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India reports the highest number of suicide deaths in the world. At this time when the Indian Government is formulating a national suicide prevention strategy, we have reviewed the current status of suicides in India, focusing on epidemiology, risk factors, and existing suicide prevention strategies to identify key challenges and priorities for suicide prevention. The suicide rate among Indian girls and women continues to be twice the global rate. Suicide accounts for most deaths in the 15-39 years age group compared with other causes of death. Hanging is the most common method of suicide, followed by pesticides poisoning, medicine overdose, and self-immolation. In addition to depression and alcohol use disorders as risk factors, several social and cultural factors appear to increase risk of suicide. The absence of a national suicide prevention strategy, inappropriate media reporting, legal conflicts in the interpretation of suicide being punishable, and inadequate multisectoral engagement are major barriers to effective suicide prevention. A scaffolding approach is useful to reduce suicide rates, as interventions provided at the right time, intensity, and duration can help navigate situations in which a person might be susceptible to and at risk of suicide. In addition to outlining research and data priorities, we provide recommendations that emphasise multilevel action priorities for suicide prevention across various sectors. We call for urgent action in India by integrating suicide prevention measures at every level of public health, with special focus on the finalisation and implementation of the national suicide prevention strategy.

Daycare is a child-care alternative that permits guardians to drop off their kids during the day for care, management, and learning. Significantly, kids who participate in early child-care programs will be more beneficial and more fruitful in school. Child-care educators can share assets, thoughts and even get together in a nearby play area. Each parent has an alternate thought of what they need for their kids. There are various sorts of child-care offerings, Private child-care chains: An organization normally runs these as establishments, Private or independent nurseries: These are controlled by people or a group of people, home-based child-care: In India, this is a typical type of child-care, most of the time run by homemakers, Daycares connected to schools: Certain schools may decide to set up child-care appended to their school, Workplace childcares: Companies now and again have nearby child-care for youngsters. Several companies offer onsite child care support for their employees. Few organizations are Google, Johnson and Johnson, Boeing, Nike, Procter, Gamble, etc. Parents have the main concern that they want to know how their child is doing while they are away. Are their child's needs being met? Are they being heard when sharing important details about their child's care? Can they reach the facility at any time, and can staff reach parents regardless of where they are during the day? A central communication hub can help parents communicate important information such as dietary restrictions or special drop-off or pick-up instructions to address all these above concerns. Also, touch-free devices that would allow teachers to communicate hands-free, leaving them to focus on their most important task caring for children, require the hour. In this paper, a framework of voice user interface-based AI enables child daycare assistance system has been proposed that reviewed by baby sitters and parents.

Sharp memory of growing children is important for education and learning of any new skills. Learning new languages is an important aspect for all children. Kids with Autism Spectrum Disorder (ASD) need extra attention to enhance the memory skills. ASD is a developmental issue that influences communication and social conduct. It leads to the slow development of certain skills in children. The reasons for ASD are yet to obscure; however, it is normal in children having parents of higher age groups. Individuals with ASD generally avoid social communications, lack confidence in the eye to eye contact, fiddle a great deal, and regularly experience the ill effects of other issues like ADHD, OCD, etc.They tend to get distracted very easily. They are often obsessive about particular things and ignorant or indifferent about everything else. ASD does not include mental retardation (MR). Even though individuals experiencing ASD are improper at conveying, they sometimes have a better than average IQ. In this paper, a memory-enhancing mobile game has been designed that would help ASD children to improve the memory and help increase the stock of words that would eventually help in communication skills. The proposed mobile game would help Children diagnose with ASD to increase their memory retention power and help parents’ track their progress using gamification concepts.

Effect of modifying cloud water to rain conversion factor above freezing level on radiation flux is studied. The linkage of cloud-radiation interaction with a better Indian summer monsoon (ISM) rainfall simulation has also been investigated. Two simulations were performed with Climate Forecast System version 2 (CFSv2), one with default revised simplified Arakawa-Schubert (RSAS) convection scheme and another with modified RSAS (RSAS-mod) in which the cloud water to precipitation conversion factor is revised. It is observed that both top of the atmosphere (TOA) and surface energy imbalances are improved in the RSAS-mod simulation. The major contribution seems to come from the enhanced reflected shortwave radiation at the TOA. In RSAS-mod, more shortwave radiation is getting reflected at the TOA and thus less shortwave is available to reach at the surface. It has happened as a consequence of enhanced planetary albedo in RSAS-mod. It is also observed that high cloud cover has increased in RSAS-mod and there is a decrease in low cloud cover. As a result of increased high cloud cover, outgoing longwave radiation has decreased significantly in RSAS-mod. It is also found from the multivariate empirical orthogonal function (MV-EOF) analysis that the composite of longwave cloud radiative forcing corresponding to phases 4 and 5 of boreal summer intraseasonal oscillation (BSISO), which signifies the active phase of ISM, is better simulated in RSAS-mod. This has helped to simulate a better seasonal rainfall.

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.

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 Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (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 ERA5 (ECMWF Reanalyses version 5) reanalyses 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 skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

Today, smartphones have become an integral part in one’s life. There are now techniques to identify the user’s emotional state as per their usage of the mobile device. In this paper, we present an attempt to classify the various interaction types such as scroll, tap, swipe, and pinch zoom based on human emotions. We run a formative statistical study where we have collected data from 60 participants over a period of 1 month. The participants were given different situations while using social networking applications and video entertainment applications and were asked to rate on 12 emotions (5 positive, 2 neutral, and 5 negative). Through the study, we find that there is significant relationship between different interaction types with satisfaction, surprise, and entertainment in emotions (p ≤ 0.05) for social networking applications. Also, there is significant relationship between different interaction types with satisfaction (p ≤ 0.05) in emotions for video entertainment applications. This result can be used while developing an interaction system. We could also develop a questionnaire that measures interaction types and emotions. The questionnaire reliability was tested using Cronbach’s Alpha.

The objective of this study is to identify and document the radiation biases in the latest National Centers for Environment Prediction (NCEP), Climate Forecasting System (CFSv2) and to investigate the probable reasons for these biases. This analysis is made over global and Indian domain under all-sky and clear-sky conditions. The impact of increasing the horizontal resolution of the atmospheric model on these biases is also investigated by comparing results of two different horizontal resolution versions of CFSv2 namely T126 and T382. The difference between the top of the atmosphere and surface energy imbalance in T126 (T382) is 3.49 (2.78) W/m 2 . This reduction of bias in the high resolution model is achieved due to lesser low cloud cover, resulting more surface insolation, and due to more latent heat fluxes at the surface. Compared to clear sky simulations, all sky simulations exhibit larger biases suggesting that the cloud covers are not simulated well in the model. The annual mean high level cloud cover is over estimated over the global as well as the Indian domain. This overestimation over the Indian domain is also present during JJAS. There is also evidence that both of the models have insufficient water vapour in their atmosphere. This study suggests that in order to improve the model's mean radiation climatology, simulation of clouds in the model also needs to be improved, and future model development activities should focus on this aspect.

A global forecast system model at a horizontal resolution of T1534 ( $${\sim }12.5\, \hbox {km}$$ ) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the north–west Indian region. Although the model has a reasonable fidelity in capturing the spatio-temporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

Previous studies have shown a slowdown in the warming rate of the annual mean global surface temperature in the recent decade and it is referred to as the hiatus in global warming. Some recent studies have suggested that the hiatus in global warming is possibly due to strong cooling in the tropical Pacific. This study investigates the possible role of the Indian Ocean warming on the tropical Pacific cooling. Despite the continued rise in sea surface temperature (SST) over the tropical Indian Ocean, SST over the tropical Pacific has shown a cooling trend in the recent decade (2002 − 2012). It is well known fact that the Indian Ocean and the Pacific Ocean are strongly coupled to each other and the Indian Ocean basin wide warming is triggered by El Nino on interannual time scale. However, in the recent decade, this relationship is weakening. The recent Indian Ocean warming is triggering a Matsuno-Gill type response in the atmosphere by generating anomalous cyclonic circulations on either side of equator over the tropical Indian Ocean and anomalous easterlies along the tropical Pacific Ocean. These anomalous easterlies result in Ekman divergence in the equatorial Pacific and produce upwelling Kelvin waves, cools the tropical Pacific and therefore indirectly contributes to the hiatus in global warming.

Newspapers are often filled with news of tempered petrol pumps and pump mafia cheating the common people using chips. Common bikers are usually unaware of such chips. They get less quantity of fuel than the amount they pay. All bikes have fuel indicators on the dashboard but these indicators do not display the accurate value in numeric format of the present fuel inside the tank. Minimum changes in fuel level inside the tank are not reflected on display. Common Indian bikers are often unable to detect such minor changes on a fuel indicator for less than 100 ml fuel value. According to the user study, it is found that most of the bikers have less trust on indicators of fuel dispenser machines of petrol pumps. This paper proposes a sensor-based fuel indicator system which is designed to indicate true fuel value inside the bike’s fuel tank. The proposed prototype of True-Fill (proposed name for a microcontroller-based system to indicate true value of petrol filled) system would help the common people of India to protect themselves from tempered petrol pump.

Computational Cognitive Science (CCS) is a new domain of interdisciplinary study to address problems related to human emotions, cognition, and socio-behavioral aspects based on computational and predictive techniques. The CCS addresses computational model development for software application to configure human emotions. People would like to buy a product which has emotional values. Therefore, it’s become essential to design emotive product that represent the expected emotive qualities of consumers. Kansai Engineering (KE) is one of the beneficial techniques to design emotive products. The term ‘Kansei’ is a Japanese word which is referred to sensitivity, sensibility, feeling and emotion. Literature suggested that KE process helps in creativity based product design. Further, creativity is defined as the tendency to generate or recognize new ideas or alternatives that may be useful to solve problems. So far, there is hardly any computer application found which generates creative and emotive design ideas based on KE process. This paper proposed a CCS based framework to design KE computational application. Consequently, the application will help designers to design emotive products in an effective way.

Background: Osteoarthritis (OA) is a chronic, degenerative joint disorder responsible for considerable morbidity, particularly in old age. Flupirtine, a new centrally acting analgesic, is devoid of the adverse effects of NSAIDs and opioid analgesics. In this study author compared the effectiveness and safety of flupirtine with tramadol in knee OA.Methods: An open label, randomized, controlled trial was done with patients of primary knee OA of both sexes, age >50 years. Patients were recruited from Rheumatology OPD of SSKM Hospital. A minimum WOMAC score of 35 was essential for recruitment. Patients with serious comorbidities were excluded. They were treated orally with either flupirtine (100mg thrice daily) or tramadol (50mg thrice daily) for 12 weeks.Results: Ninety patients were recruited and data of 42 on flupirtine and 41 on tramadol were analysed. There was significant improvement in pain, stiffness and physical function compared to baseline in both the groups. However, there was no significant difference between groups at 4, 8 and 12 weeks. Responder rate (50% reduction in pain score from baseline) was 66.67% with flupirtine and 48.78% with tramadol (p = 0.122). Flupirtine caused 4 adverse events compared to 16 with tramadol. However, both the drugs were well-tolerated.Conclusions: The effectiveness of flupirtine in knee OA is comparable to tramadol, while causing minimal adverse effects. Long-term benefits need to be explored.