Abstract

The 2023-24 Lesotho Demographic and Health Survey (2023-24 LDHS) is designed to provide data for monitoring the population and health situation in Lesotho. The 2023-24 LDHS is the 4th Demographic and Health Survey conducted in Lesotho since 2004.

The primary objective of the 2023–24 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, awareness and behaviour regarding HIV and AIDS and other sexually transmitted infections (STIs), other health issues (including tuberculosis) and chronic diseases, adult mortality (including maternal mortality), mental health and well-being, and gender-based violence. In addition, the 2023–24 LDHS provides estimates of anaemia prevalence among children age 6–59 months and adults as well as estimates of hypertension and diabetes among adults.

The information collected through the 2023–24 LDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of Lesotho’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Lesotho.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2023–24 LDHS is based on the 2016 Population and Housing Census (2016 PHC), provided by the Lesotho Bureau of Statistics (BoS). The frame file is a complete list of all census enumeration areas (EAs) within Lesotho. An EA is a geographic area, usually a city block in an urban area or a village in a rural area, consisting of approximately 100 households. In rural areas, it may consist of one or more villages. Each EA serves as a counting unit for the population census and has a satellite map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2016 PHC. Lesotho is administratively divided into 10 districts; each district is subdivided into constituencies and each constituency into community councils.

The 2023–24 LDHS sample of households was stratified and selected independently in two stages. Each district was stratified into urban, peri-urban, and rural areas; this yielded 29 sampling strata because there are no peri-urban areas in Butha-Buthe. In the first sampling stage, 400 EAs were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was carried out in all of the selected sample EAs, and the resulting lists of households served as the sampling frame for the selection of households in the next stage.

In the second stage of selection, a fixed number of 25 households per cluster (EA) were selected with an equal probability systematic selection from the newly created household listing. All women age 15–49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Woman’s Questionnaire. In every other household, all men age 15–59 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Man’s Questionnaire. All households in the men’s subsample were eligible for the Biomarker Questionnaire.

Fifteen listing teams, each consisting of three listers/mappers and a supervisor, were deployed in the field to complete the listing operation. Training of the household listers/mappers took place from 28 to 30 June 2024. The household listing operation was carried out in all of the selected EAs from 5 to 26 July 2024. For each household, Global Positioning System (GPS) data were collected at the time of listing and during interviews.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2023–24 LDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Lesotho and were translated into Sesotho. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Cleaning operations

The survey data were collected using tablet computers running the Android operating system and Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A. English and Sesotho questionnaires were used for collecting data via CAPI. The CAPI programmes accepted only valid responses, automatically performed checks on ranges of values, skipped to the appropriate question based on the responses given, and checked the consistency of the data collected. Answers to the survey questions were entered into the tablets by each interviewer. Supervisors downloaded interview data to their tablet, checked the data for completeness, and monitored fieldwork progress.

Each day, after completion of interviews, field supervisors submitted data to the central server. Data were sent to the central office via secure internet data transfer. The data processing managers monitored the quality of the data received and downloaded completed data files for completed clusters into the system. ICF provided the CSPro software for data processing and technical assistance in the preparation of the data capture, data management, and data editing programmes. Secondary editing was conducted simultaneously with data collection. All technical support for data processing and use of the tablets was provided by ICF.

Data on course enrollment by College and Course Level. Display includes FTE distribution by Division of Student Major. Data are presented by Campus, College, and Domicile. When choosing a College, the Academic Course Department data are displayed. This data includes Spring 2023 Census.

Data on course enrollment by College and Course Level. Display includes FTE distribution by Division of Student Major. Data are presented by Campus, College, and Domicile. When choosing a College, the Academic Course Department data are displayed. This data includes Fall 2023 census.

According to Cognitive Market Research, The Global Military Simulators market size will grow at a compound annual growth rate (CAGR) of 5.90% from 2023 to 2030.

The demand for the military simulator market is rising due to the advanced technology platforms that enhance operational capabilities for military personnel, as well as the increase in territorial conflicts worldwide and the rise in defense expenditure.
Demand for ground simulation remains higher in the military simulator market.
The virtual training category held the highest military simulator market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific military simulator market will experience the strongest growth until 2030.

Advancements in Simulation Technologies to Provide Viable Market Output

The development of high-fidelity simulators that replicate real-life combat scenarios with exceptional accuracy. These simulators incorporate cutting-edge technologies such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) to create immersive training environments. The incorporation of AI algorithms enables simulators to adapt scenarios in real-time based on trainee performance, making training more dynamic and effective.

In January 2021, Top Aces Corp revealed the arrival of their initial shipment of F-16 aircraft. These planes have been specifically procured for military air training purposes, catering to pilots who fly the F-16s.

(Source:www.blueskynews.aero/issue-607/Top-Aces-F16-fighter-aircraft-receives-FAA-certification.html)

Furthermore, the integration of network-centric warfare concepts allows for joint training exercises involving multiple military branches and even international partners. These technological advancements not only enhance the realism of training but also significantly reduce the costs associated with live exercises, including fuel, equipment wear and tear, and logistics. As armed forces worldwide seek to maintain a high level of readiness while optimizing their budgets, the adoption of advanced simulators is expected to grow, driving the market's expansion.

Need for Enhanced Training and Readiness to Propel Market Growth

The increasing demand for improved training and preparedness in armed forces worldwide is driving the growth of the Military Simulators Market. With geopolitical tensions and the ever-changing nature of modern warfare, military personnel need to possess adaptability and proficiency in various scenarios. Simulators provide a secure and controlled environment for soldiers, pilots, and sailors to refine their skills, engage in complex missions, and enhance their decision-making capabilities. Additionally, simulators are capable of replicating a wide range of scenarios, including cyberattacks and electronic warfare, effectively preparing military personnel for the multifaceted nature of contemporary conflicts. As defense budgets prioritize readiness and training, the demand for military simulators continues to surge.

Market Dynamics of Military Simulators

Complexity and Integration Challenges to Hinder Market Growth

Military simulators are complex systems that often need to be integrated into existing military infrastructure and training programs. Achieving seamless integration can be challenging, especially when dealing with legacy systems or a diverse range of simulation technologies. Ensuring that simulators accurately replicate various military platforms and systems, from aircraft to ground vehicles, adds to the complexity. The need for interoperability and compatibility with other defense systems further complicates the implementation of simulators. These challenges can slow down adoption rates and hinder the expansion of the Military Simulators Market.

Impact of COVID–19 on the Military Simulators Market

The COVID-19 pandemic had a multifaceted impact on the military simulator market. While the demand for military simulators remained relatively steady due to the essential nature of military training, the market experienced disruptions in production, supply chains, and installations. The pandemic caused delays in the delivery of simulators, as manufacturing facilities had to implement safety measures and deal with workforce shortages. On the positive side, there was an increased interest in virtual and remote training solutions to comply with social distancing measure...

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages from the 19th century (1805-1898) censuses of Lausanne, Switzerland. The primary language of the documents is French, although many germanic names and toponyms are also found.

The training data are formatted and provided on the model of the Bentham dataset. The format thus simply consists in a list of jpeg images, one per text segments, and their corresponding transcription, stored in a txt file. The file naming convention is 'yyyy-ppp-n', where 'y' stands for the year of publication of the census, and 'p' for the page number.

The digitized documents are provided by the Archives of the City of Lausanne.

Please note that the annotation and extraction methodology, as well as the complete evaluation of performance, including HTR benchmark and post-correction performance is published in :

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Tabular dataset resulting from automatic extraction are also available on Zenodo :

Petitpierre R., Rappo L., Kramer M., di Lenardo I. (2023). 1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History. Zenodo. doi: 10.5281/zenodo.7711640

Data on course enrollment by College and Course Level. Display includes FTE distribution by Division of Student Major. Data are presented by Campus, College, and Domicile. When choosing a College, the Academic Course Department data are displayed. This data includes Summer 2023 Census.

The Global market for Dog Training Services was USD 27.6 billion in 2022 by Cognitive Market Research, and it is projected to expand at a CAGR of 9.8% from 2023 to 2030. How Are The Main Opportunities/Drivers Influencing The Dog Training Services Market?

People's Increasing Preference For Pet Companions is Driving the Market

Numerous benefits of owning a pet include improved health. They may increase opportunities for people to interact with others, go outside, and exercise. Regular pet interaction and walks can lower blood pressure, cholesterol, and triglyceride levels. Pets can aid in the management of loneliness and anxiety by offering companionship to people. Animals provide their owners with emotional support, especially during trying times. Numerous research suggests that having a pet nearby during a stressful situation may help reduce stress.

According to Pet Sitters International (PSI), which includes professional pet sitters, the majority of their clients are dog and cat owners in March 2022. 73% of PSI members provided services for birds, 70% for freshwater fish, 59% for reptiles and amphibians, and 75% of PSI members provided services for small animals.

(Source:assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/389817/44389_unact_animals_Fish_Amphibians_Reptiles_and_Cephalopod.pdf)

Need to Establish Effective Communication Between Pets and Owners is Boosting Market Growth

Through training, a pet can learn to recognize human motions and gestures, resulting in improved understanding and communication between the owner and the pet. Pet training helps to create a communication pathway between people and their animals by enabling people to ask their pets to perform specific activities and educate them how to respond appropriately. Pet training is a special form of bonding that may be included in a pet's daily routine.

In 2019, there were 20,413 businesses offering pet care services, according to the U.S. Census Bureau. In addition, the APPA estimates that in 2020, American pet owners spent almost 103 billion USD on their animals.

Growing Trend Of A Pet Companion

(Source:www.census.gov/library/stories/2020/02/spending-on-pet-care-services-doubled-in-last-decade.html)

The Factors are Limiting the Market's Growth for Dog Training Services

Difficulty of Adopting and Raising Pets is Limiting Market Expansion

Despite the fact that some cats and dogs are rather autonomous, they still require parental care for tasks like feeding, exercise, training, and grooming. More time and attention may be required for a breed that is more dependent or a pet that has a strong affinity with other animals. When an animal is adopted into the home, it is almost like having a child. Breed study is essential before adoption. This would help the owner better predict the kind of attention the pet would need. Some animals need more care overall, including training and grooming. Along with breed, age must be taken into account.

Trend Factor for the Dog Training Services Market

As pet owners see their dogs as members of their family more and more and look for expert help in fostering proper conduct, health, and socialization, the market for dog training services is growing. Busy lifestyles and tech-savvy millennials and Gen Z are driving up demand for a wide range of delivery models, including traditional group and one-on-one in-person sessions as well as convenience-focused virtual and app-based training. In accordance with ethical and science-based methodologies, force-free and positive-reinforcement methods are now the norm. The use of technology-enhanced technologies, such as GPS trackers, wearable devices, smart collars, and AI-powered platforms, is revolutionizing training by providing real-time feedback, performance monitoring, and customized programs. Subscription and flexible payment options enhance access to specialized areas like therapy, service-dog training, breed-specific behavior work, and puppy socialization, which are growing. In addition, there is a growing trend towards holistic care, where trainers work with veterinarians and behaviorists to address both physical and mental health. These developments, which are fueled by pet humanization, technological breakthroughs, and changing consumer tastes, are indicative of continued market expansion.

Impact of COVID-19 on The Dog Training Service...

Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological and logistical factors. Advances in technology allows the utilisation of more autonomous video recording methods (i.e. Remote Operated Vehicles (ROV)) which addresses these limitations. This study used a transect-wise UVC coupled with diver operated videos (DOV). For the video analysis, a comprehensive fully automated pipeline was developed to extract frames from DOV and perform colour correction. This pipeline integrates a YOLO-based model to detect 20 Mediterranean fish species and validate the presence or absence of each species within individual transects. This study was conducted to evaluate the feasibility of using video-based methods for UVC with minimal human-input. The result of automa..., 1. Study area and data collection The training dataset (DATAT ) was gathered in eight different locations in the Mediterranean Sea along the French Riviera, following the same UVC protocol on each site (Harmelin-Vivien et al., 1985). The depth ranged from 1-37m and was carried out during the whole year in 2022 (cold and warm season) to cover the full range of conditions and possibilities of fish occurrences. The experimental dataset (DATAE) was recorded in October 2023 in and around two protected areas, one no-take zone (Cap Roux) and one Natura2000 site (Corniche Varoise), which both have elevated biodiversity. The specific coordinates and meta data can be found in the supplementary material (Table S1). A total of 64 videos, each corresponding to a transect, from 14 sites (8 on seagrass meadows and 6 on rocky substrates) were evaluated and compared. Each site consists of 3 to 6 transects, depending on the availability of video recordings and UVC data from the divers. The videos were ob..., , # Data from: Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea

https://doi.org/10.5061/dryad.f7m0cfz6f

Description of the data and file structure

1. Study area and data collection

The training dataset (DATA_T) was gathered in eight different locations in the Mediterranean Sea along the French Riviera, following the same UVC protocol on each site. The depth ranged from 1-37m and was carried out during the whole year in 2022 (cold and warm season) to cover the full range of conditions and possibilities of fish occurrences.

The experimental dataset (DATA_E) was recorded in October 2023 in and around two protected areas, one no-take zone (Cap Roux) and one Natura2000 site (Corniche Varoise), which both have elevated biodiversity. A total of 64 videos, each corresponding to a transect, from 14 sites (8 on seagrass meadows and 6 on rocky substrates) were evaluated and compared. Each site consists of...

Abstract

The National Sample Surveys (NSS) are conducted by the Government of India since 1950 to collect data on various socio-economic indicators employing scientific sampling methods. The seventy-ninth round of NSS will commence from July 2022. NSS 79th round is earmarked for collection of data for compilation of a number of SDG indicators through a „Comprehensive Annual Modular Survey (CAMS)" along with a survey on Ayurveda, Yoga, Naturopathy, Unani, Siddha, Sowa-Rigpa/Amchi and Homoeopathy (AYUSH). Comprehensive Annual Modular Survey (CAMS): CAMS is introduced to cater the emerging need of information on high-frequency socio-economic indicators that are not available from any other sources like administrative data, etc. CAMS will collect information required for the purpose of generating some SDG indicators and subindicators of Global Indices. This survey will be annual in which some of the modules may be repeated annually and some periodically with more than one year periodicity.

List of some SDG and sub-indicators of Global indices which will be generated from CAMS is given below: Proportion of population using safely managed drinking water services Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water Proportion of individuals who own a mobile phone, by sex Proportion of population covered by a mobile network, by technology Percentage of Household with a computer Percentage of individuals using internet during last 3 months, last 365 days Percentage of adult having an account at a formal financial institution Percentage of women having an account at a formal financial institution Proportion of children under 5 years of age whose births have been registered with a civil authority, by age Proportion of population that has convenient access (0.5 km/1 km) to public transport (low/ high-capacities) stop. Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sex Proportion of youth (aged 15–24 years) not in education, employment or training Mean year of schooling Out-of-pocket medical expenditure on hospitalization during last 365 days

Geographic coverage

15416 FSUs will be surveyed at all-India level for CAMS and AYUSH survey. The survey will cover the whole of the Indian union except the villages in Andaman and Nicobar Islands which are difficult to access.

Analysis unit

Randomly selected households based on sampling procedure and members of the household.

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Sampling procedure

Sample Design

Formation of sub-units (SUs): Rural areas: A rural village will be notionally divided into a number of sub-units (SU) of more or less equal population during the preparation of frame. Census 2011 population of villages will be projected by applying suitable growth rates and the number of SUs to be formed in a village will be determined apriori.

The above procedure of SU formation will be implemented in the villages with population more than or equal to 1000 as per Census 2011. In the remaining villages, no SU will be formed.

The number of SUs to be formed in the villages (with Census 2011 population 1000 or more) of the frame will be decided before selection of the samples following the criteria given below: projected population of the village no. of SUs to be formed less than 1200 1 1200 to 2399 2 2400 to 3599 3 3600 to 4799 4 4800 to 5999 5 .......and so on ....

Special case: For rural areas of (i) Himachal Pradesh, (ii) Sikkim, (iii) Andaman & Nicobar Islands, (iv) Uttarakhand (except four districts Dehradun, Nainital, Hardwar and Udham Singh Nagar), (v) Punch, Rajouri, Udhampur, Reasi, Doda, Kishtwar, Ramban of Jammu and Kashmir (vi) Leh and Kargil districts of Ladakh region and (vii) Idukki district of Kerala, numbers of SUs to be formed in a village will be determined in such a way that each SU contains 600 or less projected population. Further, SUs will not be formed in the villages in the above mentioned districts/States with population less than 500 as per Census 2011. In the remaining villages the number of SUs to be formed for these States/districts will be as follows: projected population of the village no. of SUs to be formed less than 600 1 600 to 1199 2 1200 to 1799 3 1800 to 2399 4 2400 to 2999 5 .......and so on ....

Urban areas: SUs will be formed in urban sector also. The procedure will be similar to that adopted in rural areas except that SUs will be formed on the basis of households in the UFS frame instead of population, since UFS frame does not have population. Each UFS block with number of households more than or equal to 250 will be divided into a number of SUs. In the remaining UFS blocks, no SU will be formed.

The number of SUs to be formed in the UFS blocks of the frame will be decided before selection of the samples following the criteria given below: number of households of the UFS block no. of SUs to be formed less than 250 1 250 to 499 2 500 to 749 3 750 to 999 4 1000 to 1249 5 .......and so on ....

Stratification of FSUs: Rural Sector: A Special Rural stratum, at all-India level, will be formed comprising all the uninhabited villages as per census 2011 belonging to all States/UT. For the remaining villages which are inhabited as per census 2011, districts will be basic geographical unit for stratum formation. Within each district, two Stratum will be formed: (a) The villages (i) within a distance of 5 Kms from the district headquarter or (ii) within a distance of 5 Kms from a city/town with more than 5 lakh population, will form a stratum (stratum 1). The information will be obtained from the village directory of census 2011. It will be the stratum 1 for a particular district. (b) Rest of the villages will constitute another stratum (stratum 2) of the particular district.

Urban Sector: Two or more strata will be formed in urban areas of each district: (i) each million plus city as per census 2011 will constitute separate stratum . Stratum no will be 01, 02, 03....,19 (ii) rest of the urban areas of the district. Stratum no will be 20.

Mode of data collection

Face-to-face [f2f]