bluuebunny/crossref_metadata_2025 huggingface dataset:
Fetoscopic Balloon Dilation and Stent Placement of Congenital High Airway Obstruction Syndrome Leading to Successful Cesarean Delivery
Authors: King Alice , Bedwell Joshua R. , Mehta Deepak K. , Stapleton Gary E. , Justino Henri , Sutton Caitlin , Donepudi Roopali , Sanz-Cortes Magdalena , Nassr Ahmed A. , Sun Raphael C. , Lee Timothy C. , Keswani Sundeep G. , Cassady Christopher I. , Mehollin-Ray Amy , Belfort Michael A.
Published: January 2022

Introduction: Without fetal or perinatal intervention, congenital high airway obstruction syndrome (CHAOS) is a fatal anomaly. The ex utero intrapartum treatment (EXIT) procedure has been used to secure the fetal airway and minimize neonatal hypoxia but is associated with increased maternal morbidity. Case Presentation: A 16-year-old woman (gravida 1, para 0) was referred to our hospital at 31 weeks gestation with fetal anomalies, including echogenic lungs, tracheobronchial dilation, and flattened diaphragms. At 32 weeks, fetoscopic evaluation identified laryngeal stenosis, which was subsequently treated with balloon dilation and stent placement. The patient developed symptomatic and regular preterm contractions at postoperative day 7 with persistent sonographic signs of CHAOS, which prompted a repeat fetoscopy with confirmation of a patent fetal airway followed by Cesarean delivery under neuraxial anesthesia. Attempts to intubate through the tracheal stent were limited and resulted in removal of the stent. A neonatal airway was successfully established with rigid bronchoscopy. Direct laryngoscopy and bronchoscopy confirmed laryngeal stenosis with a small tracheoesophageal fistula immediately inferior to the laryngeal stenosis and significant tracheomalacia. A tracheostomy was then immediately performed for anticipated long-term airway and pulmonary management. The procedures were well tolerated by both mom and baby. The baby demonstrated spontaneous healing of the tracheoesophageal fistula by day of life 7 with discharge home with ventilator support at 3 months of life. Conclusion: Use of repeated fetoscopy in order to relieve fetal upper airway obstruction offers the potential to minimize neonatal hypoxia, while concurrently decreasing maternal morbidity by avoiding an EXIT procedure. Use of the tracheal stent in CHAOS requires further investigation. The long-term reconstruction and respiratory support of children with CHAOS remain challenging.
Agricultural harvesting using integrated robot system
Authors: Raja Vikram , Bhaskaran Bindu , Nagaraj Koushik Karan Geetha , Sampathkumar Jai Gowtham , Senthilkumar Shri Ram
Published: January 2022

In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.
Efficient resampling features and convolution neural network model for image forgery detection
Authors: S Manjunatha , Patil Malini M.
Published: January 2022

The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.
An internet of things-based automatic brain tumor detection system
Authors: Rahman Md. Lizur , Reza Ahmed Wasif , Shabuj Shaiful Islam
Published: January 2022

Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
A new function of stereo matching algorithm based on hybrid convolutional neural network
Authors: Hamid Mohd Saad , Abd Manap Nurulfajar , Hamzah Rostam Affendi , Kadmin Ahmad Fauzan , Abd Gani Shamsul Fakhar , Herman Adi Irwan
Published: January 2022

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
Exploring the performance of feature selection method using breast cancer dataset
Authors: Assegie Tsehay Admassu , Tulasi Ravulapalli Lakshmi , Elanangai Vadivel , Kumar Napa Komal
Published: January 2022

Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X2) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X2) statistics and embedded feature selection.
Optimizing the effect of charging electric vehicles on distribution transformer using demand side management
Authors: Ganapaneni Swapna , Pinni Srinivasa Varma
Published: January 2022

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.
Slantlet transform used for faults diagnosis in robot arm
Authors: Alobaidy Muhamad Azhar Abdilatef , Abdul-Jabbar Jassim Mohammed , Al-khayyt Saad Zaghlul
Published: January 2022

The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time
 (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique
Authors: Dalal Virupaxi Balachandra , Bhairannawar Satish S.
Published: January 2022

Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more effectively.
Digital image processing methods for estimating leaf area of cucumber plants
Authors: Ngo Uoc Quang , Ngo Duong Tri , Nguyen Hoc Thai , Bui Thanh Dang
Published: January 2022

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.
Reconfigurable intelligent surfaces assisted wireless communication networks: ergodic capacity and symbol error rate
Authors: Do Dinh-Thuan , Le Chi-Bao
Published: January 2022

By enabling reconfigurable intelligent surfaces (RIS), we can deploy intelligent reflecting signals from the base station to destinations. Different from traditional relaying system, RIS relies on programmable metasurfaces and mirrors to improve system performance of destinations. We derive the formulas of main system performance metrics such as ergodic capacity and symbol error rate (SER). Based on types of modulation, we need to demonstrate other parameters which make influence to system performance. We show analytically that the number of reflecting elements along with the transmit power at the source can improve system performance. Moreover, we check the exactness of derived expressions by matching Monte-Carlo with analytical simulations. Finally, we find the best performance can be achieved at specific parameters and results are verified by explicit simulations.
New method for route efficient energy calculations with mobile-sink for wireless sensor networks
Authors: Al-juaifari Mohammad Khalaf Rahim , Mona Jammel Mohammed Ali Mohammed , Abbas Zainab Abd
Published: January 2022

Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.
Design of multi-band millimeter wave antenna for 5G smartphones
Authors: Shareef Oras Ahmed , Sabaawi Ahmed Mohammed Ahmed , Muttair Karrar Shakir , Mosleh Mahmood Farhan , Almashhdany Mohammad Bashir
Published: January 2022

The design of a millimeter wave (mmW) antenna for the 5G mobile applications is presented in this paper. The designed antenna has dimensions of 10×10×0.245 mm3. This includes the copper ground plane. The resonance of the proposed mmW antenna lies within the range of 33 GHz and 43 GHz. These frequency bands are covering the 5G proposed band in terms of the signal speed, data transmission, and high spectral efficiencies. Computer simulation technology (CST) software is used to simulate the proposed 5G antenna including the characteristics of S-parameters, gain, and radiation pattern. Simulation results show that the return loss at resonant frequencies goes -22 dB, which satisfies the requirements of 5G mobile technology.
An approach for slow distributed denial of service attack detection and alleviation in software defined networks
Authors: John Prathima Mabel , Nagappasetty Rama Mohan Babu Kasturi
Published: January 2022

Over the last few years, the need for programmable networks has captured the interest of industrialists and academicians. It has led to the development of a paradigm called software defined network (SDN). It separates the network intelligence into the control plane and forwarding logic into the data plane. This architecture gives scope to various security issues of which denial of service (DoS) is the most common and challenging to detect. This paper focuses on the detection and mitigation of a slow DoS attack called Slowloris on Apache2 server in SDN based networks. The proposed solution is called Slowloris detection and mitigation mechanism (SDMM). Mininet, an emulator, and SimpleHTTPServer are used for simulation and the same is implemented using Zodiac FX OpenFlow switch, Ryu controller and Apache2 server. SDMM algorithm detects and mitigates prolonged Slowloris attack in typical networks as well as in slow networks with low bandwidth and high delay in 240-280s with an accuracy of 100% and 98% respectively. It uses expectation of burst size as a key factor for detection.
Fog attenuation penalty analysis in terrestrial optical wireless communication-modified duo-binary return-to-zero system with various receiver pointing errors
Authors: Ali Mustafa H. , Hassan Tariq A. , Abu-Alsaad Hiba A.
Published: January 2022

In metropolitan communication infrastructures a revolutionary technique is emerge known as terrestrial optical wireless communication (OWC), which makes a high-rise building connection is possible. Even with this solution, there are many other problems like the influence of haze and fog in the propagation channel which obstruct and scatter OWC propagation light and consequently led to a big attenuation, due to propagate in temporal, angular and spatial of the light signal. Not to mention the minimum visibility that discourages the implementation of the pointing errors (PE) and tracking system. This present work aims to analyze the interrelation between multiple scattering (dense fog, heavy fog, light fog, heavy haze and light haze) and receiver PE under modified duo-binary return-to-zero (MDRZ) system. We found that PE caused by beam swag is the main controlling factor and industriously minimize the link margin, signal-to-noise ratio (SNR), and raise the bit error rate (BER) when there is an increasing the turbulence strength and the track length. We recommended to guarantee transmitter– receiver alignment by installing a variable field of view (FOV) receiver (a tracking system) to overcome the scattering impact of the fog that make render urban laser communication effective in the presence of PE.
Design of an environmental management information system for the Universidad Distrital
Authors: Torres Edwin Arturo Quintero , Beltrán William Andrés León , Céspedes Juan Manuel Sánchez
Published: January 2022

This article presents the design, development and implementation of a software tool, serving as an alternative to the problems involving management, control and reporting of processes within the institutional plan for environmental management (known as plan institucional de gestión ambiental (PIGA) by its Spanish acronym) for the Universidad Distrital Francisco José de Caldas. The software is focused on carrying out such processes to the automation setting, based on the extreme programming (XP) Agile methodology that mainly centers on the continuous development of the customer requirements to offer a more assertive tool, in line with the plan institucional de gestión ambiental in Spanish (PIGA) processes. The result is a complete satisfaction of users and a highly usable, adaptable and efficient software, inherently optimizing and automating the environmental management processes of the PIGA program. This work delivers an applet that meets the design and implementation requirements of environmental management policies. The proposed tool manages to reduce process-related times by 97%, therefore, allowing to aim efforts in other missional functions and increase the overall value offer of the organization.
Automated breast cancer detection system from breast mammogram using deep neural network
Authors: Chittineni Suneetha , Edara Sai Sandeep
Published: January 2022

All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system
Authors: Sulaiman Noor Asyikin , Abdullah Md Pauzi , Abdullah Hayati , Zainudin Muhammad Noorazlan Shah , Yusop Azdiana Md , Sulaiman Siti Fatimah
Published: January 2022

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.
A new smart approach of an efficient energy consumption management by using a machine-learning technique
Authors: Hasan Maha Yousif , Kadhim Dheyaa Jasim
Published: January 2022

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
Isolation of Arbutin from Leaves and Fruits of Buni (Antidesma Bunius L. Spreng) As Tyrosinase Enzym Inhibitor
Authors: Palensina Gea Ocktiah , Rosita Leni , Sagala Zuraida
Published: January 2022

Several studies have shown that plant extractive substances have the potential as active compounds inhibiting the enzyme tyrosinase. Arbutin is an enzyme inhibitor of tyrosinase, known as a popular whitening agent used in cosmetics because of its effectiveness in overcoming skin hyperpigmentation. The purpose of this study was to conduct a qualitative and quantitative analysis of arbutin on Buni Leaves and Fruits (Antidesma bunius L. Spreng). The raw simplicia used are mature and young buni leaves, green, red and purple buni fruits. The extraction method is maceration using methanol as solvent. The initial screening for arbutin content was carried out using thin layer chromatography (TLC) and dichlormethan:methanol 50:50 used as mobile phase. Isolation of arbutin content was carried out using Preparative TLC with the same eluent. Qualitative and quantitative analysis were performed using High Pressured Liquid Chromatography (HPLC) with mobile phase of acetonitrile: water 60:40. The tyrosinase enzyme inhibition activity test was then carried out in vitro using 96-well microplate, l-tyrosine and l-dopa were used as substrate at a wavelength of 492 nm. The Rf values obtained ??for mature buni leaves and green buni fruits, respectively 0.61 and 0,62. The retention time of HPLC chromatogram respectively 2,784 minutes and 2,758 minutes. Arbutin levels in leaves and fruits are 7.9 mg / g and 2 mg / g. The activity of the enzyme tyrosinase of mature buni leaves on L-dopa and L-tyrosine substrate were respectively stated as IC50 values ??of 88.7191ppm and 101.33347 ppm. The activity of the enzyme tyrosinase of the green buni fruit on L-dopa and L-tyrosine substrate respectively stated IC50 values ??of 198,0293 ppm and 246,1296 ppm.
Antagonistic Effect of Yeast, Acetic Acid Bacteria and Mangosteen Rind Extract on Aflatoxigenic Aspergillus flavus in Unfermented Cocoa Beans
Authors: Nurfadila Nijma , - Sutrisno , Ahmad Usman , - Samsudin
Published: January 2022

Yeasts and bacteria are two of common biocontrol agents to control mycotoxigenic fungi, meanwhile the mangosteen rind exract has xanthone and gartanin compounds for antioxidant, antiproliferation, antiinflammation, antimicrobial, and anticancer. The objectives of this research are to test the effects of yeasts, acetic acid bacteria (AAB), and mangosteen rind extract on the aflatoxigenic Aspergillus flavus growth and aflatoxin production in unfermented cocoa beans. Four yeast isolates, i.e. Issatchenkia orientalis (Io) BIO 211291, 286 and 288, and Endomyces fibuliger (Ef) BIO 132219; one bacteria isolate of Acetobacter aceti (Aa) FNCC0016; and mangosteen rind extract (MRE) were tested for their capabilities in inhibiting an aflatoxigenic A. flavus (Af) BIO 3361/747 growth using the well method (in vitro). Two yeast (Io BIO 211291 and 288) were combined with Aa and MRE in cocoa beans (in vivo). Aflatoxin production was analyzed using Thin Layer Chromathography (TLC). The result showed that interaction of Io BIO 211291 and 288, and Ef BIO 132219 on aflatoxigenic Af were interaction with inhibition zone ≥ 2 mm (type D), while the interaction type of Io BIO 211286 on Af were mutual intermingling growth, where both fungi grew into each other without any macroscopic sign of interaction (type A). The best treatment in agar media (in vitro) was Io BIO 211288 + Aa on Potato Dextrose Agar + 12 g/l MRE. The highest Io population was 5.88 log cfu/g on cocoa beans inoculated by Io BIO 211291 + MRE in 1 day after inoculation, while the highest A. aceti population was 4.74 log cfu/g on cocoa beans with Io BIO 211291 + BIO 211288 + Aa  in 3 days after inoculation. Two best treatments were Io BIO 211288 + Aa + MRE and Io BIO 211291 + BIO 211288 + Aa + MRE, because there were no A. flavus population since 3 until 11 days after inoculation. Aflatoxin in all samples treatment was lower than limit detection B1 (< 2.20 ppb),  B2 (< 3.50 ppb), (G1 < 0.54 ppb), dan (G2 < 1.00 ppb).
Tree Community Structure and Aboveground Carbon Stock of Sacred Forest in Pasaman, West Sumatera
Authors: Santhyami Santhyami
Published: January 2022

This is an analysis of the tree community’s composition and structure of Bukit Badindiang sacred forest in Nagari Simpang, Pasaman West Sumatera. The study aims to (1) to obtain a representative account of the structure and composition of tree community of the sacred forest, (2) to estimate the Aboveground Carbon Stock (C-Stock) accumulated on it. A one-hectare plot was divided into 25 subplots of 20 m × 20 m each for tree and debris data collection. In each subplots, there would be a 5m x 5m sapling subplots and 1m x 1m seedling, understorey plant and litter subplots were nested inside. A total of 446 trees were recorded, representing 139 species from 49 families with a total basal area of 38.59 m2. The most dominant tree species was Campnosperma auriculata [Importance Value (IV) of 19.19]. The other prevalent species were Ficus benjamina (IV =16.50) and Mallotus caudatus (IV =14.78). A total of 62 species (44.6%) was considered locally rare with density of 1 tree/ha. Mallotus caudatus had the highest density (37 trees/ha) and Ficus benjamina had the highest Basal Area (BA) (5.61 m2 = 14.51% of the total). Euphorbiaceae (IV= 37.40) was the dominant family. The richest families were Euphorbiaceae (10) and Lauraceae (10). The total estimated Aboveground C-Stock was 190.62 MgC/ha, with the highest C was contributed by trees (178.85 MgC/ha or 93.8% of total). Trees with diameter class of 10 – 69.99 cm stored 62% of total tree carbon. The species richness, tree density and C-Stock of Bakit Badindiang sacred forest were relatively higher than those in several disturbed lowland forests in Sumatera.
Relação do nível da atividade física com o perfil clínico, eletrocardiográfico e depressivo de estudantes de medicina
Authors: Fujioka Paula Tiemi , Nagaoka Vanessa Tieko , Heinritz Ingo Latzina , Ferraz Getulio Pinheiro Lopes , Kummer Lana , Roscani Meliza Goi
Published: January 2022

Objetivo. Conhecer o perfil clínico, os fatores cardiovasculares, perfil depressivo e a presença de distúrbios do ritmo nos estudantes de medicina da Universidade Federal de São Carlos (UFSCar), e comparar a avaliação cardiológica e qualidade de vida de estudantes inativos e ativos. Método. Estudo descritivo transversal em amostra de estudantes do curso de medicina da UFSCar, com participação de alunos de todos os anos da graduação. Em relação à coleta de dados, foram utilizadas as escalas de Beck e IPAQ, e para avaliação da função cardiovascular, foram realizados testes eletrocardiográficos. Os resultados foram expressos em média e desvio-padrão e a comparação dos grupos de estudantes ativos e inativos foi realizada por teste Qui-quadrado para variáveis categóricas e teste T para quantitativas, com nível de significância p<0,05. Resultados. Foram incluídos 85 estudantes, sendo que 3,5% apresentaram fator de risco para doença cardiovascular; 7% apresentaram perfil depressivo; 40% faziam uso de drogas lícitas e 9,5% de ilícitas; 13% apresentaram anormalidade eletrocardiográficas; 50,6% foram classificados como ativos e 49,3% como sedentários. Foi observada maior frequência de estudantes inativos do último ano do curso. O uso de drogas ilícitas foi significativo nos estudantes ativos comparados aos inativos. Conclusão. Os estudantes do último ano de graduação foram mais inativos em relação aos estudantes dos anos anteriores. O uso de substâncias lícitas e ilícitas foi mais prevalente entre estudantes ativos quando comparados aos estudantes inativos. Não houve diferença significativa nos resultados de eletrocardiografia entre os dois grupos.
Complicação óssea de um neurofibroma plexiforme – um relato de caso
Authors: Azulay Vitoria , Suchmacher Mendel , Lyra Ibson Lucas de , Filippo Gustavo Robertson
Published: January 2022

A neurofibromatose tipo I é uma doença autossômica dominante cujo diagnóstico é feito com base em critérios clínicos. As três principais manifestações: neurofibromas, manchas café com leite e nódulos de Lisch ocorrem em mais de 90% dos pacientes até a puberdade. Relatamos o caso de uma paciente jovem com diagnóstico de neurofibromatose tipo I com complicações ortopédicas devido à elefantíase causada por um neurofibroma plexiforme.
Somatic Mutations in Cardiovascular Disease
Authors: Heimlich J. Brett , Bick Alexander G.
Published: January 2022

Advances in population-scale genomic sequencing have greatly expanded the understanding of the inherited basis of cardiovascular disease (CVD). Reanalysis of these genomic datasets identified an unexpected risk factor for CVD, somatically acquired DNA mutations. In this review, we provide an overview of somatic mutations and their contributions to CVD. We focus on the most common and well-described manifestation, clonal hematopoiesis of indeterminate potential. We also review the currently available data regarding how somatic mutations lead to tissue mosaicism in various forms of CVD, including atrial fibrillation and aortic aneurism associated with Marfan Syndrome. Finally, we highlight future research directions given current knowledge gaps and consider how technological advances will enhance the discovery of somatic mutations in CVD and management of patients with somatic mutations.
Common Ancestry-Specific Ion Channel Variants Predispose to Drug-Induced Arrhythmias
Authors: Wada Yuko , Yang Tao , Shaffer Christian M. , Daniel Laura L. , Glazer Andrew M. , Davogustto Giovanni E. , Lowery Brandon D. , Farber-Eger Eric H. , Wells Quinn S. , Roden Dan M.
Published: January 2022

Background:

Multiple reports associate the cardiac sodium channel gene (
SCN5A
) variants S1103Y and R1193Q with type 3 congenital long QT syndrome and drug-induced long QT syndrome. These variants are too common in ancestral populations to be highly arrhythmogenic at baseline, however: S1103Y allele frequency is 8.1% in African Americans and R1193Q 6.1% in East Asians. R1193Q is known to increase late sodium current (
I
Na-L
) in cardiomyocytes derived from induced pluripotent stem cells but the role of these variants in modulating repolarization remains poorly understood.

Methods:

We determined the effect of S1103Y on QT intervals among African-American participants in a large electronic health record. Using cardiomyocytes derived from induced pluripotent stem cells carrying naturally occurring or genome-edited variants, we studied action potential durations (APDs) at baseline and after challenge with the repolarizing potassium current (
I
Kr
) blocker dofetilide and
I
Na-L
and
I
Kr
at baseline.

Results:

In 1479 African-American participants with no confounding medications or diagnoses of heart disease, QT intervals in S1103Y carriers was no different from that in noncarriers. Baseline APD was no different in cells expressing the Y allele (SY, YY cells) compared with isogenic cells with the reference allele (SS cells). However,
I
Na-L
was increased in SY and YY cells and the
I
Na-L
blocker GS967 shortened APD in SY/YY but not SS cells (
P
<0.001).
I
Kr
was increased almost 2-fold in SY/YY cells compared with SS cells (tail current: 0.66±0.1 versus 1.2±0.1 pA/pF;
P
<0.001). Dofetilide challenge prolonged APD at much lower concentrations in SY (4.1 nmol/L [interquartile range, 1.5–9.3]; n=11) and YY (4.2 nmol/L [1.7–5.0]; n=5) than in SS cells (249 nmol/L [22.3–2905]; n=14;
P
<0.001 and
P
<0.01, respectively) and elicited afterdepolarizations in 8/16 SY/YY cells but only in 1/14 SS cells. R1193Q cells similarly displayed no difference in baseline APD but increased I
Kr
and increased dofetilide sensitivity.

Conclusions:

These common ancestry-specific variants do not affect baseline repolarization, despite generating increased
I
Na-L
. We propose that increased
I
Kr
serves to maintain normal repolarization but increases the risk of manifest QT prolongation with
I
Kr
block in variant carriers. Our findings emphasize the need for inclusion of diverse populations in the study of adverse drug reactions.
Superiority of 24-Hour Aortic Over 24-Hour Brachial Pressure to Associate With Carotid Arterial Damage on the Basis of Pressure Amplification Variability: the SAFAR Study
Authors: Argyris Antonios A. , Mouziouras Dimitrios , Samara Stamatia , Zhang Yi , Georgakis Marios K. , Blacher Jacques , Safar Michel , Sfikakis Petros P. , Protogerou Athanase D.
Published: January 2022

Background:

Evidence suggests marginal superiority of static aortic systolic blood pressure (aSBP) compared with brachial SBP (bSBP) regarding the association with organ damage and prognosis of cardiovascular disease (CVD). The noninvasive 24-hour aSBP assessment is feasible and associates better with presence of left ventricular hypertrophy compared with 24-hour bSBP. We aimed at comparing the association of 24-hour aSBP and 24-hour bSBP with indices of arterial damage and examining the role of 24-hour SBP amplification variability (within-subjects’ SD) in this association.

Methods:

Consecutive subjects referred for CVD risk assessment underwent 24-hour aortic and brachial ambulatory BP monitoring using a validated oscillometric device (Mobil-O-Graph). Arterial damage was assessed by carotid intima-media thickness (IMT) and detection of carotid and femoral atheromatosis (plaque presence).

Results:

Cross-sectionally 501 individuals (aged 54±13 years, 57% men, 80% hypertensives) were examined. Multivariable analysis revealed superiority of 24-hour aSBP regarding the association with IMT, carotid hypertrophy and carotid—but not femoral—atheromatosis. In receiver operator characteristics analysis, 24-hour aSBP displayed a higher discriminatory ability—compared to 24-hour bSBP—for the detection of both carotid hypertrophy (area under the curve, 0.662 versus 0.624,
P
<0.05) and carotid atheromatosis (area under the curve, 0.573 versus 0.547,
P
<0.05). This effect was more prominent in individuals with above-median 24-hour SD of SBP amplification.

Conclusions:

Our results suggest that 24-hour aSBP assessment may be of significant value in clinical practice to detect site-specific arterial damage on the basis of pressure amplification variability and should be prospectively examined in clinical trials.
Self-Organization Among Delivery Platforms Workers in Neoliberal Latin American Countries. The Cases of Peru and Chile
Authors: Muñoz Karol Morales , Martinez Alejandra Dinegro
Published: January 2022

Recently in Latin America, numerous mobilizations of workers against the precariousness of work in delivery platforms have been developed. In this study, we argue that consolidation into strong organizations for defending platform workers’ interests is strongly related to the socio-political and institutional contexts they are involved in. Drawn upon the understanding of solidarity among workers as a phenomenon rooted in the labor process, as well as the relevance of socio-political and institutional context for the organizing processes among precarious workers, this study addresses the cases of self-organization of platforms deliverers in Chile and Peru. Based on ethnographic research, the results show common characteristics of workers’ self-organization, which are related to similar labor processes in delivery platforms. In addition, results shed light on the relevance of the socio-political and institutional context in providing resources for the consolidation of grassroots organizations, especially after platform counter-actions.
An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method
Authors: Sangeetha K. , Prakash S.
Published: January 2022

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done
as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method.
Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s
early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE)
technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders
(SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy
parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.
An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis
Authors: Pavithra D. , Jayanthi A. N.
Published: January 2022

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency
can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD
at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is
used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.
Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization
Authors: Sureshu S. , Vijayabhasker R.
Published: January 2022

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require
BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS
(quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at
hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling
strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged
in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented
resource scheduling approach might be utilized to enhance the viability of cloud resources.