Text Words Research Articles

MsPrompt: Multi-step prompt learning for debiasing few-shot event detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm targeting to construct a novel training set that accommodates the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection (FSED), consisting of the following two components: a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the pretrained language models (PLMs) sufficiently for tackling the context-bypassing problem, and a prototypical network module compensating for the weakness of classifying events with sparse data and boost the generalization performance.Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best baseline and achieving an outstanding debiasing performance.

Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both "spoken language" (e.g., human textual annotations) and "unspoken language" (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design. To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken "languages", we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants.

Experimental Study of Morphological Analyzers for Topic Categorization in News Articles

Natural language processing refers to the ability of computers to understand text and spoken words similar to humans. Recently, various machine learning techniques have been used to encode a large amount of text and decode feature vectors of text successfully. However, understanding low-resource languages is in the early stages of research. In particular, Korean, which is an agglutinative language, needs sophisticated preprocessing steps, such as morphological analysis. Since morphological analysis in preprocessing significantly influences classification results, ideal and optimized morphological analyzers must be used. This study explored five state-of-the-art morphological analyzers for Korean news articles and categorized their topics into seven classes using term frequency–inverse document frequency and light gradient boosting machine frameworks. It was found that a morphological analyzer based on unsupervised learning achieved a computation time of 6 s in 500,899 tokens, which is 72 times faster than the slowest analyzer (432 s). In addition, a morphological analyzer using dynamic programming achieved a topic categorization accuracy of 82.5%, which is 9.4% higher than achieve when using the hidden Markov model (73.1%) and 13.4% higher compared to the baseline (69.1%) without any morphological analyzer in news articles. This study can provide insight into how each morphological analyzer extracts morphemes in sentences and affects categorizing topics in news articles.

Interventions promoting recovery from depression for patients transitioning from outpatient mental health services to primary care: Protocol for a scoping review.

Patients with severe Major Depressive Disorder (MDD) have an increasing risk of new psychiatric hospitalizations following each new episode of depression highlighting the recurrent nature of the disorder. Furthermore, patients are not fully recovered at the end of their treatment in outpatient mental health services, and residual symptoms of depression might explain why patients with MDD have a high risk of relapse. However, evidence of methods to promote recovery after discharge from outpatient mental health services is lacking. The proposed scoping review aims to systematically scope, map and identify the evidence and knowledge gaps on interventions that aims to promote recovery from MDD for patients transitioning from outpatient mental health services to primary care. The proposed scoping review will follow the latest methodological guidance by the Joanna Briggs Institute (JBI) in tandem with the Preferred Reporting Items for Systematic reviews and Meta-Analysis-extension for Scoping Reviews (PRISMA-ScR) checklist. The review is ongoing. Four electronic databases (Medline via PubMed, PsycINFO, CINAHL, and Sociological Abstracts) were systematically searched from 20 January 2022 till 29 March 2022 using keywords and text words. The review team consists of three independent screeners. Two screeners have completed the initial title and abstract screening for all studies retrieved by the search strategy. Currently, we are in the full text screening phase. Reference lists of included studies will be screened, and data will be independently extracted by the review team. Results will be analyzed qualitatively and quantitatively. The chosen methodology is based on the use of publicly available information and does not require ethical approval. Results will be published in an international peer reviewed scientific journal, at national and international conferences and shared with relevant authorities. A pre-print has been registered at the medRxiv preprint server for health sciences (doi.org/10.1101/2022.10.06.22280499).

Intravascular Ultrasound-Guided Versus Angiography-Guided Endovascular Therapy for Femoropopliteal Artery Disease: A Scoping Review.

The objective of this study was to summarize whether the application of additional intravascular ultrasound (IVUS) can change the outcomes of endovascular treatment in femoropopliteal atherosclerotic lesions compared with conventional angiography alone based on the existing study and evidence. Studies published until September 2022 were searched from PubMed, Embase, and Cochrane databases using the methods of combination of Medical Subject Headings and free text words. The outcomes included in these studies were primary patency rate or restenosis rate and freedom from clinically driven target lesion revascularization (cdTLR) rate, and most studies were concerned about the 12-month results. Two independent authors conducted the process of study selection. And the pooled analysis was attempted. Finally, the inclusion criteria were met by 4 studies (1 randomized controlled trial and 3 retrospective studies) involving 1160 patients. IVUS played its role in measuring lesions and/or guiding wiring passage. Most of the included studies showed that IVUS could achieve a higher 12-month primary patency rate (from 70% to 90%) and higher 12-month freedom from cdTLR rate (from 83.9% to 94.7%) than angiography. The tentative pooled analysis also showed that IVUS significantly improved the 12-month primary patency rate (OR: 2.21, 95%CI: [1.38-3.55], p=0.001) and the 12-month freedom from cdTLR rate (OR: 1.70, 95%CI [1.04-2.78], p=0.03) compared with angiography alone. Intravascular ultrasound-guided endovascular treatment of femoropopliteal artery lesions may have its own advantages such as higher primary patency rate and freedom from cdTLR rate at 12 months. As a novel technology, IVUS is expected to become another beneficial option to guide clinicians performing endovascular therapy in addition to angiography and may achieve better clinical outcomes. More prospective and high-quality studies are needed in the future to investigate the role of IVUS in the process of femoropopliteal artery lesions endovascular treatment. This review summarized a few available studies to promote understanding of IVUS and reveal its potential. This novel technology shows the possibility of achieving better clinical outcomes than angiography in femoropopliteal artery lesions endovascular therapy such as higher 12-month primary patency rate shown in some studies. Currently, it is important to consider suitable technologies applied to individualized treatment. IVUS seems to provide clinicians additional option in clinical practice and benefit patients well in the future. And it needs us to conduct more high-quality studies to explore its roles and advantages in endovascular treatment.

Text Linguistics and the Paradigm of Concept

Text linguistics, which is one of the relevant fields of linguistics in our modern times, was born in the 20s of the 20th century and began to be formed in the 50s and 80s. As the name suggests, the main research object of text linguistics is the text itself. If we consider the word text in general, the word text can have two different meanings. That is, this word can be considered to be both in a broad sense - macrotext, and in its opposite narrow sense - microtext. The microtexts in a narrow sense combine to form macrotexts with a broad meaning. In this case, the concept of complex syntactic whole coincides with the concept of microtext. So, macrotexts are a collection of complex syntactic entities. While a single sentence expresses a complete idea, a complex syntactic whole expresses a complex idea, any particular information. Complex syntactic whole is a syntactic unit in which two or more sentences are connected by syntactic and formal relations. The group of sentences forming the basis of the text has been named by different scholars with different terms. For example, V.G. Kolshansky paragraph, I.R. Galperin, I.O. Moskalskaya discourse, K. Abdullayev complex syntactic whole, V. G. Admoni large syntactic whole and others used different terms. The term complex syntactic whole is more appropriate as the sentences in the text form certain unified whole.

Indications for Tube Feeding in Adults with Muscular Disorders: A scoping review.

Eating an adequate diet and maintaining a healthy body weight can be challenging for patients with muscular disorders (MD). Starting tube feeding can have a positive impact on nutritional status, functioning and quality of life. Guidelines on when to start tube feeding in adults with MD are lacking. We aim to review the scientific literature on indications to start tube feeding in adults with facioscapulohumeral dystrophy (FSHD), inclusion body myositis (IBM), muscular dystrophy type 1 (DM1), oculopharyngeal muscular dystrophy (OPMD) and congenital myopathies. This scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines. Relevant studies were identified in Pubmed, Embase and Cinahl (April 2022). The medical subject headings (MeSH) and text words used were related to FSHD, IBM, DM1, OPMD or congenital myopathies and dysphagia, enteral nutrition or malnutrition. Of 1046 unique articles, 9 case reports and 2 retrospective case series were included. Indications to start tube feeding were dysphagia, malnutrition/weight loss and respiratory infections (due to aspiration). Percutaneous endoscopic gastrostomy (PEG) tubes were used most often and complications were respiratory failure, problems with the tube itself, accidental tube removal, cutaneous symptoms, digestive symptoms, and peritonitis. Data on tube feeding in MD is scarce. Indications to start tube feeding were similar across the various MD. We call for more research in this field and suggest to include screening for dysphagia, aspiration and malnutrition in for the treatment of various MD.

A mapping exercise using automated techniques to develop a search strategy to identify systematic review tools.

The Systematic Review Toolbox aims provide a web-based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a search strategy that could be regularly and automatically run to identify eligible records for the SR Toolbox, thus reducing time on task and burden for those involved. We undertook a mapping exercise to identify the PubMed IDs of papers indexed within the SR Toolbox. We then used the Yale MeSH Analyser and Visualisation of Similarities (VOS) Viewer text-mining software to identify the most commonly used MeSH terms and text words within the eligible records. These MeSH terms and text words were combined using Boolean Operators into a search strategy for Ovid MEDLINE. Prior to the mapping exercise and search strategy development, 81 software tools and 55 'Other' tools were included within the SR Toolbox. Since implementation of the search strategy, 146 tools have been added. There has been an increase in tools added to the toolbox since the search was developed and its corresponding auto-alert in MEDLINE was originally set up. Developing a search strategy based on a mapping exercise is an effective way of identifying new tools to support the systematic review process. Further research could be conducted to help prioritise records for screening to reduce reviewer burden further and to adapt the strategy for disciplines beyond healthcare.

Protocol for a systematic review and meta-analysis of tobacco-cessation interventions delivered perioperatively

IntroductionTobacco smoking is associated with a substantially increased risk of perioperative complications. The perioperative period is an opportunity to introduce tobacco-cessation strategies. A previous systematic review provided evidence that perioperative...

Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study.

Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis. This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery. Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined. A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms. Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

Performance analysis of robustness of BERT model under attack

With the aim of testing the robustness of machine learning models, this paper tests the performance of five classification models based on IMDB datasets. Furthermore, in this work, two types of sentence embeddings generated by word2vec and BERT are added with the noise of the normal distribution with different intensities. They are fed into the Support Vector Machine for testing. The experimental results show that the performance of the model slowly decreases as the noise intensity is increased. BERT-based sentiment embedding reduces less than Word2vec-based sentiment embedding. This paper believes that this experimental phenomenon can be used as a basis to support the robustness of BERT representation learning. At the same time, this paper also noted that the previous work experimented with perturbating the BERT model by replacing the words in the original text of the IMDB dataset, causing the BERT model performance to drop sharply. However, the experiment in this paper tested the robustness of the BERT model by embedding tampering, and the experimental results were stable. In this paper, it is assumed that word substitution and embedding interference are possible situations when operating the model. Therefore, for the two different experimental phenomena, this paper plans to design more detailed experiments in the next work to check the robustness of the model.

관광해설 유튜브 댓글 분석을 통한 소비자 구매여정 연구

Purpose This study attempted to explain the decision-making process of YouTube tour interpretation by applying the consumer decision journey(CDJ) to the field of tourism research. In addition, it purpose to empirically understand the impact of YouTube tour interpretation on the tourism destination decision-making process as online tourism information during the pandemic, and make theoretical and empirical suggestions accordingly.
 Methods The impact of YouTube tour interpretation content on CDJ was proposed through literature research, and comments on tour interpretation YouTube contents were used to study the visitor decision-making process of the CDJ model using text mining techniques. Visit Seoul TV, the Seoul Tourism Foundation's official YouTube channel, selected ‘healing tour contents in the room’, and text mining and word cloud analysis conducted.
 Results As a result, comments were spread each stages on the CDJ of visitors and were most distributed in the active evaluation stage, and the length of the comments was longer as the CDJ progressed. In the word crowd analysis, expression of gratitude in the 1st stage, positive evaluation in the 2nd stage, the 3rd stage of visit intention, and the 4th stage of revisit intention were characterized, and the frequency of audit expression was high over stages 1, 2 and 3. Through this study, among the various touch points of CDJ.
 Conclusion This study provided practical implications for the need to find ways to lead to the overall management and active evaluation of potential visitors' CDJ and academic implications for using unstructured data and connecting theoretical models among social media research in the tourism field.

Image to English translation and comprehension: INT2-VQA method based on inter-modality and intra-modality collaborations.

Existing visual question answering methods typically concentrate only on visual targets in images, ignoring the key textual content in the images, thereby limiting the depth and accuracy of image content comprehension. Inspired by this, we pay attention to the task of text-based visual question answering, address the performance bottleneck issue caused by over-fitting risk in existing self-attention-based models, and propose a scenario text visual question answering method called INT2-VQA that fuses knowledge manifestation based on inter-modality and intra-modality collaborations. Specifically, we model the complementary priori knowledge of locational collaboration between visual targets and textual targets across modalities and the contextual semantical collaboration among textual word targets within a modality. Based on this, a universal knowledge-reinforced attention module is designed to achieve a unified encoding manifestation of both relations. Extensive ablation experiments, contrast experiments, and visual analyses demonstrate the effectiveness of the proposed method and prove its superiority over the other state-of-the-art methods.

Open Government Data (OGD) sites and the sharing of country-specific real-time pandemic information: An investigation into COVID-19 datasets available on worldwide OGDs

In this study, we investigate the key characteristics of more than 1500 COVID-19 datasets appearing on 35 Open Government Data (OGD) sites around the world. In addition to examining the number of datasets, data formats, star levels (a measure of data formats), and data levels, we also investigated the topical focus of individual dataset content by analyzing the text words of dataset titles and descriptions. Significant differences among the world regions were found in both the number of datasets and the number of data formats. Regional differences were also found among various star levels. In addition, regional differences were found in the topic contributions to two specific clusters. As one of the first empirical studies examining various characteristics of COVID-19 datasets on OGDs, our research fills the gaps in this area. The findings of our study will benefit not only the government in improving their OGDs’ public health data provision by ensuring accuracy, consistency, and timeliness, but also benefit those OGD users who are public health professionals who can assess the progress and identify gaps in the community, or everyday citizens who access OGD datasets to obtain an understanding of the development and risk levels of the crisis. Future research may use the same method to expand the examination to the general “health” datasets accessible from OGDs worldwide to help improve the sharing and reuse of health datasets for the general public.

Manual and non-manual sign language recognition framework using hybrid deep learning techniques

Sign language recognition is a significant cross-modal way to fill the communication gap between deaf and hearing people. Automatic Sign Language Recognition (ASLR) translates sign language gestures into text and spoken words. Several researchers are focusing either on manual gestures or non-manual gestures separately; a rare focus is on concurrent recognition of manual and non-manual gestures. Facial expression and other body movements can improve the accuracy rate, as well as enhance signs’ exact meaning. The current paper proposes a Multimodal –Sign Language Recognition (MM-SLR) framework to recognize non-manual features based on facial expressions along with manual gestures in Spatio temporal domain representing hand movements in ASLR. Our proposed architecture has three modules, first, a modified architecture of YOLOv5 is defined to extract faces and hands from videos as two Regions of Interest. Second, refined C3D architecture is used to extract features from the hand region and the face region, further, feature concatenation of both modalities is applied. Lastly, LSTM network is used to get spatial-temporal descriptors and attention-based sequential modules for gesture classification. To validate the proposed framework we used three publically available datasets RWTH-PHONIX-WEATHER-2014T, SILFA and PkSLMNM. Experimental results show that the above-mentioned MM-SLR framework outperformed on all datasets.

The Impact of Growth Hormone Therapy on Sleep-Related Health Outcomes in Children with Prader-Willi Syndrome: A Review and Clinical Analysis.

This literature review of growth hormone (GH) therapy and sleep-related health outcomes in children diagnosed with Prader-Willi syndrome (PWS) assembles evidence for the consequences of sleep deprivation and poor sleep quality: difficulty concentrating and learning at school, behavioral problems, diminished quality of life, and growth impairment. Sleep-disordered breathing (SDB) is another factor that impacts a child's well-being. We searched the electronic databases Medline PubMed Advanced Search Builder, Scopus, and Web of Science using MeSH terms and text words to retrieve articles on GH deficiency, recombinant human growth hormone (rhGH) therapy, sleep quality, SDB, and PWS in children. The censor date was April 2023. The initial search yielded 351 articles, 23 of which were analyzed for this review. The study findings suggest that while GH may have a role in regulating sleep, the relationship between GH treatment and sleep in patients with PWS is complex and influenced by GH dosage, patient age, and type and severity of respiratory disorders, among other factors. GH therapy can improve lung function, linear growth, and body composition in children with PWS; however, it can also trigger or worsen obstructive sleep apnea or hypoventilation in some. Long-term GH therapy may contribute to adenotonsillar hypertrophy and exacerbate sleep apnea in children with PWS. Finally, GH therapy can improve sleep quality in some patients but it can also cause or worsen SDB in others, leading to diminished sleep quality and overall quality of life. The current evidence suggests that the initial risk of worsening SDB may improve with long-term therapy. In conclusion, rhGH is the standard for managing patients with PWS. Nonetheless, its impact on respiratory function during sleep needs to be thoroughly evaluated. Polysomnography is advisable to assess the need for adenotonsillectomy before initiating rhGH therapy. Close monitoring of sleep disorders in patients with PWS receiving GH therapy is essential to ensure effective and safe treatment.

Handwriting word spotting in the space of difference between representations using vision transformers

Word spotting in handwritten documents is challenging due to the high intra-class and inter-class variability of handwritten forms. This paper addresses the word spotting problem in the segmentation and the training scenarios. Overall, this paper makes the following three contributions: (1) a new word text representation, called the Pyramid of Bidirectional Character Sequences (PBCS), which can solve both the word spotting problem and the word recognition problem. The use of the PBCS representation allows trained models to identify the character subsequences shared by words. Thus, words that are not seen during training can be represented and spotted. In addition, the PBCS representation encodes word texts redundantly, allowing for word discrimination. (2) A binary classification modeling of the word spotting problem in the difference space between representations, where spotting of non-vocabulary words is more efficient. Finally, (3) a new deep neural network architecture that combines the strengths of convolutional layers and transformers. We evaluated our solution on IAM and RIEMS datasets and showed that it outperforms recent state-of-the-art methods in the query-by-example scenario.

Methods of Improving Japanese-Chinese Machine Translation System through Machine Learning and Human-Computer Interaction

With globalization, the exchange of people between different countries is becoming more frequent. Due to different languages, there are serious obstacles to personnel exchanges. To a large extent, they hinder the growth of industries such as economy, culture and tourism in each country. The emergence of Machine Translation (MT) has effectively improved the problem of language barriers, and greatly reduced the workload of translators in text translation. However, MT does not have the same flexible flexibility as human translation. It just translates the text word by word, which is often difficult to meet people's higher needs. This paper proposed to build a Japanese-Chinese MT system and integrate machine learning and Human-Computer Interaction (HCI) technology into the system. To further enhance the efficiency of the system, enhancement algorithms were also applied to the system to optimize the performance of the system. From the experimental results, in terms of BLEU (Bilingual Evaluation Understudy) index, the average BLEU index of the algorithm in this paper was 8.59, and that of the traditional algorithm was 6.55. In terms of translation precision, the average precision of the algorithm in this paper was 91.53%, while that of the traditional algorithm was 87.28%. In terms of translation readability, the average readability of the algorithm in this paper was 93.32%, while that of the traditional algorithm was 89.22%. By comparison, the average BLEU index of the algorithm in this paper has increased by 2.04; the average accuracy of translation increased by 4.25%; the average readability increased by 4.1%. From the above data, it was evident that the enhancement algorithm can optimize the performance of the Japanese-Chinese MT system well.

Epidemiological Differences in Hajj-Acquired Airborne Infections in Pilgrims Arriving from Low and Middle-Income versus High-Income Countries: A Systematised Review.

This systematised review aims to compare the epidemiological patterns of Hajj-acquired airborne infections among pilgrims from low and middle-income countries (LMIC) versus those from high-income countries (HIC). A PubMed search was carried out for all published articles before February 2023, using a combination of MeSH terms and text words. The Newcastle-Ottawa Scale (NOS) was used to assess data quality. From a total of 453 titles identified, 58 studies were included in the review (LMIC = 32, and HIC = 26). In the pooled sample, there were 27,799 pilgrims aged 2 days to 105 years (male: female = 1.3:1) from LMIC and 70,865 pilgrims aged 2 months to 95 years (male: female = 1:1) from HIC. Pilgrims from both HIC and LMIC had viral and bacterial infections, but pilgrims from HIC tended to have higher attack rates of viral infections than their LMIC counterparts. However, the attack rates of bacterial infections were variable: for instance, pilgrims from LMIC seemed to have higher rates of meningococcal infections (0.015-82% in LMIC vs. 0.002-40% in HIC) based on the study population, but not Mycobacterium tuberculosis (0.7-20.3% in LMIC vs. 38% in HIC). Targeted measures are needed to prevent the spread of airborne infections at Hajj.

ANALISIS KEMAMPUAN MEMBACA CEPAT DAN PEMAHAMAN PADA SISWA KELAS IV SD NEGERI KARANG TENGAH 12 KOTA TANGERANG

This study aims to determine the speed reading skills and comprehension of fourth grade students at Karang Tengah 12 Public Elementary School, Tangerang City. This research uses a qualitative approach with descriptive methods. Data collection techniques used are tests, observations, interviews and documentation. The subjects of this study were fourth grade students at Karang Tengah 12 Public Elementary School, Tangerang City. The results of this study indicate that almost all students' speed reading skills and comprehension are good, but there are some students whose reading skills are still lacking, namely in reading fluency, reading intonation, sentence use, punctuality, confidence, not being able to read text correctly and clearly, answering questions related to the text, explaining the meaning contained in the reading, interpreting difficult words in the reading text and transcribe information in reading.