45 research outputs found
Part of speech (POS) tagging in Roman Urdu: datasets and models
No full textRoman Urdu is a prevalent medium of expression on social media, news websites, and text messages in the subcontinent, making it a valuable data source for social media and text analytics, particularly in the Indo-Pak perspective. However, despite the immense potential, limited efforts have been made in the area of Roman Urdu text analytics due to various complexities, such as a lack of a standard lexicon, the informal nature of the text, and the lack of text processing tools. The development of the Roman Urdu Part-of-Speech (POS) dataset and the implementation of a robust tagger hold immense importance for text analytics in Roman Urdu. In this work, we created a comprehensive, large-scale Roman Urdu POS dataset and developed a Roman Urdu POS tagger, laying the foundation for future advancements in advanced text analysis. Our approach involved the utilization of Hidden Markov Models, Neural Networks, state-of-the-art transformer models, and Large Language Models as baselines. In our work, we curated two distinct test datasets: one with lexical variation and the other without such variation. This approach allowed us to test the model’s robustness in handling different linguistic challenges posed by lexical variations. Our tagger yields high-quality output with an accuracy score of 96% without lexical variation and 86% on test data with lexical variations. We also evaluated state-of-the-art Large Language Models (GPT-4o and Llama-3-8B) in zero-shot and few-shot settings, with GPT-4o achieving up to 53.78% accuracy in the few-shot configuration, demonstrating a substantial performance gap compared to specialized models. This work establishes a comprehensive framework for Roman Urdu POS tagging that effectively addresses lexical variation challenges, providing essential resources and benchmarks for advancing Roman Urdu natural language processing research
RuBisCo small subunit as strong green tissue specific promoter
No full textEuropean Biotechnology Congress -- SEP 28-OCT 01, 2011 -- Istanbul, TURKEYWOS:000295310800452Transcriptomics has the potential to rapidly increase our knowl-edge of spatial and temporal gene expression and lead to newpromoters for research and development. The availability of abroad-spectrum promoters having the ability to regulate the tem-poral and spatial expression patterns of the transgene can increasethe successful application of transgenic technology and to addresslegitimate concerns raised about the safety and containment oftransgenic plants. Compared with the temporal-specific or spatial-specific expression of the toxin, constitutive expression of foreignproteins in transgenic plants may cause adverse effects. Consti-tutive overexpression of transgenes that interfere with normalprocesses in a plant underscore the need for refinement of trans-gene expression. The development of tissue-specific promoters todrive transgene expression has helped to fulfill that need. There-fore, it is desirable to use expression-specific promoters which onlyexpress the foreign gene in specific plant tissues or organs. Thisstudy highlights the uses and benefits reaped by researchers byusing green tissue specific promoter (RuBisCo small subunit) in dif-ferent crops and systems and establishes a broad range of tissuespecific promoters. Such plant promoters that are activated pre-cisely when and where they are needed would be ideal for geneticengineering strategies.European Biotechnol Themat Network Asso
The effects of workplace incivility and deviance on turnover intention and job performance among nurses in Pakistan
Full text linkHealth sector has a significant position in the services industry because it deals with the health of humans. Nurses are an indispensable part of the health sector, without their effective intervention; appropriate care of the patient is not possible to achieve. Therefore, the performance of nurses in health care settings is not compromised. Literature has identified that workplace incivility (cover and overt incivility) and workplace deviance (property deviance, production deviance, personal aggression and political deviance) can significantly influence employees’ performance (task and contextual performance), however, the mediating effect of turnover intention in this relationship can be an important contribution. Therefore, this study investigated the relationship of workplace incivility and workplace deviance with turnover intention, and job performance of nurses in the public health sector. In addition, mediating effect of turnover intention was investigated. A cross sectional study was conducted using a survey based research design and data was collected through questionnaires. Initially, 364 questionnaires were distributed to the nurses from public hospitals of Lahore, Pakistan, and 348 responded to the questionnaire. Participants of the survey were ensured of their anonymity and selected based on multistage sampling technique. Analysis using structural equation modeling found the relationship of workplace incivility and workplace deviance with turnover intention. Findings revealed that covert incivility, property deviance and personal aggression are significantly related with task performance. Moreover, overt incivility, property deviance, political deviance and personal aggression are significantly related with contextual performance. Finally, the findings revealed good support for the mediation hypotheses. In this study, turnover intention fully mediated the relationship of overt incivility, covert incivility and production deviance with task performance, while overt incivility, production deviance and political deviance mediated the relationship with contextual performance. Moreover, turnover intention partially mediated between political deviances with task performance. This study concluded that turnover intention significantly mediated the relationship between workplace incivility, workplace deviance and job performance
Workplace incivility in predicting turnover intentions and job performance: study on nurses of public sector hospitals of Pakistan
Full text linkPurpose of this study is to investigate the effect of workplace incivility on turnover intention and job performance. Data is collected from 200 nurses of public sector hospitals in Lahore Pakistan through adopted questionnaire. From the data analysis it is found that workplace incivility leads to increase in turnover intention while it negatively affects the job performance of nurses. So it is concluded that misconduct behavior directly harms the workers through increase their turnover intention and decrease the job performance and overall organizational performance
Surfactant-modified silver nanoparticle ink for high-resolution ink-jet printed narrow-gaped organic electrodes
No full textAuthentic leadership and follower’s role ethnicality: the role of leader’s ethical voice and ethical culture
No full textA ligand-based 3D pharmacophore model for the μ opioid receptor: application to the morphinan class of opioids
Full text linkA smartphone sensors-based personalized human activity recognition system for sustainable smart cities
Full text linkAccording to the Sustainable Development Agenda 2030 of the World Health Organization, maintaining physical activities have multiple societal privileges for healthier cities and societies. The amalgamation of the Internet of Things (IoT) and pervasive smartphones has become of paramount importance to produce a significant breakthrough in various domains of smart cities, including healthcare, fitness, skill assessment, and personal assistants, to support independent living. The IoT-supported devices capacitate, embedded with sensors, enabled numerous context-aware applications to recognize physical activities. There are some activity recognition applications; however, they are still deficient in recognizing activities accurately. In this paper, a novel framework for human activity recognition (HAR) is proposed using raw readings from a combination of fused smartphone sensors: accelerometer, gyroscope, magnetometer, and Google Fit activity tracking module. The proposed framework applies deep recurrent neural network (DRNN) to an extensive training dataset. The latter consists of five activity classes from 12 individuals using a deep recurrent neural network (DRNN). An extensive training dataset is used consisting of five activity classes from a group of 12 individuals. The designed android application (runs in the background) collects data from the smartphone's embedded sensors fused with the Google Fit API to validate the results proposed framework. The proposed framework shows promising results in recognizing human activities compared to other similar studies and achieves an accuracy of 99.43% for activity recognition using DRNN
2D Nanostructured MXene-Based Silver Nanoparticles for Photocatalytic Degradation of Safranin Dye
No full textDue to their unique chemical structure, MXenes have been recognized as a potential material, having a high surface area, high thermal and electrical conductivity, and a tunable band gap, showing great hydrophilicity and stability. The adsorption and reducing properties of MXene-based 2D nanomaterials make them efficient photocatalysts for degrading organic pollutants. Silver nanoparticles were synthesized over the exfoliated MXene sheets (1:50 and 1:20 by weight to silver salt) using polyvinyl pyrrolidone as a dispersant. The elemental composition and morphology of the nanocatalysts Ag20@Ti3C2Tx and Ag50@Ti3C2Tx were analyzed by X-ray diffraction (XRD) analysis, X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray (EDX) spectroscopy, and field-emission scanning electron microscopy (FESEM). FESEM micrographs reveal porous exfoliated Ti3C2Tx sheets obtained by continuously stirring Ti3AlC2 for 44 h at 60 °C, providing a platform for the growth of Ag nanoparticles. Diffused reflectance spectroscopy (DRS) indicates that the bare silver nanoparticles show a decrease in the band gap value from 2.4 to 1.35 and 1.41 eV in Ag50@Ti3C2Tx and Ag20@Ti3C2Tx, respectively, which enables the nanocomposites to show excellent catalytic performance and degrade around 99% of safranin dye within 15 min at a concentration of 5 mg Ag50@Ti3C2Tx
