Investigating steeply inclined abandoned mines for unlocking the secrets of water level recovery.

With the growing emphasis on environmental protection, many coal mines in northern China were closed. However, the cessation of pumping operations in those closed mines has caused a rise in groundwater levels, giving rise to various safety and environmental concerns. Understanding the patterns of water level recovery is vital for effectively managing abandoned mine sites and ensuring the uninterrupted production of adjacent coal mines.

The Efficacy of Non-Cultured Epidermal Cell Suspension and Excimer Lamps Combination Therapy in Vitiligo: Results of 18 Months Follow-Up.

Background: Research on evaluating the efficacy of non-cultured epidermal cell suspension (NCECS) combined with excimer lamps for the treatment of vitiligo is currently unavailable. This research aims to evaluate the efficacy of this combination in treating vitiligo.

Methods: A prospective, controlled study was conducted from November 2021 to January 2024.

Data sharing and reuse in clinical research: Are we there yet? A cross-sectional study on progress, challenges and opportunities in LMICs.

Data sharing holds promise to accelerate innovative discoveries through artificial intelligence (AI) and traditional analytics. However, it remains unclear whether these prospects translate into tangible benefits in improving health care and scientific progress. In this cross-sectional study, we investigate current data reuse practices and explore ways to enhance the use of existing data in clinical research, focusing on low- and middle-income countries.

Antibiotic resistance genes, colistin-resistant Escherichia coli, and physicochemicals in health care wastewater in Vinh Long General Hospital, Vietnam.

This study collected ten treated wastewater samples from Vinh Long General Hospital to determine their physicochemical characteristics and antibiotic properties. All treated wastewater samples collected during the monitoring periods complied with national regulations. In addition, these samples did not contain bacteria such as Salmonella, Shigella, and Vibrio cholerae.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers.

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework.