bluuebunny/crossref_metadata_2025 huggingface dataset:
A DNN-based diagnosis on autism spectrum disorder in children
Authors: Tan Shuai
Published: July 2024

The prevalence of autism spectrum disorder (ASD) witnesses a sharp increasing in recent years, and early diagnosis and intervention of ASD are critically needed. This study explored the efficacy of Deep Neural Networks (DNN) in diagnosing ASD among children aged 0 to 10. Utilizing the latest dataset derived from the ASDTests mobile application, which encompasses behavioral characteristics of over 2,000 children, we implemented a DNN model to capture complex non-linear patterns indicative of ASD. The results of comparative analysis with traditional machine learning models revealed DNN's superior accuracy in predicting ASD, indicating that the DNN achieved a significant improvement in identifying minority classes post-imbalance learning treatment. The promising results, including the 99.55% accuracy rate, paved the way for future investigations into integrating DNN with multimodal data analysis and other advanced algorithms to enhance early diagnostic processes and intervention strategies for ASD.
Investigating the capability of recurrent neural networks for identifying text-based cyberbullying incidents on social platforms
Authors: Qi Yukun
Published: July 2024

As cyberbullying escalates worldwide, the emotional toll on those targeted is profound, demanding attention. This question allows the framing of the detection of cyberbullying as a categorization puzzle, leveraging a spectrum of detection strategies from classical machine learning to advanced deep learning techniques via Natural Language Processing (NLP). It zeroes down to that capability which characterizes RNNs in parsing sequential data and interpreting the contextual nuances. This hence underscores their effectiveness and accuracy in the flagged detection of cyberbullying content. Directly compared to classical algorithms, it shows the best performance of RNN regarding accuracy, speed, and universality over different languages. The overall result of this research does, therefore, affirm the very strong promise and effectiveness of RNN frameworks toward the discrimination of cyberbullying across the various linguistic online environments, setting a firm ground for further development in cybersecurity.
The prediction of Parkinson's disease based on Pearson coefficient feature screening and machine learning
Authors: Tian Yuchen
Published: July 2024

Parkinson's disease is a common and severe type of brain disease. Its incidence rate is relatively high among brain diseases. At present, there is no very effective treatment for Parkinson's disease. So researchers have focused on diagnosing Parkinson's disease. At present, machine learning methods have been applied in the medical field and have played a very positive role in the diagnosis of diseases. It has been proven that analyzing the patient's voice and trembling condition through machine learning can accurately diagnose Parkinson's disease. In this study, before training the model, we used the Pearson coefficient feature screening method to improve the accuracy of diagnosis. Then, we conducted training on six major models (Random Forest, GBDT, Adaboost, Logistic Regression, Decision Tree, XGboost) in order to find the model with the best performance. In this study, we found that the performance of Random Forest is the best in these models (Accuracy: 91.53%, recall: 100%), then is the GBDT model (Accuracy: 91.53%, recall: 97.78%). The other four models all have a great disparity on accuracy and recall, which are the two most important metrics on the detection of diseases. The research results have demonstrated that the feature selection method based on Pearson's coefficient indeed comprehensively improves the accuracy of diagnosis for Parkinson's disease. And we also found that in the process of diagnosing Parkinson's disease, the performance of the Random Forest and GBDT models is the best.
A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms
Authors: Chen Weiyu , Sun Peiyao , Zhang Yujia
Published: July 2024

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.
Research and implementation of three-dimensional modeling techniques for Chinese dragon
Authors: Wang Zihang
Published: July 2024

This paper delves into a new trend in the field of computer science, which involves a shift from complex code editing techniques of the past towards more intuitive visual software applications. Under this trend, modeling and rendering techniques have undergone long-term development and continuous iteration, becoming indispensable tools across various domains. Using the construction of a Chinese dragon model as a case study, this paper showcases the application and development of this new trend through a detailed description of the selected software tools' characteristics, classification of production steps, and the modeling process. Special emphasis was placed on shaping key details such as the dragon's body contour, scales, eyes, etc., during the dragon model construction process, as well as how these details are manifested in software applications. Software selection and utilization were carefully aligned with practical needs and technical features to ensure the efficiency and accuracy of the modeling process. In-depth research into the modeling process revealed the potential and advantages of this new technology, receiving widespread acclaim and recognition.
Development and validation of an Adaboost model for breast cancer classification
Authors: Wang Heqing
Published: July 2024

The mutation of breast cells leads to breast cancer,a lump causing illnesses .A large amount of women who have the concern of cancer are suffering from mutation in their breast cells. Early diagnosis of breast cancer can significantly improve the survival rate of patients by allowing them to receive timely clinical treatment. Accurate classification from tumor data prevents patients from receiving unnecessary treatment. Therefore, correctly diagnosing breast cancer and classifying patients into malignant or benign groups is a hot topic. In this study, the Adaboost algorithm is used to sort breast cancer depending on whether it is benign or malignant by combining the one-hot coding approach and imbalance learning. The one-hot coding approach overcomes the bias caused by the LabelEncoder coding approach, and the synthetic minority over-sampling technique, which improves the classification performance of Adaboost by augmenting the categorization balanced data structure with more minority class samples, so that its diagnosis of breast cancer reaches 99% accuracy, which is better than other machine learning models, we propose this model can be used for the diagnosis of breast cancer, providing a more accurate and faster way to classify breast cancer.
A machine learning-based model for stroke prediction
Authors: Wu Dongchen , Zhang Xinfang , Zhu Xiaochen
Published: July 2024

In recent years, machine learning has highlighted good results in the early diagnosis and prediction of diseases. Stroke is a serious threat to human health. Early prediction of stroke is of great significance for treatment and intervention. This paper mainly investigates the application of different machine learning models in stroke prediction and compares the performance of each model. First, we collected some data, which contained 5110 entries or records and 12 different attributes. The dataset was then subjected to various preprocessing measures, such as eliminating data redundancy. Seven different machine learning models were used. Includes Logistic Regression, Support Vector Classifier (SVC, K-Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, XGBoost Classifier (XGBClassifier) and Deep Neural Networks (DNN). After model comparison, we found that when the dataset was extremely imbalanced, the AUC of DNN before feature selection was 82%, which was significantly better than other machine learning models. In addition, the characteristics of each model were analyzed to provide a reference for the selection of stroke prediction models. The results of this study provide value for the early diagnosis and intervention of stroke, and also provide new ideas for the application of machine learning algorithms in the field of stroke prediction.
Prediction of diabetes mellitus using XGBoost model
Authors: Xie Peifeng , Xu Jinghang
Published: July 2024

With the number of health-threatening diseases and deaths on the rise, medical decision support systems continue to prove effective in improving the efficiency of physicians and other healthcare providers and supporting clinical decisions. Diabetes remains one of the leading diseases responsible for many deaths worldwide. Diabetes is characterized by elevated blood glucose levels, which can have serious consequences for other organs in the body. According to the International Diabetes Alliance (IDA), 382 million people currently have diabetes and this number is expected to double to 592 million by 2035. This paper proposes a clinical prediction model for diabetes based on machine learning (ML).As traditional ML models, we consider the most commonly used classifiers, the Logic Tree (LR) and XGBoost. We compare these ML models. On the other hand, we also use deep learning (DL) models and apply a fully convolutional neural network (DNN) for diabetes prediction and recognition. The proposed models were evaluated in the public Kaggle database: prediction performance at each level was analyzed using the Precision, Recall, AUC, accuracy and F1 metrics, and overall prediction efficiency was assessed using accuracy and its macro-average: DL, LR, The overall accuracy obtained by XGBoost was 91.88%, 88.75% and 97.12% respectively. The experimental results show that XGBoost is more effective at predicting diabetes than deep learning and LR methods.
Lung X-ray image segmentation algorithm based on Multihead Self-Attention Mechanism (MSAG) optimizing Unet networks
Authors: Chen Haotian
Published: July 2024

In this paper, the Multihead Self-Attention Mechanism (MSAG) is used to optimize the Unet network for accurate segmentation of lung X-ray images. By introducing the MSAG module, the ability of the Unet network to capture global and local correlations is enhanced, which effectively improves the accuracy of the segmentation results. The introduction of the multi-head self-attention mechanism enables the network to have more powerful modelling and generalization capabilities, and can process various types of lung X-ray images stably and efficiently. The dataset is divided into training, validation and test sets according to the ratio of 4:3:3. The loss gradually converges during the training process, and the model gradually learns the data features and patterns, and the gap between them and the real labels is gradually reduced. The performance on the validation set is good and no over-fitting occurs, demonstrating the ability to generalize on unseen data. The evaluation metrics on the test set show an IoU of 0.85, a Dice of 0.92, and an Accuracy of 0.88, proving that the model can accurately extract lung features for segmentation. This study has achieved satisfactory results in the field of medical images by optimizing the network structure and introducing new techniques, which are of positive significance for improving the accuracy and efficiency of lung X-ray image segmentation.
Instrument recognition in unprocessed musical audio through transformer-based modelling
Authors: Dai Congren
Published: July 2024

This study focuses on identifying primary instruments in musical audio using an adapted Wav2Vec 2.0 model, initially intended for extracting speech features from raw audio. Modifications to the model's convolutional layers and transformer element were made to facilitate the recognition of instruments in complex audio mixes. The task of instrument recognition is approached as a multi-labelled classification problem. The effectiveness of the model is measured through accuracy, precision, recall, F1-score, and analysis via a confusion matrix. Key findings reveal the model's differential efficiency in recognising various instruments, with notable success in detecting violins, pianos, saxophones, and human voices. However, the model encounters difficulties in recognising instruments with a narrower dynamic range or lower volume, like the organ that may provide harmonic support, and those with scarce representation, such as the cello and clarinet. The research also indicates that while pre-separation of certain instruments like guitars may enhance recognition, it may not be necessary for others.
Immediate traffic flow monitoring and management based on multimodal data in cloud computing
Authors: Ding Weike , Tan Hao , Zhou Huiming , Li Zihan , Fan Chao
Published: July 2024

This study uses cloud computing platform to process multi-modal data, and constructs a traffic flow prediction model based on LSTM neural network by integrating data from multiple dimensions such as traffic flow, occupancy and speed. In the process of model construction, we fully consider the hourly characteristics and hysteresis characteristics, and carry out fine scaling and splitting of the data to improve the accuracy and generalization ability of the model. The experimental results show that our model outperforms the baseline on both the training set and the test set, which verifies its effectiveness in traffic flow prediction. By keeping our models in a cloud environment, we provide reliable tools and support for future real-time data analysis and traffic management decisions. This study provides an important reference for the development of traffic management system based on cloud computing, and also provides new ideas and methods for other fields to solve practical problems by using multi-modal data.
A master-slave teleoperation system control model based on model predictive control with fast dynamic response
Authors: Song Yixin
Published: July 2024

Robot technology is advancing with constantly expanding applications. Teleoperation system demonstrates its potential to extend human capabilities in unstructured and hazardous environments as an important branch of robotics. However, there are still issues involving insufficient position tracking accuracy and dynamic response capabilities. This article proposes a master-slave teleoperation system control model based on model predictive control (MPC) with a real-time dynamic adaptive parameter adjustment algorithm. We establish a model based on the integral barrier Lyapunov function and construct a dynamic optimization model based on MPC. Then we carry out rolling optimization and feedback correction at each moment to feed the predicted values generated by MPC back to correct the actual tracking trajectories in real-time. Additionally, we utilize an adaptive neural network and robust term compensation to adjust dynamic parameters and eliminate the effects of time delays, external disturbances, and model uncertainties. Theoretical simulation experiments are conducted on the Simulink platform, analyzing position tracking error and dynamic response capabilities of the master-slave robot respectively to verify the proposed model. The results indicate a reduction in the position tracking errors of the master with 10% and slave with 11%, while also showing an improvement in their dynamic response capabilities. The speed error ranges of the master and slave are shortened with 6% and 7% respectively.
Designofhighperformancealuminum-airbatterybasedoncheapnon-preciousmetalcathodes
Authors: Hu Jingyun
Published: July 2024

With the rapid development of areas such as mobile electronic devices and electric vehicles, environmental problems and energy problems have become increasingly prominent. Therefore, it is particularly important to develop green, safe, efficient, cheap, sustainable energy storage and large-scale application of energy storage devices. Aluminum-air battery is a device that converts the chemical energy of anode aluminum directly into electric energy through electrochemical reaction. The theoretical voltage of aluminum-air battery is high (2.75 V), large specific capacity (2.98 Ah/g), high specific energy (8.1 Wh/g), and metal aluminum is a high strength energy carrier, has the advantages of rich resources, low price, environment friendly, perfectly matching the current situation of power supply requirements. However, the existing aluminum-air batteries face the economic problem of high cost, due to the slow oxygen reduction reaction dynamics of the cathode and the use of the expensive precious metal Pt/C as a catalyst. In order to reduce the cost of aluminum-air batteries, this paper plans to use the cheap non-precious metal nano carbon materials as the cathode catalyst, and flexibly use the structural design to construct the structure of the aluminum-air battery with double cathodes, which effectively increases the cathode reaction area. In this paper, the discharge performance was studied by experimental method, and its peak power is 1.42 times that of the traditional single cathode battery, which has good catalytic performance and stability, and shows that the non-precious metal double-cathode structure battery is both practical and economical.
A design solution for a self-guarding robot based on tube worm behavior
Authors: Chen Jinzheng , Du Shihao , Huang Mingrui
Published: July 2024

Tubeworms establish benthic habitats in the abyssal zones through the aggregation of extensive clusters of chitinous tubes, embodying sessile, tubicolous organisms that engage in filter feeding. The anterior extremity of these organisms is morphologically adapted into tentacle which are capable of retracting into the organisms body at a remarkable velocity when receiving external stimulation. This high speed relies on fan worms lubricant and strong vertical muscles. This adaptive behavior of tubeworms has served as an inspiration for the Long-term underwater detector robot. Despite the prevailing focus of extant research on the intrinsic characteristics of underwater sensors, such as precision and interactivity, there remains oversight regarding the preservation of high-value and inherently delicate sensors within subaquatic robotic carriers. In response to this lacuna, the paper proposes a novel device of an autonomous robotic entity designed to reflexively retract its sensory apparatus in reaction to environmental stimulation. The solutions presented are designed for long-term underwater robots which are subject to frequent external disturbances. The shell has been designed by means of a multilayer sheath structure of a fan worm and a retraction mechanism has been designed. Solidwork is used to model the robot and apdl is employed to simulate the strength stiffness of key structures. As a result of the modelling and simulation analyses, the possibility and practicality of this self-guarding robot have been determined. It is posited that the robotic apparatus harbors a potential for application in domains such as subaqueous surveillance, bathymetric resource exploration, and aquatic quality assessment. Prospective avenues for further scholarly inquiry encompass an augmentation of the investigation into the locomotive capabilities of the robot, alongside enhancements to its autogenic safeguarding mechanisms. These advancements may help to amplify the reliability of the device within severe and abyssopelagic conditions.
Design and implementation of scrambling and decoding circuits
Authors: Sun Peixiang , Yan Zefeng , Zhang Ye
Published: July 2024

Scrambling decoding technology is now widely used in the transmission of digital signals. The circuit design is an important basis for the realization of adding and de-scrambling. So the researchers take the design and implementation of scrambling and decoding circuit as the research topic of this paper. In this study, the circuit is designed according to the demand, so that the circuit can complete the construction of pseudo-random sequences, and then the module division is carried out to determine the specific work completed by different modules. Finally, according to the designed circuit and module design simulation, the accuracy of the early design is verified. The results show that after the original data is input, the output data can be the same as the input data after the circuits scrambling operation to achieve the purpose of designing the scrambling circuit. Through this experiment, the researchers hope that the research content can better promote the security and accuracy of data transmission. At the same time, with the optimization of the existing coding technology, researchers expect to have a more efficient decoding process, reduce errors, and improve the data recovery rate.
Սասունցի Դավթի կերպարը Երվանդ Քոչարի արվեստում
Authors: Աղասյան Ա. Վ.
Published: July 2024

В творческом наследии Ерванда Кочара особое место занимают произведения, при создании которых источником вдохновения для мастера стали образы героев армянского народного эпоса «Сасунские храбрецы» («Неистовые сасунцы») и, в первую очередь, образ его главного действующего лица – Давида Сасунского. Имеем в виду семь графических полосных, данных во весь лист иллюстраций, выполненных Кочаром для русского перевода сводного текста эпоса, изданного московским и ленинградским отделениями издательства Академии наук СССР в 1939 г., а также установленный в 1959 г. на привокзальной площади Еревана, выкованный из меди конный памятник Давиду Сасунскому.
In the creative legacy of Yervand Kochar, a special place is given to the main characters of the Armenian national heroic epic “Daredevils of Sasun”, the artworks inspired by the character of David of Sasun in particular. Those are: the seven inset graphic drawings, which illustrate the Russian translation of the consolidated original text of “David of Sasun”, published in 1939 through joint efforts of the Moscow and Leningrad Publishing Houses of the USSR Academy of Sciences to mark the 1000th anniversary of our national epic, and the monumental copper equestrian statue of David of Sasun, erected in 1959 in the Railway Station Square in Yerevan.
Evaluation of Blood Nitrotyrosine and Nitric Oxide Levels in Acute Mercury Intoxication in Children
Authors: Arik Elif , Gungor Olcay , Temiz Fatih , Kurutas Ergul , Dilber Cengiz
Published: July 2024

Aim: Multiple processes have been demonstrated to elucidate the biological toxicity of mercuric chloride, among which oxidative stress has been identified as a contributing factor. The superoxide radical has the potential to induce peroxidation of lipid membranes, alter the activities of proteins and antioxidant enzymes, and modulate gene transcription. Additionally, it has the ability to swiftly deactivate Nitric Oxide (NO), resulting in impairment of endothelial function and causing harm to macromolecules, membranes, and DNA by generating more harmful radicals including peroxynitrite and hydroxyl radicals. The formation of nitrotyrosine occurs through the interaction between peroxynitrite and tyrosine residues found in proteins. Nitrotyrosine serves as a useful marker for assessing the possible cytotoxic impacts of nitric oxide. While there have been previous animal tests undertaken, the existing literature we have reviewed does not provide evidence regarding the impact of direct mercury exposure and mercury toxicity on nitrotyrosine and NO. In order to achieve our research objectives, we have devised a plan to investigate the presence of nitrotyrosine and NO in the blood serum of children who have been exposed to mercury in our study.
Materials and Methods: Our study included 65 patients, 42 girls and 23 boys, who had accidentally come into contact with mercury in the laboratories of some schools in Kahramanmaraş and Gaziantep provinces, and whose blood mercury level was over 10 μg/l and/or whose urine mercury level was over 15 μg/l. The control group of the study included a total of 23 children, 17 girls and 6 boys, who applied to the pediatric clinic with various complaints, without intoxication or neurological findings, and from whom blood samples would be taken for different diagnoses.
Results: Nitric Oxide and Nitrotyrosine levels were found to be higher in children exposed to mercury than in the control group (p<0.01). Nitric Oxide, Nitrotyrosine and mercury levels in the patients were high in both genders, and no gender-related difference was detected (p>0.05). The mean duration of mercury exposure was 45 minutes. Of the 65 patients, 20 were asymptomatic and 40 were symptomatic. The most common symptoms were headache and nausea.

Conclusion: The existing body of research predominantly focuses on investigating the association between mercury poisoning and oxidative stress biomarkers through animal studies, with a limited number of studies conducted on human subjects. Our study has made a valuable contribution to the existing literature by successfully detecting elevated levels of NO and Nitrotyrosine in children who have been diagnosed with mercury poisoning.
Emergency service management of bodypacking cases caught on the highway outside the borderline
Authors: Akkus Seyma , k Bahar , Bilgin Yasin , Sari Fatih
Published: July 2024

Objective: While drug trafficking is a major problem worldwide, body packing involving the storage of narcotic drugs in the human body represents an emergency because of the risk of deadly narcotic toxicity, intestinal obstruction and visceral perforation. We presented the evaluation of body packer cases, which have not been seen before in this region, in the emergency clinic.
Methods: This study analyzes the medical records of body packers from January to July 2023.Hematological and biochemical tests, abdominal radiographs and tomographies, and removed packages were evaluated in all cases.
Results: Nine body packer cases carrying narcotic substances were observed in this study. The mean age was 33 ± 6.3, 2 (22%) were female and 7 (78%) were male. None of the patients required emergency surgery. One patient had acute narcotic intoxication. In this patient, whose packages were observed to be intact, drug intoxication was associated with substance use. Abdominal radiography diagnosed all cases and all were recovered and discharged.
Conclusion: It is very important for public safety to diagnose body packing cases and prevent the transport of packages. In this issue, emergency physicians should have radiography and CT imaging features in asymptomatic body packing cases.
The Relationship Between Social Media Addiction and Emotion Regulation Skills and Sleep Quality of University Students
Authors: ZER NESR , TANRIVERD DERYA , ZG Safiye
Published: July 2024

Objective: This study was conducted to determine the relationship between social media addictions and emotion regulation skills and sleep quality of university students.
Methods: The research was conducted in descriptive design. The sample of the study consisted of 450 university students. Personal information form, Social Media Addiction Scale (SMAS), Cognitive Emotion Regulation Questionnaire (CERQ) and Pittsburgh Sleep Quality Index () were used to obtain the data.
Results: The mean total score of the SMAS of the students is 97.33±26.78. The mean scores of the sub-dimensions of the CERQ "Self-Blame", "Acceptance", " Rumination", "Positive Refocusing", "Refocusing on Plan", "Positive Reappraisal ", " Putting into Perspective", " Catastrophising " and "Blaming Others" are respectively; 11.23±2.78, 11.85±2.80, 13, .32±3.17, 12.19±2.81, 13.78±3.24, 13.44±3.32, 12.36±2.90, 10.40±2.95, 10.61±2.97. The mean total score of was PSQI 7.91±3.01. There is a significant positive correlation between the total score of the social media addiction scale and poor sleep quality (p≤0.005).
Conclusion: It was found that 31.5% of university students are moderately addicted to social media and their sleep quality is poor. As social media addiction increases, sleep quality worsens. As students' use of maladaptive cognitive emotion regulation strategies such as self-blame, rumination, catastrophizing and blaming others increases, their sleep quality also decreases.
Investigation of Anticancer Effects of Meteorin Like Protein on Different Human Cancer Cell Lines
Authors: Tekin Suat , ekerci G , ksel Furkan
Published: July 2024

Objective: Cancer is one of the biggest health problems threatening humanity, and the fight against cancer continues with different methods around the world. Despite advances in cancer prevention and treatment, the low success rate and tumor recurrence make the discovery of new alternative agents important. Adipokines are among those known to be associated with cancer. The relationship of the peptide-structured Meteorin like protein (Metrnl), a member of the adipokine family, discovered in 2012, with cancer remains a mystery. This study was conducted to examine the cytotoxic effects of Metrnl on human ovarian, prostate, colon and breast cancer cell lines.
Materials and Methods: In the study, ovarian (A2780), human prostate (LNCaP), colon (Caco-2) and breast cancer (MCF-7) cell lines were used. After the cells were incubated with 1, 5, 10, 50, 100 and 200 ng/ml concentrations of Metrnl for 24 hours, the cytotoxicity level in the cells was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide (MTT) assay method. determined. Comparisons between groups were made with the Kruskal Wallis H-Test.
Results: Concentrations of Metrnl significantly reduced cell viability in A2780, LNCaP, Caco-2 and MCF-7 cell lines (p<0.05).
Conclusion: These results show that Metrnl may have anticarcinogenic activity on A2780, LNCaP, Caco-2 and MCF-7 cells, but further studies are needed on this subject.
NUDGES E DIREITO DO TRABALHO: APLICAÇÃO DA ANÁLISE COMPORTAMENTAL DO DIREITO NA ARRECADAÇÃO DA CONTRIBUIÇÃO SINDICAL
Authors: Andrade Thalisson Costa
Published: July 2024

A Lei nº 13.467/2017, conhecida como Reforma Trabalhista, com o intuito de combater a proliferação de Sindicatos pelegos, alterou profundamente o art. 578, da Consolidação das Leis Trabalhistas (CLT), ocasião em que a contribuição sindical deixou de ser obrigatória e passou a ser facultativa, exigindo-se prévia e expressa autorização do contribuinte. A mencionada contribuição era a principal fonte de custeio dos Sindicatos e a alteração legislativa provocou brusca queda na receita dessas entidades, inclusive das atuantes, fato que pode ameaçar a sua sobrevivência e, por consequência, pode enfraquecer a luta dos trabalhadores por melhores condições de trabalho e de vida. Nesse contexto, por meio da revisão bibliográfica, com o apontamento das principais pesquisas e estudos acerca da temática, objetiva-se, com este trabalho, a construção de uma possível solução para o problema-tema apresentado. Essa alternativa, por sua vez, repousa nos fundamentos da Análise Econômica Comportamental do Direito, através de ferramentas de nudges, idealizadas por Richard Thaler e Cass Sunstein. Especificamente com a alteração do default rule, ou regra padrão, é possível, mantendo-se a facultatividade da contribuição sindical e a teleologia da Reforma Trabalhista no que toca à matéria, garantir maior arrecadação da contribuição sindical e, por consequência, garantir a atuação dos Sindicatos na promoção de igualdade e paz social. Em termos práticos, quanto aos Sindicatos obreiros, todos os trabalhadores estariam cadastrados automaticamente como contribuintes e essa situação tenderia a se prolongar no tempo, a menos que eles, talvez insatisfeitos com a atuação do seu representante, informem que não mais desejam contribuir.
tudents' Self-Assessment Vs. Exams Results
Authors: Gašpar Dražena ,   University of Mostar, Faculty of Economics, Bosnia and Herzegovina, Mabic Mirela , Ćorić Ivica ,   University of Mostar, Faculty of Economics, Bosnia and Herzegovina,   HERA company, Bosnia and Herzegovina
Published: July 2024

There is a generally accepted opinion that young people, born in the era of intensive use of ICT and the Internet, are much better at handling new technologies and using Internet resources than older generations. In support of this claim, it is stated that different digital technologies and the Internet have been a natural environment for these generations since birth. This paper aims to check to what extent the above statements apply to University of Mostar (SUM) students. For this purpose, the authors researched SUM students to determine how they self-assess their knowledge and use of Internet resources. On the other hand, it was necessary to use Internet resources to pass exams in certain subjects. In this paper, the authors compared the results obtained by surveying students with actual exam results. The results of the research suggest that the students have relatively good knowledge and coping skills with the tasks they solve within the individual courses of their studies. However, Insufficient mastery of the Internet and its information is indicated by lower ratings of the ability to evaluate found materials and ratings of the ability to use the advanced functions of the Google search engine.
Investigation of Seismic Behavior of the Historical Yeşiltepe Bridge
Authors: Usta Evci Pınar , Sever Ali Ekber , Şakalak Elifnur
Published: July 2024

Historic arch bridges, a common feature of Turkish infrastructure, represent a significant aspect of the country’s cultural heritage. To ensure their continued existence and preservation, it is essential to conduct a detailed examination of their structural features and behaviours. This study aimed to investigate the performance of the historic Yeşiltepe Bridge under earthquake conditions. To achieve this, the bridge was modelled using the SAP2000 finite element software, enabling a deeper understanding of its structure and the prediction of its behaviour during an earthquake. In order to ascertain the dynamic behaviour of the historical bridge, modal analysis and nonlinear time history analysis were conducted. The results of the modal analysis yielded period values, mass participation rates and mode shapes for the bridge. The time history analysis yielded displacement, base shear force and stress values for the historical structure, which were subsequently presented in graphical form. The data obtained from the study enabled the identification of the critical regions of the structure exhibiting the highest stress concentration values.
Reflections on the Spirituality of the Kanai Blood Tribe
Authors: Fox Staahtsisttayaaki Genevieve A.
Published: July 2024

The spirituality of the Kainai people, members of the Siksikaitsitapi, the Blackfoot Confederacy, can be defined as a way of being, speaking, living, or in our words Kiipaitapiiwahsinnooni. The spirituality of the Kainai Nation is embodied within the individual. Spirituality becomes a part of one's being, one's life-world, is intertwined in everything one does, thinks, speaks, helping one to achieve "matsipaitapiiysini". Matsipaitapiiysini is that state of being of harmony, of spiritual enlightenment, having kimmapiypitssini. Kimmapiypitssini is a state of being where one has empathy, respect, honours the individual, is compassionate with others -it is the highest level of being one can hope to achieve.
Contrasting Community and Individualistic Perceptions of Spirituality: Anabaptists Versus Spirit Wrestlers
Authors: Friesen John W. , De Paiva Wilson Alves
Published: July 2024

This paper elaborates two fundamental definitions of spirituality- the traditional position that there are two spheres of existence- realistic and transcendental, and humanism, which denies the existence an extraterrestrial sphere. Elaboration of Doukhobor and Mennonite belief systems reveals differences in defining spirituality, namely individualism versus community conceptualizations. Doukhobors place a high value on individual spirituality, contending that every individual has a Divine spark (Iskra) within them. Mennonites value community-defined and validated forms of spirituality.
Mindfulness in Moments of Crisis
Authors: Bearance Deb
Published: July 2024

There are various kinds and extents of crises that require individuals to have the clarity, discernment, and equanimity with which to handle the accompanying daunting challenges. Mindfulness, which is being fully aware in the present moment, is an effective strategy for dealing with the stressors of the adversity and trauma of crisis situations. Mindfulness provides a skill and a discipline that can help build a capacity for resilience. This paper explores the Eastern origin of mindfulness and its Western application, the significance of the practice of mindfulness, and the state of being of mindfulness in a crisis.
Turn All Things To Honey: The Way of Self-Knowing
Authors: Jones David C.
Published: July 2024

In India, as in the West, metaphysical thinkers tried to determine the nature of the highest truth by the intellect. But in India, mental thinking was not given "the supreme rank as an instrument in the discovery of truth, but only a secondary status. The first rank [was] always given to spiritual intuition and illumination and spiritual experience; an intellectual conclusion that contradict[ed] this supreme authority [was] held invalid." Thus the heart of the Self was "lost by the over-intellectuality of the mind of Europe" (Aurobindo). This paper focuses on the author's own spiritual experience and search for spiritual intuition and illumination. It emphasizes the teachings of the spiritual counselor and healer, Larry Wayne. The key to self-knowing for Wayne and the author was not the intellect but the turning of "all things to honey," the experience of the fullness of delight made possible only by self-love.
Muslim Nursing Student Beliefs about Possession States: An Exploratory Survey of Beliefs and Causal Attributions
Authors: Graham Jan Marie , White Diana
Published: July 2024

This study was undertaken to explore beliefs about Jinn, black magic and evil eye among Muslim nursing students at University of Calgary in Qatar (UCQ). The aim was to determine the extent and ways in which Muslim nursing students attribute physical and mental health problems to these perceived possession states. One hundred and twenty eight undergraduate nursing students who self-identified as adherents of the Islamic faith completed a survey concerning their beliefs in Jinn, black magic and evil eye. The sample included two streams of students: Bachelor of Nursing Regular Track (BNRT, N=44) and Post Diploma Bachelor of Nursing students (PDBN, N=75) who had already completed a nursing qualification and were in the process of completing the degree program. Results of the survey showed that 84.1 % of BNRT students and 73% of PDBNs believe that Jinn can possess or take over humans. The vast majority of students (BNRT 90.7%, PDBN 84%) believe in black magic while over 90% of BNRTs and PDBNs believe in the evil eye. This research adds to the limited literature available on beliefs regarding possession states among Muslim nursing students. It also provides information that can be used by faculty to better understand the prevailing beliefs and attitudes of nursing students.
Theme Issue: Self-care, Health and Well-being in the Academy
Authors: Lock Jennifer , Wilcox Gabrielle , Kjorlien Yvonne
Published: July 2024

With growing expectations of productivity, it is not always easy to achieve a balance between research, teaching and service, between competitiveness and collaboration in the academy, and between work and personal life. Both new and experienced academics are in search of achieving a "happy medium" or a notion of "balance" to meet the expectations of the academy, the students they teach, the communities they serve, and in their own personal lives. In today's competitive higher educational settings, how are academics personally and professionally attending to their well-being and the well-being of their colleagues? As well as, how are they modeling such practices to students? For example, how are academics modeling ways to take care of themselves, increase balance, healthy behaviours, happiness to students, and to their colleagues? How are institutions, through policies, expectations, and other initiatives, supporting academics in this endeavour?
Navigating a Pathway for Individual and Collective Well-being—Academics Speak Out
Authors: Kawalilak Colleen , Groen Janet , Pratt Yvonne Poitras , Danyluk Patricia
Published: July 2024

Navigating the challenging terrain of academia either as a new or seasoned academic takes thoughtful and purposeful intention. In this manuscript, two pre-tenured and two tenured faculty members at a research-intensive university engage in deep dialogue to explore challenges and opportunities whilst navigating this terrain, with a focus on supporting individual and collective well-being. We assert that surviving academia is not enough. Rather, we aim to thrive in a challenging culture, and maintain that our goal is to contribute to the personal and professional well-being of ourselves and our colleagues. Guided by auto-ethnography and dialogue, we share our storied experiences. Findings illuminate the importance of co-creating tangible spaces to support, encourage, and celebrate meaningful relations and academic work.
A Mindful Community of Praxis Model for Wellbeing in the Academy
Authors: Crowder Rachel
Published: July 2024

The reality of high levels of stress in the Canadian academy and elsewhere are pervasive and problematic. In response, increasing numbers of academic institutions are turning to mindfulness-based interventions to encourage self-care. However, such individualist approaches based on a neoliberal model of self-care do not address the faculty isolation, lack of time, and casualization of the labour force that studies cite as some probable causes for damaging levels of overwork and stress. I provide a critical reflection on the weaknesses of individualist versions of mindfulness programming to address stress in the academy and consider alternative approaches, including Freire’s popular education and engaged Buddhism models, in order to realize a more social justice and community-based approach to well-being. I propose a Communities of Praxis Framework that integrates critical consciousness, communal resistance, compassionate action, as well as mindfulness-based and other reflexive practices to improve both individual well-being and systemic change in the academy.
The Transforming Citizen: A Conceptual Framework for Civic Education in Challenging Times
Authors: Kelley Jacob
Published: July 2024

This article presents The E3 Conceptual Framework for Civic Education as an approach for critical engagement with citizenship in a democratic society during challenging times. It consists of three phases that can be deployed to facilitate critical civic education: Exposure, Emergence, and Embodiment. The E3 Conceptual Framework for Civic Education offers guidance to those scholars and practitioners committed to envisioning higher education as transformative and emancipatory. This article also shares a vignette in which The E3 Conceptual Framework for Civic Education was used to design, teach, and evaluate an undergraduate sociology course. It is my hope that the reader will transfer the knowledge in this article to their own context as they transform themselves in order to transform society.
Poetry
Authors: McDermott Sean
Published: July 2024

Poetry
Epic Limitations: Homeric Depictions of Teacher Dispositions
Authors: Smilie Ethan K. , Smilie Kipton D.
Published: July 2024

Abstract: This paper proposes an interdisciplinary use of literary texts in informing contemporary pedagogical thought. Though “soft skills” have always been a part of teachers’ expected repertoire, only fairly recently have these skills, now almost exclusively referred to as “dispositions,” become a major focus in university teacher preparation programs. As teacher preparation programs wrestle with the complexities of defining and assessing teacher dispositions, we propose that it is helpful to look to the humanities to assist with this navigation, in particular, literary texts. As an example of this process, we analyze Homer’s Iliad for dispositional insights. In the epic, Homer depicts various teacher dispositions, commenting upon both their effectiveness and their limitations in ways that can further elucidate contemporary discussions and concerns regarding their utility.
Understanding STEM PhD Production: The Interplay Among Institutional Selectivity, Race/Ethnicity, and Field of Study
Authors: Kim Dongbin , Kim Sehee , Farris Kyle
Published: July 2024

Abstract: Focusing on census data of U.S. doctoral recipients during the last 10 years, we examined PhD production and undergraduate origins with a particular emphasis on institutional selectivity. Specifically, this study had three overarching goals: (1) examining STEM PhD production with an emphasis on the selectivity of doctoral programs; (2) examining the selectivity of undergraduate origins among STEM PhDs from highly selective doctoral programs; and (3) examining whether the patterns of STEM PhD production and undergraduate origins differ by race/ethnicity. This study found significantly different patterns in PhD production overall versus PhDs from top 10 doctoral programs and in the selectivity of undergraduate institutions that produce the number of PhDs overall versus PhDs from the top 10 doctoral programs. We further found that PhD production across varied selectivity of doctoral program and its association with the undergraduate origins differ significantly across different racial/ethnic groups. Implications for policy, programs, and future research are discussed.
Can Teaching Through Dialogue Increase the Engagement of International Students in Business Studies?
Authors: Dunkwu Jude , Egbunike Obiajulu , Higham Rupert
Published: July 2024

Abstract: This study aimed to change the behaviour of adult international students in a business studies class through the use of dialogic teaching to reduce the level of reticence and improve their engagement. Dialogic teaching is the ability to harness the power of talk. Based on qualitative traditions the case study method with a constructionist epistemology was used to examine the roles of identity and culture. Among others, the empirical study noted that some students were not very comfortable with some aspects of dialogic teaching, for example its free-flowing nature. Analysis showed that there was good evidence of certain elements of dialogic teaching, such as extended contribution, encouragement of free expression of views, cued elicitation, and provision of authoritative explanation in the lesson sessions. Being a case study, this report offers a basis for future studies only.