Bayesian networks have emerged as a powerful tool for modelling complex probabilistic relationships in uncertain environments. The process of inducing Bayesian networks involves learning both the structure of the network and the parameters of the associated probability distributions. This paper provides a detailed examination of the two main approaches to Bayesian network induction: structure learning and parameter estimation, highlighting the use of both constraint-based and score-based techniques. Furthermore, the role of distribution-based methods in the estimation and inference of probabilistic models is explored. These methods, including Maximum Likelihood Estimation, Bayesian Estimation, and Monte Carlo techniques, offer robust ways to handle parameter uncertainty and facilitate efficient inference in large, complex models. This comprehensive view aims to bridge the gap between the theoretical foundations and practical applications of these methods, providing insights for researchers and practitioners in the field of machine learning and probabilistic reasoning.

Breast cancer is among the most prevalent cancers in Indonesia. One of the methods for early detection of breast cancer is utilizing ultrasound images to classify breast conditions into normal, benign tumors, or cancer. The advent of deep learning technology facilitates the image analysis process, such as the use of Transfer Learning Convolutional Neural Networks (CNNs). Generally, CNN models require large and balanced datasets to perform well in classification tasks. However, medical datasets like breast ultrasound images tend to be limited and imbalanced. The Wasserstein GAN (WGAN) is a generative data augmentation method capable of producing synthetic images by learning the pattern of the distribution of real image data. The implementation of the Wasserstein distance results in the training process of WGAN demonstrating stability from epochs 3000, 2500, and 3000 out of a total of 5000 epochs. The quality of the synthetic images improves with an increasing number of training iterations. By using WGAN, all of the evaluation metrics of each classifier are increasing, with the best accuracy score is achieved by VGG16 model with 83.33% accuracy.

Oral squamous cell carcinoma often arises from an oral potentially malignant disorder called oral leukoplakia (OL). With this work we aimed to develop a novel data-driven predictive model based on gene expression profiles to distinguish OL patients who underwent malignant transformation from those who did not. We used the Tree Augmented Naïve (TAN) Bayes classifier to predict the posterior probability of having oral cancer given the data. 86 patients were included with a median follow-up of 7.11 years. Fifty-one patients (51/86; 59%) underwent malignant transformation. We found that 16 genes were predictors of oral cancer in patients with OL and these included SLC7A11, SPINK6, SERPINA12, VIT, ATP1B3, CST6, FLRT2, ELMOD1, AZGP1, RNASE13, DIO2, ECM1, CYP4F11, SYTL4, AKR1C1, and AKR1C3. In conclusion, we showed that Bayesian gene networks are a data-driven approach which could be used also in other predictor models in oncology.

The increasing prevalence of species that are detrimental to biodiversity is a major concern, particularly for managers of National Parks. To develop effective programs for controlling weeds, it is essential to have a thorough understanding of the extent and severity of infestations, as well as the contributing factors such as temperature, rainfall, and disturbance. Predicting these factors on a regional scale requires models that can incorporate a wide range of variables in a quantifiable manner, while also assisting with on-ground operations. In this study, we present two Bayesian Network models specifically designed for six significant weed species found along the southern coast of Australia. Our models are based on empirical data collected during a coastal weed survey conducted in 2015 and repeated in 2016. We applied these models to the coastal national parks in the isolated and pristine East Gippsland region. Importantly, the prediction models were developed at two different spatial scales that directly corresponded to the scale of the observations. Our findings indicate that coastal habitats, with their vulnerable environments and prevalence of open dune systems, are particularly susceptible to weed infestations. Moreover, adjacent regions also have the potential for colonization if these infestations are not effectively controlled. Climate-related factors play a role in moderating the potential for colonization, which is a significant concern for weed control efforts in the context of global climate change.

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.

Este estudio examina la implementación de redes bayesianas para el diagnóstico de afecciones respiratorias utilizando datos clínicos. Se analizaron 5725 registros de una clínica privada en Francia, obtenidos de Kaggle, centrándose en seis variables clave: glucosa en sangre, frecuencia cardíaca, saturación de oxígeno, presión arterial, temperatura y estado de salud del paciente. El objetivo era crear un modelo que mejorara la precisión y eficiencia en el diagnóstico de infecciones respiratorias. Se desarrolló una red bayesiana para modelar las interacciones fisiológicas en condiciones respiratorias, destacando la influencia de la glucosa en sangre en la oxigenación y la interacción entre la frecuencia cardíaca y la saturación de oxígeno. La mediana de la temperatura corporal fue de 38,18 °C y la mediana de la frecuencia cardíaca fue de 92,00 lpm. El modelo, basado en sólidos principios médicos, considera tanto los mecanismos patológicos directos como las respuestas compensatorias del cuerpo. Se utilizó Netica para la simulación y análisis del modelo, proporcionando una visualización clara de las probabilidades condicionales y las relaciones entre las variables. Los hallazgos demuestran que las redes bayesianas son herramientas efectivas para apoyar el diagnóstico de afecciones respiratorias, proporcionando una evaluación integral del estado de salud del paciente. La matriz de confusión reveló una precisión del 94%, una especificidad del 100% y una sensibilidad del 86,53%. Además, la calidad de la prueba para el estado "enfermo" mostró una sensibilidad de hasta el 98,49% y una especificidad de hasta el 98,85%, con un área bajo la curva ROC (AUC) de 0,9592. Estos resultados indican que el modelo tiene un alto rendimiento en la clasificación correcta de los casos de salud y enfermedad normales. Este enfoque tiene el potencial de mejorar la precisión y la eficiencia del diagnóstico, especialmente en entornos clínicos que requieren decisiones rápidas y precisas. Este estudio destaca la aplicación innovadora de las redes bayesianas en medicina respiratoria, ofreciendo una herramienta prometedora para mejorar el diagnóstico y el manejo de pacientes con afecciones respiratorias, y estableciendo una base para futuras investigaciones en el uso de métodos probabilísticos en el diagnóstico médico.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

This study explores the use of Bayesian Neural Networks (BNNs) for estimating chlorophyll-a concentration ([CHL-a]) from remotely sensed data. The BNN model enables uncertainty quantification, offering additional layers of information compared to traditional ocean colour models. An extensive in situ bio-optical dataset is utilized, generated by merging 27 data sources across the world’s oceans. The BNN model demonstrates remarkable capability in capturing mesoscale features and ocean circulation patterns, providing comprehensive insights into spatial and temporal variations in [CHL-a] across diverse marine ecosystems. In comparison to established ocean colour algorithms, such as Ocean Colour 4 (OC4), the BNN shows comparable performance in terms of correlation coefficients, errors, and biases when compared with the in situ data. The BNN, however, further provides critical information about the distribution of [CHL-a], which can be used to assess uncertainties in the prediction. Moreover, the BNN model’s ability to provide more information for coastal waters, especially with higher spatial resolution imagery, offers valuable advantages for marine research and management. By quantifying uncertainty, the model builds confidence in [CHL-a] predictions, even in regions with limited data coverage. This is reflected by the spread in the model predictions, where the BNN can detect a range of uncertainty bounds that reflect confidence in the retrievals. Introducing BNNs as probabilistic ocean colour models represents a significant advancement, enabling more accurate and reliable predictions of [CHL-a] in the global oceans.

We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities) within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the i-entities in small example systems as a first sanity check and find that ι-entities largely fulfil simple expectations.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

This paper provides an in-depth comparative analysis of three prominent machine learning techniques: decision trees, neural networks, and Bayesian networks. Each method is explored in terms of its theoretical foundations, algorithmic structure, strengths, limitations, and real-world applications. Decision trees are celebrated for their simplicity and interpretability, making them ideal for decision-making systems, but they often struggle with overfitting and poor performance on high-dimensional data. Neural networks, while capable of achieving high accuracy and effectively handling complex, non-linear patterns, are criticized for their "black-box" nature and computational intensity. Bayesian networks distinguish themselves through their ability to model uncertainty and incorporate prior knowledge, making them highly applicable in scenarios requiring probabilistic reasoning, yet they are challenging to scale for complex, high-dimensional data sets. This comparative analysis highlights the distinctive advantages of each method, their performance across various domains such as healthcare, finance, and risk assessment, and the growing potential of hybrid models that combine the strengths of these techniques. The paper concludes by discussing future research opportunities, particularly in enhancing model interpretability and scalability while addressing domain-specific challenges.

This study is framed on the Information Theory as a constructive criterion to generate probabilistic distributions –through the elaboration of Bayesian Networks- and to reduce the uncertainty in the occurrence and relationship between two key psychological variables associated with the sports’ performance: Self-Determined Motivation and Competitive Anxiety. We analyzed 674 universitary students/athletes who competed in the 2017 Universitary Games (Universiade) in México, from 44 universities, with an average age of 21 years old (SD = 2.07), and with a sportive experience of 8.61 years of average (SD = 5.15). Methods: Regarding the data analysis, first of all a CHAID algorithm was carried out for to know the independence links among variables, and then two Bayesian networks (BN) were elaborated. The validation of the BN revealed AUC values ranging from 0.5 to 0.92. Subsequently, various instantations were carried out with hypothetical values applied to the “bottom” variables. Results showed two probability trees that have Extrinisic Motivation and Amotivation at the top, while the anxiety/activation due to the worry for performance was at the bottom of probabilities. The instantiations carried out support the existence of these probabilistic relationships, demonstrating the little influence on the competition anxiety generated by the intrinsic motivation. In conclusion, the reduction of the uncertainty made up by the restricted BN may aloe to re-introduce Information Theory principles in psychosocial studies, allowing authors to obtain useful probabilities values upon target psychological variables related with sportive performance.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Graph coloring is a manifestation of graph partitioning, wherein, a graph is partitioned based on the adjacency of its elements. Partitioning serves potentially as a compartmentalization for any structural problem. Vertex coloring is the heart of the problem which is to find the chromatic number of a graph. The fact that there is no general efficient solution to this problem that may work unequivocally for all graphs opens up the realistic scope for combinatorial optimization algorithms to be invoked. The algorithmic complexity of graph coloring is non-deterministic in polynomial time (NP) and hard. To the best of our knowledge, there is no algorithm as yet that procures an exact solution of the chromatic number comprehensively for any and all graphs within the polynomial (P) time domain. However, several heuristics as well as some approximation algorithms have been attempted to obtain an approximate solution for the same. Here, we present a novel heuristic, namely, the 'trailing path', which returns an approximate solution of the chromatic number within polynomial time, and, with a better accuracy than most existing algorithms. The ‘trailing path’ algorithm is effectively a subtle combination of the search patterns of two existing heuristics (DSATUR and Largest First), and, operates along a trailing path of consecutively connected nodes (and thereby effectively maps to the problem of finding spanning tree(s) of the graph) during the entire course of coloring, where essentially lies both the novelty and the apt of the current approach. The study also suggests that the judicious implementation of randomness is one of the keys towards rendering an improved accuracy in such combinatorial optimization algorithms. Apart from the algorithmic attributes, essential properties of graph partitioning in random and different structured networks have also been surveyed, followed by a comparative study. The study reveals the remarkable stability and absorptive property of chromatic number across a wide array of graphs. Finally, a case study is presented to demonstrate the potential use of graph coloring in protein design – yet another hard problem in structural and evolutionary biology.

This paper investigates generalized Bayesian (GB) inference in the context of a one-parameter Pareto distribution. The study focuses on the derivation of generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the Pareto distribution using type II censored data. In addition, the study investigates a one-sample prediction approach to examine generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP). To evaluate the performance of these estimation and prediction methods, Monte Carlo simulations were performed comparing GBE with GEBE and GBP with GEBP for different parameter values and learning rate parameters (LRP).

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. By focusing on the distinct aspects of infection propagation within confined spaces, our approach significantly improves the realism of temporal-dynamic contact networks, offering a powerful tool for assessing the impact of specific locations on pandemic dynamics. The resulting models shed light on the role of spatial encounters in disease spread and strengthen the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers practical insights for public health strategies and digital contact tracing efforts, aiming at more effective intervention and containment measures during pandemics.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

Orthostatic hypotension (OH) is a condition marked by a significant drop in blood pressure upon standing, resulting from impaired autonomic regulation of vascular tone and blood volume. This study investigates the prevalence, physiological mechanisms, and occupational risk factors for OH among high-risk professions in Nepal, such as traffic police, security guards, and shopkeepers. Using a cross-sectional design, participants were evaluated for blood pressure variability, autonomic function, and fluid balance. The findings reveal a high incidence of OH, linked to reduced baroreceptor sensitivity and impaired sympathetic activation due to prolonged standing and inadequate hydration. This study highlights the need for tailored clinical interventions and workplace modifications to mitigate OH in vulnerable occupational groups.

This study explores the relationship between job satisfaction, employee loyalty and two types of flexible employment arrangements; teleworking and flexi-time. The analysis relies on data derived by the Workplace Employee Relations Survey (WERS) in 2004 and 2011. A propensity score matching and least squares regressions are applied. Furthermore, Bayesian Networks (BN) and Directed Acyclic Graphs (DAGs) are employed in order to confirm the causality between employment types explored and the outcomes of interest. Finally, an instrumental variables (IV) approach based on the BN framework is proposed and applied in this study. The results support that there is a positive causal effect from these employment arrangements on job satisfaction and employee loyalty.

Sewage sludge is an organic waste generated in waste-water treatment plants with certain content of nutrients, what may potentially be used as a source of slow release fertilizer, especially as phosphorus (P) source, but it demands some pre-treatment and the content of P is much lower compared to soluble mineral fertilizers. For these reasons, composted sewage sludge was used to manufacture pelletized organomineral fertilizers, mixing it with the inorganic sources monoammonium phosphate (MAP) and AshDec® (ASD) (thermochemically incinerated sewage sludge). The fertilizers were physiochemically characterized and evaluated as to the dynamics of soil P solubilization, and the forms of P remaining according to its lability. The sources evaluated were: organic compost of sewage sludge powder (SSC), pelletized SSC (SCP), pelletized organominerals SSC+MAP (S+MAP) and SSC+ASD (S+ASD), ASD alone, all compared to a soluble mineral fertilizer (MAP), and a control without P fertilizer. A test was conducted in leaching columns with 50 g of soil (Oxisol), where the fertilizers were applied at a dose of 100 mg P/column, and 30 mL of water or 2% citric acid were added daily for 30 consecutive days. The collected leachates had pH measured and P content determined. Pelletizing process resulted in denser products and promoted more gradual P release. The organomineral S+MAP was the most water-soluble recycled source, solubilizing about 70% of the total P, while the others presented much lower solubilization (<20%). In contrast, all fertilizers showed high solubility in 2% citric acid (except for S+ASD). After leaching, the greatest amount of P remaining in the soil as labile and moderately labile. Composting and the AshDec process produced materials with slow P solubilization, which would be favoured to solubilize in acidic soils and acidic rhizosphere conditions. In turn, the organomineral mixture of SSC with a highly soluble mineral fertilizer (MAP) resulted in a promising product with intermediate nutrient solubility, better synchronized with crop demand, thus increasing efficiency in P use.

This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

The hourly and daily rainfall data which is spatially distributed are required as an input for run-off rain model. Furthermore, the run-off rain model is used to detect early flooding. The daily and hourly rainfall data have characteristics that most of data are zero. Therefore we need a model which can capture the phenomenon. A time series model involving location, which is a model that can be developed to approach the daily and hourly rainfall data, we can call Vector Autoregressive (VAR) model. The VAR model allows us for modeling rainfall data in several areas. However, in certain conditions the VAR model often occurs over-parameterization and reduces degrees of freedom. The aim of this study is to compare the VAR model with Maximum Likelihood Estimator (MLE) and Bayesian to hourly and daily rainfall data in SampeanWatershed of Bondowoso. The results showed that the hourly and daily rainfall data are fitted to VAR process of orde 5 and 1 respectively. Based on the AIC and SBC values indicate that the Bayesian is better than the MLE method. The Bayesian is able to predict parameters by producing a smaller variance covariance matrix than the MLE.

Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.

In this paper, we introduce the (ρ,q)-analogue of the p-adic factorial function. By utilizing some properties of (ρ,q)-numbers, we obtain several new and interesting identities and formulas. We then construct the p-adic (ρ,q)-gamma function by means of the mentioned factorial function. We investigate several properties and relationships belonging to the foregoing gamma function, some of which are given for the case p = 2. We also derive more representations of the p-adic (ρ,q)-gamma function in general case. Moreover, we consider the p-adic (ρ,q)-Euler constant derived from the derivation of p-adic (ρ,q)-gamma function at x = 1. Furthermore, we provide a limit representation of aforementioned Euler constant based on (ρ,q)-numbers. Finally, we consider (ρ,q)-extension of the p-adic beta function via the p-adic (ρ,q)-gamma function and we then investigate various formulas and identities.

Introduction: Early childhood caries is a significant public health problem among preschool children. The COVID-19 pandemic, which restricted dental services, altered family dynamics, affected child development, and created new demands on caregivers, may have exacerbated this issue. Objective: Analyze the occurrence and severity of caries and identify associated factors in preschool children attending public municipal daycare centers in Salvador, Bahia, Brazil, Methodology: A cross-sectional epidemiological study was conducted to investigate caries in children aged 2 to 5 years in 2022 and 2023. Caries was assessed using the decayed, extracted, filled, and missing teeth (DEFT) index, and prevalence was calculated. Sociodemographic, family dynamic, dietary, and healthcare utilization data were obtained through a questionnaire. After descriptive and exploratory analyses, robust Poisson multivariable regression was conducted at a 5% significance level, considering socioeconomic, psychosocial, and behavioral aspects. Results: A total of 440 preschoolers participated, with a mean age of 55 months. The majority were Black or mixed race (88.86%) and female (52.73%). The prevalence of caries was 30.91% and the mean DEFT score was 1.1. In the multivariable analysis, low family income (up to one minimum wage) and not playing outdoors during the pandemic were associated with a higher prevalence of caries. Conclusion: The prevalence and severity of caries remain high, and the pandemic has contributed to the maintenance of this epidemiological scenario. Public health measures and policies promoting social justice should be planned to improve oral health conditions in childhood.

In this study, we propose a conservative and compact finite difference scheme designed to preserve both the mass change rate and energy for solving the sixth-order Boussinesq equation with the surface tension. Theoretical analysis confirms that the proposed scheme achieves second-order accuracy in temporal discretization and fourth-order accuracy in spatial discretization. The solvability, convergence, and stability of the difference scheme are rigorously established through the application of the discrete energy method. Additionally, a series of numerical experiments are conducted to illustrate the effectiveness and reliability of the conservative scheme for long-time simulations.

In this paper, we introduce a new class of $(p,q)$-analogue type of Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we consider some relationships for $(p,q)$-Fubini polynomials associated with $(p,q)$-Bernoulli polynomials, $(p,q)$-Euler polynomials and $(p,q)$-Genocchi polynomials and $(p,q)$-Stirling numbers of the second kind.

Sparse direction-of-arrival (DOA) estimation of wideband signal has attracted widespread researches for its unique high-resolution performance. Numerous existing methods based on sparse Bayesian learning (SBL) don’t possess the ability to enhance sparsity even if they have already enjoyed high favor among sparse recovery approaches. In view of this, we propose a novel hierarchical Bayesian prior framework that enhance sparsity evidently and derive its corresponding iterative algorithm. On analysis, the computational complexity of the iterative are less than most existing other state-of-the-art algorithms. Not only that, proposed method possesses high angular estimation precision and sparsity performance by utilizing joint sparsity of multiple measure vector (MMV) models. Last but not least, the method obtains the ability to stabilize the estimated values between different frequencies or snapshots such that flat spatial spectrum. Extensive simulation results are presented to prove the superior performance of our methods

Polluting discharges are produced at a rate that is inexorably rising in tandem with human activity development. Eutrophication and other matters, such as offensive aquatic life, require wastewater to be treated before being discharged into the environment. Prior releasing into water bodies, the treated waters must to undertake laboratory analysis. For this purpose, an analysis using standard methods was carried out at the Laboratory Waterworks in Debrecen on various samples taken from various wastewater treatment plants (sewage treatment plants) in Hungary. The overall objective of this study was to assess the quality of the effluents from the fourteen (14) wastewater treatment plants and investigate their efficiency by measuring various physicochemical parameters and microbiological parameters as excellent indicators to evaluate the performance of the studied wastewater treatment plants. The microbiological indicator monitored was the heterotrophic count (HPC) by estimating its total number, which provides useful information for the assessment and monitoring of water samples. The determination of physicochemical parameters included biological oxygen demand (BOD5), chemical oxygen demand (COD) and nitrite -N (NO2), ammonia-N (NH3-N), nitrate-N (NO3) total phosphorus (TP), Kjeldahl- Nitrogen (organic N+ammonia-N) (TKN), total inorganic N (TI), total suspended solids (TSS) and total nitrogen (TN). The performance of the wastewater treatment plants examined was checked and the results were conclusive. In the future, solutions should be applied to these wastewater treatment plants and some recommendations are suggested.

Formaldehyde is generated via the biochemical and chemical pathways, and it widely exists in foods. Its chronic exposure causes carcinogenic, neurotoxic, reproductive, allergic, immunological, genetic and respiratory toxicities. However, food-sourced formaldehyde is usually neglected due to its rapid metabolism after intake. Compared with its residual level in processed foods, a large proportion of formaldehyde disappears because of its high reactivity. Nevertheless, its elimination reaction mechanism and the potential consequences remain unclear. In this review, the disappearing pathway of formaldehyde from two aspects, the formation of formaldehyde-derived toxins and aromas, was reviewed. This review article may help food scientists take appropriate strategies to use the beneficial reactions of formaldehyde or divert its harmful effects to beneficial ones.

Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R², making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

Granular material is one of the most common in nature. Recently, there has been a significant amount of interest in the granular materials, such as powders or sand. It is challenging to describe the motion of such kind of macroscopic size matter, because it behaves like solid, fluid or gas. Size division, pattern formation, avalanches, packing and convection - these are just a few examples from a wide range of observed phenomena that occur during the motion process of granular matter. Most of these processes are based by flow and no wonder that much effort has been devoted to obtain a hydrodynamic description in which granular materials are treated as a continuous medium. During the research of vertically shaken granular medium motion in an opened container it was found that experimental and numerical results are explained by hydrodynamic theory. This paper investigates the motion of a granular matter for a shallow, vertically shaken bed. We assume the Leidenfrost state as initial and the matter resembles a fluid heated up from below.A one-dimensional isothermal and a non-isothermal problem of granular material motion,simulatedby a hydrodynamic model are considered, and the material is assumed to be a continuous medium. The local temporal solvability of isothermal and non-isothermal initial boundary-value problem in Sobolev’s spaces is proved. This work presents a numerical study of the one-dimensional model of granular medium flow. The feature of this model is consideration of the Navier-Stokes equations accounting an interpolation form of VanderWaals constitutive equation for pressure. A finite-difference approximation for numerical solution is proposed.

This bibliometric study analysed breast cancer research trends and outputs from 2014 to 1 2023, utilising data from the Scopus database. The primary objectives include analysing publication 2 growth, citation metrics, document type distribution, leading countries, preferred journals and 3 authorship patterns. The study reveals a significant increase in breast cancer research publications, 4 with a total of 12,073 documents and 216,769 citations over the ten-year period. The United States 5 leads in publication output and citation impact, followed by China and Japan. Articles are the 6 predominant document type for total 73.4% of publications, while reviews and book chapters also 7 contribute notably. The most preferred journals for publishing breast cancer research include Breast 8 Cancer Research and Treatment, Clinical Breast Cancer, and Breast Cancer Research. Authorship 9 patterns show a trend towards multi-authored papers, with a substantial number of papers having 10 more than five authors. The study underscores the global collaborative effort in advancing breast 11 cancer research and highlights key areas for future exploration.

Background: Nanotechnology has emerged as a promising field of study, aiming to revolutionize healthcare through the innovative development of drugs, diagnostic tools, and medical devices. As a result, healthcare professionals in training are constantly faced with the challenge of understanding and applying these emerging technologies. The objective of this study is to determine the factors to consider for including the teaching of nanotechnological tools in medical specialty postgraduate programs. Methods: A study based on the Nominal Group Technique (NGT) was conducted to identify the factors that promote and hinder the implementation of nanotechnology in certain postgraduate programs where nanotechnology is employed, such as Internal Medicine, Critical Care Medicine, Family Medicine, and Clinical Pharmacology at the School of Medicine of Universidad de La Sabana. A framework analysis was used to analyze the collected data. Results: Based on the two research questions, several aspects were identified for the inclusion of nanotechnology in the medical practice of specialties. Key challenges include lack of knowledge and resource availability, misinformation, and lack of knowledge, as well as paradigmatic analysis as the main obstacles to the use of nanotechnology. Conclusions: The findings provide a first-time study on nanotechnology curricula within medical specialty programs in Colombia, with potential extrapolation to similar situations in other medical schools.

The species and bio-availability of phosphorus (P) in primary, secondary and digested sludge were fractionated and further analyzed in this study. Results showed that inorganic P (IP) was the primary P fraction in the secondary sludge and digested sludge, in which non-apatite IP (NAIP) amounted to 91.6% and 69.3% of IP, respectively. Organic P (OP), accounting for about 71.7% of total P (TP), was the dominant P composition in primary sludge. The content of bio-available P was about 9.7, 43.4, 29.8 mg-P/g-TS in primary sludge, secondary sludge and digested sludge, respectively, suggesting secondary sludge is the optimal choice when land application of sewage sludge is taken into consideration, followed by digested sludge and primary sludge. Polyphosphate and orthophosphate, comprising approximately 54.3% and 89.2% of TP, was the dominant P species in the secondary sludge and digested sludge, respectively. Monoester-P (54.6% of TP in extract) and diester- P (24.1%) were identified as OP species in primary sludge by Phosphorus-31 nuclear magnetic resonance (31PNMR). The present results would be helpful for P recovery and recycle from sewage sludge in wastewater treatment plant.

We conducted a comparative study on the development of two synthetic methods. The solvent-free synthesis is a green chemistry method developed in a bid to ameliorate environmental adverse effects of the conventional solvent-based synthesis. The synthesis of novel dichloro and polymethoxy p-nitrophenylhydrazones through solvent free technique gave moderate to high yields which were however lower than those of the solvent-based method. The established solvent-free approach has several benefits, including universality and simplicity of the approach, catalyst-free conditions, non-use of an organic solvent, quick reaction time, fast and efficient workup, and un-solvated pure products in moderate to high yields.

Reaction of the phosphonated hydrazone (2-hydrazineylidenepropyl) diphenylphosphine oxide 1 with p-fluorophenyl-isothiocyanate yields as major product the thiosemicarbazone Ph2P(=O)CH2{C=N-NH(C=S)-N(H)C6H4F}CH3 (2-(1-(diphenylphosphoryl)propan-2-ylidene)-N-(4-fluorophenyl)hydrazine-1-carbothioamide) 2 along with bis(N-p-fluorophenylthiourea) 3 as minor product. This latter product 3 was isolated as main product by direct treatment of p-FC6H4N=C=S with hydrazine in a 2:1 ratio. Both 2 and 3 were characterized by NMR. Furthermore, the molecular structure of 3 was elucidated by an X-ray diffraction study. A conformational DFT study, at the B3LYP/6311 G++ (d, p) level of theo-ry, confirmed a good match between the calculated structure and the experimental one.

This paper presents an approach to assess and improve the cybersecurity infras- tructure of developing countries. We have highlighted and addressed the critical gaps through a hybrid approach encompassing SWOT analysis, benchmarking using Data Envelopment Analysis (DEA) and, Root Cause Analysis (RCA). This approach has provided a strong basis for evaluating cybersecurity performance by combining qualitative insights with quantitative metrics. As a case study, we have compared an underdeveloped country’s (Pakistan) cybersecurity pos- ture to five highly developed countries (Lithuania, Estonia, Singapore, Spain,and Norway). Our analysis has identified cybersecurity shortcomings and pro- vided actionable recommendations within a flexible framework, applicable for all developing countries to identify and address vulnerabilities, and align their cybersecurity infrastructure with international standards.

The increasing energy consumption and industrial activities that release carbon dioxide (CO2) emissions into the atmosphere have become one of the most serious environmental issues of today. This article discusses the current status in Azerbaijan regarding the development of effective approaches to reduce carbon emissions and ensure sustainable development, and it proposes relevant strategies for managing the carbon footprint. By exploring the interrelationship between the economy and the environment, it aims to contribute to the formation of more effective policies for managing CO2 emissions in Azerbaijan. According to the results of the econometric model established within the research for the period of 1990-2023, as the Azerbaijan economy grows, per capita CO2 emissions tend to increase, which can be linked to rising industrial activity, energy consumption, and other factors driving economic growth. The presence of a positive long-term relationship between population growth and per capita CO2 emissions can be attributed to higher overall energy consumption, increased demand for goods and services, and rising transportation needs. The strong correlation between economic growth and CO2 emissions suggests that it may be too early to assert the validity of the Kuznets curve for Azerbaijan economy as a developing country.

Fluctuations in kynurenic acid (KYNA) and brain-derived neurotrophic factor (BDNF) levels in the brain reflect its neurological status. The aim of the study was to investigate the effect of transiently elevated KYNA concentration in the cerebroventricular circulation on the expression of BDNF and its high-affinity tropomyosin-related kinase receptor B (TrkB) in specific structures of the sheep brain. Intracerebroventricularly cannulated anestrous sheep were subjected to a series of four 30-min infusions of KYNA: 4×5 μg/60 μL/30 min (KYNA20, n=6) and 4×25 μg/60 μL/30 min (KYNA100, n=6) or a control infusion (n=6), at 30-min intervals. Sections of the hippocampal CA3 field, amygdala (AMG), prefrontal cortex (PCx), and the hypothalamic medial-basal (MBH) and preoptic (POA) areas were dissected from the brain immediately after the experiment. The highest concentration of BDNF protein was found in the CA3 field (P<0.01), which was 8-fold higher than in the AMG and 12-fold higher than that in the PCx (MBH and POA were not analyzed). The most pronounced BDNF mRNA expression was observed in the MBH followed by the PCx, POA, AMG and CA3, while the highest abundance of TrkB mRNA was recorded in the AMG followed by the MBH, PCx, CA3 and POA. KYNA increased (P<0.05-P<0.01) BDNF protein levels and the expression of its gene in the brain structures examined, with the effect varying by dose and brain region. KYNA, particularly at the KYNA100 dose, also increased (P<0.01) TrkB gene expression, except for the AMG, where the lower KYNA20 dose was more effective (P<0.01). These findings suggest a positive relationships between KYNA levels in the cerebroventricular circulation and BDNF-TrkB expression in specific brain regions in a sheep model. This indicates that a transient increase in CSF KYNA concentration can potentially restore BDNF production, whose deficiency underlies numerous neurological disorders.

Diospyros kaki, the most widely cultivated species of persimmon, has been long used in traditional medicine since its leaves extracts contain high amounts of flavonoids and terpenoids, endowed with potential antioxidant, anti-inflammatory, anticancer, antidiabetic and antimicrobial effects. Re-cently, many in vitro and in vivo studies have assessed different potential health benefits of per-simmon leaves; however, its anticancer activity towards colorectal cancer (CRC), the second deadliest cancer in Western countries, has not been investigated in depth. Since CRC current therapies are associated with serious side effects and show no efficacy towards patients carrying RAS/BRAF mutations, the search for new and more effective therapies has turned to plant extracts, which could help reduce conventional drugs dosages and toxicity. The effect of Diospyros kaki al-coholic extract has been investigated on E705 CRC cell line, representative of most CRC patients, and on SW480 cells, carrying a KRAS activating mutation. This extract is effective in reducing tumor cells viability, without affecting the healthy mucosa cell line CCD 841, and it triggers apoptosis in CRC cells, by disrupting mitochondrial functionality and increasing oxidative stress.

Pre-transport management activities can cause stress, injuries, and mortality in poultry. This study aimed to gather information about the current pre-transport practices (selection of unfit chickens, catching preparations, catching, and crating) for spent hens and broilers by questioning Flemish poultry farmers. The results showed that catching preparations such as an extra selection of chickens unfit for transport was performed by a minority of the poultry farmers, and that layer farmers were less in line with the EU-legislation for water and feed withdrawal than broiler farmers. All birds were caught inverted except for one broiler farmer who used mechanical catching (not yet common in Flanders). Additionally, mechanical catching may involve extra costs, increased biosecurity risks, and specific recommendations for the stable (height and width). The broiler farmers preferred mechanical catching for broiler catchers' well-being, while upright catching was considered better for animal welfare than catching more than three chickens by one/two legs, mechanically, or by wings. Awareness of the need to perform an extra selection by the poultry farmers before catching is required. Preparations like closing areas under the aviary system and removing litter (layers) can further streamline the process and reduce animal suffering.

This stability analysis method is simply represented and understood on a Bode plot of two impedances without complex equations. The external impedance of the charging system is properly quantified and designed based on the stability criteria by analyzing.

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorisation into liquid mixtures that could replace petro-leum-based materials. This study aimed to explore the chemical composition of various compo-nents of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark liquefied materials by polymerization with isocyanate, a catalyst and water as blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellu-lose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials.

Introduction: Leptin and ghrelin are two hormones that play a role in weight homeostasis. Leptin, which is produced primarily by adipocytes and is dependent on body fat mass, suppresses appetite and increases energy expenditure. Conversely, ghrelin is the “hunger hormone”, it stimulates appetite and promotes fat storage. Bariatric surgery significantly alters the levels and activity of these hormones, contributing to weight loss and metabolic improvements. Clarifying the interplay between bariatric surgery, weight loss, physical exercise, leptin, and ghrelin is essential for developing comprehensive strategies for optimizing long-term outcomes for candidates for bariatric surgery, especially sarcopenic patients. Methods: This was a randomized controlled study with two groups (n=22). The patients in both groups have obesity and sarcopenia. A Roux-en-Ygastric bypass (RYGB) procedure was performed in all patients. The intervention group participated in a structured exercise program three times per week beginning one month after surgery and lasting 16 weeks. Patient assessment was performed before surgery (baseline) and after completion of the exercise program. The control group received the usual standard of care and was assessed similarly. Results: After surgery, weight, BMI and lean mass decreased significantly in both groups from baseline to the second assessment. Leptin was not significantly different from baseline to the second assessment in the physical exercise group but was significantly lower in the control group (p=0.05). Ghrelin increased over time in both groups, but the differences were not significant. When we associated leptin (the dependent variable) with weight (the independent variable), we found that lower weight was associated with lower leptin levels. A similar relationship was also observed between leptin and sarcopenia parameters (muscle strength and mass), as well as with bone health parameters (bone mineral density and t-score). Higher ghrelin levels were significantly associated with higher t-scores and z-score (p<0.05). Conclusion: Exercise has been shown to have a significant effect on leptin and ghrelin levels after bariatric surgery. By incorporating regular physical activity into their lifestyle, bariatric patients can optimize their weight loss outcomes and improve their overall health. After the physical exercise protocol, patients in the intervention group revealed more established leptin levels, which may indicate a protected pattern concerning decreased leptin levels. An unfavorable profile was evidenced, according to which greater weight loss, sarcopenia, and osteoporosis were associated with lower leptin levels.

Among the theoretical problems with the prevailing theory of cosmology, Lambda Cold Dark Matter (λCDM), the most serious appears to be the twofold problem with the cosmological constant lambda: Dark Energy and Hubble Tension. Despite a two-decade search for systematic errors in astrometry the problems have not been resolved. This paper posits a potential source of error that may have affected astrometry related to the current and past measurements of lambda.

Differential beamforming has attracted much research since it can utilize the array with small aperture size to form frequency-invariant beampatterns and achieve high directional gains. It has recently been applied to the linear loudspeaker array to produce a broadside frequency-invariant radiation pattern. However, designing steerable frequency-invariant beampatterns for the linear loudspeaker array has yet to be explored. This paper proposes a method to design the steerable differential beamformer with the linear loudspeaker array. We first determine the target differential beampatterns according to the desired direction, the main lobe width, and the beampattern order. Then, we transform the target beampattern into the modal domain for representation. The Jacobi-Anger expansion is subsequently used to design the beamformer so that the resulting beam pattern matches the target differential beam pattern. Furthermore, based on the criterion of minimizing the mean square error between the synthesized beam pattern and the ideal one, a multi-constraint optimization problem is formulated to calculate the optimal desired weighting vector. Simulations and experimental results show that the proposed method can achieve steerable frequency-invariant beamforming over 300 Hz – 4 kHz.

N-(diisopropylphosphanyl)benzamide, PhC(O)NHPiPr2, has been synthesized in good yield following two alternative procedures that employ benzamide as the starting material. The first one is a two-step preparation, in which N-(trimetilsilyl)benzamide is reacted with PiPr2Cl, to give the title compound in good yield, whereas the second one is a straightforward synthesis which converts benzamide into N-(diisopropylphosphanyl)benzamide by reaction with PiPr2Cl in the presence of N,N-dimethylpyridin-4-amine (DMAP) and triethylamine. NMR spectroscopy and X-ray diffraction analyses have been performed to characterize the new compound and elucidate its molecular structure in the solid state. N-(diisopropylphosphanyl)benzamide adds to the limited family of amido-substituted phosphines, RC(O)NHPR’2, which can be classified as bidentate hybrid P,O-ligands, both in their neutral and anionic forms, the latter achievable by deprotonation of the NH group.

Chikungunya is mosquito born disease that is spread across continents. This research provides valuable insights into the potential functional significance of the hydrophobic tail and cysteine-rich regions in E2 proteins. These findings lay the groundwork for future investigations into the specific roles of the C-terminal domain and post-translational modifications, which could ultimately lead to a better understanding of viral mechanisms and potential therapeutic targets.

Background: Patients undergoing brain tumor resection experience neurological and cognitive (i.e., neurocognitive) changes reflected in altered performance on neuropsychological tests. These changes can be difficult to explain or predict. Brain connectivity, measured with neuroimaging, offers one potential model for examining these changes. In this study, we evaluated whether longitudinal changes in brain connectivity correlated with changes in neurocognitive abilities in patients before and after brain tumor resection. Methods: Patients underwent functional and diffusion MR scanning and neuropsychological evaluation before tumor resection followed by repeat scanning and evaluation two weeks post-resection. Using this functional and diffusion imaging data, we measured changes in the topology of the functional and structural networks. From the neuropsychological testing scores, we derived a composite score that described a patient’s overall level of neurocognitive functioning. We then used a multiple linear regression model to test whether structural and functional connectivity measures were correlated with changes in composite scores. Results: Multiple linear regression on 21 subjects showed that connectivity changes were highly correlated with changes in neuropsychological evaluation scores (R² adjusted = 0.79, p<0.001). Changes in functional local efficiency (p<0.001) and global efficiency (p<0.05) were inversely correlated with changes in composite score, while changes in modularity (p<0.01) as well as the patient’s age (p<0.05) were directly correlated with changes in composite score. Conclusion: Short interval changes in brain connectivity markers were strongly correlated with changes in the composite neuropsychological test scores in brain tumor resection patients. Our findings support the need for further exploration of brain connectivity as a biomarker relevant to brain tumor patients.

The gut, often referred to as the "second brain," has significant contributions in the maintaining of the good bacteria contribute enormous role such as digestion, produce essential vitamins, support the immune system, and protect against harmful bacteria. The beneficial flora including Akkermansia muciniphila, Adlercreutzia equolifasciens, Barnesiella, Christensenella minuta, and Oxalobacter formigenes, along with their derived bioactive metabolites emerged as a key player in maintaining host metabolic and immune health. Dietary choices such as blending of prebiotic, fermented, symbiotic, anti-inflammatory foods, and secondary metabolites from a wide variety of plants and fruits promotes the diversity, composition, and stability of beneficial intestinal microbes. The colourful plant foods rich in phytochemicals bioactive compounds such as carotenoids, flavonoids, polyphenols, alkaloids, anthocyanins, and capsaicin offer a wide array of unique properties such as analgesics, antioxidants, anti-inflammatory, antimicrobials effect and promoting the abundance of beneficial gut bacteria and their bioactive metabolites confer numerous health-promoting effects. Here, we present knowledge about most beneficial gut bacteria and their derived metabolites in terms of their sources and health benefits. Finally, we discuss best foods that skew towards promoting healthy intestinal microbes.

This article presents a method for detecting P, QRS, T, and U waves in electrocardiogram (ECG) signals through advanced signal processing techniques, including a Butterworth bandpass filter and peak finding algo- rithms. The results are evaluated against the voltage ranges recommended in the literature, underscoring the importance of accurate wave detection for the diagnosis of various cardiac conditions.

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

For Sir Ronald Fisher, it is important to consistently obtain significant p-values to support an experimental hypothesis. So, replicating experiments to obtain independent p-values is a legitimate and desirable research practice. Several simple statistics have been proposed to meta-analyze p-values, all assuming that they are genuine, i.e. observations from independent standard Uniform random variables. But, as publication bias favors the studies that report "significant" p-values, when a p&gt;0.05 is obtained for the outcome of an experiment, some researchers will "fall into temptation" and decide to replicate the experiment in the hope of getting a smaller second p-value, ideally a significant one. Consequently, if the smallest of two p-values is reported, this is a Beta(1,2) distributed "fake" p-value, not a uniformly distributed genuine p-value. This is an unacceptable scientific research practice, and moreover the detection of fake p-values is unpractical. Even when it is possible, the analytic results to accommodate their existence in combined tests are cumbersome. For an informed decision, inclusive when the presence of fake p-values in a sample of p-values to be meta-analyzed is probable, tables with simulated critical values for the usual combined testing are supplied. This will also allow comparisons to be made between several combined tests.

The global malaria community has picked up the theme of malaria elimination in greater than 90 % of the world’s population in the next decade. The recent reports of P. vivax in all sub-Saharan Africa, including Duffy negative individuals, threaten the efforts to achieve elimination. This is not only in view of strategies that are tailored only to P. falciparum elimination, but also due to currently revealed biological characteristics of P. vivax concerning the relapse patterns of hypnozoites and conservation of a large biomass at cryptic sites in the bone marrow and spleen. A typical scenario has been observed in Botswana from the period between 2008 to 2018, which palpably projects how P. vivax could endanger efforts at malaria elimination where the two parasites co-exist. The need for the global malaria community, NMPs, funding agencies and relevant stakeholders to engage in a forum to discuss and recommend clear pathways for sub-Saharan Africa regarding malaria elimination including P. vivax, is warranted.

Academic and corporate research has been focusing on analyzing stock performances, financial markets, and investment strategies for decades. According to Lynch and Rothchild (1989), certain investments can be described as plays. In this work, the authors use the term for investments in value stocks, spin-offs, and turnarounds to analyze the long-term stock performance and risk-adjusted excess returns of up to five years for those stocks. Additionally, stocks with a supe-rior return performance are identified within an investment play based on low P/E and P/B mul-tiples. The study finds that value stocks, spin-offs, and turnarounds outperform the S&P 500 on average on a one-, three- and five-year investment horizon. The analysis of the three investment plays confirms earlier studies that investment plays constitute a profitable investment opportunity. Moreover, the research exemplifies that private investors could achieve an excess return (alpha) through a passive buy-and-hold investment strategy. Overall, the analysis provides a deeper in-sight into the long-term performance of value stocks, spin-offs, and turnarounds.

Correlated binary data in a 2x2 table has been analyzed from both the frequentist and Bayesian perspectives, but a fully Bayesian hierarchical model has not been proposed. This is a commonly used model for correlated proportions when considering, for example, a diagnostic test performance where negative subjects are tested a second time. We consider a new hierarchical Bayesian model for the parameters resulting from a 2x2 table with a structural zero. We investigate the performance of the hierarchical model via simulation. We then illustrate the usefulness of the model by showing how a set of historical studies can be used to build a predictive distribution for a new study that can be used as a prior distribution for both the rate ratio and marginal probability of a positive test. We then show how the prior based on historical 2x2 tables can be used to power a future study that accounts for pre-experimental uncertainty. High quality prior information can lead to better decision making by improving precision in estimation and by providing realistic numbers to power studies.

The present work presents and discusses experimental performance and gaseous emission results of the use of ternary mixtures of diesel oil, biodiesel and ethanol in an ASTM-CFR engine. Eight different mixtures with biodiesel content equal to 8%, 20% and 60%, and ethanol content equal to 0%, 5% and 8% were analyzed. After the selection and preparation of the mixtures, the determination of the cetane number for each one was performed following the ASTM D613 standard. After cetane number evaluation, two off-standard operating conditions were selected, and performance and emissions were obtained. A thermodynamic analysis and a Bayesian inverse approach were applied and physical quantities of interest, such as indicated work, maximum pressure, rate of heat released by the fuel, ignition delay, and combustion duration, were obtained. In-cylinder pressure data were obtained through an AVL Indicating system installed in the ASTM-CFR engine. The heating values of each mixture were determined using a bomb calorimeter and thermal and indicated efficiencies were evaluated based on the total heat released by the fuel and the indicated work. Finally, a gaseous emissions analysis was conducted to evaluate the influence of the blend compositions, as well as of their cetane numbers, on the formation of NOx, CO and CO₂.

The general objective of the study is to analyze food insecurity determinants mainly related to food price at macro level. The study used a time series data from the year 1988 quarter one to 2018 quarter four. Our empirical finding on the macroeconomic model show the short term consequence of food price change is positive with food security (negative with food insecurity). The long term consequence of food price change is, however, negative with food security (positive with food insecurity) at macro level. From the variance decomposition, it can be learned that the speed by which the undernourished people decrease in number in the short term because of the inflation happened is higher than the speed by which the number of undernourished people increase in the long run. At macro level, concern should be made to the supply side for food security improvement.

Bootstrap resampling techinques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional φ(F), where F is a random distribution function. Efron’s and Rubin’s bootstrap procedures can be extended introducing an informative prior through the Proper Bayesian bootstrap. In this paper different bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e. bagging models involving decision trees. Proper Bayesian bootstrap, proposed by Muliere and Secchi, is used to sample the posterior distribution over trees, introducing prior distributions on the covariates and the target variable. The results obtained are compared with respect to other competitive procedures employing different bootstrap techniques. The empirical analysis reports the results obtained on simulated and real data.

Especially for drought periods, the higher the accuracy of reservoir inflow forecasting, the more reliable the water supply from a dam. The article focuses on probabilistic forecasting of seasonal inflow to reservoirs and determines estimates from the probabilistic seasonal inflow according to drought forecast results. The probabilistic seasonal inflow was forecasted by a copula-based Bayesian network employing a Gaussian copula function. Drought forecasting was performed by calculation of the standardized streamflow index value. The calendar year is divided into four seasons; the total inflow volume of water to a reservoir for a season is referred to as the seasonal inflow. Seasonal inflow forecasting curves conforming to drought stages produce estimates of probabilistic seasonal inflow according to the drought forecast results. The forecasted estimates of seasonal inflow were calculated by using the inflow records of Soyanggang and Andong dams in the Republic of Korea. Under the threshold probability of drought occurrence ranging from 50 to 55 %, the forecasted seasonal inflows reasonably matched critical drought records. Combining the drought forecasting with the seasonal inflow forecasting may produce reasonable estimates of drought inflow from the probabilistic forecasting of seasonal inflow to a reservoir.

This study presents the experimental and analytical evaluations on an innovative connection class under cyclic loading. The connection was an H- or I-shaped section as a short stub column (SSC) between the end-plate and the column. The stub section was bolted to the column flange on the one side and bolted to the beam along with the welded end-plate on the other side. Stiffeners were installed on the webs and flanges of the beam and stub to ensure the necessary stiffness of the connection. The variables included thickness of stub flange/web and height of stub web. The cyclic loading was applied on a sample of the proposed connection according to AISC ANSI/AISC 341-16 loading protocol. Experimental observations displayed that the proposed connection, one can either employ hot rolling sections or create the connection as a plate girder, in which case the flange thickness and stub web, as well as the distance between two flanges and their height, was obtained according to the demand and performance of the structure. The optimum cross-section of the stub was then obtained. Also, the analytical model was calibrated using the finite element method (FEM), and its performance was confirmed. The effective parameters of the proposed connection were determined by modeling in the FEM software, and the design method was presented.

Objective: In recent years, the field of Essential Thrombocythemia (ET) research has seen a significant accumulation of scientific findings, but comprehensive bibliometric analyses remain lacking. This study aims to fill that gap by performing a thorough bibliometric analysis of ET research, identifying key contributors and collaboration networks, and mapping the development trends to provide insights for future research directions. Methods: A bibliometric analysis of ET-related publications from 2001 to 2024 was conducted using CiteSpace, VOSviewer, and R packages. Data were retrieved from the Web of Science Core Collection, with a focus on publication volume, citation analysis, co-authorship networks, co-citation relationships, and citation bursts. Results: A total of 4,297 studies published in 778 journals were included in the analysis. ET research has seen rapid growth, with researcher clusters in the United States and Europe driving progress through extensive regional and international collaborations. Leading researchers, such as Ayalew Tefferi from the Mayo Clinic and Alessandro M. Vannucchi from the University of Florence, have made significant advances in ET classification, molecular mechanisms, targeted therapies, and disease management. The discovery of driver mutations, such as JAK2, has revolutionized the diagnostic and therapeutic approaches to ET. Research focus has shifted from clinical morphological diagnosis to molecular diagnostics, with the field now entering the era of targeted therapies. However, the inherent heterogeneity of ET continues to present challenges for the widespread implementation of personalized precision treatment. Conclusion: Bibliometric analysis demonstrates significant advances in ET research, particularly in molecular pathology and targeted therapies. However, the heterogeneity of ET remains a major obstacle to personalized treatment. Future research should focus on further elucidating the pathogenesis of ET and improving stratified management approaches to achieve individualized precision therapy.

Phosphorus (P) is an essential element in food production, but it is often largely unavailable for plants as it persists in soils in immobilized forms. Farmers have been using P fertilizers extensively to overcome this unavailability, leading to the exhaustion of non-renewable deposits of phosphorite, rising fertilizers prices and causing several environmental impacts such as eutrophication. Its large-scale utilization is also inefficient since most of the added P becomes quickly unavailable for the plants. Therefore, farming solutions that improve P uptake, P use efficiency of plants and P mobilization in soils are highly important. In this narrative review, we summarized and explored the link between the Silicon (Si) and P cycles in soils. Silicon is quasi-essential for plants, and it has been established that its presence in soil and uptake by plants has several benefits, especially in toxic and nutrient-deficient conditions. Here, we established that Si in soils can affect crop production in P-deficient conditions through: 1) the pH effect – Si improves organic matter decomposition and decreases Al3+ availability; the Si uptake effect – the deposited Si in the plant improves its photosynthetic performance, improves the root ability to uptake P, improves organic matter decomposition and increases P/Mn ration in plant, improving P use; and 3) the Si soil effect – Si stimulates phosphorite rocks weathering and competes with P for the binding places at the surface of Ak and Fe hydroxides, releasing P into soil solution. This positive effect of Si on P mobilization in soils and consequent use by crops should be further disseminated to farmers and other relevant stakeholders.

Cystic fibrosis (CF) diagnosis can be a challenge due to the large spectrum of pathogenic variants in the CFTR gene and the effects of complex alleles. Next-generation sequencing has improved our understanding of the contribution of these complex alleles to the wide spectrum of CF clinical symptoms and to the response to medications. Herein, we studied nine CF patients from six unrelated families carrying the complex allele p.[Ile148Thr;Ile1023_Val1024del] with a frequency of 0.18%. All the patients were from Central Mexico. This complex allele was found in trans with Class I and II pathogenic variants (PVs) such as [c.1521_1523del, p.(Phe508del), and c.3231_3232del, p.(Phe1078Profs*77)]. A targeted search of a dataset of 2,217 exomes from healthy individuals revealed that eight individuals (0.18%) carried the p.(Ile148Thr) variant, but only one (0.022%), who was also born in Central Mexico, was a carrier of the complex allele. These findings led us to hypothesize a founder effect for the p.[Ile148Thr;Ile1023_Val1024del] complex allele in Mexican patients with CF, probably through migration from the French-Canadian population in Mexico. Finally, protein modeling revealed that this complex allele disrupts the secondary structure of the CFTR protein and might alter the ion flow.

In this paper, various Ni-P composite coatings containing toner, MoS2, and nano-SiO2 particles were deposited on steel substrates by the electroless method. Then, the electrochemical properties of these coatings after a heat treatment process were compared. The microstructural evaluations were also done by using the optical and electron microscopy methods. Both Tafel polarization and electrochemical impedance spectroscopy techniques were utilized to survey the electrochemical behavior of such coatings. The surface morphology of all coatings contained cauliflower-like nodules. The X-ray diffraction patterns showed the crystalline phases of Ni and Ni3P for all coatings after the heat-treatment step. Obtained results showed that all composite coatings exhibited lower corrosion rates with respect to Ni-P coatings. Such a reduction was about 21.6-92.2%. This behavior was attributed to the presence of reinforcement as barriers for corrosive ion diffusion through the coating plus the changes in detected phases and thickness. Electrochemical impedance spectroscopy test results also demonstrated that the increase in the polarization resistance for composites coatings was about 18.4-85.3% after 1 h immersion in a 0.6M NaCl solution; however, when the immersion time increased to 24 h, such increased resistance changed to 18.1 to 73.1%. Totally, despite the lower deposition rate, the presence of MoS2 and nano-SiO2 particles were more effective than toner particles to raise the corrosion rate of the Ni-P coating.

In this paper, we investigate p-adic q-integral (q-Volkenborn integral) on Z_p of p-adic gamma function via their Mahler expansions. We also derived two q-Volkenborn integrals of p-adic gamma function in terms of q-Daehee polynomials and numbers and q-Daehee polynomials and numbers of the second kind. Moreover, we discover q-Volkenborn integral of the derivative of p-adic gamma function. We acquire the relationship between the p-adic gamma function and Stirling numbers of the first kind. We finally develop a novel and interesting representation for the p-adic Euler constant by means of the q-Daehee polynomials and numbers.

It is known that the class of p-normed spaces (0 < p ≤ 1) is an important generalization of usual normed spaces with rich topological and geometrical structure, but the most of tools and general principles in nature with nonlinearity have not been developed yet, thus the main goal of this paper is to develop tools for nonlinear analysis under the framework of p-vector spaces. In particular, we first develop the general fixed point theorems which provide solutions to answer Schauder conjecture since 1930’s in the affirmative for p-vector spaces when p = 1 (which is just general topological vector spaces); then the one best approximation result for upper semi-continuous mappings is given, which is used as a powerful tool to establish fixed points for non-self set-valued mappings with either inward or outward set conditions; and finally we establish comprehensive existence results of solutions for Birkhoff-Kellogg Problems, and the general principle of nonlinear alternative by including Leray-Schauder alternative and related results. The results given in this paper not only include the corresponding results in the existing literature as special cases, but also expected to be useful tools for the study of nonlinear problems arising from theory to practice.

The main aim of this paper is to set some correlations between Boole polynomials and p-adic gamma function in conjunction with p-adic Euler contant. We develop diverse formulas for p-adic gamma function by means of their Mahler expansion and fermionic p-adic integral on ℤ_{p}. Also, we acquire two fermionic p-adic integrals of p-adic gamma function in terms of Boole numbers and polynomials. We then provide fermionic p-adic integral of the derivative of p-adic gamma function and a representation for the p-adic Euler constant by means of the Boole polynomials. Furthermore, we investigate an explicit representation for the aforesaid constant covering Stirling numbers of the first kind.

Unmanned Aerial Vehicle (UAV) technology has recently experienced increasing development, leading to the creation of a wide variety of autonomous solutions. In this paper a guidance strategy for straight and orbital path following of fixed-wing small UAV is presented. The proposed guidance algorithm is based on a reference Vector Field as desired 16course for the UAV to follow. A Sliding Mode approach is implemented to improve robustness and effectiveness andasymptotic convergence of the aircraft to the desired trajectory in presence of constant wind disturbances is proved according to Lyapunov. The algorithm exploits the banking dynamics and generates reference signals for the inner loop ailerons control. A MATLAB&amp;Simulink® simulation environment is used to verify performance and robustness of the compared guidance algorithms. This high-fidelity model considers the six Degrees-of-Freedom (DoF) whole flight dynamics of the UAV and it is based on experimental flight test data to implement the aerodynamic behavior.

This study examines the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, essential conduits for global trade. It explores the correlation between regional instability, exemplified by Houthi actions from November 19, 2023, to February 5, 2024, and changes in maritime traffic patterns and operational efficiency. This study seeks to answer a critical question in transport geography: How does regional instability, exemplified by Houthi insurgent activities, affect maritime traffic patterns and the operational efficiency of the Red Sea and Suez Canal? Using descriptive statistics, qualitative analysis, and geospatial methods, the research highlights recent trends in maritime traffic and incidents, revealing spatial and geopolitical challenges in this crucial trade route.Findings indicate a notable decline in maritime activity in the Gulf of Aden and Suez Canal due to security concerns from Houthi attacks, prompting a significant shift to alternative routes, particularly around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade and the importance of maintaining uninterrupted maritime flow. The study also emphasizes the economic ramifications, such as increased operational costs and freight rates due to longer transit times and enhanced security measures.The research concludes with a call for improved maritime security protocols and international cooperation to protect these strategic maritime pathways. It contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, highlighting the interconnected nature of global trade and security and the need for collective action against evolving geopolitical challenges.

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the off and rainy seasons of 2021 and 2022. In this study, 30 and 50 maize plants were randomly sampled using a "W" pattern during the dry and rainy seasons, respectively. Larval density, infestation rates, and damage severity were monitored over time. Taylor's power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher infestation rate and larval density in agroecological zone AZE 8 (fisheries zone of South Benin) compared to AZE 6 (ferralitic soils zone) during the off-season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW's distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.

Objective: To explore the acceptability and feasibility of conducting comprehensive, community-based, hy-brid (in-person and virtual) mental health screening and feedback with diverse youth in the community. Methods: The Screening and Support for Youth intervention provides comprehensive assessment and thera-peutic feedback in community settings including symptoms, diagnoses, functional impairment, suicide, and clinical risk. Symptom severity, seven diagnoses and suicidality were assessed with the Kiddie Computer-ized Adaptive Test. Functional impairment was evaluated by youth self-report on the Weiss Functional Im-pairment Rating Scale. Measures were scored according to nationally developed norms and an algorithm was developed to aggregate symptoms and functioning to determine clinical risk. The results were discussed with participants in a motivational interview to promote insight and engagement in an online intervention. Results: Methodological innovations included using a QR code for recruitment, integrated in-person and virtual participation, and expansion from in-person recruitment within the schools to virtual engagement with youth throughout the community. The final sample included disproportionately more Black or African American and Hispanic youth as compared to school and community statistics, suggesting that hybrid re-search may facilitate recruitment of diverse populations. Qualitative interviews indicated that the screening and feedback raised youth awareness of their wellbeing and/or distress, its impact on their functioning, and engagement with options for improving wellbeing. Conclusion: Hybrid research, developed as a necessity during the pandemic, proved advantageous to the feasibility and acceptability of community-based recruit-ment of at risk, minoritized youth.

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

This work reports the role of structure and composition on the determination of the performances of p-type SnOx TFTs deposited by rf magnetron sputtering at room temperature, followed by a post-annealed step up to 200 °C at different oxygen partial pressures (Opp), between 0% and 20%, but where the p-type conduction was only observed between 2.8–3.8%. The role of structure and composition were evaluated by XRD and Mössbauer spectroscopic studies. The study allows to identify the best phases/compositions and thicknesses (around 12 nm) to be used that lead to the production of TFTs with a bottom gate configuration, on glasses coated with conductive Indium Tin Oxide, followed by Aluminium Titanium Oxide dielectric layer with saturation mobility of 4.6 cm²V⁻¹s⁻¹ and on-off ratio above 7 × 10⁴, operating at the enhancement mode with a saturation voltage of −10 V.

In this paper, we investigate several relations for p-adic gamma function by means of their Mahler expansion and fermionic p-adic q-integral on ℤ_{p}. We also derive two fermionic p-adic q-integrals of p-adic gamma function in terms of q-Boole polynomials and numbers. Moreover, we discover fermionic p-adic q-integral of the derivative of p-adic gamma function. We acquire a representation for the p-adic Euler constant by means of the q-Boole polynomials. We finally develop a novel, explicit and interesting representation for the p-adic Euler constant including Stirling numbers of the first kind.

In this paper, we investigate p-adic q-integral (q-Volkenborn integral) on ℤ_{p} of p-adic gamma function via their Mahler expansions. We also derived two q-Volkenborn integrals of p-adic gamma function in terms of q-Daehee polynomials and numbers and q-Daehee polynomials and numbers of the second kind. Moreover, we discover q-Volkenborn integral of the derivative of p-adic gamma function. We acquire the relationship between the p-adic gamma function and Stirling numbers of the first kind. We finally develop a novel and interesting representation for the p-adic Euler constant by means of the q-Daehee polynomials and numbers.

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion, only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize the vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligent algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by perdition of vulnerable to future poverty households? In this paper, we utilize a multidimensional and longitudinal welfare data comprising one and a half million individual data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.

The Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform that supports a large array of multi-spectral and hyper-spectral sensors. It provides accurate aerosol optical depths and remote sensing reflectances (Rrs estimates) that can be used to generate products such as absorption coefficients due to phytoplankton and detritus/Gelbstoff as well as backscattering coefficients due to particulate matter. The OC-SMART platform yields improved performance in complex environments by utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

We explore, for the first time in the literature, how the revenues of the ship-management companies respond to macroeconomic exogenous shocks. Using data for ship-management companies in Cyprus, we find evidence that a demand shock has the largest impact on revenues, exhibiting an almost one-for-one relationship. If the demand shock is permanent, we observe a ceteris paribus permanent effect on revenues. Similarly, this occurs irrespective of the final effect that demand has on the relevant freight rate, proxied via the Baltic dry and tanker (dirty and clean) indices. The BDI and the BDTI indices have smaller effect on revenues, standing at approximately 0.05% for every 1% shock, while the clean tanker index does not have an effect, most likely due to their fleet composition. In accordance with the literature, we find that a shock in the price of Brent oil increases revenues. Our results bear importance not only for ship-management companies per se, but also for countries that are ship-management hubs.

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.

Fitting parameters on spline curves produces more parsimonious models, maintaining fit quality. Smoothing the splines reduces predictive variance. Individual splines are often fit by type of variable, e.g., in age-period-cohort models. Linear and cubic splines are most common. Several smoothing criteria have been used for parameter curves, with cubic splines fit by constraining the integral of splines’ squared-second derivatives popular recently. Constraining the sum of squared second differences for linear splines is analogous. Generally the degree of smoothing is selected using cross-validation. Known spline dummy-variable matrices allow regression estimation of splines, with smoothing done via constrained regression. Smoothing criteria based on sums of squares or absolute values of parameters, as in ridge regression or LASSO, improves predictive accuracy and produces splines similar to smoothing by second-derivative constraints. Variables with very low t-statistics represent points where curve-shapes barely change. Eliminating those variables leaves knots concentrated where spline shapes change. A Bayesian version of this puts shrinkage priors on spline parameters. This yields realistic joint parameter distributions, avoids problems associated with using cross-validation for parameter estimation, and readily expands to non-linear modeling, such as interactions among variable types. Regularized regression and Bayesian spline methods are compared for two example datasets.

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.

Impulsive noise is the main limiting factor for transmission over channels affected by electromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenarios. In this work, we analyze some of the existing as well as some novel estimation algorithms. Their performance is compared, for the first time, for different channel conditions, including the Markov-Middleton scenario, where the impulsive noise switches between different noise states. Following a modern approach in digital communications, the receiver design is based on a factor graph model and implements a message passing algorithm. The correlation among signal samples as well as among noise states brings about a loopy factor graph, where an iterative message passing scheme should be employed. As it is well known, approximate variational inference techniques are necessary in these cases. We propose and analyze different algorithms and provide a complete performance comparison among them, showing that both Expectation Propagation, Transparent Propagation, and the Parallel Iterative Schedule approaches reach a performance close to the optimal, at different channel conditions.

This pandemic of COVID-19 is tedious to control. The only lockdown is the way to stop the spread of this infection. Conventional health care is facing a real challenge to operate. Primarily the challenge is to provide health care support for COVID-19 patients with limited resources and continue the health care services like earlier.Perhaps, this challenge is the same but magnitude is different from different geographical locations around the globe. In this article, we presented a Bayesian algorithm with the Code to predict cancer death due to COVID-19. This code is possible to run at different time points and different geographical locations around the world. This code will help us to get the best strategy and shift the treatment option for cancer treatment. The model would provide physicians with an objective tool for counseling and decision making at different hotspots and small areas to implement.

This paper investigates an inverse problem of option pricing in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model arising in financial markets. We identify the volatility and the drift coefficient from the measured data in financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.

An approach to the assessment of probabilistic inference is described which quantifies the performance on the probability scale. From both information and Bayesian theory, the central tendency of an inference is proven to be the geometric mean of the probabilities reported for the actual outcome and is referred to as the “Accuracy.” Upper and lower error bars on the accuracy are provided by the arithmetic mean and the -2/3 mean. The arithmetic is called the “Decisiveness” due to its similarity with the cost of a decision and the -2/3 mean is called the “Robustness”, due to its sensitivity to outlier errors. Visualization of inference performance is facilitated by plotting the reported model probabilities versus the histogram calculated source probabilities. The visualization of the calibration between model and source is summarized on both axes by the arithmetic, geometric, and -2/3 means. From information theory, the performance of the inference is related to the cross-entropy between the model and source distribution. Just as cross-entropy is the sum of the entropy and the divergence; the accuracy of a model can be decomposed into a component due to the source uncertainty and the divergence between the source and model. Translated to the probability domain these quantities are plotted as the average model probability versus the average source probability. The divergence probability is the average model probability divided by the average source probability. When an inference is over/under-confident, the arithmetic mean of the model increases/decreases, while the -2/3 mean decreases/increases, respectively.

In his best-selling book An Introduction to Probability Theory and its Applications, W. Feller established a way of ending the St. Petersburg Paradox by the introduction of an entrance fee, and provided it for the case in which the game is played with a fair coin. A natural generalization of his method is to establish the entrance fee for the case in which the probability of head is θ (0 < θ < 1/2). The deduction of those fees is the main result of Section 2. We then propose a Bayesian approach to the problem. When the probability of head is θ (1/2 < θ < 1) the expected gain of the St. Petersburg Game is finite, therefore there is no paradox. However, if one takes θ as a random variable assuming values in (1/2,1) the paradox may hold, what is counter-intuitive. On Section 3 we determine a necessary and sufficient condition for the absence of paradox on the Bayesian approach and on Section 4 we establish the entrance fee for the case in which θ is uniformly distributed in (1/2,1), for in this case there is paradox.

It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both normal and abnormal, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of abnormal-looking p-value histograms are provided and based on case studies involving real biological experiments.

Our findings illustrate that CS should be employed as a means of popularizing scientific output, facilitating the promotion of political decision-making processes, and acting as a knowledge broker to reverse consumer behavior towards more environmentally friendly and conducive to SUP usage.

We propose a multi-sensor fusion localization framework for autonomous heavy-duty trucks suitable for mining scenarios, which enables high-precision, real-time trajectory generation and map construction. The motion estimated through pre-integration of the Inertial Measurement Unit (IMU) can eliminate distortions in the point cloud and provide an initial guess for LiDAR odometry optimization. The point cloud information obtained from LiDAR can assist in recovering depth information from image features extracted by the monocular camera. To ensure real-time performance, we introduce an iKD-Tree to organize the point cloud data. To address issues arising from bumpy road segments and long-distance driving in practical mining scenarios, we can incorporate a large number of relative and absolute measurements from different sources, such as GPS information and AprilTag-assisted localization data, including loop closure as factors in the system. The proposed method has been extensively evaluated on public datasets and self-collected datasets from mining sites.Our implementation is available at https://github.com/wangyicxy/iLM-slam.

This study investigates the impact of water stress on grapevines, specifically examining the role of rootstocks and aquaporins. Two experiments on potted plants were conducted in central Chile during the summer, under conditions of high water demand, involving various rootstock genotypes and combinations of Cabernet Sauvignon (CS) grafted onto rootstocks. Significant differences were observed among plants in terms of stem water potential, stomatal conductance, and growth rate. Notably, the CS/CS combination consistently displayed the slowest growth rate, regardless of the irrigation treatment. The study also analyzed the expression levels of plasma membrane intrinsic proteins (PIPs) and tonoplast intrinsic proteins (TIPs) aquaporins in the leaves of grafted plants. Specifically, VvPIP2;2 aquaporins showed reduced expression after 14 days without irrigation, whereas VvTIP1;1 and VvTIP2;1 expression levels correlated positively with gs responses in grafted plants, suggesting their role in modulating water content in leaves under water stress. TIP aquaporins likely play a significant role in the differential responses of CS plants towards nearly isohydric or anisohydric behaviour. The CS/CS combination exhibited near isohydric behavior, correlating with lower TIP aquaporin expression, while the combination of CS onto 1103P and 101-14 showed higher expression, indicating anisohydric behavior. The findings suggest that grafted plants are more resilient to water stress, supporting the idea that rootstocks can mitigate the effects of water stress on the scion.

Breast cancer is currently the second leading cause of death worldwide, accounting for 15% of all deaths (8.2 million deaths). Approximately 20% of breast cancer diagnoses are triple-negative breast cancer (TNBC), a type of cancer that lacks receptors for estrogen (ER negative), progesterone (PR negative), and human epidermal growth factor receptor 2 (HER2/neu negative). Consequently, TNBC doesn’t respond to hormonal or targeted therapies, making it challenging to treat due to its rapid growth, metastasis, and high recurrence rate within the first three years of therapy. Alternative chemotherapies are needed to address this problem. Pharmacological dose of vitamin C (high-dose VC) has been identified as a potential treatment for some cancer cells. The present study aimed to evaluate whether VC has a therapeutic effect on TNBC using MDA-MB-231 cells as the model. MTT assay, Hoechst 33342 staining, Nuclear ID Red/Green staining, Rhodamine 123 staining, and Western blot analysis were employed to test the hypothesis that pharmacological dose of VC can kill TNBC cells. Upregulation of Apaf-1, caspases -3, -6, -7, -8, and -9, inhibition of matrix metalloproteinases (MMP-2 and MMP-9), reduction in cell cycle protein expression, and enhancement of tumor suppressor proteins such as p53 and p21 indicated that pharmacological dose of VC has promising anti-cancer properties in the treatment of TNBC.

The rapid pace of urbanization, coupled with the growing need for environmental sustainability and technological innovation, has brought smart city initiatives to the forefront of global urban development. This paper provides a comparative analysis of smart city policies from leading cities such as Oslo, Zurich, and Canberra, and derives strategic insights for Qatar. By examining the strategies, priorities, outcomes, and challenges associated with implementing smart city initiatives in these cities, the study identifies areas of alignment and divergence with Qatar's own smart city policy framework. The research highlights potential opportunities for cross-learning and suggests how Qatar can leverage lessons from international best practices to enhance its smart city initiatives. Key areas of focus include fostering strategic partnerships, promoting innovation, and ensuring effective governance and data management systems. The findings provide actionable insights to improve the effectiveness of Qatar's smart city projects, aligning them with the Qatar National Vision 2030.

Recent studies with ApoE knockout (ApoE-/-) mice deficient for PACAP (pituitary adenylate cyclase-activating polypeptide) or its receptor PAC1 provide evidence for a possible involve-ment of immune- and vasoregulatoy PACAP signaling at the PAC1 receptor in atherogenesis and plaque associated vascular inflammation. Therefore, we tested the PAC1 receptor agonist Maxadilan and the PAC1 selective antagonist M65 on plaque development and lumen stenosis in the ApoE-/- atherosclerosis model for possible anti-atherogenic effects. Adult male ApoE-/- mice were fed a cholesterol-enriched diet (CED) or standard chow (SC) treat-ed with Maxadilan, M65 or Sham. Effects of treatment on atherosclerotic plaques, lumen stenosis, apoptosis and pro-inflammatory signatures were analyzed in the brachiocephalic trunk (BT). The percentage of Maxadilan treated mice exhibiting plaques under SC and CED was lower than that of Sham or M65 treatment indicating opposite effects of Maxadilan and M65. Maxadilan ap-plication inhibited lumen stenosis under SC and CED compared to Sham. In spite of significantly increased cholesterol levels, lumen stenosis of Maxadilan-treated mice was similar under CED and SC. In contrast, M65 under SC or CED did not reveal a significant influence on lumen steno-sis. Maxadilan significantly reduced the TNF-α+ area in the plaques under CED, but not under SC. In contrast, the IL-1β+ area was reduced after Maxadilan treatment under SC, but remained unchanged under CED compared to Sham. Maxadilan reduced the caspase-3+ area in the media under both SC and CED without affecting lipid content in plaques. Despite persisting hypercholesterolemia, Maxadilan reduces lumen stenosis, apoptosis and TNF-α driven inflammation. Our data suggest that Maxadilan provides atheroprotection by act-ing downstream of cholesterol-induced vascular inflammation. This implicates the potential of PAC1-specific agonist drugs against atherosclerosis even beyond statins and PCSK-9.

Background: Urinary tract infections (UTI) are a significant health concern, particularly in women, with almost half of them experiencing at least one UTI in their lifetime. This problem is further compounded by the high recurrence rate within 6-12 months. Postmenopausal women are especially vulnerable to recurrent UTI due to the natural decline in estrogen levels, which alters the urogenital epithelium and subsequently affects the urogenital microbiome. Methods: This study included 674 female patients that were admited at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest over a period of 3 years. Results: Of the 674 women with urinary tract infection, 435 (79.3%) had more than one positive culture, and 239 (35.4%) experienced at least one recurrent urinary tract infection 6-12 months after the initial diagnosis. The mean age of women with urinary tract infection was 63 (± 15.61 years). Recurrent urinary tract infections were most prevalent in those aged 65-80 years (52%). Escherichia coli was detected in 71% of the positive cultures. Conclusions: The management of recurrent urinary tract infections in women presents a complex challenge that necessitates a multifaceted approach. The emerging understanding of the urobiome and its role in bladder health offers promising avenues for developing targeted interventions. As research in this field advances, it is imperative to integrate these new insights into clinical practice to balance traditional antibiotic treatments with novel strategies that consider the intricate microbial ecosystem of the urinary tract.