Multiple Clusters Research Articles

Modelling the elevation power angle spectrum of a MIMO channel

<p>Multiple-Input Multiple-Output (MIMO) channel modelling is constantly researched, and the innovation of its three-dimensional (3D) models is the introduction of the elevation domain parameters, in particular the elevation angle of arrival (EOA) and elevation angle of departure (EOD) where their power angle spectrum (PAS) and their angular spread (AS) greatly impact the 3D MIMO performance. PAS is the basic characteristic used to estimate and model angular dispersion in wireless channels. Propagating signal waves are constrained into a path having an angle within which the power is contained. One of the scenes that will have a significant effect on these parameters is the relative height between the BS and the building. They will also depend on the distance between the base station (BS) and the user equipment (UE). In this work, we investigate the effect of distance and height on the power angle spectrum of a street canyon and a high-rise scenario in the urban environment. The wireless Insite X3D ray tracing engine and a 3D digital map of the environment were used for the simulation. It is seen that the elevation power spectrum (EPS) in the elevation domain decreases with distance and height in both scenarios for the arrival and departure. The appearance of multiple peaks leads to a double-normal distribution in the arrival of the high-rise as height increases, which is a result of reflections from multiple clusters. The azimuth power spectrum (APS) in the azimuth domain also decreases with distance and height at the arrival but increases with distance and height at the departure. The result of the power angle spectrum in this work can be used for modelling angular dispersion as well as designing adaptive antennas for wireless communication.</p>

Do children with disabilities have the same opportunities to play as children without disabilities? Evidence from the multiple indicator cluster surveys in 38 low and middle-income countries

Play is essential for the cognitive, social, and emotional development of all children. Disparities potentially exist in access to play for children with disabilities, and the extent of this inequity is unknown. Data from 212,194 children aged 2-4 years in 38 Low and Middle-Income Countries were collected in the UNICEF supported Multiple Indicator Cluster Survey (2017-2020). Disability was assessed by the Washington Group-Child Functioning Module. Logistic regression models were applied to investigate the relationship between disability and play opportunities, controlling for age, sex, and wealth status. Meta-analysis was used to pool the estimates (overall, and disaggregated by sex), with heterogeneity assessed by Cochran's Q test. Children with disabilities have approximately 9% fewer play opportunities than those without disabilities (adjusted RR [aRR]=0.88, 95% CI=0.82-0.93), and this varied across countries. Mongolia and Democratic Republic of São Tomé and Príncipe had the lowest likelihood of play opportunities for children with disabilities ((aRR=0.26, 95% CI=0.09-0.75; aRR=0.46, 95% CI=0.23-0.93, respectively). Moreover, children with disabilities are 17% less likely to be provided with opportunities to play with their mothers (aRR=0.83, 95% CI: 0.73-0.93), which is further reduced for girls with disabilities (aRR=0.74, 95% CI: 0.60-0.90) compared to their peers without disabilities. The associations varied by impairment type, and children with communication and learning impairments are less likely to have opportunities for play with aRR of 0.69 (95% CI: 0.60-0.79) and 0.78 (95% CI: 0.71-0.86), compared to those without disabilities, respectively. Children with disabilities are being left behind in their access to play and this is likely to have negative impacts on their overall development and well-being. HK and TS are funded by HK's NIHR Global Research Professorship (NIHR301621). SR is funded by a Rhodes Scholarship. This study was funded by the Programme for Evidence to Inform Disability Action (PENDA) grant from the UK Foreign, Commonwealth and Development Office.

Health insurance uptake, poverty and financial inclusion in the Democratic Republic of Congo

AbstractThis study estimates the prevalence of health insurance coverage and associated socioeconomic factors in the Democratic Republic of Congo (DRC). Using the nationally representative household survey of the 2017/2018 DRC Multiple Indicator Cluster Survey (MICS), we applied weighted logistic regression models to identify regions and subgroups with low health insurance coverage. The study's findings revealed a low insurance coverage of less than 5%, with significant disparities across provinces and socioeconomic status. Additionally, three factors were strongly associated with the low health insurance coverage rates: education, wealth, and financial inclusion proxied by bank account ownership. Consequently, we recommend that the government, the private sector, and donors prioritize programs targeting provinces with less coverage and individuals without formal education to increase health insurance coverage. This study encourages the government to establish national programs to improve financial inclusion, which could positively impact poverty reduction and health insurance coverage. We also propose that the government initiates pilot projects for premium exemptions and through subsidies for vulnerable populations in the short term, and ensure formal employment for the majority of the population in the long term to facilitate the proper collection of premiums for individuals. Overall, this study contributes to the literature on health insurance in the DRC and sub‐Saharan Africa by identifying the socioeconomic factors that explain the prevalence of health insurance coverage. The findings of this study have important policy implications for the government, the private sector, and donors to promote health insurance coverage and achieve universal health coverage.

Deep federated learning hybrid optimization model based on encrypted aligned data

Federated learning can achieve multi-party data-collaborative applications while safeguarding personal privacy. However, the process often leads to a decline in the quality of sample data due to a substantial amount of missing encrypted aligned data, and there is a lack of research on how to improve the model learning effect by increasing the number of samples of encrypted aligned data in federated learning. Therefore, this paper integrates the functional characteristics of deep learning models and proposes a Variational AutoEncoder Gaussian Mixture Model Clustering Vertical Federated Learning Model (VAEGMMC-VFL), which leverages the feature extraction capability of the autoencoder and the clustering and pattern discovery capabilities of Gaussian mixture clustering on diverse datasets to further explore a large number of potentially usable samples. Firstly, the Variational AutoEncoder is used to achieve dimensionality reduction and sample feature reconstruction of high-dimensional data samples. Subsequently, Gaussian mixture clustering is further employed to partition the dataset into multiple potential Gaussian-distributed clusters and filter the sample data using thresholding. Additionally, the paper introduces a labeled sample attribute value finding algorithm to fill in attribute values for encrypted unaligned samples that meet the requirements, allowing for the full recovery of encrypted unaligned data. In the experimental section, the paper selects four sets of datasets from different industries and compares the proposed method with three federated learning clustering methods in terms of clustering loss, reconstruction loss, and other metrics. Tests on precision, accuracy, recall, ROC curve, and F1-score indicate that the proposed method outperforms similar approaches.

Global genomic profiling of Klebsiella pneumoniae: A spatio-temporal population structure analysis

Klebsiella pneumoniae is an important clinical bacterial pathogen that has hypervirulent and multidrug-resistant variants. Uniform Manifold Approximation and Projection (UMAP) was used to cluster genomes of 16 797 K. pneumoniae strains collected, based on core genome distance, in over 100 countries during the period 1937 to 2021. A total of 60 high-density genetic clusters of strains representing the major epidemic strains were identified among these strains. Using UMAP bedding, the relationship between genetic cluster, capsular polysaccharide (KL) types and sequence type (ST) of the strains was clearly demonstrated, with some important STs, such as ST11 and ST258, found to contain multiple clusters. Strains within the same cluster often exhibited significant diverse features, such as originating from different areas and being isolated in different years, as well as carriage of different resistance and virulence genes. These data enable the routes of evolution of the globally prevalent K. pneumoniae strains to be traced. Alarmingly, carbapenem-resistant K. pneumoniae strains accounted for 51.7% of the test strains and worldwide transmission was observed. Carbapenem-resistant and hypervirulent K. pneumoniae strains are mainly reported in China; however, these strains are increasingly reported in other parts of the world. Also identified in this study were several key genetic loci that facilitate development of a new K. pneumoniae typing method to differentiate between high- and low-risk strains. In particular, the acrR, ompK35 and hha genes were predicted to play a key role in expression of the resistance and virulence phenotypes.

Height-for-age and weight-for-age growth charts for Pakistani infants under six months: derived from a novel case selection method using multiple indicator cluster survey data

BackgroundIn the past two decades, there has been a growing recognition of the need to establish indigenous standards or reference growth charts, particularly following the WHO multicenter growth study in 2006. The availability of accurate and reliable growth charts is crucial for monitoring child health. The choice of an appropriate model for constructing growth charts depends on various data characteristics, including the distribution’s tails and peak. While Pakistan has reported some reference growth charts, there is a notable absence of indigenous charts for children under two years of age, especially for infants aged 0–6 months who are exclusively breastfed. Additionally, acquiring data poses a significant challenge, particularly for low-income countries, as it demands substantial resources such as finances, time, and expertise. The Multiple Indicator Cluster Survey (MICS) constitutes a large-scale national survey conducted periodically in low-income countries under the auspices of UNICEF. In this study, we propose methods for generating selection variables utilizing the “Novel Case Selection Method,“ as previously published. Further our approach enables to select and fit appropriate model to the MICS data, selected, and to develop the standard growth charts.MethodsOut of the 11,478 children under 6 months of age included in MICS-6 (Pakistan), 3,655 children (1,831 males and 1,824 females) met the specified criteria and were selected using the “Novel Case Selection Method”. The sample was distributed across provinces as follows: 841 (23.0%) from KPK, 1,464 (40.1%) from Punjab, 819 (22.4%) from Sindh, and 531 (14.5%) from Balochistan. This sample encompassed both rural (76.4%) and urban (23.6%) populations. Following data cleaning and outlier removal, a total of 3,540 records for weight (1,768 males and 1,772 females) and 3,515 records for height (1,759 males and 1,756 females) were ultimately available for the development of standard charts. The Bayesian Information Criterion (BIC) was employed to determine the optimal degrees of freedom for L, M, and S using RefCurv_0.4.2. Three families within the gamlss class—namely, Box Cox Cole and Green (BCCG), Box Cox T (BCT), and Box Cox Power Exponential (BCPE)—were applied, each with three smoothing techniques: penalized splines (ps), cubic splines (cs), and polynomial splines (poly). The best-fitted model was selected from these nine combinations based on the Akaike Information Criteria.ResultsThe Novel Case Selection Method yielded 3655 cases as per criteria. After cleaning the data, this method lead to selection of 3540 children for “weight for age” (W/A) and 3515 children for “height for age” (H/A). The “BCPE” family and “ps” as smoothing method proved to be best on AIC for all four curves, i.e. the W/A male, W/A female, H/A male, and H/A female. The optimum selected degrees of freedom for the curve “W/A”, for both genders were (M = 1, L = 0, S = 0). The optimum degrees of freedom for H/A male were again (M = 1, L = 0, S = 0), but for females the selected degrees of freedom were (M = 1, L = 1, S = 1). The indigenous fitted standard curves for Pakistan were on lower trajectory in comparison to WHO standards.ConclusionThis study uses the Novel Case Selection Method with introduced algorithms to construct tailored growth charts for lower and middle-income countries. Leveraging extensive MICS data, the methodology ensures representative national samples. The resulting charts hold practical value and await validation from established data sources, offering valuable tools for policy makers and clinicians in diverse global contexts.

Pt-Clusters-Equipped Antioxidase-Like Biocatalysts as Efficient ROS Scavengers for Treating Periodontitis.

Developing high-efficiency artificial biocatalysts for scavenging reactive oxygen species (ROS) is critical for treating inflammation diseases and promoting tissue regeneration. By mimicking the active sites in catalase, here, a Pt-clusters-equipped antioxidase-like biocatalysts (Pt─CN) with superior catalytic abilities for stem cell protection and periodontitis treatment are reported. Owing to the excellent effects of multiple Pt clusters, Pt─CN yields exceptional catalytic ROS-scavenging activities for multiple types of ROS. In vitro studies show that Pt─CN can effectively protect stem cell survival, adhesion, and differentiation in a high ROS levels microenvironment. Additionally, Pt─CN can reduce the M1/M2 ratio of macrophages when stimulated by lipopolysaccharide. In vivo treatment of mouse periodontitis further confirms the protection against bone loss and reduction in the inflammatory response. This study provides a basis for the application of biocatalysts with Pt catalytic center in macrophage polarization, stem cell protection, and periodontitis treatment, thus offering a new strategy for the design of high-performance artificial biocatalysts.

Efficient fuzzy-pruned high dimensional clustering with minimal distance measure

In this paper, we present a novel clustering approach that eliminates the need for predefined cluster centres and cluster counts, addressing common limitations in traditional clustering algorithms. We achieve this by allowing each data point within the problem space to serve as a potential cluster centre, determined by the presence of nearby data points. Three distinct algorithms are introduced: i) Identifying valid data points for clustering by assessing their presence within the specified vicinity of each potential cluster centre, allowing data points to belong to multiple clusters, ii) Calculating the fuzzy membership of shared data points among the clusters and iii) Retaining shared data points in the cluster with the highest membership and removing them from others. A comprehensive evaluation of this method using synthetic data and benchmark datasets from well-known problems demonstrates promising performance. Our approach offers increased flexibility, ease of use, and enhances the fidelity of cluster centres to the underlying data distribution.

Adapting a process-oriented cold hardiness model to conifers

Plant phenology has been and continues to be impacted by climate change. Process-oriented modeling of phenology reveals biological characteristics through interpretation of model results and parameter values. This paper aims to implement a cold hardiness model using historical Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) data, determine if model results improve with data clustering by seed source or growing location, and interpret model results into biological meaning. These interpretations have applications for reforestation and studies of plant phenological response to climate change.Cold hardiness data were compiled through literature review. A dynamic process-oriented cold hardiness model driven by thermal time based on daily temperatures was applied to multiple data clustering scenarios. Model results were analyzed for goodness of fit to determine model error, efficiency, and bias. A sensitivity analysis was performed to determine parameter sensitivity, and further validate the model calibration.Data clustering by seed source improved fit compared to clustering by growing location or no clustering, when applied to the full dataset. Using only temperature inputs, model results had low error when data modeled were similar. Results show that for cold hardiness acclimation a linear growing degree function with a threshold of 10 °C was adequate across testing data, as was an upper limit cold hardiness temperature of –3 °C.Interpretation of model results show that both acclimation to growing sites and seed source genetics impact cold hardiness response, though clustering by seed source improved model performance. This model could be applied to mitigate cold related risks to seedlings during production and establishment, and serve as a template for predicting phenological responses in simulated future climate scenarios.

Wound healing and Cadmium detoxification in the earthworm Lumbricus terrestris - a potential case for coelomocytes?

Earthworms are affected by physical stress, like injury, and by exposure to xenobiotics, such as the toxic metal cadmium (Cd), which enters the environment mainly through industry and agriculture. The stress response to the single and the combination of both stressors was examined in regenerative and unharmed tissue of Lumbricus terrestris to reveal if the stress response to a natural insult like injury (amputation) interferes with Cd detoxification mechanisms. We characterized the roles of metallothionein 1 (MT1) and MT2 isoforms, heat shock protein 70 as well as immune biomarkers such as the toll-like receptors (TLR) single cysteine cluster TLR and multiple cysteine cluster TLR. The role of the activated transcription factors (ATFs) ATF2, ATF7, and the cAMP responsive element binding protein as putative regulatory intersection as well as a stress-dependent change of the essential trace elements zinc and calcium was analyzed. Phosphorylated AMP activated protein kinase, the cellular energy sensor, was measured to explore the energy demand, while the energy status was determined by detecting carbohydrate and protein levels. Taken together, we were able to show that injury rather than Cd is the driving force that separates the four treatment groups - Control, Cd exposure, Injury, Cd exposure and injury. Interestingly, we found that gene expression differed regarding the tissue section that was analyzed and we hypothesize that this is due to the migration of coelomocytes, earthworm immune cells, that take over a key role in protecting the organism from a variety of environmental challenges. Surprisingly, we discovered a role for MT1 in the response to multiple stressors and an isoform-specific function for the two newly characterized TLRs. In conclusion, we gathered novel information on the relation of innate immunity, wound healing, and Cd detoxification mechanisms in earthworms.

Federated Learning-Enabled Jamming Detection and Waveform Classification for Distributed Tactical Wireless Networks

In this paper, we propose a federated learning (FL)-based jammer detection and waveform classification algorithm for distributed tactical wireless networks (TWNs). More specifically, we consider a distributed TWN with multiple clusters and various types of waveforms used in the presence of a mobile jammer. We analyze the frequency domain of the waveforms received on local servers to extract the unique cyclic frequency from each waveform’s spectral correlation function (SCF). The method is used to detect the peak values in the frequency-cyclic frequency plane. The primary signal’s SCF exhibits peaks at the unique cyclic frequency and the zero cyclic frequency. These features are then used to train local convolutional neural networks (CNNs) to detect the jamming attacks and classify the waveforms. Moreover, a practical distributed TWN is considered in which each cluster head has a partial observation of the TWN with insufficient data samples, and the proposed algorithm exploits the distributed learning feature of FL, i.e., global learning aggregation to detect the existence of jammers and distinguish the types of waveforms received throughout the TWN. We implement a rigorous TWN simulation using MATLAB toolboxes and our proposed algorithm in TensorFlow Federated. The numerical results show that our proposed algorithm outperforms the standalone local SCF-CNN algorithm. We further demonstrate that the SCF feature yields more accuracy than the In-phase and Quadrature features.

Metabolic Syndrome‐Associated Oligodendrocyte Dysfunction and Cognitive Impairment

AbstractBackgroundOne in nine people aged 65 and older in the United States suffer from cognitive decline, including dementias such as Alzheimer’s disease (AD) and AD‐related Dementias (ADRD). A major risk factor for cognitive impairment is midlife metabolic syndrome (MetS). MetS‐associated cognitive impairment progresses in a non‐cell autonomous manner and implicates a breakdown in metabolic support. This breakdown in metabolic support is likely due to disruption of metabolic crosstalk between neurons and glia, especially oligodendrocytes. Our aim was therefore to elucidate midlife MetS effects on oligodendrocytes and subsequent impact on oligodendrocyte metabolism and cognition.MethodTo accomplish this aim, middle‐aged mice (12‐month old) were fed either standard diet (SD, 10% fat) or high‐fat diet (HFD, 60% fat) for 13 weeks, followed by full assessment of metabolic parameters and cognitive function. Oligodendrocytes were then isolated for single cell RNA‐sequencing (scRNA‐seq), and protein expression of monocarboxylate transporters (MCTs).ResultWe observed that, as expected, HFD mice gained more weight and had higher blood glucose and fat mass versus SD controls. Middle‐aged HFD mice also performed worse in puzzle box and social recognition tests, reflecting impaired executive function and impaired short‐ and long‐term memory responses. scRNA‐seq analysis identified multiple oligodendrocyte clusters. One cluster of interest contained genes involved in cognition (Fam171a2, Nrsn1, Dlgap4) and metabolism (Ldhb, Slc25a5, Cox7c, Cox17), which were differentially expressed in HFD versus SD mice. This was coupled with downregulated MCT1 protein expression in HFD mouse oligodendrocytes versus oligodendrocytes from their SD counterparts. In vitro, human oligodendrocytes treated with palmitate to mimic MetS developed insulin resistance and had downregulated MCT1 protein expression.ConclusionCollectively, these results suggest that midlife MetS impairs oligodendrocyte metabolic function, suggesting a novel mechanism underlying MetS‐associated cognitive impairment.

Promoting the uniform propagation of multi-fracture through adjusting the injection rate and perforation parameter: A numerical study

The strong stress interference and fluid competitive distribution result in the non-uniform propagation of multiple fractures during horizontal well multi-stage hydraulic fracturing (HWMHF). The uniform stimulation effect can be effectively improved by adjusting the injection rate and perforation scheme. In this work, a two-dimensional fluid–solid fully coupled multi-fracture propagation model is established to investigate the pattern of multi-fracture propagation and the flow distribution. The considering factors include the fluid injection rate, the perforation parameters, and the reservoir heterogeneity. The pipe element is developed to realize the free fluid distribution among multiple fractures. The results show that multiple fractures are more likely to initiate simultaneously and propagate uniformly with a higher fluid injection rate. The stress interference among multiple fractures can be balanced and the equilibrium propagation degree of multiple fractures can be greatly improved by reducing the perforation number. In the homogeneous reservoir, when the fluid injection rate is 12 m3/min and the perforation number per cluster is reduced to 10, the difference coefficient of fluid distribution can be reduced to 3.31%. In the heterogeneous reservoir, multiple perforation clusters with different tensile strengths can generate nearly uniform fractures by increasing the fluid injection rate and reducing the perforation number. When the fluid injection rate is 12 m3/min and the perforation number per cluster is 6, the difference coefficient of fluid distribution can be reduced to 3.26%. This work is of great significance in clarifying the propagation pattern and optimizing the fluid injection rate and the perforation number to improve the uniform propagation degree of multiple fractures.

Methylomic‐based molecular subtyping of late onset Alzheimer’s Disease

AbstractBackgroundAccumulating evidence shows that the heterogeneity and temporal complexity of Alzheimer’s Disease contribute to the treatment failure. Advances in omics technologies along with development of efficient integrative analytical pipelines, provide a unique opportunity to investigate the drivers of heterogeneity at multiple molecular levels.MethodWe used genome‐wide DNA methylation data generated in postmortem prefrontal cortex samples from two independent biobanks of Brain for Dementia Research (BDR; n = 382) and the UK Brain Bank Network (UKBBN; n = 250). Multiple unsupervised clustering methods including network‐based, Bayesian and Ensemble approaches were applied on DNA methylation data quantified by the Illumina EPIC arrays in the discovery cohort (BDR). Following each clustering methods, distinct features related to the identified subtypes were captured using classification models such as sparse partial least square discriminant analysis and random forest. The most accurate models comprising the distinct features were used to classify the samples in the UKBBN cohort into the appropriate subtypes. Clinical and pathological evaluation of the identified subtypes have been performed for both discovery and replication cohortsResultWe identified well‐defined cluster of samples in the BDR cohort. We achieved acceptable prediction values in the test data to classify the subgroups using the distinct methylation features related to the identified subtypes (AUC: 0.86 – 0.96). The best performing classification model was applied on the other independent cohort and similarly labelled samples across different cohorts were extensively characterized using relevant clinical and pathological information.ConclusionWe used state‐of the‐art methods to undertake the first systematic, DNA methylomic‐based subtyping of AD. this is a vital step to unravel the heterogeneity of the disease

Identifying factors associated with child malnutrition in Ghana: a cross-sectional study using Bayesian multilevel ordinal logistic regression approach

ObjectiveIn developing countries, malnutrition is a noteworthy concern related to the well-being of people, and this study aimed to determine the factors that affect malnutrition among children below 5 years...

Can teacher quality be profiled? A cluster analysis of teachers' beliefs, practices and students' perceptions of effectiveness

AbstractA critical aim of teacher quality research is to identify and distil its multiple components. While many such components have been identified in the literature, there is a scarcity of research that has attempted to empirically investigate how these elements integrate within a larger profile of teacher quality, including how particular teachers' beliefs, behaviours and outcomes cluster into unique profiles of teachers. In this study, a group of Australian high‐school teachers completed an extensive teacher beliefs questionnaire, undertook the Visible Classroom program to record and document their use of high‐leverage teaching practices and had their students complete a survey based on perceptions of effective teaching. The results were analysed using cluster analysis to determine if distinct groups of teachers could be identified based on similarities in beliefs, behaviours and student perceptions of teaching. The results suggested multiple distinct clusters of teachers with distinguishing types of beliefs, frequency of practices and student perceptions of teaching. Most notably, the results pointed to a unique cluster of teachers who were most distinguishable in their self‐efficacy, intrinsic motivation and personal responsibility beliefs, as well as highly active teaching behaviours in the classroom. This group also possessed the highest levels of student perceptions of teaching. The findings provide new insights into the complex profiles that shape teacher quality and implications for future research.

MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog–cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

Numerical simulation study on mesoscopic metallic foam core sandwich panels under hypervelocity impact

Due to its lightweight and superior energy absorption characteristics, metallic foams are exceptionally suitable for the protective shielding of spacecraft against debris. The mesoscopic structure of such a foam plays a pivotal role in enhancing its outstanding protective performance. Numerical simulations permit us to explore the damage mechanism of its internal structure and the evolving characteristics of debris clouds. By adopting the three-dimensional Voronoi tessellation in conjunction with the algorithm of background mesh mapping, this study constructed mesoscopic finite element models that accurately reflect the internal randomness and variability in ligament width within the metallic foam. Subsequently, numerical simulations were executed employing the FE-SPH adaptive method within the LS-DYNA. Comparative experimental cases substantiated the validity of the simulations, and an exhaustive mesh sensitivity analysis was conducted. Optimizing results from these simulations following a normal impact, we delved into the propagation of stress waves within the foam core, alongside the subsequent internal structural damage. Furthermore, we explored the damage mechanism of foam sandwich panels subjected to hypervelocity impacts. According to the fragmentation process of the projectile and the evolution of debris clouds, it was discerned that the random internal structure of the foam engenders an asymmetric and skewed debris cloud, dispersing its energy and culminating in multiple concentrated particle clusters with a predilection towards a particular direction. Tracking the velocity fluctuations of these ‘dominant particles’ and the inflicted damage on the rear facesheet revealed that the agglomerated tiny fragments of the projectile are primarily responsible for perforating the rear facesheet.

Household heating fuels impact on Acute Respiratory Infection (ARI) symptoms among children in Punjab, Pakistan

Exposure to household air pollution (HAP) accounted for the loss of 86 million healthy lives in 2019, with almost half of all deaths due to lower respiratory infection among children under 5 years of age. Similarly, the situation in Punjab – Pakistan’s largest province – is also not promising. This study was conducted to examine household energy consumption and respiratory symptoms among children under the age of five in rural and urban areas of Punjab. Using data from the Multiple Indicator Cluster Survey (MICS) 2017-18, logistic regression models were applied to the data of a sample of 35,000 children under the age of five living in households with polluting heating fuels. A hypothesis was formulated to investigate the relationship between polluting heating activities and respiratory infections among children under five. Those Children who live in households having traditional space heaters without chimneys are 50% more likely to have symptoms of Acute Respiratory Infection (ARI) compared to those whose households have chimneys with traditional space heaters. When households utilize polluting heating fuel, the likelihood of children experiencing rapid, shortness of breath increases by 49%, and the likelihood of children displaying ARI symptoms characterized by coughing rises by approximately 30%. This study proposed a complete banning of polluting heating activities and replacing it with cleaner ones using financial incentives. It is pertinent to raise awareness campaigns majorly focusing on the guidelines to adopt better heat output with less harmful emissions.

Effects of parental migration on early childhood development of left-behind children in Bangladesh: Evidence from a nationally representative survey.

In Bangladesh, as in other low- and middle-income countries, parents frequently migrate to other areas, often for employment opportunities, leaving their children behind with the hope that their earnings will contribute to securing a better future for them. However, the absence of parents due to migration can have negative implications for the well-being of these left-behind children. Despite the existence of studies investigating this phenomenon, the evidence thus far has produced inconclusive findings, with no specific data available from Bangladesh. Therefore, the objective of this study was to examine the effects of parental migration on the early childhood development of left-behind children in Bangladesh. The present study utilized data from the Bangladesh Multiple Indicator Cluster Survey (MICS) conducted in 2019. A sample of 8,833 children aged 3-4 years was included in the analysis. The Early Childhood Development Index (ECDI) and its individual domains served as the outcome variables of interest. The primary explanatory variables considered in the analysis were father migration, mother migration, migration of both parents, and migration of either parent. To assess the association between the outcomes and explanatory variables, multilevel logistic regression analysis was employed, controlling for relevant covariates. Approximately 29% of all children in the study were not developmentally on track, as measured by the ECDI. When examining the individual domains of the ECDI, only 9% of the total children demonstrated developmental progress in the learning domain. Regarding the association between parental migration and ECDI outcomes, we observed a 26% decrease in the likelihood of overall ECDI among children with a migrated father (OR: 0.74, 95% CI: 0.54-0.93). This decrease became even more pronounced, reaching 37% (OR: 0.63, 95% CI: 0.48-0.97), among children with both parents migrated, compared to children with neither parent migrated. The findings of this study indicate that parental migration, especially when both parents are involved, has a substantial negative impact on the likelihood of achieving favourable ECDI outcomes for children in Bangladesh. To ensure the optimal development of children with migrating parents, it is crucial to strengthen early childhood development education programs and implement robust social safety nets. These efforts should specifically target the unique needs and challenges faced by children with migrated parents, providing them with the necessary support and resources for their holistic development.