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Dealing with radioactive waste, particularly from various industrial processes, poses significant challenges. This paper explores the use of lithium aluminate borate (Li-Al-B) glass matrix as an alternative method for immobilizing radioactive waste, focusing specifically on waste generated in tin smelting industries, known as tin slag. The study primarily concentrates on transforming tin slag, a byproduct abundant in Natural Occurring Radioactive Material (NORM), into a stable and safe form for disposal.

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Crop rotation effects on the population density of soybean soilborne pathogens under no-till cropping system.

Plant Dis

January 2025

USDA-ARS North Central Agricultural Research Laboratory, Brookings, South Dakota, United States;

Soilborne diseases are persistent problems in soybean production. Long-term crop rotation can contribute to soilborne disease management. However, the response of soilborne pathogens to crop rotation is inconsistent, and rotation efficacy is highly variable.

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Background: Digital gaming has become increasingly popular among older adults, potentially offering cognitive, social, and physical benefits. However, its broader impact on health and well-being, particularly in real-world settings, remains unclear.

Objective: This study aimed to evaluate the multidimensional effects of digital gaming on health and well-being among older adults, using data from the Japan Gerontological Evaluation Study conducted in Matsudo City, Chiba, Japan.

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Background: Evidence-based digital therapeutics represent a new treatment modality in mental health, potentially providing cost-efficient, accessible means of augmenting existing treatments for chronic mental illnesses. CT-155/BI 3972080 is a prescription digital therapeutic under development as an adjunct to standard of care treatments for patients 18 years of age and older with experiential negative symptoms (ENS) of schizophrenia. Individual components of CT-155/BI 3972080 are designed based on the underlying principles of face-to-face treatment.

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Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

J Med Internet Res

January 2025

Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.

Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.

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