Exploring clustering, deep learning, and LLMs in text classification

Natural language processing and sentiment analysis are important in the current era. Many people are working in this domain to understand the human language and try to classify it. Humans are more like to express their opinions using open text rather than pre-defined questions. This study uses different unsupervised, deep learning, and Large Language Models to classify text data. Two datasets of different topics have been chosen for training and evaluating all models. It included Apple product reviews and airline tweets. The study aims to evaluate the performance of different classification algorithms and models to see which one is more accurately classifying tweets as compared to others. This study is also important as the comparison is done with the latest model of openai which is GPT-4. The findings of this research demonstrate that among all the algorithm tested, BERT based model and GPT-4 exhibit superior performance. The Roberta-based bert model depicts 81% accuracy on the Apple dataset while the bert-based-uncased model outperformed others on the airline dataset with an impressive accuracy of 95%. GPT-4 also depicts strong results with an accuracy of 79% for the Apple dataset and 85% for airline sentiment. This is a strong indication that future model of openai or other LLM models might surpass the BERT model. These results and analysis show that LLM models like BERT and GPT-4 are more effective for sentiment classification as compared to traditional machine learning and deep learning algorithms. It is also worth noting that LLM models require less cleaning and pre-processing of datasets as those are already pre-trained models. This feature enhances efficiency and usability. This research provides potential for LLM models in text classification which also offer valuable insights for future research. Overall, this study highlights the power of the LLM model over a conventional model for sentiment data classification. It provides a detail comparison of their performance and to discuss the implificaiotn of these method for the field of natural language processing

An updated review on anti-diabetic agents and their functions: a comparative study

Chronic metabolic disease is considered by a high concentration of glucose in the blood consequent from imperfections in insulin secretion or insulin action. Currently, it is rapidly becoming an epidemic in several nations around the world affecting millions of people. Hence, it is predicted that the number of affected may double in the next couple of years. This increase may be due to the rise in the aging population, adding to an already existing burden on healthcare providers, particularly in developing countries. Based on the unusual elevation of plasma glucose diabetes is divided into two main types, comprising type (1, 2) DM, gestational diabetes mellitus, neonatal diabetes, maturity-onset diabetes of the young (MODY), and squeals induced by endocrinopathies, the consumption of steroids, along with other elements. T1 diabetes mellitus and T2 diabetes mellitus are considered inadequate insulin synthesis. Type 1 diabetes is a condition that usually affects young people, while type 2 diabetes is more common in older individuals who have unhealthy lifestyles. Both types of diabetes have different causes, symptoms, and treatments due to their distinct differences in how the body processes sugar. The aim of the present study is to learn more specifically pertaining to diabetes mellitus, its complications including clinical appearance, associated risk factors, anti-diabetic regime and its consequences at present

First Report on Clinical Feasibility of Dried Blood Spot Technique for Hemoglobin Estimation in Cholistani Cattle

Background: The dried blood spot (DBS) technique using filter papers has revolutionized the conventional blood sampling techniques through ease of blood collection, storage and transport. Various analytes (such as hormones, antigens, antibodies and hematochemical attributes) are being estimated through DBS globally. However, this technique has not yet been implied in Pakistan. This research work is the first of its kind regarding hemoglobin (Hb) estimation in Cholistani cattle (n=63) blood through DBS technique using filter paper.Methods: Three methods of Hb estimation were implied in the present study viz. through veterinary hematology analyzer (HbA), and two indirect cyanmethemoglobin methods (HbIC and HbICX) using measured (20µL) and unmeasured blood drops on the filter paper, respectively.Results: Results revealed that HbA and HbIC were non-significantly (P≥0.05) different from each other, being different from HbICX (P≤0.05). The HbICX gave overestimated values of Hb as compared to HbA and HbIC. The sensitivity, specificity, positive predictive value, and negative predictive value for HbIC were higher being 86.1%, 88.3%, 88.0%, and 86.0%, respectively as compared to the lower values of 45.0%, 12.0%, 12.0% and 45.0%, for HbICX. Bland and Altman test revealed a better level of agreement between HbA and HbIC. Around the mean difference line, there was no proportional bias in data distribution (Mean= -0.16, 95% CI= 0.34 to -0.67). Similarly, measures attained through Cronbach alpha and intraclass correlation coefficient between HbA and HbIC were higher being 0.703 and 0.825 values for single and average, respectively, as compared to the values of 0.200 and 0.333 between HbA and HbICX.Conclusion: It is concluded that the indirect cyanmethemoglobin method for Hb estimation is reliable and accurate for cattle blood, if a measured quantity of blood drop is taken on a filter paper. We recommend this DBS technique for Hb estimation in cattle blood for resource-poor settings and for livestock herds being reared distantly from the laboratories. For future, it is recommended that DBS technique with various other modifications and for other hematochemical biomarkers may be validated for livestock blood.Keywords: Dried blood spot; Hemoglobin; Cholistani cattle  

Consumer motivation by using unified theory of acceptance and use of technology towards electric vehicles

The transport sector is the leading source of growing greenhouse gas (GHG) emissions globally. To consider environmental degradation aspects due to transport, electric vehicles (EVs) have the prospect to lead road transport to electric mobility from conventional petroleum vehicles. Despite various eco-friendly benefits, the EV market penetration ratio is very low, especially in developing countries. The primary reason for low penetration is consumer limited motivation and knowledge about the EVs features. This paper uses a unified theory of acceptance and technology (UTAUT) model to assess consumer motivation and environmental knowledge towards EVs. This research used convenience random sampling to collect data and analyzed the results using the Partial Least Squares (PLS) method on the example of 199 respondents from Malaysia. The study results revealed that factors identified in the motivational context significantly influence consumer intentions to purchase EVs. Perceived environmental knowledge and technophilia have been included in UTAUT from a motivational perspective. Furthermore, a significant relationship between effort expectancy, social influence, technophilia, perceived environmental knowledge, and purchase intention towards electric vehicles has been observed, without performance expectancy. The study findings serve to inform policymakers and automakers to formulate effective marketing strategies to enhance consumer motivation, knowledge, and value creation for EVs in a sustainable era. Ultimately, the policies will help to encourage consumers to buy eco-friendly vehicles that will help reduce transport carbon emissions and attain sustainable development goals (SDGs).peer-reviewe

The effect of improving solid waste collection on waste disposal behaviour and exposure to environmental risk factors in urban low-income communities in Pakistan.

OBJECTIVE: To estimate the effect of improving waste collection services on waste disposal behaviour and exposure to environmental risk factors in urban, low-income communities in Pakistan. METHODS: We enrolled 6 low-income communities in Islamabad (Pakistan), four of which received an intervention consisting of a door-to-door low-cost waste collection service with centralised waste processing and recycling sites. Intervention communities underwent community-level and household-level mobilisation. The effect of the intervention on waste disposal behaviour, exposure to waste and synanthropic fly counts was measured using two cross-sectional surveys in 180 households per community. RESULTS: Intervention communities had less favourable socio-economic indicators and poorer access to waste disposal services at baseline than control communities. Use of any waste collection service increased from 5% to 49% in the intervention communities (difference 44%, 95%CI 41%, 48%), but the increase was largely confined to two communities where post-intervention coverage exceeded 80% and 90% respectively. An increase in the use of waste collection services was also found in the two control communities (from 21% to 67%, difference 47%, 95%CI 41%, 53%). Fly counts decreased by about 60% in the intervention communities (rate ratio 0.4, 95%CI 0.3, 0.4) but not in the control communities (rate ratio 1.52, 95%CI 1.1, 2.2). The decrease in fly counts was largely confined to the two high-coverage intervention communities. CONCLUSION: Introduction of a low-cost waste collection service has the potential for high uptake in low-income communities and for decreasing the exposure to waste and synanthropic flies at household level. Intervention success was constrained by low uptake in half of the intervention communities

Genome-Wide Analysis of Protein-Protein Interactions and Involvement of Viral Proteins in SARS-CoV Replication

Analyses of viral protein-protein interactions are an important step to understand viral protein functions and their underlying molecular mechanisms. In this study, we adopted a mammalian two-hybrid system to screen the genome-wide intraviral protein-protein interactions of SARS coronavirus (SARS-CoV) and therefrom revealed a number of novel interactions which could be partly confirmed by in vitro biochemical assays. Three pairs of the interactions identified were detected in both directions: non-structural protein (nsp) 10 and nsp14, nsp10 and nsp16, and nsp7 and nsp8. The interactions between the multifunctional nsp10 and nsp14 or nsp16, which are the unique proteins found in the members of Nidovirales with large RNA genomes including coronaviruses and toroviruses, may have important implication for the mechanisms of replication/transcription complex assembly and functions of these viruses. Using a SARS-CoV replicon expressing a luciferase reporter under the control of a transcription regulating sequence, it has been shown that several viral proteins (N, X and SUD domains of nsp3, and nsp12) provided in trans stimulated the replicon reporter activity, indicating that these proteins may regulate coronavirus replication and transcription. Collectively, our findings provide a basis and platform for further characterization of the functions and mechanisms of coronavirus proteins

Sigh in patients with acute hypoxemic respiratory failure and acute respiratory distress syndrome: the PROTECTION pilot randomized clinical trial

Background: Sigh is a cyclic brief recruitment manoeuvre: previous physiological studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity and increase release of surfactant. Research question: Is the clinical application of sigh during pressure support ventilation (PSV) feasible? Study design and methods: We conducted a multi-center non-inferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or acute respiratory distress syndrome undergoing PSV. Patients were randomized to the No Sigh group and treated by PSV alone, or to the Sigh group, treated by PSV plus sigh (increase of airway pressure to 30 cmH2Ofor 3 seconds once per minute) until day 28 or death or successful spontaneous breathing trial. The primary endpoint of the study was feasibility, assessed as non-inferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiological parameters in the first week from randomization, 28-day mortality and ventilator-free days. Results: Two-hundred fifty-eight patients (31% women; median age 65 [54-75] years) were enrolled. In the Sigh group, 23% of patients failed to remain on assisted ventilation vs. 30% in the No Sigh group (absolute difference -7%, 95%CI -18% to 4%; p=0.015 for non-inferiority). Adverse events occurred in 12% vs. 13% in Sigh vs. No Sigh (p=0.852). Oxygenation was improved while tidal volume, respiratory rate and corrected minute ventilation were lower over the first 7 days from randomization in Sigh vs. No Sigh. There was no significant difference in terms of mortality (16% vs. 21%, p=0.342) and ventilator-free days (22 [7-26] vs. 22 [3-25] days, p=0.300) for Sigh vs. No Sigh. Interpretation: Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk

Core Urdu Vocabulary for Chines Business Community in Pakistan, A Corpus-based Perspective

With the dawn of 21st century, the world has grown into a global village and the need for inter-communal interactions has also increased many times.  Urdu language is said to be one the third biggest language of the world along with Chines and English and its speakers are constantly on the rise. With the emergence of the CPEC (China Pakistan Economic Corridor), Urdu has assumed ever increasing importance due to the geo-political and geo-economic condition of the south Asian region. The undertaken study is a systematic attempt in this regard to work out a list of most frequent words of the Urdu language for the Chinese business community in Pakistan.  Schmitt (2000) asserts that that learning a non-native vocabulary is a continual process as the core vocabulary should encompass the ever changing linguistic needs of the time. The name of the Urdu corpus for the undertaken research is urTenTen that has been compiled from internet data. The corpus belongs to TenTen corpus family that is corpora of the web with more than ten billion words. The corpus has been tagged according to Unified Parts of Speech (POS) Standard in Indian Languages.  In order to process data, “sketch Engine” has been used. List of frequent words for the Chines Business Community has been retrieved from urTenTen corpus with the help of sketch engine.  The retrieved list of core Urdu vocabulary is supposed to be useful for the Chines business who is supposed to interact with the Urdu speakers of the region