Sentiment analysis (SA) is an important task because of its vital role in analyzing people's opinions. However, existing research is solely based on the English language with limited work on ...low-resource languages. This study introduced a new multi-class Urdu dataset based on user reviews for sentiment analysis. This dataset is gathered from various domains such as food and beverages, movies and plays, software and apps, politics, and sports. Our proposed dataset contains 9312 reviews manually annotated by human experts into three classes: positive, negative and neutral. The main goal of this research study is to create a manually annotated dataset for Urdu sentiment analysis and to set baseline results using rule-based, machine learning (SVM, NB, Adabbost, MLP, LR and RF) and deep learning (CNN-1D, LSTM, Bi-LSTM, GRU and Bi-GRU) techniques. Additionally, we fine-tuned Multilingual BERT(mBERT) for Urdu sentiment analysis. We used four text representations: word n-grams, char n-grams,pre-trained fastText and BERT word embeddings to train our classifiers. We trained these models on two different datasets for evaluation purposes. Finding shows that the proposed mBERT model with BERT pre-trained word embeddings outperformed deep learning, machine learning and rule-based classifiers and achieved an F1 score of 81.49%.
A new light‐driven chiral molecular switch doped in a stable blue phase (BP) liquid crystal allows wide optical tunability of three‐dimensional cubic nanostructures with a selective reflection ...wavelength that is reversibly tuned through the visible region. Moreover, unprecedented reversible light‐directed red, green, and blue reflections of the self‐organized three‐dimensional cubic nanostructure in a single film are demonstrated for the first time. Additionally, unusual isothermal photo‐stimulated less ordered BP II to more ordered BP I phase transition was observed in the system.
Thirty-day readmission rates after acute myocardial infarction (AMI) and heart failure are important patient outcome metrics. Early post-discharge physician follow-up has been promoted as a method of ...reducing 30-day readmission rates. However, the relationships between early post-discharge follow-up and 30-day readmission for AMI and heart failure are inconclusive. We used nationwide population-based data to examine associations between 7-day physician follow-up and 30-day readmission, and further associations of 7-day same physician (during the index hospitalization and at follow-up) and cardiologist follow-up with 30-day readmission for non-ST-segment-elevation myocardial infarction (NSTEMI) or heart failure.
We analyzed all patients 18 years or older with NSTEMI and heart failure and discharged from hospitals in 2010 in Taiwan through Taiwan's National Health Insurance Research Database. Cox proportional hazard models with robust sandwich variance estimates and propensity score weighting were performed after adjustment for patient and hospital characteristics to test associations between 7-day physician follow-up and 30-day readmission.
The study population for NSTEMI and heart failure included 5,008 and 13,577 patients, respectively. Early physician follow-up was associated with a lower hazard ratio of readmission compared with no early physician follow-up for patients with NSTEMI (hazard ratio HR, 0.47; 95% confidence interval CI, 0.39-0.57), and for patients with heart failure (HR, 0.54; 95% CI, 0.48-0.60). Same physician follow-up was associated with a reduced hazard ratio of readmission compared with different physician follow-up for patients with NSTEMI (HR, 0.56; 95% CI, 0.48-0.65), and for patients with heart failure (HR, 0.69; 95% CI, 0.62-0.76).
For each condition, patients who have an outpatient visit with a physician within 7 days of discharge have a lower risk of 30-day readmission. Moreover, patients who have an outpatient visit with the same physician within 7 days of discharge have a much lower risk of 30-day readmission.
Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the ...accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in speech disfluency classification. Unlike conventional methods, TranStutter leverages Multi-Head Self-Attention and Positional Encoding to capture intricate temporal patterns, yielding superior accuracy. In this study, the researchers employed two benchmark datasets: the Stuttering Events in Podcasts Dataset (SEP-28k) and the FluencyBank Interview Subset. SEP-28k comprises 28,177 audio clips from podcasts, meticulously annotated into distinct dysfluent and non-dysfluent labels, including Block (BL), Prolongation (PR), Sound Repetition (SR), Word Repetition (WR), and Interjection (IJ). The FluencyBank subset encompasses 4144 audio clips from 32 People Who Stutter (PWS), providing a diverse set of speech samples. TranStutter’s performance was assessed rigorously. On SEP-28k, the model achieved an impressive accuracy of 88.1%. Furthermore, on the FluencyBank dataset, TranStutter demonstrated its efficacy with an accuracy of 80.6%. These results highlight TranStutter’s significant potential in revolutionizing the diagnosis and treatment of stuttering, thereby contributing to the evolving landscape of speech pathology and neurodevelopmental research. The innovative integration of Multi-Head Self-Attention and Positional Encoding distinguishes TranStutter, enabling it to discern nuanced disfluencies with unparalleled precision. This novel approach represents a substantial leap forward in the field of speech pathology, promising more accurate diagnostics and targeted interventions for individuals with stuttering disorders.
This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are converted to vector form using word2vec and doc2vec ...model. These two methods are used to analyse the best approach for generating word embeddings. The generated word embeddings of post combined with other attributes like post published time, type of post and total interactions are used to train machine learning algorithms. Deep neural network (DNN), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) are used to compare the prediction of total interaction for a particular post. For word2vec, the word vectors are created using both continuous bag-of-words (CBOW) and skip-gram models. Also the pre-trained word vectors provided by google is used for the analysis. For doc2vec, the word embeddings are created using both the Distributed Memory model of Paragraph Vectors (PV-DM) and Distributed Bag of Words model of Paragraph Vectors (PV-DBOW). A word embedding is also created using PV-DBOW combined with skip-gram.
Although there have been intense efforts to fabricate large three-dimensional photonic crystals in order to realize their full potential, the technologies developed so far are still beset with ...various material processing and cost issues. Conventional top-down fabrications are costly and time-consuming, whereas natural self-assembly and bottom-up fabrications often result in high defect density and limited dimensions. Here we report the fabrication of extraordinarily large monocrystalline photonic crystals by controlling the self-assembly processes which occur in unique phases of liquid crystals that exhibit three-dimensional photonic-crystalline properties called liquid-crystal blue phases. In particular, we have developed a gradient-temperature technique that enables three-dimensional photonic crystals to grow to lateral dimensions of ~1 cm (~30,000 of unit cells) and thickness of ~100 μm (~ 300 unit cells). These giant single crystals exhibit extraordinarily sharp photonic bandgaps with high reflectivity, long-range periodicity in all dimensions and well-defined lattice orientation.Conventional fabrication approaches for large-size three-dimensional photonic crystals are problematic. By properly controlling the self-assembly processes, the authors report the fabrication of monocrystalline blue phase liquid crystals that exhibit three-dimensional photonic-crystalline properties.
Patients who have recovered from coronavirus disease 2019 (COVID-19) infection may experience chronic fatigue when exercising, despite no obvious heart or lung abnormalities. The present lack of ...effective treatments makes managing long COVID a major challenge. One of the underlying mechanisms of long COVID may be mitochondrial dysfunction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections can alter the mitochondria responsible for energy production in cells. This alteration leads to mitochondrial dysfunction which, in turn, increases oxidative stress. Ultimately, this results in a loss of mitochondrial integrity and cell death. Moreover, viral proteins can bind to mitochondrial complexes, disrupting mitochondrial function and causing the immune cells to over-react. This over-reaction leads to inflammation and potentially long COVID symptoms. It is important to note that the roles of mitochondrial damage and inflammatory responses caused by SARS-CoV-2 in the development of long COVID are still being elucidated. Targeting mitochondrial function may provide promising new clinical approaches for long-COVID patients; however, further studies are needed to evaluate the safety and efficacy of such approaches.
We study soft-input soft-output (SISO) maximum logarithmic maximum a posteriori (max-log-MAP) detection of coded generalized spatial modulation (GenSM) signals for the iterative detection and ...decoding receiver. Due to the property of GenSM, extrinsic bit log-likelihood ratio associated with each bit of the transmit antenna combination index and transmit symbol vector needs to be computed. By extending our sorting assisted successive sphere decoding algorithm (SA-SSDA) for the hard-output detection of GenSM signals, we study two-stage detection schemes here to conduct multiple single tree search sphere decoding algorithms (STS-SDAs) for the second stage computation, where the order of the multiple STS-SDAs is determined in the first stage computation. Unlike previous two-stage detectors, our emphasis is on studying the ordering metrics for determining the order of STS-SDAs and is on giving suggestions about the scenarios to use adequate ordering metrics for the purpose of complexity reduction. Accordingly, incorporating the tricks used by the SA-SSDA and STS-SDA, we arrive at a fast SA-SSTS-SDA for exactly max-log-MAP detection of GenSM signals. Our proposed detector is feasible for hardware implementation and may motivate future research on the SISO detection of coded GenSM signals.
The use of the Internet and social applications has many benefits for the elderly, but numerous investigations have shown that the elderly do not perceive online social networks as a friendly social ...environment. Therefore, TreeIt, a social application specifically designed for the elderly, was developed for this study. In the TreeIt application, seven mechanisms promoting social interaction were designed to allow older adults to use social networking sites (SNSs) to increase social connection, maintain the intensity of social connections and strengthen social experience. This study's main objective was to investigate how user interface design affects older people's intention and attitude related to using SNSs. Fourteen user interface evaluation heuristics proposed by Zhang et al. were adopted as the criteria to assess user interface usability and further grouped into three categories: system support, user interface design and navigation. The technology acceptance model was adopted to assess older people's intention and attitude related to using SNSs. One hundred and one elderly persons were enrolled in this study as subjects, and the results showed that all of the hypotheses proposed in this study were valid: system support and perceived usefulness had a significant effect on behavioral intention; user interface design and perceived ease of use were positively correlated with perceived usefulness; and navigation exerted an influence on perceived ease of use. The results of this study are valuable for the future development of social applications for the elderly.
Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful ...opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.
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