Search Results (250)

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

The primary worldwide variables limiting plant development and agricultural output are the ever-present threat that environmental stressors such as salt (may trigger osmotic stress plus ions toxicity, which impact on growth and yield of the plants), drought (provokes water stress, resulting in lowering photosynthesis process and growth rate), heavy metals (induced toxicity, hindering physiological processes also lowering crop quantity and quality), and pathogens (induce diseases that may significantly affect plant health beside productivity). This review explores the integrated effects of these stressors on plant productivity and growth rate, emphasizing how each stressor exceptionally plays a role in physiological responses. Owing to developments in technology that outclass traditional breeding methods and genetic engineering techniques, powerful alleviation strategies are vital. New findings have demonstrated the remarkable role of nanoparticles in regulating responses to these environmental stressors. In this review, we summarize the roles and various applications of nanomaterials in regulating abiotic and biotic stress responses. This review discusses and explores the relationship between various types of nanoparticles (metal, carbon-based, and biogenic) and their impact on plant physiology. Furthermore, we assess how nanoparticle technology may play a role in practices of sustainable agriculture by reducing the amount of compounds used, providing them with a larger surface area, highly efficient mass transfer abilities, and controlled, targeted delivery of lower nutrient or pesticide amounts. A review of data from several published studies leads to the conclusion that nanoparticles may act as a synergistic effect, which can effectively increase plant stress tolerance and their nutritional role. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

Sepsis-associated acute kidney injury (S-AKI) represents a significant health problem associated with adverse outcomes. Our study aimed to assess the value of serum cystatin-C (sCysC) and TNF-α (rs361525) in combination for diagnosing S-AKI patients and predicting their adverse outcomes. The study included 100 critically ill neonates and 100 controls. Patients were categorized into an S-AKI group and a non-AKI group. TNF-α (−238, rs361525) genotyping was performed using RT-PCR, and sCysC was assessed using ELISA. Our study showed a fundamental difference in the genotype frequencies of TNF-α (−238, rs361525) and SNP between S-AKI and non-AKI patients. Furthermore, there was a significant relationship between cystatin C and TNF-α (−238, rs361525), where cystatin C was higher in patients with AA alleles than in patients with GA and GG alleles. Combining GA + AA genotypes with elevated serum cystatin-C levels can serve as a potential diagnostic and prognostic biomarker for AKI development in this population. The GA/AA genotypes independently predicted S-AKI risk (OR = 6.64, p < 0.001). At the same time, elevated sCysC (>9.4 mg/L) emerged as a sensitive biomarker (AUC = 0.848) and independent predictor of adverse outcomes. Collectively, these findings contribute to the growing field of personalized medicine and represent a strategic advantage, enabling prevention-focused care rather than the treatment of established disease. Full article

Vehicle collisions with street lighting poles generate extremely high impact forces, often resulting in serious injuries or fatalities. Therefore, enhancing the structural resilience of pole bases is a critical engineering objective. This study investigates a comprehensive dynamic analysis conducted with respect to base material behavior and energy absorption of GFRP lighting pole structures under impact loads. A finite element (FE) model of a 5 m-tall tapered GFRP pole with a steel base sleeve, base plate, and anchor bolts was developed. A 500 kg drop-weight impact at 400 mm above the base simulated vehicle collision conditions. The model was validated against experimental data, accurately reproducing the observed failure mode and peak force within 6%. Parametric analyses explored variations in pole diameter, wall thickness, base plate size and thickness, sleeve height, and anchor configuration. Results revealed that geometric parameters—particularly wall thickness and base plate dimensions—had the most significant influence on energy absorption. Doubling the wall thickness reduced normalized energy absorption by approximately 76%, while increases in base plate size and thickness reduced it by 35% and 26%, respectively. Material strength and anchor bolt configuration showed minimal impact. These findings underscore the importance of optimizing pole geometry to enhance crashworthiness. Controlled structural deformation improves energy dissipation, making geometry-focused design strategies more effective than simply increasing material strength. This work provides a foundation for designing safer roadside poles and highlights areas for further exploration in base configurations and connection systems. Full article

Background/Objectives: Cancer remains one of the leading causes of mortality worldwide, while natural antioxidants have emerged as promising therapeutic agents in cancer treatment. Although fucoidan from brown algae shows anticancer potential, its efficacy is limited by bioavailability challenges, and the synergistic effects of combining it with gold nanoparticles remain unexplored. Methods: Fucoidan was extracted from Sargassum cinereum and Turbinaria decurrens. F-AuNPs were produced utilizing fucoidan as both a reducing and stabilizing agent. The nanoparticles were analyzed by UV-Vis spectroscopy, FTIR, TEM, XRD, DLS, TAG, and zeta potential evaluation. The antioxidant activity was evaluated by DPPH and FRAP tests. Cytotoxicity was determined against HepG2, THP-1, and BNL cells, utilizing MTT and SRB tests. Flow cytometry was utilized to assess the cell cycle, while molecular docking was carried out to examine binding to oncogenic proteins. Results: T. decurrens produced higher polysaccharides rich in fucoidan content (235.9 mg/g dry weight) and stated higher antioxidant activity (FRAP: 9.21 μg TE mg⁻¹; DPPH: 4.48 μg TE mg⁻¹) in comparison to S. cinereum. F-AuNPs showed potent cytotoxicity toward HepG2 cells, with IC₅₀ values and cytotoxicity toward HepG2 cells, with IC₅₀ values of 377.6 μg/mL for S. cinereum and 449.5 μg mL⁻¹ for T. decurrens. Molecular docking revealed robust binding of fucoidan to COX-2 (−7.1 kcal mol⁻¹) and TERT (−5.4 kcal mol⁻¹). Conclusions: Fucoidan and F-AuNPs reveal remarkable antioxidant and anticancer properties. Nanoparticle formulation greatly improves bioactivity, underscoring its promise as a synergistic approach for cancer treatment by influencing oxidative stress and cancer-associated pathways. Full article

This manuscript addresses the issue of irrigation water management with high efficiency and effectiveness and focuses on systems associated with significant water losses, which is sprinkler irrigation. This article presents mathematical modeling that enables the application of precision irrigation using a gun sprinkler robot. The sprinkler robot was fabricated in the Faculty of Agriculture and Natural Resources workshop at As-wan University. The experiments were conducted using 12, 14, and 16 mm nozzle sizes and three gun heights, 1.25, 1.5, and 2 m, at three forward speeds, 25, 50, and 75 m/h. The results revealed that at nozzle 12, the actual wetted diameter would be less than the theoretical diameter by a percentage of 2–5%, while at nozzle 14, it ranged from 2 to 7%, but at nozzle 16, it increased from 6 to 9%. The values of evaporation and wind drift losses were always less than 2.8 mm. The highest efficiency was achieved at the lowest forward speed (25 m/h) and using a 1.5 m gun height. The highest water application efficiency was 81.8, 82.5, and 81.1% using nozzle 12, nozzle 14, and nozzle 16, respectively. Precise irrigation control using sensor and variable rate technology will be the preferred option in the future. Full article

Emerging evidence highlights the profound and lasting impact of severe illnesses such as COVID-19, particularly among individuals with underlying comorbidities. Patients with pre-existing conditions like diabetes mellitus (DM) are disproportionately affected, facing heightened risks of both disease exacerbation and the onset of new complications. Notably, the convergence of advanced age and DM has been consistently associated with poor COVID-19 outcomes. However, the long-term metabolic consequences of SARS-CoV-2 infection, especially its role in disrupting glucose homeostasis and potentially triggering or worsening DM, remain incompletely understood. This review synthesizes current clinical and experimental findings to clarify the bidirectional relationship between COVID-19 and diabetes. We critically examine literature reporting deterioration of glycemic control, onset of hyperglycemia in previously non-diabetic individuals, and worsening of metabolic parameters in diabetic patients after infection. Furthermore, we explore proposed mechanistic pathways, including pancreatic β-cell dysfunction, systemic inflammation, and immune-mediated damage, that may underpin the development or progression of DM in the post-COVID setting. Collectively, this work underscores the urgent need for continued research and clinical vigilance in managing metabolic health in COVID-19 survivors. Full article

In this study, we analyze a time-fractional spatiotemporal $SIR$ model in a specific area $\Omega$. Taking into account the available resources, vaccines are allocated to region ${\omega }_1\subseteq \Omega$ and treatments to region ${\omega }_2\subseteq \Omega$, which may or may not coincide. Our objective is to minimize infections and costs by implementing an optimal regional control strategy. We establish the existence of optimal controls and related solutions, providing a characterization of optimal control in terms of state and adjoint functions. We employ the forward–backward sweep method to solve the associated optimality system numerically. The findings indicate that a combined strategy of vaccination and treatment is more effective in reducing disease transmission from adjacent regions. Full article

Chicory (Cichorium intybus L.) roots are valued in medicine for their potential health benefits. Producing callus from chicory roots through tissue culture technology can streamline bioactive metabolites production and ensure a sustainable supply chain. The current study explored the impact of plant growth regulators (PGRs) and light conditions on the characteristics of callus induced from C. intybus root explants. The effect of fungal elicitors [yeast extract (YE), Fusarium oxysporum, and Aspergillus niger] on bioactive metabolite production from root-derived callus was investigated. Callus color varied notably between a 16/8 h light/dark cycle and complete dark, with differences in texture based on PGR concentrations and light conditions. High weights of callus formed were generally recorded under the 16/8 h light/dark cycle. Low concentrations of YE (1 g/L) and F. oxysporum (0.25 g/L) enhanced callus biomass fresh weight, while high concentrations of A. niger (1 g/L) improved callus dry matter significantly. The content and productivity of total phenolic were maximized at 1 g/L of YE and 1 g/L of F. oxysporum. Callus cultures elicited with a higher level of A. niger recorded the higher values of total flavonoid production. High-performance liquid chromatography (HPLC) analysis revealed significant variations in chlorogenic acid, catechin, and caffeic acid levels among the different elicited cultures. A. niger at 1 g/L notably increased chlorogenic acid content, while catechin levels were enhanced by specific concentrations of YE. Catalase (CAT) activity was significantly affected by different elicitors, while only the higher level of F. oxysporum and A. niger showed a significant increase in peroxidase (POD) activity. DPPH scavenging activity was elevated by all fungal elicitors. Principal Component Analysis delineated distinct variations in callus traits in response to different elicitors, with specific treatments showcasing enhanced biomass production, bioactive compound accumulation, and antioxidant activities. Through meticulous experimentation, this study paves the way for enhancing chicory root-derived products, ensuring sustainable production and potent bioactivity. Full article

Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. While most earlier studies have addressed ECG image processing using basic filtering, fixed-region cropping, or template matching, our method uniquely focuses on automated and precise reconstruction of individual ECG leads from noisy and overlapping multi-lead images—a challenge often overlooked in previous work. This innovative segmentation strategy significantly enhances signal clarity and enables the extraction of richer and more localized features, boosting the performance of DL classifiers. The dataset utilized in this work of 12 lead-based standard ECG images consists of four primary classes. Results: Experiments conducted using various DL models—such as VGG16, VGG19, ResNet50, InceptionNetV2, and GoogleNet—reveal that segmentation notably enhances model performance in terms of recall, precision, and F1 score. The hybrid VGG19 + SVM model achieved 98.01% and 100% accuracy in multi-class classification, along with average accuracies of 99% and 97.95% in binary classification tasks using the original and reconstructed datasets, respectively. Conclusions: The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution. Full article

Myeloproliferative neoplasms, particularly the Philadelphia chromosome-negative (Ph-negative) subtypes such as essential thrombocythemia, polycythemia vera, and primary myelofibrosis, present diagnostic challenges due to overlapping morphological features and clinical heterogeneity. Traditional diagnostic approaches, including imaging and histopathological analysis, are often limited by interobserver variability, delayed diagnosis, and subjective interpretations. To address these limitations, we propose a novel framework that integrates handcrafted and automatic feature extraction techniques for improved classification of Ph-negative myeloproliferative neoplasms. Handcrafted features capture interpretable morphological and textural characteristics. In contrast, automatic features utilize deep learning models to identify complex patterns in histopathological images. The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. Our framework achieved high performance across multiple metrics, including precision, recall, F1 score, accuracy, specificity, and weighted average. The concatenated probabilities, which combine both feature types, demonstrated the highest mean weighted average of 0.9969, surpassing the individual performances of handcrafted (0.9765) and embedded features (0.9686). Statistical analysis confirmed the robustness and reliability of the results. However, challenges remain in assuming normal distributions for certain feature types. This study highlights the potential of combining domain-specific knowledge with data-driven approaches to enhance diagnostic accuracy and support clinical decision-making. Full article

Understanding information extracted from receipts is a critical task for real-world applications such as financial tracking, auditing, and enterprise resource management. In this paper, we introduce ReceiptQA, a novel large-scale dataset designed for receipt understanding through question-answering (QA). ReceiptQA contains 171,000 question–answer pairs derived from 3500 receipt images, constructed via two complementary methodologies: (1) LLM-Generated Dataset: 70,000 synthetically generated QA pairs, where each receipt is paired with 20 unique, context-specific questions. These questions are produced using a state-of-the-art large language model (LLM) and validated through human annotation to ensure accuracy, relevance, and diversity. (2) Human-Created Dataset: 101,000 manually crafted questions spanning answerable and unanswerable queries. This subset includes carefully designed templates of varying difficulty (easy/hard) to comprehensively evaluate QA systems across diverse receipt domains. To benchmark performance, we evaluate leading vision–language models (VLMs) and language models (LMs), including GPT-4o, Phi-3B, Phi-3.5B, LLaVA-7B, InternVL2 (4B/8B), LLaMA-3.2, and Gemini. We further fine-tune a LLaMA-3.2 11B model on ReceiptQA, achieving significant improvements over baseline models on validation and test sets. Our analysis uncovers critical strengths and limitations of existing models in handling receipt-based QA tasks, establishing a robust benchmark for future research. Full article

Imidacloprid (IMI) is a widely used insecticide known for its high selectivity toward insects. Ellagic acid (EA) is a plant-derived polyphenolic compound recognized for its therapeutic potential and favorable safety profile in the treatment of various diseases. This study aimed to evaluate the therapeutic effects of EA, formulated as novasomes (NOV), against IMI-induced thyroid dysfunction and to investigate the underlying mechanisms. Rats were divided into four equal groups: control, EA-NOV, IMI, and IMI + EA-NOV. Thyroid function was assessed by measuring free triiodothyronine (T3), free thyroxine (T4), and thyroid-stimulating hormone (TSH) levels. Thyroid tissues were examined to evaluate histopathological alterations, as well as to assess the oxidative/antioxidant pathway (Nrf2, SOD, TAC, MDA), inflammatory pathway (IL-1β, TNF-α, NF-κB), apoptotic pathway (Bcl, BAX), and autophagy pathway (PI3K/Akt/mTOR, P53, Beclin-1). Exposure to IMI resulted in impaired thyroid function, the upregulated gene expression of the PI3K/Akt/mTOR pathway, and downregulated P53 expression. Additionally, immunohistochemical staining revealed Beclin-1-mediated autophagy, alongside increased apoptosis, oxidative stress, and elevated levels of inflammatory cytokines. Conversely, EA improved thyroid function and ameliorated histopathological alterations by enhancing autophagy-inducing pathways. Additionally, the alleviation of oxidative stress was evidenced by the increased immunohistochemical staining of Nrf2, which promoted the synthesis and activity of antioxidant enzymes and reduced apoptotic and inflammatory markers. This study proposes the use of EA as a potential protective, naturally occurring phytoceutical against IMI-induced thyroid dysfunction, primarily through the modulation of PI3K/Akt/mTOR-mediated autophagy. Full article

Background/Objectives: Anlotinib is a novel oral antiangiogenic tyrosine kinase inhibitor (TKI) approved as a third-line treatment for advanced non-small-cell lung cancer (NSCLC). However, its efficacy in combination with docetaxel remains incompletely understood. Given the need for effective second-line therapies after platinum-based chemotherapy, this systematic review aims to evaluate the therapeutic potential of anlotinib plus docetaxel in advanced NSCLC. Methods: The PubMed, WOS, Medline, and Scopus databases were screened for published articles up to 12 April 2024. We included RCTs comparing anlotinib plus docetaxel with docetaxel alone in advanced NSCLC after receiving platinum-based chemotherapy, reporting progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) as outcomes for both groups. Results: Our systematic review included three randomized controlled trials (RCTs) with a total of 151 patients in the anlotinib plus docetaxel group and 132 in the docetaxel-only group. Meta-analysis results demonstrated that the combination therapy significantly prolonged PFS (mean difference (MD) = 2.98, 95% confidence interval (CI), 1.95–4.00; p < 0.00001) and improved ORR (risk ratio (RR) = 3.04, 95% CI = 1.77–5.24; p < 0.00001). Additionally, the DCR was notably higher in the combination group (RR = 1.58, 95% CI = 1.34–1.87; p < 0.00001). Conclusions: Anlotinib plus docetaxel appears to be more effective as a second-line treatment of advanced NSCLC than docetaxel in prolonging PFS and increasing ORR and DCR. Full article