Random Sampling Strategy Research Articles

Efficient population mean estimation via stratified sampling with dual auxiliary information: A real estate perspective

Auxiliary information is an essential component in the field of survey sampling since it enables precise estimation of population parameters like mean, variance, distribution function, and so on, which in turn guarantees the best possible outcomes. In order to estimate the population mean of a study variable, this study makes use of auxiliary information in a two-fold approach. Through a stratified random sampling scheme, we introduce a novel class of estimators that utilize auxiliary information and their corresponding ranks. By conducting a thorough evaluation based on metrics such as mean square error and percentage relative efficiency, these proposed estimators have been shown to be effective in the estimation process. Empirical validation is conducted using a real dataset sourced from the domain of real estate. Exploring the relationship between Assessed Value (X) and Sale Amount (Y) during a five-year period extending from 2017 to 2021 is the primary emphasis of the empirical validation process, which is carried out with the assistance of a real dataset of real estate data. Furthermore, in order to demonstrate that our suggested estimator is superior to conventional unbiased estimators, as well as traditional regression estimators and other estimators that have been considered in the literature, a full simulation analysis is carried out. Our proposed estimator appears to be the most effective choice after being subjected to a comparison study against a variety of preexisting approaches. The findings of this study not only make a significant contribution to the development of the methodology of survey sampling but also offer vital insights for predictive modeling within the real estate sector.

The Effects of Sheng’ Vocabulary Usage in the Learning of Kiswahili Grammar in Public Secondary Schools in Kenya

Purpose: Sheng’ is a linguistic code based primarily on the Kiswahili structure and grammar with the lexicon drawn from Kiswahili, English and the various ethnic languages of Kenya; that are mostly spoken in towns and other urban areas. The purpose of this paper was to examine the effects of Sheng’ vocabulary on the learning of Kiswahili grammar in public secondary schools in Kenya. Methodology: Krejcie & Morgan sampling table was used to sample the schools. Stratified random sampling, simple random sampling and purposive strategy were used to sample the public secondary schools to participate. Krejcie and Morgan sampling table was used to sample 55 teachers of Kiswahili and 360 students to take part in the study. Data was collected using observation schedules, focused group discussion guides and a writing task for students. Questionnaires and interview guides were used for teachers of Kiswahili. Qualitative data collected was analyzed thematically to generate information that was summarized in graphics and discussed in narrative form. Quantitative data was analyzed with the help of Statistical package for social Studies. Findings: The findings of the study further revealed that Sheng’ vocabulary influenced written Kiswahili grammar as learners wrote Sheng’ words whereas they were expected to write standard Kiswahili. Unique Contribution to Theory, Policy, and Practice: School administrators should come up with a school-based language policy so as to control learners’ language environment. The ministry of education should encourage the culture of drama, music and debate competition among schools. The Kenya Institute of Curriculum Development should also ensure language use is stipulated in Kiswahili curriculum and Sheng’ be considered as an independent language code in the country.

Parenting styles and substance abuse among Muslim students in secondary schools in Nakaloke Town Council, Mbale City, Uganda

Purpose: The study examined the relationship between parenting styles and substance abuse among Muslim students in secondary schools in Nakaloke Town Council, Mbale City, Uganda. Methodology: The study adopted a cross-sectional survey design with both quantitative and qualitative approaches. A sample of 322 students and 5 parents were selected using simple random and convenience sampling strategies respectively. A self-administered questionnaire for students and an interview guide for parents were the instruments used during data collection. Quantitative data was analyzed using correlation and multiple regressions while qualitative data was based on thematic analysis. Findings: The study revealed that there is a statistically significant relationship between authoritarian parenting style and substance abuse (r= -.850, p<.05), authoritative parenting style and substance abuse (r= -.266, p<.05), and permissive parenting style and substance abuse (r=.570, p<.05). Overall, parenting styles indicate a positive and strong effect on substance abuse (R2 = .862, p < .05). Unique contribution to theory, policy and practice: The study recommended that parents should be stricter with children so that they can behave well. Additionally, parents should always discuss their expectations and decisions with their children to help them understand why they are asked not to do something. Lastly, parents should not allow their children to do whatever they want without some control and monitoring being done.

Brain-Drain in Health Sector: Towards Sustainable Healthcare Development in Nigeria: A Study of Selected State Hospitals in Ogun State.

Abstract: This study investigates the impact of brain drain in the health sector on sustainable healthcare development in Ogun State, Nigeria, focusing specifically on selected state hospitals. The research was conducted across several state hospitals with the entire medical staff forming the target population. Utilizing a random sampling strategy, data were collected from a sample of 278 healthcare professionals, determined through Taro Yamane sampling techniques from a population of 912. Data collection was facilitated through a systematically designed containing 26-item questionnaire employing a four-point Likert scale. The reliability instrument was confirmed with a Cronbach alpha statistic, while its validity was assessed using component factor analysis. Descriptive statistics summarized the data, and inferential statistics were derived using simple linear regression analysis. The findings reveal a significant brain drain phenomenon, driven by factors such as inadequate remuneration, subpar working conditions, and limited professional growth opportunities. This has resulted in a critical shortage of healthcare providers, negatively impacting the quality and accessibility of healthcare services in Ogun state. The regression analysis further illustrates a strong correlation between brain drain and the deterioration of healthcare service delivery, which in turn hampers the region's progress towards sustainable healthcare development. The study recommends that the level of poverty must be reduce in the country; high pay incentives must be adequately provided by the government; initiatives that support youth in realizing their full potential must be developed; and a sound macroeconomic plan must be Implemented.

Few-shot learning with long-tailed labels

Few-Shot Learning (FSL) is a challenging classification task in machine learning, and it aims to recognize unseen examples of new classes with only a few labeled reference examples (i.e., the support set). The training phase of FSL typically requires a large amount of labeled examples (i.e., the base set) to effectively learn transferable knowledge, but it is usually difficult to obtain sufficient data annotation in practical applications. Existing semi-supervised FSL approaches can learn generalizable representations from partly labeled data, yet they do not sufficiently consider the real distribution of those labeled data. In this paper, we propose a new problem setting termed Few-Shot Learning with Long-Tailed Labels (FSL-LTL) to further consider a more practical semi-supervised scenario where the labeled examples are long-tailed. To effectively address this new problem, we build a novel two-stage training framework dubbed Reweighted Contrastive Embedding (RCE). In the first stage of RCE, we adopt the popular contrastive learning framework to pre-train a reliable network in a self-supervised manner. In the second stage, we integrate the semi-supervised empirical risk into a Weighted Random Sampling (WRS) strategy to fine-tune the pre-trained backbone with the aid of a consistency regularization. Experimental results demonstrate the feasibility of the proposed FSL-LTL problem setting and the superiority of our new RCE method over existing FSL approaches and semi-supervised learning methods. These results also suggest that the RCE approach is a promising solution for addressing the new FSL-LTL problem.

Comparison of 3 methods characterizing H2S exposure in water and wastewater management work.

This study evaluates the effectiveness of self-assessed exposure (SAE) data collection for characterization of hydrogen sulfide (H2S) risks in water and wastewater management, challenging the adequacy of traditional random or campaign sampling strategies. We compared 3 datasets derived from distinct strategies: expert data with activity metadata (A), SAE without metadata (B), and SAE with logbook metadata (C). The findings reveal that standard practices of random sampling (dataset A) fail to capture the sporadic nature of H2S exposure. Instead, SAE methods enhanced by logbook metadata and supported by reliable detection and calibration infrastructure (datasets B and C) are more effective. When assessing risk, particularly peak exposure risks, it is crucial to adopt measures that capture exposure variability, such as the range and standard deviations. This finer assessment is vital where high H2S peaks occur in confined spaces. Risk assessment should incorporate indices that account for peak exposure, utilizing variability measures like range and standard or geometric standard deviation to reflect the actual risk more accurately. For large datasets, a histogram is just as useful as statistical measures. This approach has revealed that not only wastewater workers but also water distribution network workers, can face unexpectedly high H2S levels when accessing confined underground spaces. Our research underscores the need for continuous monitoring with personal electrochemical gas detector alarm systems, particularly in environments with variable and potentially hazardous exposure levels.

Veterinary Drug Use Pattern in livestock and Its Public Health Significance, Gondar Town Veterinary Clinic, Northern Ethiopia

A cross-sectional and retrospective study design and simple random sampling strategy was conducted in Gondar town veterinary clinic, northern Ethiopia with the aim of evaluating drug use pattern and show public health significance of irrational veterinary drug use. A total of 2117 drugs were prescribed to 1717 veterinary patients randomly selected from the case registry in this study. The result indicates average number of drugs prescribed per case was 1.23 and the maximum of three drugs was prescribed. The percentages of antimicrobial, anthelmintic, endectocide and other drugs prescribed were 1182 (55.7%), 481 (22.7%), 426 (20.1%), and 28 (1.3%), respectively. All patients were treated without correct laboratory support. As a result, antimicrobials were inappropriately prescribed in 1.4% of viral diseases, 0.2% of external parasitic diseases, 0.8% of internal parasitic diseases, 0.3% of metabolic diseases, 0.1% of fungal diseases, and anthelmintics in 0.1% of bacterial diseases and 0.5% of external parasitic diseases. 2013/2117 (95.1%) the route of administration of the prescribed drugs was not stated. Clinic professionals were 80% animal health assistants and 20% veterinarians. The results reveal problems with correct diagnosis, low level of education of prescribers, few essential drugs, absence of standard veterinary drug lists, and inappropriate drug use; these all principals to public health significance. Therefore, the availability of key essential drugs should improve and available drugs be prescribed with its appropriate doses, routes, and regimens. Veterinarians should be made aware of the irrational use of veterinary drugs and their public health implications.

Taste Preference for Salt Predicts Salt Intake in a Chinese Population.

This study describes the association between taste preference for salt and actual salt intake, thus guiding and refining personal and public health campaigns designed to lower salt intake in China. A cross-sectional survey of 1489 residents aged 18 to 69 years was conducted in 2017 in China. A multistage random sampling strategy was used, and a combination of questionnaires and physical and laboratory measurements were conducted to collect baseline characteristics and knowledge, attitudes, and behavior (KAB) related to salt. A 24 h urine collection was obtained for sodium and potassium excretion analysis. Participants were divided into two groups, light taste preference and salty taste preference, according to their answer to the question "Compared to others, how do you think your taste preference is for salt?". The mean age of the 1489 participants was 46.26 years, 48.9% were males, over 1/3 (35.7%) were identified as hypertensive, and 317 (21.3%) self-reported a salty taste preference. The mean of 24 h urinary sodium excretion was 167.32 mmol/24 h, corresponding to 9.79 g salt/d intake, and the sodium-to-potassium ratio (Na/K) was 4.90. The 24 h urinary sodium excretion of salty taste preference (177.06 mmol/24 h) was significantly higher than that of light taste preference (164.69 mmol/24 h). The multiple logistic regression analysis showed that the salty taste preference group had significantly higher 24 h urinary sodium (ORa(95%CI) = 1.004(1.002-1.006)), diastolic blood pressure (DBP), proportion of greasy food preference, and drinking levels, but lower potassium excretion, response levels to most KAB questions, and regular physical activity compared to the light taste preference group. Self-reported taste preference for salt predicted actual salt intake, which was verified by 24 h urinary sodium monitoring. Taste preference for salt could be used as a proxy for intake in terms of targeted salt intake, nutrition, and health education.

A Comparative Study of Household Water, Sanitation and Hygiene (WASH) Covariates Across Socioeconomic Strata

Background: Water, sanitation, and hygiene (WASH) are essential for a healthy and productive life. High-quality access to these services reduces disease occurrence and spread in communities. Developing countries, including Pakistan, face significant challenges in providing these facilities. This study aims to highlight WASH-related service disparities in Pakistani households based on socioeconomic strata. Methodology: A stratified random sampling strategy was used, with a sample size of 1200 households based on the PDHS 2019 special survey. Descriptive analysis and chi-square tests were conducted to assess the association between wealth quintiles and WASH access. Results: The study found that 82.4% of households had access to improved sources of drinking water. Only 4.8% of households in the lowest quintile had access to improved drinking water sources. Water unavailability for at least one day per week was 13% higher among the lower quintiles. Forty-four percent of households using improved sanitation facilities belonged to the two highest strata, while poorer households predominantly used unimproved sanitation facilities. Conclusion: Lower-quality WASH access is more prevalent among lower wealth quintiles, increasing the risk of disease and infection. Further research is needed to inform policymakers and develop plans to reduce inequalities in access to WASH services.

Farmer’s environmental orientation as an antecedent to the intention for adopting conservational agriculture practices: the moderation analysis

PurposePakistan has long been regarded as one of the most vulnerable countries to climate change. The Food and Agriculture Organisation of the United Nations promotes conservational agricultural practices (CAP); however, they received little attention. Therefore, this study aims to explore the antecedents of farmers’ intention to adopt CAP with empirical evidence to enhance CAP in developing countries.Design/methodology/approachUsing a random sampling strategy, the data has been gathered from 483 Pakistani’s farmers of the most agriculture-producing province, Punjab and Sindh via a questionnaire survey. Regression-analysis (Haye’s process approach) is implied for testing the hypothesis.FindingsThe findings indicated that a farmer’s environmental orientation positively affects the farmer’s intention to adopt CAP. Furthermore, the farmer’s attitude towards agricultural production and the farmer’s belief in climate change also positively moderate the relationship.Practical implicationsBased on findings, this research suggests a need for efforts by the government to encourage farmers to engage themselves in technical support for the adoption of CAP. The educational campaigns and training sessions need to be arranged by the government for this purpose. This may help the farmers to adopt strategies relating to climate change concerning their education, credit access and extension services.Originality/valueThis paper explores the antecedents of farmers' intention for CAP in Pakistan. The empirical evidence previously missing in the body of knowledge will support the governments, researchers and FAO to establish a mechanism for enhancing CAP in developing countries like Pakistan. Further research is recommended to explore the outcomes of farmers' intentions to adopt more CAP to gauge the effectiveness of adaptation strategies

Unlocking Innovation: How absorptive capability unleashes the potential of motivation and leadership to catalyze innovative work behaviors in Batam.

The significance of innovative work behavior is growing as organizations strive to maintain their competitiveness by hiring freelancer employees. This study aimed to examine how absorptive capability mediates the connections between job motivation, transformational leadership, and innovation work behavior in freelancers working in the oil and gas industry. A quantitative methodology was employed through an online questionnaire, using a random sampling strategy involving 250 respondents. Furthermore, data analysis was performed using structural equation modelling. The findings indicate that job motivation and transformational leadership did not have a direct impact on innovation work behavior. However, they did have an indirect beneficial influence mediated by absorptive capability. This study revealed that absorptive skills play a vital role in facilitating the translation of job drive and transformational leadership into innovative work behaviors. The practical consequences involve firms embracing transformational leadership and establishing training programs to enhance employees' absorptive potential, thereby promoting innovation. Ultimately, cultivating absorptive skills is crucial for leadership styles and motivational elements to be evident in innovative work behaviors.

Does Patient Satisfaction and Trust Matter in the Relationship between Service Quality and Patient Loyalty in the Ghanaian Health Sector?

In today’s fast-paced society, patients expect high-quality healthcare services that meet their unique health requirements. Similarly, healthcare providers emphasize quality care, utilization of advanced technologies, and promoting a culture of excellence to deliver the best possible outcomes for their clients. This study investigated how patients’ satisfaction and trust influence the relationship between service quality and patient loyalty. The theory that underpins this study was the Anderson Health Belief Model of health services use. The research adopts a quantitative descriptive research design and used a simple random sampling strategy to select 254 respondents from a target population of 1,384 who have patronised the services of the Upper East Regional Hospital in Ghana at least twice. A survey questionnaire was designed in the form of a five-point Likert scale to collect data while Structural Equation Modeling (SEM) was utilised to analyze the data. The study's findings showed a significant and positive correlation between service quality characteristics and patient loyalty. Patient satisfaction mediates the relationship between service quality and patient loyalty. However, trust did not moderate the relationship between patient satisfaction and patient loyalty. It is recommended that healthcare organizations, in particular public health facilities, should be aware of the importance that patients place on quality dimensions and use this knowledge to improve upon patient loyalty and satisfaction.

Ratio-type estimator of the population mean in stratified sampling based on the calibration approach

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al. (2003) together its ratio-type estimators, as well as Nidhi et al. (2017) and Khare et al. (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators.

Improving Cognitive Function of the Elderly through Tai Chi Exercise Program and Cognitive Stimulation

The increasing proportion of elderly followed by the increasing life expectancy has improved the prevalence of various chronic-degenerative diseases and dementia. The promotive efforts using exercise programs, such as Tai Chi and cognitive therapy, significantly increase the physical fitness, mental well-being, and cognitive function of the elderly. This research aimed to predict the effectiveness of exercise programs for particular groups living in nursing homes. The method of this research was a quasi-experiment with a control group design. Samples are elderly living in the nursing home. The research samples were 116 elderlies living in nursing homes in Jakarta and South Sumatra. They were selected using a random sampling strategy. Statistical tests employed a dependent t-test, independent t-test, and multiple linear regression test. This study revealed that the intervention group showed different cognitive function average values before and after implementing the exercise programs (p-value = 0.001). In contrast, the control group did not show any differences. The intervention and control groups showed different cognitive function mean values after applying the exercise program (p-value = 0.057). Another finding denoted that the length of stay in the nursing home affected the elderly's cognitive functions). This research recommends policymakers and healthcare personnel use the developed exercise program to perform complementary therapy, empower the elderly, and be replaced by many orders of the healthcare system.

Domain Adaptation and Semantic Drawing Driven Sketch-to-Photo Retrieval using Collaborative Generative Representation Learning

Abstract: Sketch-based face recognition is an interesting task in vision and multimedia research yet it is quite challenging due to the great difference between face photos and sketches. In this paper we propose a novel approach for photo-sketch generation aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars we develop a fully convolutional network to learn the end-to-end photosketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning in which the architecture is stacked by only convolutional kernels of very small sizes. The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information

A New Sampling Scheme for an Improved Monitoring of the Process Mean

ABSTRACT This article introduces an innovative sampling scheme, the median sampling (MS), utilizing individual observations over time to efficiently estimate the mean of a process characterized by a symmetric (non-uniform) probability distribution. The mean estimator based on MS is not only unbiased but also boasts enhanced precision compared to its simple random sampling-based counterpart. Moreover, a new EWMA chart based on the mean estimator within the MS scheme is proposed. The performance of the EWMA charts, derived from both simple random sampling (SRS) and MS schemes, is evaluated using the metrics of steady-state average run-length and average number of items-to-signal. The findings underscore the superiority of the EWMA-MS chart over the EWMA-SRS chart. Additionally, as the magnitude of ranking errors escalates, the behavior of the EWMA-MS chart converges toward that of the EWMA-SRS chart. The practical implementation of the newly introduced EWMA chart is demonstrated through an illustrative example.

Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence.

Change-point detection is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between different states, each characterized by its distinct data distribution. Precise identification of change points in time series omics data can provide insights into the dynamic and temporal characteristics inherent to complex biological systems. Many change-point detection methods have traditionally focused on the direct estimation of data distributions. However, these approaches become unrealistic in high-dimensional data analysis. Density ratio methods have emerged as promising approaches for change-point detection since estimating density ratios is easier than directly estimating individual densities. Nevertheless, the divergence measures used in these methods may suffer from numerical instability during computation. Additionally, the most popular -relative Pearson divergence cannot measure the dissimilarity between two distributions of data but a mixture of distributions. To overcome the limitations of existing density ratio-based methods, we propose a novel approach called the Pearson-like scaled-Bregman divergence-based (PLsBD) density ratio estimation method for change-point detection. Our theoretical studies derive an analytical expression for the Pearson-like scaled Bregman divergence using a mixture measure. We integrate the PLsBD with a kernel regression model and apply a random sampling strategy to identify change points in both synthetic data and real-world high-dimensional genomics data of Drosophila. Our PLsBD method demonstrates superior performance compared to many other change-point detection methods.

A sampling greedy average regularized Kaczmarz method for tensor recovery

AbstractRecently, a regularized Kaczmarz method has been proposed to solve tensor recovery problems. In this article, we propose a sampling greedy average regularized Kaczmarz method. This method can be viewed as a block or mini‐batch version of the regularized Kaczmarz method, which is based on averaging several regularized Kaczmarz steps with a constant or adaptive extrapolated step size. Also, it is equipped with a sampling greedy strategy to select the working tensor slices from the sensing tensor. We prove that our new method converges linearly in expectation and show that the sampling greedy strategy can exhibit an accelerated convergence rate compared to the random sampling strategy. Numerical experiments are carried out to show the feasibility and efficiency of our new method on various signal/image recovery problems, including sparse signal recovery, image inpainting, and image deconvolution.

Novel process optimization based on machine learning: A study on biohydrogen production from waste resources

The biomass poultry litter and sewage sludge co-gasification is a good thermochemical method to produce hydrogen energy meanwhile to mitigate the consumption of fossil fuels. However, the operation optimization in the complex process system is important while computationally difficult because of high nonlinearity and many first-principle constraints. To address the optimization of this biohydrogen production process, a methodology framework for achieving the optimal operations is thus presented. The complete process system is first simulated and decomposed into upstream gasification process and downstream hydrogen purification for reducing computational complexity through the thermodynamic equilibrium-based simulation. Totally 1400 data points by pairing total 15 operating conditions with a composite sustainability index objective are generated through the random sampling and data classification strategies, which are then used for the construction of the artificial neural network (ANN)-based prediction models. ANN models of two subprocesses demonstrate the satisfactory prediction accuracy with R2 value of 0.98 and 0.99, respectively, which are then integrated with mixed-integer linear programming (MILP) for the optimization of upstream process and downstream process step-by-step. The MILP problems based on the ANN models are solved with lower optimization time of 45∼100 s compared to the heuristic algorithm optimization (3–6 h) based on the thermodynamic equilibrium-based simulation. The optimal sustainability index values of two processes are 0.80 and 0.91 which are both improved compared to the existing optimization results (0.79 and 0.84). This study emphasizes the optimization potential of the integration approach of machine learning-based modelling and mathematical programming for developing the optimal waste-to-energy processes.

On moving average based location charts under modified successive sampling

Ceramics are made up of water, clay, and powders. These are categorized as non-metallic and inorganic materials. It is revealed in the literature that Longquan celadon glaze had irregular cracks in glaze layers due to the relatively high content of $Na_{2}O$. Therefore, it is necessary to monitor the influence of $Na_{2}O$ in the ceramic process. Control charts are a possible tool to monitor the changes in the ceramic process. For single event issues, simple random sampling strategy is utilized; however, modified successive sampling is preferred as the favored sampling strategy at regular intervals of time when the quality of any product is evaluated. Hence, this paper is designed to propose moving average $M{A_{MSS\left( S \right)}}$ and double moving average $DM{A_{MSS\left( S \right)}}$ based control charts to detect small to moderate location shifts using the modified successive sampling technique. We have highlighted the performance evaluations of designed control charts with respect to run-length metrics, and their comparison has been made with the existing $Shewhar{t_{MSS\left( S \right)}}\;$control chart. The results revealed that the $DM{A_{MSS\left( S \right)}}$ performs more efficiently as compared to the $Shewhar{t_{MSS\left( S \right)}}$ and $M{A_{MSS\left( S \right)}}\;$control charts. Further, to demonstrate the application of the designed charts, a dataset of the chemical composition of the ceramic is also utilized.