Body-Centered Double-Square Split-Ring Enclosed Nested Meander-Line-Shaped Metamaterial-Loaded Microstrip-Based Resonator for Sensing Applications

The strong localization of the electric and magnetic fields in metamaterial-based structures has attracted a new era of radiation fields in the microwave range. In this research work, we represent a double split ring enclosed nested meander-line-shaped metamaterial resonator with a high effective medium ratio layered on a dielectric substrate to enhance the sensitivity for the material characterization. Tailoring a metallic design and periodical arrangement of the split ring resonator in a subwavelength range introduced field enhancement and strong localization of the electromagnetic field. The design methodology is carried out through the optimization technique with different geometric configurations to increase the compactness of the design. The CST microwave studio is utilized for the extraction of the scattering computational value within the defined boundary condition. The effective parameters from the reflection and transmission coefficient are taken into account to observe the radiation characteristics for the interaction with the applied electromagnetic spectrum. The proposed metamaterial-based sensor exhibits high sensitivity for different dielectric materials with low permittivity values. The numerical data of the frequency deviation for the different dielectric constants have shown good agreement using the linear regression analysis where the sensitivity is R² = 0.9894 and the figure of merit is R² = 0.9978.     

Hepatitis B among University Population: Prevalence,Associated Risk Factors,Knowledge Assessment,and Treatment Management

Background: Very few studies have been reported on hepatitis B in the State of Azad Jammu and Kashmir, Pakistan, and none of them are specific to the prevalence and causes of hepatitis B spread among educational institutes. This study aimed to estimate the prevalence of hepatitis B infection and its associated risk factors among the University of AJ and K population. Methods: An observational, cross-sectional, and analytical study was conducted with 7015 students and employees. Hepatitis B was detected by rapid immunochromatographic tests (ICTs), enzyme-linked immunosorbent assay (ELISA), and real-time quantitative PCR. A questionnaire and interview method was used to assess the disease knowledge and associated risk factors with hepatitis B through Chi-square, Fisher’s exact test, and paired t-test. Results: Of the participants, 150 (2.13%) were found positive for the hepatitis B surface antigen (57.3% male and 42.7% female). Only 0.3% participants were found fully vaccinated against the hepatitis B virus. Among ethnic groups, the Syed tribe was found more prevalent for hepatitis B infection (40.6%), while use of contaminated mourning blades (95% CI: p = 0.0001) was found as an overlooked risk factor. Hepatitis preventive awareness sessions were found to be very significant (p = 0.0001). Conclusions: The study showed that an overlooked risk factor is playing a key role in the spread of HBV in a tribe living worldwide, which must be addressed globally to eradicate hepatitis B. In Pakistan, a country-wide annual HBV vaccination program should be launched to control hepatitis B.     

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic’s short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic’s aftereffects, utilising the notion of the bullwhip phenomenon’s short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.     

Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.     

Ternary Copper Tungsten Sulfide (Cu2WS4) Nanoparticles Obtained through a Solvothermal Approach: A Bi-Functional Electrocatalyst for the Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER)

In this work, Cu₂WS₄ nanoparticles have been synthesized via a solvothermal decomposition approach using a heterobimetallic single source precursor, WCu₂S₄(PPh₃)₃. The single source precursor, WCu₂S₄(PPh₃)₃, has been characterized using multinuclear NMR spectroscopy, while Cu₂WS₄ nanoparticles have been characterized by powder X-ray diffraction (PXRD) for which Rietveld refinement has been performed to authenticate the lattice structure of the decomposed product, Cu₂WS₄. Furthermore, FESEM and EDAX analyses have been performed to assess the morphology and composition of Cu₂WS₄. An electrochemical study in acidic as well as basic media suggested that Cu₂WS₄ nanoparticles possess efficient bifunctional activity towards electrochemical hydrogen as well as oxygen evolution reactions. Linear sweep voltammetry (LSV) performed in 0.5 N H₂SO₄ indicates an onset potential for the HER of 462 mV and a Tafel slope of 140 mV dec⁻¹. While LSV performed in 0.1 M KOH indicates an onset potential for the OER of 190 mV and a Tafel Slope of 117 mV dec⁻¹.     

Fatigue Reliability Characterisation of Effective Strain Damage Model Using Extreme Value Distribution for Road Load Conditions

The aim of this paper is to characterise the fatigue reliability for various random strain loads under extreme value distribution while considering the cycle sequence effect condition in fatigue life prediction. The established strain–life models, i.e., Morrow and Smith–Watson–Topper, considered a mean stress effect and strain amplitude; nevertheless, it excluded the load sequence effect, which involves the fatigue crack closure that is subjected to overload or underload. A FESEM-EDX analysis is conducted to characterise the failure features that occurred on the leaf spring. A finite element is simulated to determine the critical region in order to obtain the strain load behaviour. In addition, the strain signal is captured experimentally at 500 Hz for 100 s under operating conditions for three different road loads based on the critical location obtained from the finite element analysis. The fatigue life correlation shows that the Pearson correlation coefficients are greater than 0.9, which indicates the effective strain damage model is linearly correlated with the strain–life models. The fatigue life data are modelled using extreme value distribution by considering the random strain loads as extreme data. The reliability rate for the fatigue life is reported to be more than 0.59 within the hazard rate range of 9.6 × 10⁻⁸ to 1.2 × 10⁻⁷ based on the mean cycle to the failure point. Hence, the effective strain damage model is proposed for a fatigue reliability assessment under extreme conditions with higher reliability and provides fatigue life prediction when subjected to cycle sequence effects.     

Aramid-wrapped CNT hybrid sol–gel sorbent for polycyclic aromatic hydrocarbons

This work describes the preparation of an analytical microextraction sorbent using a simple and versatile sol–gel hybrid composite, i.e., aramid oligomers wrapping multi-walled carbon nanotubes (CNTs) covalently bonded to a porous silica network. To overcome the inherent shortcomings of the CNTs'' solubility and dispersion in both organic phases and in the sol–gel solution, the outer surface of the CNTs was initially functionalized with carboxylic acid groups and then reacted with both aramid oligomers and 3-aminopropyl triethoxysilane (APTES). The obtained sorbent was characterized by FT-IR, scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Using sol–gel chemistry, the functionalized CNTs were coated onto SPME fibers and used in conjunction with GC-MS for the analysis of polycyclic aromatic hydrocarbons (PAHs) in water and soil samples. Excellent repeatability (run-to-run RSD% ∼ 8) and reproducibility (fiber-to-fiber RSD% ∼ 6) were achieved in addition to low LODs (0.10–0.30 ng mL⁻¹) and noticeable recovery%. The present method of sorbent preparation led to enhanced thermal and chemical stabilities, a long sorbent lifetime and good affinity towards PAHs. Moreover, the present sorbent enhanced the extraction capability by more than 30% compared to that of commercially available PDMS counterparts.

This work describes the preparation of an analytical microextraction sorbent using a simple and versatile sol–gel hybrid composite, i.e., aramid oligomers wrapping multi-walled carbon nanotubes (CNTs) covalently bonded to a porous silica network.     

Blood-Brain Barrier Permeability of Asiaticoside,Madecassoside and Asiatic Acid in Porcine Brain Endothelial Cell Model

Neurotherapeutic potentials of Centella asiatica and its reputation to boost memory, prevent cognitive deficits and improve brain functions are widely acknowledged. The plant's bioactive compounds, i.e. asiaticoside, madecassoside and asiatic acid were reported to have central nervous system (CNS) actions, particularly in protecting the brain against neurodegenerative disorders. Hence, it is important for these compounds to cross the blood-brain barrier (BBB) to be clinically effective therapeutics. This study aimed to explore the capability of asiaticoside, madecassoside and asiatic acid to cross the BBB using in vitro BBB model from primary porcine brain endothelial cells (PBECs). Our findings showed that asiaticoside, madecassoside and asiatic acid are highly BBB permeable with apparent permeability (P_app) of 70.61 ± 6.60, 53.31 ± 12.55 and 50.94 ± 10.91 × 10^?6 cm/s respectively. No evidence of cytotoxicity and tight junction disruption of the PBECs were observed in the presence of these compounds. Asiatic acid showed cytoprotective effect towards the PBECs against oxidative stress. This study reported for the first time that Centella asiatica compounds demonstrated high capability to cross the BBB, comparable to central nervous system drugs, and therefore warrant further development as therapeutics for the treatment of neurodegenerative diseases.