Sampling Plan Research Articles

Exploring the value chain of organic pineapple of Assam, India

PurposeThe global organic market is expanding, and India is in an advantageous position with the highest number of organic producers worldwide. Although many articles have been published on the value chain of organic products from India, no significant studies were found related to the value chain analysis of organic pineapple. This study aims to know the various aspects of the organic pineapple value chain, i.e. network structure, value addition at various stages of chain actors, value chain upgradation and governance structure.Design/methodology/approachThe study is explorative in nature, and primary data from various actors involved in the chain is collected and analyzed. Primary data through a structured schedule and interviews are collected from farmers and traders. A multistage sampling plan has been adopted. A sample of 75 farmers was randomly selected from the study area. For traders, snowball sampling is used due to the nonavailability of the sampling frame. A total of 10 commission agents, 10 wholesalers and 20 retailers were thus selected for the study. For objectives 1 and 4, descriptive statistics are used. For objective 2, a modified formula described by (Murthy et al., 2007) is used to calculate farmer’s net price and marketing margin. For objective 3, Garrett’s ranking technique is used to identify various constraints in upgrading the organic pineapple value chain in Assam.FindingsThis study shows that the value chain of organic pineapple is in the initial stage and proper value addition is required to have a complete regulated value chain. Six marketing channel is identified, and products are sold through farmer producer company only in case of export and trade with distant buyers. The marketing efficiency for channels II and III is 1.69 and 0.99, respectively. The degree of value addition for channel II in the hands of the commission agent, wholesaler and retailer is 11.65%, 4.56% and 12.60%, respectively. In the various constraints in upgrading the value chain, farmers rank “policy support” as a major constraint. In the governance structure, trade with distant traders and exports is done formally and through written contracts.Research limitations/implicationsThe study performs value chain analysis of organic pineapple in Cachar district of Assam, India for the year January 2022–January 2023. Future studies are encouraged related to various aspects of the supply chain and value chain of organic pineapple from various northeastern states of India and other states.Practical implicationsThe study will help policymakers and key actors to know the existing chain and frame a well-coordinated and regulated value chain.Originality/valueThis study is one of the first study to explore the value chain of organic pineapple of Cachar district of Assam, India. Implementation of these findings can help various actors to strengthen the existing value chain.

Multi-objective optimization of concrete pumping S-pipe based on DEM and NSGA-II algorithm

In the process of fresh concrete pumping, the S-pipe plays a crucial role in the pumping system. The impact and scratching of concrete aggregate on the wall of S-pipe will cause the wear of the wall and lessen its service life. The structural design of the pumping S-pipe typically relies on experience, and the cost for experiments is too expensive. In addition, the influence mechanism of structural parameters on S-pipe wear is still unclear. In view of this, to reduce the wear of S-pipe and investigate the influence of structural parameters on the wear of S-pipe, the structural parameters of S-pipe (curvature radius r1 and r2, horizontal dimension l3, and inclination angle θ) were optimised through discrete element numerical simulation combined with the non-dominated sorting genetic algorithm-II (NSGA-II) in this paper. Firstly, the wear coefficient was determined by friction and wear test. The discrete element method (DEM) model of pumping suction unit was simplified, and the S-pipe was parameterised. Secondly, sample points and their exact values of the optimization objectives were obtained by Latin hypercube sampling (LHS) plan and DEM simulations, respectively. From these, a Kriging surrogate model was constructed. Taking the average wear rate and the maximum wear of the S-pipe as the optimization objectives, a multi-objective optimization design was performed based on the NSGA-II and the Kriging models. After optimization, a series of Pareto optimal S-pipe models were obtained. Comparing the optimised model with the original model, the average wear rate and maximum wear amount of the S-pipe were reduced by 9% and 26%, respectively.

Behavioral Biases and Fear of Missing Out Impact Investment Decisions in Thailand during COVID-19 Pandemic

Purpose – the purpose of this study is to explore the Behavioral Biases and Fear of Missing Out on the Impact on Investment Decisions in Thailand during the COVID-19 Pandemic. In this research, the researcher will accentuate. Design/Methodology/Approach – The researcher used numerous components to determine the level of impact of investment decision toward customer satisfaction, including research design, sampling plan, research instruments, the validity of the pilot test, methods of data gathering and procedures, and statistical treatment of data. Findings – this study arranged determinants that influence Behavioral Biases and Fear of Missing out Impact Investment Decisions of people in Thailand Covid-19 spread. Our research was about the relationship between employee involvement and motivation which are the keys to job satisfaction and affect job performance. Moreover, job performance is also got affected by remote working and job involvement of employees. Research Limitations/Implications – there are several limitations to investigating the factors that affect job performance during work from home. The previous studies are used for specific purposes. Moreover, there is a small amount of previous research on the topic due to work from home not being standard practice before the COVID-19 pandemic.

Acceptance sampling plan based on difference in difference estimator with application

An acceptance sampling plan has been designed in this study based on the Difference-in-Difference estimator. This plan is designed for the inspection of those product units whose life follows the normal distribution. The operating characteristic function is discussed for the two respective cases of the standard deviation known and unknown. The parameters of the proposed plan are determined by minimizing the sample size and followed by the satisfying optimization rule. The results are computed and tabulated for various parametric combinations of acceptable quality levels and limiting quality levels. The computations are performed by using R statistical programming software for all respective cases. The real-life application of the proposed sampling plan has been discussed and elaborated in detail.

The Safe Return of Face-to-Face Teaching in the Post-COVID-19 Era at a University in Southern Italy: Surface Monitoring as an Early Warning System

Environmental monitoring for SARS-CoV-2 has become a useful adjunct to clinical testing because it is widely available and relatively inexpensive. During the period May–December 2022 (spring–summer: May–September–autumn: October–December), we assessed the presence and viability of the virus on surfaces in university settings in the Apulia region (Southern Italy) after the resumption of face-to-face teaching activities and evaluated surface monitoring as an early warning system. The sampling plan provided for the selection of 75% of the surface types (e.g., student and teacher desks, computer, handrail) in different materials (plasticized wood, wood, metal, plastic) present in different environments. Overall, 5.4% of surfaces (all students’ desks) resulted in positive with RT-PCR and negative with viral culture. Greater contamination was found in the spring–summer period than in the autumn (χ2 test with Yates correction = 7.6003; p-value = 0.006). The Poisson regression model showed a direct association between the average number of COVID-19 cases among university students in the seven days following sampling and the percentage of SARS-CoV-2 positive swabs on sampling day and (Intercept = 5.32498; β = 0.01847; p < 0.001). Our results show that environmental monitoring for SARS-CoV-2, especially in crowded settings such as universities, could be a useful tool for early warning, even after the end of the COVID-19 emergency.

A New Cosine-Originated Probability Distribution with Symmetrical and Asymmetrical Behaviors: Repetitive Acceptance Sampling with Reliability Application

Several new acceptance sampling plans using various probability distribution methods have been developed in the literature. However, there is no published work on the design of new sampling plans using trigonometric-based probability distributions. In order to cover this amazing and fascinating research gap, we first introduce a novel probabilistic method called a new modified cosine-G method. A special member of the new modified cosine-G method, namely, a new modified cosine-Weibull distribution, is examined and implemented. The density function of the new model possesses symmetrical as well as asymmetrical behaviors. The usefulness and superior fitting power of the new modified cosine-Weibull distribution are demonstrated by analyzing an asymmetrical data set. Furthermore, based on the new modified cosine-Weibull distribution, we develop a new repetitive acceptance sampling strategy for attributes with specified shape parameters. Finally, a real-world application is presented to illustrate the proposed repetitive acceptance sampling strategy.

Decision‐making for thrips control in soybean fields using precision agriculture principles

AbstractIn precision agriculture, sub‐areas of fields with similar features are called management zones and the inputs applied similarly in these zones. Such notion of management zones is applicable to high‐output soybean fields where thrips are an emerging problem leading to losses up to 15%. Management zones are an agricultural management strategy that can be used for decision‐making for controlling thrips on soybean fields. Thus, a 2‐year study was carried out on commercial soybean fields aimed at developing a decision‐making system for controlling soybean thrips using management zones. Three control programmes were established and assessed: Cl‐conventional control with insecticide use in the entire field; IPM‐CS‐conventional sampling plan + spray upon reaching the economic injury level (EIL; 3.43 thrips/sample); and IPM‐MZ‐spraying only specific areas of the field where thrips density reached the EIL. The decision to control reached using the IPM‐CS and IPM‐MZ programmes was 3× lower than those of the CI, which incurred in 75% mode decision mistakes. Furthermore, the decision to spray insecticides based on analysis of data for Cl was also incorrect. In 8.5% (3.6% of treated and 4.9% of non‐treated) of the field, the decisions of the IPM‐CS programme were incorrect. The total cost of the Cl programme (US$ 11.4/ha) was higher than that of the IPM‐CS and IPM‐MZ programmes (US$ 3.2 ha−1). Therefore, owing to its technical, economic, and environmental advantages, the establishment of management zones seems worthy of incorporation in integrated thrips management decision‐making systems for soybean fields.

Enhanced impulsivity, poorer planning and rigid patterns when drawing in substance use disorder: a preliminary study

Neuropsychological assessment has uncovered deficits in several executive functions in substance use disorder (SUD) individuals. Nevertheless, research has reported moderate ecological validity in current neuropsychological paradigms. In this regard, drawing is a well-known cross-cutting task that integrates complex cognitive and affective processes. Therefore, its potential for improving the ecological validity of neuropsychological assessments has been outlined. The aim of the present study was threefold. First, we analyzed the impulsivity and planning capacity of SUD individuals (n = 16) compared to controls (n = 15) through a self-reported questionnaire and a neuropsychological paradigm. Second, we explored the differences between groups in drawing variables by means of the diagnostic drawing series, a validated drawing paradigm. Finally, we examined the relationship between the neuropsychological markers and the drawing variables. Compared to controls, SUD individuals reported higher impulsivity scores and worse planning capacity. Regarding drawing variables, SUD participants needed more time to complete the artwork, occupied more space with a predominant color and reported a lower tendency to use warmer and cooler colors than controls. Additionally, across the whole sample, higher impulsivity and worse planning capacity were related to a greater use of a predominant color. Our findings suggest difficulties in functions related to inhibitory control, as well as an alternative drawing pattern in SUD individuals. Remarkably, poor inhibitory control was associated with less variability in drawing. Together, the present preliminary study seems to reinforces the use of drawing as a valid tool for adding both diagnostic and therapeutic information to classical neuropsychological paradigms.

Palliative and end-of-life care curricula in prelicensure nursing education

BackgroundHospice and palliative care are generally not taught in medical or nursing schools. AimThe study aimed to discover the palliative care self-competence of pre-licensure nursing faculty and to establish if the palliative care competencies from the American Association of Colleges of Nursing are utilized within nursing programs in the United States (U.S.). MethodsSurveys (1710) were emailed to accredited programs using a systematic sampling plan, yielding a 4.3% response rate. ResultsParticipants (N = 73), with 42% from associate degree (ADN) programs and 58% from baccalaureate (BSN) programs, reported they possessed the ability to meet the 17 palliative care competencies. Still, only ten of the 17 competencies were included in at least 75% of the programs. There were no statistically significant differences in perceived competence between faculty that taught in ADN compared to BSN programs (p = 0.138). A weak to moderate relationship was found between the faculty participants' reported palliative care self-competency and the presence of palliative care competencies in pre-licensure nursing programs in the U.S. ConclusionsThe results suggest that faculty need staff development and display an urgency to re-invigorate the promotion of palliative care nursing education for faculty and students.

Association between Chewing Difficulty and Dietary Ca, Vitamin D, and Mg Intake in Korean Older Adults: 8th Korea National Health and Nutrition Examination Survey (KNHANES) (2020–2021)

Nutrition intake plays a pivotal role in chewing difficulty (CD). This cross-sectional descriptive study aims to explore the associations between CD and the dietary intake of calcium (Ca), vitamin D, and magnesium (Mg) in adults aged 65 and older, utilizing data from the 8th Korea National Health and Nutrition Examination Survey (2020–2021). The chewing function was assessed using a 5-point scale questionnaire that inquired about discomfort experienced during mastication. “Very uncomfortable” and “uncomfortable,” two of the five response options, were categorized as being indicative of CD. Dietary intake was assessed through 24 h dietary recall interviews, and nutrient calculations were based on the 10th revised edition of the Korean Food Composition Table. Data (N = 2942) were analyzed using descriptive statistics and multi-logistic regression analyses in a composite sample plan file. Among the 2942 subjects, groups with insufficient daily nutrient intake had significantly higher odds ratios (ORs) for CD compared to their counterparts. In men, the ORs for Ca (1.56), Mg (1.75), and the combined intake of Mg (1.64) with Ca were elevated. In women, the ORs for Ca (1.74), Mg (1.53), and the combined intake of Mg (1.43) with Ca showed similar trends. After adjusting for age, family income, and family size variables, men’s ORs for Mg (1.55) and the combined intake of Mg (1.55) with Ca remained elevated, while women’s OR for Ca (1.58), Mg (1.42), and the combined intake of Mg (1.34) with Ca remained significant. Dietary vitamin D intake did not significantly impact the OR for CD. After adjusting for natural tooth numbers, self-perceived oral health, and obesity, no significant association was found between CD and these nutrients. In conclusion, this study underscores the importance of promoting the recommended daily intake of magnesium alongside dietary calcium to address CD.

Spatio-temporal distribution of the leaf crown borer Eupalamides cyparissias in coconut (Cocus nucifera L.) plantation in a tropical region

The leaf crown borer Eupalamides cyparissias (Fabricius, 1777) is an agricultural pest that deserves attention in the cultivation of coconut (Cocos nucifera L.) and other economically important palm trees (Areacaceae). Several studies address the use of geostatistics to understand the attack of pest insects in agriculture, but there are no studies on the spatial and temporal patterns of E. cyparissias in coconut trees. The presence or absence of the borer was verified monthly in all coconut plants of a plot measuring 12.5 hectares, from July 2018 to October 2020. The occurrence of the borer was verified by the appearance of two main symptoms in the coconut trees. Dispersion patterns of E. cyparissias were studied using semivariograms to determine the most suitable spatial distribution model for the species. Kriging maps were created. Exponential and spherical semivariogram models showed the best fit to the patterns of spatial dispersion of the borer, showing that the attack on the coconut plantation occurs in an aggregate, and in foci, with a range of 20 to 160 metres. The climatic data had no significant correlation with the infestation rate. These results show that geostatistics are an important tool to define reliable sampling plans for integrated pest management. It is useful to assess the spatial and temporal distribution of E. cyparissias

Employees Job Anxiety - Challenges and Strategies to Deal at a Private Hospital in Kolar - A Case Study

Purpose: Job Anxiety among employees in a private hospital setting can have a number of negative effects on both individuals and the organization as a whole. It can lead to decreased Job satisfaction, increased burnout, decreased productivity, and reduced patient safety and treatment quality. Furthermore, untreated anxiety can lead to absenteeism, high employee turnover, and higher healthcare expenditures for the firm. The goal of this analysis is to explore employee Job Anxiety in the context of a private hospital and propose appropriate techniques. Design/Methodology/Approach: Descriptive statistics and stratified random sampling were employed. The methodology includes the study Plan, study setting up, sources of data, participants, gathering data process, size of the sample, sample plan of action, questionnaire framework, and data analysis. Finding/Result: As a result, there is an important desire to investigate and propose targeted techniques aimed specifically at addressing and mitigating staff Job Anxiety in private hospitals. Such initiatives should take into account the unique features of the private hospital setting, including healthcare personnel's different roles and responsibilities, organizational culture, and available resources. By filling this knowledge gap, private hospitals can help their staff's mental health, increase Job satisfaction, and improve patient care outcomes. The data analysis of this study verified that there is no significant difference in Job Anxiety levels among Male and Female employees at a private Hospital. Originality/Value: The worth of staff Job Anxiety and methods for dealing with it in a private hospital are important because of their contribution to knowledge and potential consequences for improving the general wellness of healthcare personnel and the facility itself. Paper Type: Case study-original research findings

Optimal and economic design of modified double sampling plan under new Weibull–Pareto distribution

PurposeThe purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.Design/methodology/approachThe authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.FindingsThe results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.Originality/valueThe proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

Construction of acceptance sampling schemes for exponential lifetime products with progressive type II right censoring

Ensuring product quality is crucial for enhancing a company's competitiveness, and product lifetime serves as a vital metric, particularly in the context of electronic goods, for evaluating product quality. To meet quality requirements and mitigate risks for both producers and consumers, thorough examinations of components and final products are necessary. Acceptance sampling plans are commonly employed to achieve these objectives. When assessing product lifetime under non-normal distributions, the lifetime performance index emerges as a valuable and frequently utilized metric. In practical scenarios, lifetime tests may encounter challenges such as the absence of experimenters to observe all product lifetimes and the possibility of accidental product breakage. To address these issues, this study introduces two sampling schemes: a single sampling plan and a repetitive group sampling plan. These plans are specifically designed for exponential lifetime products, utilizing the progressively type II right censored sample as the data type for lifetime testing. The study provides tabulated plan parameters for practical implementation and presents a comparative analysis between the two proposed plans. Furthermore, a practical example is presented to demonstrate the efficacy of the developed sampling plans.

Mangrove individual tree detection based on the uncrewed aerial vehicle multispectral imagery

Mangroves are one of the most threatened and vulnerable ecosystems in the world. They dominate the coastal wetlands of tropical and subtropical regions. These forest areas are frequently difficult to access, typical methods of forest inventory are difficult undertakings. Thus, the acquisition of forest attributes through remote sensing, such as the validated individual tree detection (ITD) aim can support management and conservation proposals. This study comprehensively validated ITD from high-resolution multispectral uncrewed aerial vehicle (UAV) surveys, forest inventory data, multiresolution segmentation, mean shift segmentation and gaussian model the aim of quantifying the canopies in different areas of mangrove. The validations methods were based on the number plot size, the polygon canopy, and point canopy obtained according to simple sampling plan statistics. The results demonstrated that when using plot size inside the flight area to calibrate the ITD methods, the gaussian model and the segmentation methods can reach an acceptable quality limit (AQL) of 15%. On the other hand, this study suggests as alternative to ITD a tree like objects (TLO) method for flying through dense forests without forest inventory with AQL of 40%. This study is critical for conservation and management efforts because it applies the reliability of multispectral UAV surveys to assessing tree abundance in mangrove forests and can be applied to quantify biomass and blue carbon in dense forests.

Multiple dependent repetitive group sampling plan for Marshall-Olkin logistic-exponential distribution assuring percentile lifetime with applications

Acceptance sampling plans are commonly utilized for planning, process control, and implementation of industrial data sets. In addition to this, they also provide a solution for process quality control. They could be used for executing a decision for acceptance or rejection of a lot. A good acceptance sampling plan can be formulated depending on the requirements of the project. Usually, the plan should follow a defined lifetime distribution. Hence, the appropriate plan would not only determine the plan parameters but would also be cost and time efficient. In this article, an attribute multiple dependent repetitive group sampling (MDRGS) plan is developed assuming that the lifetime of the product follows the Marshall-Olkin logistic-exponential (MOLE) distribution. The parameters of plan are examined by considering a two-point approach. The suggested plan is contrasted with the single sampling and repeated group sampling plans. Finally, the implementation of the proposed plan is discussed using industrial as well as simulated data sets. It is concluded that the proposed plan reduces the sampling inspection cost and time as compared to existing acceptance sampling plans.

Optimal Bayesian generalized multiple-dependent state sampling plan for attributes

Over the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application.

Impact of the total measurement error on sampling plan for bulk materials – An optimal sampling plan under ToS framework

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional statistical sense, which may lead to heavy financial losses. In contrast, the Theory of Sampling (ToS) defines sampling as the collection of physical samples based on a thorough understanding of the material concerned. The goal of this research is to investigate the influence of incorrect sampling procedures on acceptance sampling in a mining value chain. To achieve this, the representative sampling approach of ToS was utilized, total measurement error was measured, and possibilities for improving sampling procedures were identified. A variable Quick Switch Sampling plan (QSS) based on the Beta distribution is applied. A nonlinear optimization model is used to estimate the plan parameters, which minimizes the necessary average sample number (ASN) while meeting the producer and consumer quality levels and maximum allowable risks using real-life data. Finally, A collaborative quality program is suggested for a solid producer-buyer relationship by using a correct sampling facility of the producer.

Economic design of multiple deferred state sampling plan for two parameter Lindley distributed mean life assurance

In this study, we propose a multiple deferred state sampling plan under two parameter Lindley distribution based on time truncated life test. One of the principal objectives of the proposed plan is to ensure that the products have higher mean life. The procedure for determining the optimal plan parameters of the proposed plan is described by two points on the operating characteristics curve approach. Tables are constructed to select the optimal plan parameters of the proposed plan for different values of shape parameters, consumer’s risk and producer’s risk. The implementation of the proposed plan is illustrated with a real life time data. In addition, the performance of the proposed plan is evaluated by comparing the average sample number and operating characteristic curve of the proposed plan to that of single sampling plan. Finally, a mathematical model for economic design of the proposed plan is developed by considering the waiting time to reduce the total inspection cost while satisfying both the producer and consumer requirements and a sensitivity analysis is also explored.

An efficient approach to monitoring pine wilt disease severity based on random sampling plots and UAV imagery

The mapping of forest pests and diseases using remote sensing presents a critical challenge in forest management and conservation. Forestry professionals have shown particular concern about the rapid decline of pine trees caused by pine wilt disease, conducting annual inspections to monitor its impact. However, achieving a balance between large-scale monitoring areas and the identification of individual trees poses a challenging issue. To tackle this issue, we employed a random sampling plan and utilized an unmanned aerial vehicle (UAV) to capture high-resolution images on two separate occasions. This methodology facilitated the comparison of temporal variations in tree health. We applied three object detection algorithms (YOLOv5, Faster-RCNN, CenterNet) to identify diseased trees in the images and introduced an enhanced vision of YOLOv5 for better performance. The improved YOLOv5 demonstrated higher precision of average precision (AP) (0.8806, 0.9004) compared to the other algorithms across two datasets. The improved YOLOv5 generated predictions that identified the topographic regions most severely affected by pine wilt nematode infestation. Geographically, the highest disease incidence was observed at altitudes between 200 and 299 m, slopes ranging from 10 to 15 degrees, and locations facing a sunny slope direction. The primary source of infection is pre-existing diseased trees that are not properly controlled. Additionally, the density of infected trees contributes to the spread, with the most severe outbreaks occurring in densely clustered over large areas. By aggregating the cumulative sampling data, we derived an estimation of the total tree mortality in the two towns. This study introduces an effective monitoring approach for pine wilt disease at the district level by integrating a random sampling protocol, unmanned aerial vehicle (UAV) strategies, and a deep learning algorithm. Our approach can assist forest managers in timely and cost-effective detection and control of pine wilt disease.