or receiving medical aid. Of these, 187 individuals chose to receive a 24-week intervention that consisted of group exercise, nutritional supplements, depression management, deprescribing and home hazard reduction. The remaining 196 individuals received usual care. We compared the short physical performance battery (SPPB) score (0–12 points), frailty phenotype scale (0–5 points) and deficit-accumulation frailty index (0–1) at baseline, 6, 18 and 30 months.

Results
After 1:1 propensity score matching (n = 117 per group), the mean SPPB scores for the intervention and comparison groups were 7.6 versus 7.6 at baseline, 10.7 versus 7.1 at 6 months (mean difference, 3.5; 95% confidence interval [CI], 2.8–4.2), 9.1 versus 7.8 at 18 months (1.3; 95% CI, 0.6–2.0) and 8.6 versus 7.5 at 30 months (1.1; 95% CI, 0.4–1.8). The intervention group had lower frailty phenotype scale (1.1 versus 1.8; difference, −0.7; 95% CI −1.0 to −0.3) and frailty index (0.22 versus 0.27; difference, −0.04; −0.06 to −0.02) at 6 months, but similar scores at 18 and 30 months. The 30-month mean institutionalisation-free survival time was 28.5 months in the intervention group versus 23.3 months in the comparison group (difference, 5.2 months; 95% CI, 3.1–7.4).

Conclusions
The 24-week multicomponent intervention showed sustained improvement in physical function, temporary reduction in frailty and longer institutionalisation-free survival over 30 months.
ASHP national survey of pharmacy practice in hospital settings: Impact of COVID-19 pandemic on pharmacy operations—2020
Authors: Pedersen Craig A , Schneider Philip J , Ganio Michael C , Scheckelhoff Douglas J 
Published: June 2021

Abstract

Purpose
Results of the 2020 ASHP national survey of pharmacy practice in hospital settings pertaining to pharmacy operational changes implemented in response to the coronavirus disease 2019 (COVID-19) pandemic are presented.

Methods
Pharmacy directors at 1,437 general and children’s medical/surgical hospitals in the United States were surveyed using a mixed-mode method of contact by email and mail. Survey completion was online.

Results
The response rate was 18.7%. Seventy-three percent of hospitals implemented changes to hospital units, including 46% that increased intensive care unit bed capacity; 94% made changes to pharmacy supply chain acquisition, changes to products, and/or increased inventory. Staffing changes were implemented by 69% of hospitals, with the most common being staffing reductions (55%) and salary reductions (16%). Medication-use changes were implemented by 86% of hospitals, with treatment guidelines for COVID-19 treatment (79%) and opening compassionate use or investigational drug studies (55%) being the most common. Changes in sterile compounding processes were implemented by 84% of hospitals. Personal protective equipment (PPE) shortages led to 71% of hospitals modifying PPE use standards in sterile compounding. Eighty-seven percent of hospitals changed operational activities, such as changing medication return practices (56%), medication reconciliation processes (46%), intravenous medication recycling (38%), and discharge counseling (37%). Hospitals experienced shortages of many medications, including albuterol inhalers (60%), sedatives and anesthetic agents (58%), neuromuscular blockers (43%), corticosteroids (34%), cardiovascular agents (24%), investigational agents (24%), and dialysis solutions (6%).

Conclusion
The pharmacy profession responded to myriad threats to operations and patient care during the COVID-19 pandemic in 2020.
DUI: the drug use insights web server
Authors: Prince Zachary , Jha Deeptanshu , Singh Rahul 
Published: June 2021

Abstract

Motivation
Substance abuse constitutes one of the major contemporary health epidemics. Recently, the use of social media platforms has garnered interest as a novel source of data for drug addiction epidemiology. Often however, the language used in such forums comprises slang and jargon. Currently, there are no publicly available resources to automatically analyze the esoteric language-use in the social media drug-use sub-culture. This lacunae introduces critical challenges for interpreting, sensemaking and modeling of addiction epidemiology using social media.

Results
Drug-Use Insights (DUI) is a public and open-source web application to address the aforementioned deficiency. DUI is underlined by a hierarchical taxonomy encompassing 108 different addiction related categories consisting of over 9000 terms, where each category encompasses a set of semantically related terms. These categories and terms were established by utilizing thematic analysis in conjunction with term embeddings generated from 7 472 545 Reddit posts made by 1 402 017 redditors. Given post(s) from social media forums such as Reddit and Twitter, DUI can be used foremost to identify constituent terms related to drug use. Furthermore, the DUI categories and integrated visualization tools can be leveraged for semantic- and exploratory analysis. To the best of our knowledge, DUI utilizes the largest number of substance use and recovery social media posts used in a study and represents the first significant online taxonomy of drug abuse terminology.

Availability and implementation
The DUI web server and source code are available at: http://haddock9.sfsu.edu/insight/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Semi-exact control functionals from Sard’s method
Authors: South L F , Karvonen T , Nemeth C , Girolami M , Oates C J 
Published: June 2021

Summary
A novel control variate technique is proposed for the post-processing of Markov chain Monte Carlo output, based on both Stein’s method and an approach to numerical integration due to Sard. The resulting estimators of posterior expected quantities of interest are proven to be polynomially exact in the Gaussian context, while empirical results suggest that the estimators approximate a Gaussian cubature method near the Bernstein–von Mises limit. The main theoretical result establishes a bias-correction property in settings where the Markov chain does not leave the posterior invariant. Empirical results across a selection of Bayesian inference tasks are presented.
The utility of remote patient management in peritoneal dialysis
Authors: Yeter Haci Hasan , Manani Sabrina Milan , Ronco Claudio 
Published: June 2021

ABSTRACT
Remote patient management (RPM) programs are one of the most crucial innovations in the peritoneal dialysis (PD) field that have been developed in the last decade. RPM programs are associated with favourable clinical outcomes by increasing the adherence of the patients to PD prescription. The literature supports that RPM is associated with increased blood pressure control and technique survival, and decreased hospitalization rate, length of hospital stay and health costs. RPM programs also facilitate patient follow-up during the coronavirus disease 2019 pandemic, increase treatment adherence and lead to better clinical outcomes. However, published data remain scarce and mainly consist of observational or retrospective studies with relatively low numbers of patients. Therefore, randomized controlled trial results will be more informative to demonstrate the effect of RPM programs on clinical outcomes.