Prospects of Moringa Cultivation in Saudi Arabia

ABSTRACT The Moringa is a fast-growing evergreen or deciduous, multipurpose tree species comprised of 13 species. They are thought to be native to India from where they have been introduced in many different tropical and subtropical countries and are now growing throughout the world including Saudi Arabia (Moringa peregrina -a native species). Moringa tree is regarded as an excellent source of nutrition for human food and as treatment of many different ailments in the indigenous system of medicine for human diseases. Leaves, green pods, flowers and roasted seeds are used as vegetable; roots are used as spice; seeds are used for cooking and cosmetic oil. Plants contain important preventive and curative compounds, which are being used as antimicrobial agent. Moringa plants are also used for many other purposes like water purifier, green manure, mycotrophic plants, reforestation, alley crop, gum, ornamental, pest control, animal feed etc. Because of its numerous economic importances, easy propagation and sustainability for cultivation under a wide range of climatic and soil conditions; the plant is suitable for cultivation in Saudi Arabia. The plant is highly drought tolerant and is widely cultivated in arid and semiarid regions. It can be cultivated in a wide range of soil type, but prefers a well-drained sandy loam or loam soil. It tolerates a soil pH 5.0-9.0 with an optimum of 6.3-7.0. Moringa is resistant to most pests and diseases. All these environmental factors and soil conditions are highly favorable for cultivation of Moringa in Saudi Arabia. M. oleifera another widely cultivated species, may be introduced along with the native species as a future crop in arid and semiarid conditions in Saudi Arabia for economic importance as well as for reducing the desertification. Further studies on population ecology and genetic variation are very important to help protect this valuable tree in Saudi Arabia

BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection.

False news articles pose a serious challenge in today\u27s information landscape, impacting public opinion and decision-making. Efforts to counter this issue have led to research in deep learning and machine learning methods. However, a gap exists in effectively using contextual cues and skip connections within models, limiting the development of comprehensive detection systems that harness contextual information and vital data propagation. Thus, we propose a model of deep learning, FakeStack, in order to identify bogus news accurately. The model combines the power of pre-trained Bidirectional Encoder Representation of Transformers (BERT) embeddings with a deep Convolutional Neural Network (CNN) having skip convolution block and Long Short-Term Memory (LSTM). The model has been trained and tested on English fake news dataset, and various performance metrics were employed to assess its effectiveness. The results showcase the exceptional performance of FakeStack, achieving an accuracy of 99.74%, precision of 99.67%, recall of 99.80%, and F1-score of 99.74%. Our model\u27s performance was extended to two additional datasets. For the LIAR dataset, our accuracy reached 75.58%, while the WELFake dataset showcased an impressive accuracy of 98.25%. Comparative analysis with other baseline models, including CNN, BERT-CNN, and BERT-LSTM, further highlights the superiority of FakeStack, surpassing all models evaluated. This study underscores the potential of advanced techniques in combating the spread of false news and ensuring the dissemination of reliable information

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Microstructure and mechanical properties of metal powder treated AISI- 430 FSS welds

Abstract: An innovative yet simple technique for the inoculation of the weld pool of commercial AISI 430 Ferritic Stainless Steel (FSS) with metal powders for grain refinement is discussed. Aluminum or titanium powder in varying amounts introduced into the weld pool via powder preplacement technique was melted under a tungsten inert gas (TIG) torch. This strategy of inoculating the welds offers dual benefits of grain refinement and constriction in the weld geometry. The addition of the metal powders constricts the HAZ by as much as 50% of the conventional weld providing a grain refinement index (GRI) of about 0.8 in titanium powder treated welds. It equally emerged that weld property is not influenced by the grain size alone but equally by the amount of delta ferrite in the microstructure

convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images has been used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among all images of the dataset, 3896 and 977 images are applied to train and test the convoHER2 model, respectively. As all the images are in high resolution, we resize them so that we can feed them in our convoHER2 model. The cancerous samples images are classified into four classes based on the stage of the cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future

Generation of a VUV-to-visible Raman frequency comb in hydrogen-filled kagom\'e photonic crystal fiber

We report the generation of a purely vibrational Raman comb, extending from the vacuum ultraviolet (184 nm) to the visible (478 nm), in hydrogen-filled kagom\'e-style photonic crystal fiber pumped at 266 nm. Stimulated Raman scattering and molecular modulation processes are enhanced by higher Raman gain in the ultraviolet. Owing to the pressure-tunable normal dispersion landscape of the fiber-gas system in the ultraviolet, higher-order anti-Stokes bands are generated preferentially in higher-order fiber modes. The results pave the way towards tunable fiber-based sources of deep- and vacuum ultraviolet light for applications in, e.g., spectroscopy and biomedicine.Comment: 4 pages, 5 figures, 1 tabl