Carbon emissions and priming effects derived from crop residues and their responses to nitrogen inputs

Crop residue-derived carbon (C) emissions and priming effects (PE) in cropland soils can influence the global C cycle. However, their corresponding generality, driving factors, and responses to nitrogen (N) inputs are poorly understood. As a result, the total C emissions and net C balance also remain mysterious. To address the above knowledge gaps, a meta-analysis of 1123 observations, taken from 51 studies world-wide, has been completed. The results showed that within 360 days, emission ratios of crop residues C (ER) ranged from 0.22% to 61.80%, and crop residues generally induced positive PE (+71.76%). Comparatively, the contribution of crop residue-derived C emissions (52.82%) to total C emissions was generally higher than that of PE (12.08%), emphasizing the importance of reducing ER. The ER and PE differed among crop types, and both were low in the case of rice, which was attributed to its saturated water conditions. The ER and PE also varied with soil properties, as PE decreased with increasing C addition ratio in soils where soil organic carbon (SOC) was less than 10‰; in contrast, the opposite phenomenon was observed in soils with SOC exceeding 10‰. Moreover, N inputs increased ER and PE by 8.31% and 3.78%, respectively, which was predominantly attributed to (NH4 )2 SO4 . The increased PE was verified to be dominated by microbial stoichiometric decomposition. In summary, after incorporating crop residues, the total C emissions and relative net C balance in the cropland soils ranged from 0.03 to 23.47 mg C g-1 soil and 0.21 to 0.97 mg C g-1 residue-C g-1 soil, respectively, suggesting a significant impact on C cycle. These results clarify the value of incorporating crop residues into croplands to regulate global SOC dynamics and help to establish while managing site-specific crop return systems that facilitate C sequestration.

Assessment of implementation barriers of blockchain technology in public healthcare: evidences from developing countries

The widespread use of Blockchain technology (BT) in nations that are developing remains in its early stages, necessitating a more comprehensive evaluation using efficient and adaptable approaches. The need for digitalization to boost operational effectiveness is growing in the healthcare sector. Despite BT's potential as a competitive option for the healthcare sector, insufficient research has prevented it being fully utilised. This study intends to identify the main sociological, economical, and infrastructure obstacles to BT adoption in developing nations' public health systems. To accomplish this goal, the study employs a multi-level analysis of blockchain hurdles using hybrid approach. The study's findings provide decision- makers with guidance on how to proceed, as well as insight into implementation challenges.

Adaptive Geometric Interval Classifier

Quantile, equal interval, and natural breaks methods are widely used data classification methods in geospatial analysis and cartography. However, when applied to data with skewed distributions, they can only reveal the variations of either high frequent values or extremes, which often leads to undesired and biased classification results. To handle this problem, Esri provided a compromise method, named geometric interval classification (GIC). Although GIC performs well for various classification tasks, its mathematics and solution process remain unclear. Moreover, GIC is theoretically only applicable to single-peak (single-modal), one-dimensional data. This paper first mathematically formulates GIC as a general optimization problem subject to equality constraint. We then further adapt such formulated GIC to handle multi-peak and multi-dimensional data. Both thematic data and remote sensing images are used in this study. The comparison with other classification methods demonstrates the advantage of GIC being able to highlight both middle and extreme values. As such, it can be regarded as a general data classification approach for thematic mapping and other geospatial applications.

GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia

The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin.