bluuebunny/crossref_metadata_2025 huggingface dataset:
Teachers’ Application of Multiple Intelligences Approach in Teaching Economics
Authors: Yidana Mumuni Baba , Arthur Francis , Ababio Bethel Tawiah
Published: March 2022

The use of Gardner’s multiple intelligences (MI) theory is touted as one of the ideal ways of teaching students with diverse intelligences. However, there appears to be paucity of research in this area of knowledge. This study, therefore, explored the application of multiple intelligences approach in the teaching of Economics. The study was a quantitative research that adopted the descriptive cross-sectional survey design. In total, 100 senior high school teachers were selected for the study. Data were collected by the use of a 5-point Likert scale questionnaire, ranging from “never” to “very frequently.” Descriptive (mean and standard deviation) and inferential statistics (MANOVA) were used to analyse the data. The study revealed that Economics teachers frequently used interpersonal intelligences in teaching Economics. Additionally, it was found out that there was a statistically significant difference in the application of bodily-kinesthetic intelligence approach in the teaching of Economics based on Economics teachers’ teaching experience. However, the findings of the study showed no statistically significant difference in the Economics teachers’ application of MI approach in the teaching of Economics based on their gender. It was, thus, recommended that Ghana Education Service, Ministry of Education, and Non-Governmental Organisations should organise seminars and conferences for teachers to deepen their understanding of the application of the MI approach in the teaching of Economics. In addition, heads of senior high schools should organise professional development programmes and conferences to enable teachers acquire information on the following domains of MI: logical-mathematical, spatial, bodily-kinesthetic, musical, and naturalistic intelligences.
Adsorptive Capacity of Calcinated Hen Eggshell Blended with Silica Gel for Removal of Lead II Ions from Aqueous Media: Kinetics and Equilibrium Studies
Authors: Bayu Abreham Bekele , Abeto Amibo Temesgen , Beyan Surafel Mustefa
Published: March 2022

In this study, a description was given for the adsorbent CaSiO3 for allure proximate examination and determination like particle density, main part density, and porosity analysis. This is performed before management of batch adsorption experiments. Both kinetics and balance studies for the adsorbent were examined. The influences of various process parameters like lead concentration, pH, adsorbent dosage, and contact temporal length for process removal were explored. The removal efficiency of CaO from eggshell was enhanced to increase after mixing it with silica coagulate compared with added scholar’s findings for the same limit. The maximum removal efficiency (99.58%) was obtained by limiting the pH, adsorbent dosage, initial lead concentration, and contact time at 4, 1.8 g, 35 g/L, and 140 minutes, respectively. Thus, blending CaO from eggshells with silica gel can increase the adsorption competency of CaO. Lead removal is well integrated into the Langmuir isotherm model with an equivalent factor of 0.991. The kinetic data of adsorption fit well into a pseudo‐first‐order model with a correlation coefficient of 0.90111. The pseudo‐second‐order model was the rate‐determining step involved in the lead adsorption process for calcium silicate (CaSiO3) adsorbents.
Security Attitude Prediction Model of Secret-Related Computer Information System Based on Distributed Parallel Computing Programming
Authors: Sun Ling , Gao Dali
Published: March 2022

In recent years, there has been an upward trend in the number of leaked secrets. Among them, secret-related computers or networks are connected to the Internet in violation of regulations, cross-use of mobile storage media, and poor security management of secret-related intranets, which are the main reasons for leaks. Therefore, it is of great significance to study the physical isolation and protection technology of classified information systems. Physical isolation is an important part of the protection of classified information systems, which cuts off the possibility of unauthorized outflow of information from the network environment. To achieve the physical isolation of the network environment and build a safe and reliable network, it is necessary to continuously improve the level of network construction and strengthen network management capabilities. At present, the realization of physical isolation technology mainly relies on security products such as firewall, intrusion detection, illegal outreach, host monitoring, and auditing. This study analyzes network security systems such as intrusion detection, network scanning, and firewall. Establishing a model based on network security vulnerabilities and making up for network security hidden dangers caused by holes are generally a passive security system. In a network, the leader of network behavior—human behavior—needs to be constrained according to the requirements of the security management system and monitoring. Accordingly, this study proposes a security monitoring system for computer information network involving classified computer. The system can analyze, monitor, manage, and process the network behavior of the terminal computer host in the local area network, to achieve the purpose of reducing security risks in the network system. Based on the evaluation value sequence, the initial prediction value sequence is obtained by sliding adaptive triple exponential smoothing method. The time-varying weighted Markov chain is used for error prediction, the initial prediction value is corrected, and the accuracy of security situation prediction is improved. According to the security protection requirements of secret-related information systems, a complete, safe, reliable, and controllable security protection system for secret-related information systems is constructed, and the existing security risks and loopholes in secret-related information systems are eliminated to the greatest extent possible. This enables the confidentiality, integrity, and availability of confidential data and information in the computer information system to be reliably protected.
Research on the Method of Predicting Consumer Financial Loan Default Based on the Big Data Model
Authors: Wang Qiuying
Published: March 2022

With the continuous advancement of Internet technology and the continuous development of big data model applications, the data model is a method of data organization and storage, which emphasizes the reasonable storage of data from the perspective of business, data access, and use. The rapid development of information technology on the Internet and the increasing consumption level of people have created conditions for the booming development of consumer finance. Compared with traditional lending methods, more and more consumers are more inclined to choose consumption channels such as the Internet and e-commerce. Consumer finance lending is more convenient and fast. The prediction of consumer financial loan default is very important, because the inaccuracy of the prediction not only leads to the loss of profits but also infringes the rights and interests of consumers. Therefore, it is very important to propose a default prediction method in the consumer finance field with good performance based on the big data model. Simulation experiment conclusions are shown as follows: (1) the pseudo

R

-squared value of the model is 0.3660, indicating that the control variable can better explain the change of

y

. (2) The chi-square test statistic is equal to 51632.31, the degree of freedom is 68, and the corresponding

P
<
0.0001

, which also shows that the entire model can significantly predict the change of

y

. (3) The regression coefficient of the number of loan performances is -0.207553, indicating that the number of consumer loans is negatively correlated with loan defaults. (4) The regression coefficient of the monthly loan frequency is 0.0500152, indicating that the customer applies the frequency of personal credit consumer loans which is positively correlated with loan defaults. (5) The accurate prediction ratio of the model is 86.11%, which further shows that the prediction model has a better effect.
Optimization of Electronic Data Investigation Mode Based on Intelligent Perception and Positioning Technology
Authors: Zhang Xin , Cheng Chao
Published: March 2022

The exploration reported here is aimed at obtaining the geographic information and ecological information of the user’s specific location to understand the real-time needs of the user and provide users with accurate and timely services. Firstly, intelligent sensing and positioning technology is introduced under the background of big data. Then, the Geographic Information Systems (GIS) and Location-Based Service (LBS) technology are sketched out. Besides, an environmental intelligent perception and positioning model is proposed based on the GIS technology, LBS technology, and visible light propagation model. Finally, the proposed model is verified by experiments. The research results demonstrate that the environmental intelligent perception and positioning model can improve the positioning ability and efficiency of the users’ location and significantly enhance the accuracy of the location of the target. The positioning accuracy of the positioning system based on GIS combined with LBS reaches more than 80%, meeting the positioning requirements in daily life. Therefore, the research reported here provides a new perspective and new method for intelligent perception, makes a guarantee for further improving the efficiency of user work and life, and sets an example for future research.
Yoga Pose Estimation and Feedback Generation Using Deep Learning
Authors: Anand Thoutam Vivek , Srivastava Anugrah , Badal Tapas , Kumar Mishra Vipul , Sinha G. R. , Sakalle Aditi , Bhardwaj Harshit , Raj Manish
Published: March 2022

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one’s own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one’s health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.
A Comparison of Metaheuristic Techniques for Solving Optimal Sitting and Sizing Problems of Capacitor Banks to Reduce the Power Loss in Radial Distribution System
Authors: Lei Tongfei , Riaz Saleem , Raziq Hira , Batool Munira , Pan Feng , Wang Jianfeng
Published: March 2022

The losses in the radial distribution system are inevitable which needs to be minimized for the proper transmission of power to the end customers. This problem can be solved by the allocation of capacitor banks at proper locations with appropriate sizing. These allocations need an efficient approach for the performance enhancement of RDS. In this paper, several metaheuristic techniques such as particle swarm optimization (PSO), Harmony search, Bat, Cuckoo, and Grey‐wolf (GW) algorithms are employed to find the size of capacitor banks. Loss sensitivity analysis is considered for the indication of candidate buses where a capacitor has to be installed to reduce the total system losses and ultimately increase efficiency. Cost‐effectiveness, power loss minimization, and voltage enhancement can be determined and compared for these 5 techniques and are implemented on the IEEE‐34 bus system to illustrate the efficacy of each of them. The results show the advantages and drawbacks of the techniques separately. The simulations are carried out in MATLAB.
Cultural Intelligence in EFL and ESP Learners: Metacognitive, Cognitive, Motivational, and Behavioral Knowledge
Authors: Karami Vahid , Izadpanah Siros
Published: March 2022

One of the most significant current discussions in language learning is cultural intelligence. This research aims to study cultural learning determinants in EFL and ESP learners concerning their metacognitive, cognitive, motivational, and behavioral knowledge toward cultural intelligence in Iran. The studies so far indicate that cultural intelligence in EFL and ESP learners has not received as much attention as warranted. Moreover, rare studies have been done in this regard in the Iranian context. In this descriptive study, a nonrandom sampling method was applied to 323 university students who were 116 EFL learners (M.A and B.A English language students of Zanjan and Islamic Azad University) and 207 ESP (from the Zanjan University of Medical Sciences). The questionnaire was valid because it was standardized, and its reliability was checked via Cronbach’s alpha (

p

 < 0.000). The result of data analysis showed that EFL students have more cultural intelligence compared to medical students. Thus, based on the results, the teachers should consider the importance of cultural factors in their classes. Regarding pedagogical implications, the findings of this study can be of great help to book designers, schoolteachers, and university lecturers.
Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning
Authors: Ghazal Taher M. , Hussain Sajid , Khan Muhammad Farhan , Khan Muhammad Adnan , Said Raed A. T. , Ahmad Munir
Published: March 2022

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.
Deep Neural Network-Based SQL Injection Detection Method
Authors: Zhang Wei , Li Yueqin , Li Xiaofeng , Shao Minggang , Mi Yajie , Zhang Hongli , Zhi Guoqing
Published: March 2022

Among the network security problems, SQL injection is a common and challenging network attack means, which can cause inestimable loop-breaking and loss to the database, and how to detect SQL injection statements is one of the current research hotspots. Based on the data characteristics of SQL statements, a deep neural network-based SQL injection detection model and algorithm are built. The core method is to convert the data into word vector form by word pause method, then form a sparse matrix and pass it into the model for training, build a multihidden layer deep neural network model containing ReLU function, optimize the traditional loss function, and introduce Dropout method to improve the generalization ability of this model. The accuracy of the final model is maintained at over 96%. By comparing the experimental results with traditional machine learning algorithms and LSTM algorithms, the proposed algorithm effectively solves the problems of overfitting in machine learning and the need for manual screening to extract features, which greatly improves the accuracy of SQL injection detection.
Computer Vision Operating System of Bank Economic Management Security under 5G Wireless Communication Technology
Authors: Jianluan Guo , Xiaoyan Wang
Published: March 2022

Based on 5G wireless communication technology, this paper proposes a secure online banking payment scheme using a password algorithm and identity authentication+SMS verification code. We use the lightweight block cipher algorithm to construct a numeric permutation table. Then, we use the digital permutation table to perform the permutation encryption operation to obtain the digital ciphertext data. Finally, we connect 5G radiofrequency identification, identity authentication, and other modules to the trusted computing platform. The research found that the online banking security encryption algorithm proposed in the paper has the characteristics of high security and high efficiency.
Existence of Solution for a Conformable Fractional Cauchy Problem with Nonlocal Condition
Authors: Hilal Khalid , Kajouni Ahmed , Chefnaj Najat
Published: March 2022

In this work, we prove the existence and uniqueness of mild solution of the fractional conformable Cauchy problem with nonlocal condition. We obtained these results by applying the fixed point theorems precisely to the fixed point theorem of Krasnoselskii and Banach’s fixed point theorem. At the end, we provide application.
Exploration for Countering the Episodic Memory
Authors: Zhou Rong , Wang Yuan , Zhang Xiwen , Wang Chao
Published: March 2022

Reinforcement learning is a prominent computational approach for goal-directed learning and decision making, and exploration plays an important role in improving the agent’s performance in reinforcement learning. In low-dimensional Markov decision processes, table reinforcement learning incorporated within count-based exploration works well for states of the Markov decision processes that can be easily exhausted. It is generally accepted that count-based exploration strategies turn inefficient when applied to high-dimensional Markov decision processes (generally high-dimensional state spaces, continuous action spaces, or both) since most states occur only once in deep reinforcement learning. Exploration methods widely applied in deep reinforcement learning rely on heuristic intrinsic motivation to explore unseen states or unreached parts of one state. The episodic memory module simulates the performance of hippocampus in human brain. This is exactly the memory of past experience. It seems logical to use episodic memory to count the situations encountered. Therefore, we use the contextual memory module to remember the states that the agent has encountered, as a count of states, and the purpose of exploration is to reduce the probability of encountering these states again. The purpose of exploration is to counter the episodic memory. In this article, we try to take advantage of the episodic memory module to estimate the number of states experienced, so as to counter the episodic memory. We conducted experiments on the OpenAI platform and found that counting accuracy of state is higher than that of the CTS model. At the same time, this method is used in high-dimensional object detection and tracking, also achieving good results.
Rootstocks for the Management of Bacterial Wilt in Eggplant (Solanum melongena L.) and Tomato (Solanum lycopersicum L.) in the Coastal Regions of India
Authors: Ramesh Raman , D’Souza Marsha , Asolkar Trupti , Achari Gauri , Gupta Mathala J.
Published: March 2022

Cultivation of solanaceous vegetables such as eggplant and tomato is severely affected by bacterial wilt in the coastal regions of India. The causal agent Ralstonia pseudosolanacearum is soilborne bacterium, highly diverse, and able to survive in soil for many years without a host. Five bacterial wilt resistant eggplant (Solanum melongena) rootstock lines were evaluated by challenge inoculation and were found to show different levels of wilt incidence. Grafts of eggplant made on two rootstocks (S0004 and Surya) recorded reduced incidence of bacterial wilt (10 to 40%) during greenhouse evaluation while in nongrafted seedlings, the wilt incidence was 80 to 100%. Field evaluation of eggplant grafts made on Solanum torvum (Turkey berry), S0004, and Surya reduced the incidence of bacterial wilt compared to nongrafted seedlings. The lowest wilt incidence (0 and 15–40%) was observed in the field evaluations where S. torvum was used as rootstock, while the nongrafted control recorded 93–100% wilt. Tomato seedlings grafted on S. torvum, Surya, and S0004 recorded very low bacterial wilt incidence (0 to 15%) under greenhouse evaluation while the nongrafted seedlings recorded 80 to 100% wilt. Reduced bacterial wilt incidence (23 to 40%) was observed in grafts of polyhouse-grown tomato hybrid (GS-600) made on S. torvum while the nongrafted seedlings were severely affected (80 to 100%) in evaluation trials conducted for two years. From this study, it is evident that grafting of susceptible eggplant and tomato on resistant rootstocks, viz., S. torvum and Surya, could be a promising strategy in bacterial wilt management.
Frame Design of Moving Image Analysis System Based on Spatiotemporal Features
Authors: Yang Jianhua
Published: March 2022

In sports, kinematic image analysis is primarily concerned with the examination of space-time characteristics, such as image analysis of the speed and acceleration of related objects. Software and hardware make up the entire system. The medical scanner, scanning workstation, and DICOM server are all part of the hardware. Our self-developed scanner is used in the medical scanner, which can collect binary, 8-bit gray, 24-bit true color, 16-bit gray, and 48-bit color images. Kinematic image analysis is used to intuitively analyze sports technology, as well as to evaluate and diagnose its rationality. This paper investigates the kinematic-based framework design of a sports image analysis system. Image analyses of displacement, speed, and time are all used in the measurement of sports technology evaluation. Displacement analysis, for example, involves position coordinates, motion displacement, motion trajectory, and so on; speed class analysis, on the other hand, involves average and maximum speed.
Application Value of Serum TK1 and PCDGF, CYFRA21-1, NSE, and CEA plus Enhanced CT Scan in the Diagnosis of Nonsmall Cell Lung Cancer and Chemotherapy Monitoring
Authors: He Xiang , Wang Ming
Published: March 2022

Objective. To assess the application value of serum thymidine kinase 1 (TK1) and PC cell-derived growth factor (PCDGF), cytokeratin 19 fragment 21-1 (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) plus enhanced CT scan in the diagnosis of nonsmall cell lung cancer (NSCLC) and chemotherapy monitoring. Methods. Between April 2019 and April 2021, 30 patients with NSCLC assessed for eligibility treated in our institution were included in the experimental group, and 30 healthy individuals screened out from physical examinations were recruited in the control group. The chemotherapy regimens included gemcitabine plus cisplatin, pemetrexed disodium plus cisplatin, and vinorelbine plus cisplatin. The application value of serum TK1, PCDGF, CYFRA21-1, NSE, CEA, and enhanced CT scan in the diagnosis and chemotherapy monitoring of NSCLC was analyzed. Results. Before treatment, the eligible patients had significantly higher serum levels of TK1, PCDGF, CYFRA21-1, NSE, and CEA than those of the healthy individuals included (

P
<
0.05

). Clinical efficacy was categorized into good and poor, and the good efficacy included complete response and partial response, with the poor efficacy including stable disease and progressive disease. Patients with good clinical efficacy had lower levels of serum TK1, PCDGF, CYFRA21-1, NSE, and CEA than those with poor efficacy (

P
<
0.05

). Joint detection showed a larger area under the curve (AUC) (0.900; 95%CI, 0.812-0.988), a higher sensitivity, and a superior detection outcome to the stand-alone detection (

P
<
0.05

). Diagnostic results were similar between joint detection and pathological examination (

P
>
0.05

). Conclusion. The application of serum TK1, PCDGF, CYFRA21-1, NSE, and CEA assay plus enhanced CT scan shows high sensitivity and diagnostic accuracy in the diagnosis and chemotherapy monitoring of nonsmall cell lung cancer and thus provides a diagnostic reference basis.
New Methods for Predicting Strain Demand of Arctic Gas Pipelines across Permafrost under Frost Heave Displacement
Authors: Li Xinze , Wu Qingbai , Jin Huijun
Published: March 2022

With increasing gas resource development in the Arctic region, gas pipeline installations in permafrost regions are becoming important. Frost heave of pipeline foundation soils may occur when a chilled gas pipeline passes through unfrozen areas with frost-susceptible soils. The stress and strain behaviors caused by the differential frost heave will directly affect the safety of the pipeline. A nonlinear finite element model (FEM) computing the mechanical responses of the buried gas pipeline subjected to frost heave load was established and successfully validated with the results of a large-scale indoor pipe-soil interaction experiment carried out in Caen in France. Utilizing C# language and object-oriented visual programming techniques, a new customized parametric strain calculation software was developed. The effects of pipe diameter, pipe wall thickness, pipe internal pressure, and peak soil resistance on the longitudinal strain of X60, X70, and X80 steel pipes have been investigated quantitatively. For the first time, a fitting semiempirical equation and trained backpropagation neural network (BPNN) for predicting pipeline strain demand subjected to frost heave load were proposed based on 2688 groups of FEM results. The comparison results have proved their high accuracy and lower running time cost. The proposed new methods can be applied in the strain-based pipeline design and safety evaluation of pipelines in service. It is in the hope of supplementing existing theory and identifying new approaches for arctic gas pipeline installations.
A Football Shot Action Recognition Method Based on Deep Learning Algorithm
Authors: Xue Ming , Chen Hongtao
Published: March 2022

Football is regarded as the world’s number one sport and is loved by all countries, and large-scale football matches are held basically every year. The key to football matches is to shoot goals, and how to improve the accuracy of football shooting requires the identification and analysis of football shooting actions. Deep learning enables machines to imitate human activities such as seeing, hearing, and thinking. It solves many complex pattern recognition problems. Especially, the deep learning algorithm is unique in the recognition of pictures with high accuracy, and it provides technical support for the recognition and analysis of football shooting actions. What this paper will discuss is the recognition method of football shooting action based on a deep learning algorithm. Experiments show that the football shooting action recognition method developed in this paper has a great effect on promoting the accuracy of football shooting, which can make the accuracy rate reach about 96%. The research in this paper has great reference value and practical significance for the team’s ability to shoot and grasp the opportunity to score.
Analysis of the Application of Feedback Filtering and Seq2Seq Model in English Grammar
Authors: Zhang Aizhen
Published: March 2022

Natural language processing (NLP) technology is widely used in grammatical error correction, but its error correction logic is complex and fault-tolerant, which leads to low accuracy. With the progress of deep learning and big data analysis technology, a new method is proposed in the technical means of English grammar error correction. This paper proposes a deep learning model-based feedback grammar error correction method, which can effectively improve the accuracy and tolerance of grammar error correction. Firstly, the Seq2Seq model with attention mechanism is proposed, and then the feedback filtering model is integrated, so that the existing errors or inefficient grammars can be corrected again, thus improving the efficiency of the model. Through a large number of text detection, the model proposed in this paper has high execution efficiency and application ability and can widely meet the needs of English translation and grammar correction.
Conocimientos y mitos de profesores colombianos sobre las altas capacidades intelectuales
Authors: Gutiérrez-Ruiz Karol
Published: March 2022

(analítico)El objetivo del estudio fue describir conocimientos y mitos de profesores colombianos sobre Altas Capacidades Intelectuales (ACI), analizando su relación con variables sociodemográficas de interés. Se utilizó una metodología no experimental, transversal descriptiva-correlacional. Participaron 584 profesores de preescolar, educación básica y media, de 160 municipios seleccionados mediante muestreo no probabilístico, que diligenciaron el cuestionario de conocimientos y mitos sobre ACI. El porcentaje general de aciertos en el cuestionario fue bajo: se encontraron niveles elevados de falsas creencias relacionadas principalmente con la naturaleza de la superdotación, su rendimiento, sus logros y sobre la intervención educativa. De igual manera, se encontró relación entre las falsas creencias sobre ACI y el género de los participantes. Estos resultados ponen de relieve la necesidad de mejorar la formación sobre ACI en la preparación del profesorado.
Palabras clave:Alta capacidad intelectual; educación inclusiva; falsas creencias; formación de docentes.
Trauma and possibles pathways to healing in The bluest eye: Pecola's story and Claudia's narration
Authors: Gomes Rosana Ruas Machado
Published: March 2022

The Bluest Eye tells the story of Pecola, a black little girl who undergoes different forms of discrimination and abuse because of the color of her skin. As result of all the pain and suffering—including an incestuous rape—which she is subjected to, the girl loses her sanity. Therefore, one of the objectives of this article is to analyze how trauma is portrayed in the novel, considering the many micro-aggressions which Pecola is exposed to and which result in insidious
Narrar ´e encadear tempos e espaços perdidos: violência de gênero e trauma em "Um amor incômodo" de Elena Ferrante
Authors: Dantas Tatianne Santos , Rosa Lia Aguirre Silveira da , Sousa Karla Julliana da Silva , Kessler Helena Pillar , Thones Ana Paula Bellochio
Published: March 2022

No presente artigo, pretendemos evidenciar como o romance Um amor incômodo, de Elena Ferrante, é atravessado por um abuso sexual que resvala na relação entre uma filha e sua mãe. Na medida em que se articula à violência de gênero, esse acontecimento apresenta-se como um ponto cego da cultura, configurando-se como traumático. Ao final do livro, após reconstituir a história de sua mãe, é possível para a narradora, Delia, lembrar do abuso que sofreu quando criança e significar pontos de sua trajetória que se apresentam como um mal-estar. O incômodo do título, que diz respeito à relação mãe e filha, atravessa a trama ressoando o incômodo da violência que é desvelada no final do romance. Como aporte teórico, tomamos as considerações da crítica literária Shoshana Felman acerca de literatura, testemunho e traumas culturais. Com o intuito de relacionar o trauma com a violência de gênero, retomamos a perspectiva de Sigmund Freud sobre o tema, assim como a discussões mais atuais, através de Cathy Caruth e Gayle Rubin. Assim, propomos que o romance de Elena Ferrante permite um esgarçamento das possibilidades narrativas do trauma do abuso sexual e da violência de gênero, enquanto pontos cegos da cultura.
It had all begun like a dream: a escrita como presença em Second Class Citizen, de Buchi Emecheta
Authors: Nunes Francisco Romário
Published: March 2022

O presente artigo analisa a construção da escrita como instrumento de presença no romance Second class citizen (1975), da escritora nigeriana Buchi Emecheta, sob o ponto de vista da personagem Adah, com o objetivo de investigar como a escrita emula o sentido da presença a partir das suas experiências. A narrativa move-se entre dois espaços divergentes, Nigéria e Inglaterra, reverberando diversas problemáticas acerca da condição feminina no contexto de descolonização. Ao longo da história, Adah luta contra o sistema de dominação masculina e promove sua independência a partir do desejo da escrita. Nesse sentido, a personagem se encontra na escrita de si, tomando consciência de sua existência e marcando sua identidade no território diaspórico. Para compreender os efeitos de linguagem e o papel da escrita como presença, fundamentamos nossas discussões em Fanon (2008), Hall (2013), Anzaldúa (1987; 2000) e Evaristo (2008), entre outras contribuições. Partimos do pressuposto de que a personagem constrói uma escrita-presença no modo como assume sua identidade real, superando a condição opressora imposta ao sujeito feminino.
Desenvolvimento do letramento literário a partir de estratégias de leitura
Authors: Fonseca Carolina Alves , Pinto Maria Beatriz
Published: March 2022

O presente artigo propõe-se a discutir o uso de estratégias de leitura conforme proposto por Solé (1998) em prol do letramento literário, apresentando uma sequência de aulas implementada em uma turma de 1º ano do Ensino Médio em um colégio de aplicação da Zona da Mata Mineira. Com o intuito de integrar à formação estética necessária a um leitor literário (BARBOSA, 2011) uma formação cidadã — discutindo temas transversais relevantes para a formação crítica e reflexiva contemporânea, com destaque para questões de gênero, raça e classe —, foi desenvolvida uma oficina literária básica (COSSON, 2018) focada no livro Quarto de despejo (1960), de Carolina Maria de Jesus. Esta sequência propunha-se, além do letramento literário, ao desenvolvimento e ao aprimoramento de habilidades de leitura, escrita, oralidade e análise linguística, promovendo a integração entre esses eixos de ensino.
Development of Advanced Heart Failure: A Population-Based Study
Authors: Subramaniam Anna V. , Weston Susan A. , Killian Jill M. , Schulte Phillip J. , Roger Veronique L. , Redfield Margaret M. , Blecker Saul B. , Dunlay Shannon M.
Published: March 2022

Background:

Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. The goals of this study were to examine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes.

Methods:

This was a population-based, retrospective cohort study of Olmsted County, Minnesota, residents with a new clinical diagnosis of HF between 2007 and 2017. Risk factors for the development of advanced HF (2018 European Society of Cardiology criteria) were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using the Andersen-Gill and Cox models, respectively.

Results:

There were 4597 residents with incident HF from 2007 to 2017. The cumulative incidence of advanced HF was 11.5% (95% CI, 10.5%–12.5%) at 6 years after incident HF diagnosis overall and was 14.4% (95% CI, 12.3%–16.9%), 11.4% (95% CI, 8.9%–14.6%), and 11.7% (95% CI, 10.3%–13.2%) in patients with incident HF with reduced, mildly reduced, and preserved ejection fraction, respectively. Key demographics, comorbidities, and echocardiographic characteristics were independently associated with the development of advanced HF. Development of advanced HF was associated with increased risks of all-cause hospitalization (adjusted hazard ratio, 3.0 [95% CI, 2.7–3.4];
P
<0.001), HF hospitalization (hazard ratio, 10.2 [95% CI, 8.7–12.1]), all-cause mortality (hazard ratio, 5.0 [95% CI, 4.5–5.6];
P
<0.001), and cardiovascular mortality (hazard ratio, 7.8 [95% CI, 6.7–9.1]).

Conclusions:

In this population-based study, development of advanced HF was common and was associated with markedly increased morbidity and mortality.
Intensive Versus Standard Treatment of Hyperglycemia in Acute Ischemic Stroke Patient: A Randomized Clinical Trial Subgroups Analysis
Authors: Torbey Michel T. , Pauls Qi , Gentile Nina , Falciglia Mercedes , Meurer William , Pettigrew Creed L. , Durkalski Valerie L. , Bleck Thomas , Bruno Askiel ,   for the Neurological Emergencies Treatment Trials Network and SHINE Trial Investigators
Published: March 2022

Background:

Benefit from blood glucose (BG) control during acute ischemic stroke may depend on glycemic parameters. We evaluated for associations between the SHINE (Stroke Hyperglycemia Insulin Network Effort) randomized treatment group and the SHINE predefined 90-day functional outcome, within-patient subgroups defined by various glycemic parameters.

Methods:

The SHINE Trial randomized 1151 patients within 12 hours with acute ischemic stroke and hyperglycemia to standard (target BG 80–179 mg/dL) or intensive (target BG 80–130 mg/dL) BG control for 72 hours. We predefined 6 glycemic parameters: acute BG level, absence versus presence of diagnosed and undiagnosed diabetes, hemoglobin A1c, glycemic gap (acute BG–average daily hemoglobin A1c based BG), stress hyperglycemia ratio (acute BG/average daily hemoglobin A1c based BG), and BG variability (SD). Favorable functional outcome was defined by the SHINE Trial and based on the modified Rankin Scale score at 90 days, adjusted for stroke severity. We computed relative risks adjusted for baseline stroke severity and thrombolysis use.

Results:

Likelihood for favorable outcome was lowest among patients with undiagnosed diabetes compared to patients with true nondiabetes (adjusted relative risk, 0.42 [99% CI, 0.19–0.94]). We did not find any relationship between the favorable outcome rate and baseline BG or any of the glycemic parameters. No differences between SHINE treatment groups were identified among any of these patient subgroups.

Conclusions:

In this exploratory subgroup analysis, intensive versus standard insulin treatment of hyperglycemia in acute ischemic stroke patient subgroups, did not influence the 90-day functional outcomes, nor did we identify associations between these glycemic parameters and 90-day functional outcomes.
Electoral Violence and Supply Chain Disruptions in Kenya's Floriculture Industry
Authors: Ksoll Christopher , Macchiavello Rocco , Morjaria Ameet
Published: March 2022

Violent conflicts, particularly at election times in Africa, are a common cause of instability and economic disruption. This paper studies how firms react to electoral violence using the case of Kenyan flower exporters during the 2008 postelection violence as an example. The violence induced a large negative supply shock that reduced exports primarily through workers' absence and had heterogeneous effects: larger firms and those with direct contractual relationships in export markets suffered smaller production and loss of workers. On the demand side, global buyers were not able to shift sourcing to Kenyan exporters located in areas not directly affected by the violence or to neighboring Ethiopian suppliers. Consistent with difficulties in ensuring against supply-chain risk disruptions caused by electoral violence, firms in direct contractual relationships ramp up shipments just before the subsequent 2013 presidential election to mitigate risk.
Exploring Sisyrinchium (Iridaceae) diversity in the Atlantic Forest Biome: three new species in S. sect. Viperella
Authors: INÁCIO CAMILA DELLANHESE , DAMASCENO LOZANO EDUARDO , ANTUNES KELLY , BÁEZ-LIZARAZO MABEL ROCIO , EGGERS LILIAN
Published: March 2022

The Southern and Southeastern regions of Brazil present the highest species richness of Sisyrinchium, and the taxa are mainly distributed in the Atlantic Forest Biome. Here, three new species belonging to Sisyrinchium sect. Viperella from this biome are described: S. caratuvense is endemic to the high altitude grasslands of eastern Paraná, S. usneoides is endemic to the campo rupestre phytophysiognomy in southern Minas Gerais, and S. iguazuanum has a wider distribution, occurring in the grasslands of western Santa Catarina and Paraná (Brazil), and eastern Misiones (Argentina). Descriptions, illustrations, comparisons to related taxa and conservation status are provided.
Arenaria izmirensis (Caryophyllaceae), a new species from Turkey
Authors: EROĞLU VOLKAN , OĞUR ERDİNÇ
Published: March 2022

Arenaria izmirensis is described as a new species from the mountain of Mahmut (district of İzmir, Western Turkey). The new species is morphologically similar to A. tmolea, A. sipylea, A. uninervia, and A. cretica from which it differs in having a procumbent habit, different habit and shapes basal leaves, densely glandular sepals, and smaller petals. A detailed description, ecological data, original drawings and photos, and the conservation status are given.
Experimental Research on Longitudinal Ultrasonic Assisted Vibration Drilling of 0Cr17Ni4Cu4Nb
Authors: Yin Chunmei , Li Zeqi , Zhang Jinhua , Yu Xuelian , Shi Yaochen
Published: March 2022

In the present study, by adding ultrasonic vibration in the longitudinal direction to assist the drill bit to drill on 0Cr17Ni4Cu4Nb stainless steel, in order to obtain high-quality deep small holes and prolong the service life of the drill bit. By establishing the movement mathematical
model of the drill bit under ordinary drilling and longitudinal vibration drilling, the movement trajectory of the main cutting edge of the drill bit in two different drilling methods is compared and analyzed, and the anti-friction mechanism of longitudinal vibration drilling is analyzed.
The conventional drilling and longitudinal ultrasonic drilling of 0Cr17Ni4Cu4Nb stainless steel are carried out on a longitudinal ultrasonic vibration drilling device. After drilling test, the wear morphology of the drill bit, the surface roughness of the hole wall and the chip morphology
are observed and analyzed by the detection equipment. The result shows: the wear degree of the drill bit after longitudinal ultrasonic vibration drilling is lower than that of conventional drilling, which can prolong the service life of the drill bit. The surface roughness of the hole wall
is reduced, and the drilling chips are more finely divided and easier to discharge.
Multistage Coordinated Control of Voltage and Reactive Power in Distribution Network Based on Pulse Random Neural Network
Authors: Liu Jun , Yang Fan , Zhang Wenjun , Gao Yan
Published: March 2022

The multi-level coordinated control method of voltage and reactive power of distribution network based on pulse random neural network is studied to improve the economy, reliability and stability of distribution network operation and improve the quality of power supply. Build three layers
of impulsive stochastic neural network short-term load forecasting model, the implementation of power network short-term load forecast, according to the forecast results, when the power system load fluctuation is bigger, with a distribution network power supply reliability, economy and stability
as the optimization goal, combined with four coordinated control method for distribution network voltage reactive power coordinated control. The experimental results show that the prediction results of this method are accurate and reliable, and the predicted load fluctuation trend is in line
with the actual situation. The method in this paper reduces the grid loss by 63.64% and the voltage deviation rate by 77.27%, effectively reducing the network loss, voltage deviation rate and power factor. The method in this paper ensures the economy of DG and can maintain the stable and reliable
operation of the power system. High continuous quality provides effective help for stable power supply of the power system.
Study of Readout Channel Signals from Electrical Probe Memory
Authors: Ou Qiao-Feng , Li Ling , Lu Shu-Long
Published: March 2022

Electrical probe memory using phase-change materials (PCM) has been considered as one of the most promising candidates for the future archival storage market. This can be attributed to the use of nanoscale probe that allows for ultra-high density and PCMs that lead to ultra-fast write/read
speed and ultra-small energy consumption. For above reasons, considerable research is devoted to researching the write performances of the electrical probe memory, while ignoring the detailed study on its readout performances. To address this issue, we investigated the impulse response and
step response of the electrical probe memory with crystalline bit embedded inside the amorphous matrix, which are the key factors to the readout channel design. Additionally, the appropriate readout excitations and the resulting DC offset are also well-established in order to ensure the reliability
of the readout performances. We also assess the tip offset interference that may have an adverse effect on resulting signals. The calculated channel impulse response signal is eventually mathematically modelled so as to understand the behavior of the readback channel in linear and non-linear
sense based on Gaussian and Lorentzian approximations.
Professional talk on cybervetting: Accounting for a contested practise
Authors: Backman Christel , Hedenus Anna
Published: March 2022

Even though recruiters’ practise of searching for information online during recruitment and selection has been a contested practise, owing to the risk of discrimination and privacy intrusions as well as poor evidence for its ability to predict work performance, it is used in recruitment. In this article, our aim is to understand how ‘professional talk’ is used as a discursive resource to legitimize contested practises such as the practise of cybervetting by recruiters. The study is based on interviews with 37 recruiters in Sweden, all of whom had experience of cybervetting jobseekers. We found that professional talk was linked to objectivity and being unemotional, having knowledge about recruitment methods and the ability to distinguish between relevant and irrelevant information. In relation to the theory on professional talk, our study contributes with empirical evidence for the normative function of professional talk. Using cybervetting, as a case of legitimizing controversial practises, we provide a theoretical contribution to the theory on professional talk by illustrating how professional talk not only fills a disciplinary function by restraining a practise but also by enabling, legitimizing and providing discursive frames for how it can be performed.