SmartFog: Training the Fog for the energy-saving analytics of Smart-Meter data

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively

An accuracy vs. complexity comparison of deep learning architectures for the detection of covid-19 disease

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%

Learning-in-the-Fog (LiFo): Deep learning meets Fog Computing for the minimum-energy distributed early-exit of inference in delay-critical IoT realms

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-Alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-Time. Motivated by this consideration, this article focuses on the optimized design and performance validation of {L} earning-{i} ext{n}-The-Fo g (LiFo), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-Tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this article are threefold, namely: (i) we design the main building blocks and supporting services of the LiFo architecture by explicitly accounting for the multiple constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an adaptive algorithm for the minimum-energy distributed joint allocation and reconfiguration of the available computing-plus-networking resources of the LiFo platform. Interestingly enough, the designed algorithm is capable to self-detect (typically, unpredictable) environmental changes and quickly self-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an Information Centric-based networking architecture, which enables the LiFo platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay LiFo performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-The-Art benchmark solutions that do not rely on the Fog support

Deepfogsim: A toolbox for execution and performance evaluation of the inference phase of conditional deep neural networks with early exits atop distributed fog platforms

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox

Effects of inhalable particulate matter on blood coagulation.

BACKGROUND: Particulate matter (PM) exposure has been linked to increased risk of cardiovascular disease, possibly resulting from hypercoagulability and thrombosis. Lung and systemic inflammation resulting from PM inhalation may activate blood coagulation, but mechanisms for PM-related hypercoagulability are still largely unknown. OBJECTIVES: To identify coagulation mechanisms activated by PM in a population with well-characterized exposure. METHODS: We measured prothrombin time (PT), activated partial thromboplastin time, endogenous thrombin potentials (ETPs) with/without exogenous triggers and with/without soluble thrombomodulin, tissue-type plasminogen activator (t-PA) antigen, D-dimer and C-reactive protein (CRP) in 37 workers in a steel production plant with well-characterized exposure to PM with aerodynamic diameter of < 1 mum (PM(1)) and coarse PM (PM(10) - PM(1)). Blood samples were collected from each subject on the first (baseline) and last (postexposure) day of a 4-day work week. We analyzed differences between baseline and postexposure levels using a paired Student's t-test. We fitted multivariate mixed-regression models to estimate the associations of interquartile range PM(1) and coarse PM exposure with parameter levels. RESULTS: None of the parameters showed any significant changes from baseline in postexposure samples. However, exposure levels were associated with shorter PT (beta[PM(1)] = -0.33 s, P = 0.08; beta[PM(coarse)] = - 0.33 s, P = 0.01), and higher ETP without exogenous triggers and with thrombomodulin (beta[PM(1)] = + 99 nm min, P = 0.02; beta[PM(coarse)] = + 66 nm min, P = 0.05), t-PA (beta[PM(1)] = + 0.72 ng mL(-1), P = 0.01; beta[PM(coarse)] = + 0.88 ng mL(-1), P = 0.04), and CRP (beta[PM(1)] = + 0.59 mg L(-1), P = 0.03; beta[PM(coarse)] = + 0.48 mg L(-1), P = 0.01). CONCLUSIONS: PM exposure did not show any short-term effect within the week of the study. The association of PM exposure with PT, ETP and CRP provides some evidence of long-term effects on inflammation and coagulation

Postural Changes in Blood Pressure Associated with Interactions between Candidate Genes for Chronic Respiratory Diseases and Exposure to Particulate Matter

BACKGROUND. Fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM2.5)] has been associated with autonomic dysregulation. OBJECTIVE. We hypothesized that PM2.5 influences postural changes in systolic blood pressure (ΔSBP) and in diastolic blood pressure (ΔDBP) and that this effect is modified by genes thought to be related to chronic lung disease. METHODS. We measured blood pressure in participants every 3-5 years. ΔSBP and ΔDBP were calculated as sitting minus standing SBP and DBP. We averaged PM2.5 over 48 hr before study visits and analyzed 202 single nucleotide polymorphisms (SNPs) in 25 genes. To address multiple comparisons, data were stratified into a split sample. In the discovery cohort, the effects of SNP x PM2.5 interactions on ΔSBP and ΔDBP were analyzed using mixed models with subject-specific random intercepts. We defined positive outcomes as p < 0.1 for the interaction; we analyzed only these SNPs in the replicate cohort and confirmed them if p < 0.025 with the same sign. Confirmed associations were analyzed within the full cohort in models adjusted for anthropometric and lifestyle factors. RESULTS. Nine hundred forty-five participants were included in our analysis. One interaction with rs9568232 in PHD finger protein 11 (PHF11) was associated with greater ΔDBP. Interactions with rs1144393 in matrix metalloprotease 1 (MMP1) and rs16930692, rs7955200, and rs10771283 in inositol 1,4,5-triphosphate receptor, type 2 (ITPR2) were associated with significantly greater ΔSBP. Because SNPs associated with ΔSBP in our analysis are in genes along the renin-angiotensin pathway, we then examined medications affecting that pathway and observed significant interactions for angiotensin receptor blockers but not angiotensin-converting enzyme inhibitors with PM2.5. CONCLUSIONS. PM2.5 influences blood pressure and autonomic function. This effect is modified by genes and drugs that also act along this pathway.National Institute of Environmental Health Sciences (T32 ES07069, ES0002, ES015172-01, ES014663, P01 ES09825); United States Environmental Protection Agency (R827353, R832416); National Institutes of Health/National Institute of Aging (AG027014); United States Department of Veterans Affairs; Massachusetts Veterans Epidemiology Research and Information Cente

Combined risk factors for melanoma in a Mediterranean population

A case–control study of non-familial melanoma including 183 incident cases and 179 controls was conducted in North-Eastern Italy to identify important risk factors and determine how combination of these affects risk in a Mediterranean population. Presence of dysplastic nevi (OR = 4.2, 95% CI = 2.4–7.4), low propensity to tan (OR = 2.4, 95% CI = 1.1–5.0), light eye (OR = 2.4, 95% CI = 1.1–5.2), and light skin colour (OR = 4.1, 95% CI = 1.4–12.1) were significantly associated with melanoma risk after adjustment for age, gender and pigmentation characteristics. A chart which identifies melanoma risk associated with combinations of these factors is presented; it can be used to identify subjects who would most benefit from preventive measures in Mediterranean populations. According to the combination of these factors, a relative risk range from 1 to 98.5 was found. Light skin colour, high number of sunburns with blistering, and low propensity to tan were significantly associated with melanoma thickness, possibly indicating that individuals with these characteristics underestimate their risk and seek attention when their lesion is already advanced. © 2001 Cancer Research Campaig

A panel study of occupational exposure to fine particulate matter and changes in DNA methylation over a single workday and years worked in boilermaker welders

Background: Exposure to pollutants including metals and particulate air pollution can alter DNA methylation. Yet little is known about intra-individual changes in DNA methylation over time in relationship to environmental exposures. Therefore, we evaluated the effects of acute- and chronic metal-rich PM2.5 exposures on DNA methylation. Methods: Thirty-eight male boilermaker welders participated in a panel study for a total of 54 person days. Whole blood was collected prior to any welding activities (pre-shift) and immediately after the exposure period (post-shift). The percentage of methylated cytosines (%mC) in LINE-1, Alu, and inducible nitric oxide synthase gene (iNOS) were quantified using pyrosequencing. Personal PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) was measured over the work-shift. A questionnaire assessed job history and years worked as a boilermaker. Linear mixed models with repeated measures evaluated associations between DNA methylation, PM2.5 concentration (acute exposure), and years worked as a boilermaker (chronic exposure). Results: PM2.5 exposure was associated with increased methylation in the promoter region of the iNOS gene (β = 0.25, SE: 0.11, p-value = 0.04). Additionally, the number of years worked as a boilermaker was associated with increased iNOS methylation (β = 0.03, SE: 0.01, p-value = 0.03). No associations were observed for Alu or LINE-1. Conclusions: Acute and chronic exposure to PM2.5 generated from welding activities was associated with a modest change in DNA methylation of the iNOS gene. Future studies are needed to confirm this association and determine if the observed small increase in iNOS methylation are associated with changes in NO production or any adverse health effect

The effect of morphine upon DNA methylation in ten regions of the rat brain

Morphine is one of the most effective analgesics in medicine. However, its use is associated with the development of tolerance and dependence. Recent studies demonstrating epigenetic changes in the brain after exposure to opiates have provided insight into mechanisms possibly underlying addiction. In this study, we sought to identify epigenetic changes in ten regions of the rat brain following acute and chronic morphine exposure. We analyzed DNA methylation of six nuclear-encoded genes implicated in brain function (Bdnf, Comt, Il1b, Il6, Nr3c1 and Tnf) and three mitochondrially-encoded genes (Mtco1, Mtco2 and Mtco3), and measured global 5-methylcytosine (5-mc) and 5-hydroxymethylcytosine (5-hmc) levels. We observed differential methylation of Bdnf and Il6 in the pons, Nr3c1 in the cerebellum, and Il1b in the hippocampus in response to acute morphine exposure (all p<0.05). Chronic exposure was associated with differential methylation of Bdnf and Comt in the pons, Nr3c1 in the hippocampus and Il1b in the medulla oblongata (all p<0.05). Global 5-mc levels significantly decreased in the superior colliculus following both acute and chronic morphine exposure, and increased in the hypothalamus following chronic exposure. Chronic exposure was also associated with significantly increased global 5-hmc levels in the cerebral cortex, hippocampus and hypothalamus, but significantly decreased in the midbrain. Our results demonstrate, for the first time, highly localized epigenetic changes in the rat brain following acute and chronic morphine exposure. Further work is required to elucidate the potential role of these changes in the formation of tolerance and dependence