446494 research outputs found
Sort by
Accuracy of Platelet Rich Plasma Devices in Cardiac Surgery:
No full textBackground: Applying platelet rich plasma (PRP) to the sternum immediately prior to approximation has been shown to enhance wound healing, lower the incidence of sternal wound infections, reduce costs associated with treating these infections and decrease post-operative pain scores. Multiple investigations have reported device-specific outcomes regarding PRP preparation yields from healthy volunteer blood donors, all with initial platelet counts in the normal range. What is missing from the literature is how accurately PRP preparations reflect device-specific yield target values, particularly under the clinical conditions encountered routinely in the cardiac surgery arena.
Methods: The Magellan® group (30 cases) and the Angel® group (30 cases) comprised the two study groups (2 groups, 60 total cases,120 samples total). Pre and post processing blood samples from each group were analyzed for platelet counts. Platelet count increases were assessed for accuracy when compared to a specific target.
Results: Individual yields from each tested device demonstrated some degree of limited variability. However, both groups mean values achieved and slightly exceeded the target value of a six-fold increase; Magellan group (M=6.58, SD=1.33), Angel group (M=6.31, SD=0.93).
Conclusion: Both devices, on average, appear capable of accurately preparing PRP to meet the specific target value of a six-fold increase over baseline under conditions routinely encountered in cardiac surgery
De la forge à l’assemblage : l’épopée d’une pièce majeure de réacteur
No full textCuves, générateurs de vapeur, pressuriseurs… Toutes ces immenses pièces en acier inoxydable qui équipent nos réacteurs nucléaires français sont passées entre les mains expertes de Framatome. Depuis les usines du Creusot où elles sont forgées jusqu’aux usines de Saint-Marcel où elles sont assemblées, leur mise au monde s’étend sur plusieurs mois. Retraçons ici le processus de confection, patient et minutieux, d’une virole de cuve – son cylindre central
Thermal Regulation of PV Panels via Bio-Based Phase Change Materials
No full textThe degradation of photovoltaic performance due high operating temperature remains a major challenge in PV technology. The integration of phase change materials offers an effective solution by absorbing the excess heat through latent heat storage during PV operation. This study numerically investigates the impact of incorporating different PCM on PV temperature regulation and electrical efficiency, under varying solar irradiance and PCM thicknesses. A transient energy balance model was developed and solved using the finite difference method FDM. Results showed the PCM integration slows PV temperature rise and enhances electrical efficiency. Among the tested materials, the bio-based PCM BWCO demonstrated effective thermal performance even at low thicknesses
Assessment of the spatio-temporal dynamics of agricultural lands in relation to rainfall variability in the Tadla plain (Morocco)
No full textThe Tadla Plain, situated in central Morocco under semi-arid climatic conditions, represents a key agricultural area at the national scale, yet it is highly exposed to climate-related constraints, particularly fluctuations in rainfall and increasing stress on irrigation water resources. Building on earlier findings that documented a 21% reduction in cultivated land between 2013 and 2023, this study examines vegetation dynamics during the subsequent period from 2023 to 2025, marked by a notable recovery in precipitation. Vegetation conditions were evaluated using the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery, in conjunction with rainfall records obtained from the NASA database. Field surveys were conducted to support and validate the remote sensing analysis. The results indicate a clear improvement in vegetation status across the Tadla Plain, with high-density vegetation areas increasing from 5.9% to 11.4% of the total surface area. In parallel, zones with moderate vegetation cover expanded from 24.6% to 29.2%. These changes closely coincide with a substantial rise in annual rainfall, which increased from an average of 240.38 mm in 2023 to 402.43 mm in 2025. The findings underline the strong dependence of agricultural systems in the Tadla Plain on climatic variability and demonstrate the effectiveness of satellite-based indicators as valuable tools for supporting sustainable land and water resource management
Yields and chemical composition of essential oils from
No full textThis study examines the valorization of three aromatic and medicinal plant species cultivated in rural nurseries in the Moroccan High Atlas (Azilal province). It aims to assess the influence of plant origin on essential oil yield and chemical composition for economic valorization. Physicochemical analyses included moisture content determination, essential oil extraction by hydrodistillation, and volatile compound identification using GC-MS. The results showed high moisture levels (65-67%), typical of young plant material, and significant yields, especially for Lavandula dentata (3.2%) and Salvia officinalis (1.97%). Each species presented a distinct chemotype influenced by ecological factors such as altitude, soil type, and sun exposure. Technical constraints related to nursery practices and artisanal extraction methods affected oil quality and stability. Improving local valorization requires better drying techniques, more efficient extraction processes, cooperative training, and a sustainable production chain adapted to local conditions
Green Technologies and Sustainability: The Role of Digital Leadership in Agricultural SMEs
No full textIn a global context marked by the transition to a more sustainable economy, this study investigates how green technologies, supported by digital leadership, contribute to strengthening organizational sustainability, particularly within the agricultural sector. Adopting a qualitative approach, the study relies on empirical data collected through an interview with the CEO of an innovative Tunisian company specializing in smart irrigation solutions. Thematic analysis reveals that the integration of green technologies not only enhances the productivity and profitability of farms, but also preserves natural resources and promotes environmentally friendly practices. The role of the manager, characterized by a proactive vision, adaptability, and a commitment to sustainability, proves to be decisive in guiding the company towards a successful ecological transition. Furthermore, the study identifies several obstacles, such as the initial cost of technologies, technical complexity, and resistance to change, while emphasizing the importance of strategic and institutional support to facilitate adoption. Ultimately, this research highlights the crucial managerial role of digital leadership in linking technological innovation and sustainability, while acknowledging the exploratory nature and limited generalizability inherent in a single-case study
Smart desalination systems coupled with green hydrogen production based on IoT
No full textKeeping water safe and creating clean energy are very important for the future. As cities get bigger, people increase, and the weather changes more, water is becoming harder to find. At the same time, moving towards cleaner energy is helping to make our air less polluted. It is thus important to apply new technologies that will allow us to make a more efficient use of water and promote cleaner energy systems. In this study, smart systems of water treatment in charge to make green hydrogen are presented. Our research aims to illustrate how the Internet of Things (IoT) can contribute to monitoring desalination systems that have benefited from significant investment, achieving energy savings and avoiding waste. After discussing several possible uses for the IoT, we return to the various practical difficulties that need to be overcome in order to achieve widespread deployment. The steps proposed below aim to increase the flexibility, efficiency, and safety of these systems, while integrating smarter and more sustainable water management practices to better mobilize this resource in the production of green hydrogen
Landfill leachate treatment strategies: Energy recovery and reduction of environmental impacts
No full textThe work commences by addressing characterization and regulatory constraints, underlining the spatio-temporal variability of leachates and the increasingly stringent requirements for their discharge. Following this, treatment technologies are reviewed, covering those from classical physico-chemical treatment schemes (coagulation and struvite precipitation) to advanced oxidation (ozonation and UV-light) and biological treatment approaches (anaerobic digestion treatment and aerobic granular sludge). The authors also look collectively at hybrid systems and focused attention on resource extraction and enhanced recovery, especially from leachates with attention being paid to the adoption of membranes enhanced for sustainability. The heart of the article discusses energy and material recovery pathways from leachates, which is considered the secondary focus of the paper treatment comes first. Attention is paid to biogas production from UASB reactors and co-digestion and nutrients recovered in the struvite form. Environmental impacts highlight the advantages that come through the donation of resource and material recovery from leachates and circularity. The article ends by looking at issues that remain in treatment such as variability of effluents, energy costs and membrane fouling as major issues and propositions for research perspectives encapsulating those areas. The authors conclude by talking about the necessity for progression to combined treatment that can turn storage sites into true biorefineries, and not just sites for storage, with the production of resources and an eye on environmental protection/security as key components
Deep Learning Solution for Monitoring Social Media Drug Sales
No full textThe illegal sale of drugs using social media is a matter of increasing concern among governments. This study proposes a deep-learning-based method to monitor such activities and identify drug-related content using an intelligent Telegram-based chatbot system. The proposed framework uses NLP to analyse textual messages and CNNs to detect drug-related images. Data collection is performed using Telegram APIs, followed by preprocessing steps to ensure data quality. The system uses a BERT-based model to classify suspicious messages and generate alerts automatically. Additionally, CNN models analyse multimedia posts containing drug-related images. Experimental results show that combining the detection of text and images significantly enhances overall accuracy and recall. Overall, the system monitors and identifies drug-related activities on Telegram in real time and demonstrates how deep-learning-based chatbot technology can support law enforcement agencies in enhancing online safety
Multi-class fault type classification in wireless sensor networks using supervised learning on simulated data
No full textWireless Sensor Networks (WSNs) are used in many vital domains such as environmental monitoring, industrial automation and smart cities. In these areas, dependable data transfer and constant operation are required. However, the existing fault detection techniques in WSNs offer mainly binary classifications; i.e., fault or fault-free without identification of the type of the fault. This is adding to inefficient and delayed troubleshooting and recovery. This work proposes a multi-class supervised machine learning-based fault classification system that can be used to identify five different network conditions: normal, no signal, high packet loss, poor Signal-to-Noise Ratio, SNR, and congestion-induced delay. To this end, the proposed system simulates different failure and recovery situations in the form of Cisco Packet Tracer by measuring important parameters such as RSSI, Packet Loss, SNR and end-to-end delay. Then, the dataset labelled is used to make a Decision Tree Classifier with accuracy above 90%. This work proposes for interpretable, light weight multi-class fault diagnoses for educational and operational improvements in WSNs