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Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

Different classes of low-dimensional superconducting systems exhibit an inhomogeneous filamentary superconducting condensate whose macroscopic coherence still needs to be fully investigated and understood. Here, we present a thorough analysis of the superfluid response of a prototypical filamentary superconductor embedded in a two-dimensional metallic matrix. By mapping the system into an exactly solvable random impedance network, we show how the dissipative (reactive) response of the system non-trivially depends on both the macroscopic and microscopic characteristics of the metallic (superconducting) fraction. We compare our calculations with resonant microwave transport measurements performed on LaAlO$_3$/SrTiO$_3$ heterostructures over an extended range of temperatures and carrier densities finding that the filamentary character of superconductivity accounts for unusual peculiar features of the experimental data.

Elevation gradients provide natural laboratories for investigating tropical tree ecophysiology in the context of climate warming. Previously observed trends with increasing elevation include decreasing stem diameter growth rates (GR), increasing leaf mass per area (LMA), higher root-to-shoot ratios (R:S), increasing leaf δ13C, and decreasing leaf δ15N. These patterns could be driven by decreases in temperature, lower soil nutrient availability, changes in species composition, or a combination thereof. We investigated whether these patterns hold within the genus Flindersia (Rutaceae) along an elevation gradient (0–1,600 m) in the Australian Wet Tropics. Flindersia species are relatively abundant and are important contributors to biomass in these forests. Next, we conducted a glasshouse experiment to better understand the effects of temperature, soil nutrient availability, and species on growth, biomass allocation, and leaf isotopic composition. In the field, GR and δ15N decreased, whereas LMA and δ13C increased with elevation, consistent with observations on other continents. Soil C:N ratio also increased and soil δ15N decreased with increasing elevation, consistent with decreasing nutrient availability. In the glasshouse, relative growth rates (RGR) of the two lowland Flindersia species responded more strongly to temperature than did those of the two upland species. Interestingly, leaf δ13C displayed an opposite relationship with temperature in the glasshouse compared with that observed in the field, indicating the importance of covarying drivers in the field. Leaf δ15N increased in nutrient-rich compared to nutrient-poor soil in the glasshouse, like the trend in the field. There was a significant interaction for δ15N between temperature and species; upland species showed a steeper increase in leaf δ15N with temperature than lowland species. This could indicate more flexibility in nitrogen acquisition in lowland compared to upland species with warming. The distinguishing feature of a mountaintop restricted Flindersia species in the glasshouse was a very high R:S ratio in nutrient-poor soil at low temperatures, conditions approximating the mountaintop environment. Our results suggest that species traits interact with temperature and nutrient availability to drive observed elevation patterns. Capturing this complexity in models will be challenging but is important for making realistic predictions of tropical tree responses to global warming.

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers - the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models - the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CARTBagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM. [ABSTRACT FROM AUTHOR]

Uttarakhand state is producing large numbers of primary and secondary teachers every year. Teacher training programmers face inadequate facilities for teacher educators and teacher trainees. Professional preparation of teacher educators and trainee teachers needs to be made more relevant and effective. The government and university should develop some procedure of regular monitoring of the faculty positions in the self-financed and other regular institutions offering B.Ed. courses. An institutional vision strategy and action plan for the State Council of Educational Research and Training (SCERT) and for the District Institutes of Education and Training (DIET) is needed. This strategy should begin with a focus on professional development of teacher educators and further research on teacher education.

Laser cleaning has been observed to alter the surface properties of steel surfaces. The change in surface properties is greatly affected by the laser parameters and therefore, the selection of appropriate laser parameters is paramount. In this work, a study on the treatment combination of laser power, scan speed, pulse repetition rate, and hatch distance on the surface hardness and the mass percentage of iron is conducted via response surface methodology using a central composite design. The factors and interactions that significantly affect the response were identified by analysis of variance (ANOVA). The microhardness was measured using a Vickers hardness testing machine and the mass percentage of iron was obtained by energy dispersive x-ray spectroscopy (EDS) analysis. The hardness value was observed to remain almost unchanged or increase by more than 200 % and steel was completely reduced to even 100 % pure iron at different laser parameters. The responses were approximated by a second-order regression model and the R-squared values for the models of hardness and mass percentage of iron are 84 % and 80 % respectively. The model can be used to predict the parameters that result in the desired surface hardness and iron content during cleaning. [ABSTRACT FROM AUTHOR]