Search Results (23,135)

This paper aims to study the oscillatory behavior of second-order neutral differential equations. Using the Riccati substitution technique, we introduce new oscillation criteria that essentially improve some related criteria from the literature. We provide some examples and compare the results in this paper with earlier results to illustrate the importance of our results. Full article

In this work, we describe the $\mathfrak{q}$-analogue of a multiplier–Ruscheweyh operator of a specific family of linear operators ${\mathfrak{I}}_{\mathfrak{q},\rho }^s(\nu ,\tau )$, and we obtain findings related to geometric function theory (GFT) by utilizing approaches established through subordination and knowledge of $\mathfrak{q}$-calculus operators. By using this operator, we develop generalized classes of quasi-convex and close-to-convex functions in this paper. Additionally, the classes ${\mathfrak{K}}_{\mathfrak{q},\rho }^s(\nu ,\tau )\left(\phi \right)$, ${\mathfrak{Q}}_{\mathfrak{q},\rho }^s(\nu ,\tau )\left(\phi \right)$ are introduced. The invariance of these recently formed classes under the $\mathfrak{q}$-Bernardi integral operator is investigated, along with a number of intriguing inclusion relationships between them. Additionally, several unique situations and the beneficial outcomes of these studies are taken into account. Full article

Background/Objectives: Hesperidin (HSP) is a potent phytochemical antioxidant and anti-inflammatory agent that protects against otitis media. However, due to its low solubility and bioavailability, a suitable delivery method is needed to overcome these problems. A hydrogel is a promising nanocarrier for controlled drug delivery in response to external stimuli, such as pH variations. Methods: Graphene oxide (GO)-based nanocarriers that encapsulate hesperidin (HSP) were further coated with a polylactic-co-glycolic acid/alginate (PLGA-Alg) hydrogel before being integrated into a green neem oil (N.O.) double emulsion to produce a synergistic effect and then characterized by different assays. Results: The nanocarriers exhibited a substantial particle size (168 ± 0.32 nm), with high encapsulation (89.86 ± 0.23%) and a zeta potential of 37 ± 0.43 mV. In vitro release studies conducted over 96 h indicated a sustained HSP release of 82% at pH 5.4 and 65% at pH 7.4. The GO-HSP-loaded neem oil double emulsion formulation exhibits substantial antibacterial activity, as evidenced by inhibition zones of 39 ± 0.02 mm against Staphylococcus epidermidis, and considerable antifungal activity against Candida albicans, with an inhibition zone of 43 ± 0.13 mm, along with biofilm inhibition activity. The formulation demonstrated antioxidant activity (5.21 µg/mL) and increased cell viability (90–95%) while maintaining low cytotoxicity in HSE-2 cells. A histopathological analysis confirmed that treatment with the nanocarriers reduced the levels of pro-inflammatory cytokines (IL-1β, TNF-α, TLR4, IL-6) and raised the levels of antioxidant markers (Nrf-2, SOD) in an in vivo rat model of otitis media. Conclusions: GO-based nanocarriers integrated into a neem oil double emulsion and coated with PLGA-Alg hydrogel deliver hesperidin with sustained release and enhanced antibacterial, antifungal, and antioxidant properties. This formulation may be used to treat otitis media and other oxidative stress diseases. Full article

The coral microbiome is highly related to the overall health and the survival and proliferation of coral reefs. The Red Sea’s unique physiochemical characteristics, such a significant north–south temperature and salinity gradient, make it a very intriguing research system. However, the Red Sea is rather isolated, with a very diversified ecosystem rich in coral communities, and the makeup of the coral-associated microbiome remains little understood. Therefore, comprehending the makeup and dispersion of the endogenous microbiome associated with coral is crucial for understanding how the coral microbiome coexists and interacts, as well as its contribution to temperature tolerance and resistance against possible pathogens. Here, we investigate metagenomic sequencing targeting 16S rRNA using DNAs from the sediment samples to identify the coral microbiome and to understand the dynamics of microbial taxa and genes in the surface mucous layer (SML) microbiome of the coral communities in three distinct areas close to and far from coral communities in the Red Sea. These findings highlight the genomic array of the microbiome in three areas around and beneath the coral communities and revealed distinct bacterial communities in each group, where Pseudoalteromonas agarivorans (30%), Vibrio owensii (11%), and Pseudoalteromonas sp. Xi13 (10%) were the most predominant species in samples closer to coral (a coral-associated microbiome), with the domination of Pseudoalteromonas_agarivorans and Vibrio_owensii in Alshreah samples distant from coral, while Pseudoalteromonas_sp._Xi13 was more abundant in closer samples. Moreover, Proteobacteria such as Pseudoalteromonas, Pseudomonas and Cyanobacteria were the most prevalent phyla of the coral microbiome. Further, Saweehal showed the highest diversity far from corals (52.8%) and in Alshreah (7.35%) compared to Marwan (1.75%). The microbial community was less diversified in the samples from Alshreah Far (5.99%) and Marwan Far (1.75%), which had comparatively lower values for all indices. Also, Vibrio species were the most prevalent microorganisms in the coral mucus, and the prevalence of these bacteria is significantly higher than those found in the surrounding saltwater. These findings reveal that there is a notable difference in microbial diversity across the various settings and locales, revealing that geographic variables and coral closeness affect the diversity of microbial communities. There were significant differences in microbial community composition regarding the proximity to coral. In addition, there were strong positive correlations between genera Pseudoalteromonas and Vibrio in close-to-coral environments, suggesting that these bacteria may play a synergistic role in Immunizing coral, raising its tolerance towards environmental stress and overall coral health. Full article

An analytical exploration of the phase decoherence equation of two qubits interacting with a coherent field with dipole–dipole interaction is introduced. The study examines the tradeoff relationships between intrinsic concurrence and first-order coherence in the qubits–cavity system while considering the impacts of decoherence and the interactions among the qubits. We affirm that the relationship between intrinsic concurrence and first-order coherence is valid. Additionally, we demonstrate that the minimum limit of intrinsic concurrence is universally applicable, although the upper limit is typically not. These connections in Heisenberg models can provide a means by which to investigate how quantum resources are allocated in spins, potentially leading to future applications in quantum information processing. It is partially but not completely possible to control the tradeoff relations between intrinsic concurrence and first-order coherence of the two-qubit cavity system; this control might involve actions that influence the system and are reflected in intrinsic concurrence and first-order coherence. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

In this paper, two different families are mixed: the exponentiated and new Topp–Leone-G families. This yields a new family, which we named the mixture of the exponentiated and new Topp–Leone-G family. Some statistical properties of the proposed family are obtained. Then, the mixture of two exponentiated new Topp–Leone inverse Weibull distribution is introduced as a sub-model from the mixture of exponentiated and new Topp–Leone-G family. Some related properties are studied, such as the quantile function, moments, moment generating function, and order statistics. Furthermore, the maximum likelihood and Bayes approaches are employed to estimate the unknown parameters, reliability and hazard rate functions of the mixture of exponentiated and new Topp–Leone inverse Weibull distribution. Bayes estimators are derived under both the symmetric squared error loss function and the asymmetric linear exponential loss function. The performance of maximum likelihood and Bayes estimators is evaluated through a Monte Carlo simulation. The applicability and flexibility of the MENTL-IW distribution are demonstrated by well-fitting two real-world engineering datasets. The results demonstrate the superior performance of the MENTL-IW distribution compared to other competing models. Full article

Background/Objectives: The widespread phenomenon of “brain rot”, named the Oxford Word of the Year 2024, refers to the cognitive decline and mental exhaustion experienced by individuals, particularly adolescents and young adults, due to excessive exposure to low-quality online materials, especially on social media. The present study is exploratory and interpretative in nature, aiming to investigate the phenomenon of “brain rot”, with a focus on its key pillars, psychological factors, digital behaviors, and the cognitive impact resulting from the overconsumption of low-quality digital content. Methods: This study employs a rapid review approach, examining research published between 2023 and 2024 across PubMed, Google Scholar, PsycINFO, Scopus, and Web of Science. It explores the causes and effects of brain rot, focusing on the overuse of social media, video games, and other digital platforms. Results: The findings reveal that brain rot leads to emotional desensitization, cognitive overload, and a negative self-concept. It is associated with negative behaviors, such as doomscrolling, zombie scrolling, and social media addiction, all linked to psychological distress, anxiety, and depression. These factors impair executive functioning skills, including memory, planning, and decision-making. The pervasive nature of digital media, driven by dopamine-driven feedback loops, exacerbates these effects. Conclusions: The study concludes by offering strategies to prevent brain rot, such as controlling screen time, curating digital content, and engaging in non-digital activities. Given the increasing prevalence of digital engagement, it is essential to explore a variety of strategies, including mindful technology use, to support cognitive health and emotional well-being. The results can guide various stakeholders—policymakers, practitioners, researchers, educators, and parents or caregivers—in addressing the pervasive impact of brain rot and promoting a balanced approach to technology use that fosters cognitive resilience among adolescents and young adults. Full article

A field trial was conducted on eight-year-old ‘Anna’ apple (Malus domestica) trees from 2021 to 2023 in northern Egypt. The objective of this study was to determine the effects of replacing mineral fertilizer with compost and microorganism applications. Treatments were prepared using combinations of three mineral fertilizer NPK (nitrogen (N), phosphorus (P) and potassium (K)) levels (75% recommended NPK rate, 50% and 25% recommended rate), with two compost levels (with/without) and two bacteria/fungi biological blend (PGPM) levels (with/without). This design resulted in 12 treatments in addition to a control treatment consisting of the full NPK recommended rate (100% NPK). Leaf nutrient concentrations, vegetative growth, fruit set percentage, fruit drop percentage, yield and fruit quality were measured in 2022 and 2023. Our results indicated that vegetative growth parameters were significantly influenced by the fertilizer treatments in both seasons. The application of 75% NPK + compost + PGPM or 50% NPK + compost + PGPM significantly increased shoot length, shoot diameter, leaf area and leaf-specific weight compared with the control (100% NPK). The greatest values of leaf nutrients and production and quality parameters were obtained with treatments 75% NPK + compost + PGPM or 50% NPK + compost + PGPM. Applying 75% NPK + compost + PGPM or 50%NPK + compost + PGPM increased total soluble solids and anthocyanin concentrations but reduced fruit nitrate and nitrite levels compared with the control (100% NPK). This study shows that it is possible to reduce mineral fertilizer application by 25–50% while improving the yield if compost and microbial inoculants are applied. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5$\mathsf{\mu }$s for 100 bits and rising to 39 $\mathsf{\mu }$s for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 $\mathsf{\mu }$s for 100 bits and rising to less than 8 $\mathsf{\mu }$s for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs. Full article

The global energy sector has been profoundly reshaped by the COVID-19 pandemic, triggering diverse reactions in energy demand patterns, accelerating the transition toward renewable energy sources, and amplifying concerns over global energy security and the digital safety of energy infrastructure. Five years after the pandemic’s onset, this study provides a taxonomy-based lesson-learned analysis, offering a comprehensive examination of the pandemic’s enduring effects on energy systems. It employs a detailed analytical framework to map short-, medium-, and long-term transformations across various energy-related sectors. Specifically, the study investigates significant shifts in the global energy landscape, including the electric and conventional vehicle markets, the upstream energy industry (oil, coal, and natural gas), conventional and renewable energy generation, aerial transportation, and the broader implications for global and continental energy security. Additionally, it highlights the growing importance of cybersecurity in the context of digital evolution and remote operations, which became critical during the pandemic. The study is structured to dissect the initial shock to energy supply and demand, the environmental consequences of reduced fossil fuel consumption, and the subsequent pivot toward sustainable recovery pathways. It also evaluates the strategic actions and policy measures implemented globally, providing a comparative analysis of recovery efforts and the evolving patterns of energy consumption. In the face of a global reduction in energy demand, the analysis reveals both spatial and temporal disparities, underscoring the complexity of the pandemic’s impact on the energy sector. Drawing on the lessons of COVID-19, this work emphasizes the need for flexible, forward-thinking strategies and deeper international collaboration to build energy systems that are both resilient and sustainable in the face of uncertainties. Full article

Background/Objectives: Fabrication of ceramic restorations with higher performance, biocompatibility, and mechanical durability, as well as excellent optical properties, is challenging. Therefore, this study was designed to investigate the mechanical properties and surface roughness of different glass ceramics manufactured with CAD/CAM and pressed techniques before and after exposure to simulated gastric acidic challenge. Methods: Lithium disilicate-based (LDS) ceramic, advanced lithium disilicate (ALDS), and zirconia-reinforced lithium silicate (ZLSC), were manufactured with two techniques. Disc-shaped (N = 336) specimens were used to test the biaxial flexural strength (BFS), while surface microhardness and surface roughness were evaluated before and after exposure to hydrochloric acid (34–37% concentration and 1.2 pH for 24 h). The results were statistically evaluated using two- and one-way ANOVA, Tukey’s post-hoc and Student’s t-test. Results: Both CAD and pressed types of LDS ceramic IPS e.max, showed significantly highest BFS and microhardness before and after acidic challenge (p = 0.01 and <0.01, respectively). ALDS and ZLSC showed significantly lower roughness before acidic aging, while all groups showed no significant difference after aging. Conclusion: Pressed groups showed better mechanical performance than CAD groups. LSD (IPS e.max types) continuously showed a better mechanical performance than modified LDS. ALDS and ZLSC showed the best performance in terms of surface smoothness, but after acidic exposure, no significant difference was observed compared to the other groups. Full article

Background and Objectives: Primary liver cancer is a major cause of mortality, ranking third among the most fatal cancers. In Egypt, liver cancer constitutes 11.75% of gastrointestinal malignancies, with HCC representing 70.5% of cases. The landscape of HCC management was revolutionized by locoregional modalities, which offer a comparable alternative to conventional techniques, with low complications and minimal invasiveness. RFA is a technique that is suitable for early-stage lesions in the liver, with a high overall survival and low complication rates. However, the associated complications cause potential mortality and morbidity. The proper selection of patients may avoid such complications. This study presents a five-year experience of radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) in Egypt, analyzing the predictors of complications and the computed tomography (CT) features associated with complications post-ablation. Materials and Methods: The study included 1000 cases (84% males with a mean age of 60), with 90% having HCC. Exclusion criteria included prior chemoembolization and non-HCC primary hepatic tumors. Patients underwent RFA at Cairo University Hospital and two private centers from January 2014 to January 2019. The workup involved clinical assessments, lab tests, and CT scans. Complications were classified as major or minor. Statistical analysis was conducted via SPSS software Version 22.0, with associations evaluated using a chi-square test. A decision tree was employed to determine the predictive values for different variables associated with the complications. Results: Overall, the rate of complications was 4%, and mortality stood low at 0.1%. Subcapsular lesions were associated with complications, as well as the lesion size, site, Child–Pugh classification, and the number of RFA sessions. Decision tree analysis determined the size of a lesion to be the most predictive factor of major complications, whereas the site of the lesion predicted the occurrence of minor complications. Conclusions: RFA offers low complication rates; however, precise patient selection is critical. The approach and imaging modality choice influence the outcomes. Clinician experience enhances early complication detection, thereby allowing for effective treatments. Full article

Background/Objectives: Brain tumors are among the most aggressive diseases, significantly contributing to human mortality. Typically, the classification of brain tumors is performed through a biopsy, which is often delayed until brain surgery is necessary. An automated image classification technique is crucial for accelerating diagnosis, reducing the need for invasive procedures and minimizing the risk of manual diagnostic errors being made by radiologists. Additionally, the security of sensitive MRI images remains a major concern, with robust encryption methods required to protect patient data from unauthorized access and breaches in Medical Internet of Things (MIoT) systems. Methods: This study proposes a secure and automated MRI image classification system that integrates chaotic and Arnold encryption techniques with hybrid deep learning models using VGG16 and a deep neural network (DNN). The methodology ensures MRI image confidentiality while enabling the accurate classification of brain tumors and not compromising performance. Results: The proposed system demonstrated a high classification performance under both encryption scenarios. For chaotic encryption, it achieved an accuracy of 93.75%, precision of 94.38%, recall of 93.75%, and an F-score of 93.67%. For Arnold encryption, the model attained an accuracy of 94.1%, precision of 96.9%, recall of 94.1%, and an F-score of 96.6%. These results indicate that encrypted images can still be effectively classified, ensuring both security and diagnostic accuracy. Conclusions: The proposed hybrid deep learning approach provides a secure, accurate, and efficient solution for brain tumor detection in MIoT-based healthcare applications. By encrypting MRI images before classification, the system ensures patient data confidentiality while maintaining high diagnostic performance. This approach can empower radiologists and healthcare professionals worldwide, enabling early and secure brain tumor diagnosis without the need for invasive procedures. Full article