Unsupervised deep learning-based reconfigurable intelligent surface aided broadcasting communications in industrial IoTs

This paper presents a general system framework which lays the foundation for Reconfigurable Intelligent Surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) is blocked completely. In this context, an RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, the DNN technique relies on unsupervised deep learning. Our numerical results show that the both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using deep learning reduces the computational latency by more than 10 times with an almost identical total ergodic spectral efficiency achieved. These numerical results reveal that while using conventional RCG method may provide unsatisfactory latency, DNN technique shows much promise for enabling RIS in ultra reliable and low latency communications (URLLC) in the context of IIoTs

Exploiting Secrecy Performance of Uplink NOMA in Cellular Networks

Funding Information: This work was supported in part by the Air Force Office of Scientific Research under Award FA9550-20-1-0090, and in part by the National Science Foundation under Grant CNS-2034218.Peer reviewedPublisher PD

Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

© 2018, King Fahd University of Petroleum & Minerals. Falls represent a major problem for the elderly people aged 60 or above. There are many monitoring systems which are currently available to detect the fall. However, there is a great need to propose a system which is of optimal effectiveness. In this paper, we propose to develop a low-cost fall detection system to precisely detect an event when an elderly person accidentally falls. The fall detection algorithm compares the acceleration with lower fall threshold and upper fall threshold values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added to our proposed method to enhance the performance and accuracy. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. A smartphone application will ensure that the notifications are delivered to the elderly person’s relatives so that medical attention can be provided with minimal delay. The system was tested by volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public datasets and it also achieved the same percentage in sensitivity and accuracy as in our recorded datasets

New method for measuring azimuthal distributions in nucleus-nucleus collisions

The methods currently used to measure azimuthal distributions of particles in heavy ion collisions assume that all azimuthal correlations between particles result from their correlation with the reaction plane. However, other correlations exist, and it is safe to neglect them only if azimuthal anisotropies are much larger than 1/sqrt(N), with N the total number of particles emitted in the collision. This condition is not satisfied at ultrarelativistic energies. We propose a new method, based on a cumulant expansion of multiparticle azimuthal correlations, which allows to measure much smaller values of azimuthal anisotropies, down to 1/N. It is simple to implement and can be used to measure both integrated and differential flow. Furthermore, this method automatically eliminates the major systematic errors, which are due to azimuthal asymmetries in the detector acceptance.Comment: final version (misprints corrected), to be published in Phys.Rev.

Stacking-Order-Dependent Excitonic Properties Reveal Interlayer Interactions in Bulk ReS₂

Rhenium disulfide, a member of the transition metal dichalcogenide family of semiconducting materials, is unique among 2D van der Waals materials due to its anisotropy and, albeit weak, interlayer interactions, confining excitons within single atomic layers and leading to monolayer-like excitonic properties even in bulk crystals. While recent work has established the existence of two stacking modes in bulk, AA and AB, the influence of the different interlayer coupling on the excitonic properties has been poorly explored. Here, we use polarization-dependent optical measurements to elucidate the nature of excitons in AA and AB-stacked rhenium disulfide to obtain insight into the effect of interlayer interactions. We combine polarization-dependent Raman with low-temperature photoluminescence and reflection spectroscopy to show that, while the similar polarization dependence of both stacking orders indicates similar excitonic alignments within the crystal planes, differences in peak width, position, and degree of anisotropy reveal a different degree of interlayer coupling. DFT calculations confirm the very similar band structure of the two stacking orders while revealing a change of the spin-split states at the top of the valence band to possibly underlie their different exciton binding energies. These results suggest that the excitonic properties are largely determined by in-plane interactions, however, strongly modified by the interlayer coupling. These modifications are stronger than those in other 2D semiconductors, making ReS2 an excellent platform for investigating stacking as a tuning parameter for 2D materials. Furthermore, the optical anisotropy makes this material an interesting candidate for polarization-sensitive applications such as photodetectors and polarimetry.</p

Hazard Analysis of Critical Control Points Assessment as a Tool to Respond to Emerging Infectious Disease Outbreaks

Highly pathogenic avian influenza virus (HPAI) strain H5N1 has had direct and indirect economic impacts arising from direct mortality and control programmes in over 50 countries reporting poultry outbreaks. HPAI H5N1 is now reported as the most widespread and expensive zoonotic disease recorded and continues to pose a global health threat. The aim of this research was to assess the potential of utilising Hazard Analysis of Critical Control Points (HACCP) assessments in providing a framework for a rapid response to emerging infectious disease outbreaks. This novel approach applies a scientific process, widely used in food production systems, to assess risks related to a specific emerging health threat within a known zoonotic disease hotspot. We conducted a HACCP assessment for HPAI viruses within Vietnam’s domestic poultry trade and relate our findings to the existing literature. Our HACCP assessment identified poultry flock isolation, transportation, slaughter, preparation and consumption as critical control points for Vietnam’s domestic poultry trade. Introduction of the preventative measures highlighted through this HACCP evaluation would reduce the risks posed by HPAI viruses and pressure on the national economy. We conclude that this HACCP assessment provides compelling evidence for the future potential that HACCP analyses could play in initiating a rapid response to emerging infectious diseases

Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics

In this paper we present compartmentalized neuron arraying (CNA) microfluidic circuits for the preparation of neuronal networks using minimal cellular inputs (10–100-fold less than existing systems). The approach combines the benefits of microfluidics for precision single cell handling with biomaterial patterning for the long term maintenance of neuronal arrangements. A differential flow principle was used for cell metering and loading along linear arrays. An innovative water masking technique was developed for the inclusion of aligned biomaterial patterns within the microfluidic environment. For patterning primary neurons the technique involved the use of meniscus-pinning micropillars to align a water mask for plasma stencilling a poly-amine coating. The approach was extended for patterning the human SH-SY5Y neuroblastoma cell line using a poly(ethylene glycol) (PEG) back-fill and for dopaminergic LUHMES neuronal precursors by the further addition of a fibronectin coating. The patterning efficiency Epatt was &gt;75% during lengthy in chip culture, with ~85% of the outgrowth channels occupied by neurites. Neurons were also cultured in next generation circuits which enable neurite guidance into all outgrowth channels for the formation of extensive inter-compartment networks. Fluidic isolation protocols were developed for the rapid and sustained treatment of the different cellular and sub-cellular compartments. In summary, this research demonstrates widely applicable microfluidic methods for the construction of compartmentalized brain models with single cell precision. These minimalistic ex vivo tissue constructs pave the way for high throughput experimentation to gain deeper insights into pathological processes such as Alzheimer and Parkinson Diseases, as well as neuronal development and function in health

The fate of acetic acid during glucose co-metabolism by the spoilage yeast Zygosaccharomyces bailii

Zygosaccharomyces bailii is one of the most widely represented spoilage yeast species, being able to metabolise acetic acid in the presence of glucose. To clarify whether simultaneous utilisation of the two substrates affects growth efficiency, we examined growth in single- and mixed-substrate cultures with glucose and acetic acid. Our findings indicate that the biomass yield in the first phase of growth is the result of the weighted sum of the respective biomass yields on single-substrate medium, supporting the conclusion that biomass yield on each substrate is not affected by the presence of the other at pH 3.0 and 5.0, at least for the substrate concentrations examined. In vivo(13)C-NMR spectroscopy studies showed that the gluconeogenic pathway is not operational and that [2-(13)C]acetate is metabolised via the Krebs cycle leading to the production of glutamate labelled on C(2), C(3) and C(4). The incorporation of [U-(14)C]acetate in the cellular constituents resulted mainly in the labelling of the protein and lipid pools 51.5% and 31.5%, respectively. Overall, our data establish that glucose is metabolised primarily through the glycolytic pathway, and acetic acid is used as an additional source of acetyl-CoA both for lipid synthesis and the Krebs cycle. This study provides useful clues for the design of new strategies aimed at overcoming yeast spoilage in acidic, sugar-containing food environments. Moreover, the elucidation of the molecular basis underlying the resistance phenotype of Z. bailii to acetic acid will have a potential impact on the improvement of the performance of S. cerevisiae industrial strains often exposed to acetic acid stress conditions, such as in wine and bioethanol production.This work was supported by Fundacao para a Ciencia e Tecnologia (FCT), Portugal Grant PTDC/AGR-ALI/102608/2008 and by project FCOMP-01-0124-FEDER- 007047 and by FEDER through POFC - COMPETE and national funds from FCT - project PEst-C/BIA/UI4050/2011. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

An Isothermal Model for Predicting Performance Loss in PEMFCs from BOP Leachates

In the development of proton exchange membrane fuel cells (PEMFCs), the cost of balance of plant (BOP) materials and their effect on PEMFC durability can retard commercialization. 1 One opportunity to decrease these costs would be to use off-the-shelf materials rather than custommade materials if leachates from the less expensive materials do not affect performance and lifetime. To understand contamination mechanisms and their impacts on PEMFCs, experimental studies conducted and found the sensitivity of performance to the low levels of contamination. 2-9 A model for contamination of a PEMFC which includes adsorption on the Pt catalyst, absorption into the membrane, and ion-exchange with ionomeric components is presented. The model predictions for three sources of voltage (i.e., performance) loss account for two-dimensional timedependent contamination along the channel and into the membrane as shown in 9 For typical parameters, the predicted voltage loss in the electrode by an ion-exchange mechanism shows slower reaction rates but greater performance losses than other mechanisms. More broadly, the model also provides a tolerance limits for contamination