The Higher Institute for Applied Sciences and Technology (HIAST) (Arabic: الْمَعْهَدُ الْعَالِي لِلْعُلُومِ التَّطْبِيقِيَّةِ وَالتِّكْنُولُوجِيِّ, romanized: al-Maʿhad al-ʿĀlī li l-ʿUlūm at-Taṭbīqīyat wa t-Tiknūlūjī) is center of Excellence for Higher Education, Research & Development in Damascus, Syria. It belongs to the Syrian Scientific Studies and Research Center (SSRC).
إن النظم الحرارية واسعة الانتشار بسبب الكلفة المنخفضة و وثوقيتها العالية وذلك عائد إلى تطوير الحساسات المصفوفية غير المبردة التي أدت إلى الاستغناء عن المسح الميكانيكي وعن التبريد. ولكن تعانيهذه النظم من الانزياح الحراريللمحرق، حيث تتغير قرينة انكسار مواد العدسات البصرية مع تغير درجة الحرارة فيتغير موقع الصورة عن الحساس مؤدياً إلى تشوه الصورة.تقدم هذه المقالة تصميماً بصرياً لنظام حراري بعدستين مصحح حرارياً خفيف الوزن وصغير الحجم. وحسب أفضل معلوماتنا، ليست هذه الحسابات موجودة في أي مرجع.
The rapid spread of ChatGPT in higher education has created growing pressure on institutions to respond to generative AI in ways that are both educationally meaningful and institutionally responsible. Although existing research increasingly shows that ChatGPT can support student learning and academic performance, these benefits are not automatic and depend on how the tool is used and guided. At the same time, current literature on ChatGPT integration remains fragmented, with most studies focusing separately on pedagogical adaptation, policy and governance, or technology adoption. This study addresses this gap by proposing a conceptual 5D framework for ChatGPT integration in higher education. The framework was developed through literature synthesis and theoretical grounding, drawing on UTAUT, TPACK, Bloom’s Taxonomy, TIM, Innovation Resistance Theory, Responsible AI Governance, and the PDCA continuous improvement model. The proposed framework includes five connected dimensions: Readiness, Pedagogical Integration, Change and Resistance, Governance and Policy, and Monitoring and Improvement. The main contribution of the study is to organize these dimensions into one coherent institution-level framework rather than treating them as separate responses. The framework offers a structured way to understand ChatGPT integration as a broader institutional process that requires preparation, educational alignment, policy direction, support during implementation, and ongoing review over time.
This paper presents a comprehensive literature survey on image captioning, covering research published between 2018 and 2025. It introduces a novel taxonomy to classify existing approaches into nine major categories, including attention-based models, transformer-based architectures, reinforcement learning, and Vision-Language Pretraining (VLP). A total of 174 peer-reviewed studies are systematically reviewed, with comparative insights drawn across different model architectures, encoding strategies, and learning paradigms. The survey also explores major benchmark datasets such as MS COCO, Flickr30K, and Conceptual Captions, along with evaluation metrics like BLEU, CIDEr, METEOR, ROUGE, SPICE, CHAIR, CLIPScore, and BERTScore. In contrast to prior surveys, this work offers a detailed comparative analysis of state-of-the-art captioning models, highlighting their strengths, limitations, and real-world applicability. Recent models such as PaLI, OSCAR, BLIP-2, and OFA are critically examined in the context of caption quality, generalization, and multimodal alignment. A key research challenge identified across methods is the persistent problem of visual-semantic hallucination, which undermines factual alignment between image content and generated captions. This survey serves as a valuable resource for both newcomers and advanced researchers by offering a structured synthesis of recent developments, challenges, and future directions in the field of image captioning.
Unmanned Aerial Vehicle (UAV) communication networks can move in three-dimensional (3D) space, which can lead to a higher Line of Sight (LoS) probability for wireless communication channels between network nodes. Non-Orthogonal Multiple Access (NOMA) techniques are one of the proposed methods to overcome emerging challenges and meet requirements in the fifth (5G) and sixth (6G) generations of cellular systems. Due to the utilisation of an imperfect Successive Interference Cancellation (SIC) receiver, the performance of the NOMA scheme is vulnerable to error propagation. This paper aims to minimise the power consumption of the NOMA-UAV uplink network, which has limited energy resources. In addition, the impact of Channel State Information (CSI) on resource allocation methods is analysed. The hybrid multiple access schemes are introduced to minimise the sum power in the NOMA-UAV uplink network, subject to minimum data rate and available resource constraints. The frequency sub-bands and transmission power are jointly optimised to decrease the impact of error propagation in the SIC receiver via three hybrid multiple access models, including (i) a hybrid NOMA and OMA scheme with user grouping on multi-NOMA sub-bands, (ii) a hybrid NOMA and OMA scheme with one NOMA sub-band, and (iii) a multi-NOMA sub-bands scheme. The iterative Sequential Quadratic Programming (SQP) algorithm is employed to solve the optimisation problems. The numerical results show the effectiveness of the proposed hybrid schemes in terms of power consumption compared to the conventional NOMA scheme as benchmarks, where the improvement ratio is more than 20%. The hybrid schemes outperform the NOMA schemes reported in other papers, where the performance was compared relative to the OMA scheme. The high diversity of available resources in the hybrid multiple access schemes contributed to achieving the best performance in terms of sum power, which negatively affects the time cost performance. Considering an imperfect SIC receiver, available resources in the NOMA-UAV uplink network are optimised at the ground base station to decrease the transmission power of the UAV communication system, which enhances the energy efficiency of UAVs.
يتزايد الاهتمام عالمياً بتكنولوجيا الطائرات المسيّرة بدون طيار في العديد من التطبيقات المدنية والعسكرية على حد سواء، وخاصةً في عمليات الاستطلاع والبحث في الزمن الحقيقي. تمتلك هذه الطائرات القدرة على إنشاء شبكة اتصالات فيما بينها في السماء يطلق عليها مصطلح Unmanned Aeronautical Ad-hoc Network (UAANET)، والتي تندرج تحت نطاق الشبكات المخصصة النقّالة Mobile Ad hoc Network (MANET). من أهم المحددات التي تحكم أداء هذه الشبكات هو بروتوكول التوجيه المُستخدم، والذي غالباً مايكون أحد البروتوكولات المصممة سابقاً لشبكات MANET. يُعد بروتوكول التوجيه Ad hoc On-demand Distance Vector (AODV) أحد أكثر البروتوكولات شيوعاً للاستخدام في نطاق هذه الشبكات. يستخدم هذا البروتوكول رسائل تحكم دورية تُدعى رسائل التعارف للحصول على معلومات الجوار واكتشاف حالات انقطاع الوصلات اللاسلكية عند حدوثها. لقد بينت العديد من الدراسات السابقة أثر هذه الرسائل على الأداء في الشبكات المخصصة النقالة MANET، وأن هذا الأثر يتعلق بشروط الشبكة ومعاملات إعداد هذه الرسائل. لذلك اعتنى هذا البحث بدراسة أداء بروتوكول التوجيه AODV في شبكات الطائرات المسيَّرة، حيث قام الباحثون بدراسة أثر تغيير إعدادات رسائل التعارف (العدد المسموح لرسائل التعارف المفقودة AHL تحديداً) على معدل ضياع الطرود في الشبكة. اشتمل البحث على دراسة تجريبية باستخدام المحاكاة الحاسوبية، ودراسة تحليلية بالاعتماد على دراسات سابقة. قام الباحثون بإجراء محاكاة لسيناريوهات شبكة UAANET وتحصيل نتائج الأداء باستخدام محاكي الشبكات OPNET.
