Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of plume temperature distribution based on the ratios of the radiation intensities of multiple CO2 lines

New inversion scheme for gas temperature distribution retrieval utilized CO2 spectrum between 2350 cm-1 and 2400 cm-1 is proposed. Inversion model is build base on neural networks. Considered spectral remote sensing method is commonly used for industrial and environmental monitoring. It is a passive single-ended sensor technique in which radiation intensity emerging from a studied object is analyzed. Quantitative investigation of heated gas radiation emission to determine temperature and gas mixture by infrared spectroscopy requires two components apart from optical radiation sensor. First appropriate spectral database and second efficient inversion techniques. In this study calculation of one-dimensional radiative transfer equation have been used for simulation of spectral radiation intensity. To increase quality of retrieval a spectrum preprocessing and feature extraction method is applied. Simulated spectra were parameterized and expressed as ratios of intensities of multiple rotational lines. Each neural network estimates temperature (NN response) at one point on studied path basing on given spectrum (NN input).
One-stage tracheostomy during surgery reduced early pulmonary infection and mechanical ventilation length in complete CSCI patients

Objective Complete cervical spinal cord injury (CSCI) is a devastating injury that usually requires surgical treatment. Tracheostomy is an important supportive therapy for these patients. To evaluate the effectiveness of early one-stage tracheostomy during surgery compared with necessary tracheostomy after surgery, and to identify clinical factors for one-stage tracheostomy during surgery in complete cervical spinal cord injury. Design Data from 41 patients with complete CSCI treated with surgery were retrospectively analyzed. Participants and interventions Ten patients (24.4%) underwent one-stage tracheostomy during surgery, thirteen (31.7%) underwent tracheostomy when necessary after surgery, and eighteen (43.9%) did not have a tracheostomy. Main results One-stage tracheostomy during surgery significantly reduced the development of pneumonia at 7 days after tracheostomy (p = 0.025), increased the PaO2 (p < 0.05), and decreased the length of mechanical ventilation (p = 0.005), length of stay (LOS) in the intensive care unit (ICU) (p = 0.002), hospital LOS (p = 0.01) and hospitalization expenses compared with necessary tracheostomy after surgery (p = 0.037). A high neurological level of injury (NLI) (NLI C5 and above), a high PaCO2 in the blood gas analysis before tracheostomy, severe breathing difficulty, and excessive pulmonary secretions were the statistically significant factors for one-stage tracheostomy during surgery in the complete CSCI patients, but no independent clinical factor was found. Conclusions In conclusion, one-stage tracheostomy during surgery reduced the number of early pulmonary infections and the length of mechanical ventilation, ICU LOS, hospital LOS and hospitalization expenses, and one-stage tracheostomy should be considered when managing complete CSCI patients by surgical treatment. GRAPHICAL ABSTRACT
Domestication and microbiome succession may drive pathogen spillover

Emerging infectious diseases have posed growing medical, social and economic threats to humanity. The biological background of pathogen spillover or host switch, however, still has to be clarified. Disease ecology finds pathogen spillovers frequently but struggles to explain at the molecular level. Contrarily, molecular biological traits of host-pathogen relationships with specific molecular binding mechanisms predict few spillovers. Here we aim to provide a synthetic explanation by arguing that domestication, horizontal gene transfer even between superkingdoms as well as gradual exchange of microbiome (microbiome succession) are essential in the whole scenario. We present a new perspective at the molecular level which can explain the observations of frequent pathogen spillover events at the ecological level. This proposed rationale is described in detail, along with supporting evidence from the peer-reviewed literature and suggestions for testing hypothesis validity. We also highlight the importance of systematic monitoring of virulence genes across taxonomical categories and in the whole biosphere as it helps prevent future epidemics and pandemics. We conclude that that the processes of domestication, horizontal gene transfer and microbial succession might be important mechanisms behind the many spillover events driven and accelerated by climate change, biodiversity loss and globalization.
On the feasibility of cross-validation in image analysis

The performance of cross-validation as a device for selecting the optimal amount of regularisation in image analysis is examined. It is shown that cross-validation results in asymptotically optimal performance, provided the amount of blur does not exceed a certain ceiling. If blur exceeds that level, then cross-validation does not yield asymptotic optimality. The ceiling is surprisingly low. For example, if a fixed scene is subject to blur and is digitised on an increasingly fine grid, then cross-validation will only yield optimal image enhancement if the amount of blur decreases as the grid becomes finer. Two types of cross-validation are studied, one taken directly from methodology for nonparametric regression, and the other involving a modification that adjusts for image blur.
Deep Reinforcement Learning Based Real-Time Renewable Energy Bidding With Battery Control

Recently, various renewable energy sources and large-scale batteries have been integrated into power grids, and renewable energy bidding and battery control become critical problems in the real-time energy market. However, bidding and control problems have been studied separately while these two problems simultaneously influence the total profit of renewable producers. In this paper, we propose a novel strategy where renewable energy bidding and battery control are collectively investigated. First, unlike the previous studies where bidding is simply the forecasted value, the proposed methods determine the bidding values considering the error compensability of the battery by switching the objective of forecasting from reducing errors to making errors compensable. After the error compensation, additional battery control is applied to utilize the energy arbitrage process considering the energy price. As there are energy price and renewable generation uncertainties, we propose a deep reinforcement learning based bidding combined with control, called DeepBid, for sequential decision making under uncertainty. Our extensive simulations with real solar and wind generation data show that the proposed DeepBid strategy substantially increases the total profit compared to existing bidding strategies by achieving as high revenues as the arbitrage strategy and as low deviation penalties as the error compensation strategy.
Monolithic integration of laser and passive elements using selective QW disordering by RTA with SiO/sub 2/ caps of different thicknesses

Area-selective disordering of an InGaAs strained quantum wells (QWs) was performed by rapid thermal annealing with thick and thin SiO/sub 2/ caps. Fabry-Perot lasers were fabricated on a selectively-disordered wafers, and a lasing wavelength difference as large as 23 nm was obtained between the lasers in 300-nm and 30-nm capped areas. We present fabrication of lasers integrated with disordered passive waveguides and demonstrate significant reduction of the passive waveguide loss from roughly 40 cm/sup -1/ to 3 cm/sup -1/. A distributed Bragg reflector laser with QW disordering in the grating area was demonstrated, and remarkable improvement of laser characteristics was achieved compared to a laser without disordering.
Clinical and Therapeutic Considerations for Older Adults with Head and Neck Cancer

Abstract Approximately 30% of patients with head and neck squamous cell carcinoma (HNSCC) are at least 70 years of age, and this percentage is expected to increase as the population increases and lives longer. Elderly patients are underrepresented in head and neck oncology clinical trials, and there is minimal evidence on the management of HNSCC for this population. Subsequently, despite their best intentions, physicians may unknowingly recommend an ill-suited course of therapy, which may result in suboptimal oncological or functional outcomes or adverse events. Surgical approaches have the potential to carry a higher risk of morbidity and mortality in older adults, especially in patients with multiple comorbidities. Definitive radiation therapy treatment in patients with HNSCC frequently involves 7 weeks of daily radiation, sometimes with concurrent chemotherapy, and this demanding treatment can be difficult for older adult patients, which may lead to treatment interruptions, potential removal of concurrent systemic therapy, compromised outcomes, and diminished quality of life. There are clinical trials currently underway investigating altered fractionation regimens and novel, less toxic systemic treatments in this population. This review provides an overview of how best to approach an older adult with HNSCC, from initial work-up to treatment selection.
Formation of oriented nanofibers using electrospinning

This letter reports on a simple, easy method for generating suspended nanofibers on a dielectric substrate using various target electrodes. By controlling the electrostatic field between the target electrodes, the nanofibers can be oriented to the electric field direction at the electrodes. The alignment of nanofibers was dependent on the applied frequency, field strength, and shape of the electrode.
Decomposing Monolithic to Microservices: Keyword Extraction and BFS Combination Method to Cluster Monolithic’s Classes

Abstract Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method.
IMPROVED THERMAL SENSITIVITY OF FIBRE BRAGG GRATINGS USING A POLYMER OVERLAYER

Fibre Bragg gratings have been shown to have a much improved thermal sensitivity when using polymer materials with large thermal expansion coefficients as driving elements. The polymers are fixed to the fibres at an elevated temperature and the fibres are under compression instead of tension at the lower temperatures subsequently used. This allows a much wider tuning range of the Bragg gratings as silica can stand much more compression than tension. A sensitivity of 0.15nm/°C in the 1550nm wavelength regime has been achieved and the Bragg wavelength has been tuned 15nm in the temperature range 0-100°C.
Prediction of medium chemical concentration with micro-ring resonators and deep learning

A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work with a silicon on-insulator ring resonator operating between the infrared wavelengths of λ=1.46 μm to λ=1. 6μm on mixtures of water, ethanol, methanol, and propanol by using simulation data obtained from finite difference eigenmode, although the same approach can be used with other designs and chemical combinations. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic various noise sources, while the DNN underwent training on the extracted features. Average Root-Mean-Square Error was for a range of concentrations from 0.0357-75% is 5.531%.
Time-Varying Risk Factors for Incident Fractures in Kidney Transplant Recipients: A Nationwide Cohort Study in South Korea

Little is known about the time-varying risk factors for fractures in kidney transplant recipients (KTRs). Using the Korea Organ Transplantation Registry, a nationwide cohort study of KTRs, the incidence, locations, and time-varying predictors of fractures were analyzed, including at baseline and post-transplant 6-month variables in KTRs who underwent KT between January 2014 and June 2019. Among 4134 KTRs, with a median follow-up of 2.94 years (12,441.04 person-years), 63 patients developed fractures. The cumulative 5-year incidence was 2.10%. The most frequent locations were leg (25.40%) and foot/ankle (22.22%). In multivariable analysis, older recipient age at baseline (hazard ratio [HR], 1.035; 95% confidence interval [CI], 1.007–1.064; p = 0.013) and higher tacrolimus trough level (HR, 1.112; 95% CI, 1.029–1.202; p = 0.029) were associated with higher risks for fractures. Pretransplant diabetes mellitus had a time-dependent impact on fractures, with increasing risk as time elapses (HR for diabetes mellitus 1.115; 95% CI, 0.439–2.832; HR for diabetes mellitus × time, 1.049; 95% CI, 1.007–1.094; p = 0.022). In conclusion, KTRs had a high risk of peripheral skeletal fractures in the first 5 years. At baseline recipient age, pretransplant diabetes mellitus and tacrolimus trough level after KT were responsible for the fractures in KTRs.
Bayesian truthing and experimental validation in homeland security and defense

In this paper we discuss relations between Bayesian Truthing (experimental validation), Bayesian statistics, and Binary Sensing in the context of selected Homeland Security and Intelligence, Surveillance, Reconnaissance (ISR) optical and nonoptical application scenarios. The basic Figure of Merit (FoM) is Positive Predictive Value (PPV), as well as false positives and false negatives. By using these simple binary statistics, we can analyze, classify, and evaluate a broad variety of events including: ISR; natural disasters; QC; and terrorism-related, GIS-related, law enforcement-related, and other C3I events.
Inverse design of robust photonic components

Recent developments in computational inverse design offer the promise of significantly reducing the footprint and allowing complex optical functionality in silicon photonic components as compared to existing conventional building blocks. However, reliable fabrication of such components is one of the major bottlenecks in its widespread adoption. A common characteristic of such designs is the presence of small features that have meaningful impact on the optical performance. Current approaches to tackle this problem consider designing for robustness, such as by co-optimizing for over- and under-etched geometries at the design stage. This is often followed with a design-for-manufacturing optimization step to meet specifications of a foundry such as minimum feature size and curvature radii. Those approaches often incur additional significant computational costs as well as a reduction in peak optical performance. In this work, we highlight our recent progress to bridge the gap between inverse design methods and their ability to deliver reliable and manufacturable designs. We observe that the so-called parameterized shape optimization methods are more likely to produce robust designs for certain classes of components, as showcased in integrated mode converter designs. For components that benefit from topological inverse design such as wavelength demultiplexers, we propose a new optimization penalty that naturally leads the optimizer towards more robust designs. In a new research direction, we also consider improving fabrication reliability by the development and use of data-driven predictive models for fabrication. Leveraging deep learning tools, we present prediction and correction models that improve fabrication outcomes for a variety of components made at an e-beam prototyping foundry.
Innovative Photonic Sensors for Safety and Security, Part III: Environment, Agriculture and Soil Monitoring

In order to complete this set of three companion papers, in this last, we focus our attention on environmental monitoring by taking advantage of photonic technologies. After reporting on some configurations useful for high precision agriculture, we explore the problems connected with soil water content measurement and landslide early warning. Then, we concentrate on a new generation of seismic sensors useful in both terrestrial and under water contests. Finally, we discuss a number of optical fiber sensors for use in radiation environments.
Nociception in fruit fly larvae

Nociception, the process of encoding and processing noxious or painful stimuli, allows animals to detect and avoid or escape from potentially life-threatening stimuli. Here, we provide a brief overview of recent technical developments and studies that have advanced our understanding of the Drosophila larval nociceptive circuit and demonstrated its potential as a model system to elucidate the mechanistic basis of nociception. The nervous system of a Drosophila larva contains roughly 15,000 neurons, which allows for reconstructing the connectivity among them directly by transmission electron microscopy. In addition, the availability of genetic tools for manipulating the activity of individual neurons and recent advances in computational and high-throughput behavior analysis methods have facilitated the identification of a neural circuit underlying a characteristic nocifensive behavior. We also discuss how neuromodulators may play a key role in modulating the nociceptive circuit and behavioral output. A detailed understanding of the structure and function of Drosophila larval nociceptive neural circuit could provide insights into the organization and operation of pain circuits in mammals and generate new knowledge to advance the development of treatment options for pain in humans.
Molecularly imprinted nanoparticles for pathogen visualisation

Saccharides displayed on the cell surface of pathogens play critical roles in many activities such as adhesion, recognition and pathogenesis, as well as in prokaryotic development. In this work, we report the synthesis of molecularly imprinted nanoparticles (nanoMIPs) against pathogen surface monosaccharides using an innovative solid-phase approach. These nanoMIPs can serve as robust and selective artificial lectins specific to one particular monosaccharide. The evaluation of their binding capabilities has been implemented against bacterial cells (E. coli and S. pneumoniae) as model pathogens. The nanoMIPs were produced against two different monosaccharides: mannose (Man), which is present mainly on the surface of Gram-negative bacteria, and N-acetylglucosamine (GlcNAc) exposed on the surface of the majority of bacteria. Herein, we assessed the potential use of nanoMIPs for pathogen cell imaging and detection via flow cytometry and confocal microscopy.
A review on the research progress on non-pharmacological therapy of Helicobacter pylori

Helicobacter pylori is a pathogenic microorganism that mainly resides in the human stomach and is the major cause of chronic gastritis, peptic ulcer and gastric cancer. Up to now, the treatment of Helicobacter pylori has been predominantly based on a combination of antibiotics and proton pump inhibitors. However, the increasing antibiotic resistance greatly limits the efficacy of anti-Helicobacter pylori treatment. Turning to non-antibiotic or non-pharmacological treatment is expected to solve this problem and may become a new strategy for treating Helicobacter pylori. In this review, we outline Helicobacter pylori’s colonization and virulence mechanisms. Moreover, a series of non-pharmacological treatment methods for Helicobacter pylori and their mechanisms are carefully summarized, including probiotics, oxygen-rich environment or hyperbaric oxygen therapy, antibacterial photodynamic therapy, nanomaterials, antimicrobial peptide therapy, phage therapy and modified lysins. Finally, we provide a comprehensive overview of the challenges and perspectives in developing new medical technologies for treating Helicobacter pylori without drugs.
KEY FRAME PARTITION MATCHING FOR VIDEO SUMMARIZATION

Summarization of video content is a complex task that requires feature selection and frame matching. To extract meaningful frames, named key frames, we have proposed partitioning of frames with Voronoi diagrams for further region matching throughout the video sequence. A unique partition metric has been used that takes into account color and textural, structural and geometric properties of Voronoi regions. Feature set designed for CBIR and CBVR has been analysed. The reasonable feature selection and incorporation into frame matching process has permitted to obtain competitive results. All these actions have allowed revealing significant changes in content while omitting slight deflections and repeats. Key frame extraction procedure has been described in detail. The proposed method has been checked on different test samples and compared with existing methods for precision and recall.
Autogeneration of Fuzzy Logic Rule-Base Controllers

A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.