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Épidémiologie des cancers chez les femmes
No full textEn 2023, la France a recensé 187 526 nouveaux cas de cancer chez les femmes. L’objectif de cette synthèse est de présenter les principales tendances et les faits marquants de l’épidémiologie des cancers chez les femmes en France, tout en résumant les principaux facteurs de risque des cancers étudiés. Le cancer du sein est le cancer féminin le plus fréquent (31 % des cas). Les cancers gynécologiques (ovaires, endomètre et col de l’utérus) représentent 9 % des cancers féminins. De plus, l’incidence de nombreux cancers non gynécologiques (côlon-rectum, poumon, pancréas, foie, bouche et pharynx, et peau [mélanome]) est en augmentation chez les femmes. La prévention, et le dépistage, dont le taux reste faible en France, sont cruciaux face à cette progression des cancers féminins, dont 37 % sont liés à des facteurs évitables (tabac, alcool, obésité, habitudes alimentaires, et infections, notamment par les papillomavirus humains)
Analysis of thermal comfort and indoor air quality in a mechanically ventilated basement bank vault
No full textIndoor air quality (IAQ) and thermal comfort are critical for health and productivity in enclosed basements. This study investigates a mechanically ventilated bank vault, comparing the performance of mixing ventilation (MV) and displacement ventilation (DV). An occupant survey captured environmental complaints, while field measurements recorded carbon dioxide (CO2), radon, fine particulates (PM2.5), and thermal parameters. Computational Fluid Dynamics (CFD) and analytical models were used to determine Air Change Efficiency (ACE), Contaminant Removal Effectiveness (CRE), Predicted Mean Vote (PMV), and Predicted Percentage Dissatisfied (PPD). The DV system, supplied at low-level inlets with ceiling exhaust, significantly improved air distribution and pollutant removal. Results showed ACE increased from 0.41 (MV) to 0.68 (DV). CRE values for CO2, radon, and PM2.5 were 1.52, 0.79, and 1.12, respectively, outperforming MV. Comfort also improved, with DV achieving a near-neutral PMV (-0.03) and 5% dissatisfaction, compared to a slightly warm PMV (0.29) and 6.7% dissatisfaction under MV. Additionally, an air curtain at entrances helped reduce particulate ingress. Overall, DV demonstrated superior IAQ and comfort, offering practical guidance for retrofitting HVAC systems in basements and other confined urban workplaces
Improving the biomethane yield of
No full textThe most common substrates for the anaerobic digestion process that release biomethane, a renewable energy source, are lignocellulose materials. Total energy recovery from lignocellulose substrates is challenging due to their recalcitrant nature. Therefore, pretreatment techniques are required for these feedstocks to enhance biomethane yield. This research investigates the impacts of pretreatment methods and co-digestion on the biomethane generation from Xyris capensis. Oxidative pretreatment using 75:25% of H2O2: H2SO4, the addition of 20 mg/L Fe3O4 nano additive, and combined oxidative and Fe3O4 nano additive were considered as pretreatment methods, and anaerobic co-digestion of duck waste and Xyris capensis at 50:50%. The substrates were subjected to anaerobic digestion under mesophilic conditions in a batch anaerobic digester to investigate the effect of the treatments on the biomethane production. Biomethane yields of 212.18, 216.41, 251.20, 309.52, and 143.21 mLCH4/g VSadded were recorded for oxidative, nano additive, combined, co-digestion, and control, respectively. The highest total biomethane generated was 309.52 mLCH4/g VSadded, 116% higher than the untreated feedstock, and was achieved when duck waste and Xyris capensis were co-digested at a 50:50 % mixing ratio. Therefore, the pretreatment of substrates and anaerobic co-digestion of Xyris capensis and duck waste produce cost-effective renewable energy that replaces fossil fuels and promotes a circular economy
Analysis of particle size influence of
No full textProvision of sustainable potable water still poses challenges in less privileged countries. Therefore, this study analyzes the influence of six different particle size (2.0 mm, 1.18 mm, 850μm, 600μm, 425μm, and 300μm) of M. oleifera seeds on coagulating surface water obtained from Ezu River, Nigeria. M. oleifera seed samples were extracted using ethanol as a solvent following oil removal by soxhlet extraction method, and added at optimal dose (1g/L). The coagulation performance was determined using jar tests and measured based on percentage decreases in turbidity, and removals in chlorides, Total hardness, alkalinity, pH, Total Suspended Solids (TSS), Total Solids (TS), Total Dissolved solids (TDS), temperature, and sulfates. The 600μm-size M. oleifera particles were seen to display maximum coagulation efficacy measured as (91%), achieving maximum removals for TS (1174-407mg/L), TSS (533-206mg/L), TDS (641-201mg/L), Chlorides (29.07-0.58mg/L), alkalinity (105-4.20mg/L), sulfates (900-9mg/L), and total hardness (136-2mg/L); all showing levels below WHO guidelines for drinking water, except for turbidity. Water pH values ranging from 6.5-8.5 and temperatures ranging from 27-30°C satisfied water chemistry standards at optimal levels. Statistically, result revealed variable treatment efficacy among M. oleifera size samples with significantly different particle size influencing purification efficacy (p < 0.05). As such, efforts towards achieving the sustainable development goal 6, can be facilitated using optimal Moringa oleifera seed size of 600μm in water purification
Development of low-cost sodium acetate trihydrate-based eutectic hydrate composites for thermal energy storage
No full textResearch into more effective energy storage materials and technologies has recently come to the forefront as a means to meet the urgent need for renewable energy. The CH3COONa⋅3H2O (sodium acetate trihydrate, SAT) offers great promise as a phase change material (PCM) for energy storage devices that operate at medium to low temperatures, but it has serious drawbacks such a strong supercooling effect and poor thermal conductivity. The use of expensive chemicals in traditional methods to improve SAT’s thermal performance has limited their industrial scale usage. This research presents SAT as a starting point, and then uses a fusion blending technique to eutectic hydrated salt composite phase change materials (CPCMs) with various ratios of disodium hydrogen phosphate dodecahydrate (DHPD) or sodium sulfate decahydrate (SSD). The findings show that the eutectic hydrates that were made successfully reduce the angle of pure SAT more than 35℃ to less than 5℃. Notably, these eutectic blends exhibit enhanced thermal conductivity (sodium acetate trihydrate /SSD-3/7 coefficient of 0.893 W·(m⋅K)-1) and remarkable latent heat capacity (SAT/SSD9/1 provides 195.42 J·g-1). In addition, the sodium acetate trihydrate eutectic hydrates were ideal for commercially significant scale applications since they do not contain costly additives, easy to produce, and work throughout a wide temperature range (20-60℃)
Analysis of RF Sheath-Driven Tungsten Erosion at RF Antenna in the WEST Tokamak
No full textThis study applies the newly developed STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to analyze tungsten (W) erosion at RF antenna structures in the WEST tokamak. STRIPE integrates SolEdge3x for edge plasma backgrounds, COMSOL for 3D RF sheath potentials, RustBCA for sputtering yields, and GITR for impurity transport and ion energy–angle distributions. Building on prior work by Kumar et al. (2025) Nuclear Fusion, 65, 076039, which validated STRIPE for WEST ICRH discharge #57877, the present study provides a spatially resolved assessment of gross W erosion at both Q2 antenna limiters under ohmic and ICRH conditions. Simulations using 2D SolEdge3x profiles in COMSOL capture rectified sheath potentials exceeding 300 V, leading to strong upper-limiter localization. Both poloidal and toroidal asymmetries are observed and attributed to RF sheath effects, with modeled erosion patterns deviating from experiment—highlighting sensitivity to sheath geometry and plasma resolution. Erosion is driven primarily by high-charge-state oxygen ions (O6+–O8+), while D+ plays a negligible role. Assuming a plasma composition of 1% oxygen and 98% deuterium, STRIPE predicts a 30-fold increase in gross W erosion from ohmic to ICRH phases, consistent with a >25-fold rise in W-I (400.9 nm) brightness. Quantitative agreement is within 5% in the ohmic phase and 30% under ICRH, demonstrating predictive capability. Importantly, the study shows that the magnitude of ICRH-driven W erosion depends strongly on the concentration of light impurities (O, B, N, C), which drive sputtering through high charge states. Cleaner plasma conditions with reduced impurity content are therefore expected to substantially mitigate antenna W sources in WEST and other toroidal fusion devices. These findings establish STRIPE as a predictive framework for RF-induced plasma–material interactions and support its application to reactor-scale antenna design
Application of entropy weighted multivariate loss function for control of process parameters during drilling hybrid composites
No full textComposites are now being used to replace traditional metallic structures in a variety of industries that including aerospace, aircraft, and military, that demand structural materials of high stiffness-to-weight and strength-to-weight ratios. This work is aimed at investigation on drilling process parameters of a hybrid composite. A hybrid composite is prepared with Epoxy as matrix with Jute and Glass fibre reinforced material. It is attempted to investigate the delamination tendency of hybrid composite along with optimization of drilling parameters. The two parameters considered are speed and feed rate. Each of this is considered at 3 levels. Response characteristics are thrust force, torque, delamination factor. L9 orthogonal array will be considered for experimentation. Standard Analysis is performed to discriminate significant and insignificant factors. Optimized process parameters are obtained for each of response characteristics. The objective is to optimize drilling process parameters of a hybrid compositeTaguchi’s L9 3-level orthogonal array was employed in this work. Entropy weighted multivariate loss function optimizes the multi-responses with a single desired response for drilled hybrid composites at the common factor level. Spindle speed and feed rate operate as control elements, the responses are dependent on torque, thrust force, and delamination. Medium spindle speeds with smaller feed rates enhance drilling success, according to the findings. The results also showed that spindle speed has a higher influence on the quality of holes drilled
Prediction of tensile strength in AA7075/SiC composites using random forest and support vector machine models
No full textIn the present study, regression approaches of machine learning were implemented to predict the tensile strength of AA7075/SiC composites fabricated under different process parameters. SiC composition, compaction pressure, sintering temperature, and sintering time were considered as independent variables, while tensile strength was considered as a dependent response. This study presents an exploratory data analysis through parametric distribution using pairplots and boxplots, showing how process parameters affect the tensile strength of AMCs. GridSearchCV was used for hyperparameter tuning in the following two optimized supervised regression models: Random Forest and Support Vector Machine. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate model performance. The Random Forest model demonstrated better predictive performance than the SVM model, as evident from R² = 0.965, MAE = 7.81, and RMSE = 9.50 versus R² = 0.838, MAE = 16.51, and RMSE = 20.43, respectively. This implies that the Random Forest algorithm is robust enough to provide a better generalization on nonlinear relationships between processing parameters and the tensile strength of composites and thus is reliable for optimization of composite manufacturing processes
Comparative additive manufacturing defect prediction accuracy with a few transfer learning implementations of deep learning models
No full textThis paper addresses the problem of a comprehensive quality assurance strategy for additively manufactured components with integrated in-situ inspection and artificial intelligence and machine learning (AIML) models. A custom test setup was created around a fused filament fabrication (FFF) 3D printing apparatus, incorporating sensors to capture real-time data concerning part quality. This image data was harnessed to formulate an AI/ML dataset for training a Convolutional Neural Network (CNN). A robust framework for predicting defects in real-time during the additive manufacturing process and validating the accuracy of AIML predictions has been presented. A test setup, generating a varied dataset, crafting AI/ML models, and optimizing the AI/ML model for precise defect prediction has been made. The proposed methodology’s practical applicability and potential to redefine quality assurance in additively manufactured parts have been presented. Results were compared between the Matlab AlexNet pre- trained model and the user-designed model on Google Colab, which has the capability of hyperparameter tuning. Performance parameters, including accuracy, loss, precision, and recall, were plotted over epochs to analyze the model’s merit after training. With the addition of hyperparameter tuning to the AlexNet, the best model was chosen as per the training accuracy. Much better accuracies were observed in the earlier epochs, with the initial loss being highly reduced compared to the non-hyperparameter-tuned model. The ResNet50 model, which did not have hyperparameter tuning capabilities, produced less accurate results. On the other hand, the loss was much lower with ResNet50 compared to both the AlexNet Matlab model and the AlexNet model on Google Colab without hyperparameter tuning. ResNet50, at the same time, gave accuracy comparable to that of the AlexNet Model with hyper-parameter tuning
Theoretical scaling of the density limit of lower hybrid current drive caused by parametric decay instability
No full textAt high plasma density, the lower hybrid current drive (LHCD) system in tokamaks suffers significant efficiency loss caused by parametric decay instability (PDI) induced in the scrape-off layer (SOL) plasma. There, the lower hybrid waves (LHWs) launched from the antenna deposit a large fraction of their power through PDI, leading to the phenomenon of nonlinear density limit of LHCD. In this paper, we present a self-consistent modelling of the power deposition of LHWs in the SOL plasma by coupling PDI to the propagation of LHWs, the power loss of LHCD is evaluated quantitively, and a theoretical scaling of the nonlinear density limit nPDI ∝
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