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The research explores forms and function of variant tag questions (VTQs) in the native and non-native Englishes. For the said purpose, patterns of VTQs in Pakistani English are compared with two native (British and New Zealand) and two non-native (Indian and Singaporean) varieties. The components of the "International Corpus of English," henceforth ICE (Greenbaum & Nelson, 1996) of the said varieties, have been used. Each ICE component consists of one- million-word corpus of the regional variety, with a common design, in order to ensure maximum comparability between the components (Nelson 1996). The ICE samples the English of adults (age 18 or over) who have been educated through medium of English to at least the end of secondary schooling. Convenient sampling technique is used to collect data. All the possible tags were extracted using AntConc 3.5.9 and the collocation were then studied. The results revealed that VTQs are under-used in non-native Englishes while least used in Pakistani English. As for the forms of VTQs, Pakistani speakers follow the native speakers and observe the grammatical rules. The functions of VTQs are analysed in Pakistani English, following Axelsson's (2011) model, and compared with that of Indian English as both varieties share almost similar linguistic and sociocultural backgrounds. The results show that declarative VTQs are preferred to seek and exchange information in both the varieties. The research suggests that further linguistic entities may be explored to observe the similarities/differences in native and non-native varieties, including Indian and Pakistani Englishes, to establish their identities.

Objective: The aim of the study was to assess the knowledge, attitudes and practices of students regarding the use of antibiotics in Punjab, Pakistan. Participants: 525 medical and non-medical students from Punjab in Pakistan. Methods: The t-test and ANOVA were used to compare the average response of respondents. Chi-square test was used to measure the association of different elements. Results: The mean age was 20.78 ± 2.10%. About 14% of the students agreed about the appropriateness of antibiotics for viral infections, and 15% of students said they stopped taking the drugs when symptoms subsided. 65.7% of students took antibiotics only when prescribed by a doctor and 54% bought antibiotics without prescription. Statistically significant results were found among the students who had heard about the antibiotic resistance (p < 0.05). Conclusion: This study will help assess the adequacy of current educational campaigns, maximize rationalization of antibiotic use, and minimize gaps in knowledge and attitudes.

With increasing interest in integrating solar power into the utility grid, multilevel inverters are gaining much more attention for medium- and high-power applications due to their high-quality waveform, low voltage stress across active components, and low total harmonic distortion in output voltage. However, to achieve these benefits, a large number of active and passive components are required. A transformer is also required to provide galvanic isolation, which increases its size and weight and reduces its power density and efficiency. In order to overcome the disadvantages posed by transformer-based inverters, research is being conducted on the transformerless topology of multilevel inverters. The first aim of this review article is to summarize traditional transformerless multilevel inverters (TMLIs) considering both single- and three-phase topologies. Secondly, the main aim of this article is to provide a detailed overview of the hybrid topologies of TMLIs that employ fewer components for photovoltaic applications. In addition, this study compares traditional and hybrid single-/three-phase topologies in terms of component count and performance factors, which will be useful to researchers. [ABSTRACT FROM AUTHOR]

Background and Objectives: Postpartum depression (PPD) is prevalent among women after childbirth, but accessing mental healthcare for PPD is challenging. This study aimed to assess the treatment gap and barriers to mental healthcare access for women with PPD symptoms living in Punjab, Pakistan. Methods: A multicenter cross-sectional study was conducted in five populous cities of Punjab from January to June 2023 by administering the questionnaire to the women using stratified random sampling. A total of 3,220 women in first 6 months postpartum were screened using the Edinburgh Postnatal Depression Scale. Of them, 1,503 women scored thirteen or above, indicating potential depressive disorder. Interviews were conducted to explore help-seeking behavior and barriers to accessing mental healthcare. Descriptive statistics along with nonparametric tests (e.g., Kruskal–Wallis, Mann–Whitney U) were used and group differences were examined. Scatter plot matrices with fitted lines were used to explore associations between variables. Classification and regression tree methods were used to classify the importance and contribution of different variables for the intensity of PPD. Results: Only 2% of women (n = 33) with high PPD symptoms sought mental healthcare, and merely 5% of women (n = 75) had been in contact with a health service since the onset of their symptoms. 92.80% of women with PPD symptoms did not seek any medical attention. The majority of women, 1,215 (81%), perceived the need for mental health treatment; however, 91.23% of them did not seek treatment from healthcare services. Women who recently gave birth to a female child had higher mean depression scores compared to those who gave birth to a male child. Age, education, and birth location of newborn were significantly associated (p < 0.005) with mean barrier scores, mean social support scores, mean depression scores and treatment gap. The results of classification and regression decision tree model showed that instrumental barrier scores are the most important in predicting mean PPD scores. Conclusion: Women with PPD symptoms encountered considerable treatment gap and barriers to access mental health care. Integration of mental health services into obstetric care as well as PPD screening in public and private hospitals of Punjab, Pakistan is critically needed to overcome the treatment gap and barriers. [ABSTRACT FROM AUTHOR]

This study investigates how Peircean semiotics can unpack the multifaceted meanings associated with House Stark, Starks’ sigil ‘direwolf’ and their motto ‘winter is coming’ in George R. R. Martin’s A game of thrones. It presents Charles S. Peirce’s semeiosis, the mathematical and Kantian origins of Peirce’s triadicity and his triadic representation of signs comprising representamen, interpretant and object. In the novel, Peirce’s representamen is the direwolf sigil, the interpretant is the motto ‘winter is coming’ and the object is House Stark. The investigators explore direct relationships between representamen–interpretant (sigil–motto), interpretant–object (motto–House Stark) and object–representamen (House Stark–sigil). They conclude how the context has changed the meaning of Starks’ sigil and motto. While the study posits that every sign and sentence in the novel functions as a Peircean sign, this investigation focuses on three elements of House Stark: the sigil, the motto and the house itself. The novel has excellent room for future research, particularly by applying Peircean semiotics as an analytical tool. This approach promises a fresh perspective on the story’s elements and a deeper understanding of how the narrative reflects and connects to real-world social structures, events and broader themes. [ABSTRACT FROM AUTHOR]

In recent years, the frequentoccurrence of natural calamities in the world has gained much attention due to infrastructure and life losses. Increase in global temperature has threatened the earth’s climate, causing unpredictable and extremeweatherevents more often. These events were both short-termas well as long-term. In spite of insufficient monitoring coverage of inner areas and high altitudes, an intensification of average yearly temperature has been observed after the 20thcentury inmany regions of Asia. Pakistan has faced a number of natural disasters including severe floods, devastating earthquakes, disturbing droughts, distressing smog, extreme temperature and torrential rainfall patterns. These calamities have affected people economically, socially, physically and mentally by posing a threat to earth, its species and their livelihood. This paper was aimed to document the natural calamities in Pakistan since the beginning of 21stcentury till date. The country is facing the problemof intense and longer summers and has witnessed two intenseheat waves. Moreover, in some areas, extensive droughts are threatening crops, freshwater supply and wildlife. In recent years, the problem of smog has emerged in Pakistan, especiallyin Punjab. The problem is getting worse in every coming year due to overpopulation, cities expansion, increased emission of greenhousegases, open burning of crop residues and extreme reliance on fossil fuels for energy purpose. So far in this century, Pakistan has faced 3 droughts, 2 heat waves, 10 severe floods, 27 major earthquakes, 2 consecutive sessions of severe smog and extreme high and lowtemperatureevents. Some of the deadliest events were; 2005 Kashmir earthquake claiming 87,350 human lives and 2010 flood impacting nearly 20 million people with 1,781 causalities. Change in climate is posing a severe threat to species, livelihood of the human beingsand the earth. Global warming is causing warming of oceans and consequently rise in sea level. Climate change affects average as well as extreme temperatures hence increasing the probability climate-relateddisasters.Thus,global warming is causing a chain of catastrophic events that makethe problem even more complex to understand.

Background: Guillain-Barre syndrome is an immune mediated poly-radiculo-neuropathy with a variable clinical presentation and prognosis. Plasma exchange and Intravenous immunoglobulin are treatment options with proven efficacy by various clinical trials. But these trials demonstrate improvement in a classical and moderate to severe GBS. In clinical setting, problem may occur in treatment of those patients who present with mild symptoms & signs, clinical variants of GBS, or when the duration of weakness is > 2 weeks from onset of symptoms. Objective: To determine the frequency of mild GBS, its clinical characteristics & to compare clinical outcome of patients treated with or without plasmapheresis. Methods: Prospective, Quasi experimental study was done in Neurology, MHL from June, 2020 to June, 2022. Diagnosis of mild GBS was made on basis of patient ability to walk without support/GBS disability score is = 2. Outcome in these patients was assessed by using MRC sum score at presentation one and 3month post treatment. Results: Patients with diagnosis of GBS (n=154) were admitted and treated during 2years.out of these patients, 22(14.28%) were diagnosed as mild GBS, 18 males and 4 females. MRC Sum score at presentation 18.5, at 1-month 19.7 and at 3 month it was 19.5 on average. MRC sum score improvement noted in patients treated with plasma-pheresis at one and three months with p value less than 0.01. There was no difference in recovery with increased number of sessions of plasma-pheresis 3 versus 5 sessions at one month and 3months with p value 0.18 &0.32 respectively. Conclusion: Mild GBS is not uncommon, remained under-reported in clinical scenario. Plasma-pheresis seems to have a beneficial role in mild form of GBS. [ABSTRACT FROM AUTHOR]

The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, including Logistic Regression, Random Forest, Decision Tree, Extra Trees classifier, Support Vector Machine (SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), GaussianNB, and Multilayer Perceptron (MLP). The machine learning models are applied to a concrete dataset acquired from Kaggle. The models underwent training using a dataset that was partitioned into an 80:20 ratio. Machine learning model evaluation involves the utilization of performance measurements such as Accuracy, Precision, Recall, and ROC curves. An exhaustive evaluation is carried out to gauge the efficacy of the models. [ABSTRACT FROM AUTHOR]

The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of machine learning algorithms in the context of intrusion detection, providing valuable insights into their efficacy and potential for enhancing cybersecurity measures. The main objective is to compare the performance of the well-known ML models using the UNSW-NB15 dataset. The performance of the models is discussed in detail with a comparison using evaluation metrics and computational performance. [ABSTRACT FROM AUTHOR]

With the increased accessibility of global trade information, transaction fraud has become a major worry in global banking and commerce security. The incidence and magnitude of transaction fraud are increasing daily, resulting in significant financial losses for both customers and financial professionals. With improvements in data mining and machine learning in computer science, the capacity to detect transaction fraud is becoming increasingly attainable. The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud. The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023, comprising around 550,000 records. The study uses this dataset to assess the performance of well-established machine learning models, measuring their accuracy, recall, and F1 score. In addition, the study includes a confusion matrix for all models to aid in evaluation and training time duration. Machin learning models, including Logistic regression, random forest, extra trees, and LGBM, achieve high accuracy and precision in the credit card fraud detection dataset, with a reported accuracy, recall, and F1 score of 1.00 for both classes. [ABSTRACT FROM AUTHOR]

Allergy is an abnormally extended and evolved sensitivity to specific substances, whereas asthma is constriction of the airways of the lungs due to swelling of internal walls of the airways. Dietary consumption of omega-3 and omega-6 supplementation may help in the prevention of allergies and asthma. The objective of this investigation was to assess the effectiveness of omega 3 and 6 supplementation on patients who were suffering from asthma or any allergy. A cross-sectional study on 100 patients with asthma and different types of allergies was conducted at different hospitals in Lahore. They were given a questionnaire to accumulate input about their dietary practices, understanding, and awareness. Data was analyzed by using SPSS 22.0. Results showed that 40% of patients had mild improvement in symptoms, 28% exhibited good responses, and 32% had no progress in their symptoms at all. About 27% of patients took omega 3 and 6 supplementations weekly, while 36% used to take them daily, and 37% never took the supplementation. It was concluded that most of those patients who took the omega-3 and omega-6 fatty acids supplementations showed mild improvement as compared to those who were not taking supplements. People taking omega 3 and 6 food sources such as fish, nuts, legumes, and meat faced fewer symptoms. [ABSTRACT FROM AUTHOR]

This review aims to assist second language (L2) educators in the developing world, burdened by complexities and obligations imposed by the pandemic era and beyond. Its objective is to enhance the understanding of blended learning and its multitude of models and practices. Blended learning revolutionizes teaching and offers world language educators versatility, adaptability, and the space to customize learning. Although widely used, there is still uncertainty about what blended learning means, how it delivers what it intends to deliver, and which blended learning model might best serve L2 educators. To address these concerns, this paper evaluates various blended learning models in academic contexts, focusing on language instruction. The findings provide valuable insights into the implications, challenges, and opportunities within the field. L2 educators are encouraged to explore suitable blended learning models and address challenges related to digital equity, educator readiness, and content alignment to maximize the benefits of blended learning in language education. [ABSTRACT FROM AUTHOR]

BACKGROUND: The increase in energy and water demand due to industrialization and urbanization requires prioritized solutions for a sustainable future. Microbial electrolysis cells (MECs) have shown huge potential for biohydrogen production along with wastewater treatment. This study examined the effectiveness of employing nickel foam as an anode material for biohydrogen production from the widely available potato industry effluent. RESULTS: Hydrogen production rate increases exponentially with the increase in applied voltage. A maximum hydrogen production rate of 0.69 ± 0.02 m3 H2 m−3 reactor volume d−1 was achieved at 0.9 V with a maximum chemical oxygen demand removal efficiency of 97% at 0.8 V. The effluent of the 0.8 V cycle had the least salinity, low total dissolved solids, 84% reduced total hardness, highest effluent clarity (4.30 NTU, and negligible quantity of heavy metals. CONCLUSION: This study successfully produced biohydrogen from potato wastewater within 5 days of operation along with wastewater treatment of the substrate. The improved performance of the system can be attributed to the unique characteristics of nickel foam, such as high porosity, large surface area and excellent conductivity. The findings of this study have implications for the sustainable treatment of domestic and agro‐industrial wastewater and the development of efficient and low‐cost bio‐electrochemical systems for renewable energy production. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]