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Abstract Diffuse pediatric-type high-grade gliomas (pedHGG), H3- and IDH-wildtype, encompass three main DNA-methylation-based subtypes: pedHGG-MYCN, pedHGG-RTK1A/B/C, and pedHGG-RTK2A/B. Since their first description in 2017 tumors of pedHGG-RTK2A/B have not been comprehensively characterized and clinical correlates remain elusive. In a recent series of pedHGG with a Gliomatosis cerebri (GC) growth pattern, an increased incidence of pedHGG-RTK2A/B (n = 18) was observed. We added 14 epigenetically defined pedHGG-RTK2A/B tumors to this GC series and provided centrally reviewed radiological, histological, and molecular characterization. The final cohort of 32 pedHGG-RTK2A/B tumors consisted of 25 pedHGG-RTK2A (78%) and seven pedHGG-RTK2B (22%) cases. The median age was 11.6 years (range, 4–17) with a median overall survival of 16.0 months (range 10.9–28.2). Seven of 11 of the newly added cases with imaging available showed a GC phenotype at diagnosis or follow-up. PedHGG-RTK2B tumors exhibited frequent bithalamic involvement (6/7, 86%). Central neuropathology review confirmed a diffuse glial neoplasm in all tumors with prominent angiocentric features in both subclasses. Most tumors (24/27 with available data, 89%) diffusely expressed EGFR with focal angiocentric enhancement. PedHGG-RTK2A tumors lacked OLIG2 expression, whereas 43% (3/7) of pedHGG-RTK2B expressed this glial transcription factor. ATRX loss occurred in 3/6 pedHGG-RTK2B samples with available data (50%). DNA sequencing (pedHGG-RTK2A: n = 18, pedHGG-RTK2B: n = 5) found EGFR alterations (15/23, 65%; predominantly point mutations) in both subclasses. Mutations in BCOR (14/18, 78%), SETD2 (7/18, 39%), and the hTERT promoter (7/19, 37%) occurred exclusively in pedHGG-RTK2A tumors, while pedHGG-RTK2B tumors were enriched for TP53 alterations (4/5, 80%). In conclusion, pedHGG-RTK2A/B tumors are characterized by highly diffuse-infiltrating growth patterns and specific radiological and histo-molecular features. By comprehensively characterizing methylation-based tumors, the chance to develop specific and effective therapy concepts for these detrimental tumors increases.

Let (M, f, g) be an (m, n)-hypermodule over an (m, n)-hyperring (R, h, k). A proper subhypermodule N of M is called n-ary 2-absorbing subhypermodule if whenever g(r1n−1, m) ⊆ N for some r1n−1 ∈ R and m ∈ M, then either g(r1n−1, M) ⊆ N or g(ri, m, 1R(n−2)) ⊆ N for some i ∈ {1, . . ., n − 1}. Various properties of n-ary 2-absorbing subhy-permodules are investigated. In particular, it is shown that if N is a subhypermodule of an (m, n)-hypermodule (M, f, g) over an (m, n)- hyperring (R, h, k), then N is n-ary 2-absorbing if and only if whenever g(I1, I2, 1R(n−3), L) ⊆ N for some hyperideals I1, I2 of R and subhyper- module L of M, then either g(I1, I2, 1R(n−3), M) ⊆ N or g(I1, 1R(n−2), L) ⊆ N or g(I2, 1R(n−2), L) ⊆ N. Also, n-ary 2-absorbing subhypermodules in multiplication (m, n)-hypermodules are studied.

Abstract Speleothem trace element records can provide seasonal resolution climate reconstructions, but the interpretation can be challenging. Aragonite samples are understudied compared to calcite samples, despite that aragonite has 10x higher uranium concentrations and thus provide excellent dating precision. Here we present a high-resolution dataset of drip water trace elements (calcium, magnesium, strontium, barium, uranium) that are commonly affected by prior carbonate (calcite / aragonite) precipitation, a process often driven by effective rainfall. Our data suggest that prior calcite and aragonite precipitation can occur at the same drip site complicating the interpretation of strontium and uranium concentrations in aragonite speleothems. However, both processes can be distinguished with the “Sinclair test” through the trendline analysis of logarithmic relationships. Furthermore, our data show that aragonite speleothems slowly growing under relatively constant conditions are ideal to record variations in prior carbonate precipitation with its barium concentrations, independent from the prior carbonate precipitation mode.

Paleoclimate information has played an instrumental role in showing how fast climate can vary and how large these changes can be. It provided the first vivid demonstration of the relationships between atmospheric greenhouse gas concentrations and surface air temperatures, as well as striking representations of climate change impacts and possible feedbacks within the climate system, such as those associated with vegetation or ice sheet changes. Here, a short review of recent advances in paleoclimate studies is provided, with the objective of showing what this information on past climates and environments can bring to research on current and possible future climates. We advocate that (1) paleoclimatic and paleoenvironmental information can be leveraged for narratives about climate change, in particular at the local and regional levels, (2) paleoclimate data is essential for out-of-range tests of climate models, since future climates are also out of the range of recent climate information used for calibrating climate models, (3) paleoclimate data, in particular for the last millennia, is essential for taking multi-centennial and multi-millennial variability into account when describing trends related to anthropogenic forcings and attributing climate change signals, in particular for extreme and rare events, and (4) paleoclimates also provide extremely valuable information for initializing the slow components of climate models. In addition, we show how paleoclimate studies can be beneficial to put recent and future climate change into context and improve our knowledge on key processes. They can both benefit from and contribute to models and knowledge based on the study of recent and future climates.

Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry with applications such as remote sensing data refinement, autonomous landing, and drone-based agriculture. However, safety concerns have prevented the widespread adoption of AI in commercial aviation. Currently, commercial aircraft do not incorporate AI components, even in entertainment or ground systems. This paper explores the intersection of AI and aerospace, focusing on the challenges of certifying AI for airborne use, which may require a new certification approach. We conducted a comprehensive literature review to identify common AI-enabled aerospace applications, classifying them by the criticality of the application and the complexity of the AI method. An applicability analysis was conducted to assess how existing aerospace standards - for system safety, software, and hardware - apply to machine learning technologies. In addition, we conducted a gap analysis of machine learning development methodologies to meet the stringent aspects of aviation certification. We evaluate current efforts in AI certification by applying the EASA concept paper and Overarching Properties (OPs) to a case study of an automated peripheral detection system (ADIMA). Aerospace applications are expected to use a range of methods tailored to different levels of criticality. Current aerospace standards are not directly applicable due to the manner in which the behavior is specified by the data, the uncertainty of the models, and the limitations of white box verification. From a machine learning perspective, open research questions were identified that address validation of intent and data-driven requirements, sufficiency of verification, uncertainty quantification, generalization, and mitigation of unintended behavior. For the ADIMA system, we demonstrated compliance with EASA development processes and achieved key certification objectives. However, many of the objectives are not applicable due to the human-centric design. OPs helped us to identify and uncover several defeaters in the applied ML technology. The results highlight the need for updated certification standards that take into account the unique nature of AI and its failure types. Furthermore, certification processes need to support the continuous evolution of AI technologies. Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community.

Liver cancer, primarily hepatocellular carcinoma, represents a major global health issue with significant clinical, economic, and psychological impacts. Its incidence continues to rise, driven by risk factors such as hepatitis B and C infections, nonalcoholic steatohepatitis, and various environmental influences. The Wnt/β-Catenin signaling pathway, frequently dysregulated in HCC, emerges as a promising therapeutic target. Critical genetic alterations, particularly in the CTNNB1 gene, involve mutations at key phosphorylation sites on β-catenin’s N-terminal domain (S33, S37, T41, and S45) and in armadillo repeat domains (K335I and N387 K). These mutations impede β-catenin degradation, enhancing its oncogenic potential. In addition to genetic alterations, molecular and epigenetic mechanisms, including DNA methylation, histone modifications, and noncoding RNAs, further influence β-catenin signaling and tumor progression. However, β-catenin activation alone is insufficient for hepatocarcinogenesis; additional genetic “hits” are required for tumor initiation. Mutations or alterations in genes such as Ras, c-Met, NRF2, and LKB1, when combined with β-catenin activation, significantly contribute to HCC development and progression. Understanding these cooperative mutations provides crucial insights into the disease and reveals potential therapeutic strategies. The complex interplay between genetic variations and the tumor microenvironment, coupled with novel therapeutic approaches targeting the Wnt/β-Catenin pathway, offers promise for improved treatment of HCC. Despite advances, translating preclinical findings into clinical practice remains a challenge. Future research should focus on elucidating how specific β-catenin mutations and additional genetic alterations contribute to HCC pathogenesis, leveraging genetically clengineered mouse models to explore distinct signaling impacts, and identifying downstream targets. Relevant clinical trials will be essential for advancing personalized therapies and enhancing patient outcomes. This review provides a comprehensive analysis of β-Catenin signaling in HCC, highlighting its role in pathogenesis, diagnosis, and therapeutic targeting, and identifies key research directions to improve understanding and clinical outcomes.

Cotton is one of the major cash crop in the agriculture led economies across the world. Cotton leaf diseases affects its yield globally. Determining cotton lesions on leaves is difficult when the area is big and the size of lesions is varied. Automated cotton lesion detection is quite useful; however, it is challenging due to fewer disease class, limited size datasets, class imbalance problems, and need of comprehensive evaluation metrics. We propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50. The proposed method offers a more precise, efficient and scalable method for automated detection of diseases of cotton crops. We have implemented the proposed method on publicly available dataset with seven disease and one health classes and have achieved highest accuracy of 95% and F-1 score of 98%. The proposed method performs better than existing state of the art methods.

Recycling waste frying oils for the synthesis of flotation reagents presents a promising avenue for sustainable waste management. Moreover, it offers a cost-effective solution for crafting a specialized collector designed to efficiently remove carbonates and enhance phosphate enrichment in froth flotation processes. This study focuses on the synthesis of an anionic collector using the saponification reaction of a frying oil sample, subsequently applied to the flotation of calcite and dolomite. To elucidate the adsorption mechanisms of the frying oil collector (FrOC) and sodium oleate, a reference collector, on fluorapatite, calcite, dolomite, and quartz surfaces, comprehensive experiments were conducted, including zeta potential measurements and Fourier transform infrared spectroscopy. Results revealed diverse adsorption affinities of the molecules towards these minerals. To assess the practical performance of the collector, flotation tests were conducted using a natural phosphate ore mixture, employing a Box-Behnken experimental design. Notably, under optimized conditions (pH 9, 1000 g/t of FrOC, 3.5 min of conditioning, and 6 min of flotation), FrOC exhibited excellent performance, with calcite and dolomite recoveries exceeding 80%, while apatite recovery in the concentrate fraction remained below 10%. This work exemplifies both circular economy practices and the distinctive approach to sustainable mineral processing.

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness.

This article presents a comparative numerical study aimed at determining the optimal pipe layout solution for the heating benches of traditional steam baths, with the goal of ensuring an optimal temperature distribution for customer comfort. This layout incorporates insulation on the lower side to direct most of the emitted heat upwards. The temperature distribution across the floor surface varies depending on the configuration of the buried pipes. Calculations were performed using the CFD software ''COMSOL Multiphysics'' to investigate the thermal behavior of an underfloor heating system. The results were used to discuss certain critical parameters that require control for the proper operation of the system. These parameters include the flow velocity inside the pipes, inlet temperatures, and pipe configuration. A new parameter designed to measure the thermal homogeneity of the floor was introduced and evaluated. In general, the findings demonstrated that the modulated spiral configuration is the best solution, minimizing pressure losses while ensuring optimal thermal homogenization across the entire floor surface.

In recent times, online users freely express their sentiments in different life aspects because of the huge increase in social networks. Sentiment Analysis (SA) is one of the main Natural Language Processing (NLP) fields thanks to its important role in identifying sentiment polarities and making decisions from the public’s opinions. The Arabic language is one of the most challenging languages for SA due to its various dialects, and morphological and syntactic complexities. Deep Learning (DL) models have shown significant capabilities, especially in SA. In particular, Long Short-Term Memory (LSTM) networks have proven perfect abilities to learn sequential data. This paper proposes a comparative study result of Word2Vec and FastText word embedding models that are used to create two Arabic SA (ASA) LSTM-based approaches. The experimental results confirm that the LSTM model with FastText can significantly ameliorate the Arabic classification accuracy.

The increasing accessibility of Virtual reality (VR) technology presents an opportunity for enhancing human immersion in several domains (Industrial, Social, Media, Cultural heritage, etc.). Moreover, it has huge impact to leverage its interaction and engagement; to un-dominate person on live, work, and diversion. The development of innovative VR-driven method is a sign of evolvement witnessed by the interaction design industry. In this respect, the work presents a complete framework for the design of virtual reality-based game; offering features, mechanisms, and 3D models that facilitate the development of interactive scenes, which reveal of the city’s historic monuments reality. The main research question focuses on empirically establishing the impact of virtual reality on video game design, including its influence on in-game cultural placement and its potential to enhance the retention of legacy game experiences within players’ memories. The results show that the game experience is felt original, enjoyable, and appealing for the user’s target group. Also, participants appreciate the use of VR technologies to explore cultural scenes and product; and to enhance empathy within a virtual experience. Furthermore, our research extends recent studies, proposing a VR-based design methodology, as well as, an assessing process. It guides the creation of visual representation of monuments, artifacts, and culture. Moreover, it improves user’s point of view and knowledge of the place they visit, within playing the WallysCar Crash Team Racing (WCTR) game. Our project intended to achieve a twofold purpose related to the Tunisian context: discovering the popular “Sfax City” with historical and patrimonial monumental heritage, while promoting the local industry creation.

The concept of integrated water resource management requires an in-depth analysis of water inflows into a river basin. Population growth and the uncertainties associated with climate change are causing increased water stress and droughts, which are impacting agriculture. Hence the need for studies on the impact of climate change on demand-supply interactions in river basins. In this study, a generic decision support system, ModSim, was used; for the first time in the region; to examine the agricultural water usage and demands over Souss basin in Morocco. ModSim was calibrated over the period from 1990 to 2019 using recorded data about physical processes and hydraulic infrastructures features and management. The simulations succeeded in replicating different deficit episodes at the various irrigated perimeters. During the simulated period from 2012 to 2019, it was observed that the water supplies for the different dams in the basin experienced a decline ranging from 38% to 89%. As a result, the average total unmet demand for surface water from reservoirs in irrigated areas reached 201 mm3 between 1990 and 2019 and the monthly average demand increases by 55% in the dry season, compared to the demands in the rest of the year. The significant amount of unmet demand across all sites suggests that demands are satisfied by the withdrawal of water from groundwater resources. The adopted approach has proven to be a useful decision support tool to understand water resources planning challenges. Water managers require such reliable tools to represent the basin's water trade-offs. Thus, additional investigation to improve the representation of groundwater/surface water interaction approaches is required to enhance the evaluation of the consequences of different uses, especially in arid and semi-arid regions with significant water stress such as Souss. A conceptual framework as well as a detailed discussion have been produced in order to guide efficient water management and governance.

Study region: The upstream part of the Essaouira basin, a data-scare region in Morocco, Northwestern Africa. Study focus: The scarcity of hydro-climate data is a significant challenge found in several regions worldwide, where qualitative and quantitative water resource information remains limited. Estimating and predicting groundwater levels (GWL) in such areas is a significant challenge in producing knowledge for effective water resource management. To address this issue, the present study aimed to use the Soil and Water Assessment Tool (SWAT) model in conjunction with downscaled total water storage (TWS) data (9 km) obtained from Gravity Recovery And Climate Experiment (GRACE) and machine learning techniques, specifically random forest (RF) and support vector machine (SVM), to estimate and predict the variation in GWL. New hydrological insights for the region: This study constitutes a first of its kind in the study area; the SWAT model was set up for 10 years, with a warm-up period from 2000 to 2001, calibration from 2002 to 2007, and validation from 2008 to 2010. The statistical indices (Coefficient of Determination (R²) ≥ 0.73, R² ≥ 0.78, Nash–Sutcliffe model efficiency coefficient (NSE) ≥ 0.67, NSE ≥ 0.80 respectively for calibration and validation) highlight a significant correlation, implying the model's capability to faithfully reproduce the streamflow. The downscaled TWS demonstrates an impressive ability to identify and monitor fluctuations in GWL. Using machine learning algorithms (RF and SVR), the prediction of GWL yielded satisfactory results, NSE = 0.78 and root mean square error (RMSE) = 0.33, NSE = 0.51 and RMSE = 0.49 for the RF and SVR, respectively. Despite some limitations, our approach provided promising results in GWL prediction, with the possibility of expanding to other data-scarce regions.

Abstract The novelty of this paper is that, based on Mawhin’s continuation theorem, we present some sufficient conditions that ensure that there is at least one solution to a particular kind of a boundary value problem with the p-Laplacian and generalized fractional Caputo derivative.

Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercussions. Previous research used datasets neglecting either good temporal or good spatial resolution, PERSIANN-CCSCDR, ERA5, and SM2RAIN-ASCAT are some of the projects aiming to remedy these limitations. This study's goal is to evaluate the accuracy of the PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT at a monthly scale and their suitability for drought assessment in a Moroccan semiarid watershed. Several statistical indices were computed, the drought SPI was calculated using PERSIANN-CCS-CDR estimates, ERA5 products, and observed records as an input in the SPI formula using Gamma distribution to simulate drought from 1983 to 2017. The preliminary comparison and evaluation results of PERSIANN-CCS-CDR estimates and ERA5 datasets showed good CC on a basin scale for monthly precipitation, with a slight overestimation of the observed precipitation shown by the PBIAS. The NSE scored 0.41 for PERSIANN-CCS-CDR and 0.72 for ERA5. The results for SM2RAIN-ASCAT showed an overestimation of the observed precipitation data. At the basin scale, the SPI3 correlation coefficients between the PERSIANN-CCS-CDR monthly estimates and observed gauge rainfall data were greater than 0.67, and the RMSE was closer to 0, outperforming ERA5 in the SPI3 evaluation. HIGHLIGHTS The use of remotely sensed precipitation data for climatological and hydrological studies.; Evaluation of one of the rarest evaluated products (PERSIANN-CCS-CDR).; Recommendation for alternative precipitation datasets for areas lacking precipitation data.;

The insular cortex, a critical hub in the brain’s sensory, cognitive, and emotional networks, remains an intriguing subject of study. In this article, we discuss its intricate functional neuroanatomy, emphasizing its pivotal role in processing olfactory information. Through concise exploration, we delve into the insula’s diverse connectivity and its involvement in sensory integration, particularly in olfaction. Stimulation studies in humans reveal compelling insights into the insula’s contribution to the perception of smell, hinting at its broader implications for cognitive processing. Additionally, we explore an avenue of research in which studying olfactory processing via insular stimulation could unravel higher-level cognitive processes. This innovative approach could help give a fresh perspective on the interplay between sensory and cognitive domains, offering valuable insights into the neural mechanisms underlying cognition and emotion. In conclusion, future research efforts should emphasize a multidisciplinary approach, combining advanced imaging and surgical techniques to explore the intricate functions of the human insula. Moreover, awake craniotomies could offer a unique opportunity for real-time observation, shedding light on its neural circuitry and contributions to higher-order brain functions. Furthermore, olfaction’s direct cortical projection enables precise exploration of insular function, promising insights into cognitive and emotional processes. This multifaceted approach will deepen our understanding of the insular cortex and its significance in human cognition and emotion.

Cigarette smoke is one of the main factors in Chronic Obstructive Pulmonary Disease (COPD), a respiratory syndrome marked by persistent respiratory symptoms and increasing airway obstruction. Perturbed NAD+/NADH levels may play a role in various diseases, including lung disorders like COPD. In our study, we investigated the preventive effect of NADH supplementation in an experimental model of COPD induced by cigarette smoke extract (CSE). N = 64 mice randomly distributed in eight groups were injected with NADH (two doses of 100 mg/kg or 200 mg/kg) or dexamethasone (2 mg/kg) before being exposed to CSE for up to 9 weeks. Additionally, NADH supplementation preserved lung antioxidant defenses by preventing the functional loss of key enzymes such as superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase, and the expression levels of glutathione (GSH) (n = 4, p < 0.001). It also reduced oxidative damage markers, such as malondialdehyde (MDA) and nitrites (n = 4, p < 0.001). A marked increase in tissue myeloperoxidase activity was assessed (MPO), confirming neutrophils implication in the inflammatory process. The latter was significantly ameliorated in the NADH-treated groups (p < 0.001). Finally, NADH prevented the CSE-induced secretion of cytokines such as Tumor Necrosis Factor alpha (TNF-α), IL-17, and IFN-y (n = 4, p < 0.001). Our study shows, for the first time, the clinical potential of NADH supplementation in preventing key features of COPD via its unique anti-inflammatory and antioxidant properties.

Spontaneous neonatal gastric perforation is rare. We report a case of a newborn from a pregnancy and birth with no complications, who presented on day 5 of life with sudden severe abdominal distension, and subsequently bilious vomiting. The radiography of the abdomen without preparation showed a massive pneumoperitoneum. The CT scan showed a pneumoperitoneum more accentuated at the supra-mesocolic level with a defect in the anterior wall of the stomach. The laparotomy found a perforation in the anterior gastric wall, which was sutured in one plane. The postoperative course was simple. The evolution of spontaneous gastric perforations in newborns is usually favorable. The key to avoiding complications is obviously to make a good diagnosis and perform the correct surgical treatment as soon as possible.

BackgroundThe Seattle Science Foundation created the Cerebrovascular Q&A series as a free web-based tool to educate physicians and physicians-in-training about cerebrovascular and endovascular neurosurgery across geographical boundaries and different levels of training.ObjectiveThis study aims to assess the educational impact and clinical implications of the Cerebrovascular Q&A webinar series, hosted by the Seattle Science Foundation.MethodsA digital anonymous, self-administered survey was sent to the live webinar participants. The survey contained questions about the socio-demographic characteristics of the participants, their perception of the content of the webinar series, and its impact on academic and clinical practice. The data collected from the Survey-Monkey platform was exported to Microsoft Excel which was used to perform all statistical analyses. The viewer metrics on Zoom and YouTube were also analyzed to understand trends observed among a diverse global cohort of participants.ResultA total of 2,057 people hailing from 141 countries had registered for the Cerebrovascular Q&A series. The response rate to the questionnaire was 12.63% (n = 260). Respondents hailed from 65 countries, of which the majority were from India (13.46%, n = 35) and United States (11.15%, n = 29). Most of the participants were male (82.69%, n = 215), while only 15.77% (n = 41) were female. The maximum number of participants were neurosurgery attendings (36.65%, n = 92) followed by neurosurgeons undergoing fellowship training (24.70%, n = 62) and students who were currently in residency training (15.54%, n = 39). 75.97% (n = 196) heard of the Cerebrovascular Q&A series through the emails from Seattle Science Foundation. 21.5% (n = 56) learned about the webinar series through social media. 75% of participants reported that the webinar content was advanced and comprehensive, and the selection of speakers was relevant. 63.08% (n = 164) found the webinars sparked innovative research ideas. Additionally, 55% (n = 143) reported changes in their clinical practice based on the acquired knowledge.ConclusionThe findings from this study reveal that webinar-based medical education in cerebrovascular neurosurgery is highly effective and influential. Web-based platforms and social media present a potent strategy to overcome barriers, emphasizing the need for targeted efforts to engage more women in medicine and neurosurgery recruitment.

Mangrove forests in the Gulf Cooperation Council (GCC) countries are facing multiple threats from natural and anthropogenic-driven land use change stressors, contributing to altered ecosystem conditions. Remote sensing tools can be used to monitor mangroves, measure mangrove forest-and-tree-level attributes and vegetation indices at different spatial and temporal scales that allow a detailed and comprehensive understanding of these important ecosystems. Using a systematic literature approach, we reviewed 58 remote sensing-based mangrove assessment articles published from 2010 through 2022. The main objectives of the study were to examine the extent of mangrove distribution and cover, and the remotely sensed data sources used to assess mangrove forest/tree attributes. The key importance of and threats to mangroves that were specific to the region were also examined. Mangrove distribution and cover were mainly estimated from satellite images (75.2%), using NDVI (Normalized Difference Vegetation Index) derived from Landsat (73.3%), IKONOS (15%), Sentinel (11.7%), WorldView (10%), QuickBird (8.3%), SPOT-5 (6.7%), MODIS (5%) and others (5%) such as PlanetScope. Remotely sensed data from aerial photographs/images (6.7%), LiDAR (Light Detection and Ranging) (5%) and UAV (Unmanned Aerial Vehicles)/Drones (3.3%) were the least used. Mangrove cover decreased in Saudi Arabia, Oman, Bahrain, and Kuwait between 1996 and 2020. However, mangrove cover increased appreciably in Qatar and remained relatively stable for the United Arab Emirates (UAE) over the same period, which was attributed to government conservation initiatives toward expanding mangrove afforestation and restoration through direct seeding and seedling planting. The reported country-level mangrove distribution and cover change results varied between studies due to the lack of a standardized methodology, differences in satellite imagery resolution and classification approaches used. There is a need for UAV-LiDAR ground truthing to validate country-and-local-level satellite data. Urban development-driven coastal land reclamation and pollution, climate change-driven temperature and sea level rise, drought and hypersalinity from extreme evaporation are serious threats to mangrove ecosystems. Thus, we encourage the prioritization of mangrove conservation and restoration schemes to support the achievement of related UN Sustainable Development Goals (13 climate action, 14 life below water, and 15 life on land) in the GCC countries.

The prevalence of diabetes mellitus (DM) among patients requiring renal replacement therapy (RRT) has been on the rise worldwide, with DM now being the primary cause of end-stage renal disease (ESRD) in roughly one-third of RRT initiations. Although renal transplantation is the optimal treatment for ESRD, its limited availability has led to in-center hemodialysis (HD) being widely used as the default RRT modality in many countries. However, peritoneal dialysis (PD) may offer a superior option for diabetic patients due to its slower ultrafiltration rate, which can help mitigate the dialysis-induced hypotension and coronary ischemia that are associated with extracorporeal circulation during HD. Despite these advantages, unfounded concerns about technique failure and increased complication rates have discouraged some clinicians from recommending PD as a first-line RRT for diabetic patients. We conducted a retrospective study comparing the incidence of complications and technique survival rates between diabetic and non-diabetic patients undergoing PD at a dialysis unit in Morocco. Our findings reveal that, diabetic patients undergoing PD experienced no significant difference in technique survival or incidence of complications compared to their non-diabetics. Nevertheless, only a small proportion (17.5%) of patients in our PD unit was diabetic, suggesting a need to improve access to PD for diabetic patients with ESRD.

We previously identified VRK3 as a specific vulnerability in DMG-H3K27M cells in a synthetic lethality screen targeting the whole kinome. The aim of the present study was to elucidate the mechanisms by which VRK3 depletion impact DMG-H3K27M cell fitness. Gene expression studies after VRK3 knockdown emphasized the inhibition of genes involved in G1/S transition of the cell cycle resulting in growth arrest in G1. Additionally, a massive modulation of genes involved in chromosome segregation was observed, concomitantly with a reduction in the level of phosphorylation of serine 10 and serine 28 of histone H3 supporting the regulation of chromatin condensation during cell division. This last effect could be partly due to a concomitant decrease of the chromatin kinase VRK1 in DMG following VRK3 knockdown. Furthermore, a metabolic switch specific to VRK3 function was observed towards increased oxidative phosphorylation without change in mitochondria content, that we hypothesized would represent a cell rescue mechanism. This study further explored the vulnerability of DMG-H3K27M cells to VRK3 depletion suggesting potential therapeutic combinations, e.g. with the mitochondrial ClpP protease activator ONC201.

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models.

Study region: The Atlas Mountains of Morocco, specifically the High Oum Er-rbiaa (HOER) and Ourika catchments. Study focus: to identify the recharge processes within the semi-arid watersheds, in the Atlas Mountains, through monthly monitoring of snow, rainfall, surface water, and groundwater isotope signal, but also the usage of remote sensing data. New hydrological insights for the region: The spatial-temporal analysis of groundwater and precipitation isotopes reveals significant spatial heterogeneity, primarily influenced by the geological variations in each aquifer. Temporal variations indicate that direct recharge occurs in response to winter precipitation, whereas a delayed response is observed during the summer when snow replenishes groundwater towards the end of the melting season. The findings are further supported by the ''Gravity Recovery and Climate Experiment'' (GRACE) dataset, which demonstrates that high values of Total Water Storage (TWS) align with groundwater isotopes. This highlights the substantial groundwater abstraction rate between March and June to compensate for the lack of precipitation during this period. The analysis of isotope data indicates that 50% of groundwater recharge in the upstream Jurassic aquifer and 80% in the downstream Triassic-Paleozoic aquifers in the HOER catchment is sourced from snowmelt. Similarly, in the Ourika catchment, snowmelt contributes 30% and 50% of groundwater recharge in the upstream and downstream portions of the catchments respectively. This disparity is due to different melting rates across altitudinal ranges and variations in the lithology of each catchment.

Study region: The downstream part of the Essaouira basin, Morocco, Northwestern Africa. Study focus: This study combines multivariate statistical analyses, stable isotopes, hydrogeochemical, hydrogeological, geological and remotely sensed data to gain a better understanding of the hydrological and groundwater salinization processes in a coastal aquifer. Hydrogeochemical data from 223 samples as well as stable isotope data collected during 10 sampling campaigns between 1990 and 2020 were compiled and subjected to a comprehensive analysis. Groundwater recharge obtained from the stable isotope approach was checked using a water balance method based on remote sensing data of actual evapotranspiration. New hydrological insights for the region: Seawater intrusion was detected only for the period of 2009–2020, nearby the ocean, whereas hyper-salinity due to evaporite dissolution occurred throughout the entire period (1990–2020) primarily in the southern part of the aquifer. Additionally, the isotopic mass balance model shows that aquifer recharge from the Ksob river area is mainly located in the northeastern region. Furthermore, based on average values of stable isotopes of rainfall and groundwater, preliminary results show that only months of November, December and January with rainfall higher than 54 mm contribute to groundwater recharge. The water balance approach confirmed these results, but also revealed a lower recharge in February, with a monthly average precipitation of 46 mm. The outcomes of this research enabled the construction of a comprehensive conceptual 3D model of the Essaouira coastal aquifer. The insights from this research are important to guide measures for sustainable water resources management.

Hammams or public baths continue to consume substantial quantities of drinking water up to 120 m3/day and discharge equivalent quantities of greywater. These hammams thus become an important source of this greywater, which can be easily treated using constructed wetlands (CWs). In this context, the present study proposes to practitioners a general method for sizing subsurface flow (SSF) CWs for the treatment of greywater discharged from hammams. It is oriented to simple applications such as irrigation, car washing and toilet flushing. Due to the complexity of quantifying the evapotranspiration (ET) of the treated water at the CWs, a practical and flexible method is presented here to calculate ET. In the end, a case study of a Moroccan hammam has been treated and discussed. It provides the designers of SSF CWs with a concrete example of the application of the proposed methodology. HIGHLIGHTS Design method for constructed wetlands with the subsurface flow for greywater treatment.; Method for calculating water evapotranspiration in constructed wetlands with the subsurface flow.; Practical reference for designers of constructed wetlands with the subsurface flow.; Contribution to facilitate the implementation of eco-systems for water reuse for non-potable purposes, thus reducing the inappropriate demand for treated drinking water.;

Abstract We present a speleothem record from western Cuba, spanning the period 98.7–84.9 ka BP. Our record shows two distinctive periods of high δ18O corresponding to dry and/or cold periods during 85–87.6 and 90.2–93.1 ka BP, synchronous with Heinrich events 8 and 9 (H8 and H9). Hence, we provide the first proxy evidence of the local Caribbean climate response to H8 and H9. Interestingly, H8 is more pronounced compared to H9, which may be a local response to lower temperatures in the North Atlantic resulting in a weak AMOC and reduced deep water formation, therefore a stronger south shift of the ITCZ. Our data complement existing speleothem records from western Cuba which, collectively, provide a nearly continuous paleoclimate time-series spanning the last 100 ka BP, indicating a consistent response to millennial-scale events as dry and/or cooler conditions. The comparison with regional paleoclimate records reveals an anti-phased relationship with South America, caused by the southern movements of the ITCZ during millennial-scale events which lead to dry conditions in the Caribbean and a stronger South American Monsoon System.

Summary: The Sahara Desert, one of today’s most inhospitable environments, has known periods of enhanced precipitation that supported pre-historic humans. However, the Green Sahara timing and moisture sources are not well known due to limited paleoclimate information. Here, we present a multi-proxy (δ18O, δ13C, Δ17O, and trace elements) speleothem-based climate record from Northwest (NW) Africa. Our data document two Green Sahara periods during Marine Isotope Stage (MIS) 5a and the Early to Mid-Holocene. Consistency with paleoclimate records across North Africa highlights the east-west geographical extent of the Green Sahara, whereas millennial-scale North Atlantic cooling (Heinrich) events consistently resulted in drier conditions. We demonstrate that an increase in westerly-originating winter precipitation during MIS5a resulted in favorable environmental conditions. The comparison of paleoclimate data with local archaeological sequences highlights the abrupt climate deterioration and the decline in human density in NW Africa during the MIS5-4 transition, which suggests climate-forced dispersals of populations, with possible implications for pathways into Eurasia.

The eddy current loss caused by the conductivity of seawater results in a relatively low transfer efficiency of underwater wireless power transfer (WPT). And the transfer distance of the current WPT system is relatively short. Considering that most of the wireless power transfer devices in practical applications are asymmetric, few studies have explored the transfer characteristics of asymmetric midrange WPT in seawater. In this study, it is experimentally found that the load voltage and transfer efficiency of an asymmetric midrange WPT system with reduced primary balancing resistance in seawater are nearly twice as high as those of a symmetric one at a 50 cm transfer distance and a 410 kHz operation frequency with a 44.4 Ω load resistance. A new circuit model of the underwater WPT system with complex impedance and complex mutual inductance is then presented, and the load voltages predicted by the model are consistent highly with the experimental values; the model is then utilized for the explanation of the experimental observations. Changing the load resistance also improves the transfer efficiency of the system; however, the eddy current loss results in a relatively low transfer efficiency of 30.9% at an optimal load resistance of 90 Ω. The asymmetric midrange underwater WPT system can be applied in scenarios where the transfer distance is prioritized.

Electric ships have been developed in recent years to reduce greenhouse gas emissions. In this system, inverters are the key equipment for the permanent-magnet synchronous motor (PMSM) drive system. The cascaded insulated-gated bipolar transistor (IGBT)-based H-bridge inverter is one of the most attractive multilevel topologies for modern electric ship applications. Usually, the fault-tolerant control strategy is designed to keep the ship in operation for a certain period. However, the fault-tolerant control strategy with hardware redundancy is expensive and slow in response. In addition, after fault-tolerant control, the ship’s PMSM may experience shock and overheating, and IGBT life is reduced due to uneven switching frequency distribution. Therefore, a stratified reconfiguration carrier disposition Sinusoidal Pulse Width Modulation (SPWM) fault-tolerant control strategy is proposed. The proposed strategy can achieve fault tolerance without any extra hardware. A reconfiguration carrier is applied to improve the fundamental amplitude of inverter output voltage to maintain the operation of the ship’s PMSM. In addition, the available states of faulty H-bridge are fully used to contribute to the output. These can improve the life of IGBTs by reducing and balancing the power loss of each H-bridge. The principles of the proposed strategy are described in detail in this study. Taking a cascaded H-bridge seven-level inverter as an example, simulation and experimental results verify that the proposed strategy, in general, has a potential future application on electric ships.

In the last few decades, the concept of Wireless Network-on-chip (WiNoC) has emerged as a promising alternative for Multiprocessor Systems on Chip (MPSOC) to achieve reliable and scalable communication. Worth recalling in this regard is that our research team has already designed, verified and evaluated Multistage Interconnection Networks (MIN) in this field. With respect to the present work, we consider proceeding with further exploring our thoughts on this research area. Firstly, we propose the design and performance evaluation of a hybrid (wireless/wired) MIN, analysing how this augmented network can potentially improve not only the average delay, but also energy consumption. Secondly, we continue with examining the implementation of our advanced DELTA-based MIN architecture on Deep Neural Network (DNN) accelerators, while accounting for its potential regularity and scalability in simultaneously maintaining an effective power efficiency and lower latency throughout the DNN operating process. In this context, several metrics have been evaluated in regard to three DNN application cases through implementation of their main respective modules.

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed.

The granular cell tumor or Abrikossoff's tumor is a rare tumor, most often benign in evolution. Malignant forms are exceptional. We report, here, a very rare case of granular cell tumor, localized in the perianal region, in a 54-year-old woman with lung metastases. CT and MRI with contrast showed a locally advanced tumor process in the right para-anal region associated with multiple ''balloon release'' lung lesions. The diagnosis was confirmed by immunostaining after surgical biopsy. Very few cases of malignant granular cell tumors with lung metastasis have been reported in the literature.

Slotted waveguides are often used when high-power and high-efficiency antennas must be designed and particularly for millimeter waves applications (automotive radar for example). But it is not easy to choose any distance between radiation slots because of the guided wavelength, to obtain a wide beam in E plane and so to cover a wide angular sector in beam scanning antennas. Moreover, the main beam depends of frequency because it is a progressive wave antenna. In this article, the authors propose an original solution based on a slotted serpentine waveguide with slots on the narrow wall of the waveguide allowing to obtain a very wide radiation pattern in E plane, and this antenna is manufactured, thanks a PCB (printed circuit board) process with empty waveguides to enhance the efficiency for the global structure. Simulations and measurements are given to validate the idea and the technological process in W band, and some different prototypes demonstrate that it could be possible to obtain a wide angular sector in a beam scanning antenna configuration.

يحاول هذا البحث تسليط الضوء على بعض الاختلافات العقيدية التي وقعت بين مالكيَّة الغربِ الإسلاميِّ، والمذاهب الفرعية التي نشأت عنها، والتعريف بأبرز علماء هذه المذاهب، وما نتج عن ذلك من آثار علمية متمثلة في التأليفات والردود المتبادلة، وكذا الآثار الاجتماعية التي رافقت ذلك.