An Insight View on Synthetic Protocol,Surface Activity,and Biological Aspects of Novel Biocompatible Quaternary Ammonium Cationic Gemini Surfactants

In this study, we prepared a novel series of diester-functionalized cationic gemini surfactants (C_m-E2O2-C_m) containing ethylene oxide as a spacer with varying alkyl chain lengths and characterized by ¹H NMR, FT-IR, elemental analysis, and ESI-MS. The physicochemical properties of the geminis were explored by tensiometry, fluorescence, dye solubilization, and Krafft point. These geminis acquire superior surface activity than the conventional surfactants. Fluorescence spectroscopy analysis affirmed that the micropolarity and aggregation number of micelles diminished with increase in the alkyl chain length. These geminis represent a new group of amphiphiles of considerably high biodegradability, better cleavability, and low toxicity as assessed by BOD test, FT-IR analysis, and HC₅₀ analysis, respectively. They also showed significant level of antimicrobial activity toward some specified bacterial strains of Gram-positive and Gram-negative by using agar well diffusion method. Furthermore, the thermogravimetric analysis provided information regarding thermal stabilities of the newly synthesized gemini surfactants.     

Removal of toxic aqueous ions using amorphous AlPO4

Aluminium phosphate (AlPO₄) was characterized using X-ray diffractometry (XRD), Fourier Transform infrared (FTIR), point of zero charge (PZC) and dissolution studies. XRD showed the sample to be amorphous, FTIR confirmed the presence of OH groups on the surface and PZC was determined at pH 3.45. The dissolution study illustrated a decrease in dissolution with an increase in the pH. Potentiometric titration data were fit to the Gaines–Thomas equation, which showed that AlPO₄ is a weakly acidic ion exchanger. Sorption studies were carried out at pH 4–6 and temperatures 293–323 K. The uptake of metal ions was observed to increase with an increase in the pH and temperature. The surface selectivity towards metal ions was found in the order Pb²⁺> Cu²⁺> Cd²⁺. Sorption data were fit to the new equation derived from the proposed mechanism for metal ion uptake. Various parameters such as stoichiometry of the surface H⁺ ion release, equilibrium constant, standard enthalpy, entropy and free energy changes were evaluated from the plots. The values of all these parameters were found to be closely related to the values reported in the literature.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset, where the response variables were divided into two classes: “1” indicated an increase in the exchange rate and “ −1” indicated a decrease in it. To better represent the dataset, we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space. Clusters that were obtained using a sampling approach were then used for data optimization. Five machine learning classifiers—the simple logistic classifier, the random forest, bagging, naïve Bayes, and the support vector machine—were applied to the optimized dataset. The results show that the simple logistic classifier yielded the highest accuracy of 82.14% for the USD and the PKR exchange rates forecasting.     

Proposed Inductor Design for High Speed Digital Communication Systems through Divergence Analysis of Self Inductance and Variability in Permeability Values of Unaltered Human Hair

The Dielectric Losses associated with inductors play more considerable role at higher frequencies. Parasitic capacity determines the magnitude of these losses. The permeability is one of the factors that affect self-inductance of a solenoid directly. Change of medium from free space to any other element like iron or plastic may change the behavior of solenoid. Human hair is primarily composed of keratin. Keratin is considered as a good insulator. However an electric charge gained by the hairs due to friction and this phenomenon is called tribo-electric effect. It gives hair an ability to become charged with static electricity. The scenario tested with coils is wrapped around human hairs and the air between them as a medium. It is hypothesized that replacement of iron by human hair will vary the self-induction. The reference or test validation platform for the experiment was an air and iron core inductor. The reason behind this choice was its wide use in various applications like power supplies, metal detectors, car ignition coils and even in high speed digital systems. The observed values of induced e.m.f for the scenario are used to analyze the deviation in self-Inductance phenomenon. The theoretical model for the air and iron core solenoid is also compared with the resulting values to calculate and reaffirm the permeability values of the human hair especially for South Asian ethnic region.     

Predicting scientific impact based on <Emphasis Type="Italic">h</Emphasis>-index

Predicting the future impact of a scientist/researcher is a critical task. The objective of this work is to evaluate different h-index prediction models for the field of Computer Science. Different combinations of parameters have been identified to build the model and applied on a large data set taken from Arnetminer comprised of almost 1.8 million authors and 2.1 million publications’ record of Computer Science. Machine learning prediction technique, regression, is used to find the best set of parameters suitable for h-index prediction for the scientists from all career ages, without enforcing any constraint on their current h-index values with R ² as a metric to measure the accuracy. Further, these parameters are evaluated for different career ages and different thresholds for h-index values. Prediction results for 1 year are really good, having R ² 0.93 but for 5 years R ² declines to 0.82 on average. Hence inferred that prediction of h-index is difficult for longer periods. Predictions for the researchers having 1 year experience are not precise, having R ² 0.60 for 1 year and 0.33 for 5 years. Considering scientists of different career ages, average R ² values for researchers having 20–36 years of experience were 0.99. For the researches having different h-index values, researchers having low h-index were difficult to predict. Parameters set comprising of current h-index, average citations per paper, number of coauthors, years since publishing first article, number of publications, number of impact factor publications, and number of publications in distinct journals performed better than all other combinations.     

Evaluation of <Emphasis Type="Italic">h</Emphasis>-index,its variants and extensions based on publication age &amp; citation intensity in civil engineering

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards.     

Framework for Analysis of Power System Operation in Smart Cities

For highly populated Asian countries like Pakistan, the smart cities may play numerous positive roles resulting in efficient utilization of available infrastructure. This can be achieved by transforming existing infrastructures into smart ones. To start with such transformation, the main emphasis would be given to energy. For this purpose, the model of a framework capable of analyzing emerging trends in upcoming power system to make it more reliable. This research work focused on the significance of implementing advanced systems like smart grids. Therefore the proposed framework addresses the provision of an integrated simulation tool for in-depth study of system to determine susceptibility of failure, critical situations and restoration scheme by adding renewable energy resources. The planned framework will be used to analyze the impact of emerging trends in power grid operation of intelligent grid which is more commonly known as the smart grid. The framework may also be used to develop contingency plans to optimize the generation, transmission and distribution schemes thus providing the more flexible solutions. The proposed framework may also be used to enhance the security features of supervisory control and data acquisition system which is essential part of such smart transformations.     

Broad‐spectrum inhibitory effect of green synthesised silver nanoparticles from Withania somnifera (L.) on microbial growth,biofilm and respiration: a putative mechanistic approach

Multi‐drug resistance in pathogenic bacteria has created immense clinical problem globally. To address these, there is need to develop new therapeutic strategies to combat bacterial infections. Silver nanoparticles (AgNPs) might prove to be next generation nano‐antibiotics. However, improved efficacy and broad‐spectrum activity is still needed to be evaluated and understood. The authors have synthesised AgNPs from Withania somnifera (WS) by green process and characterised. The effect of WS‐AgNPs on growth kinetics, biofilm inhibition as well as eradication of preformed biofilms on both gram‐positive and gram‐negative pathogenic bacteria was evaluated. The authors have demonstrated the inhibitory effect on bacterial respiration and disruption of membrane permeability and integrity. It was found that WS‐AgNPs inhibited growth of pathogenic bacteria even at 16 µg/ml. At sub‐minimum inhibitory concentration concentration, there was approximately 50% inhibition in biofilm formation which was further validated by light and electron microscopy. WS‐AgNPs also eradicated the performed biofilms by varying levels at elevated concentration. The bacterial respiration was also significantly inhibited. Interaction of WS‐AgNPs with test pathogen caused the disruption of cell membrane leading to leakage of cellular content. The production of intracellular reactive oxygen species reveals that WS‐AgNPs exerted oxidative stress inside bacterial cell causing microbial growth inhibition and disrupting cellular functions.Inspec keywords: silver, nanoparticles, nanofabrication, nanomedicine, antibacterial activity, biomedical materials, cellular biophysics, microorganisms, biomembranes, electron microscopy, oxidation, biochemistry, permeabilityOther keywords: broad‐spectrum inhibitory effect, green synthesised silver nanoparticles, Withania somnifera (L.), microbial growth, putative mechanistic approach, multidrug resistance, therapeutic strategies, bacterial infections, next generation nanoantibiotics, broad‐spectrum activity, WS‐AgNPs, growth kinetics, biofilm inhibition, gram‐positive pathogenic bacteria, gram‐negative pathogenic bacteria, bacterial respiration, membrane permeability, membrane integrity, subminimum inhibitory concentration concentration, biofilm formation, light pathogenic bacteria, electron microscopy, cell membrane, cellular content leakage, intracellular reactive oxygen species, oxidative stress, microbial growth inhibition, Ag     

FTIR and thermodynamic study of Pakistani and international crude oils in 1,4-dioxane

This paper is the continuation of our previous paper, which concerned the viscosity of crude oils in 1,4-dioxane. In this paper, we present the Fourier Transform Infrared (FTIR) spectroscopy and thermodynamic study of Pakistani and International crude oils. Degree of branching, chain length, and degree of substitution in aromatic by FTIR data were calculated. Thermodynamic parameters viz., energy of activation (E_η), free energy change of activation (ΔG^*), latent heat of vaporization (L_v), and entropy change of activation (ΔS^*) have also been evaluated.