Technology\u27s Effect on Firm Size: Manufacturing vs. Service

We develop theory that describes how increased IT investment motivates different actions within different types of industries. We contend that manufacturing firms tend to have revenue that is firm dependent, regardless of the number of employees and thus use IT to reduce costs by reducing firm size, as stated in previous theory. However, retail and service firms tend to have revenue that is tied to the number of employees and use IT to increase firm size in order to allow greater revenue. Using 629 yearly observations from 37 industries from 1985 to 2005, we find that IT investment precedes size decreases with manufacturing firms and size increases with retail and service firms. Further, impulse response functions indicate that differences in firm size differences following IT investment eventually vanish, and non- IT-investing firms eventually achieve the same firm size after several years, indicating that IT allows firms to be more responsive

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

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%

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

Age and gender specific cut-off points for body fat parameters among adults in Qatar

© 2020 The Author(s). Background: Excessive body fat is the leading cause of many metabolic disorders. Therefore, assessing levels of body fat associated with risk of disease in specific populations is crucial. The present study aimed to identify optimal cut-off values of body fat composition including total body fat, body fat percentage, visceral fat, and trunk fat, in order to predict metabolic risk in the Qatari population. Methods: This cross-sectional study was based on Qatar Biobank data of 2407 Qatari adults (1269 male and 1138 female) aged 21-70 years old. Individuals' height, weight and body fat percentage were obtained. Blood test data including lipid profile, blood glucose and HbA1c data were also obtained. The area under the curve was calculated using ROC analysis to obtain the body fat percentage associated with risk of disease. Results: The cut-off points for total fat for those aged < 40 were 34.0 kg, and for those aged ≥40 were 30.7 kg and 35.6 kg in men and women, respectively. The cut-off for body fat percent for those aged < 40 were 35.1 and 45.1%, and for those aged ≥40 were 34.8 and 46.3% in men and women, respectively. The cut-off points for trunk fat percent for those aged < 40 were 19.5 and 22.4%, and for those aged ≥40 were 21.6 and 23.4% in men and women, respectively. The cut-off points for visceral fat percent for those aged < 40 were 1.4 and 1.0%, and for those aged ≥40 were 1.9 and 1.4% in men and women, respectively. Conclusion: This study established Qatari adult-specific cut-off values of body fat for different age and gender groups.This research is funded by Qatar University. J.A. Tur is funded by CIBEROBN (CB12/03/30038), Instituto de salud Carlos III, Spain and European Regional Development Fun

High genetic diversity of measles virus, World Health Organization European region, 2005-2006

During 2005-2006, nine measles virus (MV) genotypes were identified throughout the World Health Organization European Region. All major epidemics were associated with genotypes D4, D6, and B3. Other genotypes (B2, D5, D8, D9, G2, and H1) were only found in limited numbers of cases after importation from other continents. The genetic diversity of endemic D6 strains was low; genotypes C2 and D7, circulating in Europe until recent years, were no longer identified. The transmission chains of several indigenous MV strains may thus have been interrupted by enhanced vaccination. However, multiple importations from Africa and Asia and virus introduction into highly mobile and unvaccinated communities caused a massive spread of D4 and B3 strains throughout much of the region. Thus, despite the reduction of endemic MV circulation, importation of MV from other continents caused prolonged circulation and large outbreaks after their introduction into unvaccinated and highly mobile communities