مجلة التربية والعلم

لجسيمات النانوية المعدنية (NPs) تطبيقات هائلة بسبب خصائصها الفيزيائية والكيميائية الرائعة. لقد كان تخليق NPs مصدر قلق لأن الطرق الكيميائية سامة. تم تحضير مركبات الاسبينل النانوية CuFe2O4 باستخدام طريقة الكيمياء الخضراء وتم تشخيصها باستخدام تحويل فورييه للأشعة تحت الحمراء (FTIR)، على سبيل المثال، المجهر الإلكتروني الماسح (SEM)، التحليل الطيفي EDX، وحيود الأشعة السينية (XRD). أظهر التحليل الطيفي FTIR لـ CuFe2O4 نطاقين عريضين عند 365 و547 سم-1. أظهرت نتائج حيود الأشعة السينية أن متوسط ​​حجم الجسيمات كان 30.62 نانومتر. أظهرت صور المجهر الإلكتروني الماسح (SEM) شكلاً قرصياً غير منتظم للجسيمات النانوية، والتي تتكتل بشكل أكبر في حالة التخليق الأخضر. وتُظهر صورة المجهر الإلكتروني الماسح لجسيمات CuFe2O4 النانوية شكلاً كروياً في الغالب. وتُظهر الجسيمات تكتلاً ملحوظاً، مما ينتج عنه قطر متوسط ​​يبلغ حوالي 107.24 نانومتر. تتوافق هذه النتائج بشكل جيد مع الدراسات السابقة المتعلقة بسلوك هياكل الفريت الإسبينلي النانوية. دُرست فعالية تثبيط مركبات CuFe2O4 المُحضّرة على بكتيريا الإشريكية القولونية المعزولة من مياه نهر ديالى. وقيس تثبيط مركبات الإسبينلي النانوية باستخدام خمسة تراكيز متتالية (500، 600، 700، 800، 900، و1000) ميكروغرام/مل. أظهرت النتائج أن مركب CuFe2O4 يمتلك فعالية مثبطة لنمو بكتيريا الإشريكية القولونية، حيث بلغت أعلى نسبة تثبيط للنمو 63.2% عند تركيز 1000 ميكروغرام/مل. أما عند تركيز 500 ميكروغرام/مل، فكانت أدنى نسبة تثبيط للنمو 17.6%. دُرست سمية مركبات CuFe2O4 على خط خلايا HUVEC البطانية باستخدام اختبار MTT. بلغت نسبة بقاء خلايا HUVEC بعد 48 ساعة من إضافة فيريت النحاس (CuFe2O4) بتركيز 25 ميكروغرام/مل 99.70%، وهو أدنى تركيز، بينما بلغت نسبة بقاء خلايا HUVEC عند أعلى تركيز (400 ميكروغرام/مل) 45.38%، وكانت قيمة IC50 تساوي 311.

In the past few years, cellulose fibres have reached great importance in our lives, as they are the main component of plant fibres and are characterized by their lightweight and durability, in addition to being non-toxic and considered friendly to humans and the environment. Hydroxyl groups are considered to be the effective groups in the composition of cellulose, as the presence of these groups opens wide horizons for the uses of cellulose through the chemical responses that can grow through these groups when the appropriate conditions are available for the reaction. At the same time, it is clear that laboratory performance does not automatically translate into industrial feasibility. While the scientific literature strongly emphasizes enhanced material properties, discussions about production cost, process integration, and long-term economic sustainability are comparatively limited. Therefore, we will first address a simplified explanation about cellulose, its organization, and its typical properties; and then we will discuss a simplified explanation of Nanocellulose, its qualities, its unique properties, its principal sources, and its innovation and advanced applications.

This study aimed to estimate the concentrations of several heavy metals, including cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), in fifteen springs around the Soran Independent Administration, Kurdistan Region of Iraq. Water samples were collected during two seasons (summer 2024 and winter 2025) and analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES). The results showed that most trace metals had higher concentrations in winter than in summer. The concentration ranges were: Cd (0.011–0.678 mg/L), Cr (0.300–0.548 mg/L), Cu (0.417–0.645 mg/L), Fe (0.022–0.199 mg/L), Mn (0.007–0.123 mg/L), Ni (0.002–0.047 mg/L), Pb (0.005–0.149 mg/L), and Zn (0.010–0.109 mg/L). Except for cadmium and chromium, which exceeded safe limits at all sites, the concentrations of most metals were generally below WHO guidelines for drinking water; however, lead at some sites also rendered the water unsuitable for drinking. According to the Heavy Metals Pollution Index (HPI), values above 100 in all studied springs (ranging from 405.94 to 16023.72 in summer and 811.12 to 1780.97 in winter) indicated contamination and unsuitability for human use. In contrast, based on the Metal Index (MI), most trace metals had values below 1, indicating no contamination. However, MI values for cadmium and chromium above 5 signified heavy pollution, and MI values for lead ranged from 0.5 to 22.2, varying from uncontaminated to heavily polluted. Based on these findings, this study recommends: (1) regular monitoring of Cd, Cr, and Pb concentrations; (2) implementation of pollution control measures, including regulated use of fertilizers and pesticides in surrounding agricultural areas; and (3) adoption of point-of-use water treatment systems for communities relying on these springs for drinking water

In this study, four glass samples of the front car windows, the density of samples (2.2407-2.3868) g/cm³. The chemical composition of the samples was revealed using "energy-dispersive X-ray spectroscopy (EDS)". The samples consist of varying proportions of the elements (Na, Mg, C, O, Si). Radiation attenuation measurements were performed using gamma sources (Cs-137, Am-241, Ra-226) to study the shielding parameters of these samples. Found the linear attenuation coefficient (0.172-0.507) cm-1, mass attenuation coefficient (0.0758-0.226) cm2/g, half-value layer (1.367-4.126) cm, tenth-value layer (4.542-12.936) cm, mean free path (1.972-5.618) cm, transmittance factor (77.6-91.48) %, radiation shielding efficiency (8.5-22.4) %, effective atomic number (7.630-8.530), and effective electron number (3.00-3.118). The experimental results compared with results Phy-X software, and was good. The studied samples demonstrated a good efficiency in absorbing gamma rays at low energies, with performance comparable to standard protective materials such as certain types of glass and concrete. This type of glass represents a promising option in radiation protection applications that require transparent, lightweight, low-cost, and non-toxic materials, with the potential for further development to enhance its efficiency at higher energies.

This essay describes the design and testing of a desktop computer-based automated brain tumour segmentation system for MRI images, which will support the broad adoption of advanced deep learning models in clinical practice. The proposed system integrates a DeepLabV3+ framework with a ResNeXt50 backbone and a U-Net complementary structure, and is trained on a hybrid loss function that combines Dice loss and binary cross-entropy. It was trained and evaluated using benchmark brain MRI data, such as the BraTS 2020 dataset, with a standardised preprocessing and augmentation pipeline. In contrast to most research-only implementations, the proposed work focuses on practical deployment by incorporating the entire workflow, i.e., data preprocessing, model training, inference, and quantitative evaluation, in an integrated desktop environment suitable for clinical settings. The system will facilitate effective segmentation while maintaining high diagnostic reliability. The quantitative analysis yields promising results, with a mean Dice coefficient of 0.89 ± 0.04, a sensitivity of 81.5%, a specificity of 95.9%, and an AUC-ROC greater than 0.90. It has a mean inference time of 0.65 seconds per image and moderate memory requirements, making it easy to integrate into a typical hospital computing infrastructure. The findings suggest that the tool offers a good compromise between segmentation accuracy, computational efficiency, and clinical usability. This balance may support feasible AI-based decision support in neuro-oncology.