bluuebunny/crossref_metadata_2025 huggingface dataset:
The intercultural communication competence improvement for pre-service CSL teachers: A blended learning method based on SVVR
Authors: Zhang Yan , Xu De , Wang Tianyu , Yang Kewei , Yao Xinyifeng , Cheng Mingshuang , Ge Dong
Published: August 2023

BACKGROUND: With the growing global influence of new coronary pneumonia and an increasing number of successful education case, spherical video-based virtual reality (SVVR) has arisen as an application for effective integration with teaching and learning. OBJECTIVE: This is a mixed-methods study on SVVR-based training for pre-service CSL teachers to more effectively develop their cross-cultural communication and interaction abilities. METHODS: An experimental strategy was adopted in this work. First, the processing of VR technology for designed TVET courses, including practical training courses, had been completed; The second experiment was a group experiment of one month; Finally, the hypotheses were tested by an independent sample t-test. RESULTS: Based on experiment data of the learners, this study build a multi-modal hybrid training and recognition model and confirm hybrid approach effectively improves the intercultural communication abilities of pre-service CSL teachers. CONCLUSIONS: The findings of this study confirm that information technology plays an important role in pre-service CSL teacher training, and teachers should incorporate more information technology, design more hands-on interactive activities and scenarios, and take into account the individual differences of learners in the teaching process. Several policy proposals are offered to ensure that ICT is better integrated into classroom instruction.
Oversampling method based on GAN for tabular binary classification problems
Authors: Yang Jie , Jiang Zhenhao , Pan Tingting , Chen Yueqi , Pedrycz Witold
Published: August 2023

Data-imbalanced problems are present in many applications. A big gap in the number of samples in different classes induces classifiers to skew to the majority class and thus diminish the performance of learning and quality of obtained results. Most data level imbalanced learning approaches generate new samples only using the information associated with the minority samples through linearly generating or data distribution fitting. Different from these algorithms, we propose a novel oversampling method based on generative adversarial networks (GANs), named OS-GAN. In this method, GAN is assigned to learn the distribution characteristics of the minority class from some selected majority samples but not random noise. As a result, samples released by the trained generator carry information of both majority and minority classes. Furthermore, the central regularization makes the distribution of all synthetic samples not restricted to the domain of the minority class, which can improve the generalization of learning models or algorithms. Experimental results reported on 14 datasets and one high-dimensional dataset show that OS-GAN outperforms 14 commonly used resampling techniques in terms of G-mean, accuracy and F1-score.
A hybrid classification method via keywords screening and attention mechanisms in extreme short text
Authors: Zhou Xinke , Zhu Yi , Li Yun , Qiang Jipeng , Yuan Yunhao , Wu Xingdong , Zhang Runmei
Published: August 2023

Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.
NonPC: Non-parametric clustering algorithm with adaptive noise detecting
Authors: Li Lin , Chen Xiang , Song Chengyun
Published: August 2023

Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.
A novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images
Authors: Ramachandran Sivakumar
Published: August 2023

Retinopathy of prematurity (ROP), sometimes known as Terry syndrome, is an ophthalmic condition that affects premature babies. It is the main cause of childhood blindness and morbidity of vision throughout life. ROP frequently coexists with a disease stage known as Plus disease, which is marked by severe tortuosity and dilated retinal blood vessels. The goal of this research is to create a diagnostic technique that can discriminate between infants with Plus disease from healthy subjects. Blood vascular tortuosity is used as a prognostic indicator for the diagnosis. We examine the quantification of retinal blood vessel tortuosity and propose a computer-aided diagnosis system that can be used as a tool for ROP identification. Deep neural networks are used in the proposed approach to segment retinal blood vessels, which is followed by the prediction of tortuous vessel pixels in the segmented vessel map. Digital fundus images obtained from Retcam3TM is used for screening. We use a proprietary data set of 289 infant retinal images (89 with Plus disease and 200 healthy) from Narayana Nethralaya in Bangalore, India, to illustrate the efficacy of our methodology. The findings of this study demonstrate the reliability of the proposed method as a computer-aided diagnostic tool that can help medical professionals make an early diagnosis of ROP.
Design of an energy efficient dynamic virtual machine consolidation model for smart cities in urban areas
Authors: Biswas Nirmal Kr. , Banerjee Sourav , Ghosh Uttam , Biswas Utpal
Published: August 2023

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.
Electromyographic activity of periscapular muscles in symptomatic people: Does scapular dyskinesis have an impact on it?
Authors: de Holanda Santos Laís Regina , Batista Gabriel de Amorim , da Silva Oliveira Felipe Augusto , Pitangui Ana Carolina Rodarti , de Araújo Rodrigo Cappato
Published: August 2023

BACKGROUND:

Scapular dyskinesis (SD) is a modification of the positioning and movement of the scapula and is associated with shoulder pain. Exercises to strengthen the shoulder and scapular muscles have been proposed as a therapeutic strategy to reduce pain and improve function.

OBJECTIVE:

We aimed to investigate the electromyographic activity of the periscapular and trunk muscles in individuals with shoulder pain and SD in different multi-joint exercises.

METHODS:

Thirty-two subjects with subacromial pain, of both sexes, with the presence or absence of SD, participated in this study. Volunteers performed five repetitions in five exercises (Closed Row; Pronated Row; Push-up; Scapular Push-up; T-push-up). The activity of the upper trapezius, middle trapezius, lower trapezius, serratus anterior, external oblique, and rectus abdominis muscles was recorded using electromyography.

RESULTS:

Our results showed a decrease in the electromyographic activity of the serratus anterior and external oblique during the T-push-up exercise in individuals with shoulder pain and SD. No differences were observed between groups for the trapezius and the rectus abdominis.

CONCLUSIONS:

Rehabilitation programs may initially prioritize rowing and push-up exercises to strengthen periscapular muscles. More complex exercises with greater mechanical demand on the kinetic chain, such as the T-push-up, may have to be implemented in later phases.
A novel cluster heads election method based on group sparse TSK fuzzy system for energy-efficient wireless sensor network
Authors: Zheng Xuehui , Wang Jun , Gao You
Published: August 2023

Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND).
A generic enhanced search framework based on genetic algorithm: Case study on job shop scheduling problem
Authors: Liang Zhongyuan , Zhong Peisi , Liu Mei , Zhang Chao
Published: August 2023

Optimal allocation of production resources is an urgent need for the development of industrialization. Reasonable production scheduling algorithm and excellent scheduling scheme can efficiently plan production resources, reduce production costs and shorten order completion time. Genetic algorithm has become one of the most popular algorithms for solving job shop scheduling problem because of its simplicity, versatility and good robustness. However, the genetic algorithm for solving NP-hard problems such as job shop scheduling has the problem of falling into local optimum, which leads to the decrease of solution accuracy. This study focused on the problem and proposed a generic enhanced search framework based on genetic algorithm, which named niche adaptive genetic algorithm. The niche selection mechanism and adaptive genetic operators were used to enrich the diversity of population, balance the genetic probability and enhance the global search performance of the algorithm. The working mechanism of this algorithm is analysed by testing data, and the proposed algorithm was tested on job-shop scheduling problem instances. The results show that the performance of the proposed method is 0.79 percentage points higher than that of the standard genetic algorithm, and it has the ability to search for the global optimum.
Multistage progressive learning based speech enhancement using time-frequency attentive squeezed temporal convolutional networks
Authors: Jannu Chaitanya , Vanambathina Sunny Dayal
Published: August 2023

Speech enhancement (SE) is an important method for improving speech quality and intelligibility in noisy environments. An effective speech enhancement model depends on precise modelling of the long-range dependencies of noisy speech. Several recent studies have examined ways to enhance speech by capturing the long-term contextual information. For speech enhancement, the time-frequency (T-F) distribution of speech spectral components is also important, but is usually ignored in these studies. The multi-stage learning method is an effective way to integrate various deep-learning modules at the same time. The benefit of multi-stage training is that the optimization target can be iteratively updated stage by stage. In this paper speech enhancement is investigated by multi-stage learning using a multistage structure in which time-frequency attention (TFA) blocks are followed by stacks of squeezed temporal convolutional networks (S-TCN) with exponentially increasing dilation rates. To reinject original information into later stages, a feature fusion (FF) block is inserted at the input of later stages to reduce the possibility of speech information being lost in the early stages. The S-TCN blocks are responsible for temporal sequence modelling task. The time-frequency attention (TFA) is a simple but effective network module that explicitly exploits position information to generate a 2D attention map to characterise the salient T-F distribution of speech by using two branches, time-frame attention and frequency attention in parallel. A set of utterances from the LibriSpeech and Voicebank databases are used to evaluate the performance of the proposed SE. Extensive experiments have demonstrated that the proposed model consistently improves the performance over existing baselines across two widely used objective metrics such as PESQ and STOI. The average PESQ and STOI for proposed model are boosted by a factor of 41.7% and 5.4% for Libri speech dataset, 36.10% and 3.1% for Voice bank dataset as compared to noisy speech. Additionally, we explored the generalization of the proposed TFA-S-TCN model across different speech datasets through cross data base analysis. A significant improvement in system robustness to noise is also shown by our evaluation results using the TFA module.
Classifying the bacterial taxonomy with its metagenomic data using the deep neural network model
Authors: Raman Ramakrishnan , Barve Amit , Meenakshi R. , Jayaseelan G.M. , Ganeshan P. , Taqui Syed Noeman , Almoallim Hesham S. , Alharbi Sulaiman Ali , Raghavan S.S.
Published: August 2023

Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information.
A multi granularity information fusion text classification model based on attention mechanism
Authors: Chen Jingfang
Published: August 2023

Existing research on Chinese text classification primarily focuses on classifying data information at different granularities, such as character, word, sentence, and chapter. However, this approach often fails to capture the semantic information embedded in these different levels of granularity. To enhance the extraction of the text’s core content, this study proposes a text classification model that incorporates an attention mechanism to fuse multi-granularity information. The model begins by constructing embedding vectors for characters, words, and sentences. Character and word vectors are generated using the Word2Vec training model, allowing the data to be converted into these respective vectors. To capture contextual semantic features, a bidirectional long and short-term memory network is employed for character and word vectors. Sentence vectors, on the other hand, are processed using the FastText model to extract the features they contain. To extract further important semantic information from the different feature vectors, they are fed into an attention mechanism layer. This layer enables the model to prioritize and emphasize the most significant information within the text. Experimental results demonstrate that the proposed model outperforms both single-granularity classification and combinations of two or more granularities. The model exhibits improved classification accuracy across three publicly available Chinese datasets.
A novel deep learning based underwater image de-noising and detecting suspicious object
Authors: Padmapriya S. , Umamageswari A. , Deepa S. , Faritha Banu J.
Published: August 2023

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.
Safety of botulinum toxin injections in children less than one year old: A retrospective chart review
Authors: Deshpande Nikhil , Gormley Mark E. , Deshpande Supreet
Published: August 2023

PURPOSE: Infants can have muscle hypertonia due to cerebral palsy, muscle strength imbalances due to brachial plexus palsy, refractory clubfoot, and torticollis. These muscle problems can cause significant development impairments. A child with severe sialorrhea and dysphagia from leukodystrophy can aspirate, causing respiratory problems. Botulinum toxin (BoNT) injections can improve these conditions but may lead to adverse effects from the toxin spreading to non-targeted muscles, potentially impacting breathing, swallowing, and overall strength. This is particularly concerning in infants. This study assessed the safety of BoNT injections in children less than one year of age. METHODS: This was a retrospective cohort study. RESULTS: Forty-seven patients (22 male, 25 female) received BoNT injections before one year of age (three to 12 months). Thirty-seven received one round of injections and 10 were injected on multiple occasions. Forty-five received onabotulinumtoxinA (15–100 units [U], 1.9–15.2 U/kg), one received abobotulinumtoxinA (70 U, 9.0 U/kg), and one received incobotulinumtoxinA (25 U, 3.5 U/kg). Lower extremities were treated in 15 patients, upper extremities in 38, the sternocleidomastoid in two, and the salivary glands in one. Forty-five patients had no reported complications. One experienced transient fever, vomiting, and diarrhea. The parent of another reported subjective weakness in one muscle. CONCLUSION: BoNT injections in children less than one year of age appear to be safe.
Accuracy and errors in self-assigned NAICS codes in tax return data
Authors: Oehlert Christine
Published: August 2023

Conventional wisdom holds that North American Industry Classification System (NAICS) codes chosen by people not experienced with the system are often mis-specified, but there has been little formal research into the scope of the problem. In this paper we explore prevalence of and patterns in misspecification in NAICS codes self-reported on two kinds of business tax forms. Errors are identified by comparing as-filed codes with codes validated by Statistics of Income. We find that over a third of codes are wrong, but that the errors are not random and often (though not always) seem to have logical reasons behind them.
Reliability of oral language and emergent literacy tele-assessments in autistic children: A preliminary study
Authors: Mohan Haritha S. , Tiwari Shivani , Guddattu Vasudeva
Published: August 2023

BACKGROUND:

The growing need and popularity of telepractice over conventional direct therapy by speech-language pathologists (SLPs) demand validation of oral language and literacy assessments via telepractice mode. Considering the limited research on the validation of standardised oral language and literacy tele-assessment in autistic children, it is vital to explore the feasibility of standardised oral language assessments in specific cultural and linguistic contexts.

OBJECTIVE:

To examine the reliability of standardised oral language and emergent literacy assessments delivered via telepractice in autistic children.

METHODS:

Emergent literacy and oral language skills of ten autistic children aged between 4 to 8 years were assessed using the Test of Emergent Literacy Assessment (TELA), and Assessment of Language Development (ALD) through in-person and tele-assessment with a gap of 15 days.

RESULTS:

The findings of the present study establish the reliability of standardised oral language and literacy tele-assessment in autistic children by demonstrating a high level of agreement between in-person and tele-assessment modes.

CONCLUSIONS:

Telepractice, may therefore, be a feasible and reliable mode of oral language and literacy assessment in autistic children, with adequate knowledge of the required special accommodation(s), training and strong collaboration with the facilitator(s).
Co-expression of galectin-3 and vimentin in triple negative breast cancer cells promotes tumor progression, metastasis and survival
Authors: Jeethy Ram T. , Lekshmi Asha , Darvin Pramod , Rajappan Prakash , Jagathnath Krishna K.M. , Anoop T.M. , Augustine Paul , Mathew Arun Peter , Cherian Kurian , Bhargavan Rexeena V. , Somanathan Thara , Radhakrishna Pillai M. , Santhosh Kumar T.R. , Sujathan K.
Published: August 2023

BACKGROUND:

Lack of druggable targets and complex expression heterogeneity of known targets is common among TNBC subtypes. An enhanced expression of galectin-3 in TNBCs has already been documented. We have observed a tumor progression-dependent galectin-3 expression in TNBCs compared to adjacent epithelium and non TNBCs.

OBJECTIVE:

To unravel the association of galectin- 3 in tumor progression, aggressiveness and drug resistance in TNBC patients.

METHODS:

Galectin-3 expression in 489 breast cancer tissues was correlated with clinicopathological features and the results were validated in cell lines and mouse model by silencing galectin-3 using shRNA and the proteins were profiled by western blot and qRT-PCR. Protein interaction was analyzed by GFP Trap and Mass spectrometry.

RESULTS:

Galectin-3 expression correlated with tumor stage in TNBC and a lower galectin-3 expression was associated with poor patient survival. The positive correlation between galectin-3, vimentin and CD44 expression, pinpoints galectin-3 contribution to epithelial to mesenchymal transition, drug resistance and stemness. Vimentin was found as an interacting partner of galectin-3. Duplexing of galecin-3 and vimentin in patient samples revealed the presence of tumor cells co-expressing both galectin-3 and vimentin. In vitro studies also showed its role in tumor cell survival and metastatic potential, elementary for tumor progression. In vivo studies further confirmed its metastatic potential.

CONCLUSIONS:

Tumor progression dependent expression pattern of galectin 3 was found to indicate prognosis. Co-expression of galectin-3 and vimentin in tumor cells promotes tumor dissemination, survival and its metastatic capability in TNBCs.
Subjective visual vertical and postural control in patients following cochlear implantation
Authors: Čakrt Ondřej , Slabý Kryštof , Kučerová Klára , Balatková Zuzana , Jeřábek Jaroslav , Bouček Jan
Published: August 2023

BACKGROUND: Cochlear implantation (CI) is associated with changes in the histopathology of the inner ear and impairment of vestibular function. OBJECTIVE: The objectives of our study were to evaluate patients for clinical manifestations of space perception and balance changes before surgery, compare them with asymptomatic subjects (controls), and report changes in posturography and subjective visual vertical (SVV) during the acute post-surgery period in patients. METHODS: Examination was performed using static posturography and the SVV measurement. We examined 46 control subjects and 39 CI patients. Patients were examined pre-surgery (Pre), 2nd day (D2) and then 14th day (D14) after implantation. RESULTS: Baseline SVV was not different between patients and control group. There was a statistically significant difference (p < 0.001) in SVV between subgroups of right- and left-implanted patients at D2 (–1.36±3.02° and 2.71±2.36°, right and left side implanted respectively) but not Pre (0.76±1.07° and 0.31±1.82°) or D14 (0.72±1.83° and 1.29±1.60°). Baseline posturography parameters between patients and control group were statistically significantly different during stance on foam with eyes closed (p < 0.05). There was no statistically significant difference in posturography among Pre, D2 and D14. CONCLUSIONS: CI candidates have impaired postural control before surgery. CI surgery influences perception of subjective visual vertical in acute post-surgery period with SVV deviation contralateral to side of cochlear implantation, but not after two weeks.
An Explorative Review of Artificial Intelligence Software (Chatbot) Impact on Education System
Authors: Bolarinwa J.D. , Vincent Olufunke R. , Cecilia Ajowho Adenusi
Published: August 2023

Computer programs known as chatbots can mimic conversations with real users. They are utilized more frequently in education to automate administrative activities, respond to student inquiries, and offer tailored learning support. An exploratory overview of the research on chatbot software and its prospective effects on the educational system is presented in this essay. The history of chatbots and how they have evolved in the education industry are covered in the first section of the study. The available chatbot software is then reviewed, along with its capabilities and effects on students and teachers. The requirement for high-quality data and the possibility of bias are some of the difficulties related with employing chatbots in education that are also covered in the paper. The future of chatbots in education is covered in the paper's conclusion. It implies that chatbots could fundamentally alter how we educate people. To completely comprehend how chatbots affect student learning and achievement, more research is necessary, according to the report.
Quantum Entities and the Nature of Time
Authors: Chiatti Leonardo
Published: August 2023

The conceptual statute of modern quantum theory was formulated during the 5th Solvay Conference in 1927 and since then accepted as definitive, although criticisms and discussions have continued up to the present. The more properly physical aspects of the debate seem to concern the relationship between quantum entities and spacetime. A historical circumstance that should not be ignored is that in 1927 the exploration of the realm of the fundamental constituents of matter (the so-called "elementary particles") was just in its infancy, and therefore the properties of these constituents could not play a constitutive role in the theory. Here we propose to re-read the nature of quantum entities starting from the peculiar relationship that the elementary particles that constitute them maintain with the spatial domain. In particular, it is assumed that this relationship is mediated by time and that the time accessible to elementary particles (but not to the systems they compose) is a complex variable rather than a real one. This approach helps to dissolve much of the "mystery" surrounding quantum phenomena.
Sectoral Herding Contagion on Eve of New Year
Authors: Bukhari Faisal
Published: August 2023

The purpose of this study is to investigate herding contagion on the eve of the new year (2015-2019) last three years data excluding due to Covid 19 effect on stock markets. This research work focuses on the cross-country behavioural linkages of investors between US and Chinese stock markets. This study employs a return dispersion model to identify the consensus among the participants of the market (Chang et al., 2000). The first aim of this study is to examine herding contagion by Cross Sectional Absolute Deviation (CSAD) between domestic and foreign countries. The findings of this study depict that herding contagion appeared during the new year, however, the intensity of herding contagion negligible during the new year within the aggregate and sectoral data sets. This paper highlights sectors for investors to get the maximum advantage of portfolio diversification. Therefore, stockholders require to alter their portfolios according to the situation in the market to diversify risk. The study suggests that investors and asset managers should analyse the sectoral performance of stock markets during such situations before making a portfolio.
a Analisis Kompetensi Pedagogik Guru AUD Non Linier Terhadap Proses Pembelajaran Pada TK/RA di Kelurahan Fajar Baru Kabupaten Bengkulu Utara
Authors: Abdillah Resti Abdillah
Published: August 2023

Abstrak

Tujuan penelitian ini adalah untuk mengetahui Kompetensi Pedagogik Guru AUD Non Linier Terhadap Proses Pembelajaran Pada TK/RA di Kelurahan Fajar Baru Kabupaten Bengkulu Utara. Permasalahan yang terjadi dalam penelitian ini dimana guru yang menjadi narasumber dalam penelitian ini masih kurang memiliki pengalaman dalam proses belajar-mengajar. Metode yang digunakan untuk penelitian ini menggunakan penelitian kualitatif dengan jenis penelitian (Field Research) atau penelitian lapangan. Pengumpulan data menggunakan observasi, wawancara, dan dokumentasi. Teknik keabsahan data menggunakan Triangulasi data/sumber dan Triangulasi metode. Adapun faktor yang mempengaruhi rendahnya kompetensi pedagogic guru di RA Tunas Harapan yaitu, (a) Pengalaman mengajar, (b) Penataran dan pelatihan.

 

Kata Kunci : Kompetensi Pedagogik, Guru AUD Non Linier, Proses Pembelajaran

 

Abstract

The purpose of this study was to determine the Pedagogic Competence of Non-Linear AUD Teachers on the Learning Process in Kindergarten/RA in Fajar Baru Village, North Bengkulu Regency. The problem that occurs in this study is that the teacher who is the resource person in this study still lacks experience in the teaching and learning process. The method used for this research uses qualitative research with the type of research (Field Research) or field research. Collecting data using observation, interviews, and documentation. The data validation technique uses data/source triangulation and method triangulation. The factors that influence the low pedagogic competence of teachers at RA Tunas Harapan are, (a) teaching experience, (b) upgrading and training.

 

Keywords: Pedagogic Competence, Non-Linear AUD Teachers, Learning Process.
A Pengaruh Penggunaan Media Visual Terhadap Minat Belajar Anak Di Kelompok B1 TK Nusa Kota Makassar
Authors: Lismayani Angri
Published: August 2023

This research aims to determine the profile of children's learning interest before and after the use of visual media in Group B1 of Nusa Kota Makassar Kindergarten. The approach used in this research is a quantitative approach. The type of research used is a pre-experimental design, specifically a one-group pretest-posttest design. The sample used in this study consists of 10 children. Data collection techniques include observation, interviews, and documentation. The data analysis technique used is descriptive statistical analysis and non-parametric statistics using the Wilcoxon test. The results of the study indicate that there is a difference in the average value of children's learning interest before and after treatment. This indicates the influence of the use of visual media on children's learning interest in Group B1 of Nusa Kota Makassar Kindergarten.

Keywords : learning media, visual media, interest learn
IDENTIFIKASI PERKEMBANGAN KEAKSARAAN ANAK USIA 5-6 TAHUN DI TK XAVERIUS 1 KOTA JAMBI
Authors: Tesalonika S Enjelie
Published: August 2023

Tujuan penelitian ini ialah untuk mendeskripsikan tingkat perkembangan keaksaraan pada aspek menyebutkan simbol-simbol huruf yang dikenal, aspek menyebutkan kelompok gambar yang memiliki bunyi/huruf awal yang sama, aspek mengenal suara huruf awal dari nama benda–benda yang ada di sekitarnya, aspek memahami hubungan antara bunyi dan bentuk huruf. Jenis penelitian ini termasuk penelitian deskriptif dengan pendekatan kuantitatif. Sampel penelitian ini berjumlah 17 anak. Penarikan sampel dilakukan dengan Teknik total sampling. Teknik pengumpulan data pada penelitian ini menggunakan angket. Teknik analisis menggunakan kriteria penafsiran presentase. Berdasarkan hasil dan pembahasan penelitian didapatkan: 1) Identifikasi perkembangan keaksaraan pada aspek menyebutkan simbol-simbol huruf yang dikenal pada tingkat “Sedang” (46%). 2) Identifikasi perkembangan keaksaraan pada aspek mengenal suara huruf awal dari nama benda-benda yang ada di sekitarnya pada tingkat “Tinggi” (62%). 3) Identifikasi perkembangan keaksaraan pada aspek menyebutkan kelompok gambar yang memiliki huruf awal yang sama pada tingkat “Sedang” (52%). 4) Identifikasi perkembangan keaksaraan pada aspek memahami hubungan antara bunyi dan bentuk huruf pada tingkat “Rendah” (37%).  Kesimpulan penelitian bahwa perkembangan keaksaraan anak usia dini 5-6 tahun di TK Xaverius 1 Kota Jambi berada pada tingkat “Sedang” (49%).
PERKEMBANGAN FILSAFAT ILMU DAN PERANANNYA DALAM DUNIA PENDIDIKAN DI INDONESIA
Authors: Maharani Amanda
Published: August 2023

Filsafat ilmu merupakan kajian yang sangat luas. Secara garis besar filsafat ilmu adalah cabang filsafat yang membahas masalah ilmu. Tujuannya mengadakan analisis mengenai ilmu pengetahuan dan cara bagaimana pengetahuan ilmiah itu diperoleh. Ruang lingkup filsafat ilmu pada dasarnya mencakup dua pokok bahasan utama, yaitu membahasa sifat-sifat pengetahuan ilmiah dan menelaah cara-cara mengusahakan pengetahuan ilmiah. Peran filsafat imu dalam perkembangan ilmu pengetahuan sangat besar. Secara historis dapat dikelompokkan menjadi beberapa masa, Zaman Prasejarah, Zaman Sejarah, Zaman Logam, Zaman Yunani dan Romawi, Filsafat Imu di India dan Cina, Filsafat Ilmu pada Masa Islam, Filsafat Ilmu pada Abad ke-16 dan ke-17, Filsafat Ilmu pada Abad ke-18 dan ke-19, dan Filsafat Ilmu pada Abad ke-20. Filsafat ilmu dalam pendidikan di Indonesia harus sesuai dengan nilai-nilai Pancasila, agar terciptanya manusia yang bermutu untuk bangsa dan negara.
PERAN PEMERINTAHAN DESA DALAM PENANGGULANGAN DAN PEMULIHAN PASCA BENCANA BANJIR DI DESA GEBANG ILIR KECAMATAN GEBANG KABUPATEN CIREBON
Authors: Baehaqie Muhammad Aqiel , Irfan Agus , Abijaya Sastra , Wildanu Eka
Published: August 2023

Pada dasarnya pengurangan bencana didasarkan pada suatu strategi pengkajian yang kuat dan risiko terus menerus akan ada dan perlu melibatkan aktor yang banyak. Semua itu berasal dari instansi pemerintahan, hingga komunitas lokal. Tujuan penelitian ini adalah untuk mengetahui peran dari pemerintah desa peran berkaitan dengan tugas pokok dan fungsi pelaksanaan oleh lembaga pemerintah desa gebang ilir dalam melakukan penanggulangan dan pemulihan pasca terjadinya bencana banjir. Penelitian ini menggunakan metode penelitian kualitatif adalah penelitian yang digunakan untuk meneliti pada kondisi obyek yang alamiah. Dimana peneliti adalah sebagai instrumen kunci, teknik pengumpulan data dilakukan secara triangulasi(gabungan) observasi,wawancara, dokumentasi hingga analisis data bersifat kualitatif, dan hasil.

Kesimpulan dari penelitian ini menilai bahwa dalam penanggulangan bencana kebijakan desa harus memiliki nilai kemaslahatan serta dituangkan dalam bentuk supportifitas, bahkan komunikasi sebelum terjadi bencana banjir ataupun saat terjadi bencana banjir maupun pasca bencana banjir menjadi hal yang penting dalam melakukan mitigasi bencana. Dan komunikasi dalam konteks penanganan bencana merupakan hal yang memiliki urgensi tersendiri. Pemerintah desa juga sudah mengeluarkan kebijakan secara anggaran untuk penanggulangan bencana banjir, maka diperlukannya sebuah edukasi ataupun sosialisasi kepada masyarakat mengenai pedoman penanggulangan bencana banjir untuk mengurangi ataupun meminimalisir terjadinya kerusakan-kerusakan dan hal-hal yang tidak diinginkan sangat terbatas.
SUNDA AND CIREBON LANGUAGE INTERCULTURAL COMMUNICATION PROCESS IN EARLY CHILDREN'S ENVIRONMENT
Authors: Machsunah Uun -
Published: August 2023

ABSTRACT

This study aims to determine how the intercultural communication process in the Early Childhood Education (PAUD) Learning Group (KB) environment, as well as knowing what are the factors that hinder it. The location of this research in Beber Village, Beber District, Cirebon Regency, is a place that borders Kuningan Regency with different cultural backgrounds. Descriptive qualitative. The aim is to describe the facts that have been found systematically, actually and factually, which views that only researchers are truly free and different outside the target research environment, and produce objective conclusions.

This research focuses on how the communication process and what are the factors of communication barriers that exist in the PAUD environment.

The results of the research in the intercultural communication process that occurs in the An Nur Early Childhood Education Learning Group (KB PAUD) environment are:

Communication is usually done through the adaptation stage first. If communication is blocked or deadlocked due to language differences, they use Indonesian as a 'unifying' bridge so that communication can be more effective and the message conveyed as desired.

Students of KB PAUD An Nur in their interactions with peers use a mixed language of Sundanese and Cirebon.

Teachers who teach at KB PAUD An Nur when teaching in class use Indonesian as the language of instruction. But when interacting informally follow the language style of their students, Sundanese and Cirebon language.

4.Communication that occurs in the environment of KB PAUD An Nur, as well as in general in Beber Village, Beber Sub-district experienced a mixture (acculturation) of Javanese culture Cirebon and Sunda Kuningan so that the use of language is unique.

5.The mingling of language and culture in the educational environment makes one of the riches in the cultural repertoire in Indonesia, especially in the development of Islamic education-based communities.

Keywords: Intercultural Communication Process, Language Differences, Communication Barriers, Indonesian Language
Motivation in Adults Between 30 to 60 Years Old for Learning a Foreign or Second Language
Authors: Cabrera Rivera Lucia Denisse
Published: August 2023

Learning another language, especially English, has become quite popular in the last years. Adults have very busy lives, between work, family, and personal and economical goals, learning a language can be very challenging. The following research inquires some of the reasons that adults between 30 to 60 years old have regarding learning English as a second or foreign language. The research tool used was a questionnaire that was asked to 3 participants, all of them with different backgrounds and contexts. The research concluded different motivational factors that adult students have to learn English. Some of these factors are job opportunities, personal growth or satisfaction, and public relations. The research shows how all of the participants consider learning English as something useful in the modern world.
HUBUNGAN ANTARA TINGKAT STRES DAN POLA MAKAN DENGAN KEJADIAN DISPEPSIA PADA MAHASISWA STIKES FAATHIR HUSADA TANGERANG TAHUN 2022
Authors: Hamidah Siti , Karim Abdul , Vanchapo Antonius Rino
Published: August 2023

Dispepsia merupakan istilah yang digunakan untuk sindrom atau kumpulan gejala/keluhan yang terdiri dari nyeri atau rasa tidak nyaman di ulu hati, kembung, mual, muntah, sendawa, rasa cepat kenyang, perut rasa penuh atau begah. Tujuan Penelitian ini adalah untuk mengetahui hubungan antara tingkat stres dan pola makan dengan kejadian dispepsia pada mahasiswa STIKes Fathir Husada Tangerang. Metode penelitian ini menggunakan survey analitik dengan menggunakan desain cross-sectional. Pengambilan sampel dilakukan menggunakan total sampling yaitu dengan mengambil sampel dari keseluruhan populasi sebanyak 45 orang. Anaslisis data menggunakan uji chi-square dengan program SPSS. Hasil penelitian ini menununjukan Terdapat hubungan bermakna antara tingkat stres dengan kejadian dispepsia (p value = 0.033) dan tidak terdapat hubungan antara pola makan dengan kejadian dispepsia (p value = 0.07). Kesimpulan: Terdapat hubungan bermakna antara tingkat stres dengan kejadian dispepsia dimana nilai p value = 0.033 < α = 0.05, dan tidak terdapat hubungan antara pola makan dengan kejadian dispepsia p value = 0.07 > α = 0.05.
FAKTOR YANG MEMPENGARUHI PENYALAHGUNAAN NAPZA PADA TAHANAN DI LAPAS KELAS III BATULICIN TAHUN 2023
Authors: Aprilia Helda , Udiyani Ritna , Atmaja Bayu Purnama
Published: August 2023

Penyalahgunaan Napza di Indonesia sudah sangat merajalela. Hal ini terlihat dari makin banyaknya penyalahguna dari semua kalangan dan peredaran Napza yang terus meningkat. Tujuan penelitian ini untuk mengetahui faktor yang mempengaruhi penyalahgunaan Napza pada Tahanan di Lapas Kelas III Batulicin Tahun 2023. Desain penelitian menggunakan metode pendekatan cross-secctional. Sampel dalam penelitian ini sebanyak 80 orang. Tekhnik sampling menggunakan purposive sampling dengan instrumen berupa kuesioner pengetahuan, kepribadian, keluarga, pergaulan, dan lingkungan sosial. Hasil penelitian menggunakan analisa univariat didapatkan sebagian besar responden (56%) berusia remaja akhir, (69%) responden berpengetahuan baik, (63%) responden berkepribadian introvert, (76%) responden memiliki hubungan keluarga yang tidak baik, (78%) memiliki pergaulan negatif dan (73%) memiliki lingkungan yang negatif. Kemudian analisa bivariat dengan uji fisher’s data variabel faktor pengetahuan, kepribadian, keluarga, pergaulan dan lingkungan sosial di dapatkan p value <0,05 yang artinya H0 ditolak dan H1 diterima. Kemudian hasil uji multivariat menggunakan uji regresi logistik berganda didapatkan hasil OR pada pengetahuan sebesar 0,048, keluarga 7.271, dan pergaulan 4.076. Kesimpulan dalam penelitian ini yaitu faktor keluarga merupakan faktor yang paling dominan beresiko dibandingkan dengan faktor lainnya. Saran dari peneliti diharapkan keluarga responden saling mengawasi dan mengingatkan anggota keluarganya serta menjaga hubungan baik dengan keluarga agar tidak melakukan penyalahgunaan Napza.