Search Results (326)

In the present paper, a mathematical analysis of the Gardner equation with varying coefficients has been performed to give a more realistic model of physical phenomena, especially in regards to plasma physics. First, a Lie symmetry analysis was carried out, as a result of which a symmetry classification following the different representations of the variable coefficients was systematically derived. The reduced ordinary differential equation obtained is solved using the power-series method and solutions to the equation are represented graphically to give an idea of their dynamical behavior. Moreover, a fully connected neural network has been included as an efficient computation method to deal with the complexity of the reduced equation, by using traveling-wave transformation. The validity and correctness of the solutions provided by the neural networks have been rigorously tested and the solutions provided by the neural networks have been thoroughly compared with those generated by the Runge–Kutta method, which is a conventional and well-recognized numerical method. The impact of a variation in the loss function of different coefficients has also been discussed, and it has also been found that the dispersive coefficient affects the convergence rate of the loss contribution considerably compared to the other coefficients. The results of the current work can be used to improve knowledge on the nonlinear dynamics of waves in plasma physics. They also show how efficient it is to combine the approaches, which consists in the use of analytical and semi-analytical methods and methods based on neural networks, to solve nonlinear differential equations with variable coefficients of a complex nature. Full article

The invention of antibacterial agents (antibiotics) was a significant event in the history of the human race, and this invention changed the way in which infectious diseases were cured; as a result, many lives have been saved. Recently, antibiotic resistance has developed as a result of excessive use of antibiotics, and it has become a major threat to world health. ARGs are spread across biomes and taxa of bacteria via lateral or horizontal gene transfer (HGT), especially via conjugation, transformation, and transduction. This review concerns transduction, whereby bacteriophages or phages facilitate gene transfer in bacteria. Bacteriophages are just as common and many times more numerous than their bacterial prey, and these phages are much more influential in controlling the population of bacteria. It is estimated that 25% of overall genes of Escherichia coli have been copied by other species of bacteria due to the HGT process. Transduction may take place via a generalized or specialized mechanism, with phages being ubiquitous in nature. Phage and virus-like particle (VLP) metagenomics have uncovered the emergence of ARGs and mobile genetic elements (MGEs) of bacterial origins. These genes, when transferred to bacteria through transduction, confer resistance to antibiotics. ARGs are spread through phage-based transduction between the environment and bacteria related to people or animals, and it is vital that we further understand and tackle this mechanism in order to combat antimicrobial resistance. Full article

We present an advanced restrictively preconditioned biconjugate gradient-stabilized (RPBiCGSTAB) algorithm specifically designed to improve the convergence speed of Krylov subspace methods for nonlinear systems characterized by a structured 5-by-5 block configuration. This configuration frequently arises from cell-centered finite difference discretizations employed in solving image deblurring problems governed by mean curvature dynamics. The RPBiCGSTAB method is crafted to exploit this block structure, thereby optimizing both computational efficiency and convergence behavior in complex image processing tasks. Analyzing the spectral characteristics of preconditioned matrices often reveals a beneficial distribution of eigenvalues, which plays a critical role in accelerating the convergence of the RPBiCGSTAB algorithm. Furthermore, our numerical experiments validate the computational efficiency and practical applicability of the method in addressing nonlinear systems commonly encountered in image deblurring. Our analysis also extends to the spectral properties of the preconditioned matrices, noting a pronounced clustering of eigenvalues around 1, which contributes to enhanced stability and convergence performance.Through numerical simulations that focus on mean curvature-driven image deblurring, we highlight the superior performance of the RPBiCGSTAB method in comparison to other techniques in this specialized field. Full article

This study introduces a new metric structure called the Graphical Fuzzy Cone Metric Space (GFCMS) and explores its essential properties in detail. We examine its topological aspects in detail and introduce the notion of Hausdorff distance within this setting—an advancement not previously explored in any graphical structure. Furthermore, a fixed-point result is proven within the framework of GFCMS, accompanied by examples that demonstrate the applicability of the theoretical results. As a significant application, we construct fractals within GFCMS, marking the first instance of fractal generation in a graphical structure. This pioneering work opens new avenues for research in graph theory, fuzzy metric spaces, topology, and fractal geometry, with promising implications for diverse scientific and computational domains. Full article

Heavy oil, due to its complex hydrocarbon structure and resistance to degradation, poses significant environmental challenges. There is a lack of knowledge about the biodegradability of heavy oil in the natural environment under aerobic and anaerobic conditions. In this study, we used microbial communities of water and soil samples to investigate the biodegradation of heavy oil. Gas chromatography (GC) analysis was used to measure residual oil. Under aerobic conditions, soil-derived microorganisms demonstrated significantly higher degradation efficiency—achieving up to 80.3% removal—compared to water-derived samples, which showed a maximum degradation of 52.1%. Anaerobic conditions, on the other hand, clearly slowed down degradation; the maximum degradation rates in water and soil samples were 43.7% and 11.1%, respectively. Although no clear linear relationship was found, the correlation between initial microbial populations and degradation performance revealed that higher counts of heterotrophic and oil-degrading bacteria generally enhanced biodegradation. Under anaerobic conditions, especially, persistent hydrocarbon peaks in both environments suggest the presence of recalcitrant heavy oil fractions such as polycyclic aromatic hydrocarbons. In conclusion, this study emphasizes the crucial roles microbial sources and oxygen availability play in maximizing bioremediation techniques for environments contaminated with heavy oil. Full article

Current improvements in quantum computing present a substantial challenge to classical cryptographic systems, which typically rely on problems that can be solved in polynomial time using quantum algorithms. Consequently, post-quantum cryptography (PQC) has emerged as a promising solution to emerging quantum-based cryptographic challenges. The greatest threat is public-key cryptosystems, which are primarily responsible for key exchanges. In PQC, key encapsulation mechanisms (KEMs) are crucial for securing key exchange protocols, particularly in Internet communication, virtual private networks (VPNs), and secure messaging applications. CRYSTALS-Kyber and NTRU are two well-known PQC KEMs offering robust security in the quantum world. However, even when quantum computers are functional, they are not easily accessible. IoT devices will not be able to utilize them directly, so there will still be a requirement to protect IoT devices from quantum attacks. Concerns such as limited computational power, energy efficiency, and memory constraints in devices such as those used in IoTs, embedded systems, and smart cards limit the use of these techniques in constrained environments. These concerns always arise there. To address this issue, this study conducts a broad comparative analysis of Kyber and NTRU, with special focus on their security, performance, and implementation efficiency in such environments (IOT/constrained environments). In addition, a case study was conducted by applying KEMs to a low-power embedded device to analyze their performance in real-world scenarios. These results offer an important comparison for cyber security engineers and cryptographers who are involved in integrating post-quantum cryptography into resource-constrained devices. Full article

The construction industry significantly impacts the environment, consuming 50% of natural resources and generating 20% of global greenhouse gas (GHG) emissions. In developing countries, managing construction and demolition (C&D) waste is a growing challenge due to rapid urbanization and inadequate waste management practices. This study employs life cycle assessment and life cycle costing to compare landfill and recycling scenarios for C&D waste using ISO 14040 (Environmental Management—Life Cycle Assessment—Principles and Framework) and ISO 14044 (Environmental Management—Life Cycle Assessment—Requirements and Guidelines). The study’s system boundary encompasses the entire life cycle of C&D waste management, with one ton of C&D waste as the functional unit. The results demonstrated that landfilling C&D waste is harmful due to negative impacts from transportation and landfill emissions. Recycling shows promising potential by significantly reducing environmental impacts and lowering the demand for new raw materials. The recycling scenario substantially decreased GHG emissions, saving 37 kg of CO₂ equivalents per ton of waste. Economically, recycling C&D waste proved more viable, with favorable indicators. Implementing a recycling plant in Lahore could save USD 2.53 per ton in resource costs and mitigate significant environmental impacts. This study recommends that policymakers in developing countries prioritize C&D waste recycling to enhance sustainability and support the transition to a circular economy. The findings provide valuable insights for developing effective waste management strategies, contributing to environmental conservation and economic efficiency. These recommendations guide future initiatives for sustainable C&D waste management, promoting a greener and more resilient urban environment. Furthermore, this study underlines the potential of C&D waste recycling to contribute significantly to achieving Sustainable Development Goals (SDGs), particularly sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13). Full article

In this article, we will introduce a new generalized proximal $\theta$-contraction for multivalued and single-valued mappings named ${(\mathcal{f}-{\theta }_{\kappa })}_{C_P}$-proximal contraction and ${(\mathcal{f}-{\theta }_{\kappa })}_{B_P}$-proximal contraction. Using these newly constructed proximal contractions, we will establish new results for the coincidence best proximity point, best proximity point, and fixed point for multivalued mappings in the context of rectangular metric space. Also, we will reduce these contractions for single-valued mappings, named ${\left({\theta }_{\kappa }\right)}_{C_P}$-proximal contraction and ${\left({\theta }_{\kappa }\right)}_{B_P}$-proximal contraction, to establish results for the coincidence proximity point, best proximity point, and fixed point results. We will give some illustrated examples for our newly generated results with graphical representations. In the last section, we will also find the solution to the equation of motion by using our defined results. Full article

Low-cost and sustainable technological systems are required to improve communication between deaf–mute and non-deaf–mute people. Herein, we report a novel low-cost and eco-friendly triboelectric nanogenerator (TENG) composed of recycled and waste components. This TENG can be connected to a smartphone using the internet of things (IoT), which allows the transmission of information from deaf–mute to non-deaf–mute people. The proposed TENG can harness kinetic energy to convert it into electrical energy with advantages such as a compact portable design, a light weight, cost-effective fabrication, good voltage stability, and easy signal processing. In addition, this nanogenerator uses recycled and waste materials composed of radish leaf, polyimide tape, and a polyethylene terephthalate (PET) sheet. This TENG reaches an output power density of 340.3 µWm⁻² using a load resistance of 20.5 MΩ at 23 Hz, respectively. This nanogenerator achieves a stable performance even after 41,400 working cycles. Also, this device can power a digital calculator and chronometer, as well as light 116 ultra-bright blue commercial LEDs. This TENG can convert the movements of the fingers of a deaf–mute person into electrical signals that are transmitted as text messages to a smartphone. Thus, the proposed TENG can be used as a low-cost wireless communication device for deaf–mute people, contributing to an inclusive society. Full article

Biopolymers are revolutionizing the materials landscape, driven by a growing demand for sustainable alternatives to traditional petroleum-based materials. Sourced from biological origins, these polymers are not only environment friendly but also present exciting solutions in healthcare, packaging, biosensors, high performance, and durable materials as alternatives to crude oil-based products. Recently, biopolymers derived from plants, such as lignin and cellulose, alongside those produced by bacteria, like polyhydroxyalkanoates (PHAs), have captured the spotlight, drawing significant interest for their industrial and eco-friendly applications. The growing interest in biopolymers stems from their potential as sustainable, renewable materials across diverse applications. This review provides an in-depth analysis of the current advancements in plant-based and bacterial biopolymers, covering aspects of bioproduction, downstream processing, and their integration into high-performance next-generation materials. Additionally, we delve into the technical challenges of cost-effectiveness, processing, and scalability, which are critical barriers to widespread adoption. By highlighting these issues, this review aims to equip researchers in the bio-based domain with a comprehensive understanding of how plant-based and bacterial biopolymers can serve as viable alternatives to petroleum-derived materials. Ultimately, we envision a transformative shift from a linear, fossil fuel-based economy to a circular, bio-based economy, fostering more sustainable and environmentally conscious material solutions using novel biopolymers aligning with the framework of the United Nations Sustainable Development Goals (SDGs), including clean water and sanitation (SDG 6), industry, innovation, and infrastructure (SDG 9), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), responsible production and consumption (SDG 12), and climate action (SDG 13). Full article

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems. Full article

The transformative effect of nanotechnology is revolutionizing medicine by introducing new therapeutic approaches. This study explores the utilization of aqueous extract from mushroom (Cortinarius sp.) used as a reducing agent to prepare zinc oxide myco-nanoparticles (ZnO-MNPs) in an eco-friendly manner. The synthesis of ZnO-MNPs has been confirmed by various characterization studies, including UV-vis spectroscopy, which revealed an absorption peak at 378 nm; X-ray diffraction (XRD) analysis, which revealed a wurtzite hexagonal structure; and Fourier transform infrared spectra (FTIR), which showed stabilizing agents around the ZnO-MNPs. The effectiveness of ZnO-MNPs as an anti-cancer agent was evaluated by monitoring liver biochemical parameters against hepatotoxicity caused by carbon tetrachloride (CCl₄) in Balb C mice. The results showed that the levels of catalase, glutathione (GSH), and total protein were significantly lower, while alanine aminotransferase (ALT), aspartate aminotransferase (ASAT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), melanin dialdehyde (MDA), and total bilirubin (TB) were significantly higher in each of the CCl₄ treatment groups. ZnO-MNP treatment significantly reduced the toxicological effects of CCl₄ but did not completely restore the accumulation. The antimicrobial efficacy of ZnO-MNPs was investigated and showed potential results against common pathogens, including Bacillus subtilis (29.05 ± 0.76), Bacillus meurellus (27.05 ± 0.5), Acetobacter rhizospherensis (23.36 ± 0.5), and Escherichia coli (25.86 ± 0.80), while antifungal activity was relatively lower. Moreover, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay showed that ZnO-MNPs are strong antioxidant agents. Overall, these findings highlight the effectiveness of myco-synthesized ZnO-NPs in combating pathogenic diseases, their promising role in cancer therapy, and their potential as a biomaterial option for future therapeutic applications. Full article

This study assesses the sustainability of bioethanol production from multiple agricultural feedstocks, including corn stover, wheat straw, and rice husk, using a life cycle assessment (LCA) method. The process focuses on converting lignocellulose biomass into bioethanol through advanced biotechnology, enriching energy security and supporting sustainable development in Pakistan. The process includes various stages of feedstock utilization, including cultivation, harvesting, transportation, preprocessing, and conversion, eventually yielding 1 kg of bioethanol with different inventories for each of the three feedstocks. A comparative analysis of the three feedstocks reveals that the wheat straw showed the highest environmental impacts, while rice husk exhibits the least environmental impacts and emerges as a more sustainable and viable option for bioethanol production. The economic assessment revealed the feasibility of bioethanol production, achieving a daily revenue of $9600 and a monthly income of $211,200, based on 22 working days in a single 8 h shift. The total initial capital investment cost was estimated at $478,515, while operational costs were calculated at $225,921. The external cost of the plant was evaluated at $14.23. Transitioning from grid-mix to renewable energy, such as photovoltaic systems, showed a reduction among three feedstocks. Therefore, bioethanol production not only addresses waste management challenges but also contributes to waste-to-energy conversion and renewable energy generation, aligning with public health goals and sustainable development. The findings highlight the potential of bioethanol production as a strategic solution to manage agricultural waste sustainably and reduce greenhouse gas emissions. Full article

Generative adversarial networks (GANs) prioritize pixel-level attributes over capturing the entire image distribution, which is critical in image synthesis. To address this challenge, we propose a dual-stream contrastive latent projection generative adversarial network (DSCLPGAN) for the robust augmentation of MRI images. The dual-stream generator in our architecture incorporates two specialized processing pathways: one is dedicated to local feature variation modeling, while the other captures global structural transformations, ensuring a more comprehensive synthesis of medical images. We used a transformer-based encoder–decoder framework for contextual coherence and the contrastive learning projection (CLP) module integrates contrastive loss into the latent space for generating diverse image samples. The generated images undergo adversarial refinement using an ensemble of specialized discriminators, where discriminator 1 (D1) ensures classification consistency with real MRI images, discriminator 2 (D2) produces a probability map of localized variations, and discriminator 3 (D3) preserves structural consistency. For validation, we utilized a publicly available MRI dataset which contains 3064 T1-weighted contrast-enhanced images with three types of brain tumors: meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices). The experimental results demonstrate state-of-the-art performance, achieving an SSIM of 0.99, classification accuracy of 99.4% for an augmentation diversity level of 5, and a PSNR of 34.6 dB. Our approach has the potential of generating high-fidelity augmentations for reliable AI-driven clinical decision support systems. Full article