15 research outputs found
Measuring vaccination willingness in response to COVID-19 using a multi-criteria-decision making method
No full textThe pandemic COVID-19 is continued to the massive burden of morbidity and mortality while disrupting economies and societies all over the world. At an earlier stage, wearing a face mask, social distancing, and hand hygiene were suggested to limit the transmission of this infection. The WHO, CDC, and other governing bodies were doing an effort to develop the coronavirus vaccine. Now COVID-19 vaccines are available to protect people against the coronavirus. People are hesitant about whether to receive a vaccination or do not to vaccinate. This study was aimed to analyze the COVID-19 vaccination willingness level of the general public of Pakistan to manage the COVID-19 disease. A multi-criteria decision-making method known as an analytical hierarchical method was applied to determine the COVID-19 vaccination willingness level of the public. The significant determinants of COVID-19 vaccination willingness were cues to action, perceived benefits, positive attitude, government recommendation, as well as perceived stress scoring high weights to the vaccination. Determinants of willingness to uptake the COVID-19 vaccine were individual decision, vaccine origin, adapting to change, and perceived barriers high obstacles to vaccinating. The determinants relating to the COVID-19 vaccine may help to increase the uptake of the vaccination program. The government may need communication campaigns to reinforce the benefits of the vaccine
RECENT ADVANCES IN GAS, LIQUID AND SUPERCRITICAL FLUID CHROMATOGRAPHY IN THE CONTEXT OF SUSTAINABLE/ GREEN CHEMISTRY
No full textBackground: Chromatographic techniques such as gas chromatography (GC), liquid chromatography (LC) and supercritical fluid chromatography (SFC) have long been employed in analytical chemistry. However, using or producing certain organic solvents and non-renewable gases is hazardous to the environment. Green chemistry is essential for addressing the environmental hazards associated with traditional procedures. Objectives: This review focuses on recent updates and profound insights into new, greener techniques introduced in GC, LC and SFC. Methodology: To compile this review, several electronic databases were searched, preferably from January 2019-December 2023. Results: This review highlights advancements in miniaturization, rapid methods, and eco-friendly solvents in chromatographic techniques. Integrating micro-electro-mechanical systems (MEMS) and nano-electro-mechanical systems (NEMS) in GC enhances sensitivity and resolution while reducing solvent consumption. Innovations like monolithic micro-GC chips and the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method further streamline analysis times. In LC, supercritical fluids are utilized as green mobile phases, optimizing techniques like electrochemical flow liquid chromatography (EFLC), high-performance liquid chromatography (HPLC), and ultra-high-performance liquid chromatography UHPLC. In contrast, SFC employs rapid methods, such as ultra-high-performance supercritical fluid chromatography-mass spectrometry (UHPSFC-MS). These developments collectively promote sustainability and efficiency in chromatographic practices. Conclusion: Green chromatography has become an essential tool in pharmaceutical analysis because it safeguards the operator’s health and the environment. It opens new horizons and generates new ideas for further experimentation while focusing on recent cutting-edge green technologies incorporated in GC, LC and SFC. These advancements significantly enhance safety and sustainability in analytical practices
Artificial Jellyfish Search Algorithm-Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter
No full textThis paper used an artificial jellyfish search (AJFS) optimizer suitable for selective harmonic elimination-based modulation for multilevel inverter (MLI) voltage control application. The main objective was to remove the undesired lower-order harmonics in the output voltage waveform of an MLI. This algorithm was motivated by the behavior of jellyfish in the ocean. Jellyfish have the ability to find the global best position where a large quantity of nutritious food is available. The paper applied AJFS algorithm on five, seven, and nine levels of CHB-MLI. The optimum switching angle was calculated for the entire modulation range for the desired lower-order harmonics elimination. The problem formulated to achieve the objective was solved in a MATLAB environment. The total harmonic distortion (THD) values of five-, seven-, and nine-level inverters for various modulation indexes were computed using AJFS and compared with the powerful differential evolution (DE) algorithm. The comparison of THD results clearly demonstrated superior THD in the output of CHB-MLI of the AJFS algorithm over DE and GA algorithm for low and medium values of modulation index. The experimental results further validated the better performance of the AJFS algorithm
Computational prediction for designing novel ketonic derivatives as potential inhibitors for breast cancer: A trade-off between drug likeness and inhibition potency
No full textUnlike simple molecular screening, a combined hybrid computational methodology has been applied which includes quantum chemical methods, molecular docking, and molecular dynamics simulations to design some novel ketonic derivatives. The current study contains the derivatives of an experimental ligand which are designed as a trade-off between drug likeness and inhibition strength. We investigate the interaction of various newly designed ketonic compounds with the breast cancer receptor known as the Estrogen Receptor Alpha (ERα). The molecular structures of all newly designed ligands were studied quantum chemically in terms of their fully optimized structures, 3-D molecular orbital distributions, global chemical descriptors, molecular electrostatic potentials and energies of frontier molecular orbitals (FMOs). All ligands under study show good binding affinities with the ERα protein. The ligands CMR2 and CMR4 exhibit improved molecular docking interactions. The intermolecular interactions indicate that CMR4 demonstrates better hydrophobic and hydrogen bonding interactions with protein (ERα). Furthermore, molecular dynamics simulations were conducted on ligands and reference drugs interacting with the ERα protein over a time span of 120 nanoseconds. The molecular dynamics results are interpreted in terms of ligand-protein stability and flexible behaviour based on their respective values of RMSD, RMSF, H-bonds, the radius of gyration, and SASA graphs. To analyse ligand-protein interactions throughout the entire 120 ns trajectory, a more advanced MM/PBSA method is utilized, where six selected ligands (CMR1, CMR2, CMR3, CMR4, CMR5 and CMR9) illustrate promising results for inhibition of the ERα receptor as assessed through MM/BBSA analysis. The CMR9 has the highest MM/BBSA binding free energy (−14.46 kcal/mol). The ADMET analysis reveals that CMR4 has maximum intestinal absorption (6.68) and clearance rate (0.1). All the compounds are non-toxic and safe to use. These findings indicate the potential of involving different computational techniques to design the ligand structures and to study the ligand-protein interactions for better understanding and achieving more potent synthetic inhibitors for breast cancer.The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding the work. The author from University of Bisha is thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting the work through the Fast-Track Research Support Program. For computer time, this research used the resources of Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Comparative simulation of silicon, PDMS, PGA and PMMA actuator for piezoelectric micropump
No full textA Framework to Predict the Quality of a Video for Popularity on Social Media
No full textYouTube has become a dominant force in digital media, yet current video popularity analytics remain limited in capturing the emotional and cultural dimensions of viewer engagement, particularly in underrepresented regions like Pakistan. While existing research focuses predominantly on Western markets and quantitative metrics (views, likes, comments), these approaches overlook sentiment-driven interactions critical to understanding regional audience behavior. This study bridges this gap by introducing a sentiment-aware framework for YouTube video classification in Pakistan, combining traditional popularity metrics with advanced sentiment analysis of user comments. We curated the PAK VIDEOS (2021–2023) dataset using YouTube Data APIs, comprising metadata and user comments from Pakistan's top trending videos. Leveraging Natural Language Processing (NLP) techniques, we extracted sentiment scores from comments to classify videos into four categories: non-popular, overwhelmingly positive, overwhelmingly negative, and neutral. This hybrid approach enabled a nuanced evaluation of content reception beyond quantitative metrics. Four machine learning models—random forest, stochastic gradient descent classifier (SGDC), gradient boosting, and XGBoost—were evaluated for classification. XGBoost achieved superior performance (84.3% accuracy), outperforming baseline models by up to 20%. Our framework demonstrates that integrating sentiment analysis significantly enhances popularity prediction, particularly in culturally distinct contexts
Study of the drug release profile of novel polymer-drug matrix formulations prepared by hot melt extrusion /
No full textThis publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 16/RC/3872. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission’.Hot Melt Extrusion (HME) is an emerging technology in the pharmaceutical industry for manufacturing
drug delivery devices. In the HME process, the polymer and drug are melted and mixed with the help of
heat and mechanical stresses. HME offers various advantages compared to other pharmaceutical
processes; it is a solvent-free process, it is possible to manufacture different dosage forms including
implants, tablets, granules, pellets, it can enhance the solubility and bioavailability of poorly water-soluble
drugs, and it is a continuous process. However, due to the involvement of heat and shear stresses, the
processing of heat-sensitive polymers, e.g. PLA, with drugs is challenging. Polylactide (PLA) is a
bioresorbable FDA approved biopolymer. In recent years PLA has gained particular interest in the medical
industry, and PLA-based drug-eluting implants are used in many different applications, including dental,
cardiac, orthopaedic and tissue engineering applications. The benefit of using PLA-based drug-eluting
implants is that they slowly release the entrapped drug and degrade naturally into non-toxic by-products
over time, excluding the need for any surgical method for their removal. However, despite the advantages
of HME processing, achieving consistent quality products can be challenging. One of the challenges faced
by the pharmaceutical industry is that large ratios of new drug entities belong to class BCS II, which are
poorly water-soluble drugs. Poor solubility of drugs has been a major hindrance to the development of
more effective drug delivery methods. Soluplus (polyvinyl-caprolactam polyvinyl-acetate copolymer
polyethylene glycol graft) is an amphiphilic polymer and has the ability to solubilise the poorly water soluble drugs and has been developed to enhance the bioavailability of poorly water-soluble drugs.
In this work, we explore the production of drug-loaded PLA and Soluplus products with a HME process.
Two different drugs, including ibuprofen and dexamethasone, are extruded with PLA. Further, ibuprofen
which is a poorly water-soluble drug (melting point 77°C) is extruded with Soluplus. The purpose is to
investigate the processability as well as the effect of drug loadings and processing conditions, including
temperature and screw speed, on the drug release profile. DSC is used to study the miscibility of the
polymer-drug matrix, FTIR is used to study the interaction of polymer-drug matrix, and drug-dissolution
tester is used to study the percentage drug releaseye
G-C3N4/Ag@CoWO4: A novel sunlight active ternary nanocomposite for potential photocatalytic degradation of rhodamine B dye
No full textPresent study reports the fabrication of novel sunlight active heterogeneous photocatalyst, i.e. Ag@g-C3N4/CoWO4 for potential degradation of rhodamine B dye. The ternary nanocomposite was fabricated using thermal condensation of melamine to prepare g-C3N4 followed by coupling with silver doped cobalt tungstate (Ag@CoWO4) using the hydrothermal method. The novel composite photocatalyst (Ag@g-C3N4/CoWO4) along with pristine photocatalysts (g-C3N4 and Ag@CoWO4) were well characterized in term of morphology (scanning electron microscopy), structure (Fourier Transformed Infrared spectroscopy), crystallinity (X-ray diffraction), and composition (energy dispersive X-ray). The energy band gaps of catalysts were calculated using UV–visible spectroscopic analysis (Tauc plot). The characterization analysis supports the successful assembly of Ag@CoWO4 nanoparticles on the surface of g-C3N4 nanosheets with good crystallinity. The photocatalytic potential of novel catalysts was examined through the degradation of rhodamine B dye in water. The engineered heterojunction promotes photocatalytic activity and improves photo-generated charge separation. The results of the proposed research showed boosted sunlight active photocatalytic efficiency (97% in 120 min at pH 6) of novel composite against rhodamine B dye degradation. The kinetics of the reaction was determined using different models and RSM was used as a statistical tool for interaction and individual effects of influencing parameters. The numerical values of optimized parameters endorsed the results of RSM i.e. composite dose = 10mg/100 mL, H2O2 = 15 mM, and pH = 6.Dr. Muhammad Zahid (corresponding author) is thankful to TWAS (Grant No. 15-410 RG/MSN/AS_C–FR3240288961 under TWAS-COMSTECH joint Research Grant) for equipments and the University of Agriculture Faisalabad, Pakistan for facilities to conduct this research. The valuable support from Central Lab, LUMS Pakistan for characterization of samples is highly acknowledged
Predicting literature’s early impact with sentiment analysis in Twitter
No full textTraditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature’s early impact
Author Correction: Proline-mediated redox regulation in wheat for mitigating nickel-induced stress and soil decontamination
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