Multilevel Sampling Research Articles

Multilevel importance sampling for rare events associated with the McKean–Vlasov equation

This work combines multilevel Monte Carlo with importance sampling to estimate rare-event quantities that can be expressed as the expectation of a Lipschitz observable of the solution to a broad class of McKean–Vlasov stochastic differential equations. We extend the double loop Monte Carlo (DLMC) estimator introduced in this context in Ben Rached et al. (Stat Comput, 2024. https://doi.org/10.1007/s11222-024-10497-3) to the multilevel setting. We formulate a novel multilevel DLMC estimator and perform a comprehensive cost-error analysis yielding new and improved complexity results. Crucially, we devise an antithetic sampler to estimate level differences guaranteeing reduced computational complexity for the multilevel DLMC estimator compared with the single-level DLMC estimator. To address rare events, we apply the importance sampling scheme, obtained via stochastic optimal control in Ben Rached et al. (2024), over all levels of the multilevel DLMC estimator. Combining importance sampling and multilevel DLMC reduces computational complexity by one order and drastically reduces the associated constant compared to the single-level DLMC estimator without importance sampling. We illustrate the effectiveness of the proposed multilevel DLMC estimator on the Kuramoto model from statistical physics with Lipschitz observables, confirming the reduced complexity from O(TOLr-4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}(\ extrm{TOL}_{\ extrm{r}}^{-4})}$$\\end{document} for the single-level DLMC estimator to O(TOLr-3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}(\ extrm{TOL}_{\ extrm{r}}^{-3})}$$\\end{document} while providing a feasible estimate of rare-event quantities up to prescribed relative error tolerance TOLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{TOL}_{\ extrm{r}}$$\\end{document}.

Disentangling hydrodynamic drivers of the Southern Venice (Italy) coastal aquifer via frequency decomposition analysis: Insights, challenges, and limitations

Study regionShallow coastal aquifer located in the southern part of the Venice lagoon (Italy). Study focusThis study aims to improve the understanding of coastal aquifers’ hydrodynamics by implementing systematic time-series analyses of data. A collection of non-intrinsically consistent time series from hydrological (surface water and groundwater) and meteo-mareographic monitoring networks was obtained from different institutions. Each signal was broken down through a frequency decomposition analysis, isolating the main driving forces to focus on phenomena that occur at different time and spatial scales. New hydrological insights for the regionResults highlighted that the aquifer is highly connected with the Venice lagoon, with a clear fluctuation of piezometric heads induced by tidal major constituents, decreasing landward. Besides, the effects exerted by reclamation canals and pumping stations were also determined and found to increase landward. Despite the relatively simple behaviour of piezometric heads, the groundwater salinity is influenced by additional local factors, like probe depth, wells’ screen length, and vertical salinity distribution along the aquifer. These findings suggested how to make use of limited and sparse data to enhance the conceptual model of coastal aquifer hydrodynamics, while highlighting the limitations of existing monitoring networks. This outcome justified the need for an intrinsically-consistent network of dedicated multi-level samplers to avoid intra-borehole mixing and reliably characterize the groundwater salinity distribution.

Development and Evaluation of a Robust UART Communication System with Enhanced Interference Resistance

Interference is a significant issue in UART communication, often causing data jitter and distortion, particularly in industrial environments. This study addresses these challenges by developing an anti-interference UART system that includes the design and implementation of receiving, processing, and sending modules. The system utilizes techniques such as multi-level sampling, synchronization, and cumulative decision-making to enhance data transmission accuracy and reliability. In embedded systems, electromagnetic interference (EMI) poses a major problem, leading to potential data loss and communication failures. Reliable communication is crucial for maintaining the performance and safety of industrial operations. The anti-interference UART system developed in this study aims to improve operational stability by mitigating the effects of EMI. The system’s performance was validated through extensive simulations, demonstrating its ability to maintain stable and accurate data transmission even under various interference conditions. The results show significant improvements in data integrity and communication reliability. This paper presents a comprehensive approach to designing, implementing, and validating a robust UART system that can effectively operate in challenging environments, ensuring reliable data communication and enhancing overall system performance.

A quantitative investigation in a territory of Italy on citizens' attitudes towards medicines through the COVID-19 pandemic: the importance of possible indirect effects caused by the pandemic.

The COVID-19 pandemic has greatly influenced many aspects of everyday life, particularly that of the general population health. In order to better understand the potential impacts of the COVID-19 pandemic on people's attitudes toward medicines use, a quantitative investigation was conducted in a territory of Sardinia region, Italy. Stratification of the random multilevel population sample was based on gender, age range, and territory. The methodological strategy to verify the potential approach changes towards medicines due to the COVID-19 pandemic consisted of oral interviews with adult citizens and unrecognizability preservation. Investigation, also supported by a study completed before the insurgence of the pandemic about taking medicines, interrupting treatments without consulting, and reading the information leaflet, allowed to explore citizens' attitudes before and during pandemic, and changing. The most relevant findings are the tendency towards a higher occurrence of self-interruption of treatments and an increased interest in the information leaflet (package leaflet), but not an increased self-administration of medicines. These results indicate new indirect effects of the COVID-19 pandemic that could exert an additional impact on the state of citizens' health and health systems. The study, with reference to prophylactic medical treatments and based on some considerations concerning the pandemic from its insurgence to today, also provides solutions for related problems for the present or future periods of health emergencies.

Compliance and Determinants of Spectacle Wear Among Moroccan Adults Residing Beni-Mellal Khénifra Region.

Ensuring a high level of adherence to wearing spectacles is essential to preserve eye health and achieve optimal vision correction. Comprehending the factors influencing adherence to wearing spectacles can inform strategies to improve eye care outcomes. This study aimed to assess the prevalence and factors influencing adherence to wearing spectacles among Moroccan adults residing the Beni-Mellal Khenifra region. A cross-sectional interview survey was conducted involving 389 adult spectacle wearers. Participants were recruited through a multilevel random sampling technique and interviewed using a structured questionnaire. This questionnaire collected data on demographics, spectacle prescription, usage patterns, and barriers to compliance. Compliance was assessed based on self-reported frequency and duration of spectacle wear according to the prescription. Possible factors influencing the wearing of spectacles were investigated using both univariate and multivariate regression models. This study revealed that 57.6% of participants adhered to the recommended use of spectacles. Factors associated with compliance were higher education level, longer duration of spectacle use, awareness about the importance of spectacle wear, absence of a family history, specific refractive error types, and increased severity of refractive errors. Participants reported various reasons for not adhering to the recommended use of spectacles, with the most common being forgetfulness, perceiving improved vision without spectacle, discomfort while wearing spectacle, difficulty adapting to spectacle, and loss of spectacle. Given the high prevalence of non-compliance to spectacle wear among adults in the Beni-Mellal Khenifra region, it is evident that additional efforts are required to improve understanding and education regarding the advantages of consistent spectacle usage. Targeted educational and awareness initiatives have the potential to substantially enhance adherence rates and consequently improve visual health outcomes in the region.

Exponential error reduction for glueball calculations using a two-level algorithm in pure gauge theory

This study explores the application of a two-level algorithm to enhance the signal-to-noise ratio of glueball calculations in four-dimensional SU(3) pure gauge theory. Our findings demonstrate that the statistical errors exhibit an exponential reduction, enabling reliable extraction of effective masses at distances where current standard methods would demand exponentially more samples. However, at shorter distances, standard methods prove more efficient due to a saturation of the variance reduction using the multilevel method. We discuss the physical distance at which the multilevel sampling is expected to outperform the standard algorithm, supported by numerical evidence across different lattice spacings and glueball channels. Additionally, we construct a variational basis comprising 35 Wilson loops up to length 12 and 5 smearing sizes each, presenting results for the first state in the spectrum for the scalar, pseudoscalar, and tensor channels. Published by the American Physical Society 2024

Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting.

Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.

Podoconiosis in Uganda: prevalence, geographical distribution and risk factors.

Podoconiosis is a neglected debilitating yet preventable disease. Despite its public health significance, podoconiosis is often misdiagnosed and confused with lymphatic filariasis. No appropriate diagnostic tests exist, contributing to underestimation and the absence of control interventions. A population-based cross-sectional survey was conducted in seven districts with suspected or reported cases of podoconiosis or an altitude of 1200m above sea level. Conducted from 30 January to 19 March 2023, the survey employed multilevel stratified sampling to reach eligible household members. Of the 10023 participants sampled, 187 (confidence interval 1.25 to 2.78) had clinical features of podoconiosis. The highest prevalence was recorded in Nakapiripirit (7.2% [58/809]) and Sironko (2.8 [44/1564]) and the lowest in Kasese (0.3% [5/1537]), but ranged from 1.1 to 1.8% in Zombo, Rukungiri, Gomba and Hoima districts. The duration of podoconiosis was reported to range from 1 to 57y. Factors associated with podoconiosis occurrence included advanced age, tungiasis, household cleanliness and personal hygiene. Sleeping on a bed, bathing daily, use of soap and use of footwear in at least moderate condition were protective against podoconiosis. Podoconiosis occurred in all the sampled districts and was linked to personal hygiene. Long-standing cases suggest an absence of treatment. There is potential for early intervention using a holistic care model in managing this condition. Urgent action and stakeholder engagement are essential for effective podoconiosis management.

3D GeoRemediation: A Digital Hydrogeophysical–Chemical Clone and Virtual Hydraulic Barrier with Groundwater Circulation Wells (GCWs) for Groundwater Remediation

Identification of contamination sources and delineation of plumes in the geological environment stand as pivotal elements in reconstructing the conceptual site model (CSM) and devising remediation strategies tailored to specific physicochemical traits. This study endeavors to showcase the capabilities of a 3D digital interface, seamlessly integrating multi-source data, to elucidate site-specific contamination dynamics and steer the implementation of remediation strategies harmoniously aligned with the ethos of remediation geology. In a site historically marred by chlorinated solvent contamination, the digitization of stratigraphic, piezometric, chemical, and membrane interface probe (MIP) data underpins geomodeling endeavors and yields a meticulously crafted, data-driven CSM. The hydrogeochemical and hydrogeophysical data were interpolated to build a volumetric, digital 3D model illustrating data-driven elements. The comprehensive 3D clone adeptly delineates secondary contamination sources and renders visible the contamination plume within a georeferenced framework, mirroring the nuanced interplay of stratigraphic nuances and groundwater path. A data-centric approach to modeling facilitates the design of the first hydraulic virtual barrier leveraging groundwater circulation well (GCW) technology, its geometry finely attuned to intercept the contamination plume originating from source dissolution and aligning with preferential groundwater flow trajectories. Conventional hydrochemical monitoring and multilevel sampling substantiate the discernible reduction in chlorinated solvent concentrations across various depths within the aquifer horizon, affirming the efficacy of GCWs in their virtual barrier configuration. The findings highlight the effectiveness and limited groundwater consumption of the virtual barrier compared to the on-site pump-and-stock system. This research underscores the potency of a multi-faceted evidence-driven puzzle in conceptualizing contamination mechanisms within the geological milieu, thereby fostering the application of cutting-edge, effective, and sustainable remediation strategies.

Socialist imprints and innovation strategies in a transition economy

ABSTRACT This paper contributes to research at the intersection of institutional theory and the emerging literature on institutional imprinting by studying how the persistence and decay of founding institutional imprints affect network-based innovation strategies in small firms during later stages of economic transition. In do so, we are able to investigate both the extent of imprints and the boundary conditions that serve to strengthen or weaken their persistence. We situate our study in a fast-growing but under-studied transition economy, Vietnam, applying multiple estimation methods on a multilevel panel sample of 2644 small entrepreneurs over 6 years. Our major findings are, firstly, that firms launched before transition are influenced by socialist imprints and rely more on small and concentrated informal social networks, while firms launched after transition rely more on newer formal market institutions to generate innovations, and, secondly, that management and industry experience strengthens network-based innovation strategies and, thus, amplifies the persistence of socialist imprinting in firms established prior to transition.

Determination of contamination sources and geochemical reactions in groundwater of a mine area using Cu, Zn, and S–O isotopes

To determine contamination sources and pathways, the use of multiple isotopes, including metal isotopes, can increase the reliability of environmental forensic techniques. This study differentiated contamination sources in groundwater of a mine area and elucidated geochemical processes using Cu, Zn, S–O, and O–H isotopes. Sulfate reduction and sulfide precipitation were elucidated using concentrations of dissolved sulfides, δ34SSO4, δ18OSO4, and δ66Zn. The overlying contaminated soil was possibly responsible for the contamination of groundwater at <5 mbgl, which was suggested by low δ65Cu values (0.419–1.120‰) reflecting those of soil (0.279–1.115‰). The existence of dissolved Cu as Cu(I) may prevent the increase in δ65Cu during leaching of contaminated soil in the sulfate-reducing environment. In contrast, the groundwater at >5 mbgl seemed to be highly affected by the contamination plume from the adit water, which was suggested by high SO42− concentrations (407–447 mg L−1) and δ65Cu (0.252–2.275‰) and δ66Zn (−0.105‰–0.362‰) values at a multilevel sampler approaching those of the adit seepages. Additionally, the O–H isotopic ratios were distinguished between <5 mbgl and >5 mbgl. Using δ65Cu and δ66Zn to support the determination of groundwater contamination sources may be encouraged, particularly where the isotopic signatures are distinct for each source.

Iron-rich food consumption and predictors among children aged 6-59 months old in Ethiopia: A multilevel complex sample analysis of the Ethiopian mini-demographic and health survey 2019 data.

Children with inadequate iron consumption had slower growth, weaker immunity, and poor cognitive development. Although the public health importance of iron-rich consumption in Ethiopia is known, evidence for iron-rich food consumption and predictors among children aged 6-59 months old in Ethiopia is sparse. This study aimed to assess iron-rich food consumption and predictors among children aged 6-59 months old in Ethiopia. This study used Ethiopia mini demographic and health survey 2019 (EMDHS-2019) data with a total weighted sample size of 5,112 among children aged 6-59 months old. A multilevel mixed effect logistic regression analysis was used to identify predictors of good iron-rich food consumption. The proportion of good consumption of iron-rich foods among children aged 6-59 months was 27.99% (24.22, 32.10%). The findings revealed that children born to mothers who completed primary education [AOR = 1.88, 95% CI: 1.11, 3.19], a higher education [AOR = 4.45, 95% CI: 1.28, 15.48], being born to the poorer family [AOR = 1.89, 95% CI: 1.04, 3.43], richer [AOR = 2.12, 95% CI: 1.03, 4.36], and richest [AOR = 3.57, 95% CI: 1.29, 9.93] were positively associated with good iron-rich food consumption among children aged 6-59 months old. Nevertheless, being 24-59 month-old children [AOR = 0.58, 95% CI: 0.44, 0.72], residents of the Afar [AOR = 0.23, 95% CI: 0.08, 0.67], Amhara region [AOR = 0.30, 95% CI: 0.14, 0.65], and Somali region [AOR = 0.01, 95% CI: 0.01, 0.07] were negatively associated with good iron-rich food consumption among children aged 6-59 months old. The finding revealed that there was low consumption of iron-rich foods among children aged 6-59 months in Ethiopia compared to reports from East African countries. Improving women's literacy and economic empowerment would improve iron-rich food consumption among children aged 6-59 months old. This study's findings would have implications for policymakers in Ethiopia to enhance iron-rich food consumption.

Contributory Pension Scheme and Welfare of Retirees: A case of Tertiary Institutions of Education in Southwestern Nigeria

The study investigated the effect of the Contributory Pension Scheme (CPS) on the welfare of retirees of tertiary institutions of education in the Southwestern zone of Nigeria. Using the Taro Yamane formula and a multi-level sampling technique, a sample size of 125 retirees was obtained from three tertiary institutions of education in the Southwestern part of Nigeria as follows: Osun State College of Education, Ilesa (41), Lagos State University of Education, Lagos (36) and Adeyemi College of Education, Ondo (48). The main instrument of data collection was a set of questionnaire which was administered to the retirees to collect relevant data. The instrument was validated and its reliability coefficient calculated to be 0.82 using the Cronbach Alpha formula. The data collected was analysed using the Chi-square and the one-sample t-test statistics. The results showed that the contributory pension scheme did not have significant effect on the welfare of the retirees (t = .207, p &lt; 0.05). Useful recommendations were made based on this finding.

Online harvesting of municipality websites into trusted digital repository

PurposeMunicipalities, as the front lines of service delivery, use websites as one of the tools to communicate information to the public. While it is considered a record, many organisations, including municipalities, do not manage websites as such. This study aims to explore the archiving of websites as records in the municipalities of KwaZulu-Natal (KZN) Province in South Africa by using the web archiving life cycle model.Design/methodology/approachThis study used a mixed-methods research with an explanatory design, with quantitative data collected first through content analysis of websites and qualitative data collected through interviews. Researchers used multilevel sampling, first quantitatively analysing all available websites of the municipalities (52) in KZN, and then qualitatively selecting only records managers, information managers, web administrators, communication managers and website managers or designers from municipalities because of their understanding and involvement with websites in some way.FindingsThis study established that some records on municipal websites are often in paper format in record-keeping systems, whereas others are born digital and are not captured in the systems. Municipalities lack a dedicated web online harvesting tool as well as an archiving policy or strategy to guide website archiving. Furthermore, municipalities placed a high reliance on service providers to keep their websites operational.Research limitations/implicationsIt became clear during the interviews that most of the participants were unfamiliar with web archiving. As a result, only 12 of the 56 selected participants from the municipalities provided the required information in relation to the current study as others could not provide answers. Data for other participants were not analysed.Originality/valueDue to a lack of infrastructure for ingesting digital records into archival custody, a framework for harvesting web content of value is proposed both internally in municipalities and externally to an archive repository.

Structural effect of principals change leadership on e-learning effectiveness

&lt;span lang="EN-US"&gt;The aim of this research was to investigating the structural effect of the principal change leadership on the effectiveness of online-based learning with teacher work commitment and teacher attitudes towards change as mediator variables, which were approached quantitatively. Multilevel stratified proportional random sampling technique is used in sampling, while inclusion criteria are principals and teachers of primary schools who are still active in Kutai Kartanegara, Samarinda and Balikpapan, East Kalimantan, Indonesia. The structural equation modeling (SEM) analysis technique is used in this research. The results of the study show that the principals’ change leadership has a direct effect on the effectiveness of online-based learning, as well as an indirect effect through teacher work commitment and teacher attitudes towards change. This research contributes both theoretically and practically, where the change leadership demonstrated by the principal can ideally encourage teacher work commitment and teacher attitudes towards change, which in turn can promote the effectiveness of the implementation of online learning, as an effort to meet student learning needs.&lt;/span&gt;

Incorporating Multi-Level Sampling with Adaptive Aggregation for Inductive Knowledge Graph Completion

In recent years, Graph Neural Networks (GNNs) have achieved unprecedented success in handling graph-structured data, thereby driving the development of numerous GNN-oriented techniques for inductive knowledge graph completion (KGC). A key limitation of existing methods, however, is their dependence on pre-defined aggregation functions, which lack the adaptability to diverse data, resulting in suboptimal performance on established benchmarks. Another challenge arises from the exponential increase in irrelated entities as the reasoning path lengthens, introducing unwarranted noise and consequently diminishing the model’s generalization capabilities. To surmount these obstacles, we design an innovative framework that synergizes M ulti- L evel S ampling with an A daptive A ggregation mechanism (MLSAA). Distinctively, our model couples GNNs with enhanced set transformers, enabling dynamic selection of the most appropriate aggregation function tailored to specific datasets and tasks. This adaptability significantly boosts both the model’s flexibility and its expressive capacity. Additionally, we unveil a unique sampling strategy designed to selectively filter irrelevant entities, while retaining potentially beneficial targets throughout the reasoning process. We undertake an exhaustive evaluation of our novel inductive KGC method across three pivotal benchmark datasets and the experimental results corroborate the efficacy of MLSAA.

Investigating the Role of Anger and Cognitive Malfunction in Mental Health: A Cross-Sectional Exploration Paving the Way for a Subsequent Experiment

The functional outcomes associated with subjective well-being (SWB) and the detrimental aspects of psychological distress (PD) make it essential to explore contributing factors. The present study investigated a model about the existing gap in the determining role of trait anger (TA), state anger (SA), cognitive reappraisal (CR), rumination and cognitive failure (CF) as predictors of SWB and PD. The study contributes by exploring the interaction of dispositional, situation factors and emotional regulation strategies in shaping SWB and PD in the Indian Sample. A cross-sectional survey design was employed wherein 600 young adults aged 18–40 (Mage = 22.13, SDage = 4.06) were recruited from Uttar Pradesh, India using a multi-level cluster sampling method. The data were collected using questionnaires in the field setting during August–December 2021. Regression and path analysis revealed that the proposed predictors explained significant variance in SWB and PD, i.e., R 2 = 0.24, F (5, 594) = 38.03, p < 0.01, Cohen f 2 =0.31 and R 2 = 0.35, F (5, 594) = 66.40, p < 0.01, Cohen f 2 = 0.53, respectively. The models also fit well with the statistical indices. Except CR, all predictors emerged as significant risk factors. The findings suggest that the interventions to reduce PD and enhance SWB may consider inculcating CR and reducing higher levels of TA, SA, rumination and CF to enhance an individual’s adaptive functioning. The findings pave the way for conducting a pre-planned experiment to study the outcomes of various levels of TA in regulating incidental anger (SA) employing CR and rumination.

Superpixel-wise contrast exploration for salient object detection

Salient object detection (SOD) methods typically consider SOD as a pixel-wise binary classification problem and utilize the binary cross-entropy (BCE) loss for optimization. However, the BCE loss ignores the global dependencies between pixels of the entire image, which is important for ensuring the accuracy and integrity of objects. To address this limitation explicitly, contrastive learning is introduced to enhance both the intra-pixel compactness of the foreground and inter-pixel separability between the foreground and background. Unfortunately, the random pixel sampling strategies of existing contrast algorithms cannot ensure that the sampled pixels cover all semantic contents across the entire image because of the lack of fine-grained semantic categories for pixels in SOD. Hence, in this study, a SuperPixel-wise Contrastive (SPCont) algorithm is proposed, which samples all superpixel centers to improve the diversity and representativeness of the sampled pixels. In addition, a SOD-specific hard pixel sampling strategy is designed to refine the edge details of the objects. An intra-image multi-level negative sampling approach is presented to increase the number of negative samples. Extensive experiments are conducted to demonstrate that incorporating the SPCont algorithm into the state-of-the-art network results in significant performance improvements. We expect that our work will open new paths for the application of contrastive learning to SOD.

Longitudinal associations between the rates of change in family to work enrichment, leader-member exchange, and job satisfaction

By integrating the work-home resource model with the leader-member exchange (LMX) theory, we adopt a change perspective to examine the effects of the change rate in family-to-work enrichment (FWE) on that in job satisfaction through the change rate in LMX. Using a longitudinal, multilevel sample of 360 employees in 71 teams, the results of three waves of data over eight months reveal the FWE change rate is associated positively with the LMX change rate, which, in turn, is associated positively with the job satisfaction change rate. Furthermore, we show that LMX differentiation at Time 1 moderates the FWE change rate's effect on the LMX change rate over time, such that the relation is stronger when the LMX differentiation is high at Time 1. Moreover, the LMX change rate mediates the interactive effects of the FWE change rate and Time 1 LMX differentiation on the job satisfaction change rate over time. The results also show that the initial level of FWE is related positively with the LMX change rate, but not with the job satisfaction change rate. These findings emphasize the importance of studying changes in family-work research and providing new insights into the processes of the FWE change rate's effects on the change rates of important work outcomes over time.

Assessing the prevalence of iron deficiency anemia and risk factors among children and women: A case study of rural Uttar Pradesh

BackgroundAnemia, defined as low blood haemoglobin (Hb) concentration, is a serious global health problem that relates to negative health outcomes. Anemia is a sign of poor nutrition and primarily affects children, women of childbearing age, and pregnant women. MethodsA mixed method approach was used to collect data using a multilevel sampling technique from Nagla Chandi Village, Uttar Pradesh. Survey was conducted on thirty mothers of children aged 6–59 months and thirty-three reproductive age women. Qualitative data was collected from school teachers and one ASHA worker. ResultsCommunity level anemia prevalence among women was similar to the NFHS statistics, while that of children was found to be lower in the studied community. In instances involving women, the prevalence of anemia was 64.7%, and the rates of mild and moderate anemia were 20.6% and 14.7%, respectively. Thirty-six percent of male and 44% female children were anemic. Seventy percent of male and 79% of female children were underweight. In case of adult females 18% were underweight and 26% were overweight. The risk factors of IDA and malnutrition are unhealthy eating habits, lack of intake of IFA supplements by women, lack of proper education in society, absence of Anganwadi (type of rural childcare centre in India), untreated water intake and poor hygiene practices are the major cause of IDA in this community. ConclusionsFood fortification, nutritional education, and good hygiene practices are prescribed interventions, to be implemented by Government bodies for the prevention of anemia.