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Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square.

Abstract Background Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be “suitable” only when both “efficacy” and “safety” are considered. This study presents a model, namely the “ensemble strategy model,” to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 “suitable” and “unsuitable” patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman–Pearson, and Stacking were used for model training. The “ensemble strategy concept” was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens. Results The results of the tenfold cross-validation showed that the average accuracy of the proposed “ensemble strategy model” was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity. Conclusions The “ensemble strategy model” can be used as a reference for the determination of vancomycin doses in clinical treatment.

Long-term wiring on a newborn patient could be a disguise scene for parents. Unobtrusive and reliable monitoring without wiring can be a euphoric alternative for newborns and parents in obstetrics and gynecology (OB/GYN) incubation rooms. However, reliable and continuous non-contact surveillance in an incubation room is challenging. Therefore, a novel photoplethysmography imaging (PPGi) is developed specifically for baby skins through predictive adversarial adaptation and risk-sensitive generative synchronizer. Our artificial intelligence approach does not take blind guesses from input-output pairs. We apply an intelligent step to decouple the influence of fluctuated illumination through a generative algorithm of artificial intelligence. To boost skin detection performance, we capture those pixels with periodic variations and maximize the coherence of the extraction algorithm by the generative synchronizer. The periodic variations are matched by a synthesized pulse from the output PPGi signals through the control of a risk-sensitive filter to not over-compensate the illuminate variation. Based on the sensed pulsation, we synthesize the corresponding pulsation signals on the flight to identify the living skin in a spatiotemporal image sequence. We find that our skin classifier in risk-sensitive generative synchronizer effectively improves the quality of the resulting non-contact PPGi signal. Our algorithm produces substantial accuracy in the performance of PPGi reconstruction in the critical environment of newborn care. In the limited illustration of the incubation room, our non-contact PPGi can still achieve an average accuracy of 96.62%.

Electrocardiography (ECG) is generally deemed the golden standard for diagnosing cardiovascular diseases and photoplethysmography (PPG) is unobtrusive, low-cost, and convenient for continuous monitoring. However, PPG contains insufficient information to diagnose diseases. In this study, we propose a novel method to accurately convert PPG to ECG. The banded kernel ensemble method converts a low-quality source (PPG) to a high-quality destination (ECG). Unlike neural network solutions, our algorithm requires no computation burden in the conversion task after a trained model is obtained. The proposed algorithm is then tested on a publicly available MIMIC III database. Our prediction shows excellent accuracy in the validation dataset. It offers the testing performance of under 0.314 and above 0.55 in rrmse (relative root mean squared error) and KGE (Kling–Gupta efficiency), respectively, under the scenarios of three prevalent heart diseases. The reconstructed ECG can be further used to perform heart disease detection and we obtained an average correctness rate of 81%. Our method can help a large population of high-risk, believed-healthy persons to walk into doctors’ offices before the situation becomes irreversible.

Abstract Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. Results We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Conclusions Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models. Conclusions The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.

Visual perceptual dysfunctions were common in children with developmental delays, and have severe adverse impacts on children’s daily activities. Recent studies have shown that technologies such as virtual reality and imaging can provide a motivating and engaging tool for remediating visual perceptual dysfunctions. This study was aimed to present the work we were conducting in experimenting with augmented reality (AR) for visual perceptual rehabilitation in children with developmental delay. Sixty participants (mean age = 7.96 ± 1.4) were equally divided into an experimental group trained with proposed KBTS (KBTS group) and a control group trained using traditional visual perceptual training (TVPT group). Each group completed an 8-week training program (two 30-minute sessions per week). Visual perceptual assessments (Test of Visual Perceptual Skill- 3rd Edition, Beery-Buktenica Developmental Test of Visual-Motor Integration- 6th Edition) and functional outcome measures (Vineland Adaptive Behavior Scale- Chinese Version, School Function Assessment- Chinese Version) were administered to investigate the effectiveness of the intervention. The KBTS group significantly outperformed the TVPT group in assessments of visual-motor integration, visual perceptual skills, and school functions. The effectiveness of the KBTS was supported by robust effect sizes for the pre- and post-intervention comparisons of visual perceptual and visual motor integrative functions between the KBTS group and the TVPT group. This study suggests that KBTS effectively improves visual-motor integration and overall visual perceptual functions in children with developmental delays. This study has discussed the ideas for incorporating several principles of game scenarios for visual perceptual rehabilitation, and the proposed KBTS still have raised a number of issues requiring further work. Enabling users to perform visual motor integrative tasks in an augmented reality world offers the potential for developing advanced visual perceptual skills which may be more easily transferable to activities of daily living than other techniques. Augmented reality has the potential to achieve entertaining and satisfying outcomes for visual perceptual rehabilitation, and making use of the potential applications of AR technologies should be considered more beneficial for the home-based rehabilitation during the current pandemic situations. Other clinical applications are discussed as well.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

Abstract Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.

Abstract Background This study estimates atrial repolarization activities (Ta waves), which are typically hidden most of the time from body surface electrocardiography when diagnosing cardiovascular diseases. The morphology of Ta waves has been proven to be an important marker for the early sign of inferior injury, such as acute atrial infarction, or arrhythmia, such as atrial fibrillation. However, Ta waves are usually unseen except during conduction system malfunction, such as long QT interval or atrioventricular block. Therefore, justifying heart diseases based on atrial repolarization becomes impossible in sinus rhythm. Methods We obtain TMPs in the atrial part of the myocardium which reflects the correct excitation sequence starting from the atrium to the end of the apex. Results The resulting TMP shows the hidden atrial part of ECG waves. Conclusions This extraction makes many diseases, such as acute atrial infarction or arrhythmia, become easily diagnosed.

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

This paper investigates the impact of consumer preferences on the intensity of competition for companies in a duopoly market. A classical Hotelling’s competition problem will be different if consumers are allowed to distribute non-uniformly. New results in competition intensity are established and conditions for the existence of a subgame perfect Nash equilibrium is identified through a model that considers generic distribution in consumer preferences. A competition strategy is demonstrated to depend on the signs of local change rates of the density function at the endpoints of market segments.

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

This study applies artificial immune system and parallelized finite-state machines to construct an intrusion detection algorithm for spotting hidden threats in massive number of packets. Existing intrusion detections are mostly not focused on adaptability for mixed and changing attacks, which results in low detection rate in new and mixed-type attacks. Using the characteristics of artificial immune and state transition can address the attacks in evolutionary patterns and track the anomalies in nonconsecutive packets. The proposed immune algorithm in this study is highly efficient based on a selection step in multi-island migration. Result shows that the algorithm can effectively detect mixed-type attacks and obtains an overall accuracy of 95.9% in testing data.

This study investigates the sustainability of green innovation under the influence of coopetitive diffusion. We focus on green innovations which repurpose discarded technology accessories for resale. Original manufacturers make every attempt to prevent the use of low-cost replacements. To encourage green innovation, incumbent market leaders need to be provided with business strategies which help them maximize profitability. Using a stochastic differential game from a Lotka–Volterra system, we predict a coopetitive diffusion path among participant stakeholders. We find that green innovation, combined with the positive effect of network externalities can indeed enhance total market growth. Factors such as product characteristics, product pricing, and compatibility protection strategies must interact properly for green innovation to be sustainable.

In this study, we explored a single-shot detector (SSD) backbone and its optimized algorithm hyperparameters for object detection, and proposed a systematic method for determining appropriate algorithm hyperparameter combinations for the SSD backbone. The VGG16 backbone for SSD has been used for object detection. The Resnet backbone won first place in the 2015 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), while the VGG16 backbone ranked second place in the 2014 ILSVRC. We selected the Resnet50 backbone for SSD for vehicle image detection research because the Resnet50 backbone has a high feature extraction capability. We proposed SSD with the Resnet50 backbone and its optimized algorithm hyperparameters, called the SSD-Resnet50 model, to replace SSD with the VGG16 backbone and its optimized algorithm hyperparameters, called the SSD-VGG16 model, to enhance the vehicle image detection feature extraction capability. The Taguchi method optimized the algorithm hyperparameters of the Resnet50 and VGG16 backbones, thus improving the detection accuracies of the SSD-Resnet50 and SSD-VGG16 models, respectively. Experimental results show that the SSD-Resnet50 model using 300 x 300 x 3 input images achieved a detection accuracy of an average average precision (AP) of 97.15% in three independent experiments, outperforming the SSD-VGG16 model using 300 x 300 x 3 input images with an average AP of 86.83% on the test set of vehicle images. As a result, the SSD-Resnet50 model has a higher accuracy of vehicle detection in images from the Caltech cars 1999 and 2001 datasets. [ABSTRACT FROM AUTHOR]