bluuebunny/crossref_metadata_2025 huggingface dataset:
Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment
Authors: Ahumada Sofía , Tagle Matias , Vasquez Yeanice , Donoso Rodrigo , Lindén Jenny , Hallgren Fredrik , Segura Marta , Oyola Pedro
Published: September 2022

Low-cost sensors can provide inaccurate data as temperature and humidity affect sensor accuracy. Therefore, calibration and data correction are essential to obtain reliable measurements. This article presents a training and testing method used to calibrate a sensor module assembled from SO2 and NO2 electrochemical sensors (Alphasense B4 and B43F) alongside air temperature (T) and humidity (RH) sensors. Field training and testing were conducted in the industrialized coastal area of Quintero Bay, Chile. The raw responses of the electrochemical (mV) and T-RH sensors were subjected to multiple linear regression (MLR) using three data segments, based on either voltage (SO2 sensor) or temperature (NO2). The resulting MLR equations were used to estimate the reference concentration. In the field test, calibration improved the performance of the sensors after adding T and RH in a linear model. The most robust models for NO2 were associated with data collected at T < 10 °C (R2 = 0.85), while SO2 robust models (R2 = 0.97) were associated with data segments containing higher voltages. Overall, this training and testing method reduced the bias due to T and HR in the evaluated sensors and could be replicated in similar environments to correct raw data from low-cost electrochemical sensors. A calibration method based on training and sensor testing after relocation is presented. The results show that the SO2 sensor performed better when modeled for different segments of voltage data, and the NO2 sensor model performed better when calibrated for different temperature data segments.
Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments
Authors: Kim Youngpil , Lee Kyungwoon , Park Hyunchan
Published: September 2022

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students’ hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.
Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
Authors: Zhang Yan , Wang Yong , Hu Yihua , Lin Zhi , Zhai Yadi , Wang Lei , Zhao Qingsong , Wen Kang , Kang Linshuang
Published: September 2022

Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.
A New Positioning Method for Climbing Robots Based on 3D Model of Transmission Tower and Visual Sensor
Authors: Liu Yansheng , You Junyi , Du Haibo , Chang Shuai , Xu Shuiqing
Published: September 2022

With the development of robot technology and the extensive application of robots, the research on special robots for some complex working environments has gradually become a hot topic. As a special robot applied to transmission towers, the climbing robot can replace humans to work at high altitudes to complete bolt tightening, detection, and other tasks, which improves the efficiency of transmission tower maintenance and ensures personal safety. However, it is mostly the ability to autonomously locate in the complex environment of the transmission tower that limits the industrial applications of the transmission tower climbing robot. This paper proposes an intelligent positioning method that integrates the three-dimensional information model of transmission tower and visual sensor data, which can assist the robot in climbing and adjusting to the designated working area to guarantee the working accuracy of the climbing robots. The experimental results show that the positioning accuracy of the method is within 1 cm.
Application of Fiber Bragg Gratings as a Sensor of Pulsed Mechanical Action
Authors: Kiesewetter Dmitry , Krivosheev Sergey , Magazinov Sergey , Malyugin Victor , Varzhel Sergey , Loseva Elizaveta , Koshkinbayev Sauletbek , Smailov Nurzhigit
Published: September 2022

The pulsed elongation of fiber Bragg gratings is considered in order to be used to measure the displacement or deformation rate of objects. Optimal measurement modes were determined, numerical simulation of the output signal was performed during pulsed elongation or compression of the fiber grating, and the main patterns were analyzed. The results of the application of the Bragg gratings for the experimental determination of the deformation rate of materials under pulsed magnetic action are presented. Experimentally obtained and theoretical dependencies are compared. The dependencies of the change in the grating parameters—the coefficient and the half-width of the reflection spectrum with successive shortening of the grating—are given.
Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks
Authors: Arrubla-Hoyos Wilson , Ojeda-Beltrán Adelaida , Solano-Barliza Andrés , Rambauth-Ibarra Geovanny , Barrios-Ulloa Alexis , Cama-Pinto Dora , Arrabal-Campos Francisco Manuel , Martínez-Lao Juan Antonio , Cama-Pinto Alejandro , Manzano-Agugliaro Francisco
Published: September 2022

The growing global demand for food and the environmental impact caused by agriculture have made this activity increasingly dependent on electronics, information technology, and telecommunications technologies. In Colombia, agriculture is of great importance not only as a commercial activity, but also as a source of food and employment. However, the concept of smart agriculture has not been widely applied in this country, resulting in the high production of various types of crops due to the planting of large areas of land, rather than optimization of the processes involved in the activity. Due to its technical characteristics and the radio spectrum considered in its deployment, 5G can be seen as one of the technologies that could generate the greatest benefits for the Colombian agricultural sector, especially in the most remote rural areas, which currently lack mobile network coverage. This article provides an overview of the current 5G technology landscape in Colombia and presents examples of possible 5G/IoT applications that could be developed in Colombian fields. The results show that 5G could facilitate the implementation of the smart farm in Colombia, improving current production and efficiency. It is useful when designing 5G implementation plans and strategies, since it categorizes crops by regions and products. This is based on budget availability, population density, and regional development plans, among others.
Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring
Authors: Ye Chuwei , Li Wen , Li Zhaojian , Maguluri Gopi , Grimble John , Bonatt Joshua , Miske Jacob , Iftimia Nicusor , Lin Shaoting , Grimm Michele
Published: September 2022

Driving is a ubiquitous activity that requires both motor skills and cognitive focus. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver’s physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. A cloud-based communication scheme was developed for the ease of data collection and analysis. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper.
Comparison of Different Cure Monitoring Techniques
Authors: Kyriazis Alexander , Pommer Christian , Lohuis David , Rager Korbinian , Dietzel Andreas , Sinapius Michael
Published: September 2022

The ability to measure the degree of cure of epoxy resins is an important prerequisite for making manufacturing processes for fibre-reinforced plastics controllable. Since a number of physical properties change during the curing reaction of epoxy resins, a wide variety of measurement methods exist. In this article, different methods for cure monitoring of epoxy resins are applied to a room-temperature curing epoxy resin and then directly compared. The methods investigated include a structure-borne sound acoustic, a dielectric, an optical and a strain-based observation method, which for the first time are measured simultaneously on one and the same resin sample. In addition, the degree of cure is determined using a kinetic resin model based on temperature measurement data. The comparison shows that the methods have considerable but well-explainable differences in their sensitivity, interference immunity and repeatability. Some measurement methods are only sensitive before and around the gel point, while the strain-based measurement method only reacts to the curing from the gel point onwards. These differences have to be taken into account when implementing a cure monitoring system. For this reason, a multi-sensor node is suitable for component-integrated curing monitoring, measuring several physical properties of the epoxy resin simultaneously.
COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers
Authors: Costa Yandre M. G. , Silva Sergio A. , Teixeira Lucas O. , Pereira Rodolfo M. , Bertolini Diego , Britto Alceu S. , Oliveira Luiz S. , Cavalcanti George D. C.
Published: September 2022

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning
Authors: Shen Xianhao , Bao Jindi , Tao Xiaomei , Li Ze
Published: September 2022

In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.
Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease
Authors: Farhani Ghazal , Zhou Yue , Jenkins Mary E. , Naish Michael D. , Trejos Ana Luisa
Published: September 2022

Hand tremor is one of the dominating symptoms of Parkinson’s disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.
Gamma Radiation Image Noise Prediction Method Based on Statistical Analysis and Random Walk
Authors: Li Dongjie , Deng Haipeng , Yao Gang , Jiang Jicheng , Zhang Yubao
Published: September 2022

The gamma radiation environment is one of the harshest operating environments for image acquisition systems, and the captured images are heavily noisy. In this paper, we improve the multi-frame difference method for the characteristics of noise and add an edge detection algorithm to segment the noise region and extract the noise quantization information. A Gaussian mixture model of the gamma radiation noise is then established by performing a specific statistical analysis of the amplitude and quantity information of the noise. The established model is combined with the random walk algorithm to generate noise and achieve the prediction of image noise under different accumulated doses. Evaluated by objective similarity matching, there is no significant difference between the predicted image noise and the actual noise in subjective perception. The ratio of similarity-matched images in the sample from the predicted noise to the actual noise reaches 0.908. To further illustrate the spillover effect of this research, in the discussion session, we used the predicted image noise as the training set input to a deep residual network for denoising. The network model was able to achieve a good denoising effect. The results show that the prediction method proposed in this paper can accomplish the prediction of gamma radiation image noise, which is beneficial to the elimination of image noise in this environment.
A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation
Authors: Xi Tengyan , Yuan Lihua , Sun Quanbin
Published: September 2022

In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).
Multi-Modal Deep Learning for Assessing Surgeon Technical Skill
Authors: Kasa Kevin , Burns David , Goldenberg Mitchell G. , Selim Omar , Whyne Cari , Hardisty Michael
Published: September 2022

This paper introduces a new dataset of a surgical knot-tying task, and a multi-modal deep learning model that achieves comparable performance to expert human raters on this skill assessment task. Seventy-two surgical trainees and faculty were recruited for the knot-tying task, and were recorded using video, kinematic, and image data. Three expert human raters conducted the skills assessment using the Objective Structured Assessment of Technical Skill (OSATS) Global Rating Scale (GRS). We also designed and developed three deep learning models: a ResNet-based image model, a ResNet-LSTM kinematic model, and a multi-modal model leveraging the image and time-series kinematic data. All three models demonstrate performance comparable to the expert human raters on most GRS domains. The multi-modal model demonstrates the best overall performance, as measured using the mean squared error (MSE) and intraclass correlation coefficient (ICC). This work is significant since it demonstrates that multi-modal deep learning has the potential to replicate human raters on a challenging human-performed knot-tying task. The study demonstrates an algorithm with state-of-the-art performance in surgical skill assessment. As objective assessment of technical skill continues to be a growing, but resource-heavy, element of surgical education, this study is an important step towards automated surgical skill assessment, ultimately leading to reduced burden on training faculty and institutes.
Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
Authors: Riad Mai A. , El-Ghandour Osama , Abd El-Haleem Ahmed M.
Published: September 2022

Multiservice cellular in Radio Access Network (RAN) Slicing has recently attained huge interest in enhancing isolation and flexibility. However, RAN slicing in heterogeneous networks (HetNet) architecture is not adequately explored. This study proposes a pairing-network slicing (NS) approach for Multiservice RAN that cares about quality of service (QoS), baseband resources, capacities of wireless fronthaul and backhaul links, and isolation. This intriguing approach helps address the increased need for mobile network traffic produced by a range of devices with various QoS requirements, including improved dependability, ultra-reliability low-latency communications (uRLLC), and enhanced broadband Mobile Services (eMBB). Our study displays a unique RAN slicing framework for user equipment (UE) for joint user-association. Multicell non-orthogonal multiple access (NOMA)-based resource allocation across 5G HetNet under successive interference cancelation (SIC) is seen to achieve the best performance. Joint user-slice pairing and association are optimization problems to maximize eMBB UE data rates while fulfilling uRLLC latency and reliability criteria. This is accomplished by guaranteeing the inter- and intra-isolation property of slicing to eliminate interferences between eMBB and uRLLC slices. We presented the UE-slice association (U-S. A) algorithm as a one-to-many matching game to create a stable connection between UE and one of the base stations (BSs). Next, we use the UE-slice pairing (U-S. P) algorithm to find stable uRLLC-eMBB pairs that coexist on the same spectrum. Numerical findings and performance analyses of the submitted association and pairing technique show they can all be RAN slicing criteria. We prove that the proposed algorithm optimizes system throughput while decreasing uRLLC latency by associating and pairing every uRLLC user in mini slots.
Point Cloud Completion Network Applied to Vehicle Data
Authors: Ma Xuehan , Li Xueyan , Song Junfeng
Published: September 2022

With the development of autonomous driving, augmented reality, and other fields, it is becoming increasingly important for machines to more accurately and comprehensively perceive their surrounding environment. LiDAR is one of the most important tools used by machines to obtain information about the surrounding environment. However, because of occlusion, the point cloud data obtained by LiDAR are not the complete shape of the object, and completing the incomplete point cloud shape is of great significance for further data analysis, such as classification and segmentation. In this study, we examined the completion of a 3D point cloud and improved upon the FoldingNet auto-encoder. Specifically, we used the encoder–decoder architecture to design our point cloud completion network. The encoder part uses the transformer module to enhance point cloud feature extraction, and the decoder part changes the 2D lattice used by the A network into a 3D lattice so that the network can better fit the shape of the 3D point cloud. We conducted experiments on point cloud datasets sampled from the ShapeNet car-category CAD models to verify the effectiveness of the various improvements made to the network.
Parallel Frequency Function-Deep Neural Network for Efficient Approximation of Complex Broadband Signals
Authors: Zeng Zhi , Shi Pengpeng , Ma Fulei , Qi Peihan
Published: September 2022

In recent years, with the growing popularity of complex signal approximation via deep neural networks, people have begun to pay close attention to the spectral bias of neural networks—a problem that occurs when a neural network is used to fit broadband signals. An important direction taken to overcome this problem is the use of frequency selection-based fitting techniques, of which the representative work is called the PhaseDNN method, whose core idea is the use of bandpass filters to extract frequency bands with high energy concentration and fit them by different neural networks. Despite the method’s high accuracy, we found in a large number of experiments that the method is less efficient for fitting broadband signals with smooth spectrums. In order to substantially improve its efficiency, a novel candidate—the parallel frequency function-deep neural network (PFF-DNN)—is proposed by utilizing frequency domain analysis of broadband signals and the spectral bias nature of neural networks. A substantial improvement in efficiency was observed in the extensive numerical experiments. Thus, the PFF-DNN method is expected to become an alternative solution for broadband signal fitting.
Impact of Skin on Microwave Tomography in the Lossy Coupling Medium
Authors: Meaney Paul , Geimer Shireen , Golnabi Amir , Paulsen Keith
Published: September 2022

In microwave imaging, the effects of skin on recovering property distributions of tissue underneath the surface may be significant because it has high dielectric contrast with subcutaneous fat, which inevitably causes significant signal reflections. While the thickness of skin, especially relative to the wavelengths in use, would presumably have minor effects, it can introduce practical difficulties, for instance, in reflection-based imaging techniques, where the impact of the skin is large—often as high as two orders of magnitude greater than that of signals from underlying tumors in the breast imaging setting. However, in tomography cases utilizing transmission-based measurement data and lossy coupling materials, the situation is considerably different. Accurately implementing a skin layer for numerical modeling purposes is challenging because of the need to discretize the size and shape of the skin without increasing computational overhead substantially. In this paper, we assess the effects of the skin on field solutions in a realistic 3D model of a human breast. We demonstrate that the small changes in transmission field values introduced by including the skin cause minor differences in reconstructed images.
Comparative analysis of adherence to antihypertensive therapy
Authors:   FSBEI HE Voronezh State Medical University named after N. N. Burdenko of the Ministry of Health of Russia, Lukoyanova A. S. , Khanina E. A. ,   FSBEI HE Voronezh State Medical University named after N. N. Burdenko of the Ministry of Health of Russia, Muravitskaya M. N. ,   FSBEI HE Voronezh State Medical University named after N. N. Burdenko of the Ministry of Health of Russia
Published: September 2022

Purpose of the study: to investigate the features of adherence to antihypertensive therapy at the outpatient stage.
A new approach to the organization of cleaning and disinfection at the perinatal center
Authors:   Samara Regional Clinical Hospital named after V. D. Seredavin, Kashkareva S.M. , Mosolova A.I ,   Samara Regional Clinical Hospital named after V. D. Seredavin, Shalashova N.Yu. ,   Samara Regional Clinical Hospital named after V. D. Seredavin
Published: September 2022

The authors of the article remind specialists that infectious safety is a complex of sanitary and anti-epidemic, sanitary and hygienic, treatment and preventive measures aimed at preventing the spread of infection in medical institutions, and focus on modern measures for cleaning and disinfection in the perinatal center.
The role of the functional diagnostics nurse in carrying out holter monitoring
Authors:   Samara Regional Clinical Oncology Center, Fedulaeva S.B. , Starostina A.V. ,   Samara Regional Clinical Oncology Center
Published: September 2022

The article presents the role of a nurse when performing the manipulation «setting a Holter monitor».
COMPARATIVE ANALYSIS OF BRAIN BIOELECTRICAL ACTIVITY AND CIRCULATORY SUPPLY IN ADOLESCENTS WITH ACUTE HYPOXIA
Authors: Ivanov Anatoliy Betalovich , Borukaeva Irina Khasanbievna , Abazova Zalina Khasanovna , Molov Anzor Askerbievich , Kipkeeva Tat'yana Borisovna , Shokueva Asiyat Gisaevna , Shkhagumov Kazbek Yur'evich
Published: September 2022

Brain bioelectrical activity and the degree of cerebral blood supply directly determine physiological brain activity. There is insufficient evidence on the regularity and continuity of these processes. The aim of the study is to conduct a comparative analysis of brain bioelectrical activity and circulatory supply in adolescents with acute hypoxia. Materials and Methods. The authors examined 100 adolescent males of two age groups: Group 1 consisted of boys aged 12–14 years old, Group 2 included those aged 15–17. Results. The authors revealed a positive correlation between blood supply, metabolism and brain function. In early pubertal adolescents, puberty-associated endocrine changes have a significant effect on EEG and REG indices in adolescents with hypoxia. An increase in the rheographic index is accompanied by an increase in the alpha- and a decrease in the delta-rhythm indices. In pubertal adolescents, an increase in the rheographic index causes a decrease in alpha rhythm and an increase in slow-wave delta oscillations in the frontal and occipital leads. Under short-term hypoxia, an increase in the brain filling with blood is observed. Under oxygen depletion (14%), despite the increase in pulmonary and blood minute volumes, tissue hypoxia develops in pubertal adolescents. It is especially evident in the early puberty, as there is even a decrease in oxygen consumption in adolescents aged 12–14. In adolescents with hypoxia cerebral blood flow increases.
Linear Delta Robot Controlled with PLC Based On Image Processing
Authors: ÖZTÜRK Sıtkı , KUNCAN Fatma
Published: September 2022

Delta Robot is taking an important place in industry 4.0. Its ability to work fast and with precision is the reason why this robot now is used worldwide in the industrial field. Delta robots are mostly used for packaging, 3D printing, pick and place, etc. This robot can be controlled using programmable logic controller (PLC) devices. In recent years, image processing methods have been widely used in many robot applications. In this study, it is aimed to ensure that the robot successfully moves to the determined points by using image processing methods. Images for this study were obtained by attaching a camera to the robot system. In this study, the aim is to set the robot to move based on the visual data that is taken by the camera. The coordinates of each object are defined after several steps in image processing. To be able to move the robot to the desired coordinates, inverse and forward kinematic analysis is proposed. The obtained values are transferred to PLC using OPC. The necessary coding in TIA Portal is done to control the motor drivers so that the three stepper motors can ensure the delta robot moves to the desired coordinate position.
The history of Fertility Awareness Methods
Authors: Stachowska Aneta , Kicińska Aleksandra Maria , Wierzba Tomasz Henryk
Published: September 2022

Fertility Awareness Methods (FAM) are based on the observation of physiologically occurring symptoms, so-called fertility indicators, which include: cervical mucus, basal body temperature (BBT) and changes in cervical morphology/consistency. Observations of clinical symptoms in a female patient, which change depending on periodic hormonal fluctuations, were used to determine the first and second phases of the menstrual cycle and the period around ovulation. For many years, FAMs have been successfully used by women who want to plan their motherhood in accordance with their beliefs and chosen lifestyle, in an ecological manner, without pharmacological intervention or unnecessary medical treatments.FAMs have developed over the centuries and are now a well-documented field of reproductive medicine. Initially, FAMs were used only as a tool for planning or postponing the conception of a child, now they are more and more often perceived as an effective and easy-to-use tool in the diagnosis, treatment and monitoring of the therapy of cycle disorders and infertility. The aim of the study is to present the history of FAM development, to present groundbreaking discoveries and people to whom we owe the current state of knowledge about the symptoms of human fertility. A review of the medical literature was made, also referring to historical works, which included information on breakthrough discoveries in the field of human fertility. The selection of literature was made on the basis of a review of original papers and review publications in the PubMed and Google Scholar databases with the use of the following keywords: „Fertility Awareness Methods”, „Family Planning” and the „Pearl index”.
Farklı Organik Gübre Uygulamaları ve Biçim Zamanlarının Çay Bitkisinde (Camellia sinensis (L.) O. Kuntze) Verim Üzerine Etkileri
Authors: ÇATAL Muhammed İkbal , BAKOĞLU Adil , ÇELİK Şenol
Published: September 2022

Bu çalışma 2018 ve 2019 yıllarında Rize ili Pazar ilçesi ekolojik koşullarında 2 yıl süre ile yürütülmüştür. Deneme, tesadüf bloklarında bölünmüş parseller deneme desenine göre üç tekerrürlü olarak yürütülmüştür. Denemede ana parsellere organik gübre uygulaması (Kontrol, Organoferm, Neobioplus, Bionur ve Orbitosol), alt parsellere de biçim zamanları (1., 2. ve 3. biçim) uygulanmıştır. Organik gübre uygulaması ve biçim zamanlarına bağlı olarak; ortalama verim 2018 ve 2019 yıllarında sırasıyla 527.711-904.667 kg/da olarak tespit edilmiştir. 2018 yetişme yılında en yüksek çay verimi 2. Biçim zamanında Organoferm uygulamasından 722 kg/da elde edilmiştir. 2019 yetişme yılında ise en yüksek çay verimi 1. Biçim zamanında Neobioplus uygulamasından 1260 kg/da elde edilmiştir. Tesadüf bloklarında bölünmüş parseller deneme planına göre yapılan varyans analizi sonucunda, 2018 ve 2019 yıllarında çay verimi üzerine organik gübre uygulaması ve biçim zamanının etkisi önemli, organik gübre uygulaması*biçim zamanı interaksiyonun etkisi ise önemsiz olduğu ortaya çıkmıştır.
The Effect of Financial Literacy, Cost of Technology Adoption, Technology Perceived Usefulness, and Government Support on MSMEs' Business Resilience
Authors: Lestari Elissa Dwi , Hamid Nadiah Abd , Rizkalla Nosica , Purnamaningsih Purnamaningsih , Urus Sharina Bt Tajul
Published: September 2022

Objective - The Covid-19 pandemic has made MSMEs enter a crisis period that makes them the most vulnerable sector during a crisis. Given the vital role of MSMEs in economic stability, this study identified the drivers that shape the resilience of the MSME business, which is still not widely explored. This study elaborates on the role of knowledge-based financial literacy, financial resources and the cost of technology adoption from the financial side, TAM from the technology side, and external environmental factors in the form of government support as a driver of MSMEs' business resilience. Methodology/Technique – This study collected data from 339 MSMEs operating amid the Covid-19 crisis, and data analysis was carried out using the PLS-SEM technique. Finding – The results show that financial literacy positively affects financial resources and the cost of adopting technology from a financial perspective. However, only the cost of adoption directly affects business resilience, while financial resources have no effect. In terms of technological acceptance, perceived ease of use positively impacts perceived usefulness. However, only perceived usefulness directly affects business resilience, while perceived ease of use does not. Finally, government support positively affected business resilience during the pandemic. Novelty – This paper elaborates on the financial, technological, and government support aspects as an integrated framework to examine MSME resilience. Type of Paper: Empirical JEL Classification: M10, M15, M48, O33 Keywords: MSME Business Resilience, Financial Literacy, Cost of Technology Adoption, Perceived Usefulness, Government Support. Reference to this paper should be referred to as follows: Lestari, E.D; Hamid, N.A; Rizkalla, N Purnamaningsih, P; Urus, S.T. (2022). The Effect of Financial Literacy, Cost of Technology Adoption, Technology Perceived Usefulness, and Government Support on MSMEs' Business Resilience, GATR-Global J. Bus. Soc. Sci. Review, 10(3), 132–147. https://doi.org/10.35609/gjbssr.2022.10.3(3)
The Influencing Factors on Bumiputra Entrepreneur Intention to Use Co-Working Space
Authors: Rani Nazatul Shima Abdul , Krishnan K. Sarojani Devi , Suda Khairul Azizan
Published: September 2022

Objective - This study is conducted to investigate the acceptance level of Bumiputra entrepreneurs that register with a government agency in Malaysia on the concept of co-working space related to operator supports (OS), facilities (F), and social and business networking (SBN), to use (IU).
Methodology/Technique – About 2,000 Bumiputra entrepreneurs were contacted via e-mail, and only 267 responded. The data were analysed using SPSS Software, and Cronbach's Alpha is more than 0.900 for OS, F, SBN, and IU. Hence all items can be used to measure the constructs.
Finding – Most respondents perceive OS, F, and SBN to be helpful and have a positive and significant relationship with the intention to use (IU) co-working space.
Novelty – Hence, it is recommended for the relevant government agencies with unutilized office premises to convert their office spaces into co-working spaces, as the findings have shown that most Bumiputra entrepreneurs intend to use co-working spaces and perceive them to be helpful.
Type of Paper: Empirical
JEL Classification: L26, M13.
Keywords: Co-Working Space; Facilities, Operator Support; Social and Business Networking; Intention to Use.
Reference to this paper should be referred to as follows: Rani, N.S.A; Krishnan, K.S.D; Suda, K.A. (2022). The Influencing Factors on Bumiputra Entrepreneur Intention to Use Co-Working Space, GATR-Global J. Bus. Soc. Sci. Review, 10(3), 163–171. https://doi.org/10.35609/gjbssr.2022.10.3(5)
The Impact of Challenges Experienced by Teachers and Learners on Mathematics Performance in Relation to their Socio-Economic St
Authors: Brijlall Deonarain , Ivasen Selvam Jimmy
Published: September 2022

Objective – This paper explores the challenges experienced by mathematics teachers and learners of seven high schools in South Africa. A mixed method approach involving interviews and questionnaires was used.
Methodology – The sample included 7 principals or deputy principals, 7 departmental heads who supervised mathematics, 1 mathematics teacher who taught mathematics in grades 10 to 12, and 1 mathematics teacher who taught Mathematics in grades 8 and 9. In addition, 1 learner from each grade 8 to 12 was included in the sample. Each participant was interviewed using a structured interview schedule, followed by participants completing a questionnaire.
Findings – Vygotsky's socio-cultural and conceptual development theories in mathematics education formed the basis of this study. The 'Inputs, Processes and Output" model of Howie and Kilpatrick's model of the five strands of achieving 'mathematics proficiency was adopted. We identified many systemic, societal, and pedagogical challenges that teachers and learners experienced, which affected their mathematics teaching and learning. Our findings correlated with research in the literature review.
Novelty – The researchers concluded there were always intervention strategies that could assist in minimizing or eradicating the challenges faced by the teachers and learners of mathematics, thus improving their socio-economic standing.
Type of Paper: Review
JEL Classification: A14, 19.
Keywords: Challenges, Strategies, Blended Learning, Scaffolding, Conceptual Development, Collaborative Learning.

Reference to this paper should be made as follows: Brijlall, D; Ivasen, S.J. (2022). The impact of challenges experienced by teachers and learners on Mathematics performance, in relation to their socio-economic standing, J. Bus. Econ. Review, 7(2), 112–126. https://doi.org/10.35609/jber.2022.7.2(1)
Recognizing ASEAN Nations' Realization and Commitment to the Issues of Non-Traditional Security (NTS)
Authors: Yaacob Nur Ain , Yusof Muhammad Izmer , Nurudin Suzei Mat , Zain Zarina Mohd , Mustapa Nuramira Akmal
Published: September 2022

Objective – Issues dealing with security are perhaps the most profound issues since the end of the Cold War in the 1990s. The notion is that the state's security is no longer just threatened by war but also by other circumstances and situations that might still harm the citizen's and states' well-being. In international politics, the realism paradigm considers security challenges in the classic sense that wars and military conflicts are the sole threats to the state. This viewpoint falls under the notion of traditional security. The other concept that governs state security might be more than only weapon battles and wars, and it falls under the idea and paradigm of idealism.
Methodology – This paper will highlight a few issues concerning the Non-Traditional Security paradigm (NTS). In the end, the objective of this concept paper is to see the awareness of ASEAN members on the issues of NTS and how they respond to such matters.
Findings – A total of 10 informants from different backgrounds but experts in the field of NTS will involve in the study, and the data for the study shall be collected using qualitative research methods. We hope a full picture of NTS during the COVID-19 pandemic can be presented from the findings.
Novelty – Thus, this study suggests the blueprint for NTS to help people understand NTS.
Type of Paper: Empirical
J.E.L. Classification:
Keywords: Non-Traditional Security Issues, Securitization, Awareness, ASEAN

Reference to this paper should be made as follows: Yaacob, N.A; Yusof, M.I; Nurudin, S.M; Zain, Z.M; Mustapa, N.A. (2022). Recognizing ASEAN Nations' Realization and Commitment to the Issues of Non-Traditional Security (NTS), J. Bus. Econ. Review, 7(2), 151–159. https://doi.org/10.35609/jber.2022.7.2(4)
IDENTIFICATION OF APPLE LEAF DISEASES BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK
Authors: LI Lili , WANG Bin , HU Zhiwei
Published: September 2022

In view of the obvious differences in the manifestations of the same diseases in apples at different stages of the disease, different diseases show certain similarities, and the early symptoms of the disease are not obvious. For these problems, a new model attention residual network (ARNet) was introduced based on the combination of attention and residual thought. The model introduces the multi-layer attention modules to solve the problems of early disease location dispersion and features that are difficult to extract. In order to avoid network degradation, a residual module was constructed to effectively integrate high and low-level features, and data augment technology was introduced to prevent the model from over-fitting. The proposed model (ARNet) achieved an average accuracy of 99.49% on the test set of 4 kinds of apple leaf diseases with real complex backgrounds. Compared with the models ResNet50 (99.19%) and MobileNetV2 (98.17%), it had better classification performance. The model proposed in this paper had strong robustness and high stability and can provide a reference for the intelligent diagnosis of apple leaf diseases in practical applications.
Paradigmas históricos da inclusão e da educação de pessoas com deficiência visual
Authors: Caxias da COSTA Sirlene , Mota do CARMO Selma , Batista SANTOS Carlos Alberto , Gomes Dias LIMA Artur
Published: September 2022

The present work will have the purpose of exposing the reality experienced by people with visual impairment, about the guarantee of an education with equal conditions and opportunities. To trace this reality, it was explained the historical paradigms that implied in social attitudes, with people with disabilities. These paradigms are exclusion, segregation, integration and inclusion. It was also exposed the advances in Brazilian legislation, and what they bring from guarantees to people with disabilities. Furthermore, there is dialogue about the influence of international Treaties and Conventions on the guarantees of equal conditions of such subjects. To reach a conclusion of reality, a questionnaire was applied with ten visually impaired people. In addition, a bibliographical research was carried out, with a qualitative approach. It was found that there were evolutions about the right to education for people with visual impairment, but for the inclusion of fact, it is still necessary to overcome adversities and enforce constitutional rights.
ANALISIS PENGEMBANGAN OBJEK WISATA DAN EKONOMI KREATIF DI PANTAI KARANGSONG INDRAMAYU
Authors: Haryati Yati , Khairunnisa Hafni , Soliha Wiwi
Published: September 2022

Penelitian ini bertujuan untuk mengetahui pengembangan objek wisata di Pantai Karangsong yang dapat menjadikan objek wisata lebih kreatif dan meningkatkan minat wisatawan serta mengetahui pengembangan ekonomi kreatif di Pantai Karangsong Indramayu yang dapat dijadikan ciri khas dan menjadi lebih menarik dan dapat dinikmati oleh wisatawan. Penelitian ini menggunakan metode kualitatif, data yang di kumpulkan dengan cara wawancara, observasi dan dokumentasi yang kemudian dianalisis dengan metode diskriptif analisis. Selain itu juga menggunakan analisis SWOT untuk menganalisis data terkait dengan strategi pengembangan objek wisata Pantai Karangsong dan pengenbangan ekonomi kreatif. Hasil penelitian pengembangan objek wisata di Pantai Karangsong Indramayu berdasarkan hasil analisis SWOT dihasilkan beberapa strategi pengembangan untuk objek wisata Pantai Karangsong diantaranya yaitu Menjaga kealamiahan objek wisata Pantai Karangsong Indramayu, menambahkan tempat sampah di spot-spot yang mudah di jangkau wisatawan dan memperbaiki akses jalan, menambahkan jenis atraksi. Analisis pengembangan objek wisata dan ekonomi kreatif berpengaruh pada pantai karangsong indramayu.