SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance;the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% vs. U-Net++: 84.25%), sensitivity (our: 93.28% vs. U-Net++: 89.85%) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

Innovative angles from a youth epidemiological study: Data from the HK-YES in Hong Kong

There are few specific epidemiological studies focusing on youth mental health in a non-western population. In particular, studies which are designed to include a broad range of youth-relevant potential risk and protective factors during the COVID-19 period are scarce. Such data are important for formulating programs to enhance healthy coping, as well as to facilitate the screening of risk factors. We present data from a comprehensive youth epidemiological study in Hong Kong from innovative angles which extend beyond conventional prevalence reporting. For example we investigated the change in major depressive episode states in a cohort with longitudinal data, which provide us with insight into what determines the persistence of depression. We also look into suicidal ideation and the role of rumination in this population. We also report the use of a recently identified marker for brain inhibition/excitation balance (visual stress) as a predictor associated with mental health outcomes. Finally, we studied the role of personality in mediating mental health outcomes.

COVID-19 image classification based on Deep Learning

COVID-19 is diagnosed by nucleic acid testing, aided by Computed Tomography. In order to rapidly screen CT images of COVID-19, Squeeze-And-Excitation Network based network model combined with Deep Learning is proposed, which can adapt to learn important parts of the feature channel. Firstly, the feature Squeeze is carried out along the space dimension, and the output dimension matches the number of input feature channels. Secondly, the feature channel learns the feature channel characteristics by capturing the channel dependencies in the previous step. Finally, the weight is updated to model the correlation of feature channels. The Precision, Recall and Specificity were selected to be 92.8%, 92.8% and 93.7%, the Accuracy of the model was 93.24% for the whole sample specificity. Compared with the mainstream model, the experimental results of this model are improved greatly. © 2022 ACM.

A novel isothermal digital amplification system and its application for absolute quantification of respiratory infectious virus

Herein, we establish a novel isothermal digital amplification system termed digital nicking and extension chain reaction system-based amplification (dNESBA) by utilizing the isothermal NESBA technique and the newly developed miniaturized fluorescence monitoring system (mFMS). dNESBA enables parallel isothermal NESBA reactions in more than 10,000 localized droplet microreactors and read the fluorescence signals rapidly in 150 s by mFMS. This system could identify the genomic RNA (gRNA) extracted from target respiratory syncytial virus A (RSV A) as low as 10 copies with remarkable specificity. The practical applicability of dNESBA was also successfully verified by reliably detecting the gRNA in the artificial sputum samples with excellent reproducibility and accuracy. Due to the intrinsic advantages of isothermal amplifying technique including the elimination of the requirement of thermocycling device and the enhanced portability of the miniaturized read-out equipment, the dNESBA technique equipped with mFMS could serve as a promising platform system to achieve point-of-care (POC) digital molecular diagnostics, enabling absolute and ultra-sensitive quantification of various infectious pathogens even in an early stage.Copyright © 2023

Highly Twisted Conformation Thiopyrylium Photosensitizers for In Vivo Near Infrared‐II Imaging and Rapid Inactivation of Coronavirus

Despite significant effort, a majority of heavy‐atom‐free photosensitizers have short excitation wavelengths, thereby hampering their biomedical applications. Here, we present a facile approach for developing efficient near‐infrared (NIR) heavy‐atom‐free photosensitizers. Based on a series of thiopyrylium‐based NIR‐II (1000–1700 nm) dyads, we found that the star dyad HD with a sterically bulky and electron‐rich moiety exhibited configuration torsion and significantly enhanced intersystem crossing (ISC) compared to the parent dyad. The electron excitation characteristics of HD changed from local excitation (LE) to charge transfer (CT)‐domain, contributing to a ≈6‐fold reduction in energy gap (ΔEST), a ≈10‐fold accelerated ISC process, and a ≈31.49‐fold elevated reactive oxygen species (ROS) quantum yield. The optimized SP@HD‐PEG2K lung‐targeting dots enabled real‐time NIR‐II lung imaging, which precisely guided rapid pulmonary coronavirus inactivation.

Automatic diagnosis of COVID-19 related respiratory diseases from speech.

In this work, an attempt is made to propose an intelligent and automatic system to recognize COVID-19 related illnesses from mere speech samples by using automatic speech processing techniques. We used a standard crowd-sourced dataset which was collected by the University of Cambridge through a web based application and an android/iPhone app. We worked on cough and breath datasets individually, and also with a combination of both the datasets. We trained the datasets on two sets of features, one consisting of only standard audio features such as spectral and prosodic features and one combining excitation source features with standard audio features extracted, and trained our model on shallow classifiers such as ensemble classifiers and SVM classification methods. Our model has shown better performance on both breath and cough datasets, but the best results in each of the cases was obtained through different combinations of features and classifiers. We got our best result when we used only standard audio features, and combined both cough and breath data. In this case, we achieved an accuracy of 84% and an Area Under Curve (AUC) score of 84%. Intelligent systems have already started to make a mark in medical diagnosis, and this type of study can help better the health system by providing much needed assistance to the health workers.

Genetic and environmental risk factors of acute infection-triggered encephalopathy.

Acute encephalopathy is a constellation of syndromes in which immune response, metabolism and neuronal excitation are affected in a variable fashion. Most of the syndromes are complex disorders, caused or aggravated by multiple, genetic and environmental risk factors. Environmental factors include pathogenic microorganisms of the antecedent infection such as influenza virus, human herpesvirus-6 and enterohemorrhagic Escherichia coli, and drugs such as non-steroidal anti-inflammatory drugs, valproate and theophylline. Genetic factors include mutations such as rare variants of the SCN1A and RANBP2 genes, and polymorphisms such as thermolabile CPT2 variants and HLA genotypes. By altering immune response, metabolism or neuronal excitation, these factors complicate the pathologic process. On the other hand, some of them could provide promising targets to prevent or treat acute encephalopathy.

POCT detection device based on thermal convection fluorescence PCR amplification

This article is based on the principle of thermal convection PCR and nucleic acid fluorescence intensity detection technology. The principle of thermal convection PCR is to form a temperature difference by separately controlling the upper temperature and the bottom temperature of the reaction tube. The lower temperature liquid at the upper part has relatively high density or specific gravity, and the upper and lower liquids will produce convection, which drives the flow of molecules in the tubular chamber. The reaction solution is formed into thermal convection in the reaction test tube and subjected to different temperatures, so as to meet the required conditions for the reaction of different enzymes, and realize the pre-denaturation, annealing and extension processes in the nucleic acid PCR amplification in a short time. Nucleic acid fluorescence intensity detection technology involves embedded system design for device control and signal analysis, optical system design for optical signal filtering and collection, and differential amplifier circuit design. The embedded system design is based on the development of precise temperature control system, motion system and signal analysis system based on Stm32 single-chip microcomputer. The temperature control system includes independent temperature control to control the heaters at the bottom of the reaction tube and the top of the reaction tube respectively;the motion system includes sample switching and switching of the light source in the imaging system. The optical system design includes 540nm FAM excitation light source, 570nm CY3 excitation light source and spherical lens focusing excitation system. This device uses a photodiode to convert the optical signal into an electrical signal, and then amplifies the collected electrical signal with a two-stage operational amplifier through a two-color light differential amplifier circuit, and then uses the signal analysis system to record and display the electrical signal changes in real time, and Make a qualitative analysis. This device not only has the advantages of low cost and high sensitivity, but also solves the key problem of the long time (more than 2 hours) of the whole process of real-time fluorescent quantitative PCR in the detection of new crown nucleic acid and cannot be screened quickly on site. The PCR time of this device is from 2 The hour is reduced to 30 minutes, which is suitable for POCT inspections, and achieves rapid screening goals for crowds of people, which is conducive to responding to acute nucleic acid detection and large-scale nucleic acid detection. This device is currently used with COVID-19 detection reagents to detect new coronaviruses, and realize the detection of 20 copies of nucleic acid sensitivity within 30 minutes. Four samples can be processed in batches at a time, and the sample size for single processing can be increased appropriately according to needs. This device provides rapid and sensitive screening methods for global epidemic prevention and control, and is of great significance to improve human health. This device can also be applied to other rapid nucleic acid detection fields. With different nucleic acid detection reagents, this device can detect different gene loci, and has a broad development space and application fields. © 2023 SPIE.

Smart IoMT-Based Segmentation of Coronavirus Infections using Lung CT Scans

Computed Tomography (CT) is one of the biomedical imaging modalities, which are used to confirm COVID-19 cases and/or to identify infected areas in the lung. Therefore, this article aims at assisting this crucial radiological task by proposing squeeze-and-excitation networks (SENets) within the Internet of medical things (IoMT) framework for automated segmentation of COVID-19 infections in lung CT images. The proposed SE block has been directly integrated with deep residual networks to form Seresnets based on U-Net and LinkNet models. Extensive tests were conducted on a public COVID-19 CT dataset including 20 cases and 1800+ annotated slices to evaluate the segmentation results of our proposed method. The proposed Seresnet models showed a good performance with a Dice score of 0.73, structure similarity index of 0.98, enhanced alignment measure of 0.98, and mean absolute error of 0.06. This study demonstrated a new advanced tool for radiologists to achieve automatic segmentation of the COVID-19 infected areas using CT scans. The main prospect of this research work is deploying our proposed IoMT segmentation framework in the medical diagnosis routine of positive COVID-19 patients.

Radiation source with enhanced virucide effectiveness based on a mixture of helium and iodine vapor

Subject of study. A source of spontaneous emission (a lamp) in the ultraviolet spectral range excited by a capacitive discharge was investigated. Iodine vapor and mixtures of iodine vapor with inert gases were used as the operating gas medium of the lamp. Conditions for enhancements in specific output parameters of the lamp emission were investigated. Excitation conditions under which the lamp emits predominantly at the iodine atomic line with a wavelength of 206.16 nm were determined. Aim of study. The primary aim of the study was to investigate the spectral and energy characteristics of the lamp based on the iodine vapor, which is promising for the development of a radiation source with enhanced virucidal effectiveness for ultraviolet disinfection of a human environment contaminated with pathogenic microorganisms including severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). Method. In this study, the composition and pressure of the operating medium were optimized. In addition, the excitation mode of the lamp emission was optimized by changing the repetition rate of voltage pulses. Main results. At a specific excitation power of approximately 1.3 mW/cm3 and partial pressures of iodine vapor and helium of approximately 2.5 and 7 Torr, respectively, the line of an iodine atom with a wavelength of 206.16 nm dominates in the output lamp spectrum, and the specific emission power at the outer surface of the lamp tube is approximately 3 mW/cm2. Practical significance. The emission of the investigated lamp is in the spectral range of 200–225 nm, which is promising in terms of developing technology for safe ultraviolet inactivation of pathogenic microorganisms including SARS-CoV-2. © 2022 Optica Publishing Group.

Evaluation of ventricular electrophysiological balance index in COVID-19 patients with SIRS

Background and Aim: Malignant ventricular arrhythmia is an important cause of mortality in COVID-19 patients (1-3). In our study, we aimed to investigate the cardiac electrophysiological balance index (ICEB), which predicts the risk of malignant ventricular arrhythmia in patients with COVID-19 who developed SIRS (systemic inflammatory response syndrome). Method(s): After exclusion criteria (atrial fibrillation, left bundle branch block, pre-excitation), a total of 533 COVID-19 patients, of whom 197 (37%) were SIRS, were included in the study. Result(s): The average age in the study population was 62 (49-72), and the gender distribution was 49% (261) female, 51% (272) male. The patients were divided into two groups as the control group with SIRS and the control group without SIRS. The clinical, laboratory and demographic characteristics of the patients were compared in Table 1. The QTc/QRS ratio (ICEBc) in the SIRS group was 5.1 (4.64-5.1) and was significantly higher than 4.98 (4.5-5.45) in the control group (p=0.004). The QTc interval was 450 (422-474) and 427 (407-447) significantly longer in the SIRS group than the control group (p=0.001). As a result of multivariable linear regression analysis, a significant correlation was found between ICEBc and SIRS, age, gender and CRP. Conclusion(s): Malign ventricular arrhythmias developing in COVID-19 patients are an important cause of mortality. ICEBc and QTc were significantly higher in the SIRS group than in the control group. It was thought that ICEBc could be used to predict malignant ventricular arrhythmias in the patient group developing SIRS.

WE-159. Critical Illness Neuropathy in Severe COVID-19: a case series

Introduction: Neurological complications of SARS-CoV-2 disease have received growing attention, but only few studies have described to date clinical and neurophysiological findings in COVID patients during their stay in intensive care units (ICUs). Here, we assessed the presence of either critical illness neuropathy (CIP) or myopathy (CIM) in ICU patients. Method(s): Patients underwent a neurophysiological assessment, including bilateral examination of the median, ulnar, deep peroneal and tibial motor nerves and of the median, ulnar, radial, and sural sensory nerves. Needle electromyography (EMG) was performed for both distal and proximal muscles of the lower and upper limbs. The technique of Direct Muscle Stimulation (DMS) was applied either to the deltoid or tibialis anterior muscles. Peak to peak amplitudes and onset latencies of the responses evoked by DMS (DMSamp, DMSlat) or by motor nerve stimulation (MNSamp, MNSlat) were compared. The ratio MNSamp to DMSamp (NMR) and the MNSlat to DMSlat difference (NMD: MNSlat-DMSlat) were also evaluated. Result(s): Nerve conduction studies showed a sensory-motor polyneuropathy with axonal neurogenic pattern, as confirmed by needle EMG. Both MNSamp and NMR were significantly reduced when compared to controls (p < 0.0001), whereas MNSlat and NMD were markedly increased (p = 0.0049). Conclusion(s): We have described COVID patients in the ICU with critical illness neuropathy (CIP). The predominance of CIP as compared to critical illness myopathy (CIM) has implications for the functional recovery and rehabilitation strategies in severe COVID-19. Copyright © 2022

Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

THE MONSTROSITY OF MAGNESIUM CITRATE

SESSION TITLE: Critical Gastrointestinal Case Reports SESSION TYPE: Rapid Fire Case Reports PRESENTED ON: 10/18/2022 12:25 pm - 01:25 pm INTRODUCTION: Magnesium citrate is an osmotic laxative which is occasionally used in the intensive care unit (ICU) for refractory constipation. We present a patient in whom a bowel regimen containing magnesium citrate resulted in severe hypermagnesemia with paralytic ileus, requiring renal replacement therapy. CASE PRESENTATION: 70-year-old male was admitted to the ICU for COVID-19 associated acute hypoxic respiratory failure and suffered multi-day, refractory constipation, treated with one dose of 17 grams of magnesium citrate. Vital signs were remarkable for bradycardia and hypotension. On examination, patient was lethargic and the abdomen was soft and non-distended, but there were decreased bowel sounds throughout. Subsequently, laboratory findings were notable for a magnesium level of 8.8 mg/dL and serum creatinine of 2.3 mg/dL (estimated glomerular filtration rate 28mL/min/1.73m2), all of which were previously normal at admission. Computerized Tomography of the abdomen was performed showing dilated cecum, ascending and transverse colon and moderate to large amount of intraluminal rectal stool and air. Patient was started on intravenous fluids, loop diuretics, and calcium gluconate, however, the patient required renal replacement therapy for magnesium clearance. Patient clinically improved with normalization of kidney function and magnesium levels as well as resolution of ileus. DISCUSSION: Magnesium homeostasis is regulated by gastrointestinal absorption and renal excretion, for which the kidney maintains magnesium equilibrium until creatinine clearance falls below 20 ml/min [1]. Elevated magnesium levels can decrease bowel motility by blocking myenteric neurons and interfere with excitation - contraction coupling of smooth muscle cells as well as serve as a reservoir for continuous magnesium absorption [2]. Our patient suffered acute kidney injury, likely from COVID-19 pneumonia and acute tubular necrosis from shock, placing him at increased risk for hypermagnesemia. One retrospective study identified that patients with COVID-19 are more prone to the development of hypermagnesemia, which is associated with renal failure and increased risk of mortality [3]. The magnesium load from magnesium citrate in our patient created for a seemingly out of proportion effect of hypermagnesemia-induced paralytic ileus and presumably a magnesium reservoir, refractory to conservative measures. CONCLUSIONS: The use of magnesium containing bowel regimens should be considered with caution due to the possibility of hypermagnesemia in at-risk patients, which may result in paralytic ileus and other sequelae. Hypermagnesemia reduces colonic peristalsis and interferes with magnesium equilibrium, prolonging its effects. There are rare case reports in the literature discussing this phenomenon, but should be further evaluated for specific patient susceptibility and effects on morbidity and mortality. Reference #1: Cascella, M. (2022, February 5). Hypermagnesemia. StatPearls [Internet]. Retrieved March 16, 2022, from https://www.ncbi.nlm.nih.gov/books/NBK549811/ Reference #2: Bokhari, S., Siriki, R., Teran, F., & Batuman, V. (2018, September 8). Fatal Hypermagnesemia due to laxative use. The American Journal of the Medical Sciences. Retrieved March 16, 2022, from https://www.amjmedsci.org/article/S0002-9629(17)30467-6/fulltext Reference #3: Stevens, J. S., Moses, A. A., Nickolas, T. L., Husain, S. A., & Mohan, S. (2021, July 29). Increased mortality associated with hypermagnesemia in severe covid-19 illness. American Society of Nephrology. Retrieved March 16, 2022, from https://kidney360.asnjournals.org/content/2/7/1087 DISCLOSURES: No relevant relationships by Adnan Abbasi No relevant relationships by Sarah Upson

Abstracts from the IFESS 2021 conferences

The proceedings contain 53 papers. The topics discussed include: evoked sensations with sinusoidal transcutaneous electrical stimulation at different frequencies;IMU triggered FES for robotic gait training;trans-spinal electrical stimulation for improving trunk and sitting function in tetraplegics with cervical cord injury;a combined approach to CNS excitation for hand rehabilitation: a case study using spinal stimulation and BCI;analysis of the movements generated by a multi-field FES device for upper extremity rehabilitation;and neuromuscular and functional electrical stimulation for motor recovery after COVID-19: systematic review.

Fluorescence signatures of SARS-CoV-2 spike S1 proteins and a human ACE-2: excitation-emission maps and fluorescence lifetimes.

SIGNIFICANCE: Fast and reliable detection of infectious SARS-CoV-2 virus loads is an important issue. Fluorescence spectroscopy is a sensitive tool to do so in clean environments. This presumes a comprehensive knowledge of fluorescence data. AIM: We aim at providing fully featured information on wavelength and time-dependent data of the fluorescence of the SARS-CoV-2 spike protein S1 subunit, its receptor-binding domain (RBD), and the human angiotensin-converting enzyme 2, especially with respect to possible optical detection schemes. APPROACH: Spectrally resolved excitation-emission maps of the involved proteins and measurements of fluorescence lifetimes were recorded for excitations from 220 to 295 nm. The fluorescence decay times were extracted by using a biexponential kinetic approach. The binding process in the SARS-CoV-2 RBD was likewise examined for spectroscopic changes. RESULTS: Distinct spectral features for each protein are pointed out in relevant spectra extracted from the excitation-emission maps. We also identify minor spectroscopic changes under the binding process. The decay times in the biexponential model are found to be ( 2.0 ± 0.1 ) ns and ( 8.6 ± 1.4 ) ns. CONCLUSIONS: Specific material data serve as an important background information for the design of optical detection and testing methods for SARS-CoV-2 loaded media.

Non-targeted characterization of dissolved organic matter from a wastewater treatment plant by FT-ICR-MS: A case study of hospital sewage

Hospitals generate large volumes of wastewater. Dissolved organic matter (DOM) in wastewater effluent can act as precursors of disinfection by-products, transporter of pollutants, and affect the performance of treatment plants. This study aims to characterize the composition of DOM in medical wastewater and investigate the selectivity of the hospital treatment plant in the removal of DOM. DOM was characterized by Fourier-transform ion-cyclotron resonance mass spectrometry (FT-ICR-MS) and excitation-emission matrix fluorescence spectroscopy (EEMs). DOM of medical wastewater was dominated by aliphatic and highly unsaturated compounds, a feature that is remarkably different from that of natural DOM. In the membrane bioreactor (MBR) unit, more CHNO compounds and highly unsaturated compounds were formed. After disinfection, the highly unsaturated and humic-like compounds were reduced, accompanying a decrease in aromaticity. After reverse osmosis, the highly unsaturated and CHO compounds were concentrated and removed. These steps were complementary in the removal of DOM, suggesting effective transformation and elimination of DOM. This study contributes to a better understanding of the features of DOM in medical wastewater and treatment plant performance in the removal of DOM, which is indispensable for the large-scale design and application of technologies for hospital wastewater treatment, especially in the context of the COVID-19 pandemic.

Fluorescence spectrophotometry for COVID-19 determination in clinical swab samples.

Considering the limitations of the assays currently available for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its emerging variants, a simple and rapid method using fluorescence spectrophotometry was developed to detect coronavirus disease 2019 (COVID-19). Forty clinical swab samples were collected from the nasopharyngeal and oropharyngeal cavities of COVID-19-positive and -negative. Each sample was divided into two parts. The first part of the samples was analyzed using reverse transcription-polymerase chain reaction (RT-qPCR) as the control method to identify COVID-19-positive and -negative samples. The second part of the samples was analyzed using fluorescence spectrophotometry. Fluorescence measurements were performed at excitation and emission wavelengths ranging from 200 to 800 nm. Twenty COVID-19-positive samples and twenty COVID-19-negative samples were detected based on RT-qPCR results. The fluorescence spectrum data indicated that the COVID-19-positive and -negative samples had significantly different characteristics. All positive samples could be distinguished from negative samples by fluorescence spectrophotometry. Principal component analysis showed that COVID-19-positive samples were clustered separately from COVID-19-negative samples. The specificity and accuracy of this experiment reached 100%. Limit of detection (LOD) obtained 42.20 copies/ml (Ct value of 33.65 cycles) for E gene and 63.60 copies/ml (Ct value of 31.36 cycles) for ORF1ab gene. This identification process only required 4 min. Thus, this technique offers an efficient and accurate method to identify an individual with active SARS-CoV-2 infection and can be easily adapted for the early investigation of COVID-19, in general.

Charging Techniques for UAV-Assisted Data Collection: Is Laser Power Beaming the Answer?

As COVID-19 has increased the need for connectivity around the world, researchers are targeting new technologies that could improve coverage and connect the unconnected in order to make progress toward the United Nations Sustainable Development Goals. In this context, drones are seen as one of the key features of 6G wireless networks that could extend the coverage of previous wireless network generations. That said, limited onboard energy seems to be the main drawback that hinders the use of drones for wireless coverage. Therefore, different wireless and wired charging techniques, such as laser beaming, charging stations, and tether stations, are proposed. In this article, we analyze and compare these different charging techniques by performing extensive simulations for the scenario of drone-assisted data collection from ground-based Internet of Things devices. We analyze the strengths and weaknesses of each charging technique, and finally show that laser-powered drones strongly compete with, and outperform in some scenarios, other charging techniques.

A Metric Space for Point Process Excitations

A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types. Full-rank estimation of all interactions is often infeasible in empirical settings. Models that specialize on a spatiotemporal application alleviate this obstacle by exploiting spatial locality, allowing the dyadic relationships between events to depend only on separation in time and relative distances in real Euclidean space. Here we generalize this framework to any multivariate Hawkes process, and harness it as a vessel for embedding arbitrary event types in a hidden metric space. Specifically, we propose a Hidden Hawkes Geometry (HHG) model to uncover the hidden geometry between event excitations in a multivariate point process. The low dimensionality of the embedding regularizes the structure of the inferred interactions. We develop a number of estimators and validate the model by conducting several experiments. In particular, we investigate regional infectivity dynamics of COVID-19 in an early South Korean record and recent Los Angeles confirmed cases. By additionally performing synthetic experiments on short records as well as explorations into options markets and the Ebola epidemic, we demonstrate that learning the embedding alongside a point process uncovers salient interactions in a broad range of applications.