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Lens culinaris L., has been widely recognized for its medical applications. LC-ESI-TOF-MS identified 22 secondary metabolites including phenolics, flavonoids, and anthocyanidin glycosides among its total extract (LCTE). The study aimed to apply LCTE as a biogenic material for reducing and capping the silver nanoparticles (LC-AgNPs). The ynthesized LC-AgNPs were characterized using different techniques. The UV absorption was observed at λmax 379 nm. LC-AgNPs were spherical, with 19.22 nm average size. The face cubic centre nature was demonstrated by HR-TEM and XRD. The LC-AgNPs were then evaluated for their anticancer and antimicrobial potentials. LC-AgNPs showed an extremely potent cytotoxic activity against MCF-7, HCT-116 and HepG2 cell lines (IC50= 0.37, 0.35 and 0.1 µg/mL, respectively). LC-AgNPs induced significant apoptotic effects in the three examined cancer cell lines. LC-AgNPs resulted in sequestration of cells in G1 phase of the cell cycle in both MCF-7 and HCT-116 cells, meanwhile it trapped cells at the G2 phase in HepG2 cells. Moreover, the antimicrobial activity of LC-AgNPs was highly confirmed against Klebsiella pneumoniae and Acinetobacter baumannii. Molecular docking study designated Kaempferol-3-O-robinoside-7-O-rhamnoside and Quercetin-3-D-xyloside as the topmost LCTE active constituents that caused inhibition of both Bcl-2 and IspC cancer targets in combination with the produced silver nanoparticles.

Date palm (Phoenix dactylifera L.) cultivation in the Arabian Peninsula is crucial for regional agriculture and global markets. The Arabian Peninsula is dominant in date production, contributing approximately 34 % of the global output. Recent advancements in agricultural technologies have improved fruit yield and quality, expanding date palm cultivation globally. However, sustainability challenges persist due to various abiotic stresses, such as salinity, temperature extremes, drought, soil factors, and biotic stresses, including diseases and pests. This review examines key environmental factors affecting date palm cultivation, with a focus on soil salinity, water scarcity, and climate change-related stresses. The genetic diversity among date palm varieties is emphasized, highlighting the need for breeding programs aimed at improving stress tolerance and yield. Biotechnological advancements, such as genetic transformation and genome editing, are discussed for their potential to enhance crop resilience and productivity. Additionally, remote sensing techniques are explored for their application in precision agriculture, particularly in the mapping and monitoring of date palm health and soil conditions. The significant role of artificial intelligence in accurately mapping date palm trees using multi-platform remotely sensed data is also reviewed, illustrating its potential to enhance geospatial databases and support sustainable management practices. The review concludes with recommendations for optimizing cultivar selection and management strategies tailored to local conditions, emphasizing the need for ongoing research to advance date palm cultivation on a global scale.

IntroductionThis study addresses the need for automated monitoring solutions to evaluate the daily activity patterns of camels, which is critical for improving animal welfare and farm management practices. By leveraging advanced deep learning techniques, this research aims to identify and analyze five key daily activities—sleeping, sitting, standing, eating, and drinking—using video recordings from a camel farm in Fujairah, United Arab Emirates.MethodsThe dataset was collected over two 7-day phases in November and December 2022. In Phase 1, video recordings were analyzed to monitor the activities of two camels and measure the duration of each activity. In Phase 2, the study expanded to include six camels, enabling an evaluation of individual behavioral variations. The YOLOv7 object detection algorithm was used to train and validate the model on images extracted from the recordings, achieving high accuracy in detecting and classifying the defined activities.ResultsThe results showed notable variations in activity patterns between Phases 1 and 2. Average standing time decreased from 9.8 hours (40.8%) to 6.0 hours (25.1%), and sleeping time dropped from 4.3 hours (18.0%) to 2.8 hours (11.7%). Conversely, sitting time increased from 6.2 hours (25.8%) to 9.9 hours (41.5%), and eating time rose from 3.1 hours (12.8%) to 4.6 hours (19.2%). Drinking time remained consistent at an average of 37 minutes (2.6%) across both phases. Activity peaks were observed during early mornings and after 16:00, with midday hours dominated by resting in shaded areas. Evening and nighttime activities primarily included sitting, minimal head movements, and occasional standing or walking.DiscussionThe established deep learning framework demonstrated reliable performance in detecting and analyzing camel activity patterns, offering a practical solution for continuous monitoring and improved farm management. However, further research is recommended to validate the model’s performance across different seasons and environmental conditions to enhance its robustness and adaptability.

Royal jelly (RJ) has various applications in cosmetics, health food, and apitherapy to treat chronic inflammation disorders. It is a milky white substance rich in proteins, carbohydrates, lipids, vitamins, and minerals. RJ acts as an antioxidant, anti–inflammatory, and antimicrobial agent, which helps in reducing inflammation and boosting immunity. This review provides an insight into the sensory and chemical analytical aspects of RJ, with a focus on water, 10–hydroxy–2–decenoic acid (10–HDA), protein, and sugar contents, as well as the national and international quality standards of major honey–producing countries. The chemical composition of RJ varies across locations due to external conditions, nectar sources, harvesting conditions, and honeybee species, which affect its quantity and quality. Despite having a consistent composition, the production of RJ per honeybee colony is influenced by environmental conditions, nectar source attributes, and mineral, vitamin, and amino acid content. Advanced analytical methods are required to accurately quantify the diverse ingredients of RJ, which are responsible for its biological and pharmacological effects. Understanding the intricacies of RJ is essential to unlock its therapeutic potential. Notably, apiculture research at the national level acts as sensitive bioindicators, emphasizing the global need for honeybee standards. This exploration contributes to understanding how external factors affect honeybee health and bee product quality.

This article examines the challenging of Orientalist and Western discourses and of patriarchal authority over Eastern women in Wafa Faith Hallam's memoir The Road from Morocco. The conventional representation of these women is revisited as Saadia in the memoir debunks the passivity and docility with which they are associated by exercising her agency and trespassing the sacred cultural and physical frontiers. Regardless of being introduced to confinement in the private space of a harem since her infancy, Saadia manages to liberate herself first through leaving the allegedly sacred frontiers of the house and trespassing in public space which is discursively assumed to belong to men, and second through dismantling the patriarchal authority of her husband by applying for a divorce and starting a business in spite of his refusal. This article argues that the representation of Saadia invalidates the discursive portrayals of Moroccan women as being passive and confined in the domestic private space of harem and deconstructs patriarchal authority. Keywords: Moroccan Women, Orientalism, Space, Patriarchy, Harem, The Road from Morocco, Wafa Faith Hallam, Introduction Arab women have been victimized by the early Orientalists' representation in art, fiction, travel writing, and later in cinema. They are mostly depicted as passive beings confined in the [...]

Handwriting recognition, particularly for Arabic, is a very challenging field of research due to various complex factors such as the presence of ligatures, cursive writing style, slant variations, diacritics, overlapping, and other difficult problems. This paper specifically addresses the task of recognizing offline Arabic handwritten text lines. The main contributions include the pre-processing stage and the utilization of a deep learning-based approach with data augmentation techniques. The pre-processing step involves correcting the skew of text-lines and removing any unnecessary white space in images. The deep learning architecture consists of a Convolutional Neural Network and Convolutional Block Attention Module for feature extraction, along with Bidirectional Long Short-Term Memory for sequence modelling and Connectionist Temporal Classification as a decoder. Data augmentation techniques are utilized on the images in the database to enhance the system's ability to recognize a wide range of Arabic characters and to extend the level of abstraction in patterns due to synthetic variations. Our suggested approach has the capability of precisely recognizing Arabic handwritten texts without the necessity of character segmentation, thereby resolving various issues associated with this aspect. The results obtained from the KHATT database highlight the effectiveness of our approach, demonstrating a Word Error Rate of 14.55% and a Character Error Rate of 3.25%. [JJCIT 2024; 10(3.000): 294-305]

Date palm plantations in the United Arab Emirates (UAE) are under threat from soil salinity, drought, and date palm weevils. Accordingly, monitoring and conserving date palms are crucial to preserving a vital component of the country’s agricultural heritage, economy, food security, and ecological balance. Previous studies have effectively identified date palm trees using RGB-based aerial and UAV imagery utilizing diverse deep learning methods. However, the utilization of very high-resolution satellite data for delineating individual date palm crowns remains unexplored due to the limited spatial resolution capabilities of existing satellite systems. This study primarily aimed to achieve precise and comprehensive mapping of date palm trees using WorldView-3 (WV-3) satellite data by leveraging the high representational power of the state-of-the-art vision transformers (ViT) in capturing global information from the input data. First, an in-depth analysis assessment of the various transformer-based semantic segmentation architectures, including UperNet with vision transformer and Swin transformer, SegFormer, Mask2Former, and UniFormer, was conducted. Second, the integration of spectral data on the performance of ViTs was evaluated. Moreover, the models’ generalizability and complexity effect on the segmentation effectiveness were assessed. Accordingly, a postprocessing strategy was developed to aid in delineating and counting date palm trees from semantic segmentation outputs. Results demonstrated that integration of WV-3 spectral data into the analysis resulted in a marked improvement in segmentation quality. The UniFormer, UperNet-Swin, and Mask2Former models demonstrated considerable improvements in multispectral data analysis, with increases in mean intersection over union (mIoU) of 2.17% (77.88% mIoU, 86.01% mean F-score [mF-score]), 2% (78.10% mIoU, 86.18% mF-score), and 1.15% (77.36% mIoU, 85.59% mF-score), respectively, compared with their RGB-based results. Evaluations of model transferability also indicated that Mask2Former, UniFormer, and UperNet-Swin transformers efficiently adapted to multispectral data in the Dibba region. These models achieved mIoU scores of 84.36%, 84.25%, and 83.17% and mF-scores of 90.95%, 90.87%, and 90.13%, highlighting their effectiveness and potential for broader regional application. This research highlights the efficacy and feasibility of using ViTs with WV-3 multispectral data for accurate and comprehensive surveying of date palm plantations, enabling the development of palm tree inventories and continuously updating geospatial databases.

This paper explores the impact of various distancing measures on the spread of infectious diseases, focusing on the spread of COVID-19 in the Moroccan population as a case study. Contact matrices, generated through a social force model, capture population interactions within distinct activity locations and age groups. These matrices, tailored for each distancing scenario, have been incorporated into an SEIR model. The study models the region as a network of interconnected activity locations, enabling flexible analysis of the effects of different distancing measures within social contexts and between age groups. Additionally, the method assesses the influence of measures targeting potential superspreaders (i.e., agents with a very high contact rate) and explores the impact of inter-activity location flows, providing insights beyond scalar contact rates or survey-based contact matrices. The results suggest that implementing intra-activity location distancing measures significantly reduces in the number of infected individuals relative to the act of imposing restrictions on individuals with a high contact rate in each activity location. The combination of both measures proves more advantageous. On a regional scale, characterized as a network of interconnected activity locations, restrictions on the movement of individuals with high contact rates was found to result in a $ 2 \% $ reduction, while intra-activity location-based distancing measures was found to achieve a $ 44 \% $ reduction. The combination of these two measures yielded a $ 48\% $ reduction.

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance.

In radiotherapy, X-ray or heavy ion beams target tumors to cause damage to their cell DNA. This damage is mainly induced by secondary low energy electrons. In this paper, we report the DNA molecular breaks at the atomic level as a function of electron energy and types of electron interactions using of Monte Carlo simulation. The number of DNA single and double strand breaks are compared to those from experimental results based on electron energies. In recent years, DNA atomistic models were introduced but still the simulations consider energy deposition in volumes of DNA or water equivalent material. We simulated a model of atomistic B-DNA in vacuum, forming 1122 base pairs of 30 nm in length. Each atom has been represented by a sphere whose radius equals the radius of van der Waals. We repeatedly simulated 10 million electrons for each energy from 4 eV to 500 eV and counted each interaction type with its position x, y, z in the volume of DNA. Based on the number and types of interactions at the atomic level, the number of DNA single and double strand breaks were calculated. We found that the dissociative electron attachment has the dominant effect on DNA strand breaks at energies below 10 eV compared to excitation and ionization. In addition, it is straightforward with our simulation to discriminate the strand and base breaks as a function of radiation interaction type and energy. In conclusion, the knowledge of DNA damage at the atomic level helps design direct internal therapeutic agents of cancer treatment.

The introduction of exotic plant species in the UAE poses substantial environmental challenges. Both poorly and well-adapted species can be detrimental to the local ecosystem by outcompeting native species and disrupting ecosystems. Native plants play a critical role in maintaining the ecological balance of any region. In the United Arab Emirates (UAE), native plants face significant threats due to habitat loss, climate change, and invasive species. Despite their ecological importance and potential benefits, a comprehensive and accessible database of native plant species for the UAE is currently lacking. Our review paper aims to address this gap by presenting an in-depth analysis of the native plants in Fujairah and the UAE. Our work covers almost all the native plants reported to date, which have been verified and added to our database. We provide valuable insights into the type, nature, physiology, habitat types, and other key aspects of these native plants. By presenting this information in a comprehensive and accessible manner, we hope to facilitate efforts toward biodiversity conservation and sustainable ecosystem management in the UAE. To address this issue, we have developed UAE Flora: The Native Plants Database of Emirates. This Web-based, virtual database contains over 760 plants identified from more than 400 published articles, online resources, and books from national and international authors. Each entry in UAE Flora includes over 50 data fields representing various ecological, biological, and pharmacological properties that have been manually extracted from the literature. This review paper will serve as a valuable resource for researchers, policymakers, and the public interested in promoting the conservation of native plants in the UAE. Flora will serve as a plant data repository that is the first of its kind in the United Arab Emirates.

In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined for more precise results. Therefore, the combination of human knowledge, control-flow discovery algorithms, and process mining activities is required. To this end, we present a technique for reducing the complexity of unstructured process models (Spaghetti process models) into structured ones (Lasagna process models). This framework outputs a refined normative model for improving the future Business Process (BP) auditing operations. Moreover, this work introduces the sustainability advantage that can occur using process mining techniques.

Business process modelling languages are a very prominent mechanism to understand companies’ business processes. This mechanism allows managers to communicate and transversal information through a semantic flow for business process improvement. Examples of Business Process modelling languages are Unified Modeling Language (UML) or Business Process Modeling Notation (BPMN). UML presents alternative views to model company’s functionalities. While, BPMN still have a very abstracted level to model Business Processes. In this context, there are many BPMN extensions to break down its high-level of abstraction. However, most of existing BPMN extensions model a significant increase in business process complexity that deviate it from its crucial objective. This put forward the difficulty of detailing control flow related to information systems without reaching complexity. To that end, we propose our TM4BPMN extension for breaking-down BPMN high abstraction to low-level, to maintain BPMN facility in representing BPs, using Thinging Machine (TM) techniques.

Abstract Background Different pathologies, affecting the skeletal system, were reported to display altered bone and/or cartilage innervation profiles leading to the deregulation of the tissue homeostasis. The patterning of peripheral innervation is achieved through the tissue-specific expression of attractive or repulsive axonal guidance cues in specific space and time frames. During the last decade, emerging findings attributed to the extracellular vesicles (EV) trading a central role in peripheral tissue innervation. However, to date, the contribution of EV in controlling bone innervation is totally unknown. Results Here we show that sensory neurons outgrowth induced by the bone resorbing cells—osteoclasts—is promoted by osteoclast-derived EV. The EV induced axonal growth is achieved by targeting epidermal growth factor receptor (EGFR)/ErbB2 signaling/protein kinase C phosphorylation in sensory neurons. In addition, our data also indicate that osteoclasts promote sensory neurons electrophysiological activity reflecting a possible pathway in nerve sensitization in the bone microenvironment, however this effect is EV independent. Conclusions Overall, these results identify a new mechanism of sensory bone innervation regulation and shed the light on the role of osteoclast-derived EV in shaping/guiding bone sensory innervation. These findings provide opportunities for exploitation of osteoclast-derived EV based strategies to prevent and/or mitigate pathological uncontrolled bone innervation.

The transnational belonging of diasporic communities has been addressed through several different lenses within pre-existing scholarship. Forms of attachment to the native country such as the perpetuation of traditions and conventions, the commemorations of rituals, and the exportation of objects speak to the significance of maintaining ties between the here and the there. This applies to the Moroccan diaspora. However, little research exists on the importance of native soil in the construction of identity and the burial of dead Moroccan subjects in transit. This article aims to contribute to closing this existing gap. Drawing on diaspora studies and its theorisations on the multilayered attachments and transnational ties of subjects in transit, this article argues that native soil is important to Moroccan-Belgians in the burial of their deceased relatives as it shapes their identity. Studying the Moroccan-Belgian TV series Soil (2021), this article argues that Moroccan soil constitutes an essential part of the hyphenated identity of Moroccan-Belgians. Regardless of being expensive and difficult, the repatriation of the dead to be buried in the native country’s soil, in which multitudinous axes such as religious, cultural, and social factors intersect, is a cultural imperative. The family business of ‘Omar Assurance’ in Soil repatriates the dead to Morocco. After many customers complain about the high expenses of repatriation, Ismael, one of the business owners, comes up with the idea of importing soil from Morocco and burying the dead with it. The diaspora studies informed textual analysis of the TV series corroborates that the attachments of Moroccan-Belgians to Morocco and the negotiations of their identities extend to how and where they bury their dead relatives. [ABSTRACT FROM AUTHOR]

Introduction: This study addresses the need for automated monitoring solutions to evaluate the daily activity patterns of camels, which is critical for improving animal welfare and farm management practices. By leveraging advanced deep learning techniques, this research aims to identify and analyze five key daily activities—sleeping, sitting, standing, eating, and drinking—using video recordings from a camel farm in Fujairah, United Arab Emirates. Methods: The dataset was collected over two 7-day phases in November and December 2022. In Phase 1, video recordings were analyzed to monitor the activities of two camels and measure the duration of each activity. In Phase 2, the study expanded to include six camels, enabling an evaluation of individual behavioral variations. The YOLOv7 object detection algorithm was used to train and validate the model on images extracted from the recordings, achieving high accuracy in detecting and classifying the defined activities. Results: The results showed notable variations in activity patterns between Phases 1 and 2. Average standing time decreased from 9.8 hours (40.8%) to 6.0 hours (25.1%), and sleeping time dropped from 4.3 hours (18.0%) to 2.8 hours (11.7%). Conversely, sitting time increased from 6.2 hours (25.8%) to 9.9 hours (41.5%), and eating time rose from 3.1 hours (12.8%) to 4.6 hours (19.2%). Drinking time remained consistent at an average of 37 minutes (2.6%) across both phases. Activity peaks were observed during early mornings and after 16:00, with midday hours dominated by resting in shaded areas. Evening and nighttime activities primarily included sitting, minimal head movements, and occasional standing or walking. Discussion: The established deep learning framework demonstrated reliable performance in detecting and analyzing camel activity patterns, offering a practical solution for continuous monitoring and improved farm management. However, further research is recommended to validate the model's performance across different seasons and environmental conditions to enhance its robustness and adaptability. [ABSTRACT FROM AUTHOR]

Lens culinaris L., has been widely recognized for its medical applications. LC-ESI-TOF-MS identified 22 secondary metabolites including phenolics, flavonoids, and anthocyanidin glycosides among its total extract (LCTE). The study aimed to apply LCTE as a biogenic material for reducing and capping the silver nanoparticles (LC-AgNPs). The ynthesized LC-AgNPs were characterized using different techniques. The UV absorption was observed at λmax 379 nm. LC-AgNPs were spherical, with 19.22 nm average size. The face cubic centre nature was demonstrated by HR-TEM and XRD. The LC-AgNPs were then evaluated for their anticancer and antimicrobial potentials. LC-AgNPs showed an extremely potent cytotoxic activity against MCF-7, HCT-116 and HepG2 cell lines (IC50= 0.37, 0.35 and 0.1 µg/mL, respectively). LC-AgNPs induced significant apoptotic effects in the three examined cancer cell lines. LC-AgNPs resulted in sequestration of cells in G1 phase of the cell cycle in both MCF-7 and HCT-116 cells, meanwhile it trapped cells at the G2 phase in HepG2 cells. Moreover, the antimicrobial activity of LC-AgNPs was highly confirmed against Klebsiella pneumoniae and Acinetobacter baumannii. Molecular docking study designated Kaempferol-3-O-robinoside-7-O-rhamnoside and Quercetin-3-D-xyloside as the topmost LCTE active constituents that caused inhibition of both Bcl-2 and IspC cancer targets in combination with the produced silver nanoparticles. [ABSTRACT FROM AUTHOR]