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الملخص: أهداف البحث: تلعب التغذية دورا مهما في عمل الدماغ وتطوره. فيتامين ب 6 في شكل فوسفات بيريدوكسال مطلوب للتخليق الحيوي للعديد من الناقلات العصبية. نظرا لأن فيتامين ب 6 لا يتم تصنيعه داخليا، يصبح توفر المصادر الغذائية أمرا ضروريا. بسبب مساهمته في الوظائف العصبية، يؤدي النقص الحاد في فيتامين ب 6 إلى زيادة خطر الإصابة بالاضطرابات النفسية والخرف واضطرابات النمو العصبي. هدفت الدراسة الحالية إلى إنشاء نموذج يعاني من نقص فيتامين ب 6 في حيوانات التجارب وتقييم آثار النمو العصبي في ذريتهم. طريقة البحث: تم استخدام إناث فئران ''س 57 ب ل/6 ج'' عمرها شهرين إلى ثلاثة أشهر في الدراسة. تم تقسيمهم بشكل عشوائي إلى المجموعة الضابطة وفيتامين ب 6 الناقص. تم تغذية المجموعة الضابطة بنظام غذائي منتظم يحتوي على 6 ملغ من فيتامين ب 6 / كغ من النظام الغذائي وتم تغذية المجموعة التي تعاني من نقص فيتامين ب 6 بنظام غذائي مخصص يحتوي على صفر ملغ من فيتامين ب 6 / كغ غذاء لمدة خمسة أسابيع (عدد=6). بعد خمسة أسابيع، تم تقدير فوسفات بيريدوكسال في البلازما. تم تربية الحيوانات لتولد ذرية. تم التضحية بالفئران الأمهات أيضا بعد الفطام، وتم تحديد كمية الخلايا العصبية في قرن آمون باستخدام صبغة الكريسيل البنفسجي. تم تخصيص النظام الغذائي للنسل بعد الفطام حتى عمر شهرين. تم تقييم التعلم والذاكرة باستخدام اختبار متاهة موريس المائية. النتائج: أكدت مستويات البلازما فوسفات بيريدوكسال النقص في المجموعة الناقصة بالمقارنة مع المجموعة الضابطة. أظهرت الخلايا العصبية الهرمية القابلة للحياة في منطقة ''س أ 3'' من قرن آمون فرقا كبيرا بين المجموعة الضابطة والمجموعة الناقصة. أظهر النسل المولود للفئران ذات النقص زيادة كبيرة في زمن الوصول للوصول إلى الربع المستهدف أثناء تجربة المسبار مقارنة بالمجموعة الضابطة. الاستنتاجات: يقلل نقص فيتامين ب 6 من الذاكرة في الفئران وذريتهم، مما يشير إلى أهمية فيتامين ب 6 لكل من وظائف الدماغ وتطوره. يجب القيام بالمزيد من البحث في هذه المعرفة والموقف مع مرور الوقت وتنفيذها الفعلي في الممارسة. Abstract: Objectives: Nutrition plays a critical role in the brain's function and development. Vitamin B6 in the form of pyridoxal phosphate (PLP) is required for the biosynthesis of several neurotransmitters. As vitamin B6 is not endogenously synthesized, the availability of dietary sources becomes imperative. Due to its contribution to neurological functions, severe vitamin B6 deficiency leads to an increased risk of psychiatric disorders, dementia, and neurodevelopmental disorders. This study aimed to establish a vitamin B6-deficient model in experimental animals and assess the neurodevelopmental effects in their offspring. Methods: Two- to three-month-old female C57BL/6J mice were used in the study. They were randomly divided into control and vitamin B6-deficient groups. The control group was fed a regular diet containing 6 mg vitamin B6/kg and the vitamin B6-deficient group was fed a customized diet containing 0 mg vitamin B6/kg, for 5 weeks (n = 6). After 5 weeks, plasma PLP was assessed. The animals were bred to generate offspring. The dams were killed following weaning, and the hippocampal neurons were quantified using cresyl violet staining. The offspring were assigned the respective diet post-weaning up to 2 months of age. Learning and memory were assessed using the Morris water maze test. Results: The plasma PLP levels confirmed the deficiency in the deficient group compared to the control group. The viable pyramidal neurons in the cornu ammonis 3 (CA3) region of the hippocampus showed a significant difference between the control and deficient groups. Offspring born to deficient dams showed a substantial increase in latency to reach the target quadrant during the probe trial compared to the controls. Conclusion: Vitamin B6 deficiency reduces memory in dams and their offspring, suggesting the importance of vitamin B6 for both brain function and development.

The agriculture drones are flocking and hovering over the crop fields to collect data or perform tasks related to crop management. The rise of artificial intelligence and machine learning algorithms paved the path to innovative approaches in agriculture. Apple detection on Apple farms has been a distinguished area of expertise. The apple target recognition algorithms like YOLOv7 gained a lot of reflection and accuracy to identify, recognize and detect the apples. However, the significant problems with the accurate time detection of apples include occlusions, wiring, branching, and overlapping. So, to overcome this problem, a deep learning approach of the Apple detection model is projected to rectify the margin of error with drone-based inference on the live field.Along with the accurate detection of apples, the depth of apples from a drone offers valued data for optimizing harvesting, assessing yield, discovering diseases, handling orchards, and evolving agricultural research in the apple industry. A specific multi-head attention mechanism is applied to the model to capture spatial and channel-wise dependencies concurrently. It can help capture complex interactions between regions and features, improving apple detection accuracy. The designed model is to detect apples in complex backgrounds better. The model identifies minimal objects and enhances the quality of features to achieve accurate bounding boxes, which maximizes detection accuracy. Incorporating a function to evaluate loss further increases the model's accuracy. According to a comparative study, the proposed model using the modified YOLOv7 architecture attains a good accuracy of 0.91, 0.96, and 0.92 concerning precision, recall, and F1-score, respectively.

Rescue operations for people trapped in deep borewells provide a serious problem needing both advanced robotic technology and human expertise. This abstract focuses on the creation of a revolutionary machine-human rescuing robot that is specifically designed to respond to emergencies in deep borewells. the complicated nature of deep borewell rescues, which includes technical challenges, time-sensitive situations, and the requirement for swift and accurate action. It draws attention to the drawbacks of conventional rescue techniques and promotes the creation of a flexible robotic system that can work in tandem with human rescuers. The suggested robot is built as a hybrid system that combines human control with autonomous functionality. To deliver real-time data and visual feedback from the borewell inside, it integrates specialized sensors, cameras, and communication tools. The borewell rescue robot’s, structural frame work consists of two cylindrical pipes fastened vertically with the help of T shaped square rod, these cylindrical pipes acts as a holding platform for children hands. Lower end of the pipes are provided with mechanical straps which in turn are actuated by BLCD motors with human rescuers is a crucial component of its functionality. Skilled operators can control the robot’s movements in real time thanks to remote operating interfaces and haptic feedback systems. The significance of the robot-human rescue team in deep borewell crises is emphasized in the abstract.

In the recent advancements attention mechanism in deep learning had played a vital role in proving better results in tasks under computer vision. There exists multiple kinds of works under attention mechanism which includes under image classification, fine-grained visual recognition, image captioning, video captioning, object detection and recognition tasks. Global and local attention are the two attention based mechanisms which helps in interpreting the attentive partial. Considering this criteria, there exists channel and spatial attention where in channel attention considers the most attentive channel among the produced block of channels and spatial attention considers which region among the space needs to be focused on. We have proposed a streamlined attention block module which helps in enhancing the feature based learning with less number of additional layers i.e., a GAP layer followed by a linear layer with an incorporation of second order pooling(GSoP) after every layer in the utilized encoder. This mechanism has produced better range dependencies by the conducted experimentation. We have experimented our model on CIFAR-10, CIFAR-100 and FGVC-Aircrafts datasets considering finegrained visual recognition. We were successful in achieving state-of-the-result for FGVC-Aircrafts with an accuracy of 97%.

Composite hydraulic cylinders are used to reduce the weight of construction equipment such as aerial work platforms or excavators. The weight is compensated by manufacturing hydraulic cylinders from Carbon Fiber Reinsforced Plastic (CFRP), but this is expensive. Therefore, this study investigated a hybrid hydraulic cylinder, which is a combination of CFRP and steel, considering both performance and cost. The conventional hydraulic cylinder rods are made of steel, which can prevent failure due to buckling under push load (Push) or failure under alternating push–pull load (Push–Pull) or pull only. In this paper, we discuss how the failure threshold for these two mechanisms can be increased by making the piston rod from a hybrid material. In order to develop this lightweight hybrid piston rod for hydraulic cylinders that meets the buckling strength requirements of the original steel rod, CFRP is used as a substitute, which has significant buckling strength against compressive loading and, most importantly, is lighter than steel. The substitution is done either by replacing steel completely with CFRP or by reducing the volume of steel and sheathing it with CFRP. Numerical and experimental studies are carried out to understand the strength and behavior of piston rods when they are replaced by different combinations of composite materials for the given load. For this study, two different piston rod designs with various design parameters were considered, and their respective behavior under loading was discussed. The effect of compressive loads on CFRP wrapped steel parts and buckling strength as a function of fiber orientation, stacking angle and number of CFRP layers was investigated using experiments. The study demonstrated the usefulness of steel-CFRP composites to reduce weight and their influence on buckling load in hydraulic cylinders.