Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Global-scale interdecadal variability a skillful predictor at decadal-to-multidecadal timescales for Sahelian and Indian Monsoon Rainfall

In the present study, a sea surface temperature-based index named global-scale interdecadal variability (GIV) encompassing the combined variability of Atlantic multidecadal oscillation (AMO) and interdecadal Pacific oscillation (IPO) has been proposed. The warm phase of GIV exhibits a “cold AMO-like” pattern in the Atlantic basin and a “warm IPO-like” pattern in the Pacific basin. About 84% (R ~−0.914) of Sahelian and 42% (R ~−0.647) of Indian rainfall’s temporal variance is attributed to GIV, showing substantial improvement compared to the variance explained by AMO and IPO individually. The physical mechanism for GIV-rainfall teleconnection is related to a modification of the Walker circulation. Although there is a substantial degree of uncertainty in the current generation of state-of-the-art climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), some still replicate the observed GIV’s spatial structure, its teleconnection, and associated physical mechanism. The results presented herein advance our knowledge about rainfall’s interdecadal variability and have imperative ramifications for developing skillful decadal predictions.

SLGT2 Inhibitor Rescues Myelopoiesis in G6PC3 Deficiency

The energy metabolism of myeloid cells depends primarily on glycolysis. 1,5-anhydroglucitol (1,5AG), a natural monosaccharide is erroneously phosphorylated by glucose-phosphorylating enzymes to produce 1,5-anhydroglucitol-6-phosphate (1,5AG6P), a powerful inhibitor of hexokinases. The endoplasmic reticulum transporter (SLC37A4/G6PT) and the phosphatase G6PC3 co-operate to dephosphorylate 1,5AG6P. Failure to eliminate 1,5AG6P is the mechanism of neutrophil dysfunction and death in G6PC3-deficient mice. SLGT2-inhibitor reduces 1,5AG level in the blood and restores the neutrophil count in G6PC3-deficient mice. In the investigator-initiated study, a 30 year-old G6PC3-deficient woman with recurrent infections, distressing gastrointestinal symptoms and multi-lineage cytopenia was treated with an SLGT2-inhibitor. A significant increase in all the hematopoietic cell lineages and substantial improvement in the quality of life was observed.

Substantial Improvement of High Temperature Strength of New-Generation Nano-Oxide-Strengthened Alloys by Addition of Metallic Yttrium

Oxide-dispersion-strengthened (ODS) Fe-Al-Y2O3-based alloys (denoted as FeAlOY) containing 5 vol. % of nano-oxides have a potential to become top oxidation and creep-resistant alloys for applications at temperatures of 1100–1300 °C. Oxide dispersoids cause nearly perfect strengthening of grains; thus, grain boundaries with limited cohesive strength become the weak link in FeAlOY in this temperature range. One of the possibilities for significantly improving the strength of FeAlOY is alloying with appropriate elements and increasing the cohesive strength of grain boundaries. Nearly 20 metallic elements have been tested with the aim to increase cohesive strength in the frame of preliminary tests. A positive influence is revealed for Al, Cr, and Y, whereby the influence of Y is enormous (addition of 1% of metallic Y increases strength by a factor of 2), as it is presented in this paper.

Tuberculosis and Diabetes in India: Stakeholder Perspectives on Health System Challenges and Opportunities for Integrated Care

Background India has a dual burden of tuberculosis (TB) and diabetes mellitus (DM). Integrated care for TB/DM is still in the early phase in the country and can be considerably enhanced by understanding and addressing the challenges identified from stakeholders’ perspectives. This study explored the challenges and opportunities at individual, health system and policy level for integrated care of TB/DM comorbidities in India. Methods We used an outlier case study approach and conducted stakeholder interviews and focus group discussions with relevant program personnel including field staff and program managers of TB and DM control programs as well as officials of partners in Indian states, Kerala and Bihar. Results The integrated management requires strengthening the laboratory diagnosis and drug management components of the two individual programs for TB and DM. Focused training and sensitization of healthcare workers in public and private sector across all levels is essential. A district level management unit that coordinates the two vertical programs with a horizontal integration at the primary care level is the way forward. Substantial improvement in data infrastructure is essential to improve decision-making process. Conclusion Bi-directional screening and management of TB/DM comorbidities in India requires substantial investment in human resources, infrastructure, drug availability, and data infrastructure.

Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich

This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred to as vintage effects. We estimate a spatially varying coefficients (SVC) model to investigate the spatial structures of vintage effects on SFH pricing. For SFHs in the Canton of Zurich, Switzerland, we find substantial spatial variation in the age effect. In particular, we find a local, strong vintage effect primarily in urban areas compared to pure depreciative age effects in rural locations. Using cross validation, we assess the potential improvement in predictive performance by incorporating spatially varying vintage effects in hedonic models. We find a substantial improvement in out-of-sample predictive performance of SVC models over classical spatial hedonic models.

High-Entropy-Stabilized Polyanionic Cathodes with Multiple Redox Reactions for Sodium-Ion Battery Applications

High-entropy (HE) materials containing multiple elements have created a growing interest in exploring the property limits of electrodes in energy storage and understanding the underlying chemical/physical mechanisms. Here, we show a substantial improvement in performance of HE-based cathodes in sodium-ion batteries (SIBs). Polyanionic structure has a large compositional flexibility and can incorporate many active transition-metal (TM) species, which is an ideal platform to design HE cathode materials. As a proof of concept, we show that HE sodium superionic conductor (HE-NASICON) materials can be synthesized via a facile sol-gel method. By comparing a group of HE-NASICON cathodes containing different contents of TM species, we demonstrate that the multi-Na-ions intercalation/deintercalation process is highly reversible, whereas capacity and cycling stability are improved. The HE-NASICON cathode with equal molarity of five TM species achieves a high capacity of 161 mA h g−1 and capacity retention of 85% when cycling at a high rate of 5 C over 1000 cycles. In-situ XRD and spherical-aberration-corrected transmission electron microscope (ACTEM) also demonstrate a robust trigonal phase with a volume change of merely 4.07% during the multi-Na-ions storage. These results reveal the effectiveness of HE concept in expediting high-performance polyanionic cathodes for real SIBs applications.

Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions

The effects of three drip irrigation (IR1: Farmer’s, IR2:Full (100%ETc), IR3:Deficit (80%ETc) irrigation), and two fertilization (Ft1, Ft2) treatments were studied on maize yield and biomass by applying new agro-technologies (TDR—sensors for soil moisture (SM) measurements, Precision Agriculture, Remote Sensing—NDVI (Sentinel-2 satellite sensor), soil-hydraulic analyses and Geostatistical models, SM-rootzone modelling-2D-GIS mapping). A daily soil moisture depletion (SMDp) model was developed. The two-way-ANOVA statistical analysis results revealed that irrigation (IR3 = best) and fertilization treatments (Ft1 = best) significantly affect yield and biomass. Deficit irrigation and proper fertilization based on new agro-technologies for improved management decisions can result in substantial improvement on yield (+116.10%) and biomass (+119.71%) with less net water use (−7.49%) and reduced drainage water losses (−41.02%).

Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing

The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).