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.

Graphene is a two-dimensional carbon atom with a hexagonal honeycomb-like structure in which the sp2 hybridization occurs. GO sheets are highly oxidized and have oxygen-containing groups. Properties of electronics, mechanicals, magnetics, and optics, Oxidized Methods for Brodies, Staudenmaier, Hofmann, Hummers, Improved Hummers, and Tour's Methods Reduction of Graphene oxide methods Chemical Reduction using sodium borohydride, 2,4-dinitrophenylhydrazine, Hydrazine hydrate, Thermal Reduction, Hydrothermal Reduction, Exfoliation Reduction, Catalytic Reduction, and the green synthesis route for reduction, The reduction of graphene oxide has a wide range of applications, including its use in energy storage devices for Solar cells, Supercapacitors, LIG-based sensors, River Water sensors, Battery, hydrovoltaic generators, OLED Screen, and Graphene Shielding. This review will determine the future of a few applications.

This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

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.

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.

A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.

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.

The Cancer Genome Atlas (TCGA) has classified papillary thyroid carcinoma (PTC) into indolent RAS-like and aggressive BRAF-like based on its distinct driver gene mutations. This study aimed to assess clinicopathology and pERK1/2 expression variations between BRAF-like and RAS-like PTCs and establish predictive models for BRAFV600E and RAS-mutated PTCs. A total of 222 PTCs underwent immunohistochemistry staining to assess pERK1/2 expression and Sanger sequencing to analyze the BRAF and RAS genes. Multivariate logistic regression was employed to develop prediction model. Independent predictors for the BRAFV600E mutation include a nuclear score of 3, the absence of capsules, an aggressive histology variant, and pERK1/2 levels exceeding 10% (X2=0.128, P>0.05, AUC=0.734, P<0.001). RAS mutation predictive model includes follicular histology variant and pERK1/2 expression >10% (X2=0.174, P>0.05, AUC=0.8, P<0.001). We proposed using the prediction model concurrently with four potential combination group outcomes. PTC cases included in combination of low BRAFV600E-scoring group and high RAS-scoring group are categorized as RAS-like (adjOR=4.857, P=0.01, 95% CI=1.470-16.049). PTCs included in combination of high BRAFV600E-scoring group and low RAS-scoring group are categorized as BRAF-like PTCs (adjOR=3.091, P=0.001, 95% CI=1.594-5.995). The different prediction models indicate variations in biological behaviour between BRAF-like and RAS-like PTCs, necessitating adjustments in treatment approaches.

Toll-like receptors (TLRs) play a key role in the innate immune response to numerous pathogens, including Acanthamoeba sp. The aim of this study was to determine the expression of TLR2 and TLR4 in the eyes of mice following intranasal infection with Acanthamoeba sp. Amoebae used in this study were isolated from the bronchial aspirate of a patient with acute myeloid leukemia (AML) and atypical symptoms of pneumonia. We found statistically significant differences in the expression of TLR2 and TLR4 in the eyes of immunocompetent mice at 8, 16, and 24 days post Acanthamoeba sp. infection (dpi) compared to control. Immunosuppressed mice showed significant differences in the expression of TLR2 at 16 and 24 dpi compared to uninfected animals. Our results indicate that TLR2 and TLR4 are upregulated in the eyes of mice in response to Acanthamoeba sp. We suggest that it is possible for trophozoites to migrate through the optic nerve from the brain to the eyes.

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.

In recent years, the term quantum-like has been increasingly used in different disciplines, including neurosciences, psychological and socio-economical disciplines, claiming that some investigated phenomena show “something” in common with quantum processes and, therefore, they can be modeled using a sort of quantum formalism. Therefore, the increasing use of the term quantum-like calls for defining and sharing its meaning in order to properly adopt it and avoid possible misuse. In our opinion, the concept of quantum-like may be successfully applied to macroscopic phenomena and empirical sciences other than physics when at least two conditions are satisfied: a) the behavior of the investigated phenomena show logical analogies with quantum ones; b) it is possible to find a criterion of truth based on an experiential/scientific approach applied to a probabilistic model of description of the phenomena. This is only a first, small step in the approach to the concept of quantum-like, hopefully helpful to promote further discussion and achieve a better definition.

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.

After screening nearly 1000 synthetic peptides, a synthetic peptide termed JAL-AK22 (KYEGHWYPEKPYKGSGFRCIHI) derived from the BoxA domain in Tob1 protein was found to activate both unfolded and folded proMMP-7. In addition, JAL-AK22 showed auto-proteolytic activity. Interestingly, the smaller derivative of JAL-AK22 termed JAL-TA9 (YKGSGFRMI) also possessed auto-proteolytic activity and cleaved 2 fragment peptides (MMP18-33 and MMP18-40) derived from the prodomain of proMMP-7 under physiological conditions. These proteolytic activities were inhibited by AEBSF, a serine protease inhibitor. Our results demonstrate that a small synthetic peptide consisting of only 9 amino acids has serine protease-like activity and activates proMMP-7 by cleaving the prodomain region. We thus propose calling small peptides possessing with protease-like activity Catalytides (catalytic peptides). We expect that our findings will stimulate the development of novel Catalytides and related applications.

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 present study aimed to develop a feeding strategy for pregnant sows which involved the prenatal administration of a mixture of pancreatic-like fungal enzymes, such as lipase, amylase, and protease, at (1) 1–115 days of gestation (group D1) and (2) 80–115 days of gestation (group D2) and to carry out a comparison with groups of sows that were not receiving such supplementation (negative control [NC] and positive control [PC]). It was found that the administration of the enzyme supplement resulted in a significant shortening of gestation (P ≤ 0.01). The pancreatic enzymes administered to sows had a significant effect on the number of liveborn piglets and weaned piglets, which was higher compared to the control groups that did not receive supplementation respectively: D1-12.1±1.1 and 11.12±1.1; D2- 12.8±1.3 and 11.75±0.07 vs. control groups KN-10.7±1.0 and 9.62±0,95 and KP 10.9±1.2 and 10.15±1.0 (P<0.006). Significant changes in piglet growth were observed after weaning up to 70 days of age. During this period, the most favorable growth parameters were found in groups D2 (420±91g) and PC (407±103g), in which piglets were given a mixture of pancreatic enzymes (lipase, amylase, and protease) at 3 weeks of age, and significantly higher weight gain and feed intake were observed compared to groups NC (378±114g) and D1 (381±96g) (P ≤ 0.007). In contrast, insulin levels were significantly lower in groups D1 and D2, respectively 6,8 IU/ml and 6,7 IU/ml compared to groups NC (14,6 IU/ml) and PC (16,6 IU/ml) ( (P ≤ 0.01). Piglets in group D2 had a significantly better feed conversion ratio (FCR) 1.604±0.10 compared to the other dietary groups: KN– 1.986±0.14; KP– 1.704±0.11; D1 – 1.932±0.15 (P ≤ 0.03). Histological imaging confirmed significantly thicker intestinal epithelium and intestinal mesenteron in animals from groups D2 and PC (P ≤ 0.03). Animals from the groups KP, D1 and D2 receiving enzymes showed a highly significant increase in the surface area of pancreatic follicles and pancreatic surface area compared to the group without KN supplementation (P<0.01). Furthermore, significantly higher activity of the brush border enzyme lactase was observed in groups D1; D2 and PC compared, respectively 32.90±3.99; 30.00±6.83 and 29.60±29.60 to group NC – 21.80± 3.27 (P ≤ 0.01).

Recent researches indicated that metal nanoparticles which have the unique optical properties can be used to enhance the spectral response of the photovoltaic modules. Since most of the nanoparticles have enhancement effects in a specific wavelength range, improving the spectral response of the photovoltaic modules in a broadband range is crucial for their applications in imaging, energy harvesting, and optical communication. In this study, flower-like silver particles are applied to achieve the enhancement effects in a broadband range. The optical absorption of photovoltaic modules is improved in a broad wavelength range of 400~2000 nm by immobilizing flower-like silver particles onto an amorphous Si p-i-n structure, and the peak responsivity of the spectral response is enhanced by about 10 times. Theoretical investigation further elaborates that the enhancement originates from the near-field effects of silver particles due to the interaction of different parts of the flower-like silver particles. Through these studies, we demonstrate that, utilizing the subwavelength silver particles with roughness surface can achieve the spectral response of the photovoltaic modules enhanced in broadband range, which can improve the utilization efficiency of optical energy for the applications of sensing, imaging, optical communication, and energy harvesting.

Globally, rotavirus group A (RVA) remains a major cause of severe childhood diarrhea, despite the use of vaccines in &gt; 100 countries. RVA sequencing for local outbreaks facilitates investigation into strain composition, origins, spread, and vaccine failure. In 2018, we collected 248 stool samples from children aged &lt;13 years admitted with diarrheal illness to Kilifi County Hospital, coastal Kenya. Antigen screening detected RVA in 55 samples (22.2%). Of these, VP7 (G) and VP4 (P) segments were successfully sequenced in 48 (87.3%) and phylogenetic analysis based on the VP7 sequences identified seven genetic clusters with six different GP combinations; G3P[8], G1P[8], G2P[4], G2P[8], G9P[8] and G12P[8]. The G3P[8] strains predominated the season (n=37, 67.2%) and comprised three G3 genetic clusters that fell within Lineage I and IX (the latter also known as equine-like G3 lineage). Both two G3 lineages have been recently detected in several countries. Our study is the first to document African children infection with G3 lineage IX. These data highlight the global nature of RVA transmission and the importance of increasing global rotavirus vaccine coverage.

Globin-like superfamily constitutes a superfamily of both extracellular and intracellular proteins that are found in 3 kingdoms of life. Globins are respiratory proteins that usually bind an oxygen molecule .Earlier, phylogenetic study of globin like superfamily was based on morphological character, however, molecular basis of their functional diversity is not completely understood. The present study focuses on the phylogenetic analysis of globin at molecular level. Firstly, proteins of SCOP families of globin like superfamily were retrieved from NCBI and then similar proteins are retrieved from PSI BLAST results and then pfam was used to predict the domains and two domains: Globin and Phycobilisome were analysed. Other analysis predicted the localization in cytoplasm of most of the proteins. The nucleotide sequences of same species for protein analysis were retrieved from NCBI, and phylogenetic analysis of aligned fasta files of proteins and nucleotides by using MEGA at molecular level was performed and analysis shows the difference on taxonomic tree and phylogenetic tree.

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

The use of mixed-state ionic beams in collision dynamics investigations is examined. Using high resolution Auger projectile spectroscopy involving He-like ($1s^2\thinspace^1\!S, 1s2s\thinspace^{3,1}\!S$) mixed-state beams, the spectrum contributions of the $1s2s\thinspace^3\!S$ metastable beam component is effectively separated and clearly identified. This is performed with a technique that exploits two independent spectrum measurements under the same collision conditions, but with ions having quite different metastable fractions, judiciously selected by varying the ion beam charge-stripping conditions. Details of the technique are presented together with characteristic examples. In collisions of 4 MeV B$^{3+}$ with H$_2$ targets, the Auger electron spectrum of the separated $1s2s\thinspace^{3}S$ boron beam component allows for a detailed analysis of the formation of the $1s2s(^3\!S)nl \thinspace^2\!L$ states by direct $nl$ transfer. In addition, the production of hollow $2s2p\thinspace^{1,3}\!P$ doubly- and $2s2p^2\thinspace^2\!D$ triply-excited states, by direct excitation and transfer-excitation processes, respectively, can also be independently studied. In similar mixed-state beam collisions of 15 MeV C$^{4+}$ with H$_2$, He, Ne and Ar targets, the contributions of the $1s^2$, $1s2s\thinspace^{3,1}S$ beam components to the formation of the $2s2p\thinspace^{3,1}\!P$ states by double-excitation, $1s\rightarrow2p$ excitation and transfer-loss processes can be clearly identified, facilitating comparisons with theoretical calculations.

Despite the recognized potential of nanoparticles, only a few formulations have progressed to clinical trials, and an even smaller number have been approved by the regulatory authorities and marketed. Virus-like particles (VLPs) have emerged as promising alternatives to most explored nanoparticles because of the absence of viral genetic material, their incapacity to replicate, mimicry of viral structure, and tropism conservation. Furthermore, VLPs can be surface functionalized with small molecules to improve circulation half-life and target specificity. Through the functionalization and coating of VLPs, it is possible to optimize the response properties to a given stimulus, such as heat, pH, an alternating magnetic field, or even enzymes. Surface functionalization can also modulate other properties, such as biocompatibility, stability, and specificity, deeming VLPs as potential vaccine candidates or delivery systems. In this review, we address the different types of functionalization of VLPs, their importance, and their consequent biomedical applications.

Glypicans are a family of heparan sulfate proteoglycans that are attached to the outer plasma membrane leaflet of the producing cell by a glycosylphosphatidylinositol anchor. Glypicans are involved in the regulation of many signalling pathways, including those that regulate the activities of Wnts, Hedgehog (Hh), Fibroblast Growth Factors (FGFs) and Bone Morphogenetic Proteins (BMPs), among others. In the Hh signalling pathway, glypicans have been shown to be essential for ligand transport and the formation of Hh gradients over long distances, for the maintenance of Hh levels in the extracellular matrix and for its unimpaired reception in distant recipient cells. Recently, two mechanistic models have been proposed to explain how Hh can form the signalling gradient and how glypicans may contribute to it. In this review, we describe the structure, biochemistry and metabolism of glypicans and their interactions with different components of the Hh signalling pathway that are important for the release, transport and reception of Hh.

Cardiac hypertrophy typically follows myocardial damage, a process known to involve members of the Krüppel-Like Factors (KLFs) which have pro-hypertrophic and anti-hypertrophic roles. Our study delved into the molecular mechanisms underlying KLFs-cardiac hypertrophy interplay post myocardial infraction. We induced myocardial dam-age in rats using isoproterenol. Total RNA was extracted from the left ventricle and Quantitative Real Time RT-PCR was conducted to assess the expression of KLFs, cardiac commitment genes, inflammatory markers, and certain conduction-related genes. We devel-oped a computational approach to construct a proteomic network centered on KLFs. Initial results revealed early expression (2-3 days post-induction) of KLF3, KLF4 and KLF6, followed by the subsequent expression of KLF11 and KLF15 (5-8 days after induction). In the maturation stage, KLF12 and KLF13 regulators were found upregulated. IL-6 was gradually found to be upregulated through. Interaction analysis revealed KLF3, KLF8 and KLF12 interacted with cardiac electrical functions. RT-PCR confirmed up-regulation in cardiac genes linked to electrical function and scar maturation. Our findings underscore the central role of KLFs during the modulation of the cardiac hypertrophic response. Dysregulation of KLF expression resulted in damaged myocardium, participating in the progression of abnormal hypertrophy, highlighting their potential as therapeutic targets for heart diseases.

Background: The number of people with type 2 diabetes is increasing daily, and therefore effective therapy is needed to successfully regulate glycemia and reduce the risk of associated complications. Recently, an oral formulation of the glucagon-like peptide-1 receptor agonist (GLP-1 RA) semaglutide has become available. Therefore, the aim of our study was to compare the effectiveness of the new oral formulation and the existing injectable formulation of semaglutide in terms of glycemic and body weight control in a real-world setting. Patients and methods: This was a single-center retrospective observational study conducted at the Rijeka Clinical Hospital Centre. A total of 106 patients with inadequately controlled type 2 diabetes (HbA1 ≥ 7%) on different oral or basal insulin supported oral therapy were recruited from the diabetes outpatient clinic, and data from electronic medical records were retrospectively collected and analyzed from May 2021 to November 2022. All subjects were GLP-1 RA naive and consequently prescribed 0.5 or 1.0 mg once weekly injectable semaglutide (IS) or 7 mg or 14 mg once daily oral semaglutide (OS) for at least 6 months. Glycated hemoglo-bin (HbA1c), body weight, and body mass index (BMI) were assessed prior to semaglutide administration and after a 6-month follow-up period. The primary endpoint was the change from baseline in HbA1c, and sec-ondary endpoints were the change in body weight and the proportion of participants with a reduction in body weight of ≥ 5% and ≥ 10%, respectively, 6 months after the initiation of semaglutide treatment. Results: At 6-month follow-up, no significant difference was observed between the two formulations in terms of HbA1c reduction (IS -1.1% vs OS -1.4%, p=0.126) and weight loss (IS -6.50 kg vs OS -5.90 kg, p=0.714). Exactly the same proportion of participants in both groups achieved a weight loss of ≥ 5% (56.7%, n=30). A weight loss ≥ 10% was observed in 20.7% (n=11) of participants administered IS and 15.1% (n=8) of participants adminis-tered OS, respectively (p=0.454). Conclusion: In a real-world setting, oral semaglutide as add-on therapy to ongoing antihyperglycemic treatment in patients with inadequately controlled type 2 diabetes who had not previously received GLP-1 RA demonstrated the same effectiveness as injectable semaglutide in terms of gly-cemic control and weight loss after 6 months of treatment.

Emerging evidence indicates that intracellular calcium (Ca2+) levels and their regulatory proteins play essential roles in normal stem cell proliferation and differentiation. Cancer stem-like cells (CSCs) are subpopulations of cancer cells that retain characteristics similar to stem cells and play an essential role in cancer progression. Recent studies have reported that the Orai3 calcium channel plays an oncogenic role in human cancer. However, its role in CSC remains underexplored. In this study, we explored the effects of Orai3 in the progression and stemness of oral/oropharyngeal squamous cell carcinoma (OSCC). Orai3 expression was increased in a stepwise manner during the progression of OSCC. In addition, Orai3 was highly enriched in CSC populations of OSCC. Ectopic Orai3 expression in non-tumorigenic immortalized oral epithelial cells increased intracellular Ca2+ levels, acquiring malignant growth and CSC properties. Conversely, silencing of endogenous Orai3 in OSCC cells suppressed CSC phenotype, indicating a pivotal role of Orai3 in CSC regulation. Moreover, Orai3 markedly increased the expression of inhibitor of DNA binding 1 (ID1), a stemness transcription factor. Orai3 and ID1 were highly expressed in CSCs compared to non-CSCs, implying the functional importance of the Orai3/ID1 axis in CSC regulation. Furthermore, suppression of ID1 abrogated CSC phenotype in the cell with ectopic Orai3 overexpression and OSCC. Our study reveals that Orai3 is a novel functional CSC regulator in OSCC and further suggests that Orai3 plays an oncogenic role in OSCC by promoting cancer stemness via ID1 upregulation.

Venoms are a rich source of bioactive compounds, and among them is leberagin-C (Leb-C), a disintegrin-like protein derived from the venom Macrovipera lebetina transmediterrannea snakes. Leb-C has shown promising inhibitory effects on platelet aggregation. Previous studies have demonstrated that this SECD protein specifically targets α5β1, αvβ3 and αvβ6 integrins through a mimic mechanism of RGD disintegrins. In our current study, we focused on exploring the potential effects of Leb-C on metastatic breast cancer. Our findings revealed that Leb-C disrupted the adhesion, migration, and invasion capabilities of MDA-MB-231 breast cancer cells and its highly metastatic D3H2LN sub-population. Additionally, we observed significant suppression of adhesion, migration, and invasion of Human Umbilical Vein Endothelial Cells (HUVECs). Furthermore, Leb-C demonstrated a strong inhibitory effect on fibroblast growth factor 2-induced proliferation of HUVEC. We conducted in vivo experiments using nude mice and found that treatment with 2 µM of Leb-C resulted in a remarkable 73% reduction in D3H2LN xenograft tumor size. Additionally, quantification of intratumor microvessels revealed a 50 % reduction in tumor angiogenesis in xenograft after 21 days of twice-weekly treatment with 2 µM of Leb-C. Collectively, these findings suggest the potential utility of this dinsintegrin-like protein for inhibiting aggressive and resistant metastatic breast cancer.

KLFs seem to associate with congenital heart disease-linked syndromes, malformations because of autosomal diseases, mutations that relate to protein instability, and/or loss functions such as atheroprotective activities. Ischemic damage also relates to KLF dysregulation because of differentiation of cardiac myofibroblasts or a modified fatty acid oxidation, related to the formation of a dilated cardiomyopathy; myocardial infarctions, left ventricular hypertrophy and diabetic cardiomyopathies. MicroRNA have been involved in certain regulatory loops of KLFs as they may function as critical modulators of vascular smooth muscle cells in atherosclerosis, in heart failure and as markers of endothelial damage in acute myocardial infarction.

Glioblastoma (GBM) still presents as one of the most aggressive tumors in the brain, which despite enormous research efforts remains incurable until today. As many theories evolve around the persistent recurrence of this malignancy the assumption of a small population of cells with a stem-like phenotype remains as a key driver of its infiltrative nature. In this article we research Chordin-like 1 (CHRDL1), a secreted protein, as a potential key regulator of the glioma stem-like cell (GSC) phenotype. It has been shown that CHRDL1 antagonizes the function of Bone Morphogenic Protein 4 (BMP4), which induces GSC differentiation and hence reduces tumorigenicity. We therefore employed two previously described GSCs spheroid cultures and depleted them of CHRDL1 using stable transduction of a CHRDL1-targeting shRNA. We show with in vitro cell based assays (MTT, limiting dilution and sphere formation assays), western blots, irradiation procedures and quantitative real-time PCR that depletion of the secreted BMP4 antagonist CHRDL1 prominently decreases functional and molecular stemness traits resulting in enhanced radiation sensitivity. As a result, we postulate CHRDL1 as an enforcer of stemness in GSCs and find additional evidence that high CHRDL1-expression might also serve as a marker protein to determine BMP4-susceptibility.

Purpose: The study aimed to investigate if there were any links between liver function biomarkers and immunoglobulins levels in serum, and Toll-like receptors (TLRs) and neuropilin-1 (NRP1) in COVID-19 patients. The study also aimed to assess the accuracy—sensitivity, specificity, and area under the curve (AUC) by the receiver operator curve (ROC) analysis for immunoglobulins levels and TLRs expressions. Patients and Methods: This study included 150 patients (100 patients with confirmed COVID-19 and 50 healthy volunteers as a control group). Patients with COVID-19 were subdivided into two groups according to the severity of symptoms (moderate and severe, with 50 patients each). Serum C-reactive protein (CRP), alanine aminotransaminase (ALT), aspartate aminotransaminase (AST), albumin, lactate dehydrogenase (LDH), immunoglobulin (Ig) G, and IgM levels were estimated. TLRs (TLR2 and TLR4) and NRP1 gene expression in blood samples were investigated using quantitative real-time polymerase chain reaction (qRT-PCR). ROC analysis was also applied to determine the accuracy of various detected parameters in predicting the possibility of COVID-19 infection. Results: In COVID-19 patients, serum parameters related to liver function, except serum albumin, CRP, IgG, IgM, and TLR2, TLR4, and NRP1 mRNA expression levels, significantly elevated compared to controls. Severe COVID-19 patients exhibited significantly higher liver enzymes (ALT, AST and LDH), CRP, and TLR2 mRNA expression levels and lower albumin levels than the moderate group. In the moderate and severe groups, ALT, CRP, TLR2, and TLR4 had a significant positive correlation with IgM levels. ALT, AST, LDH, CRP, TLR2, and TLR4 showed a significant positive correlation with IgG levels in both groups. In both the moderate and severe groups, NRP1 expression was found to be significantly correlated with CRP, IgG, IgM, TLR2, and TLR4. In contrast, serum albumin levels exhibited a significant negative correlation with IgG and IgM levels only in the severe group, but they showed a significant negative correlation with TLR2, TLR4, and NRP1 expression in both moderate and severe groups. Serum ALT and AST activities were positively correlated with NRP1 expression in the moderate group but not in the severe group and as well as TLR2 and TLR4 expression in both the moderate and severe groups. ROC analysis indicated that AUC was higher than 0.800 for serum IgM level and TLR4 gene expression in moderate COVID-19 group. Conclusions: The increased liver function biomarkers in serum and NRP1 expression are closely correlated with sustained activations in humoral and innate immune responses during COVID-19 infection. As a result, TLR2, TLR4, and NRP1 could be targets for limiting COVID-19 infection and impairment effects on liver function. Moreover, detection of IgM level in serum and TLR4 expression in blood have a good accuracy in predicting the possibility of infection with COVID-19 in moderate cases.

Background/Aim: Uterine leiomyoma, also known as fibroids, is the most common benign neoplasm of the female genital tract. Leiomyoma, including its subtypes, is the most common uterine tumor. The subtypes account for approximately 10% of leiomyomas. Intravenous leiomyomatosis, a uterine leiomyoma subtype, is an intravascular growth of benign smooth muscle cells, occasionally with pelvic or extrapelvic extension. Uterine leiomyosarcoma, a malignant tumor, tends to metastasize hematogenously, and distant metastasis to the lungs and liver is common. Therefore, this intravenous leiomyomatosis’ oncological features resemble those of the malignant tumor uterine leiomyosarcoma. Cancer stem cells migrate to distant organs via intravascular infiltration, leading to micrometastases. Materials and Methods: We examined the oncological properties of intravenous leiomyomatosis using molecular pathological techniques on tissue excised from patients with uterine leiomyoma. Result: CD44-positive mesenchymal tumor stem-like cells were detected in both intravenous leiomyomatosis and uterine leiomyosarcoma. The oncological nature of intravenous leiomyomatosis was found to be similar to the oncological properties of uterine leiomyosarcoma. However, in intravenous leiomyomatosis, Cyclin E and Ki-67-positive cells were rare, and no pathological findings suspected to be malignant were observed. Conclusion: It is expected that establishing a treatment method targeting cancer stem cells will lead to the treatment of malignant tumors with a low risk of recurrence and metastasis.

: Glioblastoma (GBM) is the most common and most aggressive primary brain tumor with a very high rate of recurrence and a median survival of 15 months after diagnosis. Abundant evi-dence suggests that a certain sub-population of cancer cells harbors a stem-like phenotype and is likely responsible for disease recurrence, treatment resistance and potentially even for the infil-trative growth of GBM. GBM incidence has been negatively correlated with the serum levels of 25-hydroxy-vitamin D3, while the low pH within tumors has been shown to promote the ex-pression of the vitamin D3-degrading enzyme 24-hydroxylase, encoded by the CYP24A1 gene. Therefore, we hypothesized that calcitriol can specifically target stem-like glioblastoma cells and induce their differentiation. Here, we show using in vitro limiting dilution assays, quantita-tive real-time PCR and ex vivo adult organotypic brain slice transplantation cultures that thera-peutic doses of calcitriol, the hormonally active form of vitamin D3, reduces stemness to varying extent in a panel of investigated GSC lines and effectively hinders tumor growth of responding GSCs ex vivo. We further show that calcitriol synergizes with Temozolomide ex vivo to com-pletely eliminate some GSC tumors. These findings indicate that calcitriol carries potential as an adjuvant therapy for a subgroup of GBM patients and should be analyzed in more detail in fol-low-up studies.

Toll-like receptors (TLRs) are a class of pattern recognition receptors (PRRs) family that identify pathogen-associated molecular patterns derived from microbes and activate immune cell response. Following TLRs ligation, different adaptor and transcription molecules such as myeloid differentiation primary response gene 88 (MyD88) and nuclear factor kappa B (NF-kB) are recruited that initiate inflammatory signaling pathways. The human Toll-like receptor 10 (hTLR10) is a novel member of the PRRs family with a regulatory function of immune responses because of unique cytoplasmic domains which lead to induction of both inflammatory and anti-inflammatory properties. Recent studies have reported the association of TLR10 polymorphisms with many inflammatory diseases and human cancer. Engagement of TLR10 on the surface of the epithelium and macrophages leads to the production of proinflammatory cytokines and chemokines, while other studies have proven an anti-inflammatory role of TLR10. Accordingly, TLR10 suppresses proinflammatory cytokine production via negative regulation of MyD88 and the Akt (protein kinase B) and MAPK (mitogen-activated protein kinase) signaling pathways. This review aimed to provide answers for these conflicting findings (Inflammatory and anti-inflammatory properties of TLR10) to further identify distinct biological functions of TLR10.

Bufadienolides-like chemicals, which mostly composed the active ingredient of Chansu, had been widely discovered to possess anti-inflammatory, tumor-suppressing and antipain activity, but the mechanisms of action were not clearly illuminated. In this research, in order to explore the potential mechanism of bufadienolides-like chemicals on breast cancer, a serious of bioinformatics analysis, included (1) differentially expressed genes identification combined with gene set variation analysis, (2) tissue specific co-expression network construction, (3) differentially regulated sub-networks detection with disease phenome, (4) hub gene selection and it’s relation to survival probability, and (5) similar small molecule detection were performed with gene expression profiles of bufadienolides-like chemicals. Results indicated bufadienolides-like chemicals had the most same target with valproic, estradiol and etc, could disturbed the pathways in RNA splicing, apoptotic process, cell migration, extracellular matrix organization, adherens junction organization, synaptic transmission, Wnt signaling, AK-STAT signaling, BMP signaling pathway and unfolded protein response, and had the potential ability to be used as anticancer, hormones and vasoprotectives agents.

Toll-like receptor 4 (TLR4) is a member of the TLR family. Members of the TLR family play an important role in innate immune responses and are induced by recognition of pathogen-associated molecular patterns. They are also involved in cell proliferation and apoptosis in cancer. We investigated the role of TLR4 in apoptotic cell death in gynecological cancer cells; gynecological cancer is associated with infertility and spontaneous abortion. To examine the effect of TLR4 activation on apoptotic signaling in cancer cells, cultured primary cancer cells were treated with the TLR4 agonist lipopolysaccharide (LPS). The morphology of cancer cells was compared with normal myometrial cells. Enhanced growth rate and loss of contact inhibition with cellular overlap was observed in the cancer cells. The molecular mechanism analysis revealed differential expression of tumor suppressor genes in LPS-treated cancer cells. The expression of apoptosis-related caspase-3 was increased significantly in cancer cells with TLR4 activation after exposure to LPS. Taken together, these results suggest the pro-apoptotic activity of TLR4 as a potential therapeutic target for the treatment of gynecological cancers.

Astronauts at are risk of losing 1.0 – 1.5% of their bone mass for every month they spend in space despite their adherence to high impact exercise training programs designed to preserve the musculoskeletal system. This article reviews the basics of bone formation and resorption and details how exposure to microgravity or simulated microgravity affects the structure and function of osteoblasts, osteocytes, osteoclasts, and their mesenchymal and hematologic stem cell precursors. It details the critical roles that insulin-like growth facor-1 and its receptor IGFR1 play in maintaining bone homeostasis and how exposure of bone cells to microgravity affects the function of these growth factors. Lastly, it discusses the potential of tumor necrosis factor-related apoptosis-inducing ligand, syncytin-A, and sclerostin inhibitors and recombinant IGF-1 as a bone-saving treatment for astronauts in space and during their colonization of the Moon.

Jingmen viruses are newly described segmented flavi-like viruses that have a worldwide distribution in ticks and have been associated with febrile illnesses in humans. Computational analyses were used to predict that Jingmen flavi-like virus glycoproteins have structural features of class II viral fusion proteins, including an ectodomain consisting of beta-sheets and short alpha-helices, a fusion peptide with interfacial hydrophobicity and a three domain architecture. Jingmen flavi-like virus glycoproteins have a sequence enriched in serine, threonine and proline at the amino terminus, which is a feature of mucin-like domains. Several of the serines and threonines are predicted be modified by the addition of O-linked glycans. Some of the glycoproteins are predicted to have an additional mucin-like domain located prior to the transmembrane anchor, whereas others are predicted to have a stem consisting of two alpha-helices. The flavivirus envelope protein and Jingmen flavi-virus glycoproteins may have diverged from a common class II precursor glycoprotein with a mucin-like domain or domains acquired after divergence.

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.

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.

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.

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.

Glucagon-like peptide-1 (GLP-1) is an incretin hormone that acts on the pancreas to increase insulin and decrease glucagon secretion. Albuminuria is the pivotal marker for diabetic nephropathy, which is the leading microvascular complication of diabetes mellitus. Most of the previous studies mainly focused on the clinical trials of the therapeutic renal protective effect after administration of GLP-1 receptor agonists (GLP-1 RAs), not prior to administration. Therefore, this study aimed to investigate the association between fasting plasma GLP-1 levels and albuminuria before GLP-1 RA administration. A cross-sectional study was designed to evaluate the association between fasting plasma GLP-1 levels and albuminuria in patients with type 2 diabetes mellitus (T2DM). A total of 68 T2DM participants were analyzed from the data at Wonkwang University Hospital in Iksan, Korea. Logistic regression analysis was performed to evaluate the odds ratio (OR) and 95% confidence interval (CI) of the incidence of albuminuria between the GLP-1 low group (Group L) and the high group (Group H). The OR (95% CI) for the incidence of albuminuria comparing the Group L with the Group H of fasting plasma GLP-1 levels was 3.410 (1.160-10.023), p = 0.026 after adjustment for age, gender, fasting plasma glucose, HbA1c, C-peptide, the use of ACE inhibitors or ARBs, SGLT-2 inhibitors, and DPP-4 inhibitors. As a continuous variable, the OR (95% CI) for the incidence of albuminuria associated with the reduction of each 1 pmol/L in fasting plasma GLP-1 levels was 1.673 (1.173-1.870), p = 0.018, after full adjustment. This study showed the negative association between fasting plasma GLP-1 levels and the incidence of albuminuria in Korean patients with T2DM, before GLP-1 RA administration. This result suggests that endogenous GLP-1 may play a role in attenuating albuminuria.

The red fox, found on all continents except Antarctica, occupies diverse habitats. In Croatia it is an indigenous wild species with a population density of 0.7 animals per square kilometer. While tumors in wild animals from the Canidae family are scarce, the true prevalence and diversity of tumors are likely underestimated due to limited research. So far, a limited number of tumors were observed among the red fox population, either in their natural habitat or in captivity. As part of the National rabies control program, we examined 1,890 red fox carcasses over a four-year period. Our focus was on identifying abnormalities on the skin and internal organs that suggest potential neoplastic proliferation. Five red foxes, three males and two females, were found to have growths resembling potential tumors. Their age distribution spanned from 2 to 7 years. Microscopic investigation revealed two collagenous hamartomas, two Meibomian gland adenomas and one intra-abdominal teratoma within a cryptorchid testis. This retrospective study aims to provide a comprehensive description of tumor and tumor-like lesions observed in free-range red foxes from Croatia, marking the first research of its kind in Croatia.

The effect of ferromagnetic CaMnO3 (CMO) addition on structural, magnetic, dielectric, and ferroelec-tric properties of BiFeO3 are presented. X ray diffraction and Raman investigation allowed the identifi-cation of single pseudo cubic perovskite structure. The magnetic measurement showed that the pre-pared films exhibit a ferromagnetic behavior at low temperature with both coercive field and remnant magnetization increased with increasing CMO content. However, a deterioration of magnetization was observed at room temperature. Ferroelectric study revealed an antiferroelectric like behavior with a pinched P-E hysteresis loop for 5%CMO doping BFO resulting in low remnant polarization and double hysteresis loops. Whereas, high remnant polarization and coercive field with a likely square hysteresis loop are obtained for 10 % CMO addition. Furthermore, a bipolar resistive switching behavior with a threshold voltage of about 1.8V is observed for high doped film that can be linked to the ferroelectric polarization switching.

The Burkholderia cepacia complex (Bcc) are a group of increasingly multi-drug resistant opportunistic bacteria that can cause severe pulmonary infections. This resistance is driven through a combination of intrinsic factors and the carriage of a broad range of conjugative plasmids harbouring virulence determinants. Therefore, novel treatments are required to not only treat Bcc infection but also to prevent further spread of these virulence determinants. In the search for phages infective for two clinical Bcc isolates, CSP1 phage, a PRD1-like phage was isolated. CSP1 phage was found to require pilus machinery commonly encoded on conjugative plasmids to facilitate infection of multiple Gram-negative bacteria genera including Escherichia and Pseudomonas. Whole genome sequencing and characterisation of one of the clinical Burkholderia isolates revealed it to be Burkholderia contaminans. B. contaminans 5080 was found to contain a genome of over 8 Mbp encoding multiple intrinsic resistance factors, such as efflux pump systems, but more interestingly, carried three novel plasmids encoding multiple putative virulence factors for increased host fitness, including antimicrobial resistance. Even though PRD1-like phages are broad host range, their use in novel antimicrobial treatments shouldn’t be dismissed, as the dissemination potential of conjugative plasmids is extensive. Continued survey of clinical bacterial strains is

In the chronic phase of chikungunya virus (CHIKV) infection, excessive inflammation manifests as incapacitating joint pain and prolonged arthritis. Arthritis resulted from a large influx of infiltrating immune cells driven by pro-inflammatory cytokines and chemokines originating from the toll-like receptor (TLR)-mediated innate antiviral response. This study investigated fisetin's ability to modulate TLR-mediated antiviral responses against CHIKV in Huh7 cells. The CHIKV inhibitory potential of fisetin was assessed by plaque-forming unit assay, virus yield reduction assay, and bright-field microscopy (cytopathic effect, immunofluorescence). Fisetin’s modulatory potential on TLR-mediated antiviral response was evaluated by immunofluorescence assay (expression of TLR proteins), qRT-PCR (mRNA level of antiviral genes), human cytokine array, and immunoblotting of key transcription factors. The present study showed fisetin induced the expression of the antiviral genes at an early time-point by promoting the phosphorylation of IRF3 and IRF7. Fisetin reduced excessive inflammatory cytokine responses in CHIKV-infected Huh7 cells by impeding the over-phosphorylation of NF-κB. Fisetin also reduced CHIKV-induced cytopathic effects in CHIKV-infected Huh7 cells. Altogether, our study suggests that fisetin modulates TLR-mediated antiviral responses by affecting the CHIKV-induced inflammatory responses.

A half-harmonic oscillator, which gets its name because the coordinate is strictly positive, has been quantized and determined that it was a physically correct quantization. This positive result was found using affine quantization (AQ). The main purpose of this paper is to compare results of this new quantization procedure with those of canonical quantization (CQ). Using Ashtekar-like classical variables and CQ, we quantize the same toy model. While these two quantizations lead to different results, they both would reduce to the same classical Hamiltonian if $\hbar\rightarrow0$. Since these two quantizations have differing results, only one of the quantizations can be physically correct. Two brief sections illustrate how AQ can correctly help quantum gravity and the quantization of most field theory problems.

The measurements of the proton electromagnetic form factors in the time-like region using the initial state radiation technique are reviewed. Recent experimental studies have shown that initial state radiation processes at high luminosity electron-positron colliders can be effectively used to probe the electromagnetic structure of hadrons. The BABAR experiment at the B-factory PEP-II in Stanford and the BESIII experiment at the $\tau$-charm factory BEPC-II in Beijing have measured the time-like form factors of the proton using the initial state radiation process $e^{+}e^{-}\to pbar{p}\gamma$. The two kinematical regions where the photon is emitted from the initial state at small and large polar angles have been investigated. In the first case the photon is in the region not covered by the detector acceptance and is not detected. The Born cross section and the proton effective form factor have been measured over a wide and continuous range of the the momentum transfer squared $q^2$ from threshold up to ~42 (GeV/c)$^2$. The ratio of electric and magnetic form factors of the proton has been also determined. In this report, the theoretical aspect and the experimental studies of the initial state radiation process $e^{+}e^{-}\to p\bar{p}\gamma$ are described. The measurements of the Born cross section and the proton form factors obtained in these analyses near the threshold region and in the relatively large $q^2$ region are examined. The experimental results are compared to the predictions from theory and models. Their impact on our understanding of the nucleon structure is discussed.

There is a relationship between systemic sarcoidosis (SS) and malignancy. Sarcoidosis results from an exaggerated immune response in genetically susceptible individuals. In oncologic pa-tients with sarcoidosis, tumoral antigens and antineoplastic treatment are considered potential triggering factors. The observation of a patient with granulomas in a parotid carcinoma that later developed SS led us to review previous tumors of patients with SS. The aim of the study is to see if granulomas were already present in the tumors that preceded sarcoidosis. We identified 196 sarcoidosis patients, 47 of which had a previous tumor. We were able to review 29 cases 12 of which showed tumor associated granulomas (TAGs)(41.4%). This ratio is much higher than that of the normal population. We analyzed five control patients without sarcoidosis for each tumor. In conclusion, we have observed TAGs in patients who later develop SS. In our series, the ratio of TAGs in patients with SS was significantly greater than in the normal population. To prove our findings, we suggest reviewing the history of patients with SS in search of previous tumors. The histology of such tumors should be reviewed in an attempt to identify granulomas.

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.

The ongoing pandemic of the 2019 novel coronavirus disease (COVID-19) raises a global health crisis, which has resulted in 75,778 confirmed cases with 2130 deaths in China and beyond. Atypical symptom renders it challenging to earlier recognize the 2019-nCoV carrier with the potential ability of equivalent transmission. Therefore, it is needed to gain full spectrum of COVID-19. Here we report clustered COVID-19 cases of person-to-person transmission. The symptoms of typical pneumonia are shared by the two familial members, namely son (Patient 1) and father (Patient 2). Unexpectedly, an influenza-like illness (ILI) is also caused in Patient 3 having close contact with Patient 1 at personal dinner party. Combined with clinical and epidemiological study, chest computed tomography (CT) and molecular diagnosis demonstrate that all the three cases tested positive for COVID-19 with distinct symptoms by human-to-human transmission. To the best of knowledge, it closes in part (if not all), a missing gap of clinical repertoires of COVID-19 outbreaks and underlines the possibility that neglection of cryptic/asymptomatic/mild cold-like syndromes gives biased screen in the earlier stage of COVID-19 cases.

Viruses are the most abundant biological entities in aquatic ecosystems and harbor an enormous genetic diversity. While their great influence on the marine ecosystems is widely acknowledged, current information about their diversity remains scarce. A viral metagenomic analysis of three water samples was conducted from sites on the South Scotia Ridge (SSR) near the Antarctic Peninsula, during the austral summer 2016. The taxonomic composition and diversity of the viral communities were investigated and a functional assessment of the sequences was determined. Phylotypic analysis showed that most viruses belonging to the order Caudovirales, especially the family Podoviridae (41.92-48.7%), similar to the viromes from the Pacific Ocean. Functional analysis revealed a relatively high frequency of phage-associated and metabolism genes. Phylogenetic analyses of phage TerL and Capsid_NCLDV (nucleocytoplasmic large DNA viruses) marker genes indicated that many sequences associated with Caudovirales and NCLDV were novel and distinct from known phage genomes. High Phaeocystis globosa virus virophage (Pgvv) signatures were found in SSR area and complete and partial Pgvv-like were obtained which may have an influence on host-virus interactions. Our study expands the existing knowledge of viral communities and their diversities from the Antarctic region and provides basic data for further exploring polar microbiomes.

Semiconductor materials have been shown to have better photocatalytic behavior and can be utilized for the photodegradation of organic pollutants. In this work, three-dimensional flower-like SnS₂ were synthesized by a facile hydrothermal method. Core-shell structured SiO₂@α-Fe₂O₃ nanocomposites were then deposited on the top of the SnS₂ flowers. The as-synthesized nanocomposites were characterized by X-ray diffraction (XRD), Raman spectroscopy, field emission scanning electron microscopy (FE-SEM), Brunauer-Emmett-Teller (BET) and photoluminescence spectroscopy (PL). The photocatalytic behavior of the SnS₂-SiO₂@α-Fe₂O₃ nanocomposites was observed by observing the degradation of methylene blue (MB). The results show an effective enhancement of photocatalytic activity for the degradation of MB especially for the 15 wt. % SiO₂@α-Fe₂O₃ nanocomposites on SnS₂ flowers.

Background: Benveniste’s biology experiments suggested the existence of molecular-like effects without molecules (“memory of water”). In this article, it is proposed that these disputed experiments could have been the consequence of a previously unnoticed and non-conventional experimenter effect. Methods: A probabilistic modelling is built in order to describe an elementary laboratory experiment. A biological system is modelled with two possible states (“resting” and “activated”) and exposed to two experimental conditions labelled “control” and “test”, but both biologically inactive. The modelling takes into account not only the biological system, but also the experimenters. In addition, an outsider standpoint is adopted to describe the experimental situation. Results: A classical approach suggests that, after experiment completion, the “control” and “test” labels of biologically-inactive conditions should be both associated with “resting” state (i.e. no significant relationship between labels and system states). However, if the fluctuations of the biological system are also considered, a quantum-like relationship emerges and connects labels and system states (analogous to a biological “effect” without molecules). Conclusions: No hypotheses about water properties or other exotic explanations are needed to describe Benveniste’s experiments, including their unusual features. This modelling could be extended to other experimental situations in biology, medicine and psychology.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

Anastomosing rivers are wetland systems with multiple channels, and there are many constraints limiting in-situ observations of their hydrodynamics. Therefore, there are few reports on the hydrodynamic data of such rivers. In order to obtain the hydrodynamic parameters of the river channels without hydrological observations in the anastomosing rivers, this study proposes an expression method namely expression of channel morphological parameters (ECMP) for hydrodynamic calculations. The calculation formula of the ECMP method based on the shape factor, scale factor, and gradient factor of the flow cross-section as independent variables. This method can be used to calculate the average velocity, discharge, specific stream power, gross stream power and other hydrodynamic parameters of the flow cross-section at different water levels, only requiring the measurement of morphological parameters such as the average depth, width-depth ratio, and gradient of the flow (channel) cross-section. The applicability of the ECMP method was verified using measured hydrological data. The results show that the ECMP method is a practical calculation method with high computational accuracy and convenient application for calculating river hydrodynamic parameters. It has great application value in study on fluvial geomorphology and hydrodynamics, as well as in the reconstruction of ancient river hydrology and hydrodynamics.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

Human disturbances can have severe environmental impacts on freshwater ecosystems. The main aim of this study was to detect the influences of physiochemical variables, land-cover characteristics, and river connectivity on fish assemblages in Lake Chaohu Basin, China. A cluster analysis of the river connectivity variables identified four groups of sites that characterized by significantly different connectivity gradients in local scale. These four groups of sites showed increasing connectivity from the upper reaches to the lower reaches. At the same time, among the four groups, the values of the environmental variables generally increased from the upper reaches with less human activity towards the lower reaches with more human activities. For instance, some main physiochemical variables (e.g., river width, water depth, nitrate, phosphate) significantly increased among the four groups. In contrast, fish taxa richness and diversity indices were not significantly different among the four connectivity groups. However, fish assemblages showed significant variation among the connectivity groups (p=0.026). In addition, the study determined that upper riparian land uses (e.g., woodland and grassland), flow velocity, and elevation were environmental variables regulating the variance in the fish communities, and for the connectivity variables, only river order and number of branches along a path to the left of the main stem affected the variance in the fish communities. Therefore, new practices aimed at maintaining and even increasing the riparian canopy coverage and the flow velocity of rivers should be integrated into local conservation planning for freshwater ecosystems, especially in the upper reaches of the basin.

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.

Fleets of drones have attracted a lot of attention from the research community in recent years. One of the biggest challenges in deploying such systems is localization. While GNSS localization systems can only be effective in open outdoor environments, new solutions based on radio sensors (e.g., ultra-wideband) are increasingly being used for localization in various situations and environments. However, self-localization without prior knowledge of anchor positions remains an open problem which, for example, makes it impossible to track a moving target. In this article, we provide a comparison of different variants of gradient descent-based algorithms, with a new improved variant, for solving the localization problem using relative distance measurements and multilateration. It is applied to self-localization of anchors and tracking targets using ultra-wideband distance sensors. A realistic simulation of drone tracking and anchors’ localization is performed to demonstrate the robustness and accuracy of the proposed approach.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.