IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

An adaptive discrete Newton method for regularization-free Bingham model

[EN] Developing a numerical and algorithmic tool which correctly identifies unyielded regions in yield stress fluid flow is a challenging task. Two approaches are commonly used to handle the singular behaviour at the yield surface, i.e. the Augmented Lagrangian approach and the regularization approach, respectively. Generally in the regularization approach, solvers do not perform efficiently when the regularization parameter gets very small. In this work, we use a formulation introducing a new auxiliary stress. The three field formulation of the yield stress fluid corresponds to a regularization-free Bingham formulation. The resulting set of equations arising from the three field formulation is solved efficiently and accurately by a monolithic finite element method. The velocity and pressure are discretized by the higher order stable FEM pair Q2/Pdisc 1 and the auxiliary stress is discretized by the Q2 element. Furthermore, this problem is highly nonlinear and presents a big challenge to any nonlinear solver. Therefore, we developed a new adaptive discrete Newton method, which evaluates the Jacobian with the divided difference approach. We relate the step length to the rate of the actual nonlinear reduction for achieving a robust adaptive Newton method. We analyse the solvability of the problem along with the adaptive Newton method for Bingham fluids by doing numerical studies for a prototypical configuration ”viscoplastic fluid flow in a channel”.We would like to thank the Deutsche Forschungsgemeinschaft (DFG) for their financial support under the DFG Priority Program SPP 1962. The authors also acknowledge the support by LS3 and LiDO3 team at ITMC, TU Dortmund UniversityFatima, A.; Turek, S.; Ouazzi, A.; Afaq, MA. (2022). An adaptive discrete Newton method for regularization-free Bingham model. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 180-189. https://doi.org/10.4995/YIC2021.2021.12389OCS18018

IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

Monolithic Newton-Multigrid Solver for Multiphase Flow Problems with Surface Tension

[EN] We have developed a monolithic Newton-multigrid solver for multiphase flow problems which solves velocity, pressure and interface position simultaneously. The main idea of our work is based on the formulations discussed in [1], where it points out the feasibility of a fully implicit monolithic solver for multiphase flow problems via two formulations, a curvature-free level set approach and a curvature-free cutoff material function approach. Both formulations are fully implicit and have the advantages of requiring less regularity, since neither normals nor curvature are explicitly calculated, and no capillary time restriction. Furthermore, standard Navier-Stokes solvers might be used, which do not have to take into account inhomogeneous force terms. The reinitialization issue is integrated with a nonlinear terms within the formulations.The nonlinearity is treated with a Newton-type solver with divided difference evaluation of the Jacobian matrices. The resulting linearized system inside of the outer Newton solver is a typical saddle point problem which is solved using the geometrical multigrid with Vanka-like smoother using higher order stable FEM pair $Q_2/P^{\text{disc}}_1$ for velocity and pressure and $Q_2$ for all other variables. The method is implemented into an existing software packages for the numerical simulation of multiphase flows (FeatFlow). The robustness and accuracy of this solver is tested for two different test cases, i.e. static bubble and oscillating bubble, respectively [2].Muhammad Aaqib Afaq would like to thank Erasmus Mundus INTACT project, funded by the European Union as part of the Erasmus Mundus programme and the National University of Sciences and Technology (NUST) for their financial support. The authors also acknowledge the support by LS3 and LiDO3 team at ITMC, TU Dortmund University.Afaq, MA.; Turek, S.; Ouazzi, A.; Fatima, A. (2022). Monolithic Newton-Multigrid Solver for Multiphase Flow Problems with Surface Tension. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 190-199. https://doi.org/10.4995/YIC2021.2021.12390OCS19019

Impact of Physical Exercise on Gut Microbiome, Inflammation, and the Pathobiology of Metabolic Disorders.

The gastrointestinal tract (GIT) harbors a complex and diverse microbial composition that outnumbers our own body cells and their gene contents. These microbes play a significant role in host metabolism and energy homeostasis. Emerging evidence suggests that the GIT microbiome significantly contributes to host health and that impairments in the microbiome may cause the development of metabolic diseases. The microbiome architecture is shaped by several genetic and environmental factors, including nutrition and physical activity. Physical exercise has preventive or therapeutic effects in respiratory, cardiovascular, neuroendocrine, and muscular diseases. Yet, we still have little information of the beneficial effects of physical exercise on GIT health and microbial composition. Furthermore, we are not aware whether exercise-derived benefits on microbiome diversity can beneficially influence other tissues and body organs. The aim of this article is to review the available literature on exercise-induced microbiome changes and to explain how these changes may induce inflammatory, immune, and oxidative responses that may contribute to the improvement of metabolic disorders. A systemic and comprehensive search of the relevant literature using MEDLINE and Google Scholar databases was conducted during fall 2018 and spring 2019. The search identified sixty-two research and review articles that discussed exercise-induced microbiome changes. The review of the relevant literature suggests that exercise-induced microbial changes affect the host's immune pathways and improve energy homeostasis. Microbes release certain neuroendocrine and immune-modulatory factors that may lower inflammatory and oxidative stress and relieve patients suffering from metabolic disorders. Exercise-induced changes in microbial diversity are able to improve tissue metabolism, cardiorespiratory fitness, and insulin resistance

Effect of raster angle and infill pattern on the in-plane and edgewise flexural properties of fused filament fabricated acrylonitrile–butadiene–styrene

Fused Filament Fabrication (FFF) is a popular additive manufacturing process to produce printed polymer components, whereby their strength is highly dependent on the process parameters. The raster angle and infill pattern are two key process parameters and their effects on flexural properties need further research. Therefore, the present study aimed to print test specimens with varying raster angles and infill patterns to learn their influence on the in-plane and edgewise flexural properties of acrylonitrile–butadiene–styrene (ABS) material. The results revealed that the highest in-plane and edgewise flexural moduli were obtained when printing was performed at 0 ° raster angle. In comparison, the lowest values were obtained when the printing was executed with a 90 ° raster angle. Regarding the infill pattern, the tri-hexagon pattern showed the largest in-plane modulus, and the quarter-cubic pattern exhibited the greatest edgewise flexural modulus. However, considering both the modulus and load carrying capacity, the quarter-cubic pattern showed satisfactory performance in both planes. Furthermore, scanning electron microscopy was used to investigate the failure modes, i.e., raster rupture, delamination of successive layers and void formation. The failure occurred either due to one or a combination of these modes

Prevalence of stunting among under-five children in refugee and internally displaced communities: a systematic review and meta-analysis

BackgroundA pooled estimate of stunting prevalence in refugee and internally displaced under-five children can help quantify the problem and focus on the nutritional needs of these marginalized groups. We aimed to assess the pooled prevalence of stunting in refugees and internally displaced under-five children from different parts of the globe.MethodsIn this systematic review and meta-analysis, seven databases (Cochrane, EBSCOHost, EMBASE, ProQuest, PubMed, Scopus, and Web of Science) along with “preprint servers” were searched systematically from the earliest available date to 14 February 2023. Refugee and internally displaced (IDP) under-five children were included, and study quality was assessed using “National Heart, Lung, and Blood Institute (NHLBI)” tools.ResultsA total of 776 abstracts (PubMed = 208, Scopus = 192, Cochrane = 1, Web of Science = 27, Embase = 8, EBSCOHost = 123, ProQuest = 5, Google Scholar = 209, and Preprints = 3) were retrieved, duplicates removed, and screened, among which 30 studies were found eligible for qualitative and quantitative synthesis. The pooled prevalence of stunting was 26% [95% confidence interval (CI): 21–31]. Heterogeneity was high (I2 = 99%, p < 0.01). A subgroup analysis of the type of study subjects revealed a pooled stunting prevalence of 37% (95% CI: 23–53) in internally displaced populations and 22% (95% CI: 18–28) among refugee children. Based on geographical distribution, the stunting was 32% (95% CI: 24–40) in the African region, 34% (95% CI: 24–46) in the South-East Asian region, and 14% (95% CI: 11–19) in Eastern Mediterranean region.ConclusionThe stunting rate is more in the internally displaced population than the refugee population and more in the South-East Asian and African regions. Our recommendation is to conduct further research to evaluate the determinants of undernutrition among under-five children of refugees and internally displaced populations from different regions so that international organizations and responsible stakeholders of that region can take effective remedial actions.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387156, PROSPERO [CRD42023387156]

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Molecular Assessment of Glutathion S-Transferases (GSTT1 & GSTM1) Genotypes in HCV Infection

Glutathione S-Transferases (GSTs) are super family phase II detoxification enzymes, which regulate oxidative stress. GST Mu-1 (GSTM1) and GST Theta-1 (GSTT1) are expressed frequently in liver cell and have been observed to have prominent role in neutralizing reactive oxygen species producing as results of normal metabolism, stress or infection. Oxidative stress plays an important role in the pathogenesis of HCV-induced damage because GSTs directly involved in the detoxification of reactive oxygen species. Total 76 blood samples were collected. Various risk factors regarding HCV & GSTs were evaluated. Human genomic DNA was extracted through phenol/chloroform method and GSTs genotype was analyzed observed. Among 56 patient’s samples, GSTT1 (-), GSTM1 (-) and null genotype were 21.05%, 28.95%, 23.69% respectively while 26.3% of individuals showed normal GST pattern. Similarly among the male (63.16%) the ratios of GSTT1(-), GSTM1(-) and null genotype were 11.84%, 17.11% and 18.42% respectively, while among total female (36.84%) the ratios for GSTT1(-), GSTM1(-) and Null genotypes were 9.21%, 11.84 % and 5.26 % respectively. The individuals younger than 31 years showed GSTT1(-) 2.63 %, GSTM1(-) 6.58 % and null genotype 3.95 %. While individuals elder than 31 years GSTT1(-) were 18.42% GSTM1(-), 22.37% and null genotypes were 19.74%. It has been concluded that increase in age, increases the chances of both GSTT1 & GSTM1 deletion(-), irrespective of null genotypes, however chronic HCV infection produce frequent GST’s null genotypes. Among various risk factors tobacco addiction and unhygienic life standard seems to be the risk factors