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Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF.

The accurate prediction of rotating stall inception is critical for determining the stable operating regime of a compressor. Among the two widely accepted pathways to stall, namely, modal and spike, the former is plausibly believed to originate from a global linear instability, and experiments have partially confirmed it. As for the latter, recent computational and experimental findings have shown it to exhibit itself as a rapidly amplified flow perturbation. However, rigorous analysis has yet to be performed to prove that this is due to global linear instability. In this work, an eigenanalysis approach is used to investigate the rotating stall inception of a transonic annular cascade. Steady analyses were performed to compute the performance characteristics at a given rotational speed. A numerical stall boundary was first estimated based on the residual convergence behavior of the steady solver. Eigenanalyses were then performed for flow solutions at a few near-stall points to determine their global linear stability. Once the relevant unstable modes were identified according to the signs of real parts of eigenvalues, they were examined in detail to understand the flow destabilizing mechanism. Furthermore, time-accurate unsteady simulations were performed to verify the obtained eigenvalues and eigenvectors. The eigenanalysis results reveal that at the rotating stall inception condition, multiple unstable modes appear almost simultaneously with a leading mode that grows most rapidly. In addition, it was found that the unstable modes are continuous in their nodal diameters, and are members of a particular family of modes typical of a dynamic system with cyclic symmetries. This is the first time such an interesting structure of the unstable modes is found numerically, which to some extent explains the rich and complex results constantly observed from experiments but have never been consistently explained. The verified eigenanalysis method can be used to predict the onset of a rotating stall with a CPU time cost orders of magnitude lower than time-accurate simulations, thus making compressor stall onset prediction based on the global linear instability approach feasible in engineering practice.

Electric machines emulators (EMEs) based on hardware-in-the-loop (HIL), which effectively act as emulators to mimic the actual motor behavior of Interior Permanent Magnet (IPM) machines. EME is frequently used to evaluate motor controller and motor control methodologies prior to development. The inverse magnetization motor model, which is used as the basis for real-time simulation in this paper's proposal for an electric machine emulator system based on HIL, uses FEA to create the motor model data. The nonlinear features of the motor may be successfully replicated with this motor model, and the accuracy of the electric machine emulator can be enhanced by using a straightforward and trustworthy motor controller. The real-time simulation tool typhoon HIL is used in the study to develop a hardware-in-the-loop simulation platform for an IPM electric machines emulator.

Microbial Enhanced Oil Recovery (MEOR) is an option for recovering oil from depleted reservoirs. Numerous field trials of MEOR have confirmed distinct microbial community structure in diverse production wells within the same block. The variance in the reservoir microbial communities, however, remains ambiguously documented. In this study, an 8 m long core microbial flooding simulation device was built on a laboratory scale to study the dynamic changes of the indigenous microbial community structure in the Qizhong Block, Xinjiang oil field. During the MEOR, there was an approximate 34% upswing in oil extraction. Based on the 16S rRNA gene high-throughput sequencing, our results indicated that nutrition was one of the factors affecting the microbial communities in oil reservoirs. After the introduction of nutrients, hydrocarbon oxidizing bacteria became active, followed by the sequential activation of facultative anaerobes and anaerobic fermenting bacteria. This was consistent with the hypothesized succession of a microbial ecological “food chain” in the reservoir, which preliminarily supported the two-step activation theory for reservoir microbes transitioning from aerobic to anaerobic states. Furthermore, metagenomic results indicated that reservoir microorganisms had potential functions of hydrocarbon degradation, gas production and surfactant production. Understanding reservoir microbial communities and improving oil recovery are both aided by this work.

Hollow core photonic bandgap fiber (HC-PBGF) has potential in the application of fiber optics gyroscopes (FOGs) for its properties of low nonlinearity, and excellent environmental adaptability. However, due to the structural complexity, the HC-PBGF is prone to defects during fabrication process, which will cause degradation of longitude uniformity and optical properties of fiber. The longitudinal non-uniformity of the structure limits the preparation length of HC-PBGF, while high-precision FOGs need to be made with long-distance and high-performance fibers. To facilitate the application of HC-PBGF in high-precision FOG, we studied the instability factors encountered in the fabrication process of polarization-maintaining (PM) HC-PBGF and the induced structural defects, including the fiber core, core-side hole, and fiber diameter defects. According to analyzing the optical performance variation to structural deformation rate, we conducted that the diameter defect has the greatest impact on the fiber properties, while the core-side hole defect has the weakest impact. Based on the fluctuations of optical performance, we revealed the structural deviation tolerance of PM HC-PBGF and provided theoretical reference for fiber fabrication.

Abstract Although there have been studies correlating DYRK2 with a number of human cancers, there has been no pan-cancer analysis. Therefore, through the TCGA database, we conducted a related study on the expression of DYRK2 in cancers.The expression of DYRK2 is obviously increased in some cancers, while the opposite is true in others, and there is a clear association between its expression and the prognosis of cancer patients.The mutation of DYRK2 is also significantly correlated with patients’ prognosis in certain human tumors. In addition, phosphorylation and methylation levels of DYRK2 are different between tumor tissues and adjacent normal tissues in various tumors. In the tumour microenvironment, the expression of DYRK2 correlates with cancer-associated fibroblast infiltration, such as BLCA or HNSC. In order to fully understand the role of DYRK2 in different tumors, we conducted a pan-cancer analysis.

This study compared rates of empirical-therapy use and negative patient outcomes between complicated and recurrent urinary tract infection (r/cUTI) cases diagnosed with a multiplex polymerase chain reaction or pooled antibiotic susceptibility testing (M-PCR/P-AST) vs. standard urine culture (SUC). Subjects were 577 symptomatic adults (n = 207 males and n = 370 females) presenting to urology/urogynecology clinics between 03/30/2022 and 05/24/2023. Treatment and outcomes were recorded by the clinician and patient surveys. The M-PCR/P-AST (n = 252) and SUC (n = 146) arms were compared after patient matching for confounding factors. The chi-square and Fisher’s exact tests were used to analyze demographics and clinical outcomes between study arms. Reduced empirical-treatment use (28.7% vs. 66.7%), lower composite negative events (34.5% vs. 46.6%, p = 0.018), and fewer individual negative outcomes of UTI-related medical provider visits and UTI-related visits for hospitalization/an urgent care center/an emergency room (p < 0.05) were observed in the M-PCR/P-AST arm compared with the SUC arm. A reduction in UTI symptom recurrence in patients ≥ 60 years old was observed in the M-PCR/P-AST arm (p < 0.05). Study results indicate that use of the M-PCR/P-AST test reduces empirical antibiotic treatment and negative patient outcomes in r/cUTI cases.

Abstract Zinc finger CCCH-type containing 15 (ZC3H15), a highly conserved protein involved in several cellular processes, which was responsible for tumorigenesis and may be a promising marker in myeloid leukemia (AML) and hepatocellular carcinoma (HCC). However, little is known about the biological significance and molecular mechanisms of ZC3H15 in GBM. In this study, we revealed that ZC3H15 was overexpressed in GBM and high ZC3H15 expression was associated with poor survival of patients with GBM. We found that ZC3H15 promoted the proliferation, migration, invasion, and tumorigenesis of GBM cells by activating the EGFR signaling pathway. We also revealed that ZC3H15 reduced EGFR ubiquitination, which was responsible for EGFR protein stabilization. In addition, we demonstrated that ZC3H15 inhibited the transcription of CBL, which was an E3 ubiquitin ligase for EGFR proteasomal degradation. And silencing of CBL could partly abrogate the inhibitory effects on cell proliferation, migration, and invasion of GBM cells induced by ZC3H15 knockdown. Thus, our research revealed the important roles of ZC3H15 in GBM development and provided a brand-new insight for improving the treatment of GBMs.

Abstract On‐line partial discharge (PD) monitoring is related to the safe and stable operation of power grid. However, the measured signal is inevitably affected by noise. Among a variety of noises, white noise is the most difficult to suppress and ubiquitous. It mainly determines the Cramer–Rao lower bound that cannot be crossed by all methods. This article proposes the Prony time‐series energy spectrum method to suppress white noise in a low signal‐to‐noise ratio environment. This method is divided into two parts: First, Prony time series energy spectrum is used to detect whether there is PD signals in the measured signal. Then, based on the least square method, the PD signal is estimated with the Prony model. In data analysis, this method can suppress ‐10dB white noise, while other methods have performed poorly or even failed under the same conditions. In general, this method has excellent anti‐noise performance compared to several other algorithms.

Abstract RAN binding protein 10 (RANBP10), a ubiquitously expressed and evolutionarily conserved protein, as a RAN-GTP exchange factor (GEF) to regulate several factors involved in cellular progression. Previous studies showed that RANBP10 was overexpressed in prostate cancer cells and was responsible for androgen receptor (AR) activation. However, the biological function of RANBP10 in glioblastoma (GBM) has not been studied. Here, we found that RANBP10 was overexpressed in GBM, and high RANBP10 expression was closely linked to poor survival of patients with GBM. Downregulation of RANBP10 significantly inhibited cell proliferation, migration, invasion, and tumor growth of GBM cells. In addition, we revealed that RANBP10 could suppress the promoter activity of FBXW7, and thereby increase the protein stability of c-Myc in GBM cells. Silencing of FBXW7 in RANBP10-knockdown GBM cells could partly negate the effects induced by RANBP10 downregulation. Taken together, our findings established that RANBP10 significantly promoted GBM progression by control of the FBXW7–c-Myc axis, and suggest that RANBP10 may be a potential target in GBM.

Coronavirus disease 2019 (COVID-19) pandemic has killed huge populations throughout the world and acts as a high-risk factor for elderly and young immune-suppressed patients. There is a critical need to build up secure, reliable, and efficient drugs against to the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Bioactive compounds of Ashwagandha [Withania somnifera (L.) Dunal] may implicate as herbal medicine for the management and treatment of patients infected by SARS-CoV-2 infection. The aim of the current work is to update the knowledge of SARS-CoV-2 infection and information about the implication of various compounds of medicinal plant Withania somnifera with minimum side effects on the patients’ organs. The herbal medicine Withania somnifera has an excellent antiviral activity that could be implicated in the management and treatment of flu and flu-like diseases connected with SARS-CoV-2. The analysis was performed by systematically re-evaluating the published articles related to the infection of SARS-CoV-2 and the herbal medicine Withania somnifera. In the current review, we have provided the important information and data of various bioactive compounds of Withania somnifera such as Withanoside V, Withanone, Somniferine, and some other compounds, which can possibly help in the management and treatment of SARS-CoV-2 infection. Withania somnifera has proved its potential for maintaining immune homeostasis of the body, inflammation regulation, pro-inflammatory cytokines suppression, protection of multiple organs, anti-viral, anti-stress, and anti-hypertensive properties. Withanoside V has the potential to inhibit the main proteases (Mpro) of SARS-CoV-2. At present, synthetic adjuvant vaccines are used against COVID-19. Available information showed the antiviral activity in Withanoside V of Withania somnifera, which may explore as herbal medicine against to SARS-CoV-2 infection after standardization of parameters of drug development and formulation in near future.

In ultra-dense D2D communication, the current relay selection schemes mainly assume that the relay device has the initiative to participate in data forwarding, but some rational relay devices may delay or refuse to forward due to their own considerations, thus affecting the user experience.From the perspective of selfish behavior of relay devices, a method to identify selfish relay devices in D2D communication was proposed, and then an ultra-dense D2D relay selection algorithm based on selfish behavior analysis was proposed.Firstly, three relay identification attribute factors, namely joint interest degree (JID), forwarding history ratio (FHR) and relay physical state (RPS) were defined, and their weights were calculated by triangular fuzzy function to identify relay devices with selfish behavior.Secondly, based on the TOPSIS (technique for order of preference by similarity to ideal solution) method, the final relay equipment selection scheme relay was realized.Simulation results show that the proposed algorithm can identify selfish relay devices more effectively in D2D communication.Compared with other relay selection algorithms, the proposed algorithm has made significant progress in the aspects of D2D communication forwarding success rate, system average throughput and average time delay.

This paper will first carry out experimental investigations on a test compressor which is a large-scale, two-stage and low-speed axial one to evaluate the participation of a novel casing treatment (stall precursor-suppressed casing treatment, SPS CT). The compressor performance for its pressure rise characteristics and efficiency are tested and illustrated. Besides, the time-averaged steady and the time-resolved unsteady measurement in this paper finally lead to some further analyses about the stall evolution of this compressor tending to uncover the process of the stall evolution and the mechanism of the SPS CT. The overall performance results indicate that the SPS CT can extend the compressor stall margin by 3%–11% with very limited efficiency loss. And the mechanism for such casing treatment has also been revealed as stall-precursor suppression by observing the evolution of the inception waves. In addition, modeling work have been conducted subject to the effects of SPS CT on the test two-stage axial compressor. The compressor stability model is able to investigate the effects of different geometrical parameters on the compressor stability and then make an optimal design for the SPS CT when it is applied on the test compressor.

This study proposes a novel supercontinuum (SC) shaping method using an all-fiber structure with little influence on the time domain and frequency domain of the SC spectrum. An SC is a type of laser source with a broad spectrum. However, its pump wavelength has a much greater intensity than other wavelengths, limiting the application of the SC. Our approach takes advantage of a hollow core anti-resonance fiber to attenuate the power density at the pump wavelength appropriately, thus flattening the SC spectrum. Simulation and experimental results both support the effectiveness of this method in reducing the wavelength at the pump. In our experiment, the use of a hollow core anti-resonant fiber decreased the intensity at the pump wavelength of the SC by 9 dB. By employing this method, the energy threshold can be lowered, allowing the SC to be applied to a larger range of experimental setups or applied scenarios with fewer energy requirements.

We demonstrate the damage-free delivery of nearly diffraction-limited picosecond laser pulses at 1064 nm with a maximum peak power of 3.5 MW in a 5 m air-filled anti-resonant hollow-core fiber (AR-HCF). In the air-filled AR-HCF, the transmission efficiency of picosecond pulses is degraded due to stimulated Raman scattering for the incident peak power above 3.59 MW. The temporal compression of pulses is also observed in the air-filled AR-HCF, where the self-phase modulation plays a key role in the anomalous dispersion region. By vacuuming the air in the core, a nearly constant coupling efficiency of 77% is achieved through the 5 m AR-HCF free of nonlinear effects, with M2 of the output beam less than 1.17.

Terahertz (THz) waves are widely used in the field of non-destructive testing (NDT). However, terahertz images have issues with limited spatial resolution and fuzzy features because of the constraints of the imaging equipment and imaging algorithms. To solve these problems, we propose a residual generative adversarial network based on enhanced attention (EA), which aims to pay more attention to the reconstruction of textures and details while not influencing the image outlines. Our method successfully recovers detailed texture information from low-resolution images, as demonstrated by experiments on the benchmark datasets Set5 and Set14. To use the network to improve the resolution of terahertz images, we create an image degradation algorithm and a database of terahertz degradation images. Finally, the real reconstruction of terahertz images confirms the effectiveness of our method.

In many practical fault diagnosis applications, the acquisition of fault data labels requires substantial manpower and resources, which are sometimes impossible to achieve. To address this, an unsupervised bearing fault diagnosis method based on deep clustering is proposed. In this method, an autoencoder is initially applied to the signal spectrum to learn the initial representation. Then, its potential manifold is further searched, and a Gaussian mixture model is finally used for clustering. Experiments conducted on the Case Western Reserve University bearing datasets show that the proposed method can find the optimal clusterable manifold. Moreover, its clustering performance is better than those of the current advanced baseline methods, and it is only slightly complex. Thus, the effectiveness of the proposed method is verified.

The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.

The remarkable rise of the current COVID-19 pandemic to every part of the globe has raised key concerns for the current public healthcare system. The spike (S) protein of SARS-CoV-2 shows an important part in the cell membrane fusion and receptor recognition. It is a key target for vaccine production. Several researchers studied the nature of this protein under various environmental conditions. In this work, we applied molecular modeling and extensive molecular dynamics simulation approaches at 0°C (273.15 K), 20°C (293.15 K), 40°C (313.15 K), and 60°C (333.15 K) to study the detailed conformational alterations in the SARS-CoV-2 S protein. Our aim is to understand the influence of temperatures on the structure, function, and dynamics of the S protein of SARS-CoV-2. The structural deviations, and atomic and residual fluctuations were least at low (0°C) and high (60°C) temperature. Even the internal residues of the SARS-CoV-2 S protein are not accessible to solvent at high temperature. Furthermore, there was no unfolding of SARS-CoV-2 spike S reported at higher temperature. The most stable conformations of the SARS-CoV-2 S protein were reported at 20°C, but the free energy minimum region of the SARS-CoV-2 S protein was sharper at 40°C than other temperatures. Our findings revealed that higher temperatures have little or no influence on the stability and folding of the SARS-CoV-2 S protein.

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. The aim of this study is to characterize the spatial and temporal distribution of HFOs in channel-wise instead of event-level as usual and to develop an automated the seizure onset zone (SOZ) identification by using a support vector machine (SVM) approach on the channel-wise features in a short-term recording. In this work, five consecutive patients with medically intractable epilepsy were enrolled. For each patient, ten-minute segments were defined from two hours of iEEG recordings during sleep state. A total of 17 channel-wise features including 6 rate-based, 6 duration-based, 3 amplitude-based, and 2 power-based features of HFOs were extracted from each 10-min segment, which including ripples (Rs, 80-250 Hz) and fast ripples (FRs, 250-500Hz) were detected automatically using validated detectors. Each channel-wise feature was ranked by using the Student's t-test method and the most distinctive features were selected to explore the characteristics of HFOs in each channel. A supervised-learning based SVM classifier with the selected channel-wise features or their combinations was developed to identify each channel within the independently clinician-defined SOZ or not. Over 3,816 chanel-10-min segments of iEEG recordings, the evaluated accuracy, sensitivity, and specificity of the proposed approach with the optimal combination of top five ranked features for SOZ identification are 86.6%, 73.0%, and 94.1%, respectively, for ten-fold cross-validation, and 86.0%, 79.2 %, and 91.8%, respectively, for the leave-1-out cross-validation. Compared with the recently reported SOZ detectors based on event-wise feature of HFOs, the channel-wise features and the combination with machine learning approach demonstrate its feasibility in SOZ identification with a relative higher performance and potentially reduce the time needed currently for long-term recording and manual inspection.

Sphingosine kinase 1 (SphK1) is a promising therapeutic target against several diseases including mammary cancer. The aim of present work is to identify a potent lead compound against breast cancer using ligand-based virtual screening, molecular docking, MD simulations, and the MMPBSA calculations. The LBVS in molecular and virtual libraries yielded 20,800 hits, which were reduced to 621 by several parameters of drug-likeness, lead-likeness, and PAINS. Furthermore, 55 compounds were selected by ADMET descriptors carried forward for molecular interaction studies with SphK1. The binding energy (ΔG) of three screened compounds namely ZINC06823429 (–11.36 kcal/mol), ZINC95421501 (–11.29 kcal/mol), and ZINC95421070 (–11.26 kcal/mol) exhibited stronger than standard drug PF-543 (–9.9 kcal/mol). Finally, it was observed that the ZINC06823429 binds tightly to catalytic site of SphK1 and remain stable during MD simulations. This study provides a significant understanding of SphK1 inhibitors that can be used in the development of potential therapeutics against breast cancer.

Various metabolites identified with therapeutic mushrooms have been found from different sources and are known to have antibacterial, antiviral, and anticancer properties. Over thousands soil growth-based mushroom metabolites have been discovered, and utilized worldwide to combat malignancy. In this study, psilocybin-mushroom that contains the psychedelic compounds such as psilacetin, psilocin, and psilocybine were screened and found to be inhibitors of SARS-CoV-2 Mprotease. It has been found that psilacetin, psilocin, and psilocybine bind to Mprotease with −6.0, −5.4, and −5.8 kcal/mol, respectively. Additionally, the psilacetin was found to inhibit human interleukin-6 receptors to reduce cytokine storm. The binding of psilacetin to Mprotease of SARS-CoV-2 and human interleukin-6 receptors changes the structural dynamics and Gibbs free energy patterns of proteins. These results suggested that psilocybin-mushroom could be utilized as viable potential chemotherapeutic agents for SARS-CoV-2.

Inlet swirl distortion is generally considered as a type of velocity distortion, and inlet guide vanes (IGVs) are widely used in the multi-stage compressor of aero-engines to eliminate the tangential velocity of the swirl flow. However, few studies have explored whether there still exists some negative influence of inlet swirl distortion on the compressor, even after the installation of IGVs. Therefore, in this study, the influence of various types of inlet swirl distortions on a multi-stage compressor with the installation of IGVs is investigated. A swirl distortion generator installed in the inlet duct was designed to produce various types of swirl flow patterns. When the distortion intensity increased to some degree, there still existed a decrease in the compressive capability and an obvious additional efficiency loss. The inlet twin swirl distortion was accompanied by total pressure distortion, so even with the installation of IGVs, there was still a significantly negative influence on the performance of the multi-stage compressor, especially the stall margin. Subsequently, to improve the stall margin under inlet swirl distortion, the stall precursor-suppressed (SPS) casing treatment was installed in the first stage of the multi-stage compressor. It could enhance the stall margin of the compressor with no obvious change in the characteristic curves and no additional efficiency loss under various types of inlet swirl distortions, and its mechanism was verified by capturing the dynamic pressure characteristics.

Collapse failure under external pressure is one of the common failure forms of casing. Much research has been performed on the casing body, but few on the threaded connection, in view of the general belief that the threaded connection has a thicker wall and larger collapse strength than the casing body. However, under external pressure, the sealing capacity of a worn casing connection will decrease due to deformation of the sealing structure, so the influence of sealing ability should be considered to determine the collapse strength of casing. In this paper, we established a three-dimensional finite element model of a 7” crescent-worn casing connection and calculated the collapse strength of the connection under different wear depths. Meanwhile, the stress distribution characteristics on the sealing surface were obtained and the influence of wear on the sealing performance of the casing connection under external pressure was analyzed. The results showed that when the wear rate exceeds a certain value, the collapse strength of the connection based on sealing integrity was lower than that of the casing body. Based on these, a collapse strength model for a 7” crescent-worn casing connection considering sealing integrity was developed and a safety evaluation method of the collapse strength of the worn casing string was proposed.

The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan–Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.

Foam metal is a foam-like substance with a high porosity; it has been used in flow control, vibration abatement, and acoustic absorption, mainly based on its physical properties. The aim of the current paper is to investigate the effect of foam metal casing treatments on the stability and acoustic level of a low-speed axial flow compressor. The experimental results show that the casing treatment improves the stall margin by 14.9%, without any efficiency loss. In terms of noise, the SPL of the tonal noise at the third order of BPF is reduced by 3.2 dB, while the SPL of the broadband noise is reduced up to 2.4 dB. The comparison in evolutions of the tip structure in a smooth casing condition and with a casing treatment indicates that the casing treatment affects the origination and the development of the tip leakage vortex. The working mechanism is also discussed.

Development of new drugs is a time-taking and expensive process. Comprehensive efforts are being made globally toward the search of therapeutics against SARS-CoV-2. Several drugs such as remdesivir, favipiravir, ritonavir, and lopinavir have been included in the treatment regimen and shown effective results in several cases. Among the existing broad-spectrum antiviral drugs, remdesivir is found to be more effective against SARS-CoV-2. Remdesivir has broad-spectrum antiviral action against many single-stranded RNA viruses including pathogenic SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). In this study, we proposed that remdesivir strongly binds to membrane protein (Mprotein), RNA-dependent RNA polymerase (RDRP), and main protease (Mprotease) of SARS-CoV-2. It might show antiviral activity by inhibiting more than one target. It has been found that remdesivir binds to Mprotease, Mprotein, and RDRP with −7.8, −7.4, and −7.1 kcal/mol, respectively. The structure dynamics study suggested that binding of remdesivir leads to unfolding of RDRP. It has been found that strong binding of remdesivir to Mprotein leads to decrease in structural deviations and gyrations. Additionally, the average solvent-accessible surface area of Mprotein decreases from 127.17 to 112.12 nm2, respectively. Furthermore, the eigenvalues and the trace of the covariance matrix were found to be low in case of Mprotease–remdesivir, Mprotein–remdesivir, and RDRP–remdesivir. Binding of remdesivir to Mprotease, Mprotein, and RDRP reduces the average motions in protein due to its strong binding. The MMPBSA calculations also suggested that remdesivir has strong binding affinity with Mprotein, Mprotease, and RDRP. The detailed analysis suggested that remdesivir has more than one target of SARS-CoV-2.

High-frequency oscillations (HFOs) of 80~500 Hz in the intracranial electroencephalogram (iEEG) recordings are considered as a reliable marker for epileptic location. However, a significant challenge to the clinical use of HFOs is due to the time-consuming procedure of visually identifying them. A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time-frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. The effectiveness and usefulness of the proposed method are evaluated using the clinical iEEG data acquired from five patients (28.4 ± 13.0 years) with medically intractable epilepsy. The proposed methodology presents the following significant advantages: 1) compared with the recently reported HFOs detector based on the CNN using only the 1D temporal EEG signal, the proposed method achieves a higher accuracy using the deep CNN classifier on 2D time-frequency map of HFOs, of which the evaluated sensitivity and false discovery rate (FDR) for identifying ripples are 88.16% and 12.58%, respectively, and the corresponding sensitivity and FDR are 93.37% and 8.1% for detecting fast ripples, respectively; 2) it is capable of automatically extracting the shared features of HFOs events of different patients and would be much robust, unlike other automated methodologies proposed in the literature where the characteristics of HFOs were extracted manually on the basis of researchers' knowledge, which, probably, is prone to observer bias; and 3) with the proposed STE estimation, all suspicious ripples and fast ripples could be initially found out and transformed into time-frequency map for subsequently CNN-based classification, rather than transforming and classifying the raw data, thus requiring a lower computational resource. In addition, the time occurrence of each transient event of the HFOs can be identified to be potentially useful for further seizure analysis. In conclusion, this automated detection of the HFOs combing the STE and the CNN could allow analyzing large amounts of data in a short time while assuring a relatively higher accuracy and, thus, would potentially serve to provide a clinically useful tool.

Bruxism is a sleep syndrome, in which individual involuntarily grinding and clenching the teeth. If sleep does not complete properly, then it generates many disorders such as bruxism, insomnia, sleep apnea, narcolepsy, rapid eye movement behavioral disorder, and nocturnal frontal lobe epilepsy. The aim of this paper is to draw the results in the form of signal spectrum analysis of the changes in the domain of different stages of sleep. The present research completed in three stages such as the collection of the data, analysis of the electroencephalogram (EEG) signal, and comparative analysis between bruxism patients and normal subjects. Importantly, the channels EMG1-EMG2 and ECG1-ECG2 of the EEG signal were combined for the prognosis of bruxism by using power spectral density, which mainly focused on two sleep stages such as wake (W) and rapid eye movement (REM). The total number of one-minute EEG recordings from bruxism patients and normal subjects analyzed in this work were 149 and 95, respectively. The obtained results show that the average normalized values of the power spectral density of the EMG1-EMG2 and ECG1-ECG2 channels during REM and W sleep stages are several folds higher in case of the bruxism than those in the normal. Moreover, the proposed power spectral density-based method by using the decision tree classifier shows a higher accuracy for the prognosis of sleep bruxism in comparison with previous works. In addition, the proposed approach in the prognosis of the bruxism is noise free and accurate as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective prognosis system of the human bruxism with high accuracy for medical applications.

Lack of sleep causes many sleep disorders such as nocturnal frontal lobe epilepsy, narcolepsy, bruxism, sleep apnea, insomnia, periodic limb movement disorder, and rapid eye movement behavioral disorder. Out of all, bruxism is a common behavior, which is found in 8-31% of the population. Bruxism is a sleep disorder in which individuals involuntarily grinds and clenches the teeth. The main aim of this work is to detect sleep bruxism by analyzing the electroencephalogram (EEG) spectrum analysis of the change in the domain of different stages of sleep. The present research was performed in different stages such as collection of the data, preprocessing of the EEG signal, analysis of the C4-P4 and C4-A1 channels, comparison between healthy humans and bruxism patients, and classification using decision tree method. In this study, the channels C4-P4 and C4-A1 of the EEG signal were combined for the detection of bruxism by using Welch technique, which mainly focused on two sleep stages such as S1 and rapid eye movement. The total number of EEG channels of healthy humans and bruxism patients analyzed in this work were 15 and 18, respectively. The results showed that the individual accuracy of the C4-P4 and C4-A1 channels was 81.70% and 74.11%, respectively. The combined accuracy of both C4-P4 and C4-A1 channels was 81.25%. The specificity of combined result was higher than individual. In addition, the value of theta activity during detection is consistent throughout the period, and the accuracy of S1 stage is better than rapid eye movement stage. We proposed that the theta activity of S1 could be taken for the detection of bruxism. The proposed approach in the detection of the bruxism is negligible in noise as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective detection system of the sleep bruxism with high accuracy for medical big data applications.

Early risk identification of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as TpTe/QT, JTp/JTe, and TpTe/JTp and two conduction-repolarization markers, such as TpTe/QRS and TpTe/(QT×QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classification of normal and SCD risk groups by employing machine learning classifiers, such as k-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 28 SCD and 18 normal patients. For the automated strategy, the set of five arrhythmic risk markers can predict SCD in less than one second with an average accuracy of 98.91% (KNN), 98.70% (SVM), 98.99% (DT), 97.46% (NB), and 99.49% (RF) for 30 minutes before the occurrence of SCD. Moreover, a practical and straightforward SCD index (SCDI) through a judicious integration of these markers is also proposed by using the Student's t-test. The obtained SCDIs are 1.2058 ± 0.0795 and 1.7619 ± 0.1902 for normal and SCD patients, respectively, which provide a sufficient discrimination degree with a p-value of 6.5061e-35. The present results show that both the automated classifier and the integrated SCDI can predict the SCD up to 30 minutes earlier, and that these predictions could be more practical and efficient if applied in portable smart devices with real-time requirements in hospital settings or at home.

A lightweight convolutional neural network (CNN) is presented in this study to automatically indentify atrial fibrillation (AF) from single-lead ECG recording. In contrast to existing methods employing a deeper architecture or complex feature-engineered inputs, this work presents an attempt to employ a lightweight CNN to confront current drawbacks such as higher computational requirement and inadequate training dataset, by using representative rhythms features of AF rather than raw ECG signal or hand-crafted features without any electrophysiological considerations. The experimental results suggested that this method presents the following significant advantages: (1) higher performances for indentifying AF in terms of accuracy, sensitivity, and specificity that are 97.5%, 97.8%, and 97.2%, respectively; (2) It is capable of automatically extracting the shared features of AF episodes of different patients and would be much robust and reliable; (3) with the cardiac rhythm features as input dataset, rather than complex transforming and classifying the raw data, thus requiring a lower computational resource. In conclusion, this automated method could analyze large amounts of data in a short time while assuring a relative high accuracy, and thus would potentially serve to provide a comfortable single-lead monitoring for patients and a clinical useful tool for doctors.

Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review.

In this paper, the combination of using an anti-resonant hollow-core fiber (ARHCF), working as a gas absorption cell, and an inexpensive, commercially available watch quartz tuning fork (QTF), acting as a detector in the quartz-enhanced photothermal spectroscopy (QEPTS) sensor configuration is demonstrated. The proof-of-concept experiment involved the detection of methane (CH4) at 1651 nm (6057 cm−1). The advantage of the high QTF Q-factor combined with a specially designed low-noise amplifier and additional wavelength modulation spectroscopy with the second harmonic (2f-WMS) method of signal analysis, resulted in achieving a normalized noise-equivalent absorption (NNEA) at the level of 1.34 × 10−10 and 2.04 × 10−11 W cm−1 Hz−1/2 for 1 and 100 s of integration time, respectively. Results obtained in that relatively non-complex sensor setup show great potential for further development of cost-optimized and miniaturized gas detectors, taking advantage of the combination of ARHCF-based absorption cells and QTF-aided spectroscopic signal retrieval methods.

In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

In order to explore the mechanical characteristics and failure characteristics of water-saturated frozen cement mortar with different transfixion degree joints under impact load, a cyclic freezing impact test was performed on six kinds of water-saturated frozen cement mortar specimens with different transfixion degrees (0%, 20%, 40%, 60%, 80%, 100%) by split Hopkinson pressure bar (SHPB), and the microscopic damage of specimens was tested by an MRI analyzer. The results show that the dynamic compressive strength of the water-saturated frozen cement mortar increases first and then decreases with the increase of the number of cycles, with the increase of joint transfixion degree the peak stress showing a clear linear downward trend. An 80% transfixion degree joint specimen has the weakest ability to carry impact loads, and the 40% transfixion degree joint specimen has the strongest ability to carry impact loads. With the increase of joint transfixion degree, there was a similar trend of first decreasing and then rising in the average change of porosity and the change of energy density per unit time index. The joint transfixion degree controls the crack development trend and the final destruction mode of the specimen. With the increase of joint transfixion degree, the failure sequence gradually evolved from pull-shear composite failure to split-tensioned failure.

Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

Abstract In this paper, we introduce the concept of (T,g)F $(T,g)_{F}$-contraction and investigate some fixed point theorems for such contraction in b-metric-like spaces. Moreover, an example is given to support one of our results, and an application is proposed.

We propose a novel broadband single-polarization single-mode (SPSM) photonic crystal fiber with hexagonally latticed circular airholes. Combined with our fabricated polarization maintaining fiber, it has demonstrated that the enlarged air holes in the outmost layer are superior for the confinement loss reduction. At the optimized parameters, the wavelength of SPSM region ranges from 1.52 to 2.13 μm, where only y-polarized fundamental mode exists and the confinement loss is 0.051 dB/km at 1.55 μm. The influence of realistic fabrication errors involving structure deviation on SPSM operation has also been systematically investigated.

In this work, we present femtosecond laser cutting of microchannels in a nodeless antiresonant hollow-core fiber (ARHCF). Due to its ability to guide light in an air core combined with exceptional light-guiding properties, an ARHCF with a relatively non-complex structure has a high application potential for laser-based gas detection. To improve the gas flow into the fiber core, a series of 250 × 30 µm microchannels were reproducibly fabricated in the outer cladding of the ARHCF directly above the gap between the cladding capillaries using a femtosecond laser. The execution time of a single lateral cut for optimal process parameters was 7 min. It has been experimentally shown that the implementation of 25 microchannels introduces low transmission losses of 0.17 dB (

We study the mid-infrared attenuation of antiresonant hollow-core fiber made of fused silica glass. The role of absorptive losses increases with wavelength but can be minimized by reducing the overlap of the trapped light with the silica. We show that this overlap is least at the lowest-order antiresonance condition, corresponding to the thinnest core wall, and for higher resonances scales with the core wall thickness. A record-low minimum attenuation of 18 dB/km measured in our fiber at 3.1 µm wavelength is not limited by silica absorption. We measured 40 dB/km attenuation at 4 µm wavelength, where the attenuation of bulk silica is 860 dB/m. We show that this corresponds to a modal overlap of 2.81 × 10−5 which is in good agreement with simulations, suggesting that at this wavelength, attenuation is limited by silica absorption. This enables us to predict the achievable attenuation at longer wavelengths as well. Extrinsic losses due to gaseous molecular absorption may make demonstration of such losses difficult in some spectral bands. In contrast to shorter wavelengths, where leakage loss is the primary attenuation mechanism, introducing additional elements into the cladding design is unlikely to reduce the attenuation further, and further loss reduction would require a larger core size.

The resonance band in hollow-core photonic crystal fiber (HC-PCF), while leading to high-loss region in the fiber transmission spectrum, has been successfully used for generating phase-matched dispersive wave (DW). Here, we report that the spectral width of the resonance-induced DW can be largely broadened due to plasma-driven blueshifting soliton. In the experiment, we observed that in a short length of Ar-filled single-ring HC-PCF the soliton self-compression and photoionization effects caused a strong spectral blueshift of the pump pulse, changing the phase-matching condition of the DW emission process. Therefore, broadening of DW spectrum to the longer-wavelength side was obtained with several spectral peaks, which correspond to the generation of DW at different positions along the fiber. In particular, we numerically used the super-Gauss windows with different central wavelengths to filter out these DW spectral peaks and studied the time-domain characteristics of these peaks respectively using Fourier transform method. We observed that these multiple-peaks on the DW spectrum have different delays in the time domain, which is in good agreement with our theoretical prediction. More interestingly, we found that the broadband DW with several spectral peaks can be compressed to ~29 fs after proper dispersion compensation. The results reported here, on the one hand, provide some useful insights into the resonance-induced DW generation process in gas-filled HC-PCFs. On the other hand, the DW-emission mechanism could be used to generate the ultrashort light sources with a wide spectral range through using the proper design of the resonance bands of the HC-PCFs, which has many applications in the ultrafast related experiments.

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

Two photoactivatable near infrared fluorescent proteins (NIR FPs) named “PAiRFP1” and “PAiRFP2” are formed by directed molecular evolution from Agp2, a bathy bacteriophytochrome of Agrobacterium tumefaciens C58. There are 15 and 24 amino acid substitutions in the structure of PAiRFP1 and PAiRFP2, respectively. A comprehensive molecular exploration of these bacteriophytochrome photoreceptors (BphPs) are required to understand the structure dynamics. In this study, the NIR fluorescence emission spectra for PAiRFP1 were recorded upon repeated excitation and the fluorescence intensity of PAiRFP1 tends to increase as the irradiation time was prolonged. We also predicted that mutations Q168L, V244F, and A480V in Agp2 will enhance the molecular stability and flexibility. During molecular dynamics (MD) simulations, the average root mean square deviations of Agp2, PAiRFP1, and PAiRFP2 were found to be 0.40, 0.49, and 0.48 nm, respectively. The structure of PAiRFP1 and PAiRFP2 were more deviated than Agp2 from its native conformation and the hydrophobic regions that were buried in PAiRFP1 and PAiRFP2 core exposed to solvent molecules. The eigenvalues and the trace of covariance matrix were found to be high for PAiRFP1 (597.90 nm2) and PAiRFP2 (726.74 nm2) when compared with Agp2 (535.79 nm2). It was also found that PAiRFP1 has more sharp Gibbs free energy global minima than Agp2 and PAiRFP2. This comparative analysis will help to gain deeper understanding on the structural changes during the evolution of photoactivatable NIR FPs. Further work can be carried out by combining PCR-based directed mutagenesis and spectroscopic methods to provide strategies for the rational designing of these PAiRFPs.

In this work, we present for the first time a laser-based dual gas sensor utilizing a silica-based Antiresonant Hollow-Core Fiber (ARHCF) operating in the Near- and Mid-Infrared spectral region. A 1-m-long fiber with an 84-µm diameter air-core was implemented as a low-volume absorption cell in a sensor configuration utilizing the simple and well-known Wavelength Modulation Spectroscopy (WMS) method. The fiber was filled with a mixture of methane (CH4) and carbon dioxide (CO2), and a simultaneous detection of both gases was demonstrated targeting their transitions at 3.334 µm and 1.574 µm, respectively. Due to excellent guidance properties of the fiber and low background noise, the proposed sensor reached a detection limit down to 24 parts-per-billion by volume for CH4 and 144 parts-per-million by volume for CO2. The obtained results confirm the suitability of ARHCF for efficient use in gas sensing applications for over a broad spectral range. Thanks to the demonstrated low loss, such fibers with lengths of over one meter can be used for increasing the laser-gas molecules interaction path, substituting bulk optics-based multipass cells, while delivering required flexibility, compactness, reliability and enhancement in the sensor’s sensitivity.