To verify the concept of the metasurfaced reverberation chamber (MRC) which was proposed. This paper reports on measurements in the reverberation chamber (RC) performance by using a 1-bit random coding metasurfaced stirrer. The measurement results are validated and compared with that from two mechanical stirrers (horizontal stirrer and vertical stirrer). Figures of merit such as quality factor (Q factor), number of samples, standard deviation, angle autocorrelation, average K-factor, total scattering cross section (TSCS) and the enhanced back scattering coefficient (eb) are presented. Results indicate the feasibility of the MRC technique in the operation frequency of the RC. And it is possible to enlarge the test volume of the RC by using the 1-bit random coding metasurface stirrer.

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimum number of the climate clusters. The k-mean clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, and rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. The study has also provides a methodological framework that can be used in the other river basins of Ethiopia and indeed elsewhere.

The utilization of tempered blast furnace (BF) slag through the direct fiber forming process to create high-value thermal insulation materials offers a dual benefit: it efficiently harnesses the latent heat within unused slag and substantially enhances the value of blast-furnace slag utilization. However, gauging the melting properties of iron slag under high temperatures is a challenge. In this study, we explore the melting behavior of SiO2 within a high-temperature molten pool. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device (CCD) video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines SiO2's centroid position. Given that SiO2 is proportionate to crucible pixels, the area of SiO2 is calculated through pixel statistics within these contours. Subsequently, we propose a new indirect method to process image information, yielding SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, we compare this indirect method with shape from shading (SFS), quantitative characterization, and dimensional analysis techniques. We also discuss the strengths and limitations of each method. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements. This research provides theoretical and technical support for blast-furnace slag's direct fiber forming process.

Increasing evidence points to the role of endogenous retroviruses (ERVs) in driving cancer cell proliferation. The purpose of this study was to explore the possibility of repurposing antiretroviral agents to inhibit ERVs as a new approach in cancer treatment. We found that an integrase strand-transfer inhibitor, dolutegravir (DTG), effectively inhibited the proliferation of multiple cancer cell lines and its antiproliferative potency was positively correlated with the expression levels of the human endogenous retrovirus type K (HERV-K). DTG inhibited the expression of HERV-K in multiple human cancer cell lines and the mouse mammary tumor virus (MMTV) in the murine 4T1 mammary cancer cell line. We chose the fast-growing BT-20 cell line as a model to study the in vitro antiproliferative mechanisms of DTG. BT-20 cells overexpressing both HERV-K env and pol genes became more resistant to DTG than cells transduced with vector alone. Knockdown of HERV-K also increased DTG resistance of BT-20 cells. The antiproliferative effect of DTG correlated with enhanced expression of E-cadherin and reduction in cell motility and invasiveness. Surprisingly, DTG stimulated expression of the env gene of MMTV in vivo and promoted metastasis of 4T1 tumor cells to the lungs. Taken together, our data support a role of ERVs in tumor development and encourage further search for antiretroviral agents to treat malignancies in which endogenous retroviruses are active.

The impact of nutrients on the environment, particularly on water bodies, has led to extensive studies for nutrient control. Within this context, studies have been focused on source separation of human urine from domestic wastewater to recover nutrients. Potassium is one of the most important components of human urine. However, data on potassium removal or recovery are quite limited except for some indirect information through use of zeolites for mostly ammonia removal. Potassium struvite or K-struvite (MgKPO₄·6H₂O) is a sparingly soluble salt belonging to struvite and has the potential of being used as a means of potassium and phosphate recovery from segregated human urine. This study aimed to assess the potential of K-struvite precipitation for control and recovery of nutrients. Within this context, K-struvite precipitation experiments were performed on both synthetically prepared samples and synthetic human urine solution to determine effect of operation parameters i.e. pH, stoichiometry, and temperature on potassium recovery performance. Results indicated that process performance as well as type of solid phases co-precipitated with K-struvite were closely related to initial potassium concentration, pH and reaction stoichiometry. At pH 10, the potassium recovery efficiency was maximized up to 87% by application of 100% excess dose of Mg and P for both synthetic samples and synthetic human urine solution. On the other hand, application of excess dose of K did not provide any improvement in K recovery efficiency. The effect of temperature on solubility of K-struvite was insignificant at the temperature of 24-90°C. Solid phase analyses confirmed that K-struvite was co-precipitated with either Mg₃(PO₄)₂, MgNaPO₄·7H₂O, or MgHPO₄·7H₂O depending on pH and stoichiometry instead of a pure compound.

The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

Background: Maternal diabetes mellitus (MDM) is associated with increased risks for adverse neonatal outcomes. However, the impact of MDM on neonatal outcomes in Bisha, a city in Saudi Arabia, is not well-documented. This study aims to investigate the impact of MDM on neonatal outcomes in the Maternity and Children’s Hospital (MCH), Bisha, Saudi Arabia. Methods: A retrospective cohort study was conducted on 181 pregnant women with diabetes and their neonates diagnosed at Maternity and Children’s Hospital (MCH), Bisha, Saudi Arabia, between October 5, 2020, and November 5, 2022. The primary outcome was a composite of adverse neonatal outcomes, including stillbirth, neonatal death, macrosomia, preterm birth, respiratory distress syndrome, hypoglycemia, and congenital anomalies. Logistic regression analyses were used to adjust for potential confounders. Results: The total sample size was 181. The average age of patients was 34 years (SD=6.45). The majority of the patients were diagnosed with GDM, 147 (81.2%), and pre-GDM, 34 (18.8%). Neonates born to mothers with MDM had a higher risk of adverse neonatal outcomes compared to those born to mothers without MDM (adjusted odds ratio [aOR] = 1.46, 95% confidence interval [CI]: 1.25-1.70). The risks of macrosomia (aOR = 1.74, 95% CI: 1.38-2.19), LBW (aOR = 1.32, 95% CI: 1.06-1.66), and RDS (aOR = 1.57, 95% CI: 1.28-1.93) were significantly higher among neonates born to mothers with MDM. Types of DM were statistically significant with some neonatal outcomes: hypoglycemia (p-value = 0.017), macrosomia (p-value = 0.050), and neonatal death (p-value = 0.017). Conclusion: MDM is associated with an increased risk of adverse neonatal outcomes in Bisha. Early identification and management of MDM may improve neonatal outcomes and reduce the burden of neonatal morbidity and mortality in this population.

We provide an axiomatic base for the set of natural numbers, that has been proposed as a canonical construction, and use this definition of $\mathbb N$ to find several results on finite group theory. Every finite group $G$, is well represented with a natural number $N_G$; if $N_G=N_H$ then $H,G$ are in the same isomorphism class. We have a linear order, on the quotient space of isomorphism classes of finite groups, that is well behaved with respect to cardinality. If $H,G$ are two finite groups such that $|H|=m<n=|G|$, then $H<\mathbb Z_n\leq G\leq\mathbb Z_{p_1}^{n_1}\oplus\mathbb Z_{p_2}^{n_2}\oplus\cdots\oplus\mathbb Z_{p_k}^{n_k}$ where $n=p_1^{n_1}p_2^{n_2}\cdots p_{k}^{n_k}$ is the prime factorization of $n$. We find a canonical order for the objects of $G$ and define equivalent objects of $G$, thus finding the automorphisms of $G$. The Cayley table of $G$ takes canonical block form, and we are provided with a minimal set of independent equations that define the group. We show how to find all groups of order $n$, and order them. We give examples using all groups with order smaller than $10$, and we find the canonical block form of the symmetry group $\Delta_4$. In the next section, we extend our results to the infinite case, which defines a real number as an infinite set of natural numbers. A real function is a set of real numbers, and a sequence of real functions $f_1,f_2,\ldots$ is well represented by a set of real numbers, as well. In general, we represent mathematical objects using the smallest possible data-type. In the last section, mathematical objects are well assigned to tree structures. We conclude with brief comments on type theory and future work on computational aspects of these representations.

In this paper, a new class of q-Hermite based Frobenius type Eulerian polynomials is introduced by means of generating function and series representation. Several fundamental formulas and recurrence relations for these polynomials are derived via different generating methods. Furthermore, diverse correlations including the q-Apostol-Bernoulli polynomials, the q-Apostol-Euler poynoomials, the q-Apostol-Genocchi polynomials and the q-Stirling numbers of the second kind are also established by means of the their generating functions.

We introduce a new kind of extended Hermite-based Frobenius type Eulerian polynomials and then derive diverse explicit and implicit summation equations including some symmetric formulas by utilizing series manupulation method. Multifarious summation formulas and identities given earlier for some well known polynomials such as Eulerian polynomials and Frobenius type Eulerian polynomials are generalized.

Obesity, type 2 diabetes, and different forms of cancers are among the leading human diseases and highly complex in terms of diagnostic and therapeutic approaches. Diabetes and cancer are among the most frequent and complex diseases and based on epidemiological evidence and study it can be concluded that the patients suffering from diabetes are considered to be significantly at higher risk for a number of cancer types. Both these diseases are among the highly complex and heterogeneous in nature. There are a number of evidences which support the hypothesis that these diseases interlinked and obesity may aggravate the risk(s) of both these diseases type 2 diabetes and different types of cancers. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major source which promotes a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutic. With so many known challenges, it is critical to understand the relationships and the common shared causes between type 2 diabetes and cancer which is difficult to unravel and understand. Furthermore, the real complexity arises during diagnosis from contended corroborations that specific drug(s) (individually or in combination) during diagnosis process of type 2 diabetes may increase or decrease the cancer risk or affect cancer prognosis. In this review article, we have presented the recent and most updated evidences from the studies where the origin, biological background, correlation between them have been presented or proved. Furthermore, we have summarized the methodological challenges and tasks that are frequently encountered. we have also outlined the physiological links between type 2 diabetes and cancers. Finally, we have presented and summarized the outline of the hallmarks for both these diseases diabetes and cancer.

Positive-sense single-stranded RNA (+ssRNA) viruses comprise many (re-)emerging human pathogens that pose a public health problem. Our innate immune system and in particular the interferon response form the important first line of defense against these viruses. Given their genetic flexibility, these viruses have therefore developed multiple strategies to evade the innate immune response in order to optimize their replication capacity. Already many molecular mechanisms of innate immune evasion by +ssRNA viruses have been identified. However, research addressing the effect of host innate immune evasion on the pathology caused by the viral infection is less prevalent in literature, though very relevant and interesting. Since interferons have been implicated in inflammatory diseases and immunopathology in addition to their protective role in infection, the influence of antagonizing the immune response may have an ambiguous effect on the clinical outcome of the viral disease. Therefore, this review discusses what is currently known about the role of interferons and host immune evasion in the pathogenesis of emerging viruses belonging to the coronaviruses, alphaviruses and flaviviruses.

This study delves into the impact of artificial intelligence on students’ learning.The paper pioneers the use of kmeans classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior research.Firstly, a random forest model is established with “whether AI assistance was used to complete the paper” as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students’ use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks.

One of the most used techniques for reducing drag on blunt bodies when they move at speeds above the speed of sound are spikes. An important number of numerical works based on CFD have highlighted the fluid dynamics behind its use, as well as its advantages. However, most of docu-mentation focuses mainly on modified spike physical properties, while keeping constant supersonic or hypersonic flow conditions. In this sense, it is necessary to resort to current numerical techniques as machine learning, which can be obtain the most appropriate technical solution in compared to the costly iterative simulations that can delay the rapid development and testing of new aerody-namic configurations. This investigation postulates a specific hypothesis: that the K-Nearest Neighbours algorithm, renowned for its simplicity and effectiveness in classification problems, can be extrapolated as a robust tool for predicting drag reduction on spike blunt bodies. A wide range of Mach number and aspect ratios between blunt body diameter, spike length, and spike diameter are used for the algorithm dataset. The algorithm presents good predictions of the coefficient with errors of less than 5% percent, applying a multivariable regression method. Even, in the vicinity of the hypersonic speed zone, with a minimum of 3 neighbour nodes. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties in a body moving near or above the speed of sound.

Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

Predictions of mean values of statistical variables of large scale turbulent flow by the widely used basic k-ϵ model and a new fundamentally based model are verified against published results of Direct Numerical Simulations DNS of the Navier-Stokes equations. The verification concerns turbulent channel flow at shear Reynolds numbers of 950, 2000 and 104. The basic k-ϵ model is largely based on empirical formulations accompanied by calibration constants. This contrasts with the new model where descriptions of leading statistical quantities are based on the general principles of statistical turbulence at large Reynolds number. Predicted values of major output variables such as turbulent viscosity, diffusivity of passive admixture, temperature, and fluid velocities compare well with DNS in case of the new model. Significant differences are seen in case of the basic k-ϵ model.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

The Covid-19 crisis has been one of the most relevant economic shocks in the history of the modern economy, mainly due to the restrictions on travel and social interaction taken by the authorities (quarantines, closure of schools, artistic activities, bars, and restaurants, capacity in stores), to reduce the transmission of the virus (Sars-Cov2), having consequences on the national productions of all countries (about 7% for the loss of GDP worldwide). In our study, we analyze, through clustering methods, the effects of the level of contagion, economic projections, and economic mitigation measures taken by governments for managing the pandemic. According to the analyses, it can be concluded that a low infection rate implies a lower loss of product and economic savings when applying economic mitigation measures, taking relevance in the management of sanitary measures to reduce the transmission of the virus (and the temporality of these).

This article is a study on the (k,s)-Riemann-Liouville fractional integral, a generalization of the Riemann-Liouville fractional integral. Firstly, we introduce several properties of the extended integral of continuous functions. Furthermore, we make the estimation of the Box dimension of the graph of continuous functions after the extended integral. It presents that the upper Box dimension of the (k,s)-Riemann-Liouville fractional integral for any continuous functions is no more than the upper Box dimension of the functions on the unit interval I=[0,1], which indicates that the upper Box dimension of the integrand f(x) will not be increased by the σ-order (k,s)-Riemann-Liouville fractional integral ksD−σf(x) where σ&gt;0 on I. Additionally, we prove that the fractal dimension of ksD−σf(x) of one-dimensional continuous functions f(x) is still one.

In this study, based on frost formation data provided by Harbin Meteorological Bureau, combined with geographical factors, temperature and population density, linear fitting, Mann-Kendall mutation test, pettitt method and sliding T test were used to analyze the spatio-temporal variation characteristics of frost date in Harbin Municipality and the effects of geographical factors, temperature and population density on frost regularity. The results showed that (1) The earliest year of FSD appeared was 1966 and 1967, on the 255th, i.e., August 18th, and the latest was 283 days, i.e., October 10th, 2006. The earliest year in which the FED appeared was April 24th of 2015, the 114th day of that year, and the latest was April 21st of 1974, the 141st day of that year. The year with the most frost was 2012, with 161 days, and the year with the shortest was 1966, with 123 days. (2) Throughout the study period, FSD increased by 7.8 days at a rate of -1.27d/10a, FED increased by 10.9 days at a rate of 1.77d/10a, and FFS increased by 18.9 days at a rate of 3.05d/10a. The tendency rate of FSD and FFS at each site in Harbin showed an increasing trend. For FED, the tendency rate of some sites showed an increasing trend. In general, FSD showed a delayed trend, FED showed an advanced trend, and FFS showed a prolonged trend. Using the method of unary linear regression, FSD of each site showed an increasing trend, FFS showed an increasing trend, and FED showed a decreasing trend in general. (3) Throughout the study period, FSD was mutated in 2000, and the arrival time of it in the study area averaged the 265th, i.e., September 22nd, and after that, the arrival time of it changed to the 272nd, i.e., September 29th of that year. FED was mutated in 2006, and the arrival time of it in the study area averaged 128th, i.e., April 8th. After the mutation, the arrival time of FED changed to 121st, i.e., April 1st of that year. FFS was mutated in 2004. Before the mutation, with an average arrival time of 137days in the study area .But after that, the time changed to 150 days.(4) FSD and FFS in Harbin Municipality were negatively correlated with latitude and positively correlated with temperature, while FED was positively correlated with latitude and negatively correlated with temperature. The FSD, FED and FFS in the central part of Harbin Municipality were the earliest, the latest and the longest, respectively, so the Pearson correlation coefficient method and the influence of longitude could not be reflected in the multiple regression. Several factors were judged by grey correlation analysis method. The longitude is significantly related to FSD, FED and FFS.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

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.

The membrane potential or resting potential of the neuron has been the subject of many studies. Although this theory explains the generation and maintenance of the membrane potential by direct or even facilitated diffusion, there are too many contradictions to doubt that these forces are sufficient or even at work in a process whose initial conditions are of rare complexity. The aim of this article is to show that already in the past, a competing theory has been developed whose hypothesis seems more scientifically sound. To confirm this last theory, Hirohisa Tamagawa carried out an experiment of great simplicity which makes it possible to invalidate the current theory and to question the teaching and the knowledge in Biology and Biophysics.

The generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Following the principles of chemistry, electrostatics and geometry, it becomes clear that ion channels cannot function in this way. The ions channels cannot by their location have both a filter function and be ion concentrators, and the Na/K pump by its position in the membrane and by the proposed assumptions is not able to perform its regulatory function. The current model must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena opening new research perspectives.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

There are no studies that quantitatively compare life histories among scorpion species. Statistical procedures applied to 94 scorpion species indicate that those with larger bodies do not necessarily have larger litters or longer life cycles, opposite to some theoretical predictions.

General characteristics of K changes together with their fine structure associated with ground flashes in Sri Lanka in the tropics are presented. It is found that on average there are about 2 K changes associated with each return stroke. Analysis of the fine structure of the K changes shows that the K change is a chaotic pulse burst. Some of these chaotic pulse bursts start and the others end as a regular pulse bursts. Sometimes the chaotic part occurs in between two regular pulse bursts. This is in agreement with the recent published results that claim that chaotic pulse bursts are a random superposition of regular pulse bursts. The results show that the small step fields identified in the literature as K changes are the static fields associated with these pulse bursts.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

One of the goals of modern orcharding is to have a high production of fruits that are uniform in size, organoleptic parameters, and health characteristics. The aim of the work was to study the assortment of different shoot types and their prevailing position along the cane and how shoot type can contribute to on shoot type on fruit quality in Actinidia tree. The experiment was carried out over two years in a commercial orchard of Actinidia chinensis, cv Gold 3. Along the cane, the shoots were classified as Spur shoot (SP), terminate shoot (TE), no terminate shoot (NT), and cut no terminate shoot (CNT). The data were statistically processed using ANOVA and Principal Component Analysis (ACP). Four different fruit categories were obtained from to the four shoot type to compared: a prevailing category (55% of total fruits), which is characterised by its higher soluble solids content, sweetness, and excellent health characteristics, but with a lower hardness of the pulp, which would lead to the hypothesis that harvesting could be brought forward; a second category of fruit (19% of total fruit) with the lowest soluble solids content, but a high antioxidant capacity detached from CNT shoot; another category of fruit (13% of total fruit) produced from NT shoots, with the lowest health value, but good sweetness perception; finally the category with the lowest fruit percentage over total fruit harvested (10%) from SP shoots, characterised by its smaller size. It is to be verified what the performance of each category will be after post-harvest to speculate whether it is possible to differentiate the product at harvest into quality categories or possibly to differentiate the harvest time.

Glycogen storage disease type III (GSDIII) is a hereditary glycogenosis caused by deficiency of the glycogen debranching enzyme (GDE), an enzyme, encoded by Agl, enabling glycogen degradation by catalyzing alpha-1,4-oligosaccharide side chain transfer and alpha-1,6-glucose cleavage. GDE deficiency causes accumulation of phosphorylase-limited dextrin, leading to liver disorder followed by fatal myopathy. We tested here the capacity of the new autophagosomal activator GHF-201 to alleviate disease burden by clearing pathogenic glycogen surcharge in the GSDIII mouse model Agl-/-. We used open field, grip strength and rotarod tests for evaluating GHF-201’s effects on locomotion, biochemistry panel to quantify hematological biomarkers, indirect calorimetry to quantify in vivo metabolism, transmission electron microscopy to quantify glycogen in muscle, and fibroblast image analysis to determine cellular features affected by GHF-201. GHF-201 was able to improve all locomotion parameters and partially reversed hypoglycemia, hyperlipidemia and liver and muscle malfunction in Agl-/- mice. Treated mice burnt carbohydrates more efficiently and showed significant improvement of aberrant ultrastructural muscle features. In GSDIII patient fibroblasts, GHF-201 restored mitochondrial membrane polarization and corrected lysosomal swelling. In conclusion, GHF-201 is a viable candidate for treating GSDIII as it recovered a wide range of its pathologies in vivo, in vitro, and ex vivo.

Type 2 diabetes (T2D) is a chronic systemic disease with a complex etiology, characterized by insulin resistance and mitochondrial dysfunction in various cell tissues. To explore this relationship, we conducted a secondary analysis of complete mtDNA sequences from 1,261 T2D patients and 1,105 control individuals. Our findings revealed significant associations between certain single nucleotide polymorphisms (SNPs) and T2D. Notably, the variants m.1438A>G (controls: 32 [27.6%], T2D: 84 [72.4%]; OR: 2.46; 95%CI: 1.64-3.78; p<0.001), m.14766C>T (controls: 498 [36.9%], T2D: 853 [63.1%]; OR: 2.57, 95%CI: 2.18-3.04, p<0.001), and m.16519T>C (controls: 363 [43.4%], T2D: 474 [56.6%]; OR: 1.24, 95%CI: 1.05-1.47, p=0.012) were significantly associated with diabetes probability. The variant m.16189T>C Previously reported in multiple studies in different populations, it was not found to be associated with T2D in our analysis (controls: 148 [13.39] T2D: 171 [13.56%]; OR: 1.03; 95%CI: 0.815-1.31,; p=0.83). These results provide evidence suggesting a link between some mtDNA polymorphisms and T2D, probably related to association rules and topological patterns and three-dimensional confirmation associated to regions where the changes are, rather than to point mutations in the sequence.

Osteoporosis is a common metabolic bone disease in patients with diabetes, which can develop simultane-ously with Type 2 diabetes (T2D) in postmenopausal women. Bisphosphonate (BP) is administered to pa-tients with both the conditions and may cause medication-related osteonecrosis of the jaws (MRONJ). It affects the differentiation and function of osteoclasts as well as thickness of cortical bone through bone mineralization. Therefore, this study aimed to investigate the effects of T2D on osteoclast differentiation and activity as well as cortical bone formation in postmenopausal patients with MRONJ. Tissue samples were collected from 10 patients diagnosed with T2D and Stage III MRONJ in the experimental group and from 10 patients without T2D in the control group. Histological examination was conducted, and expres-sion of dendritic cell-specific transmembrane protein (DC-STAMP) and tartrate resistant acid phosphatase (TRAP) was assessed. Cortical bone formation was analyzed using CBCT images. The number of TRAP-positive osteoclasts and DC-STAMP-positive mononuclear cells were significantly less in the experi-mental group (p < 0.05). Furthermore, the thickness and ratio of cortical bone were significantly greater in the experimental group (p < 0.05). In conclusion, T2D decreased the differentiation and function of osteo-clasts, and increased cortical bone formation in postmenopausal patients with MRONJ.

In this work, we report the carbon fiber-based wire-type asymmetric supercapacitors (ASCs). The highly conductive carbon fibers were prepared by the carbonized and graphitized process using the polyimide (PI) as a carbon fiber precursor. To assemble the ASC device, the CoMnO2-coated and Fe2O3-coated carbon fibers were used as the cathode and the anode materials, respectively. FE-SEM analysis confirmed that the CoMnO2-coated carbon fiber electrode exhibited the porous hierarchical interconnected nanosheet structures, depending on the added amounts of ammonium persulfate (APS) as an oxidizing agent, and Fe2O3-coated carbon fiber electrode showed a uniform distribution of porous Fe2O3 nanorods over the surface of carbon fibers. The nanostructured CoMnO2 were directly deposited onto carbon fibers by a chemical oxidation route without high temperature treatments. In particular, the electrochemical properties of the CoMnO2-coated carbon fiber with the concentration of 6 mmol APS presented the enhanced electrochemical activity, probably due to its porous morphologies and good conductivity. Further, to reduce the interfacial contact resistance as well as improve the adhesion between transition metal nanostructures and carbon fibers, the carbon fibers were pre-coated with the Ni layer as a seed layer using an electrochemical deposition method. The fabricated ASC device delivered a specific capacitance of 221 F g-1 at 0.7 A g-1 and good rate capability of 34.8% at 4.9 A g-1. Moreover, the wire-type device displayed the superior energy density of 60.16 Wh kg-1 at a power density of 490 W kg-1 and excellent capacitance retention of 95% up to 3,000 charge/discharge cycles.

Diabetes is a risk factor for mild cognitive impairment (MCI) and dementia. However, how the clinical characteristics of type 2 diabetic patients with MCI are linked to sarcopenia and/or its criterion remain to be elucidated. Japanese patients with type 2 diabetes mellitus were categorized into the MCI group for MoCA-J (the Japanese version of the Montreal cognitive assessment) score <26, and into the non-MCI group for MoCA-J ≥26. Sarcopenia was defined by a low skeletal mass index along with low muscle strength (handgrip strength) or low physical performance (walking speed <1.0 m/s). Univariate and multivariate-adjusted odds ratio models were used to determine the independent contributors for MoCA-J <26. Among 438 participants, 221 (50.5%) and 217 (49.5%) comprised the non-MCI and MCI groups, respectively. In the MCI group, age (61 ± 12 vs. 71 ± 10 years, p < 0.01) and duration of diabetes (14 ± 9 vs. 17 ± 9 years, p < 0.01) were higher than those in the non-MCI group. Patients in the MCI group exhibited lower hand grip strength, walking speed, and skeletal mass index, but higher prevalence of sarcopenia. Only walking speed (rather than muscle loss or muscle weakness) was found to be an independent determinant of MCI after adjusting for multiple factors, such as age, gender, BMI, duration of diabetes, hypertension, dyslipidemia, smoking, drinking, eGFR, HbA1c, and history of coronary heart diseases and stroke. In subgroup analysis, a group consisting of male patients aged ≥65 years, with BMI <25, showed a significant OR for walking speed. This is the first study to show that slow walking speed is a sole determinant for the presence of MCI in patients with type 2 diabetes. It was suggested that walking speed is an important factor in the prediction and prevention of MCI development in patients with diabetes mellitus.

Researches on flexible thermoelectric (TE) materials usually focus on conducting polymers (CPs) and CP-based composites; however, it is a great challenge to obtain high TE properties comparable to inorganic counterparts. Here, we report an n-type Ag₂Se film on flexible nylon membrane with an ultrahigh power factor ~987.4 ± 104.1 μWm⁻¹K⁻² at 300 K and an excellent flexibility (93% of the original electrical conductivity retention after 1000 bending cycles around a 8-mm diameter rod). The flexibility is attributed to a synergetic effect of the nylon membrane and the Ag₂Se film intertwined with numerous high-aspect-ratio Ag₂Se grains. A TE prototype composed of 4-leg of the hybrid film generates a voltage and a maximum power of 19 mV and 460 nW, respectively, at a temperature difference of 30 K. This work opens opportunities of searching for high performance TE film for flexible TE devices.

In order to understand the effect of Pb-CuI co-doping on the thermoelectric performance of Bi₂Te₃, n-type Bi₂Te₃ co-doped with x at% CuI and 1/2x at% Pb (x = 0, 0.01, 0.03, 0.05, 0.07, and 0.10) were prepared via high temperature solid state reaction and consolidated using spark plasma sintering. Electron and thermal transport properties, i.e., electrical conductivity, carrier concentration, Hall mobility, Seebeck coefficient, and thermal conductivity, of CuI-Pb co-doped Bi₂Te₃ were measured in the temperature range from 300 K to 523 K and compared to corresponding x% of CuI-doped Bi₂Te₃ and undoped Bi₂Te₃. The addition of a small amount of Pb significantly decreased the carrier concentration, which could be attributed to the holes from Pb atoms, thus the CuI-Pb co-doped samples show a lower electrical conductivity and a higher Seebeck coefficient compared to CuI-doped samples with similar x values. The incorporation of Pb into CuI-doped Bi₂Te₃ rarely changed the power factor because of the trade-off relationship between the electrical conductivity and the Seebeck coefficient. The total thermal conductivity(κ_tot) of co-doped samples (κ_tot ~1.4 W/m∙K at 300 K) is slightly lower than that of 1% CuI-doped Bi₂Te₃ (κ_tot~1.5 W/m∙K at 300 K) and undoped Bi₂Te₃ (κ_tot ~1.6 W/m∙K at 300 K) due to the alloy scattering. The 1% CuI-Pb co-doped Bi₂Te₃ sample shows the highest ZT value of 0.96 at 370 K. All data on electrical and thermal transport properties suggest that the thermoelectric properties of Bi₂Te₃ and its operating temperature can be controlled by co-doping.

Aims/Hypothesis): Type 1 diabetes is an immune-mediated disease with destruction of the pancreatic β-cells, a process that is conditioned by multiple genes and other factors. HLA counts as the major susceptibility gene. Significant variations in HLA genetic susceptibility to type 1 diabetes between Caucasians, African and Asian and other ethnic groups may have led to the variation in incidence of type 1 diabetes globally. Type 1 diabetes is characterized upon HLA identification. In this chapter we discuss global variations in genetic susceptibility of HLA with regard to type 1 diabetes globally with a particular attention to Arab population. Methods): Haplotype configuration of HLA class I A, B, C and Class II –DR/DQ/DP were studied in Caucasians, African and Asian and in Arab population to see if that is responsible for the exponential rise in the rate of type 1 diabetes. Results): Although Arabs have one of the highest global incidence and prevalence rates of type 1 diabetes, unfortunately, there is a dearth amount of information regarding HLA genetic susceptibility to type 1 diabetes in the Arab world. HLA haplotype configurations contribute to its risk value. However, out of an insufficient present study there are examples of misjudgment of HLA risk according to HLA alleles rather than haplotypes. Conclusion): To date HLA outlooks for the characterization of type 1 diabetes. There is an ethnicity difference in HLA characteristics which is responsible for variation in type 1 diabetes. Although Arab population have contributed heavily in the rise of burden of type 1 diabetes, however, there is significantly a dearth amount of studies on HLA in Arab population. Obviously, any future prediction, prevention or cure of the disease will be based on the HLA genetics. There is a dire need for a systematic screening of HLA for Arab population with type 1 diabetes, identification of Arab HLA-risk values and identify those who are prone to get the disease.

In this work, we introduce a new concepts for $\alpha$-type $F$-Suzuki contraction and $\alpha$-type $F$-weak-Suzuki contraction in the context of $b$-metric spaces. Compared to the $\alpha$-type $F$-contraction and $F$-Suzuki contraction mappings, these contractions are essentially weaker. For these type of contraction mappings, sufficient conditions are established for the fixed point's existence and uniqueness in $b$-metric spaces. As a result, the findings encompass several generalizations. To show the usability of our obtained results, we provide a supportive example and an application to a non-linear differential equation with fractional order.

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

Detection of PLA2R and THSD7A among primary membranous glomerulonephritis (MGN) patients transformed the diagnosis, treatment monitoring and prognosis. Anti-PLA2R can be detected in 70-90% of primary MGN patients while anti-THSD7A in 2-3% of anti-PLA2R negative primary MGN patients depending on the technique used. Serum and urine samples are less invasive and non-invasive respectively and can detect the presence of anti-PLA2R and anti-THSD7A with higher sensitivity and specificity, significant in patients’ monitoring and prognosis better than exposing patients to frequent biopsy which is an invasive procedure. Different techniques of detection of PLA2R and THSD7A in patients’ urine and sera were reviewed with the aim of providing newer and alternative techniques. We proposed the use of biomarkers (PLA2R and THSD7A) in making the diagnosis, treatment decision and follow up of patients with primary MGN. We also reviewed other prognostic renal biomarkers like retinol binding protein (RBP) and beta-2 microglobulin in order to detect progression of renal damage for early intervention.

This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA’s is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property. ERF’s apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF’s viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

Klebsiella pneumoniae, a member of the ESKAPE pathogen group, is a prominent cause of hospital-acquired infections. The WHO recognized carbapenem-resistant K. pneumoniae as a critical-one priority pathogen. These resilient superbugs have the ability to form biofilms and present a significant global threat. In the present study, we isolated and characterized a bacteriophage SAKp02, from hospital sewage, infectious to carbapenem-resistant K. pneumoniae patient isolates. SAKp02 could infect 43 of 72 clinical isolates, indicating a broad host spectrum. Whole genome analysis classified SAKp02 within the family Casjensviridae, with a 59,343 bp genome encoding 82 ORFs. Comparative genomic analysis revealed significant differences between SAKp02 and its closest viruses, indicating a distinct genetic makeup positioning it as a novel phage strain within the lineage. The SAKp02 genome comprises bacteriolytic enzymes, including holin, endolysin, and phage depolymerase, crucial for bacterial lysis and biofilm disruption. It reduced biofilm biomass by over threefold compared to the control and eradicated 99% of viable cells within a 4-hour treatment period. Scanning electron microscopy corroborated the ability of the phage to dismantle biofilm matrices and lyse bacterial cells. Safe and effective treatments are warranted, and hence, the fully characterized lytic phages with therapeutic potential against drug-resistant clinical isolates of bacteria are needed. Our study is the first to report the antibacterial and antibiofilm activity of Casjensviridae phages, and our discovery of a novel K. pneumoniae phage broadens the arsenal against the bacteria.

Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

Slave Province in Canada is an Archean granite-supracrustal terrane at the northwestern corner of the Canadian Shield. It is bordered by the Thelon–Taltson orogen (2.0 to 1.9 Ga) to the south-east and the Wopmay orogen (1.9 to 1.8 Ga) to the west. Acasta gneisses exposed in the western-most Slave province and the Wopmay rocks close to the gneisses were collected systematically for K-Ar and laser step-heating 40Ar /39Ar analyses of the biotite and amphibole. The K-Ar biotite ag-es of the four Wopmay samples range from 1816 ± 18 Ma to 1854 ± 26 Ma, which is within the reported U-Pb zircon age range of the Wopmay orogen. The K-Ar biotite ages of the fifteen Acasta gneisses range from 1779 ± 25 Ma to 1877 ± 26 Ma, except for a younger sample (1711 ± 25 Ma). The 40Ar/39Ar biotite analyses of the three samples from the Wopmay orogen give the plateau ages of 1826 ± 21 Ma, 1886 ± 13 Ma, and 1870 ± 18 Ma. On the other hand, the 40Ar/39Ar analyses of the biotite crystals from the Acasta gneiss show a variety of ages. Three samples show the plateau ages of 1877 ± 8 Ma, 1935 ± 14 Ma, and 1951 ± 11 Ma. These plateau ages are a little older than the total gas ages. The 40Ar/39Ar analyses of the amphibole crystals show more varied age rela-tions. The two samples show consistent age relations between the plateau ages (1814 ± 22 Ma and 1964 ± 12 Ma) and the total gas ages (1789 ± 23 Ma and 1967 ± 11 Ma), respectively. Some sam-ples have the apparent old age of 2000 Ma in the middle temperature fractions. These old frac-tions are due to that the originally existed amphibole crystals formed in Archean were affected by the thermal events during the Wopmay orogeny. but did not fully reset. The results suggest that the K-Ar system ages of the biotite and amphibole of the Archean Acasta gneiss that is aged from 4200 to 2600 Ma of zircon U-Pb ages were rejuvenated in the Paleo–Proterozoic ages. The heat source that rejuvenated the K-Ar system ages may have arisen due to the asthenospheric intrusion into the wedge mantle, which took place because of the subduction rollback.

Finding the ideal statistical method for grouping phenological data is always an important step for breeders to draw correct conclusions from it possibly. In this paper, K-cluster analysis is presented as a perfect tool for grouping phenological data. The present research was performed based on the phenological data of 61 strawberries (Fragaria × ananassa) cultivars of different geo-graphical origins grown in Estonian conditions. Groups of strawberry cultivars were deter-mined according to flowering and ripening time: early, middle and late, based on the sum of ef-fective temperatures above +5°C. The result of the K-cluster analysis carried out in this way makes it possible to precisely plan the ripening time of berries of different strawberry cultivars. Using such analysis data, it is possible to combine with different early, mid or late strawberry cultivars to extend the picking period. Also, this technique can be used to study the effect of cli-matic changes occurring over the years on the phenology of strawberry cultivars grown in the region.

In the particular configuration of the scalar field K-essence in the Wheeler-DeWitt quantum equation, for some age in the Bianchi type I anisotropic cosmological model, a fractional differential equation for the scalar field arises naturally. The order of the fractional differential equation is $\beta=\frac{2\alpha}{2\alpha - 1}$. This fractional equation belongs to different intervals, depending on the value of the barotropic parameter; when $\omega_{X} \in [0,1]$, the order belongs to the interval $1\leq \beta \leq 2$, and when $\omega_{X}\in[-1,0)$, the order belongs to the interval $0<\beta \leq 1$. In the quantum scheme, we introduce the factor ordering problem in the variables $(\Omega,\phi)$ and its corresponding momenta $(\Pi_\Omega, \Pi_\phi)$, obtaining a linear fractional differential equation with variable coefficients in the scalar field equation, then the solution is found using a fractional power series expansion. The corresponding quantum solutions are also given. We found the classical solution in the usual gauge N obtained in the Hamiltonian formalism and without a gauge. In the last case, the general solution is presented in a transformed time $T(\tau)$, however in the dust era we found a closed solution in the gauge time $\tau$.

This paper explores the dynamic interplay of K-12 education, the Fourth Industrial Revolution (IR 4.0), and internationalization in the Philippines. Through qualitative content analysis, key themes emerged, shedding light on the impact of K-12 education on global competitiveness, challenges in embracing IR 4.0 technologies, and opportunities arising from internationalization efforts. The findings underscore the significance of curriculum alignment with international standards, comprehensive digital literacy training, and fostering a global mindset to prepare students for the digitally-driven, interconnected world. Understanding the implications of these themes is crucial for the Philippines' education system, as it navigates towards a transformative and globally competitive future. Further, this research offers evidence-based recommendations and addresses research gaps, contributing to a broader understanding of how the convergence of these forces shapes the future of Philippine education. Future research avenues may explore long-term impacts on workforce preparedness, innovative IR 4.0 integration, and the effectiveness of internationalization efforts on educational outcomes. Eventually, this study inspires an education system that nurtures learners equipped to thrive in an ever-evolving global landscape.

Forensic document examiners can determine the authenticity of questioned documents by analyzing the ink used to create them. If an ink mismatch is found, it could be a sign of scam, backdating, or forgery. In this research a Hyperspectral Images of iVision HHID dataset is used to detect number of possible inks used in document. By using Hyperspectral Images, it’s possible to detect ink mismatch in a given document. In this research unsupervised learning method K-means is used to detect number of inks. Approximate number of clusters are determined by Elbow and Silhouette method before implementation of K-means.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

In an $m$-person symmetric game, all players are identical and indistinguishable. In this paper, we find that the payoff tensor of the player $k$ in an $m$-person symmetric game is $k$-mode symmetric, and the payoff tensors of two different individuals are the transpose of each other. Furthermore, we reformulate the $m$-person symmetric game as a tensor complementary problem and demonstrate that locating a symmetric Nash equilibrium is equivalent to finding a solution to the resulting tensor complementary problem. Finally, we use the hyperplane projection algorithm to solve the resulting tensor complementary problem, and we present some numerical results to find the symmetric Nash equilibrium.

This study aimed to investigate the possibility of using one-shot hyperspectral airborne images to recognize crops for an area with many small plots. The results showed that unsupervised clustering methods could classify crops with an accuracy of 80%, which improved to 90% when restricted to only grain crops, using a single airborne hyperspectral recording. However, additional layers such as NDVI, DTM, slope, and aspect did not improve classification accuracy. For comparison, the accuracy of clustering time series Sentinel-2 images with NDVI layers and DTM-derived data yielded an accuracy of: 74% ,Sentinel-2 time series 68% and single one registration before harvest - 39%. The results of the random forest classification were slightly less accurate due to a lack of sufficient reference data. However, it is challenging to verify the reported accuracy of crop recognition in the literature above 90% due to differences in analysis methodologies, reference data selection, pixel/object approaches, metric choice, and calculation formulas used.

Antimicrobial resistance is on the rise, and there aren't enough new treatments to combat it. This might send the modern world back to the pre-antibiotic age. The molecular hybrids of pyrazolo[3,4-b]pyridine and triazole have been designed, synthesized, and analyzed for their drug-like molecule nature and in vitro analyses for their inhibition potentials against S. Aureus and K. Pneumoniae. The compounds CY-R2-25 and CY-R2-28 have been identified as the high potential molecules in this series based on in vitro experiments. CY-R2-25 has zone of inhibition values of 15 0.82 and 14 0.7, whilst CY-R2-28 has the zone of inhibition values of 18 0.95 and 16 0.82 against S. Aureus and K. Pneumoniae, respectively. MIC and MIB values for CY-R2-25 and CY-R2-28 against S. Aureus and K. Pneumoniae are 0.25 and 0.5, respectively.

In this paper, we have established some new bounds of Fejér type Hermite-Hadamard inequality for k−fractional integrals involving r−times differentiable preinvex functions. It is noteworthy that in the past there was no weighted version of left and right sides of the Hermite-Hadamard inequality for k-fractional integrals for generalized convex functions available in literature.

Scale invariance is expected in empty Universe models, while the presence of matter tends to suppress it. As shown recently, scale invariance is certainly absent in cosmological models with densities equal to or above the critical value ρ_c = 3(H₀)²/(8πG). For models with densities below ρc, the possibility of limited effects remains open. If present, scale invariance would be a global cosmological property. Some traces could be observable locally. For the Earth-Moon two-body system, the predicted additional lunar recession would be increased by 0.92 cm/yr, while the tidal interaction would also be slightly increased. The Earth-Moon distance is the most systematically measured distance in the Solar System, thanks to the Lunar Laser Ranging (LLR) experiment active since 1970. The observed lunar recession from LLR amounts to 3.83 (±0.009) cm/yr; implying a tidal change of the length-of-the-day (LOD) by 2.395 ms/cy. However, the observed change of the LOD since the Babylonian Antiquity is only 1.78 ms/cy, a result supported by paleontological data, and implying a lunar recession of 2.85 cm/yr. The significant difference of (3.83-2.85) cm/yr = 0.98 cm/yr, already pointed out by several authors over the last two decades, corresponds well to the predictions of the scale-invariant theory, which is also supported by several other astrophysical tests.

Cancer stem cells (CSCs) are a tumor cell subpopulation that drives tumor progression and metastasis, leading to poor overall survival of patients. In colorectal cancer (CRC), hyper-activation of Wnt/β-catenin signaling by mutation of both Adenomatous polyposis coli (APC) and K-Ras increases the size of the CSC population. We previously showed that the CPD0857 inactivates Wnt/β-catenin signaling by promoting ubiquitin-dependent proteasomal degradation of β-catenin and Ras proteins, thereby decreasing proliferation and increasing apoptosis of CRC lines. CPD0857 also decreased growth and invasiveness of CRC cells harboring mutant K-Ras resistant to EGFR mAb therapy. Here, we show that CPD0857 treatment decreases proliferation and increases neuronal differentiation of neural progenitor cells (NPCs). CDP0857 effectively reduced expression of CSC markers and suppressed self-renewal capacity. CPD0857 treatment also inhibited proliferation and expression of CSC markers in D-K-Ras MT cells carrying K-Ras, APC and PI3K mutations, indicating inhibition of PI3K/AKT signaling. Moreover, CPD0857-treated xenograft mice showed regression of tumor growth and decreased numbers of CSCs in tumors. We conclude that CPD0857 could serve as the basis of a drug development strategy targeting CSCs activated through Wnt/β-catenin-Ras MAPK-PI3K/AKT signaling in CRCs.

Following legalization, cannabis has quickly become an important horticultural crop in Canada and increasingly so in other parts of the world. However, due to previous legal restrictions on cannabis research there are limited scientific data on the relationship between nitrogen (N), phosphorus (P), and potassium (K) supply (collectively: NPK) and the crop yield and quality. This study examined the response of a high delta-9-tetrahydrocannabinol (THC) Cannabis sativa cultivar grown in deep-water culture with different nutrient solution treatments varying in their concentrations (mg L^-1) of N (70, 120, 180, 250, 290), P (20, 40, 60, 80, 100) and K (60, 120, 200, 280, 340) according to a central composite design. Results demonstrated that inflorescence yield responded quadratically to N and P, with the optimal concentrations predicted to be 194 and 59 mg L^-1, respectively. Inflorescence yield did not respond to K in the tested range. These results can provide guidance to cultivators when formulating nutrient solutions for soilless cannabis production and demonstrates the utility of surface response design for efficient multi-nutrient optimization.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

More than 15,000 prescriptions and over the counter drugs are available according to the US Food and Drug Administration website. Moreover, several herbal medicines and dietary supplements are readily available to add to the list of possible drugs, which can potentially cause adverse drug interactions. These are a pressing concern for all as they can interfere with many dental procedures. Additionally, the number of geriatric patients seen in routine dental practice has increased over time. This implies that there are more patients with multiple diseases and health conditions like hypertension, diabetes, problems associated with the cardiovascular, renal and gastrointestinal systems, arthritis, osteoporosis, etc. All these require patients to be on certain medications. Furthermore, advancement in the dental field has led to more complex dental procedures (implants, grafts) being carried out in a general dental practice. These advanced and slightly more invasive treatments require the use of certain drugs before, during and after the treatment like local anesthetics, vasoconstrictors, anxiolytics, analgesics and antibiotics. All of these can possibly interact with medications a patient is already taking and can also interfere with the current dental treatment and create complications. This article aims to provide necessary information about commonly encountered systemic diseases and associated treating medications, their mechanism of action, possible complications and their management. The classes of drugs discussed include anti-platelet agents, vitamin K antagonists, novel oral anticoagulants, bisphosphonates, disease-modifying anti-rheumatic drugs and oral contraceptives.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

A Pogorelov polytope is a combinatorial simple 3-polytope realizable in the Lobachevsky (hyperbolic) space as a bounded right-angled polytope. It has no 3- and 4-gons and may have any prescribed numbers of k-gons, k ≥ 7. Any polytope with only 5-, 6- and at most one 7-gon is Pogorelov. For any other prescribed numbers of k-gons, k ≥ 7, we give an explicit construction of a Pogorelov and a non-Pogorelov polytopes. Any Pogorelov polytope different from Löbel polytopes can be constructed from the 5- or the 6-barrel by cuttings off pairs of adjacent edges and connected sums with the 5-barrel along a 5-gon with the intermediate polytopes being Pogorelov. For fullerenes there is a stronger result. Any fullerene different from the 5-barrel and the (5, 0)-nanotubes can be constructed by only cuttings off adjacent edges from the 6-barrel with all the intermediate polytopes having 5-, 6- and at most one additional 7-gon adjacent to a 5-gon. This result can not be literally extended to the latter class of polytopes. We prove that it becomes valid if we additionally allow connected sums with the 5-barrel and 3 new operations, which are compositions of cuttings off adjacent edges. We generalize this result to the case when the 7-gon may be isolated from 5-gons.

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. The performance metrics typically used in studies for prediction of diameters can be difficult to interpret and may not support comparative inferences between sampling designs or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R²), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with lidar for 190 training plots distribute across the 800 km² Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damages. However, traditional methods such as smoke detection, visual analysis, and wireless signals pose unavailability in identifying fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying indoor fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire types and sound propagation, FireSonic quantifies the Heat Release Rate (HRR) of flames by analyzing the relationship between fire heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. Experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Introduction: Type 1 diabetes mellitus is a chronic disease of autoimmune etiology that is highly prevalent in childhood. In the last decade, the possibility of using the Bacillus Calmette-Guérin vaccine for the treatment of this disease has been studied thanks to its immunomodulatory action . Objectives: The objective of this work is to carry out a systematic review of the available scientific evidence on the relationship between the tuberculosis vaccine and the treatment of diabetes in childhood. Secondarily, the aim is to study the incidence of Type 1 Diabetes Mellitus in a cohort of children from the Basque Country according to whether or not they have received the vaccine. Methods: An exhaustive bibliographic search has been carried out on the evidence available to date. For the epidemiological study in Euskadi, a comparison of accumulated incidences between vaccinated and unvaccinated population groups was made. Results: According to the literature analyzed, There is more and more evidence about the possible use of the tuberculosis vaccine in Type 1 Diabetes Mellitus. The results of our study were not conclusive, but we believe that it is necessary to continue with long-term studies to eliminate biases. Conclusions: The Bacillus Calmette-Guérin vaccine could be used for the treatment of autoimmune, allergic, infectious or oncological processes, although more studies are still necessary in this regard.

Lipoprotein (a) is a highly atherogenic, lipid particle whose concentration is genetically determined, and represents an independent risk factor for cardiovascular disease. On the other hand, metabolic syndrome represents a cluster of interconnected cardiovascular risk factors (obesity, insulin resistance, arterial hypertension, dyslipidemia) that are a direct consequence of an unhealthy lifestyle. Some evidence suggests a connection between each component of metabolic syndrome and high levels of lipoprotein (a). Still, the exact mechanisms are not fully understood and may involve complex interactions between various metabolic pathways. High levels of lp(a) are associated with insulin resistance, a key feature of type 2 diabetes mellitus and obesity. Although there is no significant association between lp(a) and other lipid levels (LDL-C, HDL-C, triglycerides), studies have shown a strong synergistic effect of genetically elevated LDL-C and Lp (a) on the development of cardiovascular events. Also, there is supporting evidence that hypertensive patients with high levels of lp(a) have higher cardiovascular risk, there is no direct association between hypertension and lp (a). Treatment of metabolic syndrome is often long-term and challenging but very important to reduce the overall cardiovascular risk, especially in individuals with higher levels of Lp (a), in whom the risk of developing cardiovascular disease is much higher.

Background and Objectives Neurofibromatosis 1 (NF1 MIM # 162200), also known as von Recklinghausen disease, is among the most common autosomal dominantly transmitted disorders characterized mainly by neurocutaneous manifestations such as café au lait spots, intertriginous freckling, various types of neurofibromas (cutaneous neurofibromas or plexiform tumors), neurological manifestations, and bone abnormalities. It occurs with an incidence of 1:3000–1:4000 cases. Cone-rod dystrophies belong to the large group of retinal dystrophies, a highly heterogeneous group of disorders clinically manifested mainly by damage to cone and rod cells and have a broad spectrum of clinical manifestations. Many genes are involved in their etiology, with more than 100 currently identified, including the CRX gene responsible for the autosomal dominant form of cone-rod dystrophy (ad CORD). Materials and Methods: The authors present an intriguing case of a patient diagnosed with Neurofibromatosis type 1 (NF1) as an infant and ocular involvement (myopia and astigmatism onset at age 15), which yielded a surprising molecular testing result. Results: Molecular DNA analysis identified a rare occurrence of two mutational variants, including a pathogenic variant in the NF1 gene, c.3871-2A, and a likely pathogenic variant in the CRX gene c.119G>A. Conclusions: The authors present this case to highlight the surprises offered by molecular testing in a condition where the diagnosis is usually based on clinical criteria, thus providing patients with competent genetic advice.

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction networks identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed, and relevant evidence was summarized. Gene information will be useful in the discovery of biomarkers and the identification of therapeutic targets for AD.

(1) Background: Little research is done on the link of diabetic kidney disease (DKD) progression and diabetic gastroenteropathy in type 1 diabetes (T1D). (2) Methods. We performed a cross-sectional study with 100 T1D patients, 27 of them had progressive DKD, defined as estimated glomerular filtration rate (eGFR) decline≥3 ml/min/year or increased albuminuria stage over the mean follow-up time of 5.89±1.73 years. Newly developed score with 17 questions on gastrointestinal (GI) symptoms was used. Faecal calprotectin was measured by ELISA. Lower GI endoscopies were performed in 21 patients. (3) Results: Gastrointestinal symptom score demonstrated high reliability (Cronbach's α=0.78). Patients with progressive DKD had higher GI symptom scores compared to those with stable DKD (p=0.019). The former group demonstrated more frequent bowel movement disorders (p=0.04). The scores correlated negatively with eGFR (r = -0.335; p = 0.001), positively with albuminuria (r=0.245; p=0.015), Hba1c (r=0.305, p=0.002), diabetes duration (r=0.251, p=0.012). Faecal calprotectin levels did not differ between DKD groups significantly. The most commonly reported histopathological findings of enteric mucosa were infiltration with eosinophils, lymphocytes, plasmacytes, presence of lymphoid follicles and lymphoid aggregates. Conclusion: progression of DKD positively correlated with gastrointestinal symptoms, but more research is needed to clarify the causal relationships of the gut-kidney axis in T1D.

Alexithymia as the inability to describe one's own feelings, is being increasingly researched. According to contemporary psychodynamic theories, negative emotions cannot be adequately named and externalized, but remain trapped in the body. Recent research shows the connection of alexithymia with numerous somatic diseases. Diabetes mellitus type 2 and obesity represent a great challenge in treatment, and the psychological profiles in these diseases are being studied more and more often. Therefore, alexithymia enters the focus of some research as one that could play a significant role namely in these diseases as the factor that makes a difference. The aim of this paper is a review of the literature with the purpose of understanding the current knowledge about the interconnection between alexithymia, obesity and type 2 diabetes mellitus.

The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades and the identification of differential glycemic responders can aid tailored treatment for prevention of pre-diabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among partic-ipants with elevated T2DM risk, to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary inter-vention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females, 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distin-guish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.

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.

The Ni-laterite deposit at the San Felipe plateau, located 30 km northwest of Camagüey, in cen-tral Cuba, is the best example of a clay-type deposit in the Caribbean region. San Felipe resulted from weathering of mantle peridotites of the Cretaceous Camagüey ophiolites. In this study, a geochemical and mineralogical characterization of two profiles (83 and 84) from the San Felipe deposit has been performed by XRF, ICP-MS, quantitative XRPD, oriented aggregate mount XRD, SEM, FE-SEM and EMPA. Core 83 with a length of 23 m, and drilled in the central part of the plateau, presents a notable concentration of cryptocrystalline quartz fragments and a rather poor content in NiO, averaging 0.87% wt. Core 84, which is 12 m long, and drilled at the border of the plateau, lacks silica fragments and presents a higher NiO content, averaging 1.79 %wt. The smectite structural formulae reveal that they evolve from trioctahedral to dioctahedral towards the top of the laterite profiles. Quantitative XRD analyses indicate that smectite is a dominant Ni bearing phase, accompanied by serpentine and minor chlorite. Serpentine, as smectite, is en-riched in the less soluble elements Fe3+, Al and Ni towards the top of the profiles. Core 83 seems to have been affected by collapses, replenishments, whereas core 84 may have remained undis-turbed.

Research on climacteric syndrome among middle-aged men remains scant than the research among women. Research is also scant on climacteric syndrome among older adults living alone, particularly men, who are more vulnerable than such females. This cross-sectional study investigated whether the prevalence of climacteric syndrome is associated with the type of household middle-aged men live in and identified the determinants of climacteric syndrome based on the household type. Six hundred middle-aged men living in multi-person households and 600 living alone were surveyed about general characteristics, diet-related factors, and climacteric syndrome. Data were analyzed using Pearson’s chi-squared test, Fisher’s exact test, and logistic regression. The risk of climacteric syndrome in single-person households was found to be 1.6 times higher than that among in multi-person households (p = 0.006). In multi-person households, income and breakfast frequency predicted climacteric syndrome (p < 0.05), while age, breakfast frequency, dinner frequency, and weekly eating out frequency predicted climacteric syndrome in single-person households (p < 0.05). Thus, dietary factors are more closely linked to the prevalence of climacteric syndrome in single-person households than in multi-person households. This highlights the need for climacteric syndrome interventions for middle-aged men whose health concerns may persist into older adulthood.

Soil aggregates, as the basic component of soil, make great contributions to the stability of soil structure and soil carbon (C) sequestration. Recently, grassland is experiencing continuous grazing, which greatly affects soil aggregation and soil C storage. However, how soil aggregates and soil C in different grassland respond to grazing remain unclear. In this study, three national fenced grassland monitoring field stations which represented mountain meadow (MM), temperate steppe (TS), temperate steppe desert (TSD) were selected, soil samples at 0–10 cm depth of inside and outside the fence were collected to explore the effect of grazing and grassland type on composition, stability and nutrients of soil aggregates. The results showed that the bulk soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN) and available phosphorus (AP) varied greatly among the three grassland types, with the highest values in MM. Soil aggregates composition showed significantly response to both grassland type and grazing, especially the proportion of soil aggregates &gt;2 mm, which significantly decreased 51.68% on average in grazing plots compared with fenced plots, whereas sharply decreased in MM and TSD. A significant decrease (on average 25.08%) of the mean weight diameter (MWD) of soil aggregates under grazing was detected across all grassland types. The effect of grazing on nutrients in macroaggregates (&gt;0.25 mm) was greater than that in microaggregates (&lt;0.25 mm). Aggregate-associated SOC concentration decreased under grazing in MM and TSD, however, SOC density showed different response to grazing across all grassland types, with minor effect on MM, decreasing SOC density in TS and increasing on TSD, respectively. The magnitude of grazing effect size on aggregate-associated SOC varied in different soil particle size, with greater response in aggregates &gt; 2 mm and the biggest value in TDS. In addition, correlation analysis showed that MWD of soil aggregates had significant negative correlation with soil bulk density, but significant positive correlation with SOC, TN and TP concentration. Results of RDA showed that BD, soil nutrients were the main influence factors of the composition and stability of aggregates. Overall, grazing had significant influence on soil aggregation, stability and SOC, played a crucial role in grassland soil stability and the accumulation of SOC.

Mountain summit in Madagascar have generally a specific habitat and that figure home a local endemicity of herpetofauna. Mantidactylus pauliani, a typically aquatic and critically endangered amphibian from Ankaratra Mountain, is among of them. This species inhabits high elevation with restricted distribution range. Our study highlights here a new data of this species and its viability with environmental change. So that, we carried out annual monitoring from 2018 to 2021. Nine fixed transects of 100 m were done along the streams between 1762 and 2378 m a.s.l. We followed the herpetofaunal standard technique for collecting data (systematic ecological niche searching and direct observation), and data analysis was performed using χ² tests and Fac-tor Correspondence Analysis. Here we present the new ecological data of M. pauliani. This spe-cies home between 1900 - 2378 m a.s.l. within the humid forest and savannah habitat. We noticed also a fluctuation in population size and migration to the upper throughout the years across the season, and the stream quality and volume of water variation. This habitat change makes it vul-nerable and the annual bushfire surrounding plays a negative impact. Knowledge and under-standing of its ecological guilds is needed for running and updating conservation activities

The objective of this study is to assess the hepatic/renal and splenic/renal echointensity ratios in ultrasonography in patients with and without diabetic nephropathy. This retrospective study included patients with diabetes mellitus who underwent ultrasound examinations at our hospital between January 2023 and May 2023. Ultrasound examinations done with renal cortical echogenicity and cortico-medullary differentiation by using B-mode ultrasonography. The hepatic/renal and splenic/renal echo intensity ratios were compared among study groups (diabetic patients with diabetic nephropathy and without nephropathy). The diabetic nephropathy group exhibited significantly higher right renal echointensity and left renal echointensity compared to the non-nephropathic group. Additionally, the splenic/renal echointensity ratio and hepatic/renal echointensity ratio were significantly lower in the diabetic nephropathy group. Urinary microalbumin levels was significantly correlated with Right renal echointensity (r=0.65, p&lt;0.001) and Left renal echointensity (r=0.69, p&lt;0.001). There was also a significant inverse correlation between urinary albumin and splenic/renal echointensity ratio (r=-0.58, p&lt;0.001). Ultrasonography, specifically the assessment of hepatic/renal and splenic/renal echointensity ratios, shows promise as a non-invasive and cost-effective method for evaluating morphological changes in the kidneys in patients with diabetic nephropathy. These findings suggest that ultrasonography can be a valuable tool for monitoring the progression of diabetic nephropathy and contributing to its early detection and management.

Edaravone is a mitochondrially targeted drug with a suggested capability to modify the course of diverse neurological diseases. Nevertheless, edaravone has not been tested yet in the context of spinocerebellar ataxia 1 (SCA1), an incurable neurodegenerative disease characterized mainly by cerebellar disorder with a strong contribution of inflammation and mitochondrial dysfunction. This study aimed to address this gap, exploring the potential of edaravone to slow down SCA1 progression in a mouse knock-in SCA1 model. SCA1154Q/2Q and healthy SCA12Q/2Q mice were getting either edaravone or saline daily for more than 13 weeks. The functional impairments were assessed via a wide spectrum of behavioral assays reflecting motor and cognitive deficits and behavioral abnormalities. Moreover, we used high-resolution respirometry to explore mitochondrial function, and immunohistochemical and biochemical tools to assess the magnitude of neurodegeneration, inflammation and neuroplasticity. Data were analyzed using (hierarchical) Bayesian regression models combined with the methods of multivariate statistics. Our analysis pointed out various previously documented neurological and behavioral deficits of SCA1 mice. However, we did not detect any plausible therapeutic effect of edaravone on either behavioral dysfunctions or other disease hallmarks in SCA1 mice. Thus, our results did not provide support for the therapeutic potential of edaravone in SCA1.

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.

(1) Background: There are links between tension-type headache and/or migraine with dysfunctions of the cervical and masticatory muscles and with psychosocial factors. The study aims to highlight the connections between primary headaches, bruxism and psychosocial issues, the effectiveness of a multidisciplinary approach in decreasing the intensity and frequency of the headache episodes and bruxism. (2) Methods: 67 patients with primary headache and bruxism were divided into two equal groups. The subjects attended manual therapy sessions twice a week and a weekly counselling session for three months. (3) Results: an overall comparison before and after three months show a significant decrease of the qualitative variables (tension-type headache, migraine, awake bruxism, limited jaw opening, anxiety and stress) after 3 months. Comparing the efficiency of the procedures after 3 months between the two groups, awake bruxism and perceived stress are significantly different. (4) Conclusions: Although the differences are not radical when comparing MT with MT+C at three months, the patients who received either manual therapy or this treatment combination showed an improvement in pain relief, in the frequency and complexity of their symptoms, a reduction in functional disability and an overall improvement of the quality of their lives after three months of treatment.

Herpes simplex virus type 1 (HSV-1) is a neurotropic virus that, occasionally, may spread to the central nervous system (CNS), being the most common cause of sporadic encephalitis. One of the main neurovirulence factors of HSV-1 is the protein ICP34.5 that, although initially seemed to be relevant only in neuronal infections, it can also promote viral replication in non-neuronal cells. New ICP34.5 functions have been discovered during the recent years, and some of them have been questioned. This review describes the mechanisms of ICP34.5 to control cellular antiviral responses and debates its most controversial functions. One of the most discussed roles of ICP34.5 is autophagy inhibition. Although autophagy is considered a defense mechanism against viral infections, current evidence suggests that this antiviral function is only one side of the coin. Different types of autophagic pathways interact with HSV-1 impairing or enhancing the infection, and both the virus and the host cell modulate these pathways to tip the scales in its favor. In this review, we will summarize the recent progress on the interplay between autophagy and HSV-1, focusing on the intricate role of ICP34.5 in the modulation of this pathway to gain the battle against cellular defenses.

Whole-genome sequencing has advanced our understanding of the population structure of the pathogenic species complex Cryptococcus gattii, which has allowed for the phylogenomic specification of previously described major molecular type groupings and novel lineages. Recently, isolates collected in Mexico in the 1960s were determined to be genetically distant from other known molecular types and were classified as VGVI. We sequenced four clinical isolates and one veterinary isolate collected in the southwestern U.S. and Argentina during 2012-2021. Phylogenomic analysis groups these genomes with those of the Mexican VGVI isolates, expanding VGVI into a clade and establishing this molecular type as a clinically important population. These findings also potentially expand the known Cryptococcus ecological range with a previously unrecognized endemic area.

To demonstrate if botulinum toxin type A (BoNT-A) improves mandibular range of motion and muscle sensibility to palpation in refractory myofascial pain (MFP) patients. METHODS: Eighty consecutive female subjects with refractory MFP, were randomly divided into four equal groups (n=20): BoNT-A low (BoNTA-L/10 U in each temporalis and 30 U in each masseter), BoNT-A medium (BoNTA-M/20 U in each temporalis and 50 U in each masseter), BoNT-A high (BoNTA-H/25 U in each temporalis and 75 U in each masseter) and saline solution 0.9% (SS, placebo control group/0.4 mL in each temporalis and 0.6 mL in each masseter). Clinical measurements of the mandibular movements included: pain-free opening, maximum unassisted and assisted opening, and right and left lateral movements. Palpation tests were performed bilaterally in masseter and temporalis muscle. Results were expressed as median, minimum, maximum, and means ± standard deviation (SD). Chi-Square Test was used to compare differences among groups. A 5% probability level was considered significant in all tests RESULTS: Regardless of dose, all parameters of mandibular range of motion significantly improve after 180 days in BoNT- A groups, when compared to the control group. Pain to palpation on muscles, significantly reduced in all BoNT- A groups regardless of dose, when compared to the control group, after 28 and 180 days of treatment. CONCLUSIONS: Independent of doses, BoNT-A improved mandibular range of motion and muscle sensibility to palpation in refractory MFP patients when compared to SS injections.

Aim of this study was to evaluate the accuracy and usability of a novel continuous glucose moni-toring (CGM) system designed for needle-free insertion and reduced environmental impact. We assessed sensor performance of two GlucoMen® Day CGM systems worn simultaneously in eight participants with type 1 diabetes. Self-monitoring of blood glucose (SMBG) was performed reg-ularly over 14 days at home. Participants underwent two standardized 5-hour meal challenges with frequent plasma glucose (PG) measurements using a laboratory reference instrument at the research center. When comparing CGM to PG the overall mean absolute relative difference (MARD) was 9.7 [2.6-14.6]%. The overall MARD of CGM vs SMBG was 13.1 [3.5-18.6]%. In the consensus error grid (CEG) analysis, 98% of both CGM/PG and CGM/SMBG pairs were in the clinically acceptable zones A and B. The analysis confirms that GlucoMen® Day CGM meets the clinical requirements for state-of-the-art CGM. The needle-free insertion technology is well toler-ated by users and reduces medical waste compared to conventional CGM systems.

Diabetes mellitus is a heterogeneous disease of complex etiology and pathogenesis. Hyperglycemia leads to many serious complications, but also directly initiates the process of β cell apoptosis. A potential strategy for the preservation of pancreatic β cells in diabetes may be to inhibit the implementation of pro-apoptotic pathways or to enhance the action of pancreatic protective factors. The HIPPO signaling pathway is proposed and selected as a target to manipulate the activity of its core proteins in therapy - basic research. MST1 and LATS2 as major upstream signaling kinases of the Hippo pathway are considered as target candidates for pharmacologically induced tissue regeneration and inhibition of apoptosis. Manipulating the activity of components of the HiPPO pathway offers a wide range of possibilities, and thus is a potential tool in the treatment of diabetes and the regeneration of β cells. Therefore, it is important to fully understand the processes involved in apoptosis in diabetic states and to fully characterize the role of this pathway in diabetes. Therapy consisting in slowing down or stopping the mechanisms of apoptosis may be an important direction of diabetes treatment shortly.

Abstract: The effect of collagen type 1 (Col I) oxidation on Adipose Tissue-Derived Mesenchymal Stem Cells (ADMSCs) remodeling is described as a model for acute oxidative stress. Morphologically, remodeling was presented by a mechanical rearrangement of adsorbed FITC-Col I and a trend for its organization in a fibril-like pattern - a process strongly abrogated in oxidized samples, but without visible changes in cell morphology. The cellular proteolytic activity was quantified in multiple samples utilizing fluorescence de-quenching (FRET effect). In the presence of ADMSCs a significant increase of native FITC-Col I fluorescence was observed, almost absent in the oxidized samples. Parallel studies in cell-free systems confirmed the enzymatic de-quenching of native FITC-Col I by Clostridial collagenase, again showing significant inhibition in oxidized samples. The structural changes in the oxidized Col I was further studied by Differential Scanning Calorimetry: an additional endotherm at 33.6°C along with the typical for native Col I at 40.5°C with sustained enthalpy (∆H) was observed in oxidized samples. Collectively, it has been evidenced that remodeling of Col I by ADMSCs is altered upon oxidation due to the intrinsic changes in the protein structure, thus presenting a novel mechanism for the control of stem cells' behavior toward collagen.

Type-2 fuzzy sets were introduced by L. Zadeh aiming at modelling some settings in which fuzzy sets (usually called type-1 fuzzy sets) are not sufficient to reflect certain uncertainty degrees - loosely speaking, they are fuzzy sets whose membership degrees are ordinary fuzzy sets. On the other hand, fiber bundles are topological entities of extreme importance in Mathematics itself and many other scientific areas, like Physics (General Relativity, Field Theory etc.), finance modelling, and statistical inference. The present work introduces a conceptual link between the two ideas and conjectures about the potential mutual benefits that can be obtained from this viewpoint.As an objective and usable product of the presented ideas, it is described a framework for defining type-2 fuzzy hedges, proper to operate on interval type-2 fuzzy sets.

Copper, which can potentially be a highly toxic agent, is an essential nutrient due to its role as a co-factor for cuproenzymes and participation in signaling pathways. In mammals, the liver is a central organ that controls copper turnover throughout the body: copper absorption, distribution, and excretion. In ontogenesis, there are two types of copper metabolism: embryonic and adult, which maintain the balance of copper in each of these periods, respectively. In the liver cells, these types are characterized by specific expression patterns and activity levels of the genes encoding ceruloplasmin, which is the main extracellular ferroxidase and copper transporter and proteins mediating ceruloplasmin metalation. In newborns, the molecular-genetic mechanisms responsible for copper homeostasis and the ontogenetic switch from embryonic to adult copper metabolism are highly adapted to milk ceruloplasmin as a dietary source of copper. In the mammary gland cells, the level of ceruloplasmin gene expression and the alternative splicing of its pre-mRNA govern the amount of ceruloplasmin in milk, and thus, the amount of copper absorbed by the newborn is controlled. In the newborns, absorption, distribution, and accumulation copper are adapted to milk ceruloplasmin. In the newborns, which are not breast-fed at the early stages of postnatal development, the control for alimentary copper balance is absent. We tried to focus on the neonatal consequences of a violation of the balance of copper in the mother / newborn system. Although there is still much to be learned, the time to pay attention to this problem came because the neonatal misbalance of copper may provoke the development of copper related disorders for future life.

This paper presents a) the novel hybrid learning method to train the type-1 non-singleton interval type-3 (IT3) fuzzy logic systems (IT3 NSFLS-1) and b) the novel method named enhanced Wagner-Hagras (EWH) IT3 NSFLS-1 fuzzy systems which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the back propagation (BP) method to train the antecedent parameters. This proposal dynamically changes the parameters of only the level alpha 0 minimizing some criterion function as new information becomes available to each level alpha k. The antecedent sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the EWH IT3 NSFLS-1 (OLS-BP) system. The algorithm was tested in a hot strip mill facility to predict the transfer bar surface temperature showing its superior capability to obtain the industrial pyrometer’s knowledge uncertainty for tuning and its better performance when compared with IT2 SFLS, IT2 NSFLS-1, GT2 SFLS, GT2 NSFLS-1, IT3 SFLS, and IT3 NSFLS-1 trained with the BP-BP algorithm.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

Malaria, leishmaniasis, and African trypanosomiasis are protozoan diseases that constitute a major global health problem, especially in developing countries; however the development of drug resistance coupled with the toxicity of current treatments have hindered their management. The implication of certain enzymes (dihydrofolate reductase, DHFR) or proteins (potassium channels) in the pathogenesis of these protozoan diseases is undeniable. In this study, a series of three DHFR inhibitors (6-5 fused heterocyclic derivatives X, Y and Z) and one K+ channel blocker (E4031) were screened for their inhibitory effect on Leishmania donovani, Plasmodium falciparum, and Trypanosoma brucei. Resazurin assay was used to assess antitrypanosomal and antileishmanial activities of test compounds, whereas the antimalarial activity was appraised through SYBR Green I test. Moreover, cytotoxicity of test compounds was evaluated on Vero, Raw 264.7 and HepG-2 cells using the resazurin-based test, whilst their pharmacokinetic properties were predicted using the online tool pkCSM. As a result, compound Y revealed a selective (selectivity index range: 2.69 - &gt;61.4; Vero, Raw 264.7 and HepG-2 cells) and broad-spectrum antiprotozoal activity against L. donovani promastigotes (IC50: 12.4 µM) and amastigotes (IC50: 4.28 µM), P. falciparum (IC50: 0.028 µM), and T. brucei brucei (IC50: 0.81 µM). In addition, compound X inhibited the growth P. falciparum (IC50: 0.0052 µM) and T. brucei brucei (IC50: 6.49 µM). In silico screening of the active antiprotozoal compounds revealed positive drug-likeness scores as none of Lipinski's rule of five’s criteria was violated by these compounds. However, in-depth pharmacokinetic and mechanistic studies are warranted to support discovery of novel antiprotozoal agents against malaria, leishmaniasis, and African trypanosomiasis by repurposing K+ channel blocker and DHFR inhibitors.

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

The present work is a numerical simulation of velocity and mixture fraction fields in a turbulent non-reaction Propane jet flow issuing into parallel co-flowing air, in isothermal condition which has been experimentally described here: http://www.sandia.gov/TNF/DataArch/ProJet.html. The objective is a better understanding of the flow structure and mixing process, a situation in which there is no chemical interaction and heat-transfer. The two-equation Realizable k-ε eddy viscosity turbulence model has been used to simulate the turbulent flow field on a 2D plane (i.e., on a 5-degree sector of the experimental domain), because Realizable k-ε more accurately predicts the spreading rate of both planar and round jets and presents the best proficiency in comparison with all versions of the k-ε models. Afterward, axial and radial profiles of mean velocities, turbulence energy, mean mixture fraction, the mixture fraction half radius (Lf), and the mass flux diagram have been numerically elicited for grid independent mesh (mesh B) and compared with corresponding experimental data to assess the numerical model. To obtain turbulence kinetic energy, k, due to lack of w' in the experimental data, the assumption of w'=v' seems to be valid. Simulations have demonstrated that mean mixture fraction (at radial profiles at locations x/D=0, 4, 15, 30 and 50) and its half radius, Lf, which characterizes jet width expanse, are prominently well-captured; Moreover, mean vertical velocity fields (in X-direction: Umean) have revealed less accuracy but still conspicuously well-captured. However, mean vertical velocity fields (in Y-direction: Vmean) have disclosed less resolution; Likewise, turbulence kinetic energy, k, have manifested moderate accuracy (between Umean and Vmean). It should be noticed that although numerical results for absolute pressure, p, have been obtained on aforementioned sections, there were no experimental data to compare with them. Thus, the corresponding numerical data has not been demonstrated in this study.