EyesOnMe: Investigating Haptic and Visual User Guidance for Near-Eye Positioning of Mobile Phones for Self-Eye-Examinations

The scarcity of professional ophthalmic equipment in rural areas and during exceptional situations such as the COVID-19 pandemic highlights the need for tele-ophthalmology. This late-breaking work presents a novel method for guiding users to a specific pose (3D position and 3D orientation) near the eye for mobile self-eye examinations using a smartphone. The user guidance is implemented utilizing haptic and visual modalities to guide the user and subsequently capture a close-up photo of the user's eyes. In a within-subject user study (n=24), the required time, success rate, and perceived demand for the visual and haptic feedback conditions were examined. The results indicate that haptic feedback was the most efficient and least cognitively demanding in the positioning task near the eye, whereas relying on only visual feedback can be more difficult due to the near focus point or refractive errors. © 2023 Owner/Author.

Hidden causality between oil prices and exchange rates

This work investigates the interactions between oil prices and exchange rates of 6 typical oil importers (China, Japan, and India) and exporters (Canada, Russia, and Saudi Arabia) from 2006 to 2022. We employ a novel method to capture their causal interactions, namely pattern causality, and compare the results to that based on the volatility spillover method. The empirical analysis supports most existing findings that oil prices are bidirectional correlated with exchange rates. However, unlike previous studies that only investigate positive and negative causalities, we highlight dark causality as a more complex interaction. Moreover, dark causality suggests that successive increases (decreases) in oil prices tend to drive the exchange rates of oil exporters to act in an oscillatory manner rather than in a purely positive or opposite trend, and vice versa. Furthermore, we also reveal that dark causality shows dominance during crises, e.g., the global financial crisis, the European debt crisis, the epidemic of COVID-19, and the Russia-Ukraine conflict. Revealing three types of causalities between oil prices and exchange rates helps policymakers develop more diversified macroeconomic policies. Moreover, the newly identified dark causality can be a useful indicator for investors to risk management. © 2023

Successful Introduction of a Novel, High Dogleg Rotary Steerable System

Rotary Steerable Systems (RSS's) have proven beneficial over conventional steerable motor systems, which has driven significant market uptake over the last several decades. Consequently, applications for RSS's have expanded and encouraged RSS technology developers to meet these ever-changing market demands. Modifications to the basic proven RSS methods, attempted independent reproductions of those methods, and other novel methods have emerged to fulfill these market demands. The initial RSS method and technique were invented and proven in the late 1990s, and common ways of creating wellbore deflection emerged, allowing the industry to attempt the classification of these methods. These classifications were an attempt to gain a better understanding of the advantages and disadvantages of each and help understand where and what version of technology to apply for an ever-increasing expansion of applications. This paper describes the attempts at classification of RSS's, then addresses the drivers, development, and field trial results of a new method for placing wells using a high dogleg automated rotary steering system (SwR;Steering-while-Rotating). The SwR was targeted to meet market changes identified in 2016 and projected out ten (10) years by the SwR technical team. Simplicity, fostering reliability and value were major design tenets. A high dogleg curve (20°/100') design, enabling "one-run, Vertical, Curve, and Lateral" bottom hole assemblies, two-way (uplink-downlink) downhole communications along with autonomous self-steering capability, also drove the SwR development. The initial SwR prototype system for 8-3/8"-9-7/8" hole sizes was built, and field trials began in Q3 of 2019. The SwR team weathered the global Covid-19 pandemic of 2020-21 with the rest of the world and deployed systems to the Middle East, while also securing and executing trial and commercial runs within North America in 2021 and 2022. Conclusions are based upon, and data is presented from thirty-seven (37) bit runs and the first 50,000 feet drilled with the SwR. The SwR team has delivered a unique steering method to the industry and presents the early run data demonstrating the SwR technology capability to drill challenging well profiles, in challenging environments. The SwR technology was designed for the RSS market of today and for several years to come improving the value proposition for modern RSS well placement technology. Copyright © 2023, Society of Petroleum Engineers.

Attention-Based Deep Learning Approach for Semantic Analysis of Chest X-Ray Images Modality

The world today is suffering from a huge pandemic COVID-19 that has infected 106M people around the globe causing 2.33M deaths, as of February 9, 2021. To control the disease from spreading more and to provide accurate healthcare to existing patients, detection of COVID-19 at an early stage is important. As per the World Health Organization, diagnosing pneumonia is a common way of detecting COVID-19. In many situations, a chest X-ray is used to determine the type of pneumonia. However, writing a report for every chest X-ray becomes a tedious and time-taking task for physicians. We propose a novel method of creating reports from chest X-rays images automatically via a deep learning model using image captioning with an attention mechanism employed through CNN–LSTM architecture. On comparing the model that does not use an attention mechanism with our approach, we found that accuracy was increased from 80% to 87.5%. In conclusion, we found that results generated with attention mechanism are better, and the report thus produced can be utilized by doctors and researchers worldwide to analyze new X-rays in lesser time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting Combination of Hierarchical Time Series: A Novel Method with an Application to CoVid-19

Multiple, hierarchically organized time series are routinely submitted to the forecaster upon request to provide estimates of their future values, regardless the level occupied in the hierarchy. In this paper, a novel method for the prediction of hierarchically structured time series will be presented. The idea is to enhance the quality of the predictions obtained using a technique of the type forecast reconciliation, by applying this procedure to a set of optimally combined predictions, generated by different statistical models. The goodness of the proposed method will be evaluated using the official time series related to the number of people tested positive to the SARS-CoV-2 in each of the Italian regions, between February 24th 2020 and August 31th 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Video Alignment of Lecture Notes Missing Corners

During the Covid-19 pandemic, generating content for live online lectures was challenging for many instructors who were not used to teaching remotely. In this paper, our major goal is to capture educational material for online classes to be efficiently delivered to students with minimal technological requirements using a smart device. For effective delivery of material, the main focus in this work is to capture the paper where the lecture content is written or drawn. However, video alignment becomes challenging since i) some of the corners of the paper may not be visible, ii) the background of text is predominantly light colored, and iii) the front text is composed of small letters aligned over the background. We propose a novel method for integrating content printed or written on a paper by computing paper corners analytically and using a machine learning algorithm. These image corners are used for image alignment and then image stitching. The image transformation is computed locally for missing areas and applied globally to maintain the legibility of the text. Our experiments quantitatively show that detecting corners analytically and using machine learning algorithms enable image stitching effectively. © 2022 IEEE.

Diagnosing and quantifying post-COVID-19 fluctuations in the architecture billings indices

Purpose: The purpose of this paper is to understand the post-COVID-19 fluctuations in the building construction demand from various angles at the national, regional, and sectoral levels. Despite the significant impact of COVID-19 on the building construction industry, a detailed quantitative analysis of the COVID-19 impact on the building construction demand is still lacking. The current study aims to (1) establish a statistical approach to quantify the COVID-19 impact on the building construction demand;(2) investigate the post-COVID-19 fluctuations in the construction demand of different building services, regional markets, and building sectors using the historical time series of the architecture billings index (ABI);and (3) identify vulnerable market and sector and discuss the post-COVID-19 recovery strategies. Design/methodology/approach: The research methodology follows four steps: (1) collecting national, regional, and sectoral ABIs;(2) creating seasonal autoregressive integrated moving average models;(3) illustrating cumulative sum control charts to identify significant ABI deviations;and (4) quantifying the post-COVID-19 ABI fluctuations. Findings: The results show that all the ABIs experienced a statistically significant decrease after COVID-19. The project inquiries index reduced more but recovered faster than billings and design contracts indices. The midwest billings index decreased the most among the regional ABIs and the commercial/industrial billing index dropped the most among the sectoral ABIs. Originality/value: This study is unique in the way that it utilized the ABI data and the approach using SARIMA models and CUSUM control charts to assess the post-COVID-19 building construction demand represented by ABI fluctuations. © 2022, Emerald Publishing Limited.

Faceshield HUD: Extended Usage of Wearable Computing on the COVID-19 Frontline

Wearable Computing brings up novel methods and appliances to solve various problems in society’s routine tasks. Also, it brings the possibility of enhancing human abilities and perception throughout the execution of specialist activities. Finally, the flexibility and modularity of wearable devices allow the idealization of multiple appliances. In 2020, the world faced a global threat from the COVID-19 pandemic. Healthcare professionals are directly exposed to contamination and therefore require attention. In this work, we propose a novel wearable appliance to aid healthcare professionals working on the frontline of pandemic control. This new approach aids the professional in daily tasks and monitors his health for early signs of contamination. Our results display the system feasibility and constraints using a prototype and indicate initial restrictions for this appliance. This proposal also works as a benchmark for the aid in health monitoring in general hazardous situations. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

SURFACE TENSION-MEDIATED PROCESSING OF WASTEWATER SAMPLES FOR SARS-CoV-2

Wastewater testing for SARS-CoV-2 has emerged as a promising tool for disease surveillance in aggregate populations. We present a novel method to rapidly extract, concentrate, and amplify viral RNA from wastewater using Exclusion-based Sample Preparation (ESP) and RT-PCR. This technology identified potential outbreaks of SARS-CoV-2 at University of Kentucky dormitories, resulting in targeted clinical testing and quarantine procedures. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Children's Impressions of Early Spelling Assessment through Handwriting on Tablets vs. Paper-based Method

Traditional paper-based children's spelling assessments were hampered due to Covid-19 because existing technologies did not provide strategic signals to teachers, such as the child's handwriting direction and how they read what they write. Our project emerged as a novel method to assess children's spelling by touchscreens in this context. Hence, this paper aims to extend community knowledge concerning children's experience and perception of handwriting spelling on tablet devices. The experiment consisted in presenting three handwriting methods (paper and pencil, finger and pen writing) and was conducted with eight Brazilian children between 4.5 and 7 years old. In addition to observation, in our experimental protocol we adopted the Fun Sorter, Again-Again Table, and the Smileyometer as evaluation tools. Our results show children were excited about handwriting using a touch pen on the tablet. Most of them even revealed they prefer the pen tablet mode to the traditional paper and pencil mode. However, the majority of children did not feel comfortable writing by finger, and it required more time than other methods. Furthermore, we observed child's handwriting using finger looks different when compared to paper and pencil, while the tracing using a touch pen is similar to the registration produced on paper. © 2022 Owner/Author.

POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection

A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with severe COVID-19 symptoms. Chest radiography is one of the promising screening approaches. Many studies reported detecting COVID-19 in chest X-rays accurately using deep learning. A serious limitation of many published approaches is insufficient attention paid to explaining decisions made by deep learning models. Using explainable artificial intelligence methods, we demonstrate that model decisions may rely on confounding factors rather than medical pathology. After an analysis of potential confounding factors found on chest X-ray images, we propose a novel method to minimise their negative impact. We show that our proposed method is more robust than previous attempts to counter confounding factors such as ECG leads in chest X-rays that often influence model classification decisions. In addition to being robust, our method achieves results comparable to the state-of-the-art. The source code and pre-trained weights are publicly available at (https://github.com/tomek1911/POTHER ). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Using Neural Networks to Model the Spread of COVID-19

The spread of the novel coronavirus across the world in 2020 exposed the tenuous nature of hospital capacity and medical resource supply lines. Being able to anticipate surge events days before they hit an area would allow healthcare workers to pivot and prepare, critically expanding capacity and adjusting to resource loads. This work aims to enable advanced healthcare planning by providing adaptive forecasts into short range COVID-19 outbreaks and surge events. Here, we present a novel method to predict the spread of COVID-19 by using creative neural network architectures, especially convolutional and LSTM layers. Our goal was to create a generalizable method or model to predict disease spread on a county-level granularity. Importantly, we found that by using an adaptive neural network model with a frequent refresh rate, we were able to outperform simple feed forward estimation methods to predict county level new case counts on a daily basis. We also show the capabilities of neural network architectures by comparing performance on different sizes of training data and geographic inputs. Our results indicate that neural networks are well suited to dynamically modeling the spread of COVID-19 on a county-level basis, but that cultural and/or geographic differences in regions prevent the portability of fully-trained models. © 2022 IEEE.

Towards Fast and Accurate Intimate Contact Recognition through Video Analysis

Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%;rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing. © 2022 IEEE.

Developing a Novel Method for Disinfection and Sterilization Using a Charged Ozonated mist

The COVID-19 virus has caused a large-scale global outbreak and has become a major public health issue 1. Although there are several vaccines, herd immunity will likely take a long time to establish, and it is not clear whether the existing vaccines are completely effective against evolved versions of the virus. The COVID-19 virus as well as other respiratory viruses can be spread through coughing, sneezing, skin contact, etc., and can enter the human body from the eyes, nasal cavity, and oral cavity. © 2021 IEEE.

Graph Convolutional Networks for Predicting State-wise Pandemic Incidence in India

In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semi-supervised learning task. By training the model with data from a small number of states whose incidence is known, we analyze the accuracy in predicting incidence levels in the remaining states and UTs in India. We explore the effect of pre-existing factors such as foreign visitor count, senior citizen population and population density of states in predicting spread. To show the robustness of this model, we introduce a novel method to choose states for training that reduces bias through random sampling in five regions that cover India’s geography. We show that GCNs, on average, produce a 9% improvement in accuracy over the best performing non-graph-based model and discuss if the results are feasible for use in a real-world scenario. © 2022 IEEE.

A Novel Method for COVID-19 Pandemic Information Fake News Detection Based on the Arithmetic Optimization Algorithm

The problem of fake news on the Internet is not new. However, in the case of a global pandemic, this kind of misinformation can be dangerous, confusing, and costly in terms of the loss of human lives. The ongoing COVID-19 pandemic has unfortunately shown the significant and remarkable spread of fake news, concerning the disease itself, vaccination, number of deaths, and so on. It is necessary to develop an effective algorithm that will be able to detect COVID-19 misinformation and help scientists to easily separate fake from true news. The research presented in this paper proposes an arithmetic optimization algorithm (AOA)-based approach that can improve the classification results by reducing the number of features and achieve high accuracy. The AOA has been utilized as a wrapper feature selection. The obtained simulation results were subject to a comparative analysis with both world-class classifiers and other nature-inspired evolutionary approaches. The results of the simulation indicate better performance of the proposed approach with AOA over other algorithms and demonstrate that it obtains superior accuracy. © 2021 IEEE.

Cost-Effective Touch-less Doorbell to Combat COVID-19

In this paper, a novel method to effectively mitigate the spread of COVID-19 at the local level due to contact with any surfaces has been introduced. Our innovation - a device called 'Touch-less Doorbell' can well prove to be an essential safety shield for the common public in their fight against this pandemic. This idea of the contactless doorbell will stand out from conventional ones installed in our houses in terms of being durable, energy-efficient and cost-effective. Once an infected person uses the doorbell, the virus holds onto that and spreads accordingly when an uninfected person touches the same. The existing doorbell itself can be reused by integrating some power electronics components without entirely replacing the existing one. Thus, the doorbell can be made to operate in the touchless as well as touch enabled mode. The touch-less doorbell circuitry comprises of an operational amplifier as a voltage comparator, a potentiometer and photodiode along with the relay setting and switching circuit. Hence, the comparator circuit and switching circuit forms the two essential parts;with the comparator circuit comparing the sensor's threshold with reference value and switching circuit (consisting of a transistor) for turning the bell ON. A detailed cost analysis of the proposed model has also been performed concluding it to be a budget friendly option without compromising on the product's quality. © 2021 IEEE.

Hybrid Conceptual Modeling for Simulation: An Ontology Approach during Covid-19

The recent outbreak of Covid-19 caused by SARS-CoV-2 infection that started in Wuhan, China, has quickly spread worldwide. Due to the aggressive number of cases, the entire healthcare system has to respond and make decisions promptly to ensure it does not fail. Researchers have investigated the integration between ontology, algorithms and process modeling to facilitate simulation modeling in emergency departments and have produced a Minimal-Viable Simulation Ontology (MVSimO). However, the 'minimalism' of the ontology has yet to be explored to cover pandemic settings. Responding to this, modelers must redesign services that are Covid-19 safe and better reflect changing realities. This study proposes a novel method that conceptualizes processes within the domain from a Discrete-Event Simulation (DES) perspective and utilizes prediction data from an Agent-Based Simulation (ABS) model to improve the accuracy of existing models. This hybrid approach can be helpful to support local decision making around resources allocation. © 2021 IEEE.

Masked Face Recognition based on Attention Mechanism and FaceX-Zoo

Recently, due to the outbreak of the COVID-19 epidemic in the world, wearing face masks has become a trend, which brings difficulties to the traditional face recognition technologies that do not actively focus on the upper part of the face. This paper proposes a novel method for masked face recognition based on attention mechanism and FaceX-Zoo (an open-source method of JD.COM). In order to make the module focus on the regions around the eyes, we integrated the CBAM (Convolutional Block Attention Module) attention mechanism into ResNet50 and MobileFaceNet network. Furthermore, the FaceX-Zoo method was used to generate masked face images to improve the module performance. Experiment results show that the proposed approach can improve the performance of masked face recognition compared with competitive approaches. © 2021 IEEE.

Differentiating COVID-19 Cases from Others by an Anatomy Similarity-Inspired Sensitive Merit from CT Images

Computed tomography (CT) of COVID-19 manifests a relatively global effect through the whole lungs, like peripheral ground glass, consolidation, reticular pattern, nodules etc. This characteristic effect renders the difficulties in differentiating COVID-19 from the normal body or other lung diseases by CT. This work presents a novel method to relieve the difficulties by reducing the global effect through the 3D whole lung volume into 2D-like domain. The hypothesis is that the lung tissue shares the similar anatomic structure within a small lung sub-volume for normal subjects. Therefore, the anatomic land-markers along the z-axis, denoted as Lung Marks are used to eliminate axial variable. Our experiments indicated that 30 Lung Marks are sufficient to eliminate the axial variable. The method computes texture measures from each 2D-like volumetric data and maps the measures on to the corresponding Lung Mark, resulting in a profile along the z-axis. The difference of the profiles between two different abnormalities is the proposed sensitive merit to differentiate COVID-19 cases from others in CT images. 48 COVID-19 cases and 48 normal screening cases were used to test the effectiveness of the proposed sensitive merit. Intensity and gradient based texture descriptors were computed from each axial cross image at the corresponding Lung Mark along the z-axis. Euclidean, Jaccard and Dice distances are calculated to generate the profiles of the proposed sensitive merit. Consistent results are observed across texture descriptor types and distance types in the texture measure between the normal and COVID-19 subjects. Uneven Profiles demonstrate the variation along the z-axis. With Lung Mark, the variation of texture descriptor has been reduced prominently. The Gradient based descriptor is more sensitive. Individual Haralick features analysis shows the 2nd and 10th dimensions are most distinguishable. © 2020 IEEE