Nanotechnology is a modern technology and has many applications that include the manufacture of molecules or particles in the range of the nanoscale. Nanotechnology from the Greek word nano, meaning "dwarf". Nanoparticles are defined as single particles whose dimensions do not exceed 100 nanometers. The unique properties and features of nanoparticles are due to their small size, in addition to their chemical composition and surface structure. Different materials at the nanoscale lead to the development of new properties in industrial products, resulting in a real and impressive increase in industrial and medical applications. In this review, we will learn about this technology, its history, and the characteristics and shapes of nanoparticles. The focus will be on the application of nanotechnology in medicine, particularly in engineering living tissues with nanoscale scaffolds that simulate the functions of the extracellular matrix (ECM) to promote tissue recovery, replacement, and regeneration. It turns out that stem cells attached to a scaffold are more successful in adapting to their environment and performing the task of regeneration. The nerve endings in the body are attached to the scaffolding by weaving between the openings. This will cause them to act as a bridge to connect the cut sections. Over time, the scaffolds will dissolve and exit the body safely, leaving intact nerves in place. Some of the successfully used scaffolds are briefly identified, such as bone scaffolds, cardiac muscle scaffolds, and spinal cord engineering.

Pneumonia remains a major global health burden, where timely recognition on chest X ray images is clinically important yet often challenged by subtle radiographic signs and variability in interpretation. This paper presents a controlled comparative evaluation of four convolutional neural network architectures, MobileNet, ResNet50, VGG, and InceptionV3, for binary classification of chest X ray images into diseased and normal cases. Experiments were conducted using a publicly available Kaggle dataset of 4,479 images under a unified preprocessing and evaluation protocol. Performance was assessed on a held out test set of 300 images, including 200 diseased and 100 normal cases, using accuracy and macro averaged precision, recall, and F1 score, supported by confusion matrix analysis. The results show that MobileNet achieved the highest test accuracy at 95.0 percent, while ResNet50 and VGG achieved 94.7 percent, and InceptionV3 achieved 92.0 percent. Confusion matrix inspection indicates that MobileNet produced the fewest false negatives for diseased cases in this setting, which is important for screening oriented use. Inference time measurements using batch size 1 at 180 × 180 input on CPU further highlight the efficiency advantage of lightweight architectures. Overall, these findings provide a reproducible benchmark to support architecture selection for computer assisted pneumonia screening and clinical triage

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The present research examines the effect of nuclear energy consumption, financial globalization, nuclear energy technology research and development (R&D) budget on the environmental quality in the context of France. The load capacity factor (LCF) is employed as a novel proxy for environmental sustainability, explaining how human actions affect environmental sustainability and how nature compensates human induced damage. Using the novel approach of Augmented Autoregressive Distributed Lag, the findings of this study show that: (i) the load capacity curve (LCC) hypothesis is valid (ii) Nuclear energy consumption and nuclear energy technology R&D promote the environmental quality by increasing LCF. (iii) The financial globalization (overall, de jure, and de facto) has a positive influence on LCF, suggesting the positive role of financial globalization in boosting environmental sustainability in France. This study presents vital policy implications that can assist policymakers in France in achieving their environmental sustainability objectives.

Medical education is being reshaped by two major technological disruptions: the rapid expansion of online learning since the COVID-19 pandemic and the equally rapid adoption of artificial intelligence (AI) in teaching, assessment, and clinical training. These developments are often treated as distinct phases of change. In reality, they are part of the same broader challenge: educational institutions have repeatedly adopted new technologies faster than they have developed the pedagogic, ethical, and regulatory frameworks needed to use them well. 1-3