Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Objective: Standardizing image acquisition protocols and image quality across cameras is an important need in imaging, in particular in multi-center clinical trials and the use of image analysis and machine learning algorithms. The objective of this study was to examine the effect of ordered subset expectation maximization (OSEM) reconstruction parameters on the quantitative image quality of cardiac perfusion SPECT images in different typical SPECT cameras and therefore assess the need to change the parameter values across cameras. Methods: The analysis was carried out by comparing the defect contrast-to-noise ratio (CNR) at 12 OSEM subset-iteration combinations. Eight frames were reconstructed using the SIMIND Monte Carlo Simulation package. An activity of 370 MBq (10mCi) and projection acquisition interval of 20 seconds per projection were used. Attenuation (AC) and scatter corrections (SC) were performed in this study for all images. Results: The 16-2 subset-iteration combination yielded the highest CNR and defect contrast values for both cameras. The difference between CNR values for two cameras was found to be close to 5%. Conclusions: Monte Carlo simulations can be useful to investigate how quantitative image quality behaves with respect to reconstruction parameters and correction algorithms in a controlled environment. In this study, the use of different camera brands did not seem to significantly affect the lesion detectability. Further simulations with more extended range of parameters and camera brands may be conducted in the future to quantify further the variability between different brands of cameras.

This study evaluates the effectiveness of Indonesia's defense industry policy from 2018 to 2023, focusing on PT Pindad, a pivotal state-owned defense enterprise. Using a Balanced Scorecard (BSC) framework, the study assesses PT Pindad’s performance across financial, customer, internal process, and learning and growth perspectives. The findings reveal strengths in financial stability (Current Ratio at 115.57% in 2023) and customer satisfaction, but challenges in Return on Investment (ROI), which fell from 6% in 2022 to 5.46% in 2023, signaling a need for further internal improvements. A mediation analysis using Shape-Restricted Regression indicates that Research and Development (R&D) serves as a crucial mediator, enhancing the impact of strategic alliances and technology transfer on PT Pindad’s self-reliance, with R&D showing a positive coefficient of β = 0.53 (p < 0.01). The systematic literature review complements these findings, underscoring the role of technology transfer, human capital development, and strategic partnerships as essential components for strengthening PT Pindad’s self-reliance and global competitiveness. Recommendations are made to enhance policy effectiveness by fostering robust technology transfer mechanisms, increasing investment in human capital, and expanding strategic partnerships. This research contributes to the literature on defense industry policies by providing a comprehensive evaluation framework that informs future policy decisions.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

Static atomic charges affect key ground-state parameters of boron quasi-planar clusters Bn, n ≤ 20, which serve as building blocks of borophenes and other two-dimensional boron-based materials promising for various advanced applications. Assuming that the outer valence shells partial electron density of the constituent B atoms are shared between them proportionally to their coordination numbers, the static atomic charges in small boron planar clusters in the electrically neutral and positively and negatively singly charged states are estimated to be in the ranges of –0.750e (B70) to +0.535e (B200), –0.500e (B7+, B8+, and B9+) to +0.556e (B17+), and –1.000e (B7–) to +0.512e (B20–), respectively.