The prediction of Indian monsoon rainfall variability, affecting a country with a population of billions, remained one of the major challenges of the numerical weather prediction community. While in recent years, there has been a significant improvement in the prediction of the synoptic-scale transients associated with the monsoon circulation, the intricacies of rainfall variability remained a challenge. Here, an attempt is made to develop a global model using a dynamic core of a cubic octahedral grid that provides a higher resolution of 6.5 km over the global tropics. This high-resolution model has been developed to resolve the monsoon convection. Reforecasts with the Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model (HGFM) have been run daily from June through September 2022. HGFM has a wavenumber truncation of 1534 in the cubic octahedral grid. The monsoon events have been predicted with a 10 d lead time. HGFM is compared to the operational Global Forecast System (GFS) T1534. While HGFM provides skills comparable to GFS, it shows better skills for higher precipitation thresholds. This model is currently being run in experimental mode and will be made operational.

Abstract. The prediction of Indian monsoon rainfall variability affecting a country with a population of billions remained one of the major challenges of the numerical weather prediction community. While in recent years, there has been a significant improvement in predicting the synoptic scale transients associated with the monsoon circulation, the intricacies of rainfall variability remained a challenge. Here, an attempt is made to develop a global model using a dynamic core of a cubic octahedral grid that provides a higher resolution of 6.5 km over the global tropics. This high-resolution model has been developed to resolve the monsoon convection. Reforecasts with the IITM High-resolution Global Forecast Model (HGFM) have been run daily from June through September 2022. The HGFM model has a wave number truncation of 1534 in the cubic octahedral grid. The monsoon events have been predicted with a ten-day lead time. The HGFM model is compared to the operational GFS T1534. While the HGFM provides skills comparable to the GFS, it shows better skills for higher precipitation thresholds. This model is currently being run in experimental mode and will be made operational.

The present study focuses on addressing the issue of too frequent triggers of deep convection in climate models, which are primarily based on physics-based classical trigger functions such as convective available potential energy (CAPE) or cloud work function (CWF). To overcome this problem, the study proposes using machine learning (ML) based deep convective triggers as an alternative. The deep convective trigger is formulated as a binary classification problem, where the goal is to predict whether deep convection will occur or not. Two elementary classification algorithms, namely support vector machines and neural networks, are adopted in this study. Additionally, a novel method is proposed to rank the importance of input variables for the classification problem, which may aid in understanding the underlying mechanisms and factors influencing deep convection. The accuracy of the ML-based methods is compared with the widely used convective available potential energy (CAPE)-based and dynamic generation of CAPE (dCAPE) trigger function found in many convective parameterization schemes. Results demonstrate that the elementary machine learning-based algorithms can outperform the classical CAPE-based triggers, indicating the potential effectiveness of ML-based approaches in dealing with this issue. Furthermore, a method based on the Mahalanobis distance is presented for binary classification, which is easy to interpret and implement. The Mahalanobis distance-based approach shows accuracy comparable to other ML-based methods, suggesting its viability as an alternative method for deep convective triggers. By correcting for deep convective triggers using ML-based approaches, the study proposes a possible solution to improve the probability density of rain in the climate model. This improvement may help overcome the issue of excessive drizzle often observed in many climate models.

Freeform liquid three-dimensional printing (FL-3DP) is a promising new additive manufacturing process that uses a yield stress gel as a temporary support, enabling the processing of a broader class of inks into complex geometries, including those with low viscosities or long solidification kinetics that were previously not processable. However, the full exploitation of these advantages for the fabrication of complex multilateral structures has been hindered by difficulties in controlling the interfaces between inks and supports. In this work, an in-depth study of the rheological properties and interfacial stabilities between a nanoclay-modified support and silicone-based inks enabled a better understanding of the impact printing parameters have on the extruded filament morphology, and thus on printing resolutions. With these improvements, the fabrication of functional multimaterial pneumatic components applied to soft robotics could be demonstrated, exhibiting superior capabilities compared to casting or traditional extrusion-based additive manufacturing in terms of geometric freedom (overhanging and multimaterial structures), tunability of the component's functionality, and robustness between different phases. Overall, the full exploitation of FL-3DP advantages enables a broader design space for features and functionalities in soft robotic components that require complex and robust combinations of materials.

Boron carbide (B4C) has emerged as a potential material, which can be used for high-energy radiation shielding in International Thermonuclear Experimental Reactor (ITER). This is one of the hardest ceramic materials with required properties, such as low density, high hardness, high toughness, high melting point, chemical inertness, outstanding thermal–electrical characteristics, and most importantly having a high cross section for the absorption of neutrons. Recently, ITER-India, Institute for Plasma Research (IPR) initiated prototype activities for the development of hot-pressed B4C blocks/pellets. ITER-India with support from an Indian Industry has demonstrated the manufacturing capability for large-scale, vacuum-hot-pressed blocks with machining feasibility for the final product (shielding block). Development activities have been carried out as per ASTM C750 (Nuclear Grade B4C Powder), ASTM C751 (Nuclear Grade B4C blocks/pellets), and other stringent requirements of ITER. The B4C blocks/pellets have been fabricated in a vacuum at the high-temperature range of 2050 °C–2100 °C and high pressure of ~30 MPa. To qualify this material as per ITER requirements, its chemical composition, mechanical, and physical properties are studied and validated. Scanning electron microscopy (SEM) studies were conducted to calculate the grain size in the microstructure. As this material is to be used in an ultrahigh vacuum (UHV) environment in ITER ports and systems, the outgassing rate of B4C is also determined. We ascertained outgassing rate $\le1\,\,\times \,\,10^{-8}$ Pam $^{3}\text{s}^{-1}\text{m}^{-2}$ at 100 °C of B4C as per the ITER vacuum handbook (IVH) as well as ITER-derived requirements for ports. The main reason to achieve this outgassing rate is the process of fabrication used as it does not include any sintering aids and additives; hence, it is a unique hot-pressing technique, which is better suited for nuclear applications. This article describes the details of the study related to B4C blocks/pellets development and toward the qualification as per shielding material requirements for ITER.

The seasonal prediction skill of climate forecast system model with two different resolutions, namely T126 and T382, is studied using hindcast data. Using novel diagnostic tools such as total variation distance and two‐state Markov Chain analysis, it is shown that increasing the horizontal resolution of the model has minimal impact on the quality of seasonal prediction. The underlying rain distribution and associated transition probabilities are very similar in both model versions. The Markov chain analysis also provides critical clues about the issues associated with convective processes in the model. Both the models produce longer (shorter) wet (dry) spells compared to the observations. Models are trying to bring the atmosphere closer to convective quasi‐equilibrium, leading to a substantial departure from observed features. Although the conventional error metrics are helpful to assess the prediction skills, the new metrics used in the study provide further insights on possible pathways to improve model moist physics.

Significance Statement The purpose of this study is to understand and unravel the large-scale mechanism behind the unprecedented heavy rainfall over Kerala, India, during August 2018 and 2019. The study brings out the importance of probabilistic rainfall predictions for extreme heavy rainfall events. The study reveals that large-scale moisture convergence plays a significant role in the extreme rain of August 2018 and 2019. The extreme rainfall of August is associated with a westward-propagating barotropic Rossby wave. The study also demonstrates that ensemble forecasts of extreme rain by the state-of-the-art prediction systems of GFS, IFS, and NCUM are skillful for longer lead times compared to deterministic models and, therefore, can provide better early warnings to the society. During August 2018 and 2019 the southern state of India, Kerala, received unprecedented heavy rainfall, which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state-of-the-art global prediction systems: the National Centers for Environmental Prediction (NCEP)-based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), and the Unified Model-based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF). Satellite, radar, and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range; sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the fifth major global reanalysis produced by ECMWF (ERA5) and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column-integrated precipitable water tendency, however, is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward-propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019. Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skillful than the deterministic forecasts, as they were able to predict rainfall anomalies (greater than three standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

Toxic metal ions are environmental pollutants that are non-biodegradable and hazardous for which World Health Organization has set a maximum permissible limit for safe drinking water. Various spectroscopic techniques are already in practice to monitor these toxic metals but electrochemical sensors have caught our attention because of their portability, low-cost and fast response. The sensitivity of these electrochemical sensors can be further improved by modifying with carbon based nanomaterials (CBNMs) because of their high conductivity, surface area and broad working potential. The review provides recent advances in the use of electrochemical sensors fabricated with CBNMs for detection of toxic metal ions.

ITER (International Thermonuclear Experimental Reactor) is a huge engineering challenge. ITER Project is in construction phase, the machine is very complex and is an assembly of hundreds of different components. Due to its large assembly and huge welding of different materials, it needs to have certain kind of reliability for safe operation. The reliability of machine is achieved in various lifecycle stages from design to manufacturing through ITER RAMI programme. Reliability and failure rate of weld influence availability of machine [1]. In this paper a study is done for the effects of irradiation on weldments and its mechanical properties and understanding various parameters which can affect the reliability of weld due to irradiation. Various papers were studied to understand changes in mechanical properties in weldment due to irradiation. Thus, by understanding various irradiation scenarios of ITER along with FMECA(Failure mode effect and criticality analysis) as tool, this work tries to systematically recognize, evaluate and prioritize the risk and potential failures. FMECA is a part of ITER RAMI programme. Failure rate data is an important input for FMECA analysis. In this study two weld seams, seam 1 and seam 2 were selected respectively in cryostat and neutral beam duct to understand irradiation condition under ITER working scenario. Flux and DPA at their Cartesian coordinates were found out with the help of various softwares for ITER project. FMECA chart is drawn to understand the severity and criticality under different irradiation scenario for these welds. Thus correlating mechanical properties of irradiated material to reliability, with FMECA as a tool will help in assessing availability of machine and lifecycle management of components. This will help in better understanding on failure rate data of non-irradiated conditions to be used for irradiated condition and changing neutron load in fusion reactors.

Thermal plumes are the energy-containing eddy motions that carry heat and momentum in a convective boundary layer. The detailed understanding of their structure is of fundamental interest for a range of applications, from wall-bounded engineering flows to quantifying surface-atmosphere flux exchanges. We address the aspect of Reynolds stress anisotropy associated with the intermittent nature of heat transport in thermal plumes by performing an invariant analysis of the Reynolds stress tensor in an unstable atmospheric surface layer flow, using a field-experimental dataset. Given the intermittent and asymmetric nature of the turbulent heat flux, we formulate this problem in an event-based framework. In this approach, we provide structural descriptions of warm-updraft and cold-downdraft events and investigate the degree of isotropy of the Reynolds stress tensor within these events of different sizes. We discover that only a subset of these events are associated with the least anisotropic turbulence in highly convective conditions. Additionally, intermittent large-heat-flux events are found to contribute substantially to turbulence anisotropy under unstable stratification. Moreover, we find that the sizes related to the maximum value of the degree of isotropy do not correspond to the peak positions of the heat-flux distributions. This is because the vertical velocity fluctuations pertaining to the sizes associated with the maximum heat flux transport a significant amount of streamwise momentum. A preliminary investigation shows that the sizes of the least anisotropic events probably scale with a mixed length scale (z(0.5)lambda(0.5), where z is the measurement height and lambda is the large-eddy length scale).

The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of truth in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.

Now with more time spent by people while travelling and increasing mobility, providing passengers with a thermally comfortable experience are one of the important targets of any bus manufacturer. Conversely, comprehensive assessment through Climatic Wind Tunnel testing is costly and not possible during early stages of vehicle design. The aim of this work has been to develop a simplified simulation methodology to model the Minibus passenger cabin for cool down test. This study presents a methodology for predicting Heating, Ventilation and Air Conditioning (HVAC) cool-down performance inside Minibus cabin using Computational Fluid Dynamics (CFD) simulation to revise the HVAC duct design and parametric optimization in order to ensure thermal comfort of occupant. Heat Load is calculated analytically and has been considered in the CFD model and occupant heat load is considered as per ASHRAE standard. CFD simulation predicted the temperature and velocity distribution inside passenger cabin. Simulated cool-down results were found to be in good agreement with the experimental results. CFD cool-down prediction is useful in order to reduce time and costs related to climatic wind tunnel and road tests. Validated CFD model is used to study the effect of air flow on cool-down performance.

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

Understanding of interaction between remote dust aerosol and Indian summer monsoon (ISM) remains dubious in literature because of wide disagreement among previous studies. This problem is revisited using version-2 of Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) datasets. ISM variability at intraseasonal timescale is not constrained by the changes in dust aerosols over the most parts of West Asia and North Africa. A new hypothesis is proposed to explain covariability of dust over the Arabian Sea and ISM circulation. Large-scale forcing modulates monsoon intraseasonal oscillations (ISOs) and creates active-like condition over the Indian landmass extending up to the Arabian Sea and the Arabian Peninsula. Organized convection induces anomalous southwesterly and northwesterly circulation as a response to enhanced latent heating. Enhanced northwesterly winds strengthen transport of dust over the Arabian Sea. Augmented dust forcing and associated warming over the Arabian Sea is unlikely to create a positive feedback because of its limited spatial extent. Findings of the present study provide a new insight into remote dust-ISM connection problem and the hypothesis proposed differs considerably from the previously proposed hypotheses.

Depression has been the leading cause of mental-health illness worldwide. Major depressive disorder (MDD), is a common mental health disorder that affects both psychologically as well as physically which could lead to loss of lives. Due to the lack of diagnostic tests and subjectivity involved in detecting depression, there is a growing interest in using behavioural cues to automate depression diagnosis and stage prediction. The absence of labelled behavioural datasets for such problems and the huge amount of variations possible in behaviour makes the problem more challenging. This paper presents a novel multi-level attention based network for multi-modal depression prediction that fuses features from audio, video and text modalities while learning the intra and intermodality relevance. The multi-level attention reinforces overall learning by selecting the most influential features within each modality for the decision making. We perform exhaustive experimentation to create different regression models for audio, video and text modalities. Several fusions models with different configurations are constructed to understand the impact of each feature and modality. We outperform the current baseline by 17.52% in terms of root mean squared error.

Behavior and teleconnections associated with canonical El Niño (~ 18–24 months; CE) and protracted El Niño (> greater than 3 years; PE) events are revisited in the present study. A careful look at seasonal mean of SST anomalies averaged over Niño3.4 region for the period 1980–2010 shows that El Niño episodes in the boreal winter of 1991 and 2002 do not turn into La Niña as CE events (1982–1983, 1986–1988, 1997–1998, 2009–2010). Unlike phase transition in canonical cases of El Niño followed by a neutral or La Niña event, El Niño episodes in the years 1991 and 2002 continued as weak El Niño for another 3 years. A typical signature of CE events in the tropical Indian Ocean (IO) is basin-wide warming, whereas in the case of PE events, warming remains highly localized and relatively weaker in magnitude. PE events are found to be associated with almost no subsurface ocean propagation in the equatorial Pacific Ocean (PO). PE events are linked to more frequent westerly wind bursts (WWBs) of weaker intensity and smaller timespan compared to CE cases. Strong IO warming during CE events generates easterlies in the equatorial western PO, which extend further towards the eastern PO as upwelling Kelvin waves. This upwelling Kelvin waves shoals thermocline through Ekman divergence and cools the sea surface temperature (SST) during the decay phase of El Niño. During PE cases, localized IO warming is incapable of generating significant atmospheric response in the form of easterlies and therefore frequent WWBs help in maintaining positive SST anomalies in the eastern PO.

This paper presents a novel approach to calculate the affine parameters of fractal encoding, in order to reduce its computational complexity. A simple but efficient approximation of the scaling parameter is derived which satisfies all properties necessary to achieve convergence. It allows us to substitute to the costly process of matrix multiplication with a simple division of two numbers. We have also proposed a modified horizontal-vertical (HV) block partitioning scheme, and some new ways to improve the encoding time and decoded quality, over their conventional counterparts. Experiments on standard images show that our approach yields performance similar to the state-of-the-art fractal based image compression methods, in much less time. (c) 2017 Elsevier Ltd. All rights reserved.

25Apr 2017 BIO POSTS: TOOTH FOR TOOTH. Randhir singh , Sandeep kaur , Sachin chadgal and Siddharth Kumar. Resident, Department of Prosthodontics, Government Dental College, Srinagar. Professor, Department of Prosthodontics, Government Dental College, Srinagar. Resident, Department of Endodontics, Government Dental College, Srinagar. Dental surgeon, PHC, Gaya, Bihar.

In this study, bath solutions have been prepared for synthesis of gold nanostructures. The role of different additives in bath formulations and their effects on gold nanostructures has been investigated. Synthesis of gold nanostructures through electroless deposition is widely performed using cyanide baths. Here, two non-cyanide gold solutions (bath A and B) are prepared in house and a commercial cyanide gold plating solution (bath C) is used. The 2-Mercaptobenzothiazole that may act as a stabilizing agent is added in Bath A. The Na L-Ascorbate that is a reducing agent is added in bath B. The zeta potential analysis was carried out that exhibited bath B as the most stable formulation. A variety of metallic substrates like silver, copper and nickel have been studied. The surface morphology of metallic substrates was characterized using SEM technique. Anisotropic gold nanostructures of different shapes like star, spherical, dendrite, polyhedral were achieved using non-cyanide baths.