The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

India's working-age population constituted over 60 percent of its total population in 2011 and was expected to grow until 2031. By 2036, a decline is expected in the share of working population from 65.1 percent in 2031 to 64.9 percent in 2036.

In 2020, there were 1,021,356 registered Indians in Canada. Between 2000 and 2020, the number of registered Indians in Canada experienced an increase, going from some 670 thousand to over one million.

Registered Indians Registered Indians in Canada are all First Nations people who are, as the name suggests, registered as an officially recognized Indian by the Canadian government. No Inuit or Métis is a Registered Indian under Canadian law, leaving only certain First Nations peoples as qualifiers. The word “Indian” is a legal term in this case and has otherwise fallen out of favor. It has been replaced by First Nations, a term used to describe all Canadian aboriginal people who are neither Métis nor Inuit.

Registered Indian status affords benefits and rights not granted to non-Registered Indians including access to reserves and self-governance within them, exemption of federal and provincial taxes to those living on reserve, and postsecondary education financial assistance. The Indian Act of 1951 established the current Indian Register and was revised in 1985 to include people that had been wrongly excluded by the original law.

The number of Registered Indians has grown significantly since 2000 and currently the largest population resides in Ontario, which also has the largest overall population of aboriginal peoples in Canada. British Columbia is home to the largest number of Indian bands, at 199 in 2020.

Abstract

The Enterprise Surveys of Micro firms (ESM) conducted by the World Bank Group's (WBG) Enterprise Analysis Unit (DECEA) in India. The survey covers nine cities: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

The primary objectives of the ESM are to: i) understand demographics of the micro enterprises in the covered cities, ii) describe the environment within which these enterprises operate, and iii) enable data analysis based on the samples that are representative at each city level.

Geographic coverage

Nine cities in India: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

Analysis unit

• Firms

Universe

The universe of ESM includes formally registered businesses in the sectors covered by the ES and with less than five employees. The definition of formal registration can vary by country. The universe table for each of the nine cities covered by ESM in India was obtained from the 6th Economic Census (EC) of India (conducted between January 2013 and April 2014), which has its own well-defined definition of registration. Generally, this entails registration with any central/government agency, under Shops & Establishment Act, Factories Act etc.

In terms of sectors, the survey covers all non-agricultural and non-extractive sectors. In particular, according to the group classification of ISIC Revision 4.0, it includes: all manufacturing sectors (group D), construction (group F), wholesale and retail trade (group G), transportation and storage (group H), accommodation and food service activities (group I), a subset of information and communications (group J), some administrative and support service activities (codes 79) and other service activities (codes 95). Notably, the ESM universe excludes the following sectors: financial and insurance activities (group K), real estate activities (group L), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Enterprise Survey of Micro firms in India 2022 was selected using stratified random sampling, following the methodology explained in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling was preferred over simple random sampling for several reasons, including: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision, along with the unbiased estimates for the whole population. b. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. c. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) d. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. e. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Two levels of stratification were used in this survey: industry and region. For stratification by industry, two groups were used: Manufacturing (combining all the relevant activities in ISIC Rev. 4.0 codes 10-33) and Services (remainder of the universe, as outlined above). Regional stratification was done across nine cities included in the study, namely: Hyderabad, Jaipur, Kochi, Ludhiana, Mumbai, Sehore, Surat, Tezpur and Varanasi.

Mode of data collection

Face-to-face [f2f]

India IN: Coverage: Social Insurance Programs: % of Population: 2nd Quintile data was reported at 21.564 % in 2011. India IN: Coverage: Social Insurance Programs: % of Population: 2nd Quintile data is updated yearly, averaging 21.564 % from Dec 2011 (Median) to 2011, with 1 observations. India IN: Coverage: Social Insurance Programs: % of Population: 2nd Quintile data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

According to the 76th round of the NSO survey conducted between July and December 2018, Andaman and Nicobar islands had a higher percentage of disabled men with a certificate of disability at 51.2 percent. The disability certificate was issued by the medical board to persons with more than 40 percent of any disability. This provides eligibility to apply for facilities, concessions and other benefits provided under various schemes.

As per Cognitive Market Research's latest published report, the Global Non-Stick Cookware market size will be $13,628.21 Million by 2028.The Global Non-Stick Cookware Industry's Compound Annual Growth Rate will 3.73% from 2023 to 2030.

The North America Non-Stick Cookware market size will be USD 4,572.27 Million by 2028.

Factors Affecting the Non Stick Cookware Market

Increasing population ratio and rapid urbanization in emerging countries

China and India are the world's biggest creating economies and furthermore two of the most crowded nations. China, which presently has more than 1.3 billion individuals, is required to develop to more than 1.4 billion by 2050, and India with a population of 1 billion will surpass China to be the most crowded nation with about a 1.6 billion population. These population giants are home to 37% of the total population today. Also, China and India have made eminent progress in their financial improvement described by a high pace of GDP development over the most recent two decades. Together the two nations account as of now for just about a fifth of world GDP.

Developing nations, for example, India and China have abounding population besting the one-billion imprints; both experienced the progress from a shut economy to a more market–situated commitment with the outside world in exchange and speculation; and both to date are in the procedures of industrialization and modernization joined by significant rates of economic growth.

The rapid urbanization in many countries including developed nations over the past 50 years appears to have been joined by unnecessarily elevated levels of grouping of the urban population in extremely enormous urban communities. In any case, in a develop arrangement of urban communities, economic activity is increasingly spread out. Since forever, urban areas have been the primary habitats of learning, culture and development.

It is not surprising that the world's most urban countries tend to be the richest and have the highest human development. Progressing rapid urbanization can possibly improve the prosperity of social orders. Albeit just around a large portion of the world's kin live in urban areas, they create in excess of 80 percent of Global Domestic Product (GDP).

Due to growing population and urbanization people spending capacity has also increased gradually. People give preference to the health development. Additionally, increasing urbanization results in surging nuclear family which enhances the demand for kitchen appliances and cookware. Moreover, rise in working-class population prefers quickly made home-cooked healthy food with the help of modern kitchen appliances that results in mounting of demand for non-stick cookware.

Following graph shows the, world's population who lives in urban area. Also, every region provides the growth ratio of their population from year 1990 till forecast year 2050. All in one this analysis shows how population growth impacts on rapid urbanization. According to graph, Asia Pacific region’s population growth is expected to grow in forecast period.

Varieties of non-stick cookware and wide availability in retail channels

Restraints for Non-Stick Cookware Market

Availability of substitute products. (Access Detailed Analysis in the Full Report Version)

Opportunities for Non-Stick Cookware Market

Rise in disposable income and spending habits. (Access Detailed Analysis in the Full Report Version)

Introduction of Non Stick Cookware

A non-stick cookware is a kitchen cookware such as non-stick pans that has a non-stick surface engineered to reduce the ability of other materials to stick to it. It ensures quick proper cooking of the food in the cookware without sticking. The commonly used non-stick coating cookware is Teflon, ceramic coated cookware.

There are various benefits of non-stick cookware such as affordable, lightweight, easy to handle provides easy cleaning of food. The non-stick cookware in form of frying pans, saucepan, griller, casseroles are made up of different coating material such as Teflon, ceramic coated, anodized aluminum, these are durable, user-friendly, scratch resistant and are stable at temperature till 300 degree Celsius. They use less oil and allows even heat distribution that enhances the flavors of dish and quick heating enables quicker cooking of t...

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 19 of the 28 states in India which covers 96% of the population. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households.

Kind of data

Sample survey data [ssd]

Sampling procedure

World Health Survey Sampling India has 28 states and seven union territories. 19 of the 28 states were included in the design representing 96% of the population. India used a stratified multistage cluster sample design. Six states were selected in accordance with their geographic location and level of development. Strata were defined by the 6 states:(Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal), and locality (urban or rural). There are 12 strata in total. The 2000 Census demarcation was used as the sampling frame. Two stage and three stage sampling was adopted in rural and urban areas, respectively. In rural areas PSUs(villages) were selected probability proportional to size. The measure of size being the 2001 Census population in the village. SSUs (households) were selected using systematic sampling. TSUs (individuals) were selected using Kish tables. In urban areas, PSUs(city wards) were selected probability proportional to size. SSUs(census enumeration blocks), two were randomly selected from each PSU. TSU (households) were selected using systematic sampling. QSU (individuals) were selected as in rural areas. A sample of 379 EAs was selected as the primary sampling units(PSU).

SAGE Sampling The SAGE sample was pre-determined as all PSUs and households selected for the WHS/SAGE Wave 0 survey were included. Exceptions are three PSUs in Assam which were replaced as they were inaccessible due to flooding. And a further six PSUs were omitted for which the household roster information was not available. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: 1)Households with a WHS/SAGE Wave 0 respondent aged 50-plus: all members aged 50-plus including the WHS/SAGE Wave 0 respondent were eligible for the individual interview. 2)Households with a WHS/SAGE Wave 0 respondent aged 47-49: all members aged 50-plus including the WHS/SAGE Wave 0 respondent aged 47-49 was eligible for the individual interview. 3)Households with a WHS/SAGE Wave 0 female respondent aged 18-46: all females members aged 18-49 including the WHS/SAGE Wave 0 female respondent aged 18-46 were eligible for the individual interview. 4)Households with a WHS/SAGE Wave 0 male respondent aged 18-46: three households were selected using systematic sampling and one male aged 18-49 was eligible for the individual interview. In the households not selected, all members aged 50-plus were eligible for the individual interview.

Stages of selection Strata: State, Locality=12 PSU: EAs=375 surveyed SSU: Households=10424 surveyed TSU: Individual=12198 surveyed

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. A Womans Questionnaire was administered to all females aged 18-49 years identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Hindi, Assamese, Kanada and Marathi. SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=88% Cooperation rate=92%

Individual: Response rate=68% Cooperation rate=92%

According to the 76th round of the NSO survey conducted between July and December 2018, India's capital territory of Delhi had a higher percentage of disabled men with a certificate of disability at 38.2 percent. The disability certificate was issued by the medical board to persons with more than 40 percent of any disability. This provides eligibility to apply for facilities, concessions and other benefits provided under various schemes.

Demographics of the population.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

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Data submitted is based on farm surveys among 660 farmers in the Siriguppa taluk of Bellary district in the Indian state of Karnataka. The data includes four waves of surveys among same farmers over five years from 2012-13 to 2016-17. The first survey – baseline survey – was carried out in the first year of the project before the implementation of the experimental intervention. The intervention was on providing farmers with crop cultivation information ranging from land preparation to harvesting, information on credit and insurance, the price of inputs and outputs, etc. This intervention was carried out every year except 2014-15. After the baseline survey, two more surveys were carried out apart from the endline survey. These surveys retrospectively record information on several aspects of farming, social network, and household consumption.

Abstract Recent years have witnessed renewed appreciation that agriculture could play a significant role in the pursuit of Millennium Development Goals. In this context, the role of information dissemination through information and communication technology (ICT) in improving rural welfare is highlighted. However, some fear that with ICT technological disparity will arise, and existing socio-economic inequality and poverty will be further exacerbated. This study will use randomised experiment and surveys before and after the experiment to investigate the impact of ICT on rural welfare in the Indian state of Karnataka. The two key aims of this project are: (1) to unravel the linkage between information access and agricultural growth, rural development, reduction of poverty, and income and social inequality; (2) to identify the role of ICT as a potential instrument of rural information and empowerment for inclusive growth. The randomised experimental methodology proposed here involves facilitating information access on key agriculture related services to households in some villages and not in others. Combining data from both surveys and the experiment, we investigate the impact of information dissemination on agricultural practices, household incomes, social network, risk coping mechanism and caste disparity. India's development priorities include poverty reduction and faster, more inclusive growth. Due to widespread rural poverty and high population growth, India must increase agricultural productivity. In the current debate among academics and policy makers on inclusive growth in India, there is a growing concern that poor people, especially in rural India, have benefited very little from rapid economic growth. Asymmetric information coupled with poor skill sets are considered the root cause, and inability of the rural poor to take advantage of opportunities in the markets, created by technology advancement and policy changes. Addressing the problem of asymmetric information is expected to empower the rural poor to take advantage of the market opportunities as well as overcome the skill set deficits in the long run, and therefore, enhances inclusiveness. The action research proposed in the current project using experimental methodology does precisely this - benefits the rural community directly, where e-governance facilities installed and access to range of information provided. The information will include both public and private services in the areas of education, health, agriculture, employment, financial inclusion, etc. These services will directly cater to the needs of the village inhabitants, local government as well as business. In recent years, there is a proliferation of government welfare programs for the poor to be delivered in the rural areas. But several of these services have not been delivered due to weak last mile organisational linkage. Proper design and use of the telecentres can help overcome this difficulty to a large extent and effectively reach the rural poor. With public access to information on these services, there can be some scope for transparency and lower corruption. Apart from directly benefiting the rural people, this project will inform the ongoing debate on some of the concerns raised.

India IN: Coverage: Social Insurance Programs: % of Population: 3rd Quintile data was reported at 18.584 % in 2011. India IN: Coverage: Social Insurance Programs: % of Population: 3rd Quintile data is updated yearly, averaging 18.584 % from Dec 2011 (Median) to 2011, with 1 observations. India IN: Coverage: Social Insurance Programs: % of Population: 3rd Quintile data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Smart Waste Management Market Outlook

The global smart waste management market size was estimated to be USD 2.4 billion in 2023 and is projected to reach USD 7.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.6% during the forecast period. This robust growth is driven by the increasing need for efficient waste collection and disposal solutions that leverage advanced technologies like IoT, AI, and data analytics. The rising global population and the consequent increase in waste generation have made traditional waste management methods inefficient, resulting in greater adoption of smart waste management solutions. The focus on sustainability and environmental preservation is also pushing governments and private entities to invest in smarter and more effective waste management systems.

One of the primary growth factors for the smart waste management market is the increasing urbanization across the globe, which leads to higher waste production rates in urban areas. As more people migrate to urban centers, the volume of waste generated by residential, commercial, and industrial sectors rises significantly. This creates an urgent need for efficient waste management solutions that can handle increased waste volumes while minimizing environmental impact. Moreover, governments are increasingly implementing stringent regulations regarding waste management, further driving the adoption of smart waste solutions that can ensure compliance with these regulations. This regulatory pressure, combined with the demand for more sustainable urban living conditions, is leading to higher investments in smart waste management technologies.

Another significant factor contributing to the market's growth is the advancement and integration of technology in waste management processes. The development of smart sensors, RFID technology, and data analytics tools has revolutionized the way waste management operations are conducted. These technologies enable real-time monitoring of waste levels, optimization of collection routes, and predictive analytics for better decision-making. Additionally, the integration of IoT in waste management allows for seamless communication between waste bins, collection trucks, and central monitoring systems, enhancing efficiency and reducing costs. As technological innovation continues to progress, the capabilities and benefits of smart waste management systems are expected to expand, propelling market growth.

The growing awareness and emphasis on environmental sustainability and circular economy principles are also fueling the demand for smart waste management solutions. Modern consumers and businesses are increasingly conscious of their environmental footprint and are demanding waste management practices that reduce landfill usage, promote recycling, and enable resource recovery. Smart waste management systems facilitate these practices by providing the tools required for effective sorting, recycling, and energy recovery from waste materials. As sustainability becomes a major focus for both policy makers and corporations, the market for smart waste management is anticipated to grow significantly as these solutions align with global environmental goals and initiatives.

Regionally, North America currently holds a significant share in the smart waste management market, largely due to well-established waste management infrastructure and early adoption of smart technologies. The region's strong regulatory framework and governmental support for sustainable waste management initiatives further boost market growth. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, rising population, and increased investments in smart city projects. Countries like China and India are at the forefront of adopting innovative waste management solutions to tackle the challenges posed by mounting waste generation, providing substantial growth opportunities for the market. Europe also represents a significant market for smart waste management, supported by stringent environmental regulations and a strong focus on recycling and resource recovery.

Component Analysis

The smart waste management market is segmented by components into hardware, software, and services. The hardware segment includes smart bins, sensors, and RFID tags, which are essential for implementing intelligent waste management systems. These devices are crucial as they provide the real-time data needed to optimize waste collec

According to the 76th round of the NSO survey conducted between July and December 2018, Bihar had a higher percentage of disabled men with a certificate of disability at 36.1 percent. The disability certificate was issued by the medical board to persons with more than 40 percent of any disability. This provides eligibility to apply for facilities, concessions and other benefits provided under various schemes.

This dataset corresponds to paper titled "A Mathematical Model for COVID-19 Considering Waning Immunity, Vaccination and Control Measures". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

The Indian Clinical Trials Market size was valued at USD 2.21 billion in 2023 and is projected to reach USD 3.86 billion by 2032, exhibiting a CAGR of 8.3 % during the forecasts period. The Indian Clinical Trials’ market therefore entails research studies to determine the BLA in exposing the efficacy, safety and advantages of the drug, biological or a medical device among the Indian people. Despite being challenged by innumeracy and response rate issues, it plays an important role in clinician development programmes because of the patient population variation in India, the availability of a large treatment naive population, and the low cost of research. Clinical research in India ranges from phase-I to phase-IV and post marketing studies to meet different therapeutic segments including, oncology, cardiovascular, and infectitious. Other trends include growth in partnership between domestic corporate structures as well as global ones, with several research establishments in India, improvements in the existing guidelines, and trends toward the use of technical solutions in data storage and the global recruitment of patients. The market growth is brought about by India’s strong healthcare facilities, talented population and geographical location concerning clinical research solutions.

Gene flow is a critical ecological process that must be maintained in order to counteract the detrimental effects of genetic drift in sub-divided populations, with conservation benefits ranging from promoting the persistence of small populations to spreading adaptive traits in changing environments. We evaluated historical and contemporary gene flow and effective population sizes of leopards in a landscape in central India using non-invasive sampling. Despite the dramatic changes in land use patterns in this landscape through recent times, we did not detect any signs that the leopard populations have been through a genetic bottleneck and they appear to have maintained migration-drift equilibrium. We found that historical levels of gene flow (mean mh = 0.07) were significantly higher than contemporary levels (mean mc = 0.03) and populations with large effective population sizes (Satpura and Kanha Tiger Reserves) are the larger exporters of migrants at both time scales. The greatest decline in historical versus contemporary gene flow is between pairs of reserves that are currently not connected by forest corridors (i.e, Melghat-Pench mh-mc= 0.063; and Kanha-Satpura mh-mc= 0.054). We attribute this reduction in gene flow to accelerated fragmentation and habitat alteration in the landscape over the past few centuries, and suggest protection of forest corridors to maintain gene flow in this landscape.

Odds of multiple logistic regression for PCF.

Description of logistic regression models.