Baghdad Science Journal

Altered environmental conditions in aquatic ecosystems through Dissolved Organic Carbon (DOC) input and increased temperature influence phytoplankton communities. To assess the impacts of climate change as both single and combined effects of DOC inputs and temperature on phytoplankton growth, we conducted an outdoor experiment including four sets of conditions in triplicate: a) control, b) DOC treatment, c) temperature treatment, and d) DOC and temperature treatment. We added 10 mg/L of glucose (C₆H₁₂O₆) as a source of DOC. The warming treatment increased the water temperature by 3°C above the control (p-value < 0.05). The DOC treatment significantly affected pH, CO₂, and DOC (p-values = 0.004, 0.01, and 0.003, respectively). Dissolved oxygen was impacted by both DOC treatment (p-value = 0.0003) and temperature treatment (p-value = 0.003), whereas NO3-1 and PO₄-3 were unaffected by either (p-value > 0.05). Both DOC and warming treatments significantly changed the physicochemical properties of the water, influencing phytoplankton biomass. The phytoplankton biomass(chl-a) showed significant positive correlations with temperature (r = 0.5, p-value = 0.0002), CO₂ (r = 0.64, p-value < 0.05), DOC (r = 0.6, p-value < 0.05), NO3-1 (r = 0.5, p-value < 0.05), and PO₄ -3(r = 0.5, p-value = 0.0001), while being negatively correlated with pH (r = -0.5, p-value = 0.001). Dissolved oxygen also showed a positive correlation with phytoplankton biomass (r = 0.63, p-value < 0.05). We concluded that increasing DOC input is crucial for mitigating the negative effects of warming on phytoplankton growth, as DOC provides a primary source of CO₂ necessary for photosynthesis.

An effective method for the in vitro propagation of Tulipa gesneriana was developed using Murashige and Skoog (MS) medium. Seeds were first cultured on hormone-free MS medium, achieving %90 germination after 30 days. Seedlings were obtained, and flowering stem explants excised from these seedlings were used for callus induction. The best callus formation was obtained on MS medium supplemented with thidiazuron (TDZ1.5mg/L) and 2,4-dichlorophenoxyacetic acid (2,4-D1.5mg/L). Shoot proliferation was most effective on MS medium containing 2.0 mg/L benzyladenine (BA) and 0.2 mg/L indole-3-butyric acid (IBA), and bulb formation was promoted by 0.5 mg/L BA, producing 3–4 bulbs per explant, while root induction was achieved with 1.0 mg/L naphthalene acetic acid (NAA), generating up to 5 roots per plantlet. Plantlets with well-developed shoots and roots exhibited survival rates ranging from 70-90% during acclimatization in peat moss and peat moss: soil (1:1) substrates. This study provides a reliable method for large-scale tulip propagation and conservation.

Water pollution is a global issue that affects both human health and the environment. The increasing use of the methylene blue (Mb) dye in various industrial applications poses significant risks. The study aimed to use hydrochloric acid-modified Ziziphus Spina Christi powdered leaves (ZSC) as an effective and low-cost adsorbent for removing Mb dye from effluents. Batch experiments were conducted with contact times of 10–180 minutes, initial dye concentrations of 10–60 mg/L, temperatures of 25–60°C, pH values of 5–9, and sorbent dosages of 0.25-2 g per 40 ml of dye solution. The results showed that the highest removal efficiency of Mb dye was 97.51% using an initial concentration of 10 mg/L, 1.5 g of adsorbent, pH 8, at 50 °C, a contact time of 60 minutes, a particle size of 0.075 μm, and a stirring speed of 150 rpm. The ZSC was characterized using SEM and FTIR techniques, and the adsorption kinetics were found to follow the pseudo-first-order model, with a correlation coefficient of 0.969. In general, the modified ZSC is an auspicious, friendly, and cost-effective adsorbent for removing Mb dye from aqueous solutions.

The rise of Methicillin-resistant Staphylococcus aureus (MRSA) poses a serious global health threat and reduces the efficacy of current treatments. The present study investigates the antibacterial and antibiofilm properties of exoproducts from Pseudomonas aeruginosa against MRSA. The confirmation of the identities of P.aeruginosa and S.aureus was achieved through the sequencing of the 16S rRNA gene. Using the agar diffusion assay, the cell-free supernatants (PA-CFS) derived from both the extensively drug-resistant (XDR) P. aeruginosa strain and the reference strain ATCC 27853 demonstrated notable antibacterial activity against MRSA, exhibiting inhibition zones ranging from 28.67 ± 0.33 to 33.33 ± 0.33 mm and 26.67 ± 0.33 to 28.66 ± 0.33 mm, respectively. The minimum inhibitory concentration (MIC) assays demonstrated the capability of PA-CFS (XDR) and PA-CFS (ATCC 27853) to suppress MRSA growth at a concentration of 1 × 107 CFU/mL, with stability noted across varied pH and temperature conditions. Biofilm quantification revealed that a 1/2 × MIC of PA-CFS significantly hindered biofilm formation, achieving inhibition rates of 22.301% and 23.886% for PA-CFS (ATCC 27853) and PA-CFS (XDR), respectively. The staphylolytic activity indicated a substantial lysis of MRSA populations influenced by PA-CFS. PCR analysis validated the existence of the lasA virulence gene in the two strains of P. aeruginosa. GC–MS investigation of ethyl acetate extracts from PA-CFS indicated the presence of 22 distinct peaks for the PA-CFS (ATCC 27853) strain and 8 peaks for the PA-CFS (XDR) strain. These findings indicate that P. aeruginosa exoproducts significantly contribute to microbial competition by overcoming challenges posed by MRSA

Heavy metals in dust are considered one of the most important environmental consequences of human activities. The objectives of this study are to investigate the ecological risk of heavy metals on human health in the dust of schools in Kut City, Iraq. The dust samples were collected from 25 primary schools and kindergartens. The pH, EC, TDS, and concentrations of heavy metals (Pb, Ni, Cr, Cd, Zn, Co, Mn) were measured.
The heavy metals were analyzed using an atomic absorption spectrophotometer. The contamination factor (CF), enrichment factor (EF), and pollution load index (PLI) were calculated. The results showed that the mean concentration of Pb, Ni, Cr, Cd, Zn, Co, and Mn in the schools' dust were 37, 45, 60, 0.15, 73, 24, and 110 mg/kg, respectively. The mean concentration of all heavy metals was higher than the background concentration values of these metals, except for Mn and Ni.
The highest amounts of Pb, Ni, and Cr were observed in schools 13, 7, and 1. The amounts of Cd were also significantly higher in schools 13, 7, and 23 than in other schools. The significant correlation between heavy metals shows that these metals have the same origin. The CF of most heavy metals was mainly in moderately contaminated (1 ≤ CF < 3). The EF for most elements was mainly in the class of significant enrichment (5 ≤ EF < 20). The PLI values ranged from 1.29 to 2.36, and 100% of the sampling fell into the polluted class.

Tembawang agroforestry represents a traditional forest management system practiced by Dayak communities in Kalimantan and is recognized for its ecological and livelihood functions. This study examines land cover changes and carbon stock distribution in Tembawang agroforestry in Cipta Karya Village, West Kalimantan. The research applied a combination of field surveys and remote sensing using Sentinel satellite imagery from 2019 and 2023. Vegetation data were collected through observation plots to assess species composition, density, and biomass for carbon estimation using allometric equations. A total of 17 tree species were identified across Tembawang and rubber forest areas. Four species showed the highest density values: Durio zibethinus, Hevea brasiliensis, Elateriospermum tapos, and Ochanostachys sumatrana, with densities ranging from 25 to 268.75 ind ha⁻¹ and an overall mean of 260.42 ± 110.00 ind ha⁻¹. Estimated aboveground carbon stocks varied widely between 5.13 and 632.11 Mg C ha⁻¹, with an average of 269.71 ± 181.79 Mg C ha⁻¹. Land cover analysis indicated no significant change in secondary dryland forest over the past five years. However, mixed dryland agricultural areas, including parts of Tembawang, gardens, and cultivated fields, decreased by approximately 100 ha. The spatial distribution of carbon stocks across secondary dryland forest and mixed agricultural land ranged from 0 to 453.96 Mg C ha⁻¹, reflecting substantial variability in carbon storage across the landscape. These findings highlight the role of Tembawang agroforestry in maintaining carbon stocks while undergoing moderate land use changes

Dung beetles (Scarabaeidae: Scarabaeinae) play a crucial role in dung removal and nutrient cycling. We assessed the distributional patterns of diversity of the tribe Onitini across 12 subdivisions in Gujranwala, Gujrat, and Sialkot districts of Punjab, Pakistan. Surveys were conducted twice a month with an inter-visit interval of ~2 weeks by placing three pitfall traps along each 1000 m transect, each spaced ~330 m apart. Data were collected from nine traps placed along three transects at each location within the subdivisions. Gujrat district demonstrated the highest species richness, recording all 11 species, followed by Sialkot and Gujranwala. Onitis castaneus was the dominant species in all districts in winter. Onitis excavatus was most abundant in spring and summer seasons, both in the Gujranwala and Sialkot districts. In summer, O. subopacus was dominant in Sialkot. PERMANOVA indicated no significant differences in species composition among districts (p = 0.278, R² = 0.084), while NMDS revealed strong overlap among divisions. SIMPER analysis showed variations in the relative abundance among districts, resulting in high Bray–Curtis dissimilarities. The spatial dissimilarity observed was mainly contributed by O. castaneus, O. excavatus, and O. subopacus. Onitis singhalensis and O. crassus were recorded exclusively from Gujrat, indicating local habitat association. Dung beetle assemblages were also significantly influenced by seasons (R² = 0.165). The land use types also influenced abundance patterns, specifically in districts. This study provides the first region-wide ecological comparison of Onitini assemblages in Punjab and establishes a baseline for future assessments of dung beetle diversity and ecosystem functioning under agricultural intensification

Spirulina platensis is a supplement that has a good impact on the growth performance of lambs, but information about the amount of this substance in feed efficiency and environmental impact is still required to be determined. The study aimed to examine the effect of the supplementation level of Spirulina platensis in the basal diet on the productivity and environmental costs of Thin-tailed lambs fattening. Twenty-four male lambs (aged 4 months; weighing 14.99 kg) were allocated to a completely randomized design with 4 treatments and six replications in each treatment. The treatments were 1) 100% basal diet without Spirulina supplementation (T0); 2) 99.5% basal diet + 0.5% Spirulina platensis supplementation (T1); 3) 99.0% basal diet + 1.0% Spirulina platensis supplementation (T2); and 4) 98.5% basal diet + 1.5% Spirulina platensis supplementation (T3). The parameters observed were dry matter intake (DMI), nutrient utilization, rumen fluid and blood profile, productivity, feed cost and nitrogen excretion and methane emission. The results showed that DMI of T3 was the highest among the treatments (985.66 g/d; p<0.05). Retained protein of T2 (70.82 g/d) and T3 (72.49) was similar (p>0.05) and higher than that of others (p<0.05). Acetic, propionic and butyric acids 3h after feeding in T3 were higher (p<0.05) than those of the others (178. 21 mMol, 69.82 mMol and 12.42 mMol, respectively). Lambs fed T2 and T3 were similar (averaged 186.7 g/d; p>0.05) in average daily gain (ADG) and higher than that of the others. Feed conversion ratio (FCR) of T2 (4.74) was not significantly different from T3 (p>0.05) but higher (p<0.05) than that of T0 and T1, while T0, T1 and T3 were not significantly different (p>0.05). Feed cost per gain (FC/G) of T2 (Rp 31,996/kgADG) was the lowest (p<0.05) among the others. Nitrogen excretion per kg ADG of T2 and T3 was similar (p>0.05; averaged 0.035 g/kgADG) and lower (p<0.05) than that of T0 and T1. Methane emission per kg ADG of T2 (11.52 L/ADG) was the lowest (p<0.05) among the other treatments. In conclusion, the supplementation of Spirulina platensis up to 1% (T2) increased productivity, reduced feed cost and environmental cost of Thin-tailed lambs production.

Jordan indigenous chickens (JIC) are threatened with extinction since they are crossed with exotic chickens to enhance their low levels of production. The study objective was to investigate the origin, biodiversity, and phylogeographic structure of JIC‎ using mtDNA sequencing in reference to exotic chickens. We sampled 483 males and females by taking a blood spot on an FTA card. We also sampled ancient, commercial, and fancy‎ chickens from Jordan as references. The sampling regions were the main cities and rural areas of the Middle, North and South governorates of Jordan. The extracted DNA from blood samples was sequenced for the mtDNA D-loop region. The results showed that most of the genetic diversity occurred within JIC rather than between populations. The highest expected heterozygosity was for JIC (0.666), whereas the lowest was for Cochin (0.503) chickens. In addition, the coefficients of differentiation (Fst) ranged from the lowest (0.271) between indigenous and Cochin (fancy) chickens to the highest (0.104) between Pharaonic chickens and Lamborghini. Particularly, a UPGMA-based evolutionary analysis showed high differentiation between JIC and the ancient chickens, indicating a lack of close phylogeographic relationship. This study provides maternal genetic evidence that the primary origin of JICs is linked to Indian chickens because they possess haplotypes of Asian and African origin, with additional groups indicating past and recent crossbreeding with exotic and commercial breeds... Finally, it is recommended to establish a genetic conservation program for JIC for their current-day diversity, to ensure better resilience to the current global warming hitting Jordan in recent years

Video streaming services, including YouTube, Netflix, and Amazon, account for a large portion of global internet traffic. Therefore, maintaining high visual quality is very important for user satisfaction and overall Quality of Experience (QoE) for these users. However, fluctuating bandwidth often causes frame drops and motion discontinuities, resulting in visual artifacts and degraded QoE. Although existing video frame interpolation methods attempt to address these challenges, most remain limited in their ability to sustain performance under bandwidth-constrained environments. To overcome these limitations, IncepU-Net has been introduced. IncepU-Net is proposed as an enhanced U-Net architecture that integrates multi-scale inception modules and attention-gated skip connections for intelligent video frame interpolation in bandwidth-limited settings. IncepU-Net was trained and evaluated on the Vimeo90K and UCF101 datasets. The model at the testing stage achieved 35.19 dB PSNR and 0.958 SSIM using Vimeo90K, and 30.08 dB PSNR and 0.933 SSIM on UCF101. The IncepU-Net surpasses several state-of-the-art interpolation methods. These results demonstrate that IncepU-Net effectively interpolates missing frames, mitigates playback stutter, and maintains motion continuity. Which establishing it as a robust and practical solution for improving QoE in real-world video streaming applications, bridging the gap between restricted network delivery and perceptually meaningful frame synthesis

It is a new digital process inspired by the pointillist technique of artist Georges Seurat and seeks to recreate Impressionist paintings in digital form in record time, as opposed to the long duration needed to create a painting by hand. The proposed algorithm breaks down the original image into the components of primary colors with the help of the Self-Organizing Maps algorithm. It's like how Seurat mixed his colours, which, when viewed from afar, creates a complete picture from small dots of color. The methodology has a number of technical improvements, such as a halo effect around the main components of the painting, Kirsch filters to determine the edges with the highest precision, the use of HSV color space to fine-tune the color gradations and the utilization of Gaussian functions to enhance the distribution of dots in space. The high efficiency of the proposed methodology was proved by experimental results in which PSNR values of 35.1, 33.0 and 32.4 dB were achieved with the help of the color palettes of 72, 24 and 16 colors, respectively. The processing time of the methodology was also significantly lower as compared to the conventional processes. Such a method opens new opportunities in digital art, providing viable solutions to the automation of the process of creating art at the same time as maintaining the aesthetic qualities of the products of handiwork. It might also benefit AI-driven image processing methods, especially in the area of preserving the artistic heritage and the creation of artistic content.

Deaf communities still struggle with communication, partly due to the inefficiency of current sign language recognition systems, their poor generalization, and their inability to manage regional and linguistictions. This work suggests a novel architecture that blends attention-based spatiotemporal processing (RTC-ModeRNN-BSF) with a reinforcement threshold–controlled ModeRNN to solve these problems. The model adapts its computation based on the complexity of the input gesture, using between 2 and 8 attention slots, while gradually reducing exploration during training (↋: 0.9→0.1). Dual-stream memory pathways are optimized using joint log-likelihood maximization (J-Star) and computational pruning (Q-Max) to capture both immediate sequential patterns (Ct) and hierarchical spatiotemporal dependencies (Mt). The hybrid gradient descent using the Adam W optimizer ensures dependable convergence while avoiding feature memorization. The proposed system converges 47% faster than conventional techniques, with an average classification accuracy of 99% across datasets of American Sign Language (ASL), Indian Sign Language (ISL), and Chinese Sign Language (CSL). Furthermore, it shows notable cross-lingual adaptation with 78.5% accuracy on unseen sign languages without retraining, consistently maintaining 93–97% performance under real-world challenges such as partial occlusion, changing lighting, and increasing signing speeds.

Predicting stock market movements is a complex and challenging task due to many factors that can influence stock prices. While there are various approaches and techniques that researchers and analysts have explored, it\'s crucial to note that accurately forecasting stock prices consistently is complicated. Financial data can be noisy and subject to errors. Minor inaccuracies in data can lead to inaccurate predictions. In this research, we propose a new deep learning technique with an optimization algorithm to overcome the limitations in stock market prediction. This research has five stages to predict closing prices using historical data. The input data is initially collected from three datasets (Apple,Microsoft,&Tesla). This data must undergo punctuation removal, outlier removal, replacing missing values, data normalization, and data cleaning performed in the pre-processing stage for accurate prediction. After that, the features are extracted using the Improved ResNeXt approach to improve the prediction performance. Finally,a novel DL technique of EfficientNet V2 is employed to predict the future stock closing prices based on historical data. To improve the prediction accuracy, the study utilizes the Multi-Strategies Improved Beluga Whale Optimization Algorithm (MSIBWOA) to optimize the hyper-parameters. For determining the results of the DL stock forecasting approaches, the Accuracy, Mean Squared Error (MSE), Pearson\'s Correlation (R), Normalized Root Mean Squared Error (NRMSE), and Root Mean Squared Error (RMSE) metrics were used. The performance of the proposed method is compared with other “state-of-the-art” techniques. In comparison to the most advanced baseline algorithms, the evaluations demonstrate a significant improvement in the prediction\'s performance.

Thousands of new websites are published every day, which pose significant challenges for web classification and cybersecurity. URL classification datasets, including general and cybersecurity-specific ones, face challenges such as class imbalance, noise, and ambiguous data, which can significantly affect model performance. This study proposes a novel Fine-Tuned FastText Unsupervised Embedding Augmentation Technique (FFUEAT). Datasets (DMOZ and Phishing) have been used to evaluate the performance of the proposed technique, achieving F1-scores of 0.8639 with DistilBERT and 0.8891 with BERT. The performance of sparse minority classes, achieving accuracy increases of 20.88% for \'home\', 58.04% for \'news\', and 11.18% for the \'kids\' categories of the publicly available DMOZ dataset using the FFUEAT technique. When applied to the cybersecurity dataset (Phishing), leveraging a BiLSTM model, the proposed technique achieved F1-scores of 0.98 for legitimate sites and 0.99 for phishing URLs. The impact of class imbalance, noise and ambiguous data in URL classification datasets is reduced by applying the FFEUEAT technique. This method offers a promising way to improve web classification and cybersecurity threat detection, contributing to better online content management and safety.

This paper proposes a method utilizing an evolutionary algorithm (EA) to improve the learning process within the artificial neural network (ANN). The study utilized two models: the neuroevolutionary algorithm and the GANet tool. These algorithms were trained on two clinical diabetes datasets to discern health conditions. Each Feedforward Artificial Neural Network (FANN) layer in a neuroevolutionary algorithm is broken into multiple ANNs according to the number of neurons. The evolutionary technique later utilizes crossover and mutation operations to produce an ideal FANN. Enhanced artificial neural networks were subsequently incorporated to provide an innovative and robust functional artificial neural network for practical use. The GANet tool offers a novel mutation methodology. This method enables neurons to transform into multi-layered subnetworks. The tests utilizing diabetes data attained enhanced prediction accuracy. This demonstrates the importance of optimizing neural networks. It creates a strong basis for the automation of AI design and the advancement of medical diagnostics in the future. The proposed tools exhibited superior efficiency relative to alternative methods such as decision trees and support vector machines.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that is the leading cause of dementia. Early diagnosis is essential for interepting the progression of cognitive decline and favoring the clinical outcome, although conventional diagnostic means may not be able to identify early symptomatic forms. In this manuscript, we propose a residual attention-based hybrid deep learning model with the EfficientNetB1 backbone for improving the classification of AD based on structural MRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and ADNI1 datasets. The network structure integrates two-dimensional (2D) convolutional feature extraction, multi-head self-attention for building the global context information, and residual learning. The proposed method can acquire both the local anatomical characters and the global brain structure dependencies effectively. It outperforms several state-of-the-art methods in terms of diagnostic accuracy on both models, reaching macro-averaged accuracy of 99.13% and 99.37% over ADNI and ADNI1 data, respectively, based on three anatomical planes (axial, sagittal, and coronal). This then led to biologically feasible areas from attention maps, which enhanced interpretability. These findings indicate that our model is an effective and interpretable approach to early and accurate classification of AD, offering the potential to be deployed in clinical settings. In future, the model will be evaluated for its usability and pragmatic effectiveness in clinical use to confirm sufficient applicability for neurologists and radiologists.

Facial expression recognition (FER) is a significant challenge due to the complexity of multi-class categorization and the subtle differences between emotional factors, with existing deep learning models often struggling to leverage hierarchical local features for accurate distinction. To find a solution, this study proposes a two-method approach, aiming to enhance FER accuracy by refining classification through the arousal-valence dimension and improving feature extraction via an adaptive attention mechanism to resolve the difficulty of differentiating semantically close expressions. The proposed methodology combines (1) a two-level classification strategy, first, performing fine-grained classification, and second, multi-class categorization by grouping expressions into neutral, positive, and negative classes based on emotional dimensions. (2) a hierarchical attention model that mitigates the challenge of fine-grained expression recognition by stacking global, regional, and local attention in a redesigned spatial hierarchy, first learning a robust semantic grouping (neutral, positive, and negative) of expressions. This approach reduces high-level confusion and provides a clearer pathway for subsequent fine-grained classification. This module extract emotion feature from facial images, all implemented on an enhanced VGG-16 backbone. The experiments on CK+, FER+, AffectNet, and RAF-basic datasets demonstrate that the proposed model achieves superior accuracy and robustness compared to existing methods, effectively capturing multi-level features and leveraging hierarchical subdivision for improved performance. The results confirm that our approach, through its fusion of multi-class grouping and hierarchical spatial attention, offers a more reliable and sophisticated solution for real-world emotion recognition applications.

Simultaneous saccharification and fermentation (SSF) is considered an efficient approach for bioethanol production from starchy biomass, although its performance is often affected by multiple variables acting at different stages of the process. In the present study, Plackett–Burman experimental design was used as a screening method to evaluate factors affecting ethanol production from cassava flour using Saccharomyces cerevisiae. Eleven variables related to both liquefaction and SSF stages were examined, including temperature, pH, processing time, agitation, enzyme loading, nitrogen supplementation and inoculum level, arranged in twelve experimental runs. As the experimental design was performed without replication, the effect of each variable was evaluated based on the magnitude of standardized effects and interpreted using a Pareto chart. It was observed that not all variables contributed equally to ethanol production. Among the factors tested, SSF temperature showed the most dominant effect, followed by liquefaction pH and liquefaction time within the range studied. Other variables appeared to have a relatively smaller contribution under the present conditions. These results suggest that the use of Plackett–Burman design is suitable for initial screening of variables in the SSF system, particularly when dealing with multiple factors. The outcome of this study can be used as a basis for further optimization using an experimental design with replication to obtain a more reliable estimation of the effects of significant variables.

The erythropoietic dysfunction and iron insufficiency associated with β-Thalassemia (β-TH) underscore the importance of augmenting traditional diagnostic techniques with independent biomarkers, beyond standard panels. This single-center case–control study (n = 120 β-TH major, 55; β-TH intermedia, 25; control, 40) examined whether circulating phosphatidylinositol-3-kinase (PI3K) distinguishes patients from controls and correlates with hemoglobin fractions. The serum PI3K was significantly higher in β-TH (p < 0.001), with excellent discrimination tested in serum (AUC = 0.934; optimal cut-off > 1301 pg/mL; sensitivity 100%, specificity 80%). In a multivariable model, HbA₂% and HbF% were both positively and independently associated with PI3K. These results are biologically consistent with the interaction of erythroid stress, iron homeostasis, and PI3K–AKT signaling, and suggest that PI3K provides unique and complementary diagnostic value in parallel with traditional markers. While human serum circulating PI3K in β-TH data with formal diagnostic parameters are quite scarce. These results suggest that PI3K may serve as a non-invasive complementary biomarker and support multi-center, longitudinal validation with cut-offs confirmation, temporal responsiveness around transfusion/chelation, and supplemental clinical utility in prediction models. Any therapeutic implications targeting the PI3K pathway remain exploratory and require disease-specific studies.

Epilepsy is a common brain disorder, characterized by spontaneous recurrent seizures, with associated neuropsychiatric and cognitive comorbidities and increased mortality. Assessment of serum Eotaxin and related biomarkers offers useful information about neuroinflammatory activity and metabolic imbalance in epilepsy, and may have potential value as diagnostic and prognostic tools. The aim of this study was to evaluate the association between Eotaxin, Intelectin-1, Kynurenine, and Proprotein Convertase-1 and epilepsy. The study consisted of 90 subjects who were classified into three groups: 30 healthy controls, 31 patients with focal epilepsy, and 30 patients with generalized epilepsy. Serum samples were obtained, and biomarker concentrations were measured using the ELISA technique. Statistical analysis was performed using GraphPad software. The results of this study show a significant increase in serum Eotaxin, Intelectin-1, and kynurenine concentrations in the epileptic patients’ group as compared with the normal group. The results also show a significant decrease in the levels of proprotein convertase in the two epileptic patient groups as compared to healthy subjects. These findings suggest that serum Eotaxin and related biomarkers may provide mechanistic insights into epileptogenic processes, support early detection, and help prevent complications in patients with epilepsy.

The use of persistent plastic in food packaging continues to raise practical and environmental concerns, prompting interest in biodegradable alternatives derived from biomass. In this work, cellulose derived from coconut dregs was used as a film-forming material. The cellulose was isolated using a combination of chemical and ultrasonic treatments, followed by grafting with glycidyl methacrylate (GMA). Citronella oil (CitO) was incorporated at different loadings to produce GraftCell/CitO0.15, GraftCell/CitO0.6, and GraftCell/CitO1.2 films. Fourier-transform infrared (FTIR) analysis confirmed grafting, while structural changes were reflected in an increase in the crystallinity index from 10.55% to 16.04%. Thermogravimetric (TGA) analysis further revealed a shift in the onset degradation temperature from 254.6 °C (Cell) to 369.6 °C (GraftCell), with a maximum decomposition temperature reaching 477.8 °C, indicating improvement of thermal resistance after modification. The mechanical response varied depending on CitO content. The GraftCell/CitO1.2 film exhibited a tensile strength and elongation at break of 25.63 MPa and 21%, respectively. Approximately three times higher than that of the untreated cellulose film. Shelf-life testing revealed that grapes wrapped with the GraftCell/CitO1.2 film remained visually acceptable for up to 9 days, compared to conventional plastic packaging (ony 5 days). Indicating an approximate 80% improvement in preservation performance. These findings highlight the potential of the GraftCell/CitO1.2 film as a sustainable biodegradable packaging material.

This study has assessed the novel technology developed for our in-house NAG-5SX1-1D-SSP analyzer by utilizing the established reaction of the zinc (II) ion with ammonium thiocyanate and mercury (II) chloride to produce a white precipitate. This analyzer was used to access turbidity by calculating the reflection of incident light. The method maintains linear behavior over a concentration range from 0.05 to 18 mmol. L-1, with a detection limit of 0. 489µg.sample-1 and excellent precision (RSD% > 1). These results confirmed the reliability and robustness of the developed instrument for Zn (II) determination in various pharmaceutical formulations. Successful detection of Zn (II) ion was achieved in three drug formulations by using the standard addition approach. A comparative analysis was conducted between the newly devised approach and the conventional method (UV-Vis spectrophotometer) at a wavelength of 227 nm through the implementation of an individual and paired t-test. The data obtained underwent rigorous mathematical treatment, revealing no significant variance between the two methodologies in the analysis of three distinct drugs at a 95% confidence level.

Herein, silica nanoparticles (SNs) with different functional groups (propyl amine, propyl thiol, propyl carboxylic acid and unmodified hydroxyl) were loaded with the anticancer agent lomustine (LMS) and conjugated with Folic Acid (FA) to produce FA-NH₂-SNs, FA-SH-SNs, FA-COOH-SNs, and FA-OH-SNs, respectively. The nanoparticles (NPs) were characterized using Fourier Transform Infrared Spectroscopy (FTIR) and Dynamic Light Scattering (DLS). Encapsulation Efficiency (EE) and drug release rate were measured using UV Spectrophotometer. The in vitro cytotoxicity toward HeLa and VERO cells was assessed using MTT assay. LMS encapsulation efficiency exceeded 40% and FTIR spectra confirmed a successful conjugation with FA. Particle size increased after LMS loading and FA conjugation, ranging from 284 to 308 nm. Zeta potential measurements showed that all NPs became more negative after FA conjugation. The largest shift occurred in NH₂-SNs, changing from about − 4 to −14 mV. One-way ANOVA showed statistically significant differences (p < 0.05) among most formulations for drug release rates and cytotoxicity which were lower compared to free drug. The cytotoxicity was higher against HeLa compared to VERO cells due to folate-receptor targeting. The order of cell toxicity effect was almost related to their release rate behaviors where FA-NH2-SNs exhibited the lowest percentages (18% and 40%, respectively). Thus, the type of surface functional group significantly influenced the efficiency of FA conjugation, which in turn modulated the in vitro characteristics of the loaded drug where higher conjugation correlated with reduced release rates and cytotoxicity.

In this study, a novel analytical reagent based on arsenazo III immobilized on a polymer matrix composed of polyacrylonitrile, polyethylene polyamine, and dichloroethane (PPD) was developed and applied for the spectrophotometric determination of Pb(II) ions. The proposed method demonstrated standard analytical and metrological characteristics for the highly accurate determination of Pb(II) ions, as evidenced by a relative standard deviation (Sr) of 0.06 and a minimum detectable concentration (Cmin) of 0.24 μg/L. The accuracy and reproducibility of the method were evaluated using Student’s t-test and Fisher’s F-test. Comparison of the experimental and tabulated values (experiment=1.09; ttable=2.83, Fexperiment=2.52; Ftable=4.47) confirmed the reliability of the proposed analytical procedure. At pH 5.8 in an acetate buffer medium, the Pb(II)–arsenazo III complex immobilized on the PPD matrix exhibited a maximum analytical signal at a wavelength of 640 nm. The measurement results followed the Beer–Lambert–Bouguer law, showing a linear relationship in the Pb(II) concentration range of 0.04-2.4 μg/mL. The method is simple, rapid, cost-effective, and allows direct Pb(II) ion determination in water samples with high sensitivity and selectivity.

Topological indices in graph theory are considered a very important topic because of its practical importance in some branches of sciences, especially in chemistry, physics and networks, because they are closely related to chemical properties and physical properties, including boiling point, melting point, vapor pressure, molar volume etc. There are several types of topological indices, the most important of which are the indices that depend on the degrees of vertices, especially adjacent or non-adjacent vertices. The Zagreb indices for adjacent vertices and non-adjacent vertices, multiplicative and eccentricity for adjacent vertices in a connected simple graph are considered topological indices that have an impact on knowing many chemical and physical properties. In this paper, we discussed the Zagreb type topological indices of generalized caterpillar graphs, as well as for the molecules alkanes with formula C_p H_((2p+2)), which is a special case of the statement caterpillar graph and studied some of its properties. In the application section, we presented that the first and second Zagreb coindices (corresponding indices) give high correlation among other Zagreb indices when predicting entropy and ascent factor of alkanes.

This study is introducing two new different pairwise related to Lindelo ̈f bitopological spaces. Lindelo ̈f spaces are among of the most important topological spaces with important applications in various branches of mathematics. Therefore, many studies on Lindelo ̈f topological spaces and pairwise Lindelo ̈f bitopological spaces were presented in previous papers. However, those two new pairwise Lindelo ̈f bitopological spaces are more general than what has been studied before. Therefore, the study defines a new pairwise interior operator and a new pairwise closure operator, furthermore, it reviews their characteristics where the researchers provide new strongly pairwise axioms (the strongly pairwise regular and the strongly pairwise normal axioms) in bitopological spaces. Moreover, many illustrative examples and counterexamples are reviewed, and a study was conducted on the relationship between the strongly pairwise regular axioms and the pairwise regular axioms, and the relationship between the strongly pairwise normal axioms and the pairwise normal axioms. In addition, this study presents some important results in strongly regular and strongly normal bitopological spaces. Finally, the findings showed some relationships between the new concepts of Lindelo ̈f bitopological spaces.

Web phishing attacks have been continually evolving over the past few years, which has led customers to lose their trust in online services and e-commerce. To identify phishing data, a variety of methods and systems based on a blacklist of phishing websites are used. However, the rapid development of technology has given rise to increasingly complex techniques for creating user-attracting websites. Therefore, current blacklist-based techniques are unable to identify the recently launched phishing data, such as zero-day phishing websites. Machine learning techniques have been used in several recent studies to detect phishing data and function as an early warning system for such attacks. However, majority of these methods selected the most significant features of websites based on frequency analysis or human experience. In order to improve the classification of phishing data, this research proposes the hybrid MI-AN method for intelligent phishing data classification using three phishing datasets. We have used six machine learning models with the proposed MI-AN feature selection method. The experimental results indicated that the proposed MI-AN method achieved significant improvements in classification accuracy. Compared to other models, the multi-layer perceptron achieves higher accuracy (97.07%) on dataset 1, the random forest model achieves higher accuracy (96.60% and 97.78%) on both datasets 2 and 3. We have also compared proposed method to a previously published research work, and our method achieves better performance. Overall, the proposed MI-AN method consistently enhanced model performance across all phishing datasets, demonstrating robustness, adaptability, and effectiveness in eliminating irrelevant features to achieve better generalization.

The principal factor in the proofs of many so-called intersection results and associated best approximation theorems—such as the famed Brouwer fixed-point theorem) is Knaster-Kuratowski-Mazurkiewicz theorem (KKM), which was extended from R^l to Hausdorff vector spaces by Ky Fan. This kind of theory necessitates introducing some form of abstract convex structure on a topological space. In both pure and applied mathematics, the theory of approximation plays a central role. The connection between best approximation and fixed-point theory provides a powerful tool for analyzing the nonlinear maps, the convergence of iterative processes, and the solving of variational problems.
This paper is dedicated to presenting a new extension of the well-known Prolla’s theorem in the field of invariant approximation from a normed space to a kind of metric space. First, we will introduce the basic concepts of vector metric spaces, define the best approximation, and describe its relationship to fixed points. Second, we will substantiate the best approximation theorem using the state of KKM-map (where the convex combination of any finite elements in Θ⊂Π entirely belonged to the union of their images under the KKM-map,N,N:Θ→2^Π). This theoretical construction has been applied to specific cases that were previously discussed as the best approximations in earlier papers.

The current investigation focuses on developing a semi-analytical technique aimed at estimating the solutions of magnetohydrodynamics flow with Casson tri-hybrid nanofluid (TiO_2-SiO_2-Al_2 O_3 \/blood) flowing over a stretchable surface along two-dimensional space. To solve the obtained nonlinear coupled differential equations in the problem formulation, the proposed semi-analytical approach makes use of the Shehu reduced differential transform Padé method (SRDTPM). Several physical parameters, such as slip effect, porosity, magnetic strength, and Casson number, were considered for examining the effect on momentum, while radiation parameter, heat generation/absorption, temperature-dependent heat sink/source term, and Biot number were analyzed for energy transfer. The results revealed an inverse relation between fluid velocity (both primary and secondary velocities) and porosity/magnetic strength, whereas an increase in the value of the Casson number resulted in higher secondary velocity. Further, it is observed that higher values of the heat sink/source terms and Biot numbers led to an increase in the fluid temperature.

Iron oxide nanoparticles (Fe₂O₃) and iron oxide core–silver/silver chloride shell nanoparticles (Fe₂O₃@Ag/AgCl) were green-synthesized using mint leaf extract .X-ray diffraction (XRD) results showed that Fe₂O₃ formed in two phases, γ-Fe₂O₃ and α-Fe₂O₃, while Fe₂O₃@Ag/AgCl formed in one phase, α-Fe₂O₃, with crystallite sizes increasing from 1.89 nm for Fe₂O₃ to 22.42 nm for Fe₂O₃@Ag/AgCl. Transmission electron microscopy (TEM) revealed that Fe₂O₃ had a heterogeneous structure with nanoclusters, while Fe₂O₃@Ag/AgCl exhibited a uniform Ag/AgCl coating around the iron oxide core, indicating successful coating. FTIR analysis identified peaks corresponding to Fe–O bonds at 559.56 cm⁻¹ and 472.05 cm⁻¹ with the presence of functional groups due to the components of the plant extract. Atomic force microscopy (AFM) showed that Fe₂O₃@Ag/AgCl had a smoother surface and larger crystal size (58 nm) compared to Fe₂O₃ (39 nm), with lower surface roughness of 3 nm compared to 12 nm for Fe₂O₃. UV–Vis analysis showed absorption in UV region with a plasmonic peak in the visible range, with Eg were 3.57 eV and 3.41 eV for Fe₂O₃ and Fe₂O₃@Ag/AgCl, respectively. Cytotoxicity test was performed on normal rat embryo fibroblast (REF) cells and growth inhibition test of MDA breast cancer cells using a concentration (50 µg / ml) with a series of dilutions )100%, 50%, and 25(% for two periods )24 and 48 (hours, Fe₂O₃@Ag/AgCl showed greater toxicity to (REF) cells, but within the permissible limits and more effective growth inhibition of breast cancer (MDA) cells compared to Fe₂O₃, especially after 48 hours.