Assessment of spatiotemporal risks for nationwide groundwater nitrate contamination.

National assessments of groundwater contamination risks are crucial for sustaining high-quality groundwater supplies. However, traditional methods often treat groundwater contamination risk as a steady-state indicator without considering spatiotemporal variation in risk-both geographically and over time- caused by anthropogenic and climate influences. In this work, XGBoost, a tree-based algorithm, was applied to comprehensively analyze the drivers of groundwater contamination from nitrate, using data on13 physical features (as used by the index-based ranking method DRASTIC) and 30 anthropogenic features from 1985 to 2010 in the contiguous United States (CONUS). The results indicate that physical features controlling the transport processes, particularly those affecting contaminant travel time from land surface to groundwater (depth to water table and transmissivity), were the dominant factors for nitrate contamination in groundwater. This was followed by features representing the potential nitrogen loading. Positive correlations between most features and nitrogen loading years were found, suggesting their growing influence on contamination risk. Based on the drivers identified for nitrate concentrations exceeding 10 mg/L in groundwater and their varying temporal contributions, this study proposes a reformulated index-based method for contamination risk assessment. With this method an overall accuracy of around 70 % was achieved based on the validation data set. The predicted high-risk areas are mainly intensive irrigation regions, such as the High Plains, northern Midwest, and Central Valley. This new approach contributes to a more accurate and effective assessment of the contamination risks of groundwater on a regional and national scale under temporally varying environmental conditions.

Adoption of ICT4D and its determinants: A systematic review and meta-analysis.

Various Digital Agricultural Technologies (DAT) have been developed and implemented around the world. This study aims to estimate the overall adoption rate and identify the determinant factors for a better adoption perspective after decades of innovation and dissemination. A systematic review was conducted on published studies that reported adoption rates and determinant factors using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. We used meta-regression and the partial correlation coefficient to estimate the effect size and establish the correlation between socioeconomic characteristics and the adoption of various technologies reported. Fifty-two studies with 32400 participants met the selection criteria and were included in the study. The results revealed an overall pooled adoption rate of 39 %, with the highest adoption rates in developing countries in Africa and South America. Socioeconomic factors such as age, education, gender, and income were found to be the main determinants and should be considered when designing technology for sustainable adoption. The study also found that young farmers were more susceptible to adoption. Moreover, farmers with higher income levels and educational attainment are more likely to use technology linked to agricultural production, market access, and digital advising, implying that high-income farmers with more education are more tech-savvy. However, this does not exclude low-income and low-educated farmers from adopting the technologies, as many models and strategies with socioeconomic considerations were developed. It is one of the reasons behind the underlying enthusiasm for digital agricultural adoption in low and middle-income countries.

Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature.

The meta-analysis dataset presented is a convenience sample from 218 separate studies of agricultural technology adoption in Africa, Asia, and Latin America. Each study uses survey data to estimate a form of multiple regression of adoption of a technology (dependent variable) with a diverse array of predictor variables. Fifteen predictor variable categories are included in this dataset: Age, education, gender, household size, farming experience, land size, soil fertility, land slope, distance to inputs/outputs, access to credit, land tenure, livestock ownership, non-farm income, access to extension, and organization membership. Data have been cleaned and transformed to common units. A total of 384 statistical models are recorded, with a total of 2875 effect size estimates.

Automated 3D bone ablation with 1,070 nm ytterbium-doped fiber laser enabled by inline coherent imaging.

BACKGROUND AND OBJECTIVE: Laser osteotomy bears well-identified advantages over conventional techniques. However, lack of depth control and collateral thermal damage are barriers to wide clinical implementation. Flexible fiber delivery and economical benefits of ytterbium-doped fiber lasers make them desirable for laser osteotomy. In this work, we demonstrate automated bone ablation with a 1,070 nm industrial-scale fiber laser to create 3D target structures with minimal thermal side-effects. MATERIALS AND METHODS: Fresh and dry ex vivo cortical bone samples are ablated using 50-100 µs laser pulses of 15-30 mJ. In situ inline coherent imaging monitors ablation dynamics with micron precision and on microsecond timescales. Ablation depth is extracted by on-the-fly processing of ICI data, enabling feedback control of depth (via laser pulse number). Final ablated morphology, measured by an ex situ stylus profiler, is compared to the target shape. Histological examination is performed to quantify the thermal side-effects of laser ablation. RESULTS: Percussion drilled hole depth is highly variable for fixed laser parameters (880 ± 151 µm on fresh bone and 1038 ± 148 µm on dry bone) due to nondeterministic ablation. ICI-enabled depth control is implemented to achieve precise ablation of complex 3D features. The RMS deviation between target and ablated morphology is 12.6 µm. The heat-affected zone is found to be 5-10 µm on fresh and dry bone. CONCLUSIONS: An ytterbium-doped fiber laser is utilized for cortical bone ablation with limited thermal side-effects. In situ real-time ICI measurement enables characterization of bone ablation dynamics. Furthermore, ICI closed-loop feedback realizes depth-controlled ablation on heterogeneous bone. This proof-of-principle study shows great promise for ICI-guided laser osteotomy.