The study of the connection between isotopic depletion in precipitation and stratiform rains is limited. This study reports the relationship between the temporal characterization of stable isotopes in precipitation in Lucknow of Central India that has been lacking up to the present. Stable isotope analyses were carried out on collected precipitation samples from three locations in the city of Lucknow in northern India, from July 2018 to September 2018 is reported here. A least-squares regression of the isotope data was used to obtain the local meteoric water line (LMWL), and the obtained LMWL was δD = 7.75 δ18O + 7.2 (R2 = 0.99). The average δ18O values of the samples ranged from − 16.27 to 0.45‰ while δD values varied from − 112.42 to 10.51‰ respectively.

India's diverse vegetation and landscapes provide an opportunity to understand the responses of vegetation to climate change. By examining pollen and fossil records along with carbon isotopes of organic matter and leaf wax, this review uncovers the rich vegetational history of India. Notably, during the late Miocene (8 to 6 Ma), the transition from C3 to C4 plants in lowland regions was a pivotal ecological shift, with fluctuations in their abundance during the late Quaternary (100 ka to the present). In India, the global phenomenon of C4 expansion was driven by the combined feedback of climate variations, changes in substrate conditions, and habitat disturbances. The Himalayan region has experienced profound transformations, including tree-line migrations, shifts in flowering and fruiting times, species loss, and shifts in plant communities due to changing monsoons and westerlies. Coastal areas, characterized by mangroves, have been dynamically influenced by changing sea extents driven by climate changes. In arid desert regions, the interplay between summer and westerlies rainfall has shaped vegetation composition. This review explores vegetation and climate history since 14 Ma and emphasizes the need for more isotope data from contemporary plants, precise sediment dating, and a better understanding of fire's role in shaping vegetation. ▪ This review highlights diverse vegetation and landscapes of India as a valuable source for understanding the vegetation-climate link during the last 14 Ma . ▪ A significant ecological shift occurred during 8 to 6 Ma in India, marked by the transition from C3 to C4 plants in the lowland regions. ▪ This review emphasizes the importance of more isotope data, precise sediment dating, and a better understanding of fire's role in shaping vegetation. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 52 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sub-optimal wheat productivity in the eastern Indo-Gangetic plain of India can largely be attributed to delayed sowing and the use of short duration varieties. The second week of November is the ideal time for sowing wheat in eastern India, though farmers generally plant later. Late-sowing farmers tend to prefer short-duration varieties, leading to additional yield penalty. To validate the effect of timely sowing and the comparative performance of long- and short-duration varieties, multi-location on-farm trials were conducted continuously over five years starting from 2016–2017. Ten districts were selected to ensure that all the agro-climatic zones of the region were covered. There were five treatments of sowing windows: (T1) 1 to 10 November, (T2) 11–20 November, (T3) 21 to 30 November, (T4) 1–15 December, and (T5) 16–31 December. Varietal performance was compared in T3, T4, and T5, as short-duration varieties are normally sown after 20 November. There is asymmetry in the distribution of samples within treatments and over the years due to the allocation of fields by farmers. Altogether, the trial was conducted at 3735 sites and captured 61 variables, including yield and yield attributing traits. Findings suggested that grain yields of long-duration wheat varieties are better even under late sown scenarios.

The dual isotopes of dissolved NO3- (n = 43) has been used to delineate the nitrate sources and N-cycling processes in the Ganga river. The proportional contribution of nitrate from different sources has been estimated using the Bayesian mixing model. The seasonal NO3- concentration in the lower stretch of the river Ganga varied between 4.1 and 64.1 mu M with higher concentration during monsoon and post-monsoon season and lower concentration during the pre-monsoon and winter season. The temporal variation in the isotopic values ranged between +0.0 and +9.6%o for 815NNO3- and -1.2 to +11.0%o for 818ONO3-. The spatial NO3- concentration during the post-monsoon season varied between 23.2 and 57.7 mu M, with higher values from the middle and lower values from the lower stretch of the river Ganga. The isotopic ratio during the post-monsoon season varied between -1.0 and +11.3%o for 815NNO3- and -4.6 to +5.2%o for 818ONO3-. The temporal dataset from the lower stretch of the river Ganga showed the dominance of nitrate derived from the nitrification of soil organic matter (SOM) (average -53.4%). The nitrate contribution from synthetic fertilizers was observed to be higher during the postmonsoon season (34.7 +/- 23.4%) compared to that in the monsoon (25.5 +/- 19.5%) and pre-monsoon (22.2 +/- 19.6%) season. No significant seasonal variations were observed in the nitrate input from manure/sewage (-13.9%). Spatial samples collected during the post-monsoon season showed higher contribution of synthetic fertilizer in the lower stretch (34.6 +/- 22.7%) compared to the middle stretch (21.1 +/- 18.2%), which indicates greater influence of the agricultural activity in the lower stretch. The dual isotope study of dissolved NO3established that the nitrate in the Ganga river water is mostly derived from the nitrification of incoming organic compounds and is subsequently removed via assimilatory nitrate uptake. The study also emphasises significant nitrification and assimilatory nitrate removal processes operating in the mixing zone of the Ganga river and Hooghly estuary.

Flame-spray pyrolysis offers a scalable approach to the synthesis of tailored nanostructured catalysts for biomass conversion.

Quantitative delineation of water sources in a large river system is essential for the sustainable use of water. In the present study, we have tested two different methodologies to estimate the contribution from different water sources in the river Ganga. The first model uses stable isotopes and physicochemical parameters of water to delineate the contribution of glacier-melt, groundwater, and surface runoff in different stretches of the river Ganga. The end member-based mixing model provides glacier-melt contribution of similar to 31.2%, similar to 5.5%, and similar to 0.5% in the upper, middle, and lower stretch of the river Ganga. The model showed maximum contribution from groundwater (similar to 66.3%) in the middle stretch and surface runoff (similar to 57.2%) in the lower stretch of the river Ganga. However, the uncertainties in the estimates from the three-component mixing model were significantly high due to temporal variability in the end member values. To provide estimates with lower uncertainty, an alternate method (discharge dependent budget estimation [DDBE]) has been proposed which delineates the contribution of groundwater and surface runoff in small segments of the river. The DDBE model confines the calculation for budget estimates to smaller segments of the river and hence leads to lower uncertainties in the results, providing improved systematic estimation of water budget for large river systems. In the present study, estimates from the DDBE model suggest that groundwater contributes similar to 79% of the additional water whereas the contribution from surface runoff is similar to 21% in the middle and lower stretch of the river Ganga. The DDBE model was successfully applied in 5 small segments along the stretch of the river Ganga and the methodology used shows great potential for systematic delineation of water sources in large river system.

The paraffin-to-olefin (P/O) ratio in gasoline fuel is a critical metric affecting fuel properties and engine efficiency. In the conversion of dimethyl ether (DME) to high-octane hydrocarbons over BEA zeolite catalysts, the P/O ratio can be controlled through catalyst design. Here, we report bimetallic catalysts that balance the net hydrogenation and dehydrogenation activity during DME homologation. The Cu-Zn/BEA catalyst exhibited greater relative dehydrogenation activity attributed to higher ionic site density, resulting in a lower P/O ratio (6.6) versus the benchmark Cu/BEA (9.4). The Cu-Ni/BEA catalyst exhibited increased hydrogenation due to reduced Ni species, resulting in a higher P/O ratio (19). The product fuel properties were estimated with an efficiency merit function and compared against finished gasolines and a typical alkylate blendstock. Merit values for the hydrocarbon product from all three BEA catalysts exceeded those of the comparison fuels (0–5.3), with the product from Cu-Zn/BEA exhibiting the highest merit value (9.7).

Systems pharmacology helps to understand the complex relationships between biological systems, drugs, and infection model; Leishmania major being one of them. It has aided the drug discovery process by addressing the concerns about economic stress, drug toxicity, and the emergence of resistance. Two million new leishmaniasis cases are reported annually, and >350 million people are at risk globally due to the parasite Leishmania. Try-panothione reductase (TryR) from the parasite-specific redox metabolism is a promising target. In the discipline of medicinal chemistry, benzimidazole is a strong pharmacophore and exhibits a broad range of biological ac-tivities. In the current study, benzimidazole derivatives were explored using computational, enzyme kinetics, biological activity, cytotoxic impact characterization, and in-silico ADME-Tox predictions, followed by their confirmation through in-vitro and animal experiments to discover novel inhibitors for TryR from Leishmania major. During rigorous in-silico screening, two benzimidazole derivatives were chosen for further experimenta-tion. In-vitro testing revealed that compound C1 has a higher binding affinity for the TryR protein. Treatment with compound C1 caused significant morphological changes in the parasite, including size reduction, membrane blebbing, loss of motility, and improved anti-leishmanial efficacy. The compound C1 had significant anti-leishmanial potential against L. major promastigotes and demonstrated apoptosis-mediated leishmanicidal ac-tivity (apoptosis-like cell death). Furthermore, BALB/c female mice treated with C1 reduced parasite burden. Our findings depicts that C1 successfully lowered the parasite load and has a therapeutic impact on infected mice making C1 as a promising lead compound that, with additional modifications, may be exploited to create novel anti-leishmanial therapies.

This dataset provides detailed information on rice production practices being applied by farmers during 2018 rainy season in India. Data was collected through computer-assisted personal interview of farmers using the digital platform Open Data Kit (ODK). The dataset, n = 8355, covers eight Indian states, viz., Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Odisha, Punjab, Uttar Pradesh and West Bengal. Sampling frames were constructed separately for each district within states and farmers were selected randomly. The survey was deployed in 49 districts with a maximum of 210 interviews per district. The digital survey form was available on mobile phones of trained enumerators and was designed to minimize data entry errors. Each survey captured approximately 225 variables around rice production practices of farmers' largest plot starting with land preparation, establishment method, crop variety and planting time through to crop yield. Detailed modules captured fertilizer application, irrigation, weed management, biotic and abiotic stresses. Additional information was gathered on household demographics and marketing. Geo-points were recorded for each surveyed plot with an accuracy of <10 m. This dataset is generated to bridge a data-gap in the national system and generates information about the adoption of technologies, as well as enabling prediction and other analytics. It can potentially be the basis for evidence-based agriculture programming by policy makers.

Advanced catalytic materials play an enabling role in producing renewable fuels and chemicals from biomass, thereby helping meet the global climate-change goals set forth by the Intergovernmental Panel on Climate Change. Herein, we present a multiscale approach to accelerate the catalyst-process development cycle for catalytic fast pyrolysis (CFP) of biomass over Mo2C. Mo2C has been shown to possess co-localized acidic and metallic sites and exhibit high activity for deoxygenation of biomass pyrolysis model compounds. However, critical knowledge gaps remain regarding the effectiveness of this catalyst for CFP of whole biomass. We address these knowledge gaps and demonstrate thatMo2C is effective at deoxygenating biomass-pyrolysis products in the presence of H2 but that it undergoes rapid selective and non-selective deactivation. The knowledge gaps addressed fromthis integrated study, targeting appropriate experiments across scales and feed types, enabled identification of critical modifications for advancing the CFP catalyst- process development cycle.

The synthesis of branched hydrocarbons for high-octane gasoline and sustainable aviation fuel directly from CO2-rich syngas in a single reactor holds potential to decrease capital and operating costs and increase overall energy and carbon efficiencies in a biorefinery. Here, we report the cascade chemistry of syngas to hydrocarbons under mild reaction conditions in a single reactor with C4+ single-pass yields of 13.7-44.9%, depending on the relative catalyst composition employing our dimethyl ether homologation catalyst, Cu/BEA zeolite. With co-fed CO2 at a concentration representative of biomass-derived syngas, 2.5:1:0.9 for H-2:CO:CO2, a hydrocarbon yield of 12.2% was observed with similar selectivity to C4+ products compared to the CO2-free feed. Definitive evidence of CO2 incorporation into the hydrocarbon products was demonstrated with isotopically labeled (CO2)-C-13 co-feed experiments, where mass spectrometry confirmed the propagation of C-13 into the C4+ hydrocarbons, highlighting the feasibility to co-convert CO and CO2 in this single reactor approach.

Ni-modified beta zeolite (Ni/BEA) catalysts activate carbon-hydrogen bonds in light alkanes, as demonstrated through isobutane reaction testing. Controlled synthesis of Ni/BEA allows for efficient introduction of ion-exchanged Ni sites at varying Ni loadings (0.43% - 1.8%). These catalysts exhibit site time yields (STY) for H2 production that increase with increasing Ni loading. A detailed analysis of secondary reactions and carbon deposition based on the relative molar flowrates of product C and H indicates that the observed increase in H2 STY with increasing Ni loading is likely attributed to both increasing alkane activation activity and increasing formation of hydrogen-deficient aromatic products retained within the catalyst pores. In situ diffuse-reflectance UV-visible-NIR absorbance and X-ray absorption spectroscopies indicate isolated, 4-coordinate Ni(2+) species for all loadings. Quantum mechanics/molecular mechanics modeling identifies two distinct Ni(2+) sites consistent with the structural characterization, but with differing relative stabilities due to their coordination environment. Computed reaction energetics for isobutane dehydrogenation demonstrate that the more stable Ni(2+) species at a six-membered 4Si-2Al ring, Ni-6MR, is less active for isobutane dehydrogenation than the less stable Ni(2+) at the five-membered 3Si-2Al ring, Ni-5MR. The differing local structure of the isolated cationic Ni sites in Ni/BEA offers a possible rationalization for the increased H2 STY observed at greater Ni loadings. (c) 2022 Elsevier Inc. All rights reserved.

With the necessity to develop antileishmanial drugs with substrate specificity, trypanothione reductase (TryR) has gained popularity in parasitology. TryR is unique to be present only in trypanosomatids and is functionally similar to glutathione in mammals. It protects against oxidative stress exerted by the host defense mechanism. The TryR enzyme is essential for the survival of Leishmania parasites in the host as it reduces trypanothione and aids in neutralizing hydrogen peroxide produced by the host macrophages during infection. Henceforth, it becomes vital to decipher their functional stability and behaviour in the presence of denaturants. Our study is focused on structural, functional and behavioural stability aspects of TryR with different concentrations of Urea, Guanidinium chloride, alcohol based compounds followed by extensive molecular dynamics simulations in a lipid bilayer system. The results obtained from the study reveal an interesting insight into the possible mechanisms of modulation of the structure, function and stability of the TryR protein.

Modern end-to-end speech recognition models show astonishing results in transcribing audio signals into written text. However, conventional data feeding pipelines may be sub-optimal for low-resource speech recognition, which still remains a challenging task. We propose an automated curriculum learning approach to optimize the sequence of training examples based on both the progress of the model while training and prior knowledge about the difficulty of the training examples. We introduce a new difficulty measure called compression ratio that can be used as a scoring function for raw audio in various noise conditions. The proposed method improves speech recognition Word Error Rate performance by up to 33% relative over the baseline system.

The Southern Ocean (SO) is highly energetic and sensitive parts of the earth climate system. The regional scale upper ocean variability is highly dominant and energetic in SO. The interaction between the atmosphere and the ocean through mixed layer modulate the heat and nutrient exchange between the upper ocean surface and the dark deep ocean. Its space time variability modulates the distribution of carbon and water mass formation that influence the physical and biological pumps of SO. The mixed layer depth (MLD) generally calculated by climatological fields either by in-situ data or by numerical simulation. Here, we demonstrate the variability of mixed layer depth of SO in the domain 7° E–80° E; 72° S–45° S using a regional high resolution coupled ocean sea ice model. The model run is performed on a horizontal resolution at 9 km with open boundaries for a period of 20 years (1994–2013). The spatial and temporal variation of model-derived MLD is compared against the MLD of estimation the circulation and climate of the ocean 2 (ECCO2) reanalysis. The model has qualitatively as well as quantitatively good resemblance with ECCO2 reanalysis. The month-to-month MLD variation is very prominent, however, the model performance in simulation of seasonal MLD in open ocean is quite well compared to the higher latitude sea ice formation and melting domain. Along with this, an attempt has been made to understand and quantify the influence of air-sea forcing (near-surface zonal and meridional winds and air temperature) on the variability of mixed layer depth. The study shows the near-surface wind forcings have higher contribution to changing the MLD compare to atmospheric temperature, however, the effect of air temperature is also significant and prominent also in the study domain.

The extreme pandemic has changed the traveling habit as people are skeptical about traveling through the infection outbreak regions. Therefore, it has become a need to find alternative routes whenever traveling in a city kind of regions. In the current paper, we have proposed a simple yet smart system for predicting the safest route between source and destination amongst other alternative routes. The safest route is with minimum exposure to localities affected by the spread of communicable diseases like COVID-19. It is done by considering different quality measures assuming that there will be available information on the active number of infection cases from the governing authorities. However, the only information taken into account is the location of the confirmed active cases without using any other sensitive information of the patients. The proposed system will help common people while traveling in the pandemic situation. It will also be helpful for the local administration in restricting movement to and from containment zones.

Water isotope‐based hydrological and paleoaltimetry studies in high mountain areas are generally done using the isotopic composition of river discharge. However, rivers capture a basin averaged signal of regional precipitation and are less likely to register the meteorological processes intrinsic to distinct hydrological fractions at different altitudes. This has been observed in the Khumbu (Mt. Everest) Himalayan watersheds of Dudh Kosi Basin (DKB), where the δ 18 O values of snowpack and stream water vary non‐uniformly with altitude while the δ 18 O values of river water show a uniform relationship. Snow exhibits the highest (+0.9 to −4.4‰/100 m) isotopic lapse rate (ILR), followed by streams (+0.2 to −0.4‰/100 m) and rivers (−0.05‰/100 m). Sublimation, catchment vegetation, diurnal temperature, cloud type, and insolation play a significant role in controlling the isotopic composition of snowpack and stream water. Similarly, the isotopic composition of small streams disproportionately represents the meteoric water composition of an area, as rainfall immediately joins the stream‐runoff while the snow melts gradually around the year. To map the isotopic heterogeneity in DKB surface waters, we have modeled the isoscape for surface runoff using the isotopic composition of snow and stream water, and remotely sensed parameters. Accordingly, we simulate the isoscapes for snow and stream‐runoff via multi‐regression models which extrapolate the observed data as a function of the controlling factors. The amount‐weighted summation of both the isoscapes (relative contribution (%) *δ 18 O value) constitutes the hydropool. The hydropool model incorporates spatiotemporal variation in ILR computed from the δ 18 O values of surface runoff.

This paper proposes orthogonal frequency division multiple access (OFDMA) based multiuser hybrid cooperative- D2D (C-D2D) communication system with the best user selection. In the proposed model, the listen-before-talk (LBT) algorithm with an energy detection method has been used to check the WiFi spectrum availability. Based on the spectrum availability, the system adapts to any one of the two modes, namely in-band C- D2D or out-band C-D2D. Further, the best user selection scheme is applied to a system wherein multiple D2D users coexist with a cellular user in the same cell. In particular, among the M available D2D pairs, selected D2D user helps the cellular user by relaying D out of N subcarriers to the base station (BS). Based on the availability of Wi-Fi, either N-D subcarriers of the cellular spectrum or N subcarriers of the Wi-Fi spectrum will be shared among M D2D users for D2D transmission. Closedform expressions of outage probability for cellular and D2D users are derived. Results show that the proposed hybrid C-D2D communication system with the best user selection outperforms the conventional C-D2D framework.

While Direct Seeded Rice (DSR) has numerous potential benefits to smallholder farmers in the Eastern Gangetic Plains of South Asia, it's out-scaling has been limited by both a lack of demand by farmers and limited supply of DSR services by machinery owners. This contrasts with the comparatively more rapid scaling of zero tillage wheat in the region. This trend is yet to be fully explored, particularly when focus has been placed almost exclusively on understanding DSR adoption though the lens of farm-level agronomic, economic and environmental performance. Given that limited DSR service provision is likely to be governed outside of these considerations, this study explores with zero tillage drill owners the decision processes they apply in deciding how to use their zero tillage drills. Respondents highlight a complex web of interrelated considerations that highlight the additional complexities of DSR as compared to existing practices. Using a novel 'Decision-making Dartboard' qualitative framework, these complexities are unpacked and a set of potential changes to the assumed theory of change for DSR scaling are identified, including considerations for selection of potential DSR service providers and responsibilities for promotion and extension of DSR to overcome the prevalent negative perceptions of DSR held broadly across the communities explored. The proposed framework and analysis process are also potentially useful for exploration of other farmer decision making processes more broadly.

The Automated Speech Recognition (ASR) task has been a challenging domain especially for low data scenarios with few audio examples. This is the main problem in training ASR systems on the data from low-resource or marginalized languages. In this paper we present an approach to mitigate the lack of training data by employing Automated Curriculum Learning in combination with an adversarial bandit approach inspired by Reinforcement learning. The goal of the approach is to optimize the training sequence of mini-batches ranked by the level of difficulty and compare the ASR performance metrics against the random training sequence and discrete curriculum. We test our approach on a truly low-resource language and show that the bandit framework has a good improvement over the baseline transfer-learning model.