Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

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.

Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

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.

The results of electron-beam processing of Mn-Zn ferrite samples using pulsed and continuous electron beams in the forevacuum pressure range (10 Pa) are presented. We find that continuous electron-beam processing leads to surface structuring of the ferrite, changes in elemental composition on the surface, and electrical property modification. The degree of ferrite parameter changes exhibits a threshold behavior. For surface processing temperatures below 900 °C, changes are barely noticeable, while for temperatures over 1100 °C the surface resistance decreases by more than an order of magnitude. Electron-beam processing with millisecond pulse duration and pulse energy density exceeding 15 J/cm² results in the formation of low zinc content melt islands, while the remaining surface area (outside the islands) elemental content and ferrite properties remain largely unchanged. The thickness of the modified layer depends on the processing mode and can be controlled over the range 0.1–0.5 mm. Because of its low resistance, the modified layer can be utilized to enhance the RF-absorbing properties of the ferrite.

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as Diffusion, Consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with Supervised Contrastive Learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised Contrastive Learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared to traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

Rhotekin is an effector protein for small GTPase Rho. This protein consists of a Rho binding domain (RBD), a pleckstrin homology (PH) domain, proline-rich regions and a C-terminal PDZ-binding motif. We and other groups have identified various binding partners for Rhotekin and proposed their possible physiological roles. However, functions of Rhotekin per se are largely unknown and information about its physiological roles are fragmentary. In this review, we summarize known features of Rhotekin in neuronal tissues and cancer cells. We also describe characteristics of binding partners for Rhotekin and predicted roles of their interaction.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

Phosphatase and tensin homologue (PTEN) and SH2-containing-inositol-5’-phosphatase 2 (SHIP2) are structurally and functionally similar. They both consist of a Phosphastase (Ptase) domain and an adjacent C2 domain, and both proteins dephosphorylate phosphoinositol-tri(3,4,5)phosphate, PI(3,4,5)P3; PTEN at the 3-phophate and SHIP2 at the 5-phosphate. Therefore, they play pivotal roles in the PI3K/Akt pathway. Here, we investigate the role of the C2 domain in membrane interactions of PTEN and SHIP2, using molecular dynamics simulations and free energy calculations. It is generally accepted that for PTEN the C2 domain interacts strongly with anionic lipids and therefore significantly contributes to membrane recruitment. In contrast, for the C2 domain in SHIP2 we previously found much weaker binding affinity for anionic membranes. Our simulations confirm the membrane anchor role of the C2 domain in PTEN, as well as its necessity for the Ptase domain in gaining its productive membrane binding conformation. In contrast, we identified that the C2 domain in SHIP2 undertakes neither of these roles, which are generally proposed for C2 domains. Our data support a model in which the main role of the C2 domain in SHIP2 is to introduce allosteric interdomain changes that enhance catalytic activity of the Ptase domain.

Pin1 is a peptidyl-prolyl isomerase responsible for isomerizing phosphorylated S/T-P motifs. Pin1 has two domains that each have a distinct ligand binding site, but only its PPIase domain has catalytic activity. Vast evidence supports interdomain allostery of Pin1, with binding of a ligand to its regulatory WW domain impacting activity in the PPIase domain. Many diverse studies have made mutations in Pin1 in order to elucidate interactions that are responsible for ligand binding, isomerase activity, and interdomain allostery. Here, we summarize these mutations and their impact on Pin1’s structure and function.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

Although there are a large number of databases available for regulatory elements, bottleneck has been created by the lack of bioinformatics tools for predicting types of mechanisms underlying actions of regulatory elements. To reduce the gap, we developed ARabidopsis Transcription regulatory Factor Domain-domain interaction Analysis Tool- Liquid-liquid phase separation (LLPS), Oligomerization, GO analysis (ART FounDATion-LOG), a useful toolkit for protein-nucleic acid interactions (PNI) and protein-protein interactions (PPI) analysis based on domain-domain interaction (DDI). LLPS, protein oligomerization, structural properties of protein domains, and protein modifications are major components in orchestrating spatio-temporal dynamics of PPI and PNI. Our goal is to integrate PPI/PNI information into development of prediction model for identifying important genetic variants in peach. The program unified inter-database relational keys by protein domains for facilitating inference from the model species. Key advantage of the program lies in the integrated information of related features: LOG, structural characterization of domain (e.g. domain linker, intrinsically disordered regions, DDI, domain-motif (peptide) interaction, beta-sheet and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use the program. The program may be applied to other eukaryotic organisms. The program codes and data are freely available for download at and https://sourceforge.net/projects/artfoundation-log/.

The high transmissibility and replication of SARS-CoV-2 have been attributed to enhanced protein functions which are dependent on protein folding. Our in silico study endeavored to scrutinize SARS-CoV-2 ORF1ab by analyzing the conserved folding patterns of its transcribed proteins. Accordingly, the findings indicated that SARS-CoV-2 ORF1ab shares domain-specific fold-fingerprints with a spectrum of unrelated organisms. Closer observation revealed slight changes in folding patterns engendered with small variation in the intrinsic amino acid sequence. By correlating with the evolvability-potential of RNA-viruses and COVID-19 pandemic, we hypothesize that SARS-CoV-2 could undergo fast recombination with the host, SARS-CoV-2 minor variants and other viral species resulting in a reservoir of SARS-CoV-2 quasispecies. It is highly possible that natural selection will cause a future emergence of evolved SARS-CoV-2-descendants. Nonetheless, we hope that this insightful study will assist in elucidating SARS-CoV-2 protein functionalities, development of vaccines, and the possibility and nature of future emergence.

The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

In archaea, the interaction of HerA helicase and NurA nuclease is required for 3' end resection during DNA double-stranded break repair, and its disruption results in unfaithful DNA repair. Interactions transpire by the four conserved domains, including the N-terminal HAS domain (HerA-ATP synthase), which serves as a binding site for the exonuclease NurA. As a HerA hexamer between its two subunits, the conserved C-terminal brace extension functions as an adjacent brace. DBD serves as a location for DNA binding, which causes the DNA strands to be propelled into the HerA pocket to begin unwinding. The putative pore entrance is made up of four helix coils, which provide a suitable charged surface for guiding DNA into the pore. The crystal structure of the complex HerA-NurA allows for the identification of key residues for protein-protein interaction. This allows us to predict that most archaeal HerA orthologs will be able to interact with one another. Archaeal HerA is a homo-hexameric assembly featuring a DNA-binding domain that is conserved. It also features distinctive ATP and NurA binding domains. Following the structure and sequence conservation criteria, we find one HerA ortholog in Pyrobaculum calidifontis and multiple HerA paralogs, but the role of none is established yet.Interestingly, orthologous HerA is part of an Mre11-Rad50 gene neighborhood while paralogous HerA is not, which could be misleading. As a result, it sought to describe the criteria that may be used as a possible fingerprint for identifying orthologous genes, as well as the cloning procedures and tactics that would aid in the confirmation of the HerA helicase in P. calidifontis. Their confirmation is based on PCR cloning and characterization following HerA construct creation.

Computing the rotation tensor is vital in the analysis of deformable bodies. This paper describes an explicit expression for the SO(3) rotation tensor R of the deformation gradient F, and successfully establishes an intrinsic relation between the exponential mapping Q = exp A and the deformation F. As an application, Truesdell's simple shear deformation is revisited.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

In this paper, it is shown that the bivariate beta statistical manifold of the first kind with three parameters has a gradient system Hamiltonian and is completely integrable. It is shown that this system admits a Lax pair representation. The question here is how to construct a gradient system and show that it is completely integrable while determining the integrable curves on the three-parameter bivariate beta manifold of the first kind admitting a potential function?To do this, we prove the existence of the potential on the manifold using ovidiu in Ovidiu, then using Amari's theorems in Amari, we show that this coordinate system admits a dual coordinate system. This will allow us to determine the Riemannian metric on the variety and to construct the gradient system. We linearise this system using Nakamura's method. We prove that it is Hamiltonian and completely integrable using the theorem in \cite{mama-proceeding}. It is shown that the Bivariate Beta of the first kind with three parameters, is an exponential function. The gradient system is linearizable. It is proven that the associate gradient system is Hamiltonian with $ \mathcal{H} $ which is in involution and satisfying $d\mathcal{H}=0.$ Therefore, the gradient system obtained is a sub-dynamical system of a $4$-dimensional system and is a completely integrable system. We show that the gradient system on the statistical model has the following Lax pair representation: $ \dot{L}= \left[L, N\right] $ where $L$ is a symmetric matrix and $N$ is the diagonal matrix. The gradient system defined by the Bivariate Beta family of the first kind with three parameters odd-dimensional manifold is a completely integrable Hamiltonian system.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

The incidence of cutaneous melanoma has been increasing in the last decades among fair-skinned population. Despite etiology is complex and multifactorial, the exposure to ultraviolet radiation (UVR) is the most consistent modifiable risk factor associated with melanoma. Several factors influence the amount of UVR reaching Eart’s surface. The aim of our study is to explore the association between melanoma and altitude in an area characterized by a mixed geographic morphology, such as in the Veneto region. For this purpose, 2752 melanoma patients, who referred to our attention between 1998 and 2014, were included in this study. For each patient we extracted demographic, histological and clinical-survival data. Head/neck and acral melanoma were more common in patients from the hills and the mountains, while the prevalence of limb and trunk melanoma was higher in patients living in plain and coastal areas. With increase of altitude, the Breslow thickness, ulceration and mitotic rate get worse but no significant difference was observed in overall and disease free-survival. Geographical area of origin of melanoma patients and the “coast-plain-hill gradient” could help to estimate the influence of different sun exposure and to explain the importance of vitamin D level in skin-cancer control.

To extend the calculation power of tensor analysis, we introduce four new definition of tensor calculations. Some useful tensor identities have been proved. We demonstrate the application of the tensor identities in continuum mechanics: momentum conservation law and deformation superposition.

Effective control of domain walls or magnetic textures in antiferromagnets promises to enable robust, fast, and non-volatile memories. The lack of net magnetic moment in antiferromagnets implies the need for creative ways to achieve such a manipulation. Here, we investigate changes in magnetic force microscopy (MFM) imaging and in magnon-related mode in Raman spectroscopy of virgin NiO films under a microwave pump. After the MFM and Raman studies, a combined action of broadband microwave (0.01-20 GHz, power scanned from −20 to 5 dBm) and magnetic field (up to 3 kOe) were applied to virgin epitaxial (111) NiO and (100) NiO films grown on (0001) Al2O3 and (100) MgO substrates, following which the MFM and Raman studies were repeated. We observed a suppression of the magnon-related Raman mode subsequent to the microwave exposure. Based on MFM imaging, this effect appears to be caused by the suppression of large antiferromagnetic domain walls due to the possible excitation of antiferromagnetic spin oscillations localized within the antiferromagnetic domain walls.

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.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

In order to investigate the vertical distribution pattern of soil fauna community in low-altitude mountain areas. On July 8th, 2022, a low hill was selected as the study area to collect soil arthropods were collected through trap. Leaf litter, vegetation type and distribution quantity were investigated at each sampling site while soil fauna were collected. Also, the soil physical and chemical parameters were measured simultaneously. The results of one-way ANOVA showed that there were significant differences (P

We prove an analog of the removable singularity theorem for bounded harmonic functions on stratified sets. The harmonic functions are understood in the sense of the soft Laplacian. The result can become one of the main technical components for extending the well-known Poincaré–Perron’s method of proving the solvability of the Dirichlet problem for the soft Laplacian.

The Ca Mau Peninsula situated in the Mekong Delta of Vietnam, features a low-lying terrain. Over recent years, the region has encountered the adverse impacts of climate change, leading to both land subsidence and rising sea levels. In this study, we assessed the land subsidence susceptibility in the Ca Mau peninsula utilizing three Boosting machine learning models: AdaBoost, Gradient Boosting, and Extremely Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), Groundwater levels, distance to roads, Digital Terrain Model (DTM), normalized vegetation index (NDVI), geology, soil composition, and proximity to rivers and streams. A dataset comprising 1950 subsidence sample points was employed for the model, with 1910 points obtained from the PSInSAR Radar method, and the remaining points derived from leveling measurements. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the ROC curve, Area under the curve (AUC), Specificity, Sensitivity, and Overall Accuracy. The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC above 0.9 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau peninsula.

The transition region is a very thin but most peculiar layer in the solar atmosphere, located between the solar chromosphere and the corona. It is a key region for understanding the coronal heating, the solar eruptions triggering, and the origin of solar winds. Here, the gradients of all physical parameters are very high, including plasma density, ionization degree, temperature, and magnetic fields, etc. Therefore, the transition region should be highly dynamic, such as fast energy releasing and transporting, plasma heating, and even particle accelerating. They include two physical aspects: thermal and non-thermal processes. The thermal processes can be observed from the ultraviolet (UV) and extreme ultraviolet (EUV) emission by multi-wavelength images. The non-thermal processes, however, should be related to radio emissions especially at millimeter wavelengths, and the frequency range should span from several GHz to beyond 300 GHz, and the emission mechanism should be highly related to the magnetic fields and variations. The ultra-wide broadband spectral observations at millimeter wavelengths with high spectral resolutions may provide rich dynamic information about the transition region, such as non-thermal energy release and transmission, the flows of plasma and energetic particles, the magnetic fields and their variations, the generation and transportation of various waves, and the formation and evolution of the source regions of solar eruptions. Here, we discussed the spectral characteristics of millimeter-wave emission from the transition region and proposed a new conception of ultra-wide broadband millimeter-wave spectrometers to observe them.

Polynomial recurrence bounds for a class of stochastic differential equations with a gradient type drift and an additive Wiener process are studied without Lyapunov functions.

The present study is aimed at designing anti-reflective (AR) engraving on the Input-Output surfaces of a rectangular light-guide. We estimate AR efficiency, by the transmittance level in the angular range, determined by the light-guide. Using nano-engraving, we achieve a uniform high transmission over a wide range of wavelengths. In the past, we used smoothed conical pins or indentations on the faces of light-guide crystal as the engraved structure. Here, we widen the class of pins under consideration, following the physical model developed in the previous paper. We analyze the smoothed pyramidal pins with different base shapes. The possible effect of randomization of the pins parameters is also examined. The results obtained demonstrate optimized engraved structure with parameters depending on the required spectral range and facet format. The predicted level of transmittance is close to 99\%, and its flatness (estimated by the standard deviation) in the required wavelength range is 0.2\%. The theoretical analysis and numerical calculations indicate that the obtained results demonstrate the best transmission (reflection) we can expect for a facet with the given shape and size for the required spectral band. The approach is equally useful for any other form and of the facet. We also discuss a simple way of comparing experimental and theoretical results for a light-guide with the designed input and output features. In this study, as well as in our previous work, we restrict ourselves to rectangular facets. We also consider the limitations on maximal transmission produced by the size and shape of the light-guide facets. The theoretical analysis is performed for an infinite structure and serves as an upper bound on the transmittance for smaller-size apertures.

Achieving devices to be transparent is the task considered in the last time in many aspects and for different purposes. One issue of these problems is making anti-reflective surfaces in a wide wavelength range and keeping it flat enough. The authors of the previous publication showed that one could enhance a flat facet's transmission efficiency by suitable engraving. They used smoothed conical fingers and holes. Here, we widen the class of anti-reflective metasurfaces under consideration, following the requirements of the model developed in the previous paper. We involve also smoothed pyramidal fingers. The obtained results provide the improved engraved structure, with parameters dependent on the required spectral range, and the facet format. The predicted level of transmittance is close to 99\%, and the flatness(estimated by the standard deviation) as 0.2\%. This improvement is significant enough for high and broadband transmittance. In this work, we show that the randomization of the engraving parameters does not provide any significant effect for small facets. We also discuss a simple way of comparing experimental and theoretical results for a waveguide with the considered input and output features. In this study, as well as in our previous work, we restrict ourselves by rectangular facets. We also discuss the limitations that originated from the size and shape of the waveguide facets.

Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D cell arrays has been extensively used for drug discovery because of its usability and applicability. Herein, we developed a microfluidic spheroid culture device (μFSCD) with a concentration gradient generator (CGG) that enabled cells to form spheroids and grow in the presence of cancer drug gradients. The device is composed of concave microwells with several serpentine micro-channels which generate a concentration gradient. Once the colon cancer cells (HCT116) formed a single spheroid (approximately 120 μm in diameter) in each microwell, spheroids were perfused in the presence of the cancer drug gradient irinotecan for 3 days. The number of spheroids, roundness, and cell viability, were inversely proportional to the drug concentration. These results suggest that the μFSCD with a CGG has the potential to become an HTS platform for screening the efficacy of cancer drugs.

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

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.

The earthquake alert of the early warning systems is based on the delay that the S waves have in reference to the P waves and on the interpretation of the P waves’ specific parameters. One of the most used parameters for estimating the moment magnitude of an earthquake is the characteristic period measured in the first 3 seconds of the appearance of the P wave. The typical method determines the characteristic period in the time domain, based on the velocity wave and the displacement wave. In the present work, we present a method for estimating the frequency of the characteristic period. This method includes zero padding of the P wave sequence, conversion of the extended sequence from the time domain to the frequency domain, the identification of local frequency maxima and the calculation of the weighted average of the frequency based on the identified maxima. Tests conducted on synthetic signals, as well as standard deviation evaluation tests for simultaneous recordings on several stations, reveal better performance to the usual method.

Neuronal PAS domain protein 2 (NPAS2) is a hemeprotein comprising a basic helix-loop-helix domain (bHLH) and two heme-binding sites, the PAS-A and PAS-B domains. This protein acts as a pyridine nucleotide-dependent and gas-responsive CO-dependent transcription factor and is encoded by a gene whose expression fluctuates with circadian rhythmicity. NPAS2 can heterodimerize with ARNTL in the activating limb of the transcriptional/translational feedback loop operating the circadian clock circuitry. NPAS2 can functionally substitute for the core transcription factor and histone/protein acetylase CLOCK in the molecular clockwork endowing central oscillators in the suprachiasmatic nuclei of hypothalamus as well as in peripheral oscillators in the cell of other body tissues, such as the liver and kidney.

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.

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.

The problem of intensification of melt crystal growth process has been analyzed using CdTe as an actual material. Numerical simulation of 100 mm diameter CdTe crystal growth by VGF technique has been carried out. The heat-mass transfer was controlled by introducing of low-frequency oscillating baffle into the melt, so called axial vibrational control (AVC) technique. The baffle configuration has been optimized to destroy solid «tails», which were formed near the crucible walls at high cooling rates due to the low thermoconductivity and the corresponding latent heat. Analysis of CdTe homogeneity range showed that during fast crystal cooling Te micro precipitations were formed resulting from decay of oversaturated Cd-riched nonstoichiometric solid solution at Bridgman crystal growth technique. A VGF grown CdTe crystal after full crystallization stays inside the phase field of the high temperature wurtzite polymorph. This makes it possible to go through the polymorph transition without Te micro-precipitating using the advantages of VGF specific feature of very slow cooling.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.

Pollution of urban surface dust with hazardous elements (HEs) is a serious environmental issue due to its toxicity and potential hazardous effects. Surface dust samples were collected from core urban, urban, and suburban gradients in the Urumqi city of the arid NW China, and the concentrations of six HEs, such as arsenic (As), cadmium (Cd), nickel (Ni), lead (Pb), mercury (Hg), and chromium (Cr), were determined. The pollution load index (PLI) and the US EPA health risk assessment model were applied to analyze and compare the pollution levels and potential health risk of HEs in surface dusts in different urbanization gradients. The obtained results indicate that the average concentrations of Hg, Cd, and Ni in surface dust decrease in the order of core urban > urban > suburban, whereas the average concentrations of As, Cr, and Pb decrease in the order of urban > core urban > suburban. The PLI of HEs in surface dust decreased in the order of core urban > urban > suburban. The concentrations of HEs in the core urban and urban gradients are relatively higher than in the suburban gradient. Furthermore, the total non-carcinogenic and carcinogenic risk index of investigated HEs in surface dust decrease in order of urban > core urban > suburban, for both adults and children. In addition, the pollution of surface dust with HEs in all urbanization gradients is more harmful to the children's health than to the adults. Overall, the potential non-carcinogenic and carcinogenic health risks of the investigated HEs, instigated primarily by oral ingestion of surface dust, are found to be within the acceptable range. However, urbanization can effects the accumulation of HEs in surface dust, and Cr is the main non-carcinogenic risk factor, whereas Cd is the main carcinogenic risk factor among the analyzed HEs in surface dust in all urbanization gradients.

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

The robust control of high precision electromechanical systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improves the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The sterling performance is revealed with intensive tracking simulation experiments and demonstrates the improvement in the accuracy and response time of the controller.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

Cesiumtrifluoroacetate (CsTFA) is a key chemical for RNA-based stable isotope analyses to link the structure and function of microbial communities. We report a protocol to easily synthesize CsTFA from Cesiumcarbonate (Cs₂CO₃₎ and Trifluoroacetate (TFA) and show that self-synthesized CsTFA behaves similar to commercial CsTFA in the separation of isotopically labelled and unlabelled E. coli RNA.

Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow. Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimal distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.

In automated manufacturing systems, most of the manufacturing processes including machining are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Ceramic cutting tools are used to machine hard materials. Ti[C,N] mixed alumina ceramic cutting tools are widely used to machine hardened steel and Stainless Steel due to its superior mechanical properties. Martensitic stainless steel has wide applications in screws, bolts, nuts and other engineering applications. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Using the dataset obtained from experimental machining tool life model has been developed using Gradient Descent algorithm. The model was validated using co-efficient of determination. The accuracy of the machine learning model was tested using the test data and 99.83% accuracy was obtained. Tool life model based on Gradient Descent Algorithm was successfully implemented for the tool life of Ti[C,N] mixed alumina ceramic cutting tool.Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article; yet reasonably common within the subject discipline.)

Synthetic polymer-based gradient foams have considered as promising category of functionally graded materials with unique properties. In this study, the carbon dioxide (CO₂) foaming technology has used for PET-PEN (Polyethylene Terephthalate - Polyethylene Naphthalate) copolymer towards porous functional materials with thermal insulation with reasonable mechanical strength. Through scanning electron microscope based morphological characterization, a potential to fabricate gradient foam structures with micro-pores has identified. It has shown that variation of post-foaming temperature can tune the pore size distribution although the very high post-foaming temperature tends to cause structural instability. Thermal measurement data set the limits of operation, confirmed by simultaneous differential scanning calorimeter and thermo-gravimetric analysis. Mechanical stress and thermal conductivity also has measured to find rationale of thermal insulation with reasonable mechanical strength and to elucidate the actual 3D grid foam of copolymer.

We have developed a cast microfluidic chip for concentration gradient generation that contains a thin (~5 μm^2 crosssectional area) microchannel. Durable 2 μm-high microchannel mold features with a smooth bell-shaped sidewall were fabricated by exposing SU-8 photoresist to diffused 185 nm UV light emitted by a low-cost ozone lamp from the backside of the substrate to ensure sufficient crosslinking of small regions of the SU-8 photoresist. An H-shaped microfluidic configuration was used, in which the thin channel was able to maintain constant diffusion fronts beyond purely static diffusion confirmed with experiment. We also demonstrated the long-term effects of a gradient of nerve growth factor on axon elongation by primary neuronal cells cultured in the microfluidic channel.

The early-season area estimation of the winter wheat crop as a strategic product is important for decision makers. Classification of multi-temporal images is an approach which is affected by many factors like appropriate training sample size, proper frequency and acquisition times, vegetation indices (VIs) type, temporal gradient of spectral bands and VIs, appropriate classifier and missed values because of cloudy conditions. This paper addresses the impact of appropriate frequency and acquisition times and VIs type along with the spectral and VI gradient on random forest (RF) classifier when missed values exist in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, the study area was selected on an overlapping area between two LDCM paths. In our developed method, the miss values of cloudy bands for each pixel are retrieved by the mean of k-nearest ordinary pixels. Then the multi-temporal image analysis is performed by considering different scenarios provided by decision makers in terms of desired crop types that should be extracted at early-season in the study areas. The classification results obtained by the RF decrease by 1.6% when temporally missed values retrieved by the proposed method, which is an acceptable result. Moreover, the experimental results demonstrated that if temporal resolution of Landsat 8 increased to one week the classification task can be conducted earlier with almost better results in terms of OA and kappa. The impact of incorporating VIs along with the temporal gradients of spectral bands and VIs as new features in RF demonstrated that the OA and Kappa are improved 3.1% and 6.6%, respectively. Furthermore, the obtained result showed that if only one image from seasonal changes of crops is available, the temporal gradient of VIs and spectral bands play the main role to discriminate remarkably wheat from barley. The experiments also demonstrated that if both wheat and barley merge to a single class the crop area can be estimated two months earlier with 97.1 and 93.5 in terms of OA and kappa, respectively.

Modern companies accumulate a vast amount of customer data that can be used for creating a personalized experience. Analyzing this data is difficult and most business intelligence tools cannot cope with the volume of the data. One example is churn prediction, where the cost of retaining existing customers is less than acquiring new ones. Several data mining and machine learning approaches can be used, but there is still little information about the different algorithm settings to be used when the dataset doesn't fit into a single computer memory. Because of the difficulties of applying feature selection techniques at a large scale, Incremental Probabilistic Component Analysis (IPCA) is proposed as a data preprocessing technique. Also, we present a new approach to large scale churn prediction problems based on the mini-batch Stochastic Gradient Decent (SGD) algorithm. Compared to other techniques, the new method facilitates training with large data volumes using a small memory footprint while achieving good prediction results.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

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.

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.

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.

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.

Qinghai spruce (Picea crassifolia) is a ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. We here analyzed several biological indicators among naturally re-generating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physico-chemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the biological indicators and correlations between the indicators and soil parameters were then assessed at each elevation. The results showed that soil stoichiometry was more sensitive to changes in el-evation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon to nitrogen ratio (C/N), the carbon to phosphorus ratio, and soil organic carbon (SOC) contents. None of the analyzed soil parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling diameter at each elevation. Finally, soil pH, SOC, C/N, and the carbon to phosphorus ratio significantly affected variations in seed-ling height at each elevation. This study provides a strong theoretical basis for further under-standing of the mechanisms associated with Qinghai spruce regeneration, ulti-mately contrib-uting to rational protection and management strategies for this important natural resource.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

A detailed literature review is performed in this study to address solutions for the full-body design and control of an aerial manipulator. Deep Reinforcement Learning methods are growing to be utilized recently to cope with various uncertainties. The pros and cons of these theories will be explained as well as introducing the advantages of Fuzzy Reinforcement Learning methods. State-of-the-Art, possible challenges, potential approaches, and a summary of desired precision devices are discussed in this study.

Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.

Landslides impact on human activities and socio-economic development especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides i.e. topographic, hydrologic, soil, forest, and geologic factors are prepared from various sources based on availability and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performing field survey. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories content 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models i.e. Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.757 and the testing accuracy is 0.74. Similarly, training accuracy of XGBoost is 0.756 and testing accuracy is 0.703. The prediction of DNN revealed acceptable agreement between susceptibility map and the existing landslides with training and testing accuracy of 0.855 and 0.802, respectively. The results showed that, the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to 3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser, whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal obtained by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of high measurand resolution (50 mK at 3-cm spatial resolution), thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, compared to the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser, therefore it is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double sideband or single sideband modulated probe light. The results confirm the validity of the proposed approach.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

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.

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.

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.

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.

Currently, the world is struggling with the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Prion-like domains are critical for virulence and the development of therapeutic targets; however, the prion-like domains in the SARS-CoV-2 proteome have not been analyzed. In this in silico study, using the PLAAC algorithm, we identified the presence of prion-like domains in the SARS-CoV-2 spike protein. Compared with other viruses, a striking difference was observed in the distribution of prion-like domains in the spike protein, since SARS-CoV-2 was the only coronavirus with a prion-like domain found in the receptor-binding domain of the S1 region of the spike protein. The presence and unique distribution of prion-like domains in the SARS-CoV-2 receptor-binding domains of the spike protein is particularly interesting, since although the SARS-CoV-2 and SARS-CoV S proteins share the same host cell receptor, angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 demonstrates a 10- to 20-fold higher affinity for ACE2. Finally, we identified prion-like domains in the α1 helix of the ACE2 receptor that interact with the viral receptor-binding domain of SARS-CoV-2. Taken together, the present findings indicate that the identified PrDs in the SARS-CoV-2 receptor-binding domain (RBD) and ACE2 region that interact with RBD have important functional roles in viral adhesion and entry.

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.

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.

Technical components should be produced sustainably and are subject to growing demands in terms of durability and reliability. To meet these expectations, the development of thermochemical processes is auspicious. Titanium alloys, which have a comparatively high gas solubility, allow temporary hydrogen (H) loading, often called thermo hydrogen treatment (THT). THT causes lattice deformation and reduces the β transformation temperature. These effects enable an adjustment of the microstructure, which improves the mechanical properties as compared to those produced conventionally. Furthermore, the process is applicable to complex geometries that cannot be surface hardened by mechanical surface treatment techniques. The investigation presented intends to realize a local microstructure gradient in additively via laser-powder bed fusion (L-PBF) manufactured cylindrical Ti–6Al–4V specimens by changing the distribution and morphology of strengthening precipitates as well as the β grain size depending on the distance to the surface, which should improve the material fatigue properties. To evaluate the mechanical properties of such THT-induced microstructure gradients, the resulting change in fatigue life in the LCF and HCF range was determined by stress-controlled cyclic deformation tests. Besides, the resulting microstructure was characterized by means of XRD and TEM. In addition to the additively manufactured and thermo hydrogen treated cylindrical specimens, conventionally produced tube-like specimens were thermo hydrogen treated and examined. For the THT-induced microstructure gradients, numerically simulated H concentration profiles as well as experimentally determined hardness profiles were used for the evaluation of the THT process. The study shows that H-loading at 500°C and H-degassing at 750°C, followed by aging at 550°C, allows the establishment of a microstructure gradient that shows an 8% improvement in fatigue properties compared to a reference condition without THT. The reason for this improvement is an increased volume fraction secondary alpha phase in the near-surface area of the specimens. Moreover, the results show that the process is applicable a tube-like specimen with varying wall thicknesses.

In this paper, a novel nonlocal strain gradient anisotropic elastic shell model is developed to analyse the vibrations of simply supported single‒walled carbon nanotubes (SWCNTs). Sanders‒Koiter shell theory is used to obtain the strain‒displacement relationships. Eringen nonlocal elasticity theory and Mindlin strain gradient theory are adopted to derive the constitutive equations, where the anisotropic elastic constants are expressed via Chang molecular mechanics model. The complex variable method is used to analytically solve the equations of motion and to obtain the natural frequencies of SWCNTs. First, the anisotropic elastic shell model is validated via comparisons with the results of molecular dynamics simulations reported in the literature. Then, the effect of nonlocal and material parameters on the natural frequencies of SWCNTs with different geometries and wavenumbers is analysed. From the numerical simulations it is obtained that the natural frequencies decrease with increasing nonlocal parameter, while they increase with increasing material parameter. Moreover, the decrease of natural frequencies with increasing SWCNT radius is exponential as the material parameter increases, while it is linear as the nonlocal parameter increases. Finally, as the number of waves increases, the natural frequencies linearly vary with increasing nonlocal parameter, while they exponentially increase with increasing material parameter.

Applications of gradient method for nonlinear optimization in development of Gradient Neural Network (GNN) and Zhang Neural Network (ZNN) are investigated. Particularly, the solution of the matrix equation AXB=D which changes over time is studied using the novel GNN model, termed as GGNN(A,B,D). The GGNN model is developed applying GNN dynamics on the gradient of the error matrix used in the development of the GNN model. The convergence analysis shows that the neural state matrix of the GGNN(A,B,D) design converges asymptotically to the solution of the matrix equation AXB=D, for any initial state matrix. It is also shown that the convergence result is the least square solution which is defined depending on the selected initial matrix. A hybridization of GGNN with analogous modification GZNN of the ZNN dynamics is considered. The Simulink implementation of presented GGNN models is carried out on the set of real matrices.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Machine learning (ML) is widely used in the field of crop-growing information identification based on high-resolution remote sensing images. With Baoying County in Jiangsu Province, China, as the study area, this paper used Sentinel-2 images during the winter wheat growth period to construct its spectral, textural, and topographic features during its growth period and proposes a winter wheat-growing area extraction method based on the extreme gradient boosting (XGBoost) algorithm, which was compared with traditional ML algorithms such as the support vector machine (SVM), classification and regression tree (CART), and random forest (RF) algorithms. The results indicated that (1) a winter wheat-growing area identification model based on the XGBoost algorithm was successfully constructed based on Sentinel-2 images, considering 27 spectral, textural, and topographic features; (2) the constructed model could effectively extract winter wheat in the study area with an overall accuracy of 93.43% and only a small error compared with the actual winter wheat-growing area in Baoying County, meeting the accuracy requirement for crop identification in the study area; and (3) the deep learning algorithm XGBoost outperformed the three traditional ML algorithms, among which the RF algorithm was better than the SVM and CART algorithms, both of which had poor identification performance and a large error compared with the actual growing area. This paper provides a scientific basis for the accurate extraction of winter wheat-growing areas and further research on winter wheat growth monitoring and yield estimation.

How to effectively suppress thermal cracks in the metal laser additive manufacturing process is still one of the key issues to be solved in the field of laser additive manufacturing. Metal tin, with wide liquid phase working temperature range, high boiling point, low viscosity, high thermal conductivity and excellent electrical conductivity. The use of tin as an auxiliary thermal management material in the metal additive manufacturing process is expected to achieve effective regulation of the temperature field and stress field of the formed part, thereby inhibiting the initiation of cracks and obtaining formed parts with the target grain structure and high reliability. This paper presented a novel liquid metal-assisted laser additive manufacturing method (LMAAM). A numerical model for the laser additive manufacturing of tin-assisted titanium alloys was established. The differences of the flow field, temperature field and stress field of the formed parts with tin and without tin were compared and analyzed. The influence of the interaction position between the tin liquid level and the forming part on the temperature field and stress field of the forming part was deeply studied. The laser additive manufacturing experiment of tin-assisted titanium alloy was carried out, and the experimental results were basically consistent with the simulation results, which verified the validity of the model. LMAAM technology has proven to be an effective method for additive manufacturing of highly reliable formed parts.

The mechanical properties and stability of hydrophobic surface structures prepared by traditional methods are still the main technical bottlenecks restricting the broad application of hydrophobic systems on workpiece surfaces. In this contribution, we propose a technique called selective laser shock peening (SLSP) to enable large-scale high efficient, low-cost manufacturing of hydrophobic metal surfaces with enhanced mechanical properties for durable applications. Using the method of experimental investigation combined with numerical calculation, the hydrophobic properties, mechanical properties, and tribological properties of the samples prepared under SLSP, all-laser shock peening (ALSP), and non-laser shock peening (NLSP) are studied. The SLSP process could prepare a 3D gradient structure material with surface structures, a two-phase (strong phase, soft phase) distribution on the surface, and a multi-level gradient distribution in the thickness direction. Compared with the 2D gradient structure prepared by the traditional process, 3D gradient structures by SLSP have more significant advantages in improving the wetting behavior and the mechanical properties of the material, which proves SLSP to be a novel method to fabricate functional metal surface structures, with highly high engineering application value.

Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

For the solution existence condition of the Navier-Stokes equation, we propose a conjecture as follows: "\emph{The Navier-Stokes equation has a solution if and only if the determinant of flow velocity gradient is not zero, namely $\det (\bm \nabla \bm v)\neq 0$.}"

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.