bluuebunny/crossref_metadata_2025 huggingface dataset:
Sistema de Monitoramento em Tempo Real da Cor e Temperatura dos Grãos de Café Durante a Torrefação
Authors: Cardoso Rennan Alves , Borém Flávio Meira , Barbosa Bruno Henrique Groenner
Published: October 2021

A cor e a temperatura dos grãos de café são duas variáveis muito importantes de serem monitoradas para avaliar o grau de torra do mesmo, durante uma torrefação. No entanto, os torradores comerciais atuais não possuem sistema de monitoramento dessas variáveis em tempo real. Nesse sentido, este trabalho propõem um sistemas de medição de cor de grãos de café e um sistema supervisório para monitoramento das temperaturas envolvidas durante o processo. Este sistema foi projetado para operação em torradores de tambor rotativo horizontal. Para tal, foi construído um sistema de visão computacional e dois termopares tipo k foram posicionados de forma apropriada no interior do torrador. O sistema de visão é composto por uma estrutura obtida por meio de impressão 3D que sustenta e direciona uma câmera digital de baixo custo (Webcam USB) e um sistema de iluminação para capturar imagens (formato RGB) de grãos no torrador. Os termopares foram conectados à um chassi NI CompactDAQ USB, para condicionar e converter o sinal analógico para digital. Todos os elementos, câmera e chassi, foram conectados em um computador pessoal e um programa desenvolvido em LabVIEW foi utilizado para adquirir imagens e temperaturas e fazer os processamentos necessários. Cada imagem foi adquirida a frequência de 2 Hz e, posteriormente, segmentada e analisada de forma que a cor dos grãos seja fornecida na escala AGTRON, muito utilizada em processos de torrefação. As temperaturas foram adquiridas a uma frequência de 3 Hz e, a partir de uma interface gráfica implementada, os valores de cor e temperatura são apresentados ao usuário em tempo real. O sistema aqui projetado mostrou-se adequado para o monitoramento das temperaturas e cor em tempo real, durante a torrefação.
Probability Estimation of Direct Hydrocarbon Indicators Using Gaussian Mixture Models
Authors: Lemos John B. , Barbosa Matheus R. S. , Troccoli Edric B. , Cerqueira Alexsandro G.
Published: October 2021

This work aims to delimit the Direct Hydrocarbon Indicators (DHI) zones using the Gaussian Mixture Models (GMM) algorithm, an unsupervised machine learning method, over the FS8 seismic horizon in the seismic data of the Dutch F3 Field. The dataset used to perform the cluster analysis was extracted from the 3D seismic dataset. It comprises the following seismic attributes: Sweetness, Spectral Decomposition, Acoustic Impedance, Coherence, and Instantaneous Amplitude. The Principal Component Analysis (PCA) algorithm was applied in the original dataset for dimensionality reduction and noise filtering, and we choose the first three principal components to be the input of the clustering algorithm. The cluster analysis using the Gaussian Mixture Models was performed by varying the number of groups from 2 to 20. The Elbow Method suggested a smaller number of groups than needed to isolate the DHI zones. Therefore, we observed that four is the optimal number of clusters to highlight this seismic feature. Furthermore, it was possible to interpret other clusters related to the lithology through geophysical well log data.
Use of Convolutional Neural Networks in the Diagnosis of Corn Diseases
Authors: Lima Paulo Victor Cunha , Costa Edson Magalhães , Holanda Maria Eliana da Silva , Oliveira Dhian Kelson Leite , Espírito Santo Esley Teixeira , Ramos Luiz Henrique Dias , Soares Lucas Henrique Martins , Santos Isadora Mendes , Araújo Fabrício Almeida , Silva Jakelyne Machado Lima , Souza Junior Gilberto Nerino , Braga Marcus de Barros
Published: October 2021

The detection of corn (maize) crop diseases is traditionally carried out by farmers, based on their experience accumulated over a period of field practice. However, the visual observation may represent a risk of error due to subjective perception. This article presents an approach based on Deep Learning to identify diseases that affect corn crops. A public database with 3,852 images of maize plant leaves was used, dividedinto four classes: healthy corn, exserohilun leaf spot (northern leaf blight), common corn rust (common rust) and cercosporiosis (cercospora leaf/gray leaf). The proposed model used Convolutional Neural Networks (CNN) techniques for image classification. The four experiments indicated results with an average accuracy above 94.5%. These results in the identification and diagnosis of plant diseases can contribute significantly as atool to the improvement of the production chain that affect corn crops. All data are available at https://github.com/npcaufra/classificacao-doencas-milho .
Previsão do Preço do Açúcar: Uma Análise Comparativa entre Modelo Linear e Redes Neurais
Authors: Barchi Tathiana M. , Bassetto Priscila , Biuk Lucas H. , Costa Lucas F. P. , Puchta Erickson D. P. , Correa Fernanda Cristina , Andrade Mauren L. S. C. , Kachba Yslene Rocha , Siqueira Hugo Valadares
Published: October 2021

O Brasil desempenha um papel importante na produção mundial de subprodutos gerados a partir da cana-de-açúcar, principalmente açúcar e etanol. O açúcar ́e uma das mais antigas commodities comercializadas pelo país. Realizar uma previsão adequada dos preços de tal produto tem impacto direto no sistema econômico, pois auxilia os gestores nos planejamentos estratégicos e minimiza os riscos futuros com uma avaliação mais precisa de suas tendências de mercado. Assim, o presente estudo tem como objetivo realizar a previsão do preço do açúcar, usando os modelos autorregressivo (AR), Perceptron de Multiplas Camadas (MLP), Máquinas de Aprendizado Extremo(ELM) e Redes Neurais com Estados de Eco (ESN). Os resultados experimentais evidenciaram que os modelos que apresentaram melhores desempenhos para um passo a frente nas bases CEPEA e NY No. 11, foram ELM e MLP, respectivamente.
Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches
Authors: Samantaray Sandeep , Ghose Dillip Kumar
Published: October 2021

Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.
Towards pragmatism in climate risk analysis and adaptation
Authors: Wilby Robert Leonard , Lu Xianfu , Watkiss Paul , Rodgers Charles Andrew
Published: October 2021

The Asia-Pacific region is extremely vulnerable to climate variability and change. This reflects high exposure to hydroclimatic hazards such as tropical cyclones, floods, droughts, and heatwaves. Rapidly growing cities and low-lying coastal zones/estuaries also face threats from sea level rise and storm surges. However, climate model projections remain very uncertain about most of these risks, so water infrastructure and operations need to consider a range of plausible futures. Against this background, the Asian Development Bank (ADB) has been developing frameworks, tools, and capacities in climate risk and adaptation assessment and management. Project teams are often operating in data-scarce situations and under significant time constraints, so the emphasis has been on creating pragmatic guidance and training resources. This paper charts the transition of climate risk management (CRM) within the ADB from a predominantly scenario-led to decision-led approach to adaptation. Examples are given of light-touch procedures for screening climate risks, strengthening the transparency and rigour of scenario analysis, raising awareness of a broad range of adaptation options, streamlining identification of CRM options, and embedding allowances for climate change in detailed engineering designs. Such practical innovations would benefit communities of practice beyond the Asia-Pacific region.
Climate change adjustments in engineering design standards: European perspective
Authors: Kundzewicz Zbigniew W. , Licznar Paweł
Published: October 2021

The European Commission Flood Risk Directive review shows that while many nations have embraced the concepts of flood risk management, there is still quite more to do in delineating risk–cost-effective measures and developing cost estimates and financing of those measures. Not mentioned are the necessary changes to existing design standards and protocols which will have to change in order to properly encompass climate change and variability, with associated uncertainties. Adjustments in engineering design standards and changes in hazards are examined, based on trend detection in observational records and projections for the future. Issues of urban and transport (motorways and railways) drainage design are also examined. Furthermore, risk reduction strategies are discussed. Finally, a way of accounting for non-stationarity in determining design precipitation and design floods is tackled. Climate change adjustments in engineering design standards, such as design precipitation and design floods, are reviewed via examples from Europe.
The Effect of Plant Metabolites on Coronaviruses: A Comprehensive
Review Focusing on their IC50 Values and Molecular Docking Scores
Authors: Hashempour-Baltork Fataneh , Khosravi-Drani Kianoush , Farshi Parastou , Kaya Eda Ceren
Published: October 2021

:

Coronaviruses have caused worldwide outbreaks in different periods. SARS (severe acute
respiratory syndrome) was the first emerged virus from this family, followed by MERS (Middle East
respiratory syndrome) and SARS-CoV-2 (2019-nCoV or COVID 19), which is newly emerged. Many
studies have been conducted on the application of chemical and natural drugs for treating these coronaviruses
and they are mostly focused on inhibiting the proteases of viruses or blocking their protein
receptors through binding to amino acid residues. Among many substances which are introduced to
have an inhibitory effect against coronaviruses through the mentioned pathways, natural components
are of specific interest. Secondary and primary metabolites from plants, are considered as
potential drugs to have an inhibitory effect on coronaviruses. IC50 value (the concentration in which
there is 50% loss in enzyme activity), molecular docking score and binding energy are parameters
to understand the ability of metabolites to inhibit the specific virus. In this study we reviewed 154
papers on the effect of plant metabolites on different coronaviruses and data of their IC50 values,
molecular docking scores and inhibition percentages are collected in tables. Secondary plant metabolites
such as polyphenol, alkaloids, terpenoids, organosulfur compounds, saponins and saikosaponins,
lectins, essential oil, and nicotianamine, and primary metabolites such as vitamins are
included in this study.
Altering Amino Acid Profile in Catharanthus Roseus (L.) G. Don Using Potassium and Ascorbic Acid Treatments
Authors: Sahi Neda , Mostajeran Akbar , Ghanadian Mustafa
Published: October 2021

Background:

Catharanthus roseus (L.) G. Don is the main source of alkaloids anticancer drugs. Alkaloids are derived from amino acids and can lead to changes in these valuable compounds.

Objective:

This experiment evaluated the variation of amino acids under potassium and ascorbic acid treatments.

Methods:

Different concentrations (1.5, 3.16, 15 and 30 mM) and forms (K2SO4 and KNO3) of potassium (K+) were added to plants via Hoagland’s nutrient solution. Ascorbic acid (AsA) (750 mg L-1) was sprayed on the leaves surfaces on days 68 and 78. Amino acids were extracted from 90-day-old plant leaves, and different amino acids were determined by High-Performance Liquid Chromatography with fluorescence detection.

Results:

Amino acids increased in K+ deficiency (1.5 mM), but changes in negatively charged amino acids were lower. In contrast, N-rich amino acids showed the biggest change. In excessive K+, branched-chain and aromatic amino acids decreased, while the least amount of the other amino acids was observed in the plants treated with optimum K+.

Conclusion:

The exterior of AsA and excessive K+ cause branched-chain and aromatic amino acids, aspartic acid and glutamic acid to decrease.
Impact of Overloaded School Backpacks: An Emerging Problem
Authors: Barbosa João , Marques Mário C. , Neiva Henrique P. , Santos Catarina , Branquinho Luís , Ferraz Ricardo , Marinho Daniel A.
Published: October 2021

The possible adverse effects of overloading students' backpacks are a public concern and should be considered by the scientific and educational community. This topic has gained particular importance due to the childhood development process, which can increase the promotion of future disorders (e.g., back pain, low back pain, spinal column deviations). In this brief review, we critically analyze the impact of excess load in students' backpacks and attempt to identify solutions that can be useful to minimize the effects of this problem. It is necessary to find a viable alternative to classic backpacks that can contribute to minimizing the effects of backpack loads on children.
Ultrasound-Guided Sciatic and Femoral Block in Patient with Recent Embolism Stroke Undergo Amputation Above the Knee
Authors:   Anesthesiology and Intensive Care, Universitas Sebelas Maret Indonesia, Mubarak Propan Hanggada Satyamakti
Published: October 2021

Background: Patients undergoing amputation of the lower extremities usually have poor circulation and other comorbidities and, therefore, present a challenge to the anesthesiologist. The combination of sciatic and femoral nerve block is reported to be an effective anesthetic technique for patients undergoing knee surgery, and it is known to be an effective method of postoperative analgesia in patients undergoing major surgery on the subject's lower extremities. Case: A 70-year-old man with a recent embolism stroke, death of his limb on the right cruris region, and underwent an amputation above the knee. Preoperative physical examination showed GCS E4 V, motor aphasia M6, blood pressure 131/82 mmHg, pulse 114 bpm, RR 20 times/minute with bodyweight 70 kg, height 160 cm, SpO2 97%. There was cyanosis in the right cruric region with motor power of 1/5 1/5. The value of leukocytes 18,600/ul, platelets 309,000/ul, Prothrombin Time (PT) 15.8 seconds, Activated partial thromboplastin time (APTT) 25.9 seconds, International normalized ratio (INR) 1,330, neutrophils 81.7%, lymphocytes 12.6%, creatinine 1.8 mg/dl, urea 137 mg /dl, blood sugar at the time 258 mg/dl, Cl 95 mmol/l. This case report suggests that ultrasound-guided peripheral nerve blocks may be useful for major lower extremity surgery in patients with severe hemodynamic impairment. Conclusion: Perioperative management of embolism stroke patients undergoing above-knee amputation requires special attention in selecting anesthetics. Ultrasound-guided peripheral nerve blocks minimize patient hemodynamic changes and provide better postoperative pain control.
Vraag & Antwoord
Authors: Schauvliege S. , Claerebout E.
Published: October 2021

Noodslachting bij het rund

Moniezia spp. bij runderen
Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model
Authors: Liang Chia-Wei , Yang Hsuan-Chia , Islam Md Mohaimenul , Nguyen Phung Anh Alex , Feng Yi-Ting , Hou Ze Yu , Huang Chih-Wei , Poly Tahmina Nasrin , Li Yu-Chuan Jack
Published: October 2021

Background

Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates.

Objective

The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year.

Methods

Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works

Results

We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively.

Conclusions

The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.
Exploring Reasons for Delayed Start-of-Care Nursing Visits in Home Health Care: Algorithm Development and Data Science Study
Authors: Zolnoori Maryam , Song Jiyoun , McDonald Margaret V , Barrón Yolanda , Cato Kenrick , Sockolow Paulina , Sridharan Sridevi , Onorato Nicole , Bowles Kathryn H , Topaz Maxim
Published: October 2021

Background

Delayed start-of-care nursing visits in home health care (HHC) can result in negative outcomes, such as hospitalization. No previous studies have investigated why start-of-care HHC nursing visits are delayed, in part because most reasons for delayed visits are documented in free-text HHC nursing notes.

Objective

The aims of this study were to (1) develop and test a natural language processing (NLP) algorithm that automatically identifies reasons for delayed visits in HHC free-text clinical notes and (2) describe reasons for delayed visits in a large patient sample.

Methods

This study was conducted at the Visiting Nurse Service of New York (VNSNY). We examined data available at the VNSNY on all new episodes of care started in 2019 (N=48,497). An NLP algorithm was developed and tested to automatically identify and classify reasons for delayed visits.

Results

The performance of the NLP algorithm was 0.8, 0.75, and 0.77 for precision, recall, and F-score, respectively. A total of one-third of HHC episodes (n=16,244) had delayed start-of-care HHC nursing visits. The most prevalent identified category of reasons for delayed start-of-care nursing visits was no answer at the door or phone (3728/8051, 46.3%), followed by patient/family request to postpone or refuse some HHC services (n=2858, 35.5%), and administrative or scheduling issues (n=1465, 18.2%). In 40% (n=16,244) of HHC episodes, 2 or more reasons were documented.

Conclusions

To avoid critical delays in start-of-care nursing visits, HHC organizations might examine and improve ways to effectively address the reasons for delayed visits, using effective interventions, such as educating patients or caregivers on the importance of a timely nursing visit and improving patients’ intake procedures.
Multiparameter Continuous Physiological Monitoring Technologies in Neonates Among Health Care Providers and Caregivers at a Private Tertiary Hospital in Nairobi, Kenya: Feasibility, Usability, and Acceptability Study (Preprint)
Authors: Ginsburg Amy Sarah , Kinshella Mai-Lei Woo , Naanyu Violet , Rigg Jessica , Chomba Dorothy , Coleman Jesse , Hwang Bella , Ochieng Roseline , Ansermino J Mark , Macharia William M
Published: October 2021

BACKGROUND

Continuous physiological monitoring technologies are important for strengthening hospital care for neonates, particularly in resource-constrained settings, and understanding user perspectives is critical for informing medical technology design, development, and optimization.

OBJECTIVE

This study aims to assess the feasibility, usability, and acceptability of 2 noninvasive, multiparameter, continuous physiological monitoring technologies for use in neonates in an African health care setting.

METHODS

We assessed 2 investigational technologies from EarlySense and Sibel, compared with the reference Masimo Rad-97 technology through in-depth interviews and direct observations. A purposive sample of health care administrators, health care providers, and caregivers at Aga Khan University Hospital, a tertiary, private hospital in Nairobi, Kenya, were included. Data were analyzed using a thematic approach in NVivo 12 software.

RESULTS

Between July and August 2020, we interviewed 12 health care providers, 5 health care administrators, and 10 caregivers and observed the monitoring of 12 neonates. Staffing and maintenance of training in neonatal units are important feasibility considerations, and simple training requirements support the feasibility of the investigational technologies. Key usability characteristics included ease of use, wireless features, and reduced number of attachments connecting the neonate to the monitoring technology, which health care providers considered to increase the efficiency of care. The main factors supporting acceptability included caregiver-highlighted perceptions of neonate comfort and health care respondent technology familiarity. Concerns about the side effects of wireless connections, electromagnetic fields, and mistrust of unfamiliar technologies have emerged as possible acceptability barriers to investigational technologies.

CONCLUSIONS

Overall, respondents considered the investigational technologies feasible, usable, and acceptable for the care of neonates at this health care facility. Our findings highlight the potential of different multiparameter continuous physiological monitoring technologies for use in different neonatal care settings. Simple and user-friendly technologies may help to bridge gaps in current care where there are many neonates; however, challenges in maintaining training and ensuring feasibility within resource-constrained health care settings warrant further research.

INTERNATIONAL REGISTERED REPORT

RR2-10.1136/bmjopen-2019-035184
Addressing Burgeoning Unmet Needs in College Mental Health (Preprint)
Authors: Singh Manpreet Kaur , Malmon Alison , Felten Obi
Published: October 2021

UNSTRUCTURED

If we can measure the psychosocial impact of mental health challenges for college students, we can triage precious mental health resources through personalized measurement-based care, treatment matching, and a peer-support network. Thus, digitally derived measurement-based self-care, peer care, and clinician-delivered care can be deployed in a way that is meaningful to a student’s needs, preferences, and acceptability, and translated back for daily use and decision making. Indeed, if students can utilize a deeper understanding of behaviors to adopt or change, they can also make decisions about when to seek treatment. This ecological approach to the experience of students is advantageous not only for health promotion, but also better contextualizes symptoms to social determinants of health, early life stress exposure, adversity, and life events. While preliminary mental health digital applications and their uptake are encouraging, we have yet to tap the full potential of a more integrated approach to optimize mental health for all college students. Establishing the foundations of this path forward have never been more imperative.
Reimagining Connected Care in the Era of Digital Medicine (Preprint)
Authors: Mann Devin M , Lawrence Katharine
Published: October 2021

UNSTRUCTURED

The COVID-19 pandemic accelerated the adoption of remote patient monitoring technology, which offers exciting opportunities for expanded connected care at a distance. However, while the mode of clinicians’ interactions with patients and their health data has transformed, the larger framework of how we deliver care is still driven by a model of episodic care that does not facilitate this new frontier. Fully realizing a transformation to a system of continuous connected care augmented by remote monitoring technology will require a shift in clinicians’ and health systems’ approach to care delivery technology and its associated data volume and complexity. In this article, we present a solution that organizes and optimizes the interaction of automated technologies with human oversight, allowing for the maximal use of data-rich tools while preserving the pieces of medical care considered uniquely human. We review implications of this “augmented continuous connected care” model of remote patient monitoring for clinical practice and offer human-centered design-informed next steps to encourage innovation around these important issues.
Exploring a Tailored Online Emotion Regulation Approach for Emotional Eaters (Preprint)
Authors: Schwartz Lysanne M. , Dol Aranka , Bode Christina , van Gemert-Pijnen Lisette
Published: October 2021

BACKGROUND

Emotional eating is a complex problem fostering obesity and resulting from maladaptive emotion regulation. Until now, attempts to decrease emotional eating with traditional behavioral weight loss interventions have shown little effect. It seems that they can be improved regarding targeting the specific needs of emotional eaters.

OBJECTIVE

To spark advancements in the treatment of emotional eating, the current study explored a tailored online approach with the aim to positively influence affect (positive and negative) and emotion regulation by studying three exercises: a bodyscan, an opposite action, and a positive reframing exercise.

METHODS

An embedded mixed-method design (questionnaire data (t0, t1, t2) and perceived usefulness of exercises (t2)) was used to evaluate the effects of a two-week online quasi-experiment with three groups. In total, 80 participants, recruited by convenience sampling, with self-proclaimed emotional eating difficulties finished baseline measurements. Assignment of exercises was tailored, based on the first measurement. The study sample was predominantly female (95%), with ages ranging from 18 till 66 (M_age=38, SD=14.25). Wilcoxon signed-rank tests and thematic analyses were used to answer the research questions.

RESULTS

The observed changes for two of the three tailored exercises (i.e., bodyscan and positive reframing exercise) were in line with the study expectations and showed a small increase in positive affect and a small decrease in negative affect and decrease in overall emotion dysregulation. Although the quantitative results did not reach significance, the qualitative data highlighted components of both tailored exercises which were deemed as useful by participants and may have contributed to mood outcomes and emotion regulation.

CONCLUSIONS

Overall, the results of the current study provide promising starting points for the further development of online tailored interventions for emotional eaters. Future studies should identify working components and explore the contexts in which exercises are most effective to assign to emotional eaters by employing factorial experimental design studies and experience sampling methods respectively.
Percepción diferencial de la intervención para la convivencia entre escolares de Santo Domingo, República Dominicana
Authors:   Universidad de Extremadura, Bringas-Molleda Carolina , Mercedes-Acosta Josefina ,   Universidad Autónoma de Santo Domingo, Álvarez-García David ,   Universidad de Oviedo, Almonte-Mata Margarita ,   Universidad Autónoma de Santo Domingo, Rodríguez-Díaz Francisco Javier ,   Universidad de Oviedo
Published: October 2021

La convivencia escolar refiere una de las dificultades a resolver en el ámbito académico. El es-tudio plantea analizar las propiedades psicométricas de la escala M-EP, adaptado a la República Dominicana, investigar la percepción de normas de convivencia escolar por parte de los estu-diantes de la comunidad educativa de Santo Domingo, y su influencia en la aparición de situa-ciones violentas dentro de marco escolar. Participaron 1945 estudiantes procedentes de centros educativos de Santo Domingo, de edades comprendidas entre 11 y 17 años. Los instrumentos utilizados fueron: El M-EP, que evalúa medidas de mejora de la convivencia escolar percibida, y el CUVE3-EP, que evalúa la frecuencia con la que el alumnado considera que ocurren diferentes tipos de violencia escolar. Los resultados se discuten con el objeto de orientar una intervención más eficaz.
Preventing Accidental Overdoses by Innovating a Restrictive Prescription Pill Dispenser Vial (RPPDV)
Authors: Chunduri Roshan
Published: October 2021

Abstract: Rather than attempting to prevent all overdoses, the Prescription Pill Dispenser Vial (RPPDV) aims to prevent accidental overdoses by controlling how many pills a user can access within a twenty-four hour period. The RPPDV is much like a traditional pill vial. The body is similar to a conventional pill vial; however, it is modified to prevent the cap's opening without a special screw. The cap is heavily modified and uses a CR2032 or CR2025 coin cell battery to power a gate and the unlocking of the pill cap. The RPPDV works first when the user clicks the red button on the cap of the RPPDV, which opens the pill gate and opens space into a pill container section. This section is large enough for only one pill (or two if multiple pills are required simultaneously). Once a tablet has entered the pill container section, the button must be pressed again to close the pill gate. The user cannot open the lid to the pill container section until the pill gate is closed (see Fig 1.4.1). The RPPDV is similar to an escrow service, with the pill holder section acting as a middleman. The size of the pill holder container prevents access to multiple pills at the same time. The number of times the user presses the button is recorded and timed with a 555 timer set for twenty-four hours. For example, if a drug has a maximum dose of three pills per day, the button could be capped at four cycles or eight total pushes (to allow a grace cycle if a tablet is broken or stuck). Similarly, the gate cannot be opened unless the lid to the pill container section is closed. The production price of the RPPDV is significantly higher than a traditional pill vial and can get higher depending on the additional features added to the RPPDV. The necessary components to function include CR2032 or CR2025 lithium batteries, a 555 timer, and a coin battery holder, along with plastic and metal molds. The base model alone produces an estimated average cost per unit of $1.33 USD (see Fig 4.2.1). Additional features include adding a Bluetooth Low Energy (Bluetooth LE) component to customize the number of unlocks per day and RFIDs for storage and tracking. Prices vary depending on the specifications of the RPPDV.
Image Caption Generator Using Deep Learning
Authors: Kameswari A. V. N.
Published: October 2021

Abstract: When humans see an image, their brain can easily tell what the image is about, but a computer cannot do it easily. Computer vision researchers worked on this a lot and they considered it impossible until now! With the advancement in Deep learning techniques, availability of huge datasets and computer power, we can build models that can generate captions for an image. Image Caption Generator is a popular research area of Deep Learning that deals with image understanding and a language description for that image. Generating well-formed sentences requires both syntactic and semantic understanding of the language. Being able to describe the content of an image using accurately formed sentences is a very challenging task, but it could also have a great impact, by helping visually impaired people better understand the content of images. The biggest challenge is most definitely being able to create a description that must capture not only the objects contained in an image, but also express how these objects relate to each other. This paper uses Flickr_8K dataset and Flickr8k_text folder that contains Flickr8k.token which is the main file of our dataset that contains image name and their respective caption separated by newline(“\n”). CNN is used for extracting features from the image. We will use the pre-trained model Xception. LSTM will use the information from CNN to help generate a description of the image. In our Flickr8k_text folder, we have Flickr_8k.trainImages.txt file that contains a list of 6000 images names that we will use for training. After CNN-LSTM model is defined we give an image file as parameter through command prompt for testing image caption generator and it generates the caption of an image and its accuracy is observed by calculating bleu score for generated and reference captions. Keywords: Image Caption Generator, Convolutional Neural Network, Long Short-Term Memory, Bleu score, Flickr_8K
PHYSIOTHERAPY REHABILITATION IN YOUNG PATIENT WITH IDIOPATHIC SCOLIOSIS
Authors: Chitale Neha
Published: October 2021

The cause of scoliosis is unknown. Males and females have almost the same rate of scoliosis; however, females have a 10-fold higher chance of curve progression. While many with scoliosis may not experience serious complications, it can lead to rib deformity and respiratory compromise, as well as cosmetic issues and emotional discomfort in some patients. However, it is the most common spinal deformity in adolescents. Physiotherapy Rehabilitation helps in correcting the deformity if functional deformity. A 20 year old female presented with dorso-lumbar scoliosis, L 4 – L5 disc bulge, Sacralization of L5 S1 diagnosed as adolescent idiopathic scoliosis. She had decreased muscular strength of back muscles, abdominals and pelvic floor muscles. She also complaint of radiating pain with tingling numbness in both lower limbs with restricted range of motion. Multiple tender points were present throughout her spinal musculature with muscle tightness. The Adam's forward bent test and a scoliometer calculation will help direct the use of radiologic tests for Cobb angle measurement and orthopaedic referrals. Proper conservative management with Prompt physical therapy which leads to achieve functional goals .The case report suggests that prompt structured physical rehabilitation led to improving functional goals progressively and significantly which is a major aspect leading to a successful recovery and better living.
LUNG NODULE SEMANTIC SEGMENTATION WITH BI-DIRECTION FEATURES USING U-INET
Authors: Joshua Eali Stephen Neal
Published: October 2021

It's difficult to detect lung cancer and determine the severity of the disease without a CT scan of the lungs. The anonymity of nodules, as well as physical characteristics such as curvature and surrounding tissue, suggest that CT lung nodule segmentation has limitations. According to the study, a new, resource-efficient deep learning architecture dubbed U-INET is required. When a doctor orders a computed tomography (CT) scan for cancer diagnosis, precise and efficient lung nodule segmentation is required. Due to the nodules' hidden form, poor visual quality, and context, lung nodule segmentation is a challenging job. The U-INET model architecture is given in this article as a resource-efficient deep learning approach for dealing with the problem. To improve segmentation operations, it also includes the Mish non-linearity functions and mask class weights. Furthermore, the LUNA-16 dataset, which included 1200 lung nodules, was heavily utilized to train and evaluate the proposed model. The U-INET architecture outperforms the current U-INET model by 81.89 times and reaches human expert level accuracy.
THE KNOWLEDGE, PERCEPTION AND BEHAVIOR AMONG DENTAL PRACTITIONERS TOWARDS DIAGNOSIS OF ORAL PATHOLOGICAL LESIONS BY BIOPSY- A CROSS-SECTIONAL SURVEY
Authors: Gupta Sanjeev K.
Published: October 2021

The foundation of diagnosing the oral pre malignant and the malignant lesions is biopsy; and the Behavior of taking biopsy should be a familiar Behavior for all dental professionals hence; the main aim of current cross-sectional survey was to explore factors associated with the knowledge, perception and Behavior of Dental practitioners towards diagnosis of Oral Pathological lesion by Biopsy. Current study is a Descriptive Cross-sectional study conducted among 163 dental practitioners including both general dental (GP) practitioners and the specialists excluding Oral pathologists working in private dental set up. To explore knowledge, the perception and Behavior and factors associated with it among dental practitioners, a close ended questionnaire was framed consists of 30 questions divided into 3 parts. Most of study participants {59 (36.19%)} were within 36-45 years age group. Male respondents {109(66.87%)} were more than female respondents. average OPD per month was 1-20 patients by 100 (61.34%) of dental practitioners. knowledge scores regarding Diagnosis of Oral Pathological lesion by Biopsy among all study participants {84 (51.53%)} was low. Perception scores were positive among 64 (39.27%) study participants. behavior scores among majority of Dental practitioners {97 (59.50%)} was poor. From above it was concluded that Dental practitioners had low knowledge, positive attitude and poor behavior regarding towards diagnosis of Oral Pathological lesion by Biopsy. Age in years, Gender, designation, Years of Practice, Average no. of patients with oral lesion seen in the months, Average No. of biopsy done or referred were some of the factors significantly associated with knowledge, the perception and Behavior of Dental practitioners for diagnosis of Oral Pathological lesion by Biopsy.
ANALYTICAL METHOD DEVELOPMENT AND VALIDATION FOR LIMIT OF N-NITROSODIMETHYLAMINE BY LC-MS/MS OF EMPAGLIFLOZIN, LINAGLIPTIN AND METFORMIN HCL EXTENDED-RELEASE TABLETS
Authors: Shrivas Narayan
Published: October 2021

As a result of the devotion of The Limit of N-Nitrosodimethylamine (NDMA), a rapid and selective LC/MS/MS technique was created and validated for Empagliflozin, Linagliptin, and Metformin Hydrochloride Extended-Release Tablets. The ionization mode of Atmospheric pressure chemical ionization (APCI) was used with high-performance liquid chromatography-tandem mass spectrophotometry (LC-MS/MS). Separation of N-Nitrosodimethylamine (NDMA) was performed on Inertsil ODS-4 (250 mm X 4.6 mm), 5μm column with a run time of 40 minutes. A mixture of Methanol and Buffer solution (630 mg of ammonium format into 1000 mL of purified water) in the ratio of (10:90, v/v) was used as the mobile phase A. A mixture of acetonitrile and methanol comprised the mobile phase B in the ratio of (50:50) % v/v. Analytes were extracted from Empagliflozin, Linagliptin, and Metformin Hydrochloride Extended-Release Tablets. According to the International council for Harmonization of technical requirement for Pharmaceuticals for Human Uses (ICH) standards, the accuracy, precision, selectivity, recovery, and stability of the method have been validated. Over a concentration range of 0.4 -3.6 ng/mL, the technique exhibited linearity. for N- Nitrosodimethylamine of Empagliflozin, Linagliptin, and Metformin Hydrochloride Extended-Release Tablets with an acceptable correlation coefficient applies (1/X2) linear regression with weights A pharmaceutical study can benefit from this method because it is simple, fast, precise, and accurate, making it ideal for pharmaceutical research.
REPAIR OF POWER HYDRAULIC CYLINDERS USING NEW POLYMER COMPOSITE MATERIALS
Authors:   National Research Mordovian State University named after N.P. Ogarev, Radaykina Elena A. , Kotin Aleksandr V.
Published: October 2021

The development and implementation of a new repair technology based on the use of polymer parts made of high-performance antifriction composites in tribo-couplings of power hydraulic cylinders will reduce the cost and repair time, significantly increase the reliability of the entire hydraulic system. (Research purpose) The research purpose is in increasing the efficiency of repairing of power hydraulic cylinders of agricultural machinery by restoring tribo-conjugations with new antifriction composites based on modified caprolon. (Materials and methods) The article presents the study of the physical and mechanical characteristics of samples of polymer composites according to well- known and original methods. Authors used computer programs and original methods of equipment suppliers in the study of compounding processes, rheological and tribotechnical tests. Theoretical research using the provisions of theoretical mechanics, elasticity theory and thermophysics has been conducted. A numerical study of the introduction of a spherical indenter into a sample of a viscoelastic composite material was carried out using the ANSYS software package, the MAPDL module. (Results and discussion) The article analyzes the obtained data on the elastic-strength, rheological and tribotechnical characteristics of the studied compositions of polyamide composites. The three-component composite containing 2 percent shungite, 0.5 percent graphite, 6 surfactants has the best complex of these properties. It has been proposed to restore the operability of the piston assembly and the front cover during the repair of the power hydraulic cylinder to change their design with the installation of guide support rings made of polyamide composite into the cover and piston of the hydraulic cylinders. This will lead to a significant reduction in the wear rate of the tribo-coupling parts and reduce the complexity of repairing hydraulic cylinders. (Conclusions) Restoration of tribo-couplings of power hydraulic cylinders with the installation of a guide ring made of a new polymer composite ensures a decrease in the maximum contact stresses, the rate of their increase with an increase in the gap and, as a result, a decrease in the wear rate of the contacted parts.
EXPERIMENTAL SELECTION OF ELECTRIC SPARK TREATMENT FOR ELECTRODES OBTAINED BY VARIOUS METHODS
Authors:   Federal Scientific Agroengineering Center VIM, Romanov Il’ya V.
Published: October 2021

Electric spark surface treatment is widely used in repair production. Its high versatility is ensured by a wide range of electrical modes and the possibility of using all conductive materials as electrodes. The selection of optimal electrical modes of the electric spark processing installation is important for obtaining high-quality coatings when using a specific electrode. This problem is especially acute for new electrode materials obtained from machine-building waste, since such electrodes are still poorly studied. (Research purpose) The research purpose is in experimentally selecting optimal modes of electric spark processing of steel parts for each of the electrodes under consideration for using the obtained data in further research. (Materials and methods) The article presents electrodes made of bronze grade BrAZh9-4 according to GOST 18175-78 and from secondary bronze powder. The coatings were applied using the Vestron AI-007 electric spark treatment unit on steel samples. The analytical scales Acculab ALC-210d4 and the micrometer MG H25 GOST 6507-90 were used. (Results and discussion) All coatings were applied to samples with an equal area in three layers under different processing modes. The increase in the thickness and mass of the electrode material on the sample surface was measured. The article presents formulas and graphs based on the results of the work. (Conclusions) For each electrode, the optimal mode of electric spark processing was chosen, which would further help to compare the electrodes by the quality of the coating formed. The performance characteristics of sintered secondary bronze powder, almost similar to the characteristics of standard materials, prove the feasibility of obtaining such powders by the energy-efficient method described above.
Mobile Phone (Smartphone) as a Farmer’s Tool in Digital Farming
Authors:   Federal Scientific Agroengineering Center VIM, Kurbanov Rashid K. , Lichman Gennadiy I. , Zakharova Natal’ya I.
Published: October 2021

Remote sensing in agriculture is one of the rapidly developing areas of observation of agricultural biological objects. Combining the latest sensor technologies with smartphones allows us to develop powerful platforms for solving various tasks in agriculture. (Research purpose) The research purpose is in analyzing and evaluating the usage of mobile phones and smartphones to monitor the state of the field and agricultural crops in order to obtain information necessary for making optimal management decisions. (Materials and methods) The article presents the results of scientific research and reviews on the use of mobile phones and smartphones to obtain information about agricultural crops in the precision farming system. (Results and discussion) The software for smartphones in agriculture is considered as a subdivision of precise agricultural technologies for monitoring and protecting crops. Smartphones with the required software enable farmers to receive information from various sensors. Authors reviewed the existing simplest mobile applications for smartphones that allow detecting and identifying biotic stresses by comparing photos of plants taken with images from the library, and applications whose work is based on photographing biological objects and sending them to an expert. It has been revealed that mobile applications created on the basis of convolutional neural networks of deep learning, capable of making independent decisions, are gaining popularity. (Conclusions) The use of smartphones equipped with various sensors and additional software opens up new opportunities for farmers, providing data collection, storage, transmission and processing for making management decisions. The article presents the disadvantages of mobile applications. Further expansion of the scope of smartphones for crop monitoring is possible when developing new software using convolutional neural networks of deep learning.