bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image
Authors: Ashok Kumar L. , Jebarani M. R. Ebenezar , Gokula Krishnan V.
Published: April 2023

Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches.
Lung Disease Classification using Dense Alex Net Framework with Contrast Normalisation and Five-Fold Geometric Transformation
Authors: Rana Poonam , Sharma Vineet , Kumar Gupta Pradeep
Published: April 2023

lung disease is one of the leading causes of death worldwide. Most cases of lung diseases are found when the disease is in an advanced stage. Therefore, the development of systems and methods that begin to diagnose quickly and prematurely plays a vital role in today's world. Currently, in detecting differences in lung cancer, an accurate diagnosis of cancer types is needed. However, improving the accuracy and reducing training time of the diagnosis remains a challenge. In this study, we have developed an automated classification scheme for lung cancer presented in histopathological images using a dense Alex Net framework. The proposed methodology carries out several phases includes pre-processing, contrast normalization, data augmentation and classification. Initially, the pre-processing step is accompanied to diminish the noisy contents present in the image. Contrast normalization has been explored to maintain the same illumination factor among histopathological lung images next to pre-processing. Afterwards, data augmentation phase has been carried out to enhance the dataset further to avoid over-fitting problems. Finally, the Dense Alex Net is utilized for classification that comprises five convolutional layers, one multi-scale convolution layer, and three fully connected layers. In evaluation experiments, the proposed approach was trained using our original database to provide rich and meaningful features. The accuracy attained by the proposed methodology is93%, which is maximum compared with the existing algorithms
Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data
Authors: Rajendra K. , Subramanian S. , Karthik N. , Naveenkumar K. , Ganesan S.
Published: April 2023

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.
Evaluating Performance of Beacon Enabled 802.15.4 Network with Different Bit Error Rate and Power Models
Authors: Bamber Sukhvinder Singh
Published: April 2023

This research finds the most suited Bit Error Rate (BER) and Power Model for IEEE 802.15.4 network. A total of three BER models and three Power Models were used for testing purposes. Their respective algorithms have been developed as well. alt_ber was found to be most suited as BER model and dra_power was found to be most suited for power model for 802.15.4 based networks. The relationship between BER and throughput has been established and the same has been confirmed by simulating the network. The most suited BER and power model settings were confirmed from the graphs obtained from network performance. This research paper can further be used for future references by all the researchers who aim to study this particular aspect of 802.15.4 based networks.
Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors
Authors: Patil Smital D. , Deore Pramod J.
Published: April 2023

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected.  In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system.
ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System
Authors: Patil Anitha , Govindaraj Suresh Kumar
Published: April 2023

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16.
A Smart Remote Monitoring System for Prenatal Care in Rural Areas
Authors: Dhanwanth Batini , Saravanakumar R. , Tamilselvi T. , Revathi K.
Published: April 2023

The complications in maternity especially the women lives in rural sector can be reduced through regular monitoring of their vitals like blood pressure, SpO2 and fetal growth. The internet of things (IoT) is the modern technology bridges the gap between the traditional clinical setting with its consumers as well promotes the telemedicine industry into great levels of accessing proactive healthcare facilities. The predominant aim of this work is to bring a remote monitoring device which assesses the significant health indicators of the pregnant women and their fetus status cost effectively. In order to build such kit, the biosensors like heart rate, SpO2, pressure, temperature and load cell which gives the weight of the fetus are integrated into Arudino board. The sensor readings are processed through ThingSpeak. The timely medical attention is proposed upon observing abnormal physiological vitals of the women which is implemented through a buzzer system in this device. Like such devices in realism help to predict the pregnancy risk and decrease the mortality rate.
A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms
Authors: Rayavarapu Swarajya Madhuri , Prashanthi Tammineni Shanmukha , Kumar Gottapu Santosh , Lavanya Yenneti Laxmi , Rao Gottapu Sasibhushana
Published: April 2023

Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals.
Significance of Artificial Intelligence in the Production of Effective Output in Power Electronics
Authors: Anandpwar Winit , Barhate Shweta , Limkar Suresh , Vyawahare Mohini , Ajani Samir N. , Borkar Pradnya
Published: April 2023

The power electronics (PE) industry is expected to play a significant role in the development of energy conservation and global industrialization trends of the 21st century. Due to the technological advancements that have occurred in the field, such as transportation and communication, the need for efficient and quality products is becoming more prevalent. The importance of power electronics is acknowledged in the automated industries that are constantly striving to improve their efficiency and effectiveness. Due to the increasing global energy consumption, the need for more energy-efficient technologies is also becoming more prevalent. Around 87% of our energy is derived from fossil fuels, while 6% is generated from nuclear power plants and 7% from renewable sources. Due to the increasing concerns about the environment and safety issues associated with nuclear plants and fossil fuels, the need for energy conservation is becoming more prevalent. This is also expected to be achieved through the development of power electronics. In the coming decades, the development of artificial intelligence (AI) tools, such as neural network, expert system, and fuzzy logic, is expected to bring a new era to the field of motion control and power electronics. Despite the technological advancements that have occurred in the field, these tools have not yet reached the power electronics sectors. In this paper, the AI tools and their applications in the field of power electronics and motion control are discussed.
Enhancing and Detecting the Lung Cancer using Deep Learning
Authors: Aluka Madhavi , Dixit Rajeev , Kumar Pankaj
Published: April 2023

In the medical sector cancer detection became the most challenging task. Here a lot of research is carried out by the scientific fraternity. Most medical issues are getting better answers because to modern technology like artificial intelligence and models based on neural networks. In this the first half part of the paper discuss about the CNN model by using regularization and augmentation techniques for getting the better accuracy result. The second part delas with developing and demonstrating an application for detecting the lung cancer using the deep learning (DL). Here the application is built using flask which works based on the Python programming language. This acts as an application programming interface (API) between the cloud server and the proposed application mod el. Heroku cloud platform was used as a service base to launch the software and to use the application with highest reliability. The internal functionality of the anticipated model is created on convolutional neural network (CNN) architecture with ten layers to obtain high accuracy. The model demonstrated a considerable training and validation accuracy of 94% and 92% respectively.
Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning
Authors: Ranga Kamal Kumar , Nagpal Chander Kumar , Vedpal Vedpal
Published: April 2023

Foreign tourism has gained immense popularity in the recent past. To make a rational decision about the destination to be visited one has to go through variety of social media sources with very large number of reviews, which is a tedious task. Automated analysis of these reviews is quite complex as it involves non structured text data having slang terms also. Moreover, these reviews are pouring in continuously. To overcome this problem, this paper provides a Big Data analytics-based framework to make appropriate selection of the destination on the basis of automated analysis of social media contents based upon the adaptation and augmentation of various tools and technologies. The framework has been implemented using Apache Spark and Bidirectional Encoder Representation Transformers (BERT) deep learning models through which raw text review are analysed and a final score based on five metrics is obtained to recommend destination for visit.
Adaptation of the hearing analyzer under vibration noise exposure in volunteers: Ways of prevention
Authors: Korkmazov Musos Yu. , Dubinets Irina D. , Lengina Maria A. , Korkmazov Arsen M. , Koroleva Anastasia N. , Fastakovskaya Kristina S. , Romanyugo Grigory D. , Bidzhiev Ali S.
Published: April 2023

BACKGROUND: The negative effect of impulse noise and vibration when using small arms on the auditory analyzer in athletes is less investigated and justifies the need for an extended study of volunteers with normal hearing. The study of the initial characteristics of the impulse noise and vibration effects of sports weapons on the auditory analyzer will make it possible to determine the early risk factors for the occurrence of high-frequency hearing loss in shooters by the number of shots from sports weapons and the recovery time of auditory function.
AIMS: To conduct a comparative analysis of the effect of impulse noise and vibration exposure depending on the technical characteristics of small arms on volunteers with normal hearing and, based on the results obtained, unify the methods of forecasting and prevention.
MATERIALS AND METHODS: At the Department of Otorhinolaryngology of the South Ural State Medical University of the Ministry of Health of Russia, 30 fifth-year students were examined following continuous recruitment of volunteers between September 2022 and November 2022. All interviewed participants gave voluntary written consent to participate in the survey and to the test level of impulse noise. Based on the current regulatory documents, all volunteers were surveyed and underwent audiological and vestibulometric studies, the technical characteristics of noise and vibration exposure as a risk factor for the early development of presbycusis were determined, and ways of preventing hearing loss through lifestyle were proposed.
RESULTS: The comparative analysis of the influence of impulse noise and vibration effects showed a dependence on the weight of the small arms cartridge, number of shots, and adaptation time of the auditory analyzer in volunteers with normal hearing considering the changes in the high-frequency spectrum of increasing audiometry thresholds from the average value of indicators.
CONCLUSIONS: The use of active explanatory propaganda among young people on a careful attitude to the physiological state of the cochleovestibular apparatus avoids the risk of early presbycusis development.
Padevyj toxidrome bee, causes and prevention
Authors: Rozhkov Konstantin A. , Lunegova Irina V.
Published: April 2023

Nowadays, feeding honeybees is not only considered a way to provide them with energy, but also as a factor that affects the normal functioning of all the bees body systems and the prevention of various diseases. This study investigated the quality of natural and artificial carbohydrate feed for honeybees, as well as presented data on their digestibility in the conditions of the northwestern region of the European part of Russia. A correlation was found between the digestibility of carbohydrate feeds and the weight of the rectum of worker bees at the end of the wintering period before the first flight. The methodology for conducting the research was also presented. The authors concluded on the prospects of studying the digestibility of carbohydrate feed, as well as determining the amount of honeydew and the speed of honey crystallization, as important components of the organization and planning of wintering of honeybees and improving beekeeping technology.
Bunga Matahari sebagai Simbol Perempuan dalam Cipta Kreasi Batik Lukis dengan Teknik Pewarnaan Glow In The Dark
Authors: Prasetyan Alfia Bella , Ratnawati Ike , Sidyawati Lisa
Published: April 2023

Perempuan dan bunga matahari merupakan dua wujud yang berbeda tetapi memiliki sifat yang sama. Bunga matahari memiliki keunikan mengikuti arah matahari bergerak dan filosofi bunganya dihubungkan dengan simbol feminitas atau sifat perempuan. Lemah lembut dan sabar terkadang masih diposisikan tidak adil dalam suatu budaya yang disebut patriarki. Sistem sosial yang menempatkan perempuan di kelas terbawah, kekuasaan lebih  didominasi laki-laki sehingga banyak kasus pelecehan dan kekerasan atas tubuh perempuan. Dari permasalahan tersebut masih sering terjadi di era modern ini sehingga penulis menjadikan latar belakang dalam pembuatan karya, yang mana makna dibalik karya ini diharapkan bisa menjadi jembatan khususnya bagi perempuan untuk menyuarakan penderitaannya. Tujuan penciptaan ini mendeskripsikan ide gagasan konsep, proses kreatif, hasil dan penyajian batik lukis yang terinspirasi dari bunga matahari sebagai simbol perempuan. Penulis mengembangkan metode Alma M. Hawkins pada pengciptaannya  melalui tiga tahapan, yaitu tahap eksplorasi, improvisasi, dan perwujudan. Penyajian hasil karyanya meng-hasilkan enam batik lukis dengan judul: Nirmala, Gundah, Sadrah, Nirwana, Angkara, dan Senandika. Menggunakan dua teknik yaitu teknik colet dan canting tulis untuk pewarna glow in the dark, hasil akhir penciptaan ini diharapkan dapat menjadi inspirasi dalam mengembangkan teknik baru pada batik lukis modern.
Kata kunci: bunga matahari, simbol, perempuan, batik lukis, glow in the dark
The Sunflowers as a Symbol of Women in the Creation of Batik Painting Creations using Coloring Techniques Glow in The Dark
Women and sunflowers are two different forms but have the same characteristics. Sunflowers have the uniqueness of following the direction of the moving sun, a social system that places women in the lowest class, power is dominated by men so that there are many cases of harassment and violence against women's bodies. These problems still often occur in this modern era, so the author uses the background in making works, where the meaning behind this work is hoped to be a bridge, especially for women to voice their suffering. The purpose of this creation is to describe the concept ideas, creative processes, results and presentation of painted batik inspired by sunflowers as a symbol of women. The author develops Alma M. Hawkins' method of creation through three stages, namely the stages of exploration, improvisation, and embodiment. The presentation of his work resulted in six painted batiks with the titles: Nirmala, Gundah, Sadrah, Nirwana, Angkara, and Senandika. Using two techniques, namely the dab and canting techniques for glow in the dark coloring, the final result of this creation is expected to be an inspiration in developing new techniques in modern painting batik.
Keywords: sunflowers, symbols, woman, painted batik, glow in the dark
Penataan dan Pemanfaatan Sarana Prasarana Pembelajaran Pendidikan Formal dan Nonformal
Authors: Rasyad Ayu Anindya , Ulfatin Nurul , Timan Agus
Published: April 2023

Abstract: This study aims to identify and describe the arrangement and utilization of learning infrastructure for formal and non-formal education. This research uses qualitative methods with the type of case study research. The research data which was conducted at Pondok Pesantren An-Nur II Malang was collected using interview, observation, and documentation techniques. Then analyzed through an interactive process through the stages of data reduction, presentation, and verification. The results of this study are: (1) the types of infrastructure at the An-Nur II Malang Islamic Boarding School, (2) the arrangement of the infrastructure facilities at the An-Nur II Islamic Boarding School Malang; (a) the principles of structuring educational facilities and infrastructure include the relationship between spaces, land area, spatial framework, building location; (b) types of arrangement of educational infrastructure include movable property, immovable property, consumable movable property, non-consumable movable object; (c) supporting and inhibiting factors in the arrangement of facilities and infrastructure; (d) solutions in overcoming inhibiting factors in structuring facilities and infrastructure. (3) utilization of infrastructure facilities in Islamic Boarding School An-Nur II Malang; (a) use of learning tools; (b) use of visual aids; (c) the use of learning media; (d) supporting and inhibiting factors in the use of infrastructure; (e) solutions in overcoming inhibiting factors in the use of infrastructure.

Abstrak: Penelitian ini bertujuan untuk mengetahui dan mendeskripsikan penataan dan pemanfaatan sarana prasarana pembelajaran pendidikan formal dan nonformal. Penelitian ini menggunakan metode kualitatif dengan jenis penelitian studi kasus. Data penelitian yang dilaksanakan di Pondok Pesantren An-Nur II Malang ini dikumpulkan menggunakan teknik wawancara, observasi, dan dokumentasi. Kemudian dianalisis melalui suatu proses interaktif melalui tahap reduksi, penyajian, dan verifikasi data. Adapun hasil penelitian ini yaitu: (1) jenis-jenis sarana prasarana di Pondok Pesantren An-Nur II Malang, (2) penataan sarana prasarana di Pondok Pesantren An-Nur II Malang; (a) prinsip-prinsip dalam penataan sarana prasarana pendidikan meliputi hubungan antar ruang, luas tanah, kerangka ruang, lokasi bangunan; (b) jenis-jenis penataan sarana prasarana pendidikan meliputi barang bergerak, barang tidak bergerak, barang bergerak habis pakai, barang bergerak tidak habis pakai; (c) faktor pendukung dan penghambat dalam penataan sarana prasarana; (d) solusi dalam mengatasi faktor penghambat dalam penataan sarana prasarana. (3) pemanfaatan sarana prasarana di Pondok Pesantren An-Nur II Malang; (a) pemanfaatan alat pelajaran; (b) pemanfaatan alat peraga; (c) pemanfaatan media pembelajaran; (d) faktor pendukung dan penghambat dalam pemanfaatan sarana prasarana; (e) solusi dalam mengatasi faktor penghambat dalam pemanfaatan sarana prasarana.
美国大学外语教学发展趋势及其对中文教育的启示
Authors: Wang Yanhao
Published: April 2023

本研究基于美国现代汉语协会所进行的一系列调查，分析了2002年至2016年期间美国大学外语课注册人数的发展趋势。研究指出，自上世纪五十年代以来，美国大学生对外语课的兴趣一直在波动上升，但总体增长率较低。2009年前后由于经济危机等因素，大学生对外语等人文学科的兴趣大幅度下降。中文课注册人数在2013年达到峰值，但随后锐减13%。本研究认为，为了推动中文教育在美国大学内的发展，教师应增强内容的实用性并鼓励学生进一步学习高级课程，国家也应该积极推动软实力建设，从而吸引更多的学生主动学习中文。

This research, based on reports published by Modern Language Association, analyzed enrollments in languages other than English in United States institutions of higher education from 2002 to 2016. Results show that American college students’ interest on foreign languages have been increasing unsteady since the 1950s, but the increasing rates kept low; and given the factors like the Great Depression since 2009, college students’ interests on foreign language and other disciplines of humanity have dropped abruptly. The enrollment of Chinese classes has reached its peak in 2013 but then experienced a 13% decrease. This study suggests, to promote Chinese education in the US, teachers should develop practical contents and encourage students to study advanced classes, and government of China should develop its soft power to attract more students to study Chinese.
Práctica de danza como actividad saludable a lo largo de la vida. Revisión de alcance
Authors: Ollora Triana Natalia , Isidori Enmanuele , López Perea Eva María
Published: April 2023

Los tiempos vividos por el COVID-19 han posicionado a las artes escénicas y dentro de estas a la danza en una situación de reconocimiento. Como práctica en sus diferentes estilos, ofrece la capacidad de adaptarse a las necesidades de la población y a diferentes situaciones sociales. La afirmación de ser una actividad con múltiples beneficios para la salud integral lleva a considerar la necesidad de revisar hoy la documentación existente. Los objetivos del estudio son averiguar las aportaciones de la literatura encontrada en los últimos años en relación con los beneficios de la práctica de danza en personas de cualquier edad ausentes de enfermedad, delimitar si esta actividad es apropiada para la salud integral en cualquier etapa natural de la vida y conocer los beneficios concretos de esta práctica. Se realiza una revisión de alcance de documentos publicados entre el 2012 y el 2022 incluídos, utilizando el software Atlas Ti 8 para el análisis cualitativo. Se constata un incremento de publicaciones que integran la danza como herramienta para desarrollar beneficios saludables, generando beneficios psicológicos y mentales en niños; físicos, mentales y sociales en adolescentes y jóvenes; físicos, psicológicos y emocionales en adultos y en la vejez físicos, emocionales y sociales. Se concluye que la práctica de danza mejora diferentes aspectos de la salud integral en todas las etapas de vida.
Impacto del confinamiento en el desarrollo curricular de Educación Física en Educación primaria y Secundaria
Authors: Soto González José Ignacio , Marín Regalado María Nieves , Martín Sánchez José Alberto , Caracuel Cáliz Rafael Francisco
Published: April 2023

Debido a la pandemia por el COVID-19, el estado de alarma y el confinamiento domiciliario, la docencia pasó a ser virtual en cuestión de unos pocos días y los docentes tuvieron que adaptar todo su trabajo de manera inexorable. Ante esta situación totalmente inédita en la historia de nuestro sistema educativo mediante esta investigación se ha indagado y descrito el impacto del periodo de confinamiento en el currículo escolar de educación física en Educación Primaria (EP) y Educación Secundaria obligatoria (ES). El instrumento utilizado ha sido un cuestionario realizado en Google Forms mediante la técnica Delphi. La muestra está́ compuesta por 100 docentes de las etapas de Educación Primaria y Educación Secundaria en España, en concreto en la comunidad autónoma Andaluza. Entre las principales conclusiones destacamos que el profesorado ha considerado que los objetivos de salud y los de condición física, fueron los objetivos más trabajados del área. Los docentes consideran que no se consiguieron llevar a cabo los tiempos motrices establecidos, ni la programación, pero si se consiguió que el alumnado realizara una tarea semanal con una implicación de aproximadamente una hora. Se concluyo que el impacto en el aprendizaje fue grande pues no se consiguieron trabajar todos los contenidos y las tareas planteadas no eran evaluables.
Work Performance of Nurses Working in Covid-19 Dedicated Tertiary Public Hospitals, Bangladesh
Authors: Sultana Sharmin , Islam ANM Shamsul , Islam MD. Tajul , Azam Md. Rahat Ul , Kundu Poly Rani , Haque Quazi Umme La-sani , Hossain Md. Belaet , Uddin Md. Imtiaz
Published: April 2023

Work performance of nurses refers to the set of nursing activities or behaviors that are performed by nurses and directed toward the recovery and well-being of the patients assigned to their care. During the COVID-19 pandemic, nurses perform a pivotal role in managing COVID-19 patients in hospitals. The study aimed to assess the level of work performance of nurses working in COVID-19-dedicated tertiary public hospitals. A descriptive type of cross-sectional study was conducted from January to December, 2021 within 223 nurses. The sample was selected based on a non-probability purposive sampling technique. The data was collected by using a self-administered structured questionnaire (6DSNP and self-administered variables) from two COVID-19 dedicated tertiary public hospitals (Kuwait Bangladesh Friendship Government Hospital and Sheikh Russel Gastroliver Institute & Hospital). The study findings mentioned that 68.2% of respondents were between the age of 21-30 years and the mean age was 28.6 years and SD= 4.66. In the educational level, 67.3% of respondents had a diploma degree, 62.8% of respondents had more than 7 years of working experience, 67.3% of respondents dislike the night shift & 68% mentioned short manpower is the cause of disliking that shift, their average work performance on the planning of direct patient-centered work was 3.52 ±.773, critical care of direct patient-centered work was 3.79 ±.382, teaching/collaboration of direct patient-centered work was 3.33±.712, communication of direct patient-centered work was 3.68±.532, and the additional variable of direct patient work was 3.77±.465. In addition to their average performance in the leadership of non-patient-centered work was 3.49±.688, professional development of non patient-centered work was 3.35±.693, and also their performance on indirect patient work was 3.53 ±.703. The average work performance of the respondents was 3.55 ±.618 which mentioned their performance was good. Quality of nursing care should be improved by enhancing nurses' competency, which can influence the growth of faith in people's minds to treat their health inside the country as well as reduce the trend to move abroad for better treatment.
Lo Que Debemos Tener En Cuenta Antes De Diseñar Procesos De Aprendizaje
Authors: Luis Bretel B
Published: April 2023

Este documento pretende responder, desde la revisión bibliográfica de la literatura pedagógica y de la psicología de la educación contemporánea y de la manera más simple ysintética posible, a las siguientes preguntas: ¿Qué es aprender? ¿Qué tipos de aprendizaje podemos tener? ¿Cómo aprendemos cada tipo? Y ¿Qué tiempo requerimos para hacerlo? Esperamos que su contenido pueda ayudar a tomar decisiones acertadas sobre cómo diseñar cursos que hagan posible aprendizajes exitosos.
Nitazoxanide – A New Option in Biliary Ascariasis
Authors: Nasreen Hasina , Hassan Mahtabuddin
Published: April 2023

This prospective study was conducted in the in-patient department of medicine, Chittagong Medical College Hospital (CMCH), Chittagong, Bangladesh from February 2016 to September 2016. The study included 70 patients admitted to emergency department of medicine. Clinical assessment was performed in all the cases. Detailed history was taken as per site, severity and nature of pain. Nitazoxanite is a newly recommended antihelminthic agent elicits its activity by interfering with pyruvate: ferredoxin oxidoreductase (PFOR) enzyme dependent electron transfer reaction, which is essential for anaerobic energy metabolism. It is given by the oral route with good bioavailability and is well tolerated, with primarily mild gastrointestinal side effects. Nitazoxanide (500mg) tablet was given twelve hourly for 3 days. Relieve of pain after taking antihelminthic was observed and serial ultrasonogram was performed to determine the worm present or absent in biliary tree. In this study majority of the patients presenting biliary ascariasis were females (74.3%) and the most common age group affected was within 18 to 35 years of age. Among the total respondents, 88.57% were cured, while only 11.43% were not cured off their diseases.
Seroprevalence of Low Pathogenic Avian Influenza (H9) in Sonali Chickens of Joypurhat
Authors: Rahman Md. Ziaur , Rashid S.M. Harun-ur- , Azam Md. Golam , Md. Nuruzzaman  , Rahman Md. Gausur
Published: April 2023

The present study was designated to know the sero-prevalence status of H9 antibodies in Sonali chickens at Joypurhat district in Bangladesh by initial screening of AIV type A by ELISA followed by H9 LPAI detection by HI test. 180 sera samples from 18 flocks were collected and screened for Avian Influenza Virus type A antibodies by ELISA. All ELISA positive samples were tested by HI test, specific for H9 antibodies. On ELISA, sero-prevalence for AIV type A were 14.4% and 3.3% in older aged group (≥11weeks) and growing aged group (≤10 weeks) respectively, with an overall sero-prevalence of 8.89% in Sonali chickens at Joypurhat district. Among these AIV sero-positive samples, 92.31% 0f older aged group and 66.67% of growing aged group chickens found sero- positive to H9 antibodies by HI test with an overall 87.5%. Out of 180 sera samples, only 14  samples found positive to H9 antibodies, indicating 7.78% sero-prevalence in Sonali chickens of Joypurhat district. This study revealed higher sero-prevalence of AIV antibodies as well as H9  antibodies in older aged group which might be due long-time exposure to infections. Sero- prevalence of both AIV type A and H9 LPAI are higher in older aged Sonali chickens. The findings of the present study suggest that the overall seroprevalence of AIV type A and H9 LPAI in Sonali chickens are 8.89% and &.78% respectively, at Joypurhat district in Bangladesh and also suggest that H9LPAI virus in circulation in Sonali chickens at Joypurhat district in Bangladesh.
Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models
Authors: Feyzioğlu Ahmet , Taspınar Yavuz Selim
Published: April 2023

Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.
Detection and comparison of Proliferative Diabetic Retinopathy using Watershed Algorithm and K-Means Clustering Algorithm
Authors: Naz F. , Rani D.J. , Rajakumari R.
Published: April 2023

Aim: The aim of this research work is for the presence of Innovative Proliferative Diabetic Retinopathy Detection, using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between Watershed Algorithms and K-Means Clustering Algorithm. Materials and methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for Watershed Algorithm and (N=24) for K-means clustering algorithm in accordance with total sample size calculated using clinicalc.com by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval, G power as 80%. The PSNR was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample test using SPSS software. There is a statistical significant difference between Watershed Algorithm and K-means clustering algorithm with p<0.001, p<0.05 (PSNR=10.8205) using Watershed Algorithm showed better results in comparison to K-Means Clustering Algorithm (PSNR=9.7350). Conclusion: Watershed Algorithms were found to give higher PSNR than in K-Means Clustering Algorithms for the Innovative Proliferative Diabetic Retinopathy Detection.
Analysis and Comparison of Random Forest Algorithm for Prediction of Cardiovascular Disease over Support Vector Machine Algorithm with Improved Precision
Authors: Gadde R. , Kumar N.S.
Published: April 2023

Aim: To find the best algorithm for the prediction of innovative cardiovascular disease accurately, with fewer errors between Random Forest and Support Vector Machine classifiers. Materials and Methods: Data collection containing various data points for predicting innovative cardiovascular disease from UCI machine learning repository. Classification is performed by Random Forest classifier (N=20) over Support Vector Machine (N=20) total sample size calculation is done through clinical. com. The accuracy was calculated using Matlab software and the outputs are graphed using SPSS software. Results: comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistical indifference between Random Forest and Support Vector Machine. Support Vector Machine algorithm (87.38%) showed better results in comparison to Random Forest (83.50%). Conclusion: Support Vector Machine algorithm appears to give better accuracy than Random Forest for the prediction of innovative Cardiovascular Disease.
Implementation of inclisiran in United Kingdom primary care for patients with atherosclerotic cardiovascular disease or its-risk equivalents: rationale and design of Victorion-spirit, a pragmatic phase IIIb, randomised controlled study
Authors: Wilson Paul M. , Dixon Samantha , Vell Tracey , Bower Peter , Rootkin Linda , Williams Claire , Strong Adam , Lawrence David , Elvey Rebecca , Williams Owen , Stein Norman , Gibson J. Martin
Published: April 2023

Background: Translational gaps exist in implementing health innovations rapidly in clinical practice. Pragmatic effectiveness and implementation studies, therefore, play a pivotal role in understanding how high-value health innovations could be deployed and delivered in healthcare systems to reduce barriers to adoption and provide more rapid patient benefit. Victorion-spirit is an ongoing pragmatic, 9-month, phase IIIb, open-label, multicentre, randomised controlled study evaluating the implementation, patient experience, and delivery of the subcutaneous lipid-lowering therapy, inclisiran sodium 300 mg, in participants with elevated low-density lipoprotein cholesterol (LDL-C) who are on established lipid-lowering medication, or have been recommended lipid-lowering therapy, but are unable to tolerate treatment.
Methods: Victorion-spirit utilises a type 1 hybrid effectiveness-implementation design, where the primary objective is to demonstrate superiority of inclisiran with or without (±) behavioural support versus standard of care (SOC; e.g., statin and/or other lipid-lowering therapies) + behavioural support in terms of percentage reduction in LDL-C from baseline to Day 270 in a primary care setting. Secondary objectives will evaluate implementation of inclisiran ± behavioural support versus SOC + behavioural support through assessment of patient satisfaction and patient activation/empowerment after treatment at Day 90; adherence to cardiovascular disease self-management; and serious adverse event profile. Additionally, a process evaluation ascertaining the views of patients, providers and national health service (NHS) commissioners will explore barriers and enablers to integrating inclisiran within primary care.
Conclusions: The results of Victorion-spirit have potential to change our approach to lipid management and inform further implementation efforts in healthcare systems, such as the NHS.
Trial registration: ClinicalTrials.gov NCT04087400.
Conducting a three-country clinical trial during the COVID-19 pandemic: experience and future considerations
Authors: Baedorf Kassis Sylvia , Lu Weidong , White Sarah A. , Giobbie-Hurder Anita , Tanasijevic Anna , Jung Hyun-Jung , Zhang Xiping , Shin Im H. , Park Sung H. , Jeong Young J. , Yao Chang , Ligibel Jennifer , Bierer Barbara E.
Published: April 2023

The global SARS-COV-2 pandemic has significantly impacted the delivery of clinical care as well as the conduct of international clinical trials. A coordinated, multinational acupuncture study, consisting of three parallel randomized studies with a planned pooled analysis of individual patient data, was initiated in 2019 with the goal of assessing whether acupuncture relieved hot flash symptoms in hormone receptor-positive breast cancer patients prescribed adjuvant endocrine therapy. Eligibility included persistent hot flashes on endocrine therapy. Participants were randomly assigned to receive either immediate or delayed acupuncture in equal proportions; the primary endpoint was assessed at week 10, after completion of the immediate acupuncture treatments and before the delayed treatment sessions began. The trial was conducted in China, South Korea and United States of America (USA) and was in the midst of enrollment and study procedures when the COVID-19 pandemic began. Despite numerous challenges, the study was nonetheless completed successfully. We deployed a process evaluation method to describe each site’s experiences in conducting this multinational study during the pandemic. Using these observations, we offer measures for the planning and conduct of future studies, taking into account preparedness considerations in the event of exigent and demanding global circumstances.
Acute pancreatitis, a significant mortality predictor in acute organophosphate poisoning: an observational study in a tertiary care centre in north India
Authors: Tomar Aditya , Singh Reena , Kumar Vinay , Kaur Samarjeet
Published: April 2023

Background: Organophosphate compounds are one of the most common agents used as poison and acts by accumulation of acetylcholine hormone at neuronal synapses resulting in the symptoms like excessive salivation, vomiting, urination, and increased serum amylase and lipase levels. APACHE 2 score along with ultrasound modality can be used to assess the acute pancreatitis. The primary aim of the study is to find correlation of serum amylase and lipase levels with duration of ICU stay and with radio-logical variables like bulky pancreas.
Methods: This observational study conducted at GSVM medical college, Kanpur from December 2020 to October 2022 included 58 out of total 94 admitted patients with acute organophosphate intoxicationn on the basis of inclusion and exclusion criteria. Patients were divided in 3 groups as mild, moderate and severe using q SOFA score at the time of admission.
Results: Acute organophosphate poisoning was more prevalent among 20-40 years of age group. Mean serum amylase level values in q SOFA category 0, 1, 2, 3 were 65.2, 82.0, 118.4, 329.9 IU/l respectively and that of mean serum lipase levels on day 1 of admission values were 42.0, 44.3, 38.6 and 115.5 IU/l respectively. Serum amylase levels were positively correlated with duration of ICU stay and were better predictor for acute pancreatitis.
Conclusions: In this study we concluded that serum amylase is a better predictor of duration of ICU stay and acute pancreatitis in patients admitted with acute organophosphate poisoning.
Prediction of esophageal varices and risk of bleeding in liver cirrhosis by aspartate aminotransferase to-platelet ratio index and fibrosis-4 index
Authors: Kumar Shailesh , Kumar Vinay , Giri Richa , Agarwal Saurabh , Gautam S. K.
Published: April 2023

Background: Liver cirrhosis is the end-stage for chronic liver disease. During the course of cirrhosis repeated endoscopy is recommended. As this intervention is expensive and often poorly accepted by patients who may refuse further follow up, there is a need for non-invasive methods to predicts the progression of portal hypertension as well as the presence and size of esophageal varices. This study was aimed to assess the value of aspartate aminotransferase (AST)-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) for predicting esophageal varices in cirrhotic patients. Objectives of the study were: prediction of esophageal varices and risk of bleeding in liver cirrhosis by APRI, and prediction of esophageal varices and risk of bleeding in liver cirrhosis by FIB-4.
Methods: It was a single centre, observational study in 100 patients of chronic liver disease. Patients were included in the study after applying inclusion and exclusion criteria. Complete blood count (CBC), liver function test (LFT), kidney function test (KFT), SE, viral marker, ultrasonography (USG) whole abdomen, and upper gastrointestinal endoscopy (UGIE) was done for all patients. Child-Pugh-Turcotte (CTP) score was calculated for every patient.
Results: The APRI and FIB-4 shows moderate diagnostic accuracy in predicting the presence of esophageal varices and variceal bleed.
Conclusions: In conclusion, the APRI and FIB-4 shows moderate diagnostic accuracy in predicting the presence of esophageal varices and variceal bleed. They help in starting prophylactic therapy earlier to prevent the bleeding and other complications of varices. Although endoscopy remains the primary modality for diagnosis of esophageal varices but these non-invasive parameters can also play an effective role in conjunction with endoscopy in predicting the presence of esophageal varices.