Radio Frequency Interference (RFI) detection and characterization play a critical role to in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, this paper proposes an efficient end-to-end method using the latest advances in deep learning to extract the appropriate features of the RFI signal. Moreover, this study utilizes the benefits of transfer learning to determine both the type of received RFI signals and their modulation types. To this end, the scalogram of the received signals is used as the input of the pre-trained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pre-trained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

We are of the opinion that during the design of a binary classifier one ought to consider adding an “I don’t know” answer. We provide the case for the introduction of this third category when a human needs to make a decision based on the answer from a binary classifier. We discuss the costs and potential benefits of its introduction. Colloquially, we have used the term “I don’t know”, but formally we refer to it as NotAvailable. A procedure to define NotAvailable predictions in binary classifiers, called all leave-one-out models (ALOOM), is presented as proof of the concept. Furthermore, we discuss the potential benefits of applying ALOOM in real life applications.

This paper studied the solar PV frequency control in the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) systems. The studied frequency control approaches include droop frequency control, inertia control, and droop-inertia-combined frequency control. The control effects of different frequency controls of PV in the EI and ERCOT are studied using actual high PV penetration interconnection grid models to provide suggestions to the future revision of future PV frequency control standards.

We report the results of an analysis of the functional capabilities of the KTP crystal and its isomorphs for nonlinear-optical frequency conversion of all types of interaction in the transparency range of the crystal. The possibility of implementing angle-, wavelength- (frequency-) and temperature-noncritical phase matching is shown.

The lactic acid bacteria (LAB) have great potential to produce homoexopolysaccharides (HoPS), have been the subject of extensive research efforts, given their health benefits and physicochemical properties. The HoPS functional properties are determined by structural characteristics of varied molecular weights, types of glycosidic linkages, degrees of branching and chemical composition. The dextransucrases (DSases) are responsible of the synthesis of a kind of HoPS (dextran polymers), which are among the first biopolymers produced at industrial scale with applications in medicine and biotechnology. The concept of glycodiversification opens additional applications for DSases. In that sense the design and characterization of new DSases is of prime importance. Previously, we described the isolation and characterization of a novel extracellular dextransucrase (DSR-F) encoding gene. In this study, from DSR-F, we design a novel chimeric dextransucrase DSR-F-∆SP-∆GBD-CBM2a, where DSR-F-∆SP-∆GBD is fused to the carbohydrate-binding module (CBM2a) of the β-1-4 exoglucanase/xylanase Cex (Xyn10A) of Cellulomonas fimi ATCC 484. This dextransucrase variant is active and without alteration in its specificity. The DSR-F-∆SP-∆GBD-CBM2a is purified by cellulose affinity chromatography for the very first time. Our results indicate that new hybrids and chimeric DSases with novel binding capacity to cellulose can be designed to obtain glyco-biocatalysts from renewable lignocellulosic materials.

Maxwell equation for the electric field has been solved for any medium by suggesting the wavenumber and angular frequency be complex quantities. This accounts for the field decay by interaction with the medium.This expression for the time and special decay of the electric field in a medium is used to construct a new wave function sensitive to the medium physical properties. This new wave function, unlike the conventional one, differentiates between a beam of particles in a vacuum and that enters a medium which is an attenuated due to the scattering effect.Another expression for time decaying electric field was obtained using Newton’s laws for frictional medium. This expression shows that the electric field diminishes due to friction.Fortunately, this time decaying part of the electric field is typical to that derived from Maxwell’s equations. Finally, a new Schrodinger equation sensitive to the medium properties was derived. This equation, fortunately, describes some scattering processes for Protein scattering, scattering of x-rays, opto-acoustic phonons, and Raman scattering for some materials successfully .

Cantilever beams are the most widely used form of strain-driven energy harvesting, which uses piezoelectric materials. Although researchers are constantly seeking improved power output from cantilever-beam-driven piezoelectric energy harvesters, a systematic and fundamental analysis of the effect of beam geometry on the power capacity of energy harvesters warrants further investigation. Most of the previous research accounts for beams that are fully coated with piezoelectric material. While a larger coating area increases energy output, it also escalates costs as piezoelectric materials are very expensive. Considering the high cost of piezoelectric materials when dealing with limited piezoelectric material, enhancing power output can be achieved by employing a larger base beam and coating a portion of it at the fixed end. As such, the aim of this work is to investigate a wide variety of cantilever beam shapes (e.g., trapezoidal, triangular, V-cut, concave, and convex) with partial piezoelectric material coating on the base beam to maximize the power output capacity of the harvester. To ascertain a comparable argument, the surface area, volume, and mass of all the considered beam shapes are kept consistent, as these parameters influence the power output of the harvester. The geometry of each shape is systematically varied to understand the effect of geometric configuration on the output power density. Finally, the power capacities of all types of beams are compared, and a design is proposed to obtain the maximum power output. It was found that when the surface area of both the beam and piezoelectric material is kept constant and piezoelectric material is located at the fixed end, a trapezoidal beam with a smaller base width and larger free end width is the most efficient in generating power as long as the structural integrity is maintained. The finding is completely opposite as was suggested by previous studies, which proposed that a trapezoidal beam with a smaller free end yields higher output. Instead, this study demonstrates that an inverted trapezoidal beam produced higher output, considering partial coating compared to the full beam coating used in previous studies. Additionally, a correlation is presented on how the beam resonance frequency shifts with variation in beam parameters.

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to $6.02\%$ compared to state-of-the-art baselines.

This paper aims to provide an overview of the complete process in the development of a Domain-Specific Language (DSL). It explains the construction steps such as preliminary research, language implementation, and evaluation. Moreover, it provides details for different key components which are commonly found in the DSLs such as the abstraction layer, DSL metamodel, and the applications. It also explains the general limitations related to the Domain-Specific Languages for Workflows.

(1) Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. (2) Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. (3) Results: The PSNR increases 1.5 dB on average compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

Six Half-ring with varying linewidth from 120nm to 370nm were connected in series on five corners. The magnetization reversal processes were investigated by the measurement of anisotropic magnetoresistances (AMR). The number of switching jumps in the AMR loops, from zero to five, varied with the longitudinal applied field. These discrete jumps result from domain wall nucleating and depinning on the corners. The larger external field applied the fewer number of jumps in the MR curve. This reproducible and particular-respondence of domain wall device in pattern of half-ring wire might be the new promising magnetoelectronic devices.

The cytoskeleton is a master organizer of cell cortex, organelles positioning and membrane trafficking. Therefore, playing a decisive role in establishment of apico-basal polarity. Septins are critical cytoskeleton components which function in apico-basal polarity remain weakly investigated. Here, using 3D culture system, we demonstrate that septin9 localizes at basolateral membrane (BM). Its depletion induces an inverted polarity phenotype, decreasing -catenin at BM and increasing the expression of the transforming growth factor (TGF) and the epithelial-to-mesenchymal transition (EMT) markers. Similar effects were observed upon deleting its two polybasic domains, and the mutant become cytoplasmic and apical. The cysts with an inverted polarity phenotype display an invasive phenotype, with src and cortactin accumulating at the peripheric membrane. Inhibition of TGF-receptor and RhoA rescues the polarized phenotype. Although the cysts from overexpressed-septin 9, overgrowth and present a filled lumen. Both phenotypes correspond to tumor features. This suggests that septin 9 expression and assembly through the two PB domains are essential for establishing and maintaining apico-basal polarity against tumor development through a mechanism involving TGF-dependent EMT and RhoA activity.

This work presents an alternative to harnessing wind energy with an Electromagnetic Frequency Regulator (EFR) coupled to a hydrostatic transmission and associated with a horizontal axis wind turbine, a bidirectional frequency inverter and a secondary energy source, in a hybridized system. The hydrostatic transmision is composed by a fixed displacement axial piston pump and a variable displacement, swash plate, axial piston motor. Feedback linearization was used as a technique to control the motor geometric displacement, and a prediction algorithm for the steady state rotations of the armature and the electromagnetic field has been developed. A 5\,kW project was simulated on a Scilab platform, with combinations of constant or variable load, and constant or variable wind speed. The results indicated that the system was able to supply the generator load, adapting to fluctuations in wind speed. The possibility of storing wind energy through the inverter has also been proven. The system can accumulate energy in batteries during the fastest wind regimes, to use it when the turbine power is lower than the load.

These days, numerous organizations and Analysis Centers (AC) offer various Ambiguity Resolution (AR) products using various methodologies. To use it for time-frequency transfer, there is no associated study. This paper chooses 16 MGEX stations with external high-precision atomic clocks to constitute 15 international time comparison links and uses AR products data from CNES, SGG, CODE, and PRIDE laboratory, using three ambiguity-fixed strategies, to thoroughly evaluate the effects of various strategies and AR products for high-precision time-frequency transfer. We reach the following results by using the IGS final clock product as a reference and comparing it to ambiguity-float. With various ambiguity-fixed procedures, the time stability STD of time transfer is increased for a single GPS, and the improvement ranges from 10% to 40%. The frequency stability has barely improved; up to 40%, the most notable improvement comes from FCB with GRM products. The time stability STD of combinations has improved after the addition of the Galileo system compared to the single GPS, and the improvement ranges from 2% to 9%. Most strategies have improved, while a few techniques have weakened with the GEC (GPS+Galileo+BDS) combination. We feel that the stability has not significantly increased with the system's increase within short-term stability after comparing multiple groups of linkages.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

This work examines drought and wet events based on Standardized Precipitation-Evapotranspiration Index (SPEI) over Kenya from 1981 to 2016. Spatiotemporal analysis of dry and wet events is conducted for 3 and 12-month SPEI. The drought incidences were observed during the period 1984, 1987, 2000, 2006, 2009, 2015, and 2016 for SPEI-3 whilst the SPEI-12 demonstrated the manifestation of drought during the year 2000 and 2006. SPEI clearly shows that the wettest period, 1997 and 1998 that coincide with the El Nino event in both time steps. SPEI -3 shows a reduction in moderate drought events while severe and extreme cases were on increase towards the end of the twentieth century. Conversely, SPEI-12 depicts an overall increase in severe drought occurrence over the study location with observed intensity of -1.54 and cumulative frequency of 64 months during the study period. The trend of wet events is upwards in the western and central highlands while the rest of the regions show increase in dry events during the study period. Moreover, moderate dry/wet events predominate whilst extreme events occur least frequent across all grid cells. It is apparent that the study area experiences mild extreme dry events in both categories although moderately severe dry events dominate most parts of the study area. High intensity and frequency of drought is noted in SPEI-3 while least occurrences of extreme events are recorded in SPEI-12. Although drought event prevails across the study area, there is evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at projection of droughts over the study area based on regional climate models.

In biology molecules and macromolecules like sugars, proteins, DNA, and RNA are of utter importance. Detecting their presence as well as their conformation is still a challenge in many cases. It is well known that the vibrational states of such molecules lie from the infrared to the TeraHertz range. Spectroscopy can be used to detect such compounds and probe their conformation. Still, terahertz spectroscopy on biosample is a challenge for two main reasons: water absorption; and the small size of the samples. The sample volume is smaller than the cube of the TeraHertz wavelength; the light matter interactions are thus extremely reduced. In this paper, we present the design, fabrication, characterization and the first typical uses of a biophotonic device aiming at increasing light matter interaction to enable terahertz spectroscopy of minute samples on a broad band (0.2–2 THz). We demonstrate time domain spectroscopy experiments on few µl samples showing the validity of our approach.

In E. coli DNA replication forks stall on average once per cell cycle. When this occurs, replisome components disengage from the DNA, exposing an intact, or nearly intact fork. Consequently, the fork structure must be regressed away from the initial impediment so repair can occur. Regression is catalyzed by the powerful, monomeric DNA helicase, RecG. During this reaction, the enzyme couples unwinding of fork arms to rewinding of duplex DNA resulting in the formation of a Holliday junction. RecG works against large opposing forces enabling it to clear the fork of bound proteins. Following subsequent processing of the extruded junction, the PriA helicase mediates reloading of the replicative helicase DnaB leading to the resumption of DNA replication. The single-strand binding protein (SSB) plays a key role in mediating PriA and RecG functions at forks. It binds to each enzyme via linker/OB-fold interactions and controls fork loading sites in a substrate-dependent manner that involves helicase remodeling. Finally, it is displaced by RecG during fork regression. The intimate and dynamic SSB-helicase interactions play key roles in ensuring fork regression and DNA replication restart.

Six mode shapes, including bending and torsion, were documented for five different basketball rims and backboards at the United States Military Academy, West Point, New York, USA. The frequency and damping ratio of each mode shape were also determined. The empirical process began with the time-domain excitation and response of each rim-backboard system. The impulse of excitation came from an impact hammer separately applied to sequentially, to each node. The sinusoidal response was gathered from an accelerometer at a fixed location, node 1. Each time-domain excitation-response was then converted to a frequency domain Bode plot for each node by a B&K 2034 Signal Analyzer, giving transfer functions of output/input versus frequency. Structural Measurements System (SMS) Software was used to fit mode shapes to the Bode plots. Each of the six mode shapes were fitted to the Bode plots of each node at a specific modal frequency. Each of the six mode shapes were a function of the locations of the nodes, and the Bode plot gathered at each node. The first and second modes were critical for showing that the Energy Rebound Testing Device statistically correlated with the energy transferred to the rim and backboard. A known perturbation mass was selectively attached to the rim, to help isolate the dynamic masses and spring rates for the rim and backboard, to ascertain the kinetic energy transferred to the rim had a 95.67% inverse correlation with rim stiffness.

This study explores the relationship between mental and physical therapeutic through three dimensions: man-environment relationships; a sense of place and symbolic landscapes. The study used a combination of qualitative and quantitative research methods. Local residents living in the coastal area of Xinglin Bay are the research objects. Quantitative data analysis revealed that the frequency of residents' visits was an important variable affecting their physical and treatment perceptions. In addition, the significance is higher for females than for males. The text analysis shows that the environment, especially the ecological restoration of water quality and migratory birds, is of great significance to the identity of local residents. Daily activities contribute to a sense of place and are what make the coast a place of therapeutic landscape function.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

We demonstrate that the conductivity of graphene on thin-film polymer substrates can be accurately determined by reflection-mode air-plasma-based THz time-domain spectroscopy (THz-TDS). The phase uncertainty issue associated with reflection measurements is discussed, and our implementation is validated by convincing agreement with graphene electrical properties extracted from more conventional transmission-mode measurements. Both the reflection and transmission THz-TDS measurements reveal strong nonlinear and instantaneous conductivity depletion across an ultra-broad bandwidth under relatively high incident THz electrical field strengths.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

Background: Variations in several clopidogrel-pharmacogenes have been linked to clopidogrel response variability and clinical outcomes. We aimed to determine the frequency distribution of major polymorphisms on CYP2C19, PON1, ABCB1 and P2RY12 pharmacogenes in Puerto Ricans. Methods: This was a cross-sectional, population-based study of 200 unrelated “Guthrie” cards specimens from newborns registered in the Puerto Rican Newborn Screening program (PRNSP) between 2004 and 2014. Taqman® SNP assay techniques were used for genotyping. Results: Minor Allele Frequencies (MAF) were 46% for PON1 (rs662), 41% for ABCB1 (rs1045642), 14% for CYP2C19*17, 13% for CYP2C19*2, 12% for P2RY12-H2 and 0.3% for CYP2C19*4. No carriers of the CYP2C19*3 variants were detected. All alleles and genotype proportions were found to be in Hardy-Weinberg equilibrium (HWE). Overall, there were no significant differences between MAFs of these variants in Puerto Ricans and the general population (n=453) of the 1,000 Genome project, except for the Yoruba in Ibadan from Nigeria (YRI, West-African ancestry; p<0.05). As expected, the prevalence of these markers in Puerto Ricans most resembled those in the 181 subjects from reference populations of the Americas. Conclusions: These prevalence data provide a necessary groundwork for future clinical studies of clopidogrel pharmacogenetics in Caribbean Hispanics.

We evaluated the closure of idiopathic full-thickness macular holes (MHs) the day after surgery in minimizing the burden and maximizing patient outcomes. Herein, 25-gauge pars plana vitrectomy, internal limiting membrane peeling, and fluid-gas (20% sulfur hexafluoride) were performed for the treatment. Patients were instructed to remain in the facedown position until the confirmation of MH closure, and the position was discontinued in cases where the closure was confirmed. In total, 43 eyes of 43 patients, whose average age was 69.7±8.6 years, were enrolled in this study. We used swept-source (SS)-optical coherence tomography (OCT) for the confirmation of MH closure for gas-filled eyes and used spectral domain (SD)-OCT for the reconfirmation of MH closure after the gas volume was reduced to less than half of the vitreous cavity. MH closure was confirmed in 40 eyes (93%, the closure group) on the next day after surgery. The time from surgery to SS-OCT imaging was 24.7 hours. Although facedown positioning was terminated in cases where MH closure was confirmed, there were no cases in which the MH was re-opened afterward. The basal and minimum MH size was significantly larger in the non-closure group than that in the closure group (p = 0.027, p = 0.043, respectively). Therefore, checking with SS-OCT the day after surgery and terminating facedown positioning in cases where MH closure was confirmed would be a useful method, removing great burden for the elderly without sacrificing the MH closure rate.

We report an all-optical radio-frequency (RF) spectrum analyzer with a bandwidth greater than 5 terahertz (THz), based on a 50-cm long spiral waveguide in a CMOS-compatible high-index doped silica platform. By carefully mapping out the dispersion profile of the waveguides for different thicknesses, we identify the optimal design to achieve near zero dispersion in the C-band. To demonstrate the capability of the RF spectrum analyzer, we measure the optical output of a femtosecond fiber laser with an ultrafast optical RF spectrum in the terahertz regime.

Introduction The prevalence of adolescent electronic cigarette (e-cigarette) use has increased in most countries. This study determines the relation between the frequency of e-cigarette use and the frequency and intensity of cigarette smoking. Furthermore, it evaluates the association between the reasons for e-cigarette use and the frequency of its use. Materials and Methods Participants were 68,043 middle and high school students aged 13–18 years from the 2015 Korean Youth Risk Behavior Web-Based Survey. Of the 68,043 participants, we analyzed 6,655 adolescents with an experience of e-cigarette use. Results The prevalence of ever using and current (past 30 days) use of e-cigarettes was 10.1% and 3.9%, respectively. Of the ever e-cigarette users, approximately 40% used e-cigarettes for ≥1/month and 8.1% used e-cigarettes daily. Daily e-cigarettes users were 10 times greater among daily cigarette smokers than among cigarette users for <1/month (18.1% vs. 1.8%) and 16 times more prevalent among those smoking ≥20 cigarettes/day than among those smoking <1 cigarette/month (38.9% vs. 2.4%). The most common reason for e-cigarette use was curiosity (22.9%), followed by less harmful than conventional cigarettes (18.9%), smoking cessation (13.1%), and indoor use (10.7%). Curiosity was the most common reason among less frequent e-cigarette users; however, smoking cessation and indoor use were the most common reasons among more frequent users. Conclusions Results showed a positive relation between frequency or intensity of conventional cigarette smoking and frequency of e-cigarette use among Korean adolescents, and frequency of e-cigarette use differed according to the reason for the use of e-cigarettes.

FRAP technique have been used for decades to measure movements of molecules in 2D. Data obtained by FRAP experiments in cell plasma membranes are assumed to be described through means of two parameters, a diffusion coefficient D (as defined in a pure Brownian model) and a mobile fraction M. Nevertheless, it has also been shown that recoveries can be nicely fit using anomalous sub-diffusion. FRAP at variable radii has been developed using the Brownian diffusion model to access geometrical characteristics of the surrounding landscape of the molecule. Here we performed numerical simulations of continuous time random walk (CTRW) anomalous subdiffusion and interpreted them in the context of variable radii FRAP. These simulations were compared to experimental data obtained at variable radii on living cells using the PH domain of the membrane binding protein EFA6 (exchange factor for ARF6, a small G protein). This protein domain is an excellent candidate to explore the structure of the interface between cytosol and plasma membrane in cells. By direct comparison of our numerical simulations to the experiments, we show that this protein does not exhibit anomalous diffusion in BHK cells. The non Brownian PH-EFA6 dynamics observed here is more related to spatial heterogeneities such as cytoskeleton fences effects.

The World Health Organization has developed the Integrated Care of Older People (ICOPE) strategy, a program based on the measurement of intrinsic capacity (IC) as “the composite of all physical and mental attributes on which an individual can draw”. Multicomponent interventions appear to be the more effective approach to enhance IC and to prevent frailty and dependence, being adapted physical activity is the preventive intervention that has shown more evidence in the treatment of frailty and risk of falls. The present study aims to describe the development of a multi-domain group-based intervention addressed to frail older people living in the community aimed to improve and/or maintain intrinsic capacity by means of promoting physical activity, healthy nutrition, and psychological wellbeing in frail older people. We used the GUIDED checklist to describe the development process of AMICOPE (Aptitude Multi-domain group-based intervention to improve and/or maintain IC in frail Older PEople). The intervention was built upon the ICOPE framework and it is described with Template for Intervention Description and Replication (TIDieR) guidelines and it includes dietary advice, cognitive stimulation strategies, medication review, goal setting, and activities to strengthen social support and manage depressive symptoms, as well as strength, balance and flexibility exercise using the Vivifrail program. The study represents the first stage of the UK Medical Research Council framework for developing and evaluating a complex intervention. The next step should be carrying out a feasibility study for the AMICOPE intervention, and in a later stage, assessing the effectiveness in a randomized controlled trial.

The evolutionary transition from single-celled to multicellular individuality requires organismal fitness to shift from the cell-level to the cell group. This reorganization of fitness occurs by allocating the two components of fitness, survival and reproduction, between two specialized cell types in the multicellular group – soma and germ, respectively. How does the genetic basis for such fitness reorganization evolve? One possible mechanism is the co-option of life-history genes present in the unicellular ancestors of a multicellular lineage. Single-celled organisms must regulate their investment in survival and reproduction in response to environmental changes, particularly decreasing reproduction to ensure survival when stressed. Such stress response life-history genes can provide the foundation for the genetic basis for cellular differentiation in multicellular lineages. The regA gene family in volvocine green algae provides a model system to study how this co-option occurs. We discuss the origin and evolution of the volvocine regA gene family, including regA – the gene that controls somatic cell development in the model organism Volvox carteri. We hypothesize that the co-option of life history trade-off genes is a general mechanism involved in the transition to multicellular individuality, making volvocine algae and the regA family a useful template for similar investigations in other lineages.

Identifying anti-spike antibodies that exhibit strong neutralizing activity against current dominant circulating variants and antibodies that are escaped by these variants have important implications in the development of therapeutic and diagnostic solutions as well as in improving understanding of the humoral response to SARS-CoV-2 infection. We characterized seven anti-RBD monoclonal antibodies for their binding activity, pairing capability and neutralization activity to SARS-CoV-2 and three variant RBDs (UK, SA and BR P.1) via lateral flow immunoassays. The results allowed us to group these antibodies into three distinct epitope bins. Our studies showed that two antibodies had broadly potent neutralizing activity against SARS-CoV-2 and these variant RBDs and that one antibody did not neutralize the SA and BR P.1 RBDs. The antibody escaped by the SA and BR P.1 RBDs retained binding activity to SA and BR P.1 RBDs but was unable to induce neutralization. Further, we demonstrated that the lateral flow immunoassay can be a rapid and effective tool for antibody characterization, including epitope classification and antibody neutralization kinetics. From these studies, the potential contributions of the mutations (N501Y, E484K and K417N/T) contained in these variants’ RBDs on antibody pairing capability, neutralization activity and therapeutic antibody targeting strategy are discussed.

The suspended test masses of gravitational-wave (GW) detectors require precise alignment to be able to operate the detector stably and with high sensitivity. This includes the continuous counter-acting of seismic disturbances, which, below a few Hertz, are not sufficiently reduced by the seismic isolation system. The residual angular motion of suspended test masses is further suppressed by the Angular Sensing and Control (ASC) system. However, in doing so, the angular motion can be enhanced by the ASC at higher frequencies where the seismic isolation system is very effective. This has led to sensitivity limitations between about 10 Hz and 25 Hz of the LIGO detectors in past observation runs. The observed ASC noise was larger than simple models predict, which means that more accurate detector models and new simulation tools are required. We present Lightsaber, a new time-domain simulator of the ASC in LIGO. The simulation is a nonlinear simulation of the optomechanical system consisting of the high-power cavity laser beam and the last two stages of suspension in LIGO including the ASC. The main noise inputs are power fluctuations of the laser beam at the input of the arm cavities, read-out noise of sensors used for the ASC, displacement noise from the suspension platforms, and noise introduced by the suspension damping loops. While the plant simulation uses local degrees of freedom of individual suspension systems, the control is applied on a global angular basis, which requires a conversion between the local and global bases for sensing and actuation. Some of the studies that can be done with this simulation concern mis-centering of the beam-spot (BS) position on the test masses, the role of laser power fluctuations for angular dynamics, and the role of the various nonlinear dynamics.

We address weighted composition operators ψCφ from α-Bloch spaces to Bers-type spaces of bounded holomorphic functions on Y, where Y is a generalized Hua domain of the first kind, and obtain some necessary and sufficient conditions for the boundedness and compactness of those operators.

AbstraIncorporating knowledge of indigenous forage legumes or associated nutritional attributes in cultivated pastures has the potential to improve grazing conditions, rangeland conditions, and cattle productivity. Climate change and other site-specific factors such as land sizes and other grazing management practices play a significant role in food security, job creation, and environmental health. Several research results have reported that the utilization of forage legumes in most farming systems has proved to increase herbage yield and soil fertility. The major aim of this review is to elucidate the potential benefits of intercropping indigenous forage legumes such as cowpea (Vigna unguiculacta L.Walp) and Dolichols (Lablab purpureaus) intercropped with Chloris gayana, this is to assesses the principal advantages, productivity, and overall performance of forage legumes grown in conjunction with grasses. Moreover, this review seeks to provide insights into the optimal management practices for intercropping forage legumes with grasses. The promising feature of intercropping South African pastoral systems must target the ability of forage legumes in terms of performance and utilization of agro-symbiotic relationships of N2 fixation symbiotic relationship in pastures. Moreover, this review aims also to address this knowledge gap by assessing the effects of defoliation frequency on the biomass production of selected forage legumes when intercropped with Chloris gayana in different growing seasons. By examining how varying defoliation frequencies influence biomass production, this review aimed to provide practical recommendations for optimizing forage management practices and promoting sustainable forage production systems. Through this investigation, we can gain insights into the complex dynamics between the defoliation frequency, intercropping systems, and biomass production.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

Rice blast fungus (Magnaporthe oryzae) causes massive yield losses annually worldwide. The fungus secreted bunch of effector proteins target different rice cellular compartments to facilitate its infection. However, most of intensively studied effectors are small secreted proteins with high presence/absence polymorphism in the fungus, and effectors conserved among different species were rarely investigated. In this study, we identified MoPce1, a CAP/PR domain containing protein common in different species, as an important virulence factor from a screening of 145 putative core effectors (PCE). MoPCE1 is required for pathogenicity but not the asexual development. Ectopic expression of MoPCE1sp in ZH11 background compromised the plant resistance. We also found that MoPce1 lacks the conserved cysteine residuals in CAP domain, and is BIC-localized in invasive hyphae and nuclei-localized when ectopically expressed in tobacco leaves and rice protoplasts. These results suggested that MoPce1 may bind novel ligand(s) rather than sterol. Indeed, we found that MoPce1 could interact with OsDi19-5, a transcription factor in rice. We inferred from these results that MoPce1 is required for pathogenicity by suppressing the immune response in rice, likely through the interaction with OsDi19-5.

Lipases have tremendous potential for industrial use, particularly, those mostly active against water-insoluble substrates, such as triglycerides composed of long-chain fatty acids. However, in most cases, mutants often need to be constructed to achieve optimal performance for such substrates. Protein engineering techniques have been reported as strategies for improving lipase characteristics by introducing specific mutations in the cap domain of esterases or in the lid domain of lipases, or through lid domain swapping. Here, we have improved the lipase character of a lipase, retrieved from the Marine Metagenomics MarRef Database and assigned to the Actinoalloteichus genus (WP_075743487.1), through site-directed mutagenesis and by substituting its lid domain (FRGTEITQIKDWLTDA) by the one of Rhizopus delemar (previously Rhizopus oryzae) lipase (FRGTNSFRSAITDIVF). Results demonstrated that the redesigned mutants gain activity against bulkier triglycerides such as glyceryl tridecanoate and tridodecanoate, olive oil, coconut oil, and palm oil. The absence of a residue in the new lid, present in the original lid, appears to be a key aspect to the increase in lipase character, although full activity recovery is only obtained with lid swapping.

The inherent dependency of deep learning models to labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling.

We employ another approach to quantize electromagnetic fields in the coordinate space, instead of the mode (or Fourier) space, such that local features of photons can be efficiently, physically, and more intuitively described. To do this, coordinate-ladder operators are defined from mode-ladder operators via the unitary transformation of systems involved in arbitrary inhomogeneous dielectric media. Then, one can expand electromagnetic field operators through the coordinate-ladder operators weighted by non-orthogonal and spatially-localized bases, which are propagators of initial quantum electromagnetic (complex-valued) field operators. Here, we call them QEM-CV-propagators. However, there are no general closed form solutions available for them. This inspires us to develop a quantum finite-difference time-domain (Q-FDTD) scheme to numerically time evolve QEM-CV-propagators. In order to check the validity of the proposed Q-FDTD scheme, we perform computer simulations to observe the Hong-Ou-Mandel effect resulting from the destructive interference of two photons in a 50/50 quantum beam splitter.

Ontology is a formalized representation of the problem area (PrA). Representation of the PrA in the form of an domain ontology is often used in the process of development of intelligent software systems and used as a knowledge base. The process of building an ontology is complex and requires an expert in the PrA. A large number of researchers are working to solve this problem. The basis of our approach is the use of a pipeline of different linguistic methods of text analysis. The set of rules developed by us is used to build an ontology based on the content analysis of a text resource. This article describes the method of building a domain ontology based on the linguistic analysis of content of text resources, presents an example of the proposed approach, and also presents the architecture of our pipeline.

Pulsed electric fields of specific frequencies emitted by a glass plasma antenna tube have been shown to slow the growth of Acute Lymphoblastic Leukemia cells by as much as 43% when exposed to a series of frequencies between 156kHz and 162kHz. This narrow band of frequencies is termed a “Destructive Cancer Resonant Frequency Formant” (DCRFF).

This article proposes a measurement solution designed to monitor instantaneous frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping. A program in Python takes care of receiving the data, calculating the frequency, and finally transferring the measurement results to a database. The frequency is calculated with two different methods, which are compared in the article. The stored data is visualized using the Grafana platform, thus demonstrating its potential for comparing scientific data. The system as a whole constitutes an efficient low cost solution as a data acquisition system.

Background: Actually, resonance frequency analysis (RFA) is the most extended method for measuring implant stability. The implant stability quotient (ISQ) is the measure obtained by the different RFA devices, however, inter- and intra- rater reliability and validity of some devices remains unknown. Methods: Thirty implants were placed in 3 different pig mandibles. ISQ was measured axial and parallel with Osstell® Beacon, Penguin® and MegaISQ® by 2 different operators and one operator performed a test-retest. Intraclass correlation coefficient was calculated to assess the intra- and inter-rater reliability. Pearson correlation coefficient was used to assess the validity. Results: The higher inter- and intra- rater reliability was obtained by Penguin® when measuring axial. The highest ISQ values were obtained using Penguin® in an axial measurement; the lowest, using the MegaISQ® in an axial measurement. The highest correlation values with the other devices were obtained by MegaISQ® measuring axially. Conclusion: Penguin® had a good reliability for measuring ISQ both inter- and intra- rater. Osstell® had good validity for measuring ISQ both axial and parallel and MegaISQ® had the best validity for measuring ISQ axial.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

Autosomal dominant isolated foveal hypoplasia (FVH1) is a rare condition of foveal hypoplasia (FH) that lacks any other ocular manifestations. FVH1 is associated with hypomorphic mutations in the PAX6 gene. We report our findings in 17 patients with PAX6 mutations associated with FVH1 or FH with aniridia and corneal opacities. Patients with three mutations, p.V78E, p.V83F and p.R128H, in the C-terminal subdomain of the paired domain (CTS) consistently had severe FH. Luciferase assays indicated that the transcriptional activities of these mutations were significantly reduced comparable to that with truncation mutation of p.G65Rfs*5. Patients with p.P20S in the N-terminal subdomain of the paired domain, and a patient with p.N365K in the proline-serine-threonine-rich domain (PSTD) had mild FH. A patient with p.Q255L in the homeodomain had severe FH. The P20S and Q255L mutants did not affect the transcriptional activity. Mutant N365K had a retained DNA binding activity but a reduced transcriptional activity due to a low PSTD transactivation. These findings demonstrated that mutations associated with FVH1 underlie a functional divergence between DNA binding ability and transcriptional activity. Broadly distributed mutations in the PAX6 gene, not limited to the CST region, were responsible for FVH1.

This paper proposes a linear eddy-current feature to determine the radius of a metallic ball in a non-contact manner. An electromagnetic eddy-current sensor with two coils is placed co-axially to the metal ball during measurement. It is well known that the distance between the sensor and test piece (i.e. lift-off) affects eddy-current signals. In this paper, it is found that the peak frequency feature of inductance spectrum is linear to the lift-off spacing between the centre of coil and ball. Besides, the slope of peak frequencies versus lift-offs is linked to the radius of ball. The radius of metallic balls is retrieved from the experimental and embedded analytical result of the slope. Measurements have been carried out on 6 metallic balls with different radii. The radius of the metallic ball can be retrieved with an error of less than 2 %.

Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18.

Transition delaying is of great importance for the drag and heat flux reduction of hypersonic flight vehicles. The first mode within low frequency and the second mode within high frequency exist simultaneously during the transition of hypersonic boundary layer. This paper proposes a novel bi-frequency synthetic jet to suppress low- and high-frequency disturbances at the same time. Orthogonal table and variance analysis are used to compare the control effects of jet with different frequencies, amplitudes and positions. Linear stability analysis results show that, low frequency synthetic jet can suppress the first mode when it is arranged upstream of synchronization point, while the second mode control effect is relatively weak. The higher the high frequency is, the stronger the suppression effect is on the first mode. For the second mode, the suppression effect is only at f2=89.09kHz. The larger the amplitude, the weaker the promoting effect for the first mode and the second mode, and the more obvious the suppressing effect. For the cases with synthetic jet downstream of synchronization point, all levels of the three parameters promote the unstable mode. In terms of the growth rate with the spanwise wave number, the control effect of the same factor and level under different spanwise wave number is different. In order to obtain the optimal control effect on transition, the three factors and the arrangement position of the synthetic jet should be selected as follows: the position is arranged in the upstream, with f1 = 17.82kHz, f2 = 89.9kHz, a =0.007, so that the maximum growth rate of the first mode is reduced by 9.06% and that of the second mode is reduced by 1.28% compared with the uncontrolled state.

The mammalian/mechanistic target of rapamycin complex 1 (mTORC1) is activated by the small G-protein, RHEB-GTPase. On lysosome, RHEB activates mTORC1 by binding the domains of N-heat, M-heat, and FAT, which allosterically regulates ATP binding in the active site for further phosphorylation. The crucial role of RHEB in regulating growth and survival through mTORC1, makes it a targetable site for anti-cancer therapeutics. However, the binding kinetics of RHEB to mTORC1 is still unknown at the molecular level. Therefore, we studied the kinetics by in vitro and in-cell protein-protein interaction (PPI) assays. For this, we used the split-luciferase system (NanoBiT®) for in-cell studies, and prepared proteins for the in vitro measurements. Consequently, it was shown that RHEB binds to the whole mTOR both in the presence or absence of GTPɣS, with five-fold weaker affinity in the presence of GTPɣS. Also, RHEB bound to the truncated mTOR fragments of N-heat domain (60-167) and M-heat domain (967-1023) in a GTP independent manner. Furthermore, RHEB bound to the truncated kinase domain (2148-2300) with higher affinity also in GTP independently. In conclusion, RHEB binds two different binding sites of mTOR, which probably regulates the kinase activity of mTOR through multiple different molecular mechanisms.

The ANSYS 2024R1 student edition was used to create a finite element model of a basketball rim and backboard. This finite element model included the use of steel for the rim and its mount, a tempered glass backboard, and an aluminum frame behind the backboard. After a mesh was created, fixed support boundary conditions were applied to the four corners of the aluminum frame. The subsequently calculated mode shapes and frequencies were compared to empirical modal analysis previously done at the United States Military Academy at West Point, New York. Four mode shapes and frequencies agreed rather well between the theoretical finite element analysis and previously published empirical modal analysis, specifically where the rim was vibrating in the vertical direction, which was the direction that the accelerometer was aligned for modal analysis. However, three mode shapes missed by the empirical modal analysis were found where the vibration of the rim was confined to the horizontal plane, which was orthogonal to the orientation of our accelerometer.

Abstract: The worldwide spread of SARS-CoV-2 has led to a significant economic and social burden on a global scale. Even though the pandemic has concluded, there remains apprehension regarding the emergence of highly transmissible variants capable of evading immunity induced by either vaccination or prior infection. The success of viral penetration is the specific amino acid residues of the Receptor Binding Motif (RBM) involved in viral attachment. This region interacts with the cellular receptor ACE2, triggering a neutralizing antibody (nAb) response. In this study, we have evaluated the immunogenicity of serum from individuals who received either a single dose or a combination of different vaccines against the original SARS-CoV-2 strain and mutated linear RBM. Despite a modest antibody response to the wild-type SARS-CoV-2 RBM, the Omicron variants exhibit four mutations in the RBM (S477N, T478K, E484A, F486V) that result in even lower antibody titers. The primary immune responses observed were directed towards IgA and IgG. While nAbs typically target the RBD, our investigation has unveiled a reduced seroreactivity within the RBD's crucial subregion, the RBM. This deficiency may have implications for the generation of protective nAbs. Evaluation of S1WT and S2WT RBM peptides binding to nAbs using microscale thermophoresis revealed a higher affinity (35 nM) for the S2WT sequence (GSTPCNGVEGFNCYF), which includes the FNCY patch. Our findings suggest that the linear RBM of SARS-CoV-2 is not an immunodominant region in vaccinated individuals. Comprehending the intricate dynamics of the humoral response, its interplay with viral evolution, and host genetics is crucial for formulating effective vaccination strategies, not only targeting SARS-CoV-2 but also anticipating potential future coronaviruses.

Global economic and social burden was caused by the SARS-CoV-2 spread worldwide. Despite the end of the pandemic, there is a concern about virulent evolving variants of the virus which can bypass the humoral immune response induced by vaccination or infection. Crucial to the viral entrance, amino acid residues in the RBM region, which interacts with cellular receptor ACE2, can elicit neutralizing antibody response. Herein we determine the immunogenicity of one-dose or heterologous dose vaccinated serum against wild-type and mutated RBM region. Despite low antibody response to wild-type SARS-CoV-2 RBM, omicron variants possess four mutations in RBM (S477N, T478K, E484A, F486V) that induce even less antibody titers. The most predominant responses were against IgA and IgG. While neutralizing antibodies (nAbs) predominantly target the RBD, our investigation revealed a diminished seroreactivity within the RBD's crucial region, the receptor-binding motif (RBM), potentially impacting the production of protective nAbs. S1WT and S2WT RBM peptides binding to nAbs were evaluated through microscale thermophoresis, and higher affinity (35 nM) was obtained for sequence S2WT (GSTPCNGVEGFNCYF), containing the FNCY patch. Our data indicates that SARS-CoV-2 RBM is not an immunodominant region in vaccinated individuals. Understanding the intricate dynamics of the humoral response and its interplay with viral evolution and host genetics is essential for the formulation of effective vaccination strategies, not only against SARS-CoV-2 but also for future emerging coronaviruses.

Frequency combs (FCs)—spectra containing equidistant coherent peaks—have enabled researchers and engineers to measure the frequencies of complex signals with high precision thereby revolutionising the areas of sensing, metrology and communications and also benefiting the fundamental science. Although mostly optical FCs have found widespread applications thus far, in general FCs can be generated using waves other than light. Here, we review and summarise recent achievements in the emergent field of acoustic frequency combs (AFCs) including phononic FCs and relevant acousto-optical, Brillouin light scattering and Faraday wave-based techniques that have enabled the development of phonon lasers, quantum computers and advanced vibration sensors. In particular, our discussion is centred around potential applications of AFCs in precision measurements in various physical, chemical and biological systems in conditions, where using light, and hence optical FCs, faces technical and fundamental limitations, which is, for example, the case in underwater distance measurements and biomedical imaging applications. This review article will also be of interest to readers seeking a discussion of specific theoretical aspects of different classes of AFCs. To that end, we support the mainstream discussion by the results of our original analysis and numerical simulations that can be used to design the spectra of AFCs generated using oscillations of gas bubbles in liquids, vibrations of liquid drops and plasmonic enhancement of Brillouin light scattering in metal nanostructures. We also discuss the application of non-toxic room-temperature liquid-metal alloys in the field of AFC generation.

The increasing penetration of renewable energy sources in power grids highlights the role of battery energy stor- age systems (BESSs) in enhancing the stability and reliability of electricity. A key challenge with the renewables’, specially the BESSs, integration into the power system is the lack of proper dynamic model for stability analysis. Moreover, a proper control design for the power system is a complicated issue due to its complexity and inter-connectivity. Thus, the application of decentralized control to improve the stability of a large- scale power system is inevitable, especially in distributed energy sources (DERs). This paper presents an optimal distributed hybrid control design for the interconnected systems to suppress the effects of small disturbances in the power system employing utility-scale batteries based on existing battery models. The results show that i) the smart scheduling of the batteries’ output reduces the inter-area oscillations and improves the stability of the power systems; ii) the hybrid model of the battery is more user-friendly compared to the Western electricity coordinating council (WECC) model in power system analysis.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

The article is devoted to issues related to the propagation and transformation of vortexes in the optical range of frequency. Within the framework of the traditional and modified model of slowly varying envelope approximation (SVEA), the process of converting vortex beams of the optical domain into vortex beams of the terahertz radio range based on nonlinear generation of a difference frequency in a medium with a second-order susceptibility is considered. The modified SVEA splits a slowly varying amplitude into two factors, which makes it possible to more accurately describe the three-wave mixing process. The theoretical substantiation of the rule of vortex beams topological charges conversion is given – the topological charge of the output radio-vortex beam is equal to the difference between the topological charges of the input optical vortex beams. A numerical simulation model of the processes under consideration has been implemented and analyzed.

Matrix Metaloproteinase-2 (MMP-2) is an extracellular Zn²⁺ protease specific to type I and IV collagens. Its expression is associated with several inflammatory, degenerative, and malignant diseases. Conformational properties, domain movements, and interactions between MMP-2 and its associated metal ions were characterized using a 1.0 µs molecular dynamics simulation. Dihedral principle component analysis revealed 10 families of conformations with the greatest degree of variability occurring in the link region connecting the catalytic and hemopexin domains. Dynamics cross correlation analysis indicated domain movements corresponding to opening and closing of the hemopexin domain in relation to the fibronectin and catalytic domains facilitated by the link region. Interaction energies were calculated using the MMPBSA-interaction entropy analysis method and revealed strong binding energies for the catalytic Zn²⁺ ion 1, Ca²⁺ ion 1, and Ca²⁺ ion 3 with significant conformational stability at the binding sites of Zn²⁺ ion 1 and Ca²⁺ ion 1. Ca²⁺ ion 2 diffuses freely away from its crystallographically defined binding site. Zn²⁺ ion 2 plays a minor role in conformational stability of the catalytic domain while Ca²⁺ ion 3 is strongly attracted to the highly electronegative sidechains of the Asp residues around the central β-sheet core of the hemopexin domain.

A printed Vivaldi antenna that is optically transparent, ultra-wideband (UWB), and reconfigurable has been developed, fabricated, and tested at millimeter wave frequencies. It covers a broad frequency range of 20-30 GHz by using three PIN diodes. The diodes control the current flow to direct the beam of the antenna. The results of numerical simulations and measurements match at millimeter wave frequencies. The design of this antenna is unique as it allows for a reduction in size and ease of integration while also providing the ability to change the radiation pattern by up to 300 degrees, making it suitable for 5G and 6G communications. Additionally, this antenna can also be useful for RF applications that require dynamic switching of radiation patterns and cognitive radio.

Eukaryotic cells contain membranes with various curvatures, from the near-plane plasma membrane to the highly curved membranes of organelles, vesicles, and membrane protrusions. These curvatures are generated and sustained by curvature-inducing proteins, peptides, and lipids, and describing these mechanisms is an important scientific challenge. In addition to that, some molecules can sense membrane curvature and a thereby be trafficked to specific locations. The description of curvature-sensing is another fundamental challenge. Curved lipid membranes and their interplay with membrane-associated proteins can be investigated with molecular dynamics (MD) simulations, given the right tools. Various methods for simulating curved membranes with MD are discussed here, including tools for setting up simulation of vesicles, and methods for sustaining membrane curvature. The latter are divided into methods that exploit scaffolding virtual beads, methods that use curvature-inducing molecules, and methods applying virtual forces. The variety of simulation tools allow the researcher to closely match the conditions of experimental studies of membrane curvatures.

It is widely, but not universally, believed that the lipids of the plasma membrane are not uniformly distributed, but that "rafts'' of sphingolipids and cholesterol float in a "sea'' of unsaturated lipids. The physical origin of such heterogeneities is often attributed to a phase coexistence between the two different domains. We argue that this explanation is untenable for several reasons. Further we note that the results of recent experiments are inconsistent with this picture. However they are quite consistent with an alternate explanation, namely that the plasma membrane is an emulsion of the two kinds of regions. To show this, we briefly review a simplified version of this theory and its phase diagram. We also explicate the dependence of the predicted domain size on four physical parameters. Among them are the spontaneous curvature of the membrane and its bending modulus and surface tension. Taking values of the latter two from experiment, we obtain domain sizes for several different cell types that vary from 58 to 88 nm.

Domain walls were studied for permalloy wires with different depth trenches (500 nm wide, 30 nm thick, 7.5 µm long, the depth of the trench was 0, 4, 5, 8, 10, 12 and 15nm). Permalloy (Ni80Fe20) wires were fabricated by electron beam lithography and Ar ion milling. When the depth of trench is smaller than 12 nm, the switching field (Hs) is increasing with the deeper trench. However, the depth of trench is bigger than half depth of thickness, the Hs is decreasing with the deeper trench. We believed that Hs increased as the trench depth increased was because the magnetic diploes force increased between the magnetic poles on two sides of the trench. The domains of the wires were divided by the trenches and the domain walls were pinned on the trenches these were confirmed by magnetic force microscopy images.

This algorithm is designed to perform Discrete Fourier Transforms (DFT) to convert temporal data into spectral data. This algorithm obtains the Fourier Transforms by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this DFT algorithm is that it can produce spectral data at any user-defined resolution.

GCN1 is recognized as a factor that is essential for the activation of GCN2, which is a sensor of amino acid starvation. This function is evolutionarily conserved from yeast to higher eukaryotes. However, recent studies have revealed non-canonical functions of GCN1 that are independent of GCN2, such as its participation in cell proliferation, apoptosis, and the immune response, beyond the borders of species. Although it is known that GCN1 and GCN2 interact with ribosomes to accomplish amino acid starvation sensing, recent studies have reported that GCN1 binds to disomes (i.e. ribosomes that collide each other), thereby regulating both the co-translational quality control and stress response. We propose that GCN1 regulates ribosome-mediated signaling by dynamically changing its partners among RWD domain-possessing proteins via unknown mechanisms. We recently demonstrated that GCN1 is essential for cell proliferation and whole-body energy regulation in mice. However, the manner in which ribosome-initiated signaling via GCN1 is related to various physiological functions warrants clarification. GCN1-mediated mechanisms and its interaction with other quality control and stress response signals should be important for proteostasis during aging and neurodegenerative diseases, and may be targeted for drug development.

Abstract: Background: Adolescents frequently self-report pain, according to epidemiological research. The knee is one of the sites where the pain is most common. One of the main factors contributing to the number of years people spend disabled and having substantial personal, societal, and economic burdens globally is musculoskeletal disorders. Insidious, slowly building onset that neither the adolescent nor the parent is aware of [5]. Patellofemoral Pain (PFP) is a clinical knee pain commonly affecting adolescents. In the present study, the authors aimed to estimate the frequency of knee pain in Saudi adolescents. Methods: This cross-sectional survey carried out from June to November 2022 included 676 adolescents, ages 10 to 18. The data are presented as frequencies and percentages for categorical variables. Analysis of variance (ANOVA) was used to compare means between groups, while the chi-square test was used for the comparison of categorical variables. Statistical significance was set at p < 0.05. Results: Adolescents were invited to take part in the study; 57.5% were female and 42.5% were male, and 68.8% were aged between 15 and 18. The prevalence of knee pain was considerably high among females (26%), while it was 19.2% among males. Moreover, age was a significant predictor for knee pain, and BMI was as well. We also found a significant difference between BMI classification and stiffness of the knee (p = 0.008). There is a difference between adolescents who do physical activities in leisure and difficulty bending (p = 0.03). Conclusion: Our study noted a high rate of knee pain among adolescents, so we need to raise awareness about risk factors. Adolescent knee pain can be prevented with conservative methods and some minor lifestyle/activity modifications.

Controlling the collective behavior of micro/nanomotors with ultrasound may enable new functionality in robotic, medicine and other engineering disciplines. Currently, various collective behaviors of nanomotors, such as assembly, reconfiguration and disassembly, have been explored by using acoustic fields with a fixed frequency, while regulating their collective behaviors by varying the ultrasound frequency still remains challenging. In this work, we design an ultrasound manipulation methodology allowing nanomotors to exhibit different collective behaviors by regulating the applied ultrasound frequency. This work has important implications for design of artificial actuated nanomotors and optimize their performances.

Successfully engaging in social communication requires efficient processing of subtle socio-communicative cues. Voices convey a wealth of social information, such as gender, identity and the emotional state of the speaker. We tested whether our brain can systematically and automatically differentiate and track a periodic stream of emotional utterances among a series of neutral vocal utterances. We recorded frequency-tagged EEG responses of 20 neurotypical male adults while presenting streams of neutral utterances at 4 Hz base rate, interleaved with emotional utterances every third stimulus, hence at 1.333 Hz oddball frequency. Four emotions (happy, sad, angry, and fear) were presented as different conditions in different streams. To control the impact of low-level acoustic cues, we maximized variability among the stimuli and included a control condition with scrambled utterances. This scrambling preserves low-level acoustic characteristics but ensures that the emotional character is no longer recognizable. Results revealed significant oddball EEG responses for all conditions, indicating that every emotion category can be discriminated from the neutral stimuli, and every emotional oddball response was significantly higher than the response for the scrambled utterances. These findings demonstrate that emotion discrimination is fast, automatic, and is not merely driven by low-level perceptual features.

A novel kind of power spectrum is constructed, the inter-spike spectrum, which transforms any signal into its spike-frequency domain. This method clearly shows the apparent cycles in the data and overcomes the problems for spike-train-like signals when using the obvious idea of Fourier-transforming it. We invent this instructive approach with the idea of transforming the τ-recurrence rate of a recurrence plot (RP), which often has a spiky appearance. The τ-recurrence rate is the density of recurrence points along diagonals of the RP, which are parallel to the main diagonal with a distance of τ. In this context the inter-spike spectrum can be interpreted as a nonlinear power spectrum of a potentially high dimensional system which constitutes the RP. The proposed measure is robust to noise and is able to detect and analyze bifurcations.

The signal processing technique is one of the principal tools for diagnosing power quality (PQ) issues in electrical power systems. The Discrete Fourier Transform (DFT) is a frequency analysis technique used to process power system signals and identify PQ problems. However, the DFT algorithm may lead to spectral leakage and picket-fence effect problems for asynchronously sampled signals that contain harmonic, inter-harmonic, and flicker components. To resolve this shortcoming, a hybrid method for frequency estimation based on a second-level DFT approach and a frequency-domain interpolation algorithm to obtain the accurate fundamental frequency of a power system is proposed in this paper. This method uses a second-level DFT to compute the cosine and sine parts for the fundamental frequency components of the acquired signals. Then, a frequency-domain interpolation approach is adopted to determine the amplitude ratio for the cosine and sine parts of the system's fundamental frequency. To demonstrate the performance of the proposed frequency estimation method, the observation window used by this paper to evaluate different estimation algorithms is 200 ms. According to the IEC standards, a 200 ms acquisition window is recommended for power system quality assessment. A set of mixed signals with harmonic, inter-harmonic, and flicker components with the fundamental frequency deviation is used. The evaluation results demonstrate the superiority of the new method over other approaches for assessing asynchronously sampled signals contaminated with noise, harmonic, inter-harmonic, and flicker components.

This paper presents a novel approach for accurate counting and localization of tropical plants in aerial images that is able to work in new visual domains in which the available data is not labeled. Our approach uses deep learning and domain adaptation, designed to handle domain shift between the training and test data, which is a common challenge in this agricultural applications. This method uses a source dataset with annotated plants and a target dataset without annotations, and adapts a model trained on the source dataset to the target dataset using unsupervised domain alignment and pseudolabeling. The experimental results show the effectiveness of this approach for plant counting in aerial images of pineapples under significative domain shift, achieving a reduction up to 97% in the counting error when compared to the supervised baseline.

The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM), is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and with aggressive in the clinic. Thus, it’s important to select a proper drug to block tumor cell migration for clinic treatment. In this study, we proposed that lactoferrin (LFcinB11) may be a more ideal candidate for inhibition of NCAM polysialylation comparing with CMP and low molecular weight heparin (LMWH), which were determined based on our NMR studies. On the other hand, because neutrophil extracellular traps (NET) is the most dramatic stage in cell death process, and NETs’ release are related to pathogenesis of autoimmune and inflammatory disorders, with proposed involvement in glomerulonephritis, chronic lung disease, sepsis and vascular disorders. In this study, the molecular mechanism of inhibition of NETs’ release using LFcinB11 as inhibitor was also determined. Based on these results, LFcinB11 is proposed to be bifunctional inhibitor for inhibiting both NCAM polysialylation and NETs releases.

(1) Background: To fight for COVID-19 pandemic, immunity against SARS-CoV-2 should be achieved not only through natural infection but also vaccination. Controversies exist about the effect of COVID-19 vaccination on previously infected persons; (2) Methods: A prospective cohort was undergone to collect sera from unvaccinated survivors and vaccinated persons--with and without COVID-19 pre-infection. The sera were analyzed for the anti-Receptor Binding Domain (RBD) titers by ELISA and for the capacity to neutralize the pseudovirus of the Wuhan-Hu-1 strain by luciferase assays; (3) Results; Neither the antibody titers nor the neutralization capacity was significantly different between the three groups. However, the correlation between the antibody titers and the percentage of viral neutralization derived from sera of unvaccinated survivors was higher than that from vaccinated persons with pre-infection (Spearman correlation coefficient (r) = -0.8558; 95% CI, -0.9259 to -0.7288), p &lt;0.0001 vs -0. 581; 95% CI, -0.7679 to -0.3028; p = 0.0002, respectively), indicating the capacity to neutralize the virus is better among the unvaccinated individuals. (4) Conclusions: Vaccines induced anti-RBD titers as high as the natural infection with lower neutralization capacity, and it did not boost the immunity in pre-infected persons.

Substrate binding proteins (SBP) bind to specific ligands in the periplasmic region and bind to membrane proteins to participate in transport or signal transduction. Typical SBPs consist of two α/β domains and recognize the substrate by hinge motion between two domains. Conversely, short length Rhodothermus marinus SBP (named as RmSBP) exists around the methyl-accepting chemotaxis protein. We previously determined the crystal structure of RmSBP consisting of a single α/β domain, but the substrate recognition mechanism is still unclear. To better understand the short length RmSBP, we performed comparative structure analysis, computational substrate docking, and X-ray crystallographic study. RmSBP shares a high level of similarity in α/β domain with other SBP proteins, but it has a distinct topology in the C-term region. The substrate binding model suggested that conformational change in the peripheral region of RmSBP was required to recognize the substrate. We determined the crystal structures of RmSBP at pH 5.5, 6.0, and 7.5. RmSBP showed structural flexibility of the β1-α2 loop, β5-β6 loop, and extended C-term domain based on the electron density map and temperature B-factor analysis. These results provide information that will further the understanding of the function of the short length SBP.

Ombrian curves, i.e. curves linking rainfall intensity to return period and time-scale, are well-established engineering tools, crucial to the design against storm waters and floods. Whereas at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modelling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modelling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure except for a spatially varying scale parameter which is itself modelled by a spatial smoothing model for the 24 h average rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13 700 km2 water district of Greece characterized by varying topography and hydrometeorological properties.

This paper presents the simplified and quick way to obtain an experimental measure of the modulus of elasticity(E) of a piece of metal rod using the Smartphone microphone and through the experimental configuration in mode of unsupport beam Euler-Bernoulli(E-B). To understand it, it is required to have knowledge about flexural vibration of E-B beams and to know the analysis of the spectogram of the audio signal. The methodology consists in measuring the flexural resonance frequency through an app installed on a Smartphone and using the E formula deduced from the E-B theoretical framework. The results of the experimental measurements have a discrepancy of less than 5% with respect to the factory value, for a carbon steel rod with circular cross section using the impact excitation technique. It describes the precision and accuracy of the experimental measurement. It is useful, because it helps to classify the materials in the Faculty of Engineering and it helps to make a more efficient quality control.

The frequency synthesizer is a critical component in Cs beam clock technology. In this paper, we present a demonstration of a direct microwave frequency-synthesis chain for a cesium-beam atomic clock, which utilizes frequency multiplication and a dual-phase-locked loop mode. A detailed analysis of the frequency-synthesis chain is conducted, and a mathematical model is established. The phase settling time and system stability are simulated, measured, and verified. The experimental results for the phase settling time align with the simulation outcomes. The phase settling time can be adjusted within the range of 644.5 µs to 1.5 ms, and the absolute phase noise values are -63.7 dBc/Hz, -75.7 dBc/Hz, -107.1 dBc/Hz, and -122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively. Additionally, the Ramsey fringes are detected, and the Allan deviations of the 10 MHz output from the cesium-beam atomic clock are measured to be 2.99×10−12 at 1s and 8.02×10−14 at 10,000 s.

The increase in the number of firms manipulating financial reports has misled shareholders' investment decisions and resulted in an indelible blot on foreign investors’ trust. Due to earnings management (EM) practice, managers' inefficiency, and lack of transparency in Iraq companies. This study tested the influence of the corporate governance mechanisms (CG), (board independence, audit committee, meeting frequency) on EM based on agency theory, as well, to link between EM and firm's performance (FP) in Iraqi listed companies and the impact of moderating role of corporate social responsibility (CSR) based on the Stakeholder Theory. The study's sample consists of 65 companies for the 2013-2018 financial years. Data were collected mainly from the annual reports (secondary data) of the Iraqi listed firms. This study uses the M-score model to detect EM practices as practical techniques in detecting earnings manipulation practices. The panel static model estimators. Hence, this paper adds to the CG literature from the perspective of stakeholder theory using Iraq's unique industrial environment. Based on the research results, policy-makers might use the study‘s findings to recognize the essential roles of several CG mechanisms in alleviating the opportunistic practices in Iraq. Further, companies should also be encouraged to enhance the CSR disclosure quality.

Fiber distributed optical fiber acoustic sensor (DAS) is generally used in distributed long-distance acoustic/vibration measurement. We found that DAS with excellent performance of low self-noise and anti-fading in combination with the plastic structure in daily life as an acoustic transducer can achieve high-sensitivity acoustic measurement at a single point rather than designing a state-of-the-art acoustic transducer or using special enhanced scattering fiber. The simple acoustic transducer we proposed for DAS can reach the sensitivity level of -106.5dB re. 1rad/μPa at a sensing range of 5.1 km, which can meet many demands on the industrial site.

Cancer stem cells remain a challenge to isolate and characterize because of their plastic phenotype. Using a microfluidic lab-on-a-chip based on ultra-high frequency dielectophoresis, we measured the electromagnetic signature of colorectal cancer cells and demonstrated that cancer stem cells show a distinct and lower electromagnetic signature than differentiated cells. The release of extracellular vesicles from tumor cells can drive tumor progression and metastasis development. As extracellular vesicles from cancer stem cells carry more aggressive content, we treated colorectal cancer cells with these vesicles to test whether the lab-on-a-chip can detect a change in phenotype. The electromagnetic signature of treated cells is modified in comparison to untreated cells and sometimes even when no biological change is observed. The lab-on-a-chip provides rapid and relevant result without prior labeling compared to conventional biological approaches. It could be useful in the clinic for early detection of cancer stem cells in the tumor mass and for monitoring the aggressive potential of extracellular vesicles in the bloodstream in order to adapt therapeutic management and prevent relapse.

A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3×10-3 Hz and a -90° to 90° AOA measurement range with errors of less than ±0.5° are realized. The safety and robustness of the system to environmental disturbance are improved, and it is more suitable for the modern electronic warfare system.

The superharmonic imaging of tissue has the potential for high spatial and contrast resolutions, compared to the fundamental and second harmonic imaging. For this technique, the spectral bandwidth of an ultrasound transducer is divided for transmission of ultrasound and reception of its superharmonics (i.e., higher than the second harmonic). Due to the spectral division for the transmission and reception, transmitted ultrasound energy is not sufficient to induce superharmonics in media without using contrast agents, and it is difficult that a transducer has a -6-dB fractional bandwidth of higher than 100%. For the superharmonic imaging of tissue, thus, multi-frequency array transducers are the best choice if available; transmit and receive elements are separate and have different center frequencies. However, the construction of a multi-frequency transducer for intravascular ultrasound (IVUS) imaging is particularly demanding because of its small size of less than 1 mm. Here, we report a recently developed dual-element focused IVUS transducer for the third harmonic imaging of tissue, which consists of a 35-MHz element for ultrasound transmission and a 105-MHz element for third harmonic reception. For high quality third harmonic imaging, both elements were fabricated to have the same focus at 2.5 mm. The results of tissue mimicking phantom tests demonstrated that the third harmonic images produced by the developed transducer had higher spatial resolution and deeper imaging depth than the fundamental images.

The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of high-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes are investigated. The main gas-phase and solid products of plasma-chemical transformations are determined. Thermodynamic analysis of SiCl4 + H2 and GeCl4 + H2 systems for optimal process parameters was carried out. Using the example of hydrogen reduction of SiCl4 by the method of numerical modeling, gas-dynamic and thermal processes for this type of discharge are investigated. The impurity composition of gas-phase and solid reaction products is investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, is shown.

In this paper, we study a differential operator of parabolic type with a variable and unbounded coefficient, defined on an infinite strip. Sufficient conditions for the existence and compactness of the resolvent are established, and an estimate for the maximum regularity of solutions of the equation Lu=f∈L_2(Ω) is obtained. Two-sided estimates for the distribution function of approximation numbers are obtained. As is known, estimates of approximation numbers show the rate of best approximation of the resolvent of an operator by finite-dimensional operators. The paper proves the assertion about the existence of positive eigenvalues among the eigenvalues of the given operator and finds two-sided estimates for them.

Light pen 3D vision coordinate measurement systems are increasingly widely used due to their advantages, such as small size, convenient carrying and wide applicability. The posture of the light pen is an important factor affecting accuracy. The pose domain of the pen needs to be given so that the measurement system has a suitable measurement range to obtain more qualified parameters. The advantage of the self-calibration method is that the entire self-calibration process can be completed at the measurement site without any auxiliary equipment. After the system camera calibration is completed, we take several pictures of the same measurement point with different poses to obtain the conversion matrix of the picture, and then use spherical fitting, the generalized inverse method of least squares, and the principle of position invariance within the pose domain range. The combined stylus tip center self-calibration method calculates the actual position of the light pen probe. The experimental results show that the absolute error is stable below 0.0737 mm and that the relative error is stable below 0.0025 mm. The experimental results verify the effectiveness of the method; the measurement accuracy of the system can meet the basic industrial measurement requirements.

Background: Oscillation and pulmonary expansion (OPE) therapy can decrease postoperative pulmonary complications in a general surgical population, but its effect after cardiac surgery has not been reported, to our knowledge. We hypothesized that using an OPE device after cardiac surgery before extubation would decrease pulmonary complications. Methods: This retrospective cohort study included adults undergoing elective open cardiac surgery at our institution from January 2018 through January 2019, who had an American Society of Anesthesiologists score of 3 or greater. For mechanically ventilated patients after cardiac surgery, a new OPE protocol was adopted July 1, 2019, comprising an initial 10-minute OPE treatment administered in-line with the ventilator circuit, then continued treatments for 48 hours after extubation. Severe postoperative respiratory complications (primary outcome measure) included need for antibiotics, increased supplemental oxygen use, and prolonged hospital length of stay (LOS). Demographic, clinical, and outcomes data were compared for patients before (usual care with postextubation hyperinflation) and after protocol implementation (OPE). Results: Of 104 patients, 54 patients received usual care, and 50 received OPE. Usual-care recipients had more men (74% vs 62%; P=.19) and were older (median, 70 vs 67 years; P=.009) than OPE recipients. The OPE group had a significantly shorter hospital LOS than the usual-care group (mean, 6.2 vs 7.4 days; P=.04). Other measures improved with OPE but did not reach significance: shorter ventilator duration (mean, 0.6 vs 1.1 days with usual care; P=.06) and shorter LOS in the intensive care unit (mean, 2.7 vs 3.4 days; P=.06). On multivariate analysis, intensive care unit LOS was significantly shorter for the OPE group (mean difference, 0.85 days; 95% CI, 1.65 to 0.06; P=.04). The OPE group had a lower percentage of postoperative complications (10% vs 20%). Conclusion: OPE therapy after cardiac surgery is associated with decreased ICU and hospital LOS.

Triangular resonators re-shaped with the Sierpinski geometry and U-shaped resonators have been designed, linking them with single-pole-double-through (SPDT) RF MEMS switches to provide frequency tuning for potential applications in the K-Band. Prototypes of band-stop narrowband filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, have been studied in coplanar waveguide (CPW) configuration, and the tuning was obtained by switching between two paths of the devices loaded with different resonators. As a result, dual-band operation or fine-tuning can be obtained depending on the choice of the resonator, acting as a building block. The studied filters belong to the more general group of devices inspired by the metamaterial design.

A parallel shallow water flow model is introduced in this paper. The explicit-time finite volume approach is adopted to solve the 2D shallow water equations on an unstructured triangular mesh. The proposed scheme is second-order accurate in temporal and spatial terms using the two-stage Runge-Kutta and the monotone upwind scheme for conservation law (MUSCL) methods, respectively. Based on Message Passing Interface (MPI) and OpenACC, a multi-GPU model is presented with the METIS library to produce the domain decomposition. A CUDA-aware MPI library through GPUDirect for peer-to-peer (P2P) transfer between two GPUs and overlapping computation and MPI communication are used to speed up MPI memory exchange and the performance of the code. A 2D circular dam break test with wet and dry downstream beds and grid resolutions of about 2 million cells is considered to verify the accuracy of the code, and good results were achieved compared to the numerical simulations of published studies. Compared with the multi-CPU version of the 6-core CPU, maximum speedups of 56.18 and 331.51 were obtained using the single-GPU and multi-GPU versions, respectively. Results indicate that acceleration performance improves as the mesh resolution increases.

Tropical cyclones (TC) are dangerous weather events and accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and still 19 kts after simple linear correction even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems because of the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use TC knowledge to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in IBTrACS in the North Atlantic. The method may have wide applicability, such as extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.

Dysfunction of the WW domain-containing adaptor with coiled-coil, WAC, gene underlies a rare autosomal dominant disorder, DeSanto-Shinawi syndrome (DESSH). DESSH is associated with facial dysmorphia, hypotonia, and cognitive alterations, including attention deficit hyperactivity disorder and autism. How the WAC protein localizes and functions in neural cells is critical to understanding its role during development. To understand the genotype-phenotype role of WAC, we developed a knowledgebase of WAC expression, evolution, human genomics, and structural/motif analysis combined with human protein domain deletions to assess how conserved domains guide cellular distribution. Then assessed in a cell type implicated in DESSH, cortical GABAergic neurons. WAC contains conserved charged amino acids, phosphorylation signals, and enriched nuclear motifs, suggesting a role in cellular signaling and gene transcription. Human DESSH variants are found within these regions. We also discovered and tested a nuclear localaization domain that impacts the cellular distribution of the protein. These data provide new insights into the potential roles of this critical developmental gene, establishing a platform to assess further translational studies, including the screening of missense genetic variants in WAC. Moreover, these studies are essential for understanding the role of human WAC variants in more diverse neurological phenotypes, including autism spectrum disorder.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

Individuals with metabolic syndrome have increased risk for developing health conditions, in-cluding cardiovascular diseases and stroke. Modifiable risk factors, such as exercise and diet, are key components in the prevention and control of metabolic syndrome. Specifically, dietary pat-terns and habits are extremely successful in controlling more than one of the metabolic syn-drome risk factors. Meal timing and frequency have been associated with type 2 diabetes, cardi-ovascular diseases, and other chronic conditions. However, there is limited evidence linking metabolic syndrome to meal timing and meal frequency. This review summarizes and discusses how meal timing and frequency impact metabolic outcomes in adults.

The experimental verification for the computational method sometimes varies due to numerous factors such as the manufacturing process and the materials' property change due to environmental aspects. In this work, we performed verification of experimental and computational evaluation of a hybrid composite moderate thick plate. The experiment was performed with simplistic approaches and without the advanced tools of preparing composite materials. This is due to the fact that most of the students in many developing countries around the world cannot have access to such equipment. As such, in this research, we are presenting cheap and easy preparation methods, with some details, for even equipment calibration and some tricks to attain a reliable composite structure for educational purposes. Moreover, the software and solvers used in this study are freely provided by the supplier for educational purposes. This study examined two methods for producing carbon and glass/polyester composite plates and discussed which one was best based on mechanical properties for different volume fractions, random stacking sequences, and ply angles (using OCTAVE's random estimation program). It also determined the three natural frequencies experimentally and with the aid of ANSYS. Less than 6% separated the experimentally determined natural frequencies from the calculated results.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

Tree pruning is a management tool in agroforestry systems for reducing shade, enhancing nutrient cycling or providing fodder. However, little information is available on the effect of pruning management on plant growth, non-structural carbohydrate (NSC) levels in the roots and N2 fixation of Sesbania sesban. A glasshouse experiment was conducted to assess the effect of pruning frequency on biomass production, NSC levels and N2 fixation of Sesbania sesban. Treatments included pruning at (i) 3 months only, (ii) 3 and 6 months, and (iii) 3, 6 and 9 months, with each pruning removing shoot biomass above 50% of the initial height. Increased pruning frequency decreased above- and belowground dry matter (DM), and root NSC levels. The decrease in aboveground DM correlated with reduced levels of starch, sugar and total non-structural carbohydrates. Pruning more frequently significantly decreased nodulation, the percentage N derived from the atmosphere and N2 fixed. It could be concluded that more frequent prunings decreases biomass and NSC levels which could reduce the regeneration capacity of trees since they rely on NSCs to regrow. The suppressive effects of increased pruning frequency on N2 fixation could decrease the desirable benefits of legume trees on soil N fertility improvement.

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, after the initial cluster selections are made. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

Modifiable THz spectral shapes are important tools that facilitate the comprehensive study of phonon dynamics in condensed matter systems. The generation of narrow bandwidth THz spectra with tunable center frequency which are suitable THz forms needed to achieve such objectives are currently less studied from the table top laser-induced plasma emitters’ perspective. This experimental research is aimed at developing a robust two-color laser induced plasma set-up comprising of a temporal pulse stretcher and an Optical Parametric Amplifier that generates chirped and wavelength tunable pulses respectively. By focusing and independently controlling the ω and 2ω arms of the chirped pulses resulting after the interaction with a β-BBO crystal, I aim to generate narrow bandwidth THz signal (from plasma) scalable at MV/cm intensity and tunable in a wide THz spectral range, in addition to varying the frequency ratio mix.

The vast rise in the number of Internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faced some issues, such as complexity in sensing leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, an adaptive threshold matched filter detector and a cooperative matched filter detector are utilized in this paper to detect the spectrum. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values.

A The present investigation was undertaken in foothill regions of Uttarakhand from July-2016 up to December-2018. A total of thirty four different sites ranging from the roadside areas, grasslands to forests were studied and Mushroom fruiting bodies were collected. A total of One Hundred sixty six fruiting counts were obtained and 68 mushroom genera belonging to 15 orders and 43 families were identified. During collection visits mushroom were apparent from organic debris of diversified habitats ranging from humid soil; grassland; leaf litter; living tree trunk; dead wood log of forest zone. Maximum fruiting bodies (75%) were obtained between July to September and minimum i.e. 6% between November – February. Among the collected mushroom Stereum rugosum, Crepidotus variabilis, Laccaria laccata, Schizophyllum commune, Ganoderma applantum, Cantharellus cibarius were more prevalent. Out of all collected mushroom sample the frequency of Mushroom belonging to order Agaricales was 45.18% followed by Polyporales i.e., 27.7%. The collected mushroom were cultured on PDA medium and their mycelial forms were preserved for further studies.

Qualitative and quantitative values estimation is a crucial aspect of social data science and plays a vital role in social and psychological research. Numerous methods of personal values measurement such as Schwartz Value Survey (SVS) and its re-modified versions have been proposed and invented, but many are challenged with one or more limitations. In this article, an alternative approach in a new paradigm of values measurement called Kabiru’s Value Survey (KVS) was proposed to measure the relative degree of importance or significance attached to particular value-actions of an individual(s) in a given population over a period. The methodological innovations in KVS unlike other existing methods of human value measurement include the use of habitually reported practices associated with a particular value-action in place of judging the series of Schwartz hypothetical statements, the use of multiple dependent variables rather than a single coded hypothetical statement. In terms of data analysis, a new approach of multivariate compression is introduced which merges the chain of multiple dependent variables into one coded scale. Psychometric result interpretation is also another innovative aspect of KVS methodology. Finally, the proposed methodology however solved some of the criticized limitations of the other methods of human value measurement.

Approximately 300 different types of blood groups are identified so far, the ABO and Rh antigens are still the clinically most significant and genetically most polymorphic of all human blood group systems to date. A total of 200 unrelated individuals from Uttar Pradesh were studied for the phenotype and allele frequency distribution of ABO and Rh (D) blood groups. In total 200 samples analyzed, phenotype B blood type has the highest frequency 36.5% (n=73), followed by O (34.5%; n=69), A (20.5%; n=41) and AB (8.5%; n=17). The O, A and B frequencies were 0.5849, 0.1571 and 0.2580 respectively. The overall phenotypic frequencies of ABO blood groups were B>O>A>AB. The variation in phenotypic frequencies between male and female might be due to small sample size of male sample. The allelic frequency of Rh-negative was 0.2.