Coverage Problems in Rechargeable IoT Networks

Coverage is required by various applications that leverage Internet of things (IoT) networks to monitor one or more targets. A specific coverage requirement could be to ensure all targets, e.g., the ingress/egress of a building, are monitored by a sensor node for the longest time period possible. Another requirement could be coverage quality, which relates to the number of samples collected by sensor devices. The coverage quality or lifetime of targets, however, is limited by the energy of sensor devices. This is because the operational time of fixed or mobile sensor nodes, e.g., unmanned aerial vehicles (UAVs), is a function of their available energy. In this respect, many works have proposed to equip sensor nodes with energy harvesting (EH) capabilities. Henceforth, this thesis aims to address targets coverage problems in EH IoT networks. First, it focuses on maximizing complete targets coverage lifetime using static devices. To this end, the problem at hand is to decide the set cover in each time slot, where each set cover ensures all targets are under the coverage of a device. In this respect, it presents a mixed integer linear program (MILP) to determine complete targets coverage. Further, it shows how the MILP can be represented as a factor graph, which can then be used by the belief propagation algorithm to compute set covers over time using the residual energy of devices. The second aim focuses on an IoT network with UAVs and solar-powered charging stations. It considers K-coverage of targets, where at least K targets must be covered by a UAV. To this end, this thesis first formulates an UAVs assignment problem as an MILP. Although the MILP yields the optimal assignment, it does so using non-causal energy arrivals information at charging stations. To this end, this thesis proposes two solutions that use only causal energy arrivals information. In particular, these approaches employ model predictive control (MPC) or Monte Carlo tree search (MCTS). Further, they use a Gaussian mixture model (GMM) to estimate future energy arrivals. Lastly, this thesis investigates the use of UAVs as aerial relays to boost coverage quality. The problem at hand is to decide the activation and data transmission rate of devices, and the location of UAVs. These quantities are optimized using an MILP. Further, this thesis proposes two heuristic solutions that decouple the MILP into different sub-problems; each sub-problem is then solved alternately. Moreover, the MPC solution relies only on causal energy arrivals information

Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet

With the rise of food safety traceability, unmanned supermarkets and autonomous shopping, the automatic identification technology of agricultural products such as vegetables in circulation and sales has become an urgent problem. This paper designs an intelligent vegetable identification system based on MobileNet to solve intelligent identification problem of vegetable sales in supermarkets. The system includes main control core, visual processing module, pressure sensor, voice broadcasting module and display module. When the system detects that there are vegetables to be weighed, the visual processing module completes the classification of vegetables, broadcasts the name, unit price and total price of vegetables by voice, and displays the weight, unit price and total price by OLED. The machine vision processing module is constructed by deep separable convolution (DSC). It realizes the separation of channels and regions, so it has high computing efficiency and is more suitable for embedded devices with low memory space. The experimental results show that the overall recognition rate of five vegetables reaches 97.33% under three kinds of illumination. The system has the advantages of stability, intelligence and convenience

Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/

Unraveling the mechanisms of intervertebral disc degeneration: an exploration of the p38 MAPK signaling pathway

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD

Unraveling Molecular Mechanisms of Antibiotic Resistance Through Multiscale Simulations and Explainable Machine Learning

Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical vi mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs

Unraveling Molecular Mechanisms of Antibiotic Resistance Through Multiscale Simulations and Explainable Machine Learning

Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical vi mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs

Modeling the loyalty of individuals towards banks

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