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Maize (Zea mays) is a critical crop for global food security and economic stability. However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. These models also suffer from high computational complexity when deeper layers are introduced to handle fine-grained details. Transformer-based models, on the other hand, provide long-range dependencies but come with significant computational overhead, limiting their use in real-time agricultural applications. To overcome these challenges, we propose MSCPNet, a novel architecture that combines a truncated MobileNetV2 backbone with a Multi-Scale Convolutional PoolFormer block. The truncated backbone ensures that only essential layers for general feature extraction are retained, enhancing the model’s adaptability across domains. The Multi-Scale Convolutional PoolFormer block captures both local and global dependencies through parallel convolutional branches of varying kernel sizes, while the PoolFormer module efficiently handles feature aggregation without the heavy computational cost associated with traditional attention mechanisms. This design allows the model to balance computational efficiency and high accuracy, making it highly suitable for real-time maize disease detection. Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. Furthermore, the model was evaluated on the PlantVillage dataset for tomato leaf disease classification, where it achieved an accuracy of 99.32%, precision of 99.32%, recall of 99.33%, F1-score of 99.32%, and an MCC of 0.9925. These results demonstrate the effectiveness and efficiency of MSCPNet in disease classification across different domains.

Abstract Background The WHO considers anemia in pregnancy a severe public health issue when prevalence surpasses 40%. In response, we conducted a systematic review and meta-analysis to examine anemia among pregnant women in Egypt, focusing on its prevalence, determinants, and associated complications. Methods We conducted a systematic literature search for studies published between January 1, 2010, and August 18, 2024, to identify studies from Egypt reporting on anemia in pregnant women, including its prevalence, associated determinants, and complications. A meta-analysis was conducted using a random-effects model to estimate pooled prevalence, odds ratios (OR), and standardized mean differences (SMD). Sensitivity analyses and publication bias were performed. All statistical analyses were conducted using R software. Results Eighteen studies met the eligibility criteria with a total sample size of 14,548. The overall prevalence of anemia among pregnant women was 49% (95% CI: 42–57), with no significant difference between Upper and Lower Egypt (P = 0.66). The sensitivity analysis demonstrated the absence of influential outliers and Egger’s test indicated no evidence of publication bias (P = 0.17). Anemia prevalence was significantly higher in the third trimester (65%) compared to the second trimester (47%) (P = 0.03). Among anemic pregnant women, most cases were mild (47%) and moderate (47%). The determinants of anemia among pregnant women included being over 30 years old (OR: 1.95), residing in rural areas (OR: 1.76), illiteracy (OR: 1.93), birth spacing

Abstract Glycogen Storage Disease Type Ib (GSD-Ib) is a rare autosomal recessive metabolic disorder caused by mutations in SLC37A4, leading to a deficiency in glucose-6-phosphate translocase. This disorder is characterized by impaired glycogenolysis and gluconeogenesis, resulting in clinical and metabolic manifestations. We report a three-month-old Moroccan female patient presenting with doll-like facies, hepatomegaly, dysmorphic features, and developmental delays. Laboratory analysis revealed hypoglycemia, elevated triglyceride levels, hypercalcemia, and neutropenia. Genetic testing confirmed a homozygous pathogenic variant in SLC37A4 and a heterozygous variant of uncertain significance in TBX1. Initial management included a lactose-free and galactose-free diet, multivitamin supplementation, and granulocyte colony-stimulating factor (G-CSF) therapy to address neutropenia. A novel aspect of this case involves hypercalcemia as an unusual finding in GSD-Ib and the co-occurrence of a variant in the TBX1 gene, which is not typically associated with the disease but may contribute to the patient’s clinical presentation. These findings add a new dimension to our understanding of GSD-Ib and suggest potential avenues for future research to elucidate these genetic interactions and their impact on clinical outcomes.

Hydraulic motion platforms are widely utilized in various environments due to their capacity to bear heavy loads and provide long actuator strokes. The predominant control system employed for hydraulic motion platforms is the electro-hydraulic servo system (EHSS) due to its high accuracy and robustness. However, compared to other types of hydraulic valves, the electro-hydraulic servo valve (EHSV) is less durable. An alternative system to EHSS is the electro-hydraulic proportional system (EHPS), which employs an electro-hydraulic proportional valve (EHPV) instead of the EHSV. The EHPV resolves the durability issue at the expense of accuracy and system error. The optimization of durability and accuracy is the core of this study. The tracking behavior and response of both Three degrees of freedom (3 DOF) motion platforms using EHSS and EHPS are investigated across different frequency ranges.

This study utilizes density functional theory (DFT) with the Tran–Blaha modified Becke–Johnson (TB-mBJ) exchange potential in the WIEN2k framework to comprehensively investigate the structural, electronic, elastic, and optical properties of Rb2TlIrF6 and Rb2TlRhF6 double perovskite compounds. Structural stability assessments affirm that both materials maintain their integrity under standard conditions, demonstrating robust frameworks. The calculated elastic constants reveal mechanical stability, elasticity, and directional anisotropy, emphasizing their resilience and adaptability in varied environments. Electronic band structure calculations categorize Rb2TlIrF6 and Rb2TlRhF6 as direct bandgap semiconductors, with respective bandgaps of 2.61 eV and 1.94 eV at the L–L symmetry points, underscoring their potential in optoelectronic applications. The optical properties were analyzed through key parameters: refractive index n(ω), optical conductivity σ(ω), reflectivity R(ω), absorption coefficient α(ω), and extinction coefficient k(ω). Additionally, the real and imaginary parts of the dielectric function ∊1(ω) and ∊2(ω) were evaluated, indicating effective light interaction and absorption properties in the visible to UV range. These findings suggest that Rb2TlIrF6 and Rb2TlRhF6 exhibit advantageous optoelectronic characteristics suitable for semiconductor and next-generation electronic devices.