COMSATS University Islamabad

We present a theory for determining the linear dimensions of compact rectangular microwave patch antennas on metamaterial substrates with a high real part of the effective relative permittivity. This theory demonstrates that significant miniaturization of the volume profile of such antennas is achievable with enhanced performance using a metamaterial substrate instead of a dielectric substrate. It is assumed that the metamaterial substrate is a host dielectric medium with periodically embedded metallic inclusions. The proposed theory is based on a simple analytical algorithm design to minimize the volume profile of the antenna patch. It establishes a relationship between the effective relative permittivity of the substrate, the resonant frequency, and the substrate thickness. The proposed approach achieves up to 80% reduction in the antenna volume profile. Notably, the proposed optimization approach does not impose any restrictions on the geometry of the metamaterial unit cell used to create the antenna substrate except for the case of positive values of the effective relative permittivity and permeability. Furthermore, it does not require substantial computational resources for designing the linear dimensions of patch antennas. The derived relations are intended to be used along with modern electromagnetic simulators for the CAD design of compact microwave metamaterial patch antennas with a rectangular patch and the substrate with cylindrical copper inclusions of circular cross section. The proposed optimization theory is validated through an electromagnetic simulator based on the finite difference time-domain method. Moreover, appropriate computer simulations have shown that employing metamaterials in place of conventional dielectric materials to create the substrate not only leads to the miniaturization of the antenna but also enhances its overall performance.

The Delay Tolerant Network (DTN) communication model addresses the challenges of data transmission in environments characterised by intermittent connectivity, frequent disconnections, and limited resources. The Store, Carry, and Forward paradigm is implemented to facilitate the transfer of messages to distant destinations. The efficiency of this paradigm relies heavily on message storage and forwarding, which directly influence network throughput and protocol effectiveness. Storage policies are designed to manage buffer occupancy while forwarding mechanisms focus on transmitting messages to nodes that may only be intermittently connected. Current message forwarding protocols utilize various metrics, such as encountering patterns, social communities, and location data, to make transmission decisions. However, these protocols often overlook critical factors like bandwidth, buffer space, and energy consumption, which can deplete the already scarce network resources. The Social Resource Consumption-Based Message Forwarding (SRC-MF) protocol for DTNs in urban areas introduces a novel social metric called resource awareness. This metric integrates resource consumption metrics with social parameters, enabling a more accurate assessment of a node’s capability to forward messages. The performance of the SRC-MF protocol, along with existing protocols such as Epidemic, PRoPHET, and MaxProp, is evaluated using several key metrics, including buffer consumption, energy consumption, bandwidth consumption, delivery ratio, message loss, and forwarding accuracy, under real-time mobility conditions.

In this paper, we investigate the structural and topological properties of two-dimensional nickel-based organometallic frameworks (2D Ni-MOFs) using various topological descriptors and entropy measures. The frameworks are analyzed by calculating degree-based, edge-based, and Shannon entropy measures to quantify structural complexity and connectivity patterns. Additionally, logarithmic regression models are applied to establish the relationship between entropy measures and topological indices. The results demonstrate that entropy measures effectively capture the structural characteristics of the networks, with logarithmic models providing excellent fits, as evidenced by high $R^2$ values. Violin plots are used to represent the distribution of entropy values and how they correlate with the respective topological indices. This article offers insightful information on the application of topological descriptors and entropy values in the exploration of complex networks and offers a useful approach to interpreting the structural characteristics of two-dimensional nickel-based organometallic frameworks.

In the drug delivery systems and biomedical fields, pH‐sensitive hydrogels have found potential applications. In this research study, a novel hydrogel of chitosan/poly (vinyl alcohol), cross‐linked with tetraethoxyorthosilane, is synthesized for drug delivery by a solution casting method. Graphene oxide (GO) and iron sulfide (FeS 2 ) nanoparticles were incorporated into hydrogels to enhance their swelling properties in external media, which made them fascinating in drug delivery applications. FeS 2 nanoparticles were synthesized by the solid‐state method and characterized by x‐ray diffraction (XRD). The crystallinity of synthesized nanocomposite hydrogels and the dispersion of nanoparticles into the matrix of hydrogels were characterized by the XRD technique. In the nanocomposite hydrogel, the existence of integrated constituents and crosslinking was interpreted by FT‐IR. The surface morphology of hydrogels was exhibited by SEM. The swelling characteristics of hydrogel were examined by studying their behavior in distinct phases, such as aqueous, pH, and electrolyte solutions. The maximum swelling of 0.20 g/g, 0.19 g/g, and 0.07 g/g was observed in CS/PVA/GO + FeS 2 (TA‐3), CS/PVA/GO (TA‐1), and CS/PVA/FeS 2 (TA‐2) hydrogel, respectively. The most suitable hydrogel with high swelling performance was loaded with a model drug. Cefadroxil monohydrate (CMO), an antibacterial drug, was selected as a model drug for loading. In vitro drug release behavior was studied in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF). The release profile of the cefadroxil monohydrate drug was investigated by using a UV spectrophotometer at 264 nm. The release profile exhibited 1.2% release in SGF (pH 1.2) and 92.15% controlled release in SIF (pH 6.8). These results indicate that the synthesized nanocomposite hydrogels hold the potential as promising antibacterial drug delivery systems.

Objective This study aims to fabricate dual drug-loaded nanofibrous films made from polyvinyl alcohol (PVA) and chitosan, incorporating cefadroxil and mupirocin to meet the critical needs of burn wound care. Methods Electrospinning was utilized to fabricate cefadroxil- and mupirocin-loaded polyvinyl alcohol PVA/Chitosan nanofibers. Characterization of structural and morphological properties of these nanofibers was done through Fourier Transform IR Spectroscopy, Scanning Electron Microscopy, Thermal analysis by TGA, and XRD spectroscopy. The kinetic profiles of the drug release mechanisms were considered to determine the release of cefadroxil and mupirocin. Antibacterial activity was determined against the bacteria Staphylococcus aureus and Pseudomonas aeruginosa, while the wound healing efficacy was tested in a rabbit model using full-thickness wounds. Results SEM analysis demonstrated the formation of uniform and smooth nanofibers possessing a well-defined morphology. FTIR spectroscopy confirmed the successful incorporation of cefadroxil and mupirocin into the PVA/Chitosan matrix. TGA analysis indicated the thermal stability of the nanofibers, while XRD results suggested that the drugs were either molecularly dispersed or in an amorphous state within the biopolymeric blend. Drug release studies showed distinct profiles, with an initial burst release followed by sustained drug release. Over 80% of mupirocin was released within the first 2 hours, while cefadroxil exhibited a cumulative release exceeding 60%. Antibacterial assays showed significant inhibition zones, with the largest being 20 mm against Staphylococcus aureus. In vivo studies utilizing a full-thickness rabbit wound model revealed that the drug-loaded nanofibers accelerated wound contraction, achieving approximately 90% closure by day 17, compared to less than 70% for the control. Conclusion The study demonstrates that cefadroxil-mupirocin nanofiber films provide superior antibacterial activity and faster wound healing rates, highlighting their potential in advanced burn wound management.

Salinity is a significant constraint for crop production globally, alongside other stresses such as drought. While exogenously applied proline has been shown to mitigate the negative effects of these stresses, the comparative effects on maize genotypes, including their fluorescence responses and antioxidant enzyme activities under salinity stress, remain underexplored. In this regard, a hydroponic study was conducted using two maize genotypes, Monsanto-6525 and Pioneer-33H25, exposed to four treatments including, salinity (75 mM NaCl), proline (30 mM), and proline (30 mM) + salinity (75 mM NaCl), and a control. Foliar application of proline (control, 30 mM) was done at 15 mL per plant after 20 days of transplantation. Results showed that proline application positively influenced maize growth (shoot and root dry weights), physiological responses (total chlorophyll contents, relative water contents, membrane stability index), as well as proline and ionic compositions, including shoot and root sodium (Na) and potassium (K) concentrations. The Na was reduced, and K was improved in both maize genotypes by foliar-applied proline, however, this improvement was more perceived in Monsanto-6525 cultivar when compared with Pioneer-33H25. In addition, chlorophyll fluorescence parameters and antioxidant enzyme activities (SOD, CAT, total APX) were significantly modulated under salt stress, with proline treatment helping to enhance antioxidant defense and photochemical efficiency. Overall, the study suggests that foliar application of proline can be a promising strategy for improving maize tolerance to salinity stress, with cultivar-specific responses indicating the potential for targeted application.

Bioactive natural compounds and their analogues play a significant role in the treatment of fatal diseases such as malaria, pneumonia, hepatic disorders, heart diseases and various types of cancer. Existing chemoprotective treatments for different types of cancer employ numerous natural compounds from medicinal plant extracts, fruits, vegetables, herbs, microorganisms and marine life. One of these bioactive compounds is betaine, which is a natural constituent with remarkable antioxidant and anticancer properties. The betaine scaffold and its derivatives have demonstrated suppressive effects on the progression, metastasis and angiogenesis of cancer cells. Although multiple studies have reported that betaine and its derivatives demonstrate remarkable efficiency in treating cancer or preventing tumor growth, a review summarizing the literature on this important anticancer agent is lacking to date. Thus, the present review article provides a detailed overview of the reported literature on the use of betaine and its derivatives in the prevention of different types of cancers and discusses the inhibitory activities of betaine against various types of tumorigenesis and its mechanism of action. The synergistic effects of betaine with phytochemicals and nanostructure-based medication delivery systems and its molecular modification for lowering the risk of cancer in humans are also discussed in detail.

The aim of current work is to perform the numerical solutions of the fractional order nonlinear mathematical lungs cancer model by applying a stochastic computing approaches. The fractional kind of the derivatives is used to present the reliable and accurate performance of the lungs cancer model in comparison with the integer order derivatives. The lungs cancer model is categorized into the cells of cancer in lung tissue, cancer cells spread to the remaining body parts, and cells of immune in lung tissue. Three cases of the fractional order derivatives between 0 and 1 have been used to present the numerical solutions of the model in order to check, which fractional values perform better closer to 0 or 1. A construction of single hidden layer is performed by applying the radial basis activation function, whereas the tests of optimization are provided through the Bayesian regularization. A dataset is collected through the Runge–Kutta solver, which is used to reduce the mean square error by data separating into training, verification and testing with reasonable percentages. The proposed stochastic computing process contains a radial basis merit function, twenty neurons, and the optimization through the Bayesian regularization. The correctness of proposed solver is perceived via outputs overlapping, and minor absolute error. Moreover, the reliability of scheme is approved through different tests including state transition, regression coefficient performances, and error histogram values.

In this study, we apply the Natural Transform Iterative Method (NTIM), a relatively new and efficient analytical tool for solving fractional differential equations (FDEs). NTIM combines the Natural Transform and Daftardar-Jafari polynomials to construct approximate solutions without the need for discretization, small parameters, or linearization. This method is particularly effective for handling fractional partial differential equations due to its simplicity and rapid convergence. In comparison with other established techniques, such as the Homotopy Perturbation Method (HPM), Adomian Decomposition Method (ADM), and Fractional Homotopy Analysis Transform Method (FHATM). NTIM demonstrates improved accuracy and computational efficiency. The method is validated through numerical and graphical comparisons with exact solutions, showing its potential for broader applications in nonlinear fractional systems.

The biopolymers known as polyhydroxyalkanoates (PHA’s) are composed of a variety of microorganisms that are responsible for their preparation and composition. These naturally occurring polymers possess certain physical characteristics that are identical to those of plastics derived from petroleum, such as polypropylene. Based on this information, it can be deduced that these natural polymers possess the capability of functioning as an alternative to plastics that are generated from petroleum. Polyhydroxybutyrate (PHB), polyhydroxyvalerate, and their copolymer (PHBV) are the PHA’s that are found in the greatest abundance in a variety of microorganisms from across the world. In this research, we have made a chemical structure model of PHB and determined its eccentricity tables and also calculated eccentricity-based TI’s. For PHB, the computation of eccentricity-based TI’s provides important information about its structural characteristics and possible uses. Additionally, the application of TI’s may improve polymer performance prediction models, directing the future development of more sustainable and effective materials.

Based on AdS/CFT correspondence, our study delineates a crucial correlation between the dynamics of Einstein ring and LQG parameter a , horizon $\rho _{h},$ ρ h , observational positions and the wave source $\omega ,$ ω , which aptly reflects the influence of space-time geometry on the Einstein ring. The augmentation of a and $\rho _{h}$ ρ h leads to the growth of the peak of absolute amplitude of the response function, which is generated due to diffraction of the scalar wave on the AdS boundary. The special optical system helps us to construct holographic images on the screen. The results indicate that the smaller $\rho _{h}$ ρ h shows a series of concentric striped patterns for an observer, and the brightest ring is close to the boundary and gradually shrinks with the aid of $\rho _{h}.$ ρ h . Our analysis reveals that the Einstein ring, which corresponds to the photon ring in geometric optics, constantly exhibits high levels of observational intensity at the north pole. With changes in observation positions, the ring gradually transforms into a bright arc-like shape and ultimately changes into a light spot on the screen’s left. The impact of variations of a on the Einstein ring is also presented in the bright curves, where the peak of the curves slightly moves towards the boundary with the increase of a . The influence of $\omega$ ω reflects on the ring; the width of the ring sharply reduces as $\omega$ ω grows, which can also be confirmed from the corresponding brightness profiles. Finally, the comparison between wave optics and geometric optics outcomes is examined in depth.

This paper investigates several Corrected Euler–Maclaurin-type inequalities for different function classes using Riemann–Liouville fractional integrals. The results, which are derived from special cases of theorems and illustrative examples, are subsequently presented. Furthermore, the authors have developed fractional Corrected Euler–Maclaurin-type inequalities for bounded functions. In addition, the research has acquired fractional Corrected Euler–Maclaurin-type inequalities for Lipschitzian functions. Finally, the study concludes with the derivation of fractional Corrected Euler–Maclaurin-type inequalities for functions of bounded variation.

People in various occupations are chronically exposed to a wide range of workplace pollutants that affect overall health. Among these, certain heavy metals pose a risk even at low concentrations over prolonged exposure. This study aims to examine the health effects of specific heavy metals with chronic exposure in different occupational groups within the Pakistani population. A case–control approach was used, comprising 404 individuals with chronic exposure and 170 age- and gender-matched controls. Volunteers from seven different occupational groups participated in the study, including pump attendants, automobile mechanics, woodworkers, garment workers, furniture shop workers, electric/electronics workers, and office employees. Heavy metals were identified using the ICP-MS method, while renal, hepatic, pancreatic, and lipid parameters were assessed through standard colorimetric and enzymatic techniques. Heavy metals, including Mn, Ni, Co, Cu, and Pb, were detected at varying concentrations, with higher levels observed in exposed groups compared to the control group. Hepatic markers (ALT, AST, ALP, LDH, and GGT), renal function tests (BUN and creatinine), pancreatic parameters (insulin, glucose, amylase, lipase, and HOMA-IR), and lipid profile components (triglycerides, LDL, HDL, and total cholesterol) were found to be dysregulated in the exposed group. The findings conclude that the studied occupational groups face multiple health risks due to continuous exposure to the mixture of heavy metals. Notably, the presence of Pb was associated with multiple organ toxicity. Additionally, trace levels of heavy metals were found in workers not directly involved in metal-related tasks, raising concerns in developing countries where workplace safety measures are often inadequate.

Automated anomaly detection in surveillance videos is crucial for ensuring public safety by reducing human intervention and improving decision-making accuracy. This study proposes SurveillanceNet, a novel two-stream spatio-temporal anomaly recognition framework that efficiently extracts spatial and temporal features for multi-class anomaly classification. The spatial stream utilizes DenseNet201 to extract features from individual RGB frames, while the temporal stream employs SG3I images to capture motion patterns without the computational overhead of optical flow or 3D CNNs. The extracted features are fused and processed by a stacked LSTM for sequence classification. Extensive experiments on the UCF Crime dataset demonstrate the effectiveness of the proposed approach, achieving an AUC of 88.0%, outperforming several CNN-LSTM and C3D-based methods while remaining competitive with transformer-based architectures. Unlike recent deep learning models that rely on memory-intensive transformers or computationally expensive optical flow, SurveillanceNet strikes a balance between accuracy and efficiency, making it suitable for real-time surveillance applications. The results highlight the model’s robustness in capturing spatio-temporal dependencies while addressing inter-class similarity challenges. Future research will focus on enhancing dataset diversity, incorporating advanced attention mechanisms, and exploring hybrid architectures for improved anomaly detection performance.

Achieving a clean atmosphere requires the implementation of environmentally sustainable strategies to mitigate the detrimental effects of fossil fuels, which are associated with high sulfur content, substantial greenhouse gas emissions, and limited reserves. Biodiesel, with its low sulfur content, represents a promising and sustainable alternative. Ionic liquids are green catalysts that align with the principles of sustainability and green chemistry, and are used increasingly in biodiesel production. The conversion of CO2 – a major contributor to global warming – into value‐added products offers a viable strategy to mitigate climate change. However, comprehensive reviews addressing the use of ionic liquids as catalysts in biodiesel production and their application in the conversion of CO2 and sulfur compounds into valuable chemicals are lacking. This review provides an in‐depth analysis of recent advances in biodiesel production using ionic liquid‐based catalysts, including magnetic, enzymatic, and photocatalytic systems, with a focus on modifications to their acidic and basic properties. Notably, polyoxometalate‐based ionic liquids have demonstrated complete sulfur removal from diesel. The review also explores the utility of CO2‐derived products in sustainable biodiesel production and evaluates the roles of ionic liquids and deep eutectic solvents as green solvents and catalysts. Greener synthesis pathways for these catalysts and their potential for commercialization are evaluated through techno‐economic assessments.

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37.40 GHz) and n259 (42.43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 × 3.3 λ 2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls × Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4× 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 to 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of <0.5, ensuring good diversity gain of 9.99 dB, minimum isolation of 18 dB, and total efficiency of 86 % at mm-wave n260 band and 89 % at mm-wave n259 band. The measured and simulated results are in good agreement, confirming its viability for future 5G mm-wave IoT devices.

This paper proposes a compact, low-profile, two-port MIMO antenna for portable devices operating in next-generation 5G wireless networks. The proposed design consists of two semi-circular radiating elements on FR4 substrate (εr = 4.4 and h = 1.6 mm). The overall antenna size is 0.40λ × 0.32λ (50 mm × 20 mm) at a center frequency of 4.85 GHz. A key innovation is the incorporation of complementary split-ring resonators (CSRR) in the ground plane, which significantly enhances isolation between the antenna elements. The CSRR technique effectively mitigates mutual coupling, achieving an isolation level below − 15dB, which is critical for MIMO systems to maintain signal integrity and reduce inter-port interference. The proposed antenna offers a broad 47% impedance bandwidth, covering 3.7–6 GHz. This wide bandwidth enables support for various communication standards, including the 5G N79 band, WLAN 4.9–5.0 GHz, and Wi-Fi 5 GHz. Experimental results closely match the simulated data, demonstrating good return-loss performance, high diversity gain over 9.9dB, and a low envelope correlation coefficient below 0.005, ensuring efficient MIMO operation by minimizing inter-port interference. The omnidirectional radiation patterns across the XZ- and YZ-planes further validate the antenna’s ability to provide stable and uniform coverage.

Spatio-temporal variations of mercury concentrations in air and soil were measured near 20 formal e-waste recycling facilities and 8 background locations in 8 provinces in Türkiye between June 2021 and May 2022. Annual average Gaseous Elemental Mercury (GEM) concentrations in air at the studied formal e-waste facilities averaged 34 ng/m3 (range from 2.2 to 273 ng/m3), exceeding by more than an order of magnitude average levels of 2.2 ng/m3 (range from 1.6 to 2.6 ng/m3) at background sites. Total mercury concentration (THg) concentration in soils near formal e-waste processing facilities of 0.88 mg/kg dw (range from 0.17 and 12 mg/kg dw) similarly exceeded levels of 0.053 mg/kg dw (range from 0.01 to 0.11 mg/kg dw) in background soils. No clear seasonality in air or soil mercury concentrations were observed, possibly due to variations in the magnitude of recycling operations and in the type of consumer products being recycled in different seasons. Indices such as Enhancement Factor (EF) and Geoaccumulation Index (Igeo) used to define pollution degree/classification in studied areas. EF and Igeo values showed that 25 % and 5 % of air and soil samples taken near formal e-waste facilities, respectively, are very strongly polluted (EF and Igeo are > 3). Based on the overall mean Igeo index, 33.75 % of these soil samples are moderately to extremely contaminated (Igeo is greater than 1). Formal e-waste recycling facilities in Türkiye clearly are a source of mercury to the surrounding environment.

Heat distribution in multi-layer tissues, especially in the skin, is crucial for improving medical treatments, such as hyperthermia and laser therapy. Precise modeling of the thermal properties of skin layers is essential for achieving successful treatment outcomes. This study aims to investigate thermal dynamics in multi-layer tissues using high-order modeling techniques to optimize laser-based treatments. The local thermal non-equilibrium model is utilized in this study, incorporating the theory of porous media to account for the different thermal properties of blood and tissue in multi-layer biological tissues. The model is solved using a numerical method. The results show temperature distribution and thermal damage in multi-layer tissues under the influence of laser intensity, duration of laser exposure, and tissue porosity. As the laser intensity and duration increase, temperature distribution and thermal damage also increase. Porosity is inversely related to temperature distribution, with higher porosity leading to decreased temperature distribution.