The increased rate of cyber threats such as fraud and other forms of attack, including phishing, malware, and denial-of-service attacks, has led to a growing demand for secure methods to ensure the security of sensitive information that is transferred among users. Video steganography has become a very important element in dealing with these security issues, as communication has become more dependent on multimedia content. Hiding information is now an area that is being developed very fast with the introduction of Deep Learning (DL)-based steganography methods. This framework introduces Vid_Steg_DenseUNet, a video steganography method which efficiently extracts multi-level features using edge-preserving U-Net and DenseNet through Invertible Neural Networks (INN) interactions. The main objectives of this approach include improving the perceptual fidelity of the stego video and enhancing the quality of the recovered secret image. These improvements are evaluated using objective imperceptibility metrics through pixel-level and structural analyses, such as PSNR and SSIM. The model was trained on the DIV2K images and Ultra Video Group (UVG) videos. The proposed model hides a secret color image in each video frame, and its performance is assessed using quantitative difference metrics. Comparative results with existing state-of-the-art methods, such as recent DL-based video steganography frameworks, show that this end-to-end embedding approach delivers promising performance in terms of human visual accuracy, with a PSNR score of 37.054 and an SSIM score of 0.9754. The results also demonstrate that the model achieves enhanced security and high resistance to detection techniques.
Coverage optimization in WSNs is critical for disaster warning and industrial monitoring, but is challenging due to multi-modality and high-dimensionality. Particle swarm optimization (PSO) offers fast convergence and simple parameter tuning, yet suffers from premature convergence and parameter sensitivity in multi-peak problems. To address these issues, we propose a Quad-module Ring-Competitive PSO (QRC-PSO). It comprises four heterogeneous subgroups with distinct parameter configurations for global exploration, local exploitation, balanced search, and perturbation enhancement. Subgroups evolve independently but exchange elite particles via a ring-topology migration strategy: every 20 iterations, the best three particles of each subgroup move clockwise to the next subgroup and replace its three worst ones, enabling high-quality solution diffusion while preserving diversity. Simulations on a 100 m × 100 m field with 20 and 30 nodes show that QRC-PSO achieves coverage rates of 84.69% and 98.62%, outperforming GA, standard PSO, APSO, LPSO, ALPSO, GWO, and DE. Tests on a 500 m × 500 m area with 500 and 750 nodes further confirm its superiority. These results demonstrate that the proposed subgroup structure and competitive mechanism effectively overcome traditional PSO weaknesses, making QRC-PSO an efficient and reliable solution for WSN coverage optimization.
The increasing volume of videos produced and shared via smart devices has made indexing and retrieving them a clear challenge. Current retrieval systems, such as specialized video retrieval systems, rely on text-based searches, which often misrepresent and frequently result in erroneous descriptions of the requested video, particularly when it comes to videos about certain specialities like security or medical procedures that are hard to describe. As a result, we developed a model that handles information-rich video queries, making it easier to find related videos that are specifically relevant to the user's needs. This paper proposes a new method for indexing and retrieving videos, aiming to preserve small-sized information while being a powerful and fast retrieval tool. The process involves three stages: preprocessing and indexing, which involves preparing each video with three basic keys; extracting features using a hybrid Transformer-based encoder with multiple algorithms and neural networks; and storing them in a lightweight CSV file. The final stage utilizes the modified RSA Reptile Search Algorithm to retrieve videos efficiently, achieving 1.0 at Accuracy@10, Precision@10, and mAP, while Recall remains a challenge due to the nature of the data. This approach requires lightweight resources and efficient modifications to handle the massive number of video files.
Internet of Things (IoT) devices are vulnerable to zero-day attacks because most of them have weak or no inherent security due to the resource constraints of the devices. This weakness underscores the growing need for anomaly-based intrusion detection systems tailored to IoT networks. Nevertheless, general anomaly detection traditionally has a high number of false positives that drain analysts' time. Also, a semantic difference exists between the system's results and the operators' interpretations. We introduce a machine learning-based framework to tackle these issues in traditional systems in this paper by combining large language models (LLMs). Our model is effective in identifying possible threats as well as filling the semantic gap. The framework uses isolation forests to detect anomalies and random forests to measure device integrity. To further improve the assessment of anomalies and increase interpretability, system insights are further refined using GPT-4o mini, an LLM. The model gives statistical summaries of the IoT traffic, a risk score, and an explanation in easy language, which is easy to understand and therefore makes the process of decision-making easier. Such a novel system reduces the reliance on dedicated network operators and allows non-technical users to better understand and act on the results of the system.
In this study, Sn nanoparticles and Sn/Zn core-shell nanoparticles were synthesized using (PLA) techniques. Pulsed laser ablation with a (1064 nm) wavelength, (6 Hz) frequency, (300 mJ) energy, and (250 pulses) number of shots was used to subject colloidal dispersions of Sn and Zn, and bimetallic Sn@Zn core/shell nanoparticles were generated by the metal target in 15 mL of distilled water (DW). The investigation of nanoparticles was done by X-ray diffraction (XRD), atomic force microscopy (AFM), UV-VIS spectroscopy, FESEM, zeta potential, and transmission electron microscopy (TEM). The efficiency of the prepared nanomaterial was assessed for cytotoxicity, as the test was performed on normal cell lines and the prostate cancer line (PC3). This study employed a nanosecond timeframe to synthesize high-purity Sn and Sn@Zn core/shell, with subsequent analysis of their properties via multiple methodologies. Coating Sn nanoparticles with Zn nanoparticles produces composite nanoparticles with relatively low toxicity. The zeta potential analysis indicated that the core-shell of Sn and Sn@Zn had a moderate electrostatic potential. It is excellent for anti- prostate cancer applications. From these results, it was found that the Sn@Zn core-shell nanoparticles can kill cancer cells more than pure Sn nanoparticles.
In this paper, we introduce a new class of dynamical systems, Proximal Dispersive Group Spaces (PDG - spaces), to study the structure of topological group actions. In these spaces, we observe a mixture of two different behaviors: points come close to each other (proximal convergence) under some group elements, while remaining separated (dispersive separation) under others. This framework generalizes the classical theory to allow for selective convergence without global asymptotic collapse. We give a formal definition of PDG - spaces and study some of their basic properties, like invariance under group actions and incompatibility with equicontinuous systems. Our results show the existence of non-trivial minimal subsets that are both proximal and dispersive. In this paper, we prove that PDG - spaces constitute a special intermediate regime between purely proximal and distal systems.
Spinel ferrites are valued for their tunable electrical and magnetic properties. Rare-earth substitution, such as with La3+, is known to modify their cation distribution and magnetic interaction. In this study, LaxNi1-xFe2O4 nanoparticles (x = 0.0, 0.5, 0.7, and 0.9) were synthesized via the sol-gel method. Their properties were characterized using X-ray diffraction (XRD), vibrating sample magnetometry (VSM), and field-emission scanning electron microscopy (FE-SEM). XRD confirmed a single-phase cubic spinel structure for all compositions. A gradual peak shift with increasing La3+ content indicated lattice expansion, attributable to the larger ionic radius of La3+ compared to Fe3+ and Ni2+. Crystallite size, calculated via the Scherrer equation, also increased with La3+ content. VSM measurement showed typical ferrimagnetic hysteresis loops. Saturation magnetization (Ms) decreased progressively as non-magnetic La3+ ions replaced magnetic Fe3+ and Ni2+ ions, reducing the number of active magnetic sites. This substitution consequently lowered the remnant magnetization (Mr) and coercivity (Hc). FE-SEM images revealed predominantly spherical particles with varying agglomeration, attributed to magnetic and surface forces. The grain size was consistent with XRD estimates. Higher La3+ concentration led to increased agglomeration, reduced shape regularity, and altered particle size distribution and porosity. These results demonstrate that La3+ addition induces significant structural changes, weakens magnetic performance, and alters surface morphology, providing valuable insights for tailoring LaxNi1-xFe2O4 nanoparticles for technological applications.
This study aims to investigate the estimation of the parameters of the inverse exponential Rayleigh distribution using the Maximum Likelihood Estimation (MLE) and Ranked Set Sampling (RSS) methods, on account of their importance in modeling across lifespan and reliability data. The model is used on empirical data representing the fracture stress of carbon fibers for both single and saturated fibers, in order to determine the accuracy of the estimation methods in given applications. In addition, the classical model is expanded to a fuzzy model to focus on inherent uncertainty in real-world applications. This is realized by designing fuzzy numbers using Shih-skewed membership functions, which offer greater flexibility in illustrating asymmetry in the data. Yager algorithms are used to transform fuzzy values into crisp and clear estimates. At the end, the key distributional functions, consisting of the probability density function, cumulative distribution function, survival function, and hazard function, are derived and analyzed under both classical and fuzzy approaches. Finally, the accuracy of the estimation methods is tested with the Mean Squared Error (MSE), where the result suggests that the RSS method excels the MLE method, in particular when paired with fuzzy techniques, yielding more precise estimation in the face of uncertainty.
The unsteady flow of a tangential hyperbolic fluid with temperature in the presence of a sublimation medium and an exponential surface was discussed in this article. The law of conservation of mass, energy, and momentum is essential to the model of matter. To make it easier to identify solutions, the partial equations were converted into ordinary equations. We refer to this approach as the homotopic method. To explain the results, the Mathematica program was used to find the solutions and graphically depict them. The impact of the parameters on velocity, temperature was discussed, which are Random motion factor (Nb), Thermo-migration factor (Nt), Lewis number (le), Unsteady factor (A), Prandtl number (pr), Weissenberg number (we), and porosity parameter (P). The results shown in the graphs indicate that an increase in medium porosity affects the flow dynamics. Specifically, it conduces to gain in the velocity f while reducing the secondary velocity g. In addition, the flow speed is influenced by the unsteady factor; as A increases, the flow velocity rises. However, without P, an increment in unsteady factor cause a decline in velocity. Furthermore, an increase in we correlates with a decrease in velocity, and a temperature rise. The results also reveal that increasing p r decreases the temperature, but when Nt, Nb increase, the temperature rises and reduces the rate of heat transfer. Conversely, temperature decreases as le increases. Moreover, temperature changes slightly as P increases.
The locating-chromatic number of a graph combines proper vertex coloring with vertex identification through distances to color classes. Although this parameter has been studied for many graph families, general results for bipartite graphs remain limited. Bipartite graphs contain structural symmetries, especially within each partite set, making it difficult to obtain distinct color codes. This paper establishes lower and upper bounds for the locating-chromatic number of bipartite graphs using neighborhood equivalence classes in the two partite sets. The bounds describe the effect of identical neighborhoods on the number of distinguishable color codes. They are shown to be tight, and regular complete bipartite graphs are identified as extremal examples. The paper also considers corona products of regular complete bipartite graphs and complements of complete graphs, for which bipartiteness is preserved. Exact values of the locating-chromatic number are obtained for all relevant numbers of attached vertices, indicating how the number and arrangement of pendant vertices affect the coloring process. These results provide a basis for studying locating colorings in bipartite and corona graphs and complement existing results in the literature.
This study presents a precision-engineered carbon nanotube (CNT)-doped SnO2/Si heterojunction photodetector, wherein interfacial physics governs performance beyond individual material capabilities. Stepwise CNT loadings (0, 0.03, and 0.07 wt.%) were deposited by vacuum thermal evaporation to systematically reconstruct the crystallographic, morphological and band-alignment landscape. A-sure X-ray diffraction demonstrates that the rutile phase remains, and atomic force microscopy and scanning electron microscopy confirm grain passivation at the nanoscale. High sub-bandgap light sensitivity can be achieved, benefiting from the built-in field-assisted carrier separation at the SnO2/Si interface, whereby the redshifted absorption edge is indicative of intentional bandgap engineering. CNT networks become shuttle buses for carriers with variable illumination, transferring photogenerated carriers through the heterojunction with lower recombination and higher mobility, reducing rise and fall times for improved temporal response. At 0.07 wt. At 1%CNT, this architecture provides a peak photoconductive gain of up to ∼104 and a maximum spectral responsivity of 350 A/W over a broadband ultraviolet–near- infrared (UV–NIR), illustrating a paradigm shift for self-powered, silicon-compatible transparent photodetectors.
In a production system, rework plays a key role in reducing waste and efficiently managing manufacturing costs. The proposed model addresses a system with batch arrivals, a single server, and a two-stage bulk service with inspection, two rework cycles, and multiple vacations. First, batches are processed in stage 1, some of which may require rework during the same cycle, albeit with a certain probability, while others proceed to stage 2. After stage 2, batches are inspected, and if evidence of nonconformities is found, they are reworked with a certain probability. After inspection or rework, batches are processed according to the current queue length. If the queue length is equal to or greater than a specific limit (Q ≥ a), the server returns to serving the next batch; otherwise, the system enters multiple vacations, which help maximize server availability and minimize idle time. The rework management model is applied in the paint manufacturing industry to streamline rework operations and enhance manufacturing performance. The supplementary variable technique is used to build a model for studying the probability-generating function of the queue size. Performance measures are then illustrated with numerical examples, and the cost-effectiveness of the results is shown using graphs. The study presents noteworthy findings by comparing the neuro-fuzzy technique results against outcomes obtained through Adaptive Neuro-Fuzzy Inference System (ANFIS) validation.
Solid dispersions are one of the most promising strategies to overcome the poor aqueous solubility of some drug substances. However, the physical and chemical instability of solid dispersions has limited their use. The purpose of this study was to assess the risk of factors affecting the dissolution and chemical stability of solid dispersion products of poorly-water-soluble drugs. Five poorly-water-soluble drug substances were subjected to forced degradation studies, pH-dependent stability testing and excipient-drug substance compatibility testing and testing for effect of manufacturing parameters then the experimental data were analysed. FT-IR results and compatibility studies showed that carrier type variability and drug-to-carrier ratio could critically affect the chemical stability of the solid dispersions. Anova results showed that mixing time, evaporation temperature and milling size were the statistically-significant manufacturing parameters affecting the dissolution stability and/or degradation percentage of poorly-soluble drugs. Results of photostability studies showed that light had high risk to the chemical stability of poorly-soluble drugs. By applying Quality by Design (QbD) principles it was concluded that besides polymorphism, zeta potential, dose–to-solubility ratio and water content, carrier type, drug-to-carrier ratio, mixing time, evaporation temperature, milling size and light were determined as the critical factors affecting the dissolution stability and chemical degradation of poorly-soluble drugs. This conclusion will be the basis for subsequent design of acceptable robust specifications and successful stability protocols allowing to obtain a physically and chemically-stable solid drug product.
The current study was conducted to compare and evaluate the increasing or decreasing levels of LaDXS1 ``1-deoxy-D-xylulose 5-phosphate synthase'' gene expression of four treatments of callus initiate on Murashige and Skoog medium (MS) with various application of 2,4-dichlorophenoxyacetic acid (2,4-D) and Benzyl adenine (BA) from leaves of Lavandula Angustifolia consider one of species used for oil extraction through total RNA extraction from callus for the four treatments then reverse-transcribed to complementary DNA (cDNA) then amplified by RT-qPCR technique as a relative quantitation method .The results demonstrated that gene expression of LaDXS1 was varied between treatments. The highest gene expression of LaDXS1 in the second treatment was recorded at 2.282 gene folding as compared with control group which record 1 gene fold, while the lowest treatment is 1.580 copy number of gene. Our conclusion is that LaDXS1 gene expression elevated by using very economical and available hormonal combination has been affected to increase lavender oil production in callus.
Dung beetles perform various functions in ecosystems, such as nutrient cycling, aeration of soil, and dung burial activities that increase soil properties. Genus Onthophagus is well known for its role in dung removal and decomposition. We appraised the role of Onthophagus ramosellus and Onthophagus gazella in dung removal under field conditions. Species-specific functional features like body size and dry body weight associate these dung beetle species with tunneling depth and dung removal. We installed mesocosms (18 cm height and 12 cm diameter) at three sites in district Gujrat, i.e., Chak, Chopala, and Kot Mir Hussain. Treatment groups used in the trials included: (i) T1: O. gazella (one pair), (ii) T2: O. ramosellus (three pairs), (iii) T3: O. gazella (one pair) + O. ramosellus (three pairs), and (iv) T0: dung-only control. The data were collected from experimental units after 24, 48, 72, and 96 hours. Dung removal was determined gravimetrically by measuring the initial and final dry weights of dung pats and correcting for moisture loss using controls. O. gazella had greater body length (11.58 ± 0.08 mm), width (6.10 ± 0.07 mm), and dry weight (37.65 ± 0.22 mg) than O. ramosellus (p <0.001). O. gazella excavated deeper tunnels and removed significantly larger quantities of dung. Although the three-way interaction was not significant, the treatment × duration interaction was significant. In T3, O. gazella (one pair) + O. ramosellus (three pairs) demonstrated lower dung removal. Overall, O. gazella showed stronger morphometric traits, deeper tunneling, and greater dung removal.
This study presents a simple method for synthesizing a novel distorted octahedral copper(II) compound. The structural and electronic properties of the compound were determined using single-crystal X-ray diffraction (SCXRD). A three-coordinate Schiff base ligand (NNO system) was synthesized by condensation and fully characterized using Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H-NMR), carbon-13 nuclear magnetic resonance (13C-NMR), electrospray ionization mass spectrometry (ESI-MS), thermogravimetric/differential gravimetric analysis (TGA/DTG), and elemental analysis (CHN). The ligand is linked to the copper(II) ion via pyridine nitrogen, azomethine nitrogen, and enol oxygen, forming five-membered rings. The crystal structure reveals a unique and highly stable three-dimensional framework, characterized by continuous intrinsic porosity with a pore size of 5.44 angstroms and a unit cell density of 996 atoms. The compound exhibits exceptional thermal and dynamic stability, with non-covalent interactions playing a pivotal role in crystal arrangement. Hirshfeld surface analysis shows that π…π stacking interactions between phenolic rings are the primary contributor to structural stability, accounting for 45% of the total non-covalent forces. Furthermore, its distinctive structural properties make it an effective template for capturing and sequestering small molecules that are well suited to its pore dimensions, such as water (H2O), methane (CH4), Ethane (C2H6), Propane (C3H8), and carbon dioxide (CO2). Its insolubility in water and mild alcohols further enhances its performance, and the compound is expected to have strong potential for applications in catalysis, polymer chemistry, antioxidants, and corrosion inhibition. This promising activity is driven by the synergistic effects of its uncoordinated hydroxyl functional groups, active heteroatoms, and robust geometric arrangement.
Medicinal plants continue to be a source of antifungal agents, and the green synthesis of nanoparticles has enhanced the bioactive potential of Glycyrrhiza glabra L. In the present study, ultrasonic-assisted chloroform- and ethanol-extracted leaves & roots were prepared, whereas aqueous extracts were used for green synthesis of Ag NPs. Phytochemical evaluation of the extract showed the availability of terpenoids, phenols, flavonoids, tannins, saponins, and resins. Formation of Ag NPs was confirmed by a UV–Vis plasmon resonance peak at 400–438 nm, FT-IR functional groups (O–H, C=O, and C–N), spherical morphology from FE-SEM, EDX signal of silver atoms, and fcc planes in XRD at (111), (200), and (220). In the field, in the case of Ross-308 poultry, Aspergillus fumigatus (74%) was dominant, followed by A. niger (16%), A. flavus (6%), and A. terreus (4%), which were confirmed by ITS1/ITS4 sequencing. In the DPPH assay (0.625–5 mg/mL), crude extracts exhibited concentration-dependent antioxidant activity, with significant differences (P≤ 0.05) compared to ascorbic acid at different concentrations. Root and leaf Ag NPs showed significantly lower scavenging activities than those of ascorbic acid, with higher IC50 values. MIC of antifungal ranged from 1.56–3.125 mg/mL from extracts, while it was between 1.56–50 mg/mL for Ag NPs, and species-wise susceptibility patterns were observed. Thus, these results indicated that the Glycyrrhiza glabra L. plant extracts and their Ag NPs interact to have antioxidant and antifungal activities, revealing the possibility of using them as environmentally friendly antimicrobial agents.
Plankton serve as live food for aquatic organisms and are indicators of water fertility, which is related to its abundance and diversity. Some live feeds also form a symbiotic relationship with macroalgae, known as epiphytic plankton, which is found in estuarine waters. The Brondong estuary area, Brondong District, Lamongan, East Java, with abundant Caulerpa sp. macroalgae, exemplifies these characteristics. This study aimed to identify the abundance, diversity, uniformity, and dominance indices of zooplankton, epiphytic microalgae, phytoplankton, and chlorophyll a in the Brondong estuary. Given that research on identifying epiphytic plankton species attached to Caulerpa sp. is lacking, this study investigates zooplankton, phytoplankton, and chlorophyll-a in the environment, correlating them with water quality. Stratified random sampling was conducted in May, June, and July 2023, with biota collection lasting for approximately 18–20 hours. The study identified two types of macroalgae, namely Caulerpa racimosa and Caulerpa lentimofera, and6 genera of epiphytic phytoplankton, namely Nitzschia, Asterionella, Ceratium, Rhizoclonium, Pleurosigma, and Oedogonium. The identified zooplankton consisted of four genera, namely Nauplius larva, Penaeus larva, Canalus, and Acartia. Based on chlorophyll a levels in the Brondong estuary, it falls into the mesotrophic or nutrient-rich category. The results obtained demonstrate that plankton can serve as an aquatic bioindicator that reflects the quality of water sources for aquaculture, and demonstrate the potential for domesticating organisms into aquaculture products. The high abundance of organisms in the Brondong waters results from the direct and indirect supply of nutrients from the mainland and the favorable water quality, which contributes to their fertility.
Cystic echinococcosis is a globally prevalent zoonotic infection, including in the Kurdistan region of Iraq. It arises when Echinococcus granulosus eggs are ingested, leading to the larval stages that mainly effect the internal organs of humans and livestocks. This study aimed to explore the genetic variability and sequence polymorphisms of E. granulosus and to evaluate the protoscolicidal potential of Moringa oleifera leaf extract. Protoscoleces PSCs were isolated from hydatid cysts collected from infected humans, sheep, cattle and goat. Among the 73 hydatid cysts examined, 63 (86.3%) were fertile. DNA was extracted from PSCs, the mitochondrial cytochrome oxidase subunit 1 (cox1) gene was amplified for molecular characterization. DNA sequencing and phylogenetic analyses were performed to identify E. granulosus genotypes. Phylogenetic analysis demonstrated the predominance of G1 and G3 genotypes. In the experimental assays, PSCs were treated with ethanolic M. oleifera leaf extract. GC–MS profiling revealed major bioactive constituents (phytol, eugenol acetate, trans-isoeugenol and vitamin E). The addition treatment included four concentrations of the extract were 250, 400, 500, and 750 mg/mL across various exposure periods 5, 15 and 30 minutes, and demonstrated strong protoscolicidal activity, achieving 100% mortality at 750 mg/mL after 30 min., while the lower mortality rate found at the concentration 250mg/ml was 13%. Scanning electron microscopy PSCs were demonstrated ultrastructural alterations with including loss of hooks, shedding of microtriches, cellular shrinkage, and lysis it was reflecting severe structural damage. Overall, these results suggest that M. oleifera leaf extract represents a promising alternative protoscolicidal agent.
Illicium verum, a star anise evergreen tree, is of great ethnopharmacological, medicinal, and biological interest. Objective: To develop an aqueous extract from its fruits and to evaluate its antioxidant potential, particularly in terms of DNA protection. The extraction method was straightforward; total phenolics (123 mg GAE/g) and flavonoids (51.3 mg QE/g) were quantified, and antioxidant (DPPH radical scavenging and iron (III) reducing) properties determined. The total antioxidant capacity (TAE) of the extract was estimated, and its protectiveness on human genomic DNA was examined. In addition, an in silico computer docking simulation was conducted to describe the molecular interactions of the extract's phenolic compounds with DNA. The extract showed a marked antioxidant profile (TAE; 25.63 ± 2.83 mg vitamin C equivalent and potent DPPH IC50 equal to 1.63 mg/mL). Its FRAE activity was low compared with vitamin C; interestingly, despite having lower free radical scavenging activity than vitamin C, the extract was effective in DNA protection, with 96% prevention against damage. This is further supported by the in silico study, which demonstrated strong binding forms with binding energy scores between –8.87 and –4.71(kcal/mol) with hydrogen bonds. The Illicium verum aqueous extract was rich in phenols, with robust antioxidant activity, and had a significant protective effect against human genomic DNA damage, as evidenced by in vitro and in silico analysis.
