There are no theorems (proven theories) in the biological sciences. We propose that the 3 31 nt minihelix tRNA evolution theorem be universally accepted as one. The 3 31 nt minihelix theorem completely describes evolution of type I and type II tRNAs from ordered precursors (RNA repeats and inverted repeats). Despite diversification of tRNAome sequences, statistical tests overwhelmingly support the theorem. Furthermore, the theorem relates the dominant pathway for the origin of life on Earth, specifically, how tRNAomes and the genetic code may have coevolved. Alternate models for tRNA evolution (i.e., 2 minihelix, convergent and accretion models) are falsified. In the context of the pre-life world, tRNA was a molecule that via mutation could modify anticodon sequences and teach itself to code. We relate the clearest history to date of the chemical evolution of life on Earth. From analysis of tRNA evolution, ribozyme-mediated RNA ligation was a primary driving force in evolution of complexity during the pre-life to life transition on Earth.

Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real world radiation thermometry is grey body radiation thermometry, which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability, therefore real body radiation thermometry is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation-thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses a digital twin model of the system of measurement to compensate for the loss of data, caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model with variable scalar inputs is proposed, in which the scalar input parameters are calculated to correspond to the system of measurement. The method for calculating the effective parameters is proposed and verified with the theoretical model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of radiation thermometry of grey bodies, according to which the sum of instrumental emissivity and instrumental reflectivity equals to 1, does not hold in radiation thermometry for real bodies. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity is analysed by simulated measurements along the temperature ranges of various radiation thermometers, enabling traceable non-contact temperature measurements with known measurement uncertainty under any known conditions.

The study investigated both the static and dynamic behavior of silicate materials through a series of experimental and numerical tests. Compression tests were conducted on cubic samples, three-point bending tests on beams, and perforation tests on silicate plates. In the compression tests, stress-strain curves were generated, enabling the calibration of the Concrete Damaged Plasticity (CDP) model for silicate materials. The tensile strength of the silicate was assessed using three-point bending tests, while dynamic perforation tests determined the impact resistance of silicate when subjected to a rigid projectile. The perforation tests provided insight into the failure mechanisms of silicate plates under projectile impact at velocities approaching the ballistic limit. Additionally, the numerical simulations for all the experimental tests were performed using the Abaqus software, in order to validate the accuracy of the material behavior model and confirmed the appropriateness of the calibrated parameters for the chosen model. The results showed a strong qualitative and quantitative correlation with the experimental data, demonstrating the robustness of the adopted approach.

This study investigates the theoretical modeling and experimental analysis of composites made from randomly oriented flax fibers reinforced with recycled expanded polystyrene (EPS) waste. The effect of flax fiber volume fractions (20%, 30%, and 40%) on the composites' tensile strength and tensile modulus were examined. Several theoretical models namely the rule of mixtures (ROM) series, modified Bader and Bowyer, Hirsch, Cox-Krenchel, and Einstein-Guth model were compared against the experimental results of the formulated composites. The theoretical predictions of tensile strength from the Hirsch and modified Bader and Bowyer models showed close alignment with experimental results. Cox-Krenchel and Hirsch's models better predict the tensile modulus of a composite at 30% fiber loading. Additionally, this research accentuates the potential of utilizing natural fibers in combination with recycled EPS materials to produce eco-friendly composites with better performance for construction applications, such as house panels and automobile interior components.

The gene sets of the two cells produced by each division of the fertilized egg are different, and these differences gradually accumulate. When the similarity between a certain cell and a certain type of cell is below a threshold, the differentiation ability in that direction is lost. When all cells can only have one developmental direction, the expression of zygotic genes is turned on. When a stem cell differentiates, it first divides into two cells. One of two cells, similar to the next level cell, becomes the next level cell, while the other, similar to the mother cell, continues to become the other stem cell. When a cell undergoes dedifferentiation, it expresses a group of genes. When the gene set is similar to the upper level stem cell, the cell undergoes dedifferentiation and becomes the upper level stem cell.

This treatise represents a significant contribution to the field by integrating quality constraint functions and predicates into Possibility Computation Tree Temporal Logic (PoCTL), thus giving rise to Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL). We provide a comprehensive definition of QFCTL's syntax, conduct an in-depth analysis of its logical characteristics, outline a precise model checking algorithm for QFCTL, and perform a meticulous complexity assessment of said algorithm. Moreover, by enriching QFCTL with a quantitative characteristic predicate operator, we innovate, culminating in the development of an enhanced Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL*). The treatise explores the logical facets of QFCTL*, formulates a bespoke model checking algorithm for QFCTL*, and conducts a rigorous analysis of its algorithmic complexity. To attest to the practical utility and robust expressive power of QFCTL and QFCTL*, we present a model checking example that serves as empirical evidence of the efficacy of the proposed model checking algorithms.

Cultivation is advocated as a solution for sustainable exploitation of medicinal plants. Understanding environmental factors influencing plant species distribution will eliminate the introduction of medicinal plants indiscriminately to inappropriate cultivation regions. The study investigated environmental conditions for the distribution of Artemisia afra and mapped out potential areas for its cultivation in South Africa. Soil samples were collected in the Free State Province in South Africa for analysis. To identify suitable environmental conditions for the natural distribution of A. afra, the South African National Botanical Institute database and physically collected Global Positioning System points were used in maximum entropy model. Monthly long-term average interpolated weather surfaces were used to estimate the effect of climate change on future climate suitability for A. afra distribution. Sixty-one percent of soil samples from different A. afra populations were clay loam soils with slightly acidic to neutral pH. The carbon source utilization, Shannon Weaver Index, and species richness were positively correlated to one group of fourteen soil samples, and species evenness was positively correlated to the second group, consisting of four samples. Climate change will only affect the distribution of A. afra in the very long term. The current study provides critical information for identifying suitable cultivation areas for A. afra, while supporting conservation efforts from an ecological point.

The reasonableness and accuracy of engineering design are often assessed through the use of a variety of structural design analysis software, which are then compared and verified. However, it is challenging for a single analysis software to meet the diverse and complex design requirements. In order to meet the specific engineering requirements, it is necessary to convert the MIDAS result model into an ANSYS structural model and conduct a nonlinear analysis and simulation in ANSYS. Nevertheless, the existing interface is unable to facilitate direct conversion of the model. Accordingly, this paper presents a Python-based ANSYS APDL program that enables the complete conversion of MIDAS GEN structural models to ANSYS finite element models. The program is capable of converting a range of data, including material, section, element, connection, load, node mass, constraint, time history function, and so forth. The program is capable of converting specific connection units, including elastic and general connection units. Additionally, the beam-column section direction, beam end freedom release, rigid element, and special anti-rocking structure of the structure can be considered. Ultimately, the theatre model is transformed. Following a comparison of the analysis results, it was found that the mass and mode of the model before and after the transformation were essentially identical. The maximum error of the first six orders of the structure is 2.95%, with the structural displacement under gravity load remaining essentially unchanged. The research and analysis demonstrate the accuracy and reliability of the MIDAS GEN conversion ANSYS program. The conversion program significantly reduces the time required for direct modeling in ANSYS, enhancing work efficiency. The study has considerable practical significance for the seismic sway design and analysis of buildings based on vibration isolation design.

This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.

The spread of false information can significantly harm public opinion, underscoring the importance of accurately identifying untrustworthy news. This paper presents an innovative machine learning (ML) tool, TMining, designed to evaluate news credibility and facilitate various text-mining tasks. By examining a range of ML methodologies alongside preprocessing techniques, we aim to boost the system's effectiveness. Our research meticulously assesses different datasets, highlights the impact of applying stemming techniques, and employs Local Interpretable Model-Agnostic Explanations (LIMEs) to shed light on the rationale behind model predictions. The outcomes reveal a notable enhancement in both the precision and clarity of the news verification process. The ultimate version of the model has been made available as an Application Program Interface (API), and its source code has been shared openly, encouraging further exploration and collaboration within the scientific community. This initiative advances our ability to discern manipulative context from fictitious content and promotes transparency and understanding in the domain of ML applications.

Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine-learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt-Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.

The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model and the Friedmann-Lemaitre-Robertson-Walker metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study shows that galaxy formation was most rapid in the early universe, especially within the redshift range of , with notable peaks around at and These two characteristic redshift values, predicted by our standard Friedmann redshift model used in this work, are confirmed by the observational data utilized in this research. The research also highlights that dark matter plays a significantly more critical role than dark energy in the process of galaxy formation. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to go beyond the domination of local galaxy formation and evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

The article is focused mainly on verifying the suitability of data from experimental milling heat-treated beech wood and on investigation of the effect of technical and technological parameters of milling on the energy consumption of this process. The independent parameters of machining process are cutting speed, feed speed, rake angle, and hydrothermal modification of experimental wood material. Based on analysis of variance it can be argued that the greatest statistically significant effect on energy consumption have cutting speed and rake angle of the tool while the feed speed had the least influence. The measured data of cutting power during milling were used to build a regression model and validate it, while the most suitable type of model with correlation of 87 % is Classification and Regression Tree followed by model created by Random Forests method.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

Art's unique style and creativity are essential in defining a work's identity, conveying emotions, and shaping audience perception. Recent advancements in diffusion models have revolutionized art design, animation, and gaming, particularly in generating original artwork and visual identities. However, traditional creative processes face challenges such as slow innovation, high costs, and limited scalability. Consequently, deep learning has emerged as a promising solution for enhancing painting-style creative design. In this paper, we present the Painting-Style Design Assistant Network (PDANet), a groundbreaking network architecture designed for advanced style transformation. Our work is supported by the Painting-42 dataset, a meticulously curated collection of 4,055 artworks from 42 illustrious Chinese painters, capturing the aesthetic nuances of Chinese painting and offering invaluable design references. Additionally, we introduce a lightweight Identity-Net, designed to enhance large-scale text-to-image (T2I) models by aligning internal knowledge with external control signals. This innovative Identity-Net seamlessly integrates image prompts into the U-Net encoder, enabling the generation of diverse and consistent images. Through extensive quantitative and qualitative evaluations, our approach has demonstrated superior performance compared to existing methods, producing high-quality, versatile content with broad applicability across various creative domains. Our work not only advances the field of AI-driven art but also offers a new paradigm for the future of creative design.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

Antimicrobial peptides (AMPs) are regarded as a promising alternative to traditional antibiotics in the face of ever-increasing resistance. Our research has focused on crotalicidin (Ctn), an AMP from a snake venom, and a fragment thereof, Ctn[15-34], with improved in vitro antimicrobial and anticancer activities and remarkable serum stability. As the retroenantio versions of both AMPs maintained favorable profiles, in this work we evaluate the in vivo efficacy of both the na-tive-sequence AMPs and their retroenantio counterparts in a murine infection model with Aci-netobacter baumannii. A significant reduction in bacterial levels is found in the mice treated with Ctn[15-34]. However, contrary to expectations, the retroenantio analogues either exhibit toxicity or lack efficacy when administered to mice.

The Covid19 pandemic and the current wars in some countries have put incredible pressures on the global economy. Challenges for the U.S. include not only economic factors, major disruptions and reorganizations of the supply chains, but also those of national security and global geopolitics. This unprecedented situation makes predicting economic crisis for the coming years crucial yet challenging. In this paper, we propose a method based on various machine-learning models to predict the probability of a recession for the US economy in the next year. We collect the U.S’ monthly macroeconomics indicators and recession data from January of 1983 to December of 2023 to predict the probability of an economic recession in 2024. The performance of the individual economics indicator for the coming year was predicted separately, and then all of the the predicted indicators were used to forecast a possible economic recession. Our results showed that the U.S. will face a high probability of being in a recession period in the last quarter of 2024.

To increase the calculation speed of the computational fluid dynamics (CFD) based simulation for the gas-solid flow in fluidized beds, a Markov chain model (MCM) is developed to simulate the particle movement in a two-dimensional (2D) circulating fluidized bed (CFB) riser. As a statistic model, the MCM takes the CFD-discrete element method (DEM) results as samples to calculate the transition probability matrix of particles, which can describe the macroscopic regularities of the particle movement and guide the numerical simulation of the particle motion based on the Monte Carlo method. The particle distribution snapshots, residence time distribution (RTD) and mixing obtained from both the CFD-DEM and the MCM are compared. Results show that the computational speed of the MCM is faster than that of the CFD-DEM by about 2 orders of magnitude, and the difference of the mean particle residence time calculated by the two models is less than 2%. Besides, the MCM can well describe the time-averaged particle mixing compared to the CFD-DEM.

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) and innovative trend analysis (ITA) were examined. Additionally, we utilized hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network (i.e. CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. The results demonstrate the following essential points: (1) The trend analysis results of the total monthly rainfall values indicate a significant decreasing trend with the negative z-score (MK= -3.7541 and MMK= - 4.0773). ITA also indicated a significant downwards trend of rainfall in the study area. (2) As the forecasting horizon increases, the prediction accuracy of the models and CEEMDAN-combined models gradually improves, reaching their optimum performance at the 12-month horizon. (3) CEEMDAN efficiently stabilizes time-series data, resulting in greater prediction accuracy in the hybrid model compared to individual models at all timescales. (4) The RMSE and R2 values for the hybrid CEEMDAN-ARIMA-LSTM model at a 12-month forecasting horizon are 0.042 and 0.995, demonstrating the applicability of this hybrid approach for drought forecasting.

Swampy land in Banjarmasin has special characteristics for building house constructions. The foundation for very low soil bearing capacity requires special treatment as well. Many building collapses are caused by foundation failures due to mismatched building loads and foundation functions, as well as construction material errors. The need for house design follows the development of building functions. Nowadays, a one-story house is no longer sufficient due to limited land. The need to elevate houses to two stories or to develop houses (renovations) often leads to foundation construction errors. The commonly used foundation, wooden pile foundation, has limited load-bearing capacity, necessitating modification steps in the form of foundation reinforcement. Several steps, including material combinations or combinations of various types of foundations, can be implemented. Therefore, a specific model is required to obtain a foundation design that can accommodate construction needs. The aim of the research is to create a foundation design model capable of reinforcing wooden pile foundations for both house construction development and as a reference for building one-story and two-story houses. In this research, field observations will be conducted at various house construction sites in Banjarmasin, both residential and individual objects. Data will be presented descriptively and analyzed to obtain an applicable model as a reference for house construction on swampy land. The model will provide an applicable depiction and will be tested against foundation construction implementation methods.

The effect of the heterogeneity in sex factor is considered in the Markovian manpower modeling model. The estimation of the transition probability matrix of the staff flow was done using the maximum likelihood method. The expected future durations of the individuals in the manpower system were obtained under the aggregated and disaggregated (male and female) staff flow as well as their variances. Test for the heterogeneity of the staff based on sex factor was performed and the result of the heterogeneity test shows that there is difference between the disaggregated staff flows. The computed entrant probabilities also indicate that the male group has higher probabilities of attaining higher grades than the female group.

This study explores an optimal strategy for muscle force estimation by integrating simplified cost functions with detailed muscle models, demonstrating significant advantages over traditional complex methods. Using elbow flexion as a case study, we characterize the interplay between muscle models and cost functions, showing that accurate muscle force estimations can be achieved effectively without electromyography (EMG) data while also substantially reducing computational demands. Validated against established bio-mechanical data, our models confirm their accuracy in predicting muscle behavior. This approach has profound implications for enhancing rehabilitation protocols and athletic training, providing a more accessible and adaptable tool for muscle force analysis. It broadens the applicability of muscle force estimation in clinical and sports settings and paves the way for future innovations in bio-mechanical research.

This study investigates the ergodic behavior of brain waves through an innovative multidisciplinary approach, integrating insights from neuroscience, physics, economics, and biology. By examining the statistical properties and dynamic characteristics of alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) brain waves, we argue that the ergodic nature of these neural oscillations can be comprehensively understood and quantified using analytical tools from diverse scientific disciplines. Our research begins with a thorough review of current neurophysiological understanding of brain wave generation and propagation. We then introduce the concept of ergodicity, originating from statistical physics, and explore its applicability to neural systems. The study employs advanced time series analysis techniques and stochastic process models, borrowed from economics and physics, to analyze large-scale electroencephalographic (EEG) datasets. Key findings suggest that brain waves exhibit ergodic properties under specific physiological conditions, with implications for understanding neural information processing and brain state transitions. We propose a novel framework that characterizes the ergodic behavior of brain waves across different frequency bands and cognitive states. This framework provides new insights into the relationship between local neural dynamics and global brain function.Furthermore, we discuss the biological implications of ergodic brain waves, considering evolutionary perspectives and potential adaptive advantages. Our analysis reveals how the ergodic properties of neural oscillations may contribute to the brain's ability to balance stability and flexibility, a crucial aspect of adaptive behavior and cognition. The study's findings have significant implications for multiple fields. In neuroscience, our framework offers new tools for analyzing brain function and may inform the development of innovative therapeutic interventions for neurological disorders. In physics and complex systems theory, it provides a concrete biological example of ergodic behavior in a highly complex, non-linear system. For the field of economics, it demonstrates the successful application of financial modeling techniques to biological time series data. This research contributes to a more holistic understanding of the brain as a complex, adaptive system operating at the intersection of physics, biology, and information processing. It also opens new avenues for future research, including investigations into the relationship between ergodic properties of brain waves and consciousness, cognitive flexibility, and the emergence of complex behavior from neural ensembles.

Artificial intelligence technologies based on deep convolutional neural networks and large lan-guage models have made significant breakthroughs in many tasks such as image recognition, target detection, semantic segmentation and natural language processing, but also face the problem of contradiction between the high computational capacity of the algorithms and the limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deploy-ment and cost reduction in edge computing. This paper analyze various existing quantization methods, showcase the deployment accuracy of advanced techniques, and discuss the future challenges and trends in this domain.

Good communication skills are essential for prospective workers, particularly college students, to clearly convey their abilities and understanding during job interviews. Despite the high demand for strong communication skills among employers, many students struggle with inadequate experience and skills, leading to reduced confidence and lower chances of securing desired jobs. Existing tools and resources for improving communication skills are limited, especially free ones, leaving students underprepared and less confident in interviews. To address this issue, a comprehensive solution is needed that provides free, customizable tools to enhance students' interview communication skills. Invisor is a web-based self-interview training application that leverages AI and Machine Learning to analyze users' behavior, responses, and facial expressions during simulated interviews. Accessible via desktop, laptop, or mobile devices, Invisor offers interactive and voice-recorded feedback to help students prepare for interviews effectively. By employing AI technologies, Invisor generates personalized interview questions based on job descriptions, enhancing the realism of the interview experience and providing tailored feedback to improve users' responses. Developed using agile methodologies, Invisor has undergone iterative enhancements, integrating user and stakeholder feedback promptly to improve usability and functionality. The combination of AI-driven simulations and agile development practices enables Invisor to meet current user needs and anticipate future demands, providing a robust tool for interview preparation in a competitive job market. This paper outlines the development and implementation of Invisor, demonstrating its effectiveness in enhancing interview skills and confidence among students and job seekers.

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e. understandable-by-humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and allows for flexibility and user-control in varying the shade of the opacity of black-box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions \textit{a~priori} compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open-source R package, `sparseR`) to the test in a predictive model "bakeoff" (i.e. a benchmarking study of ML algorithms applied "out-of-the-box," that is, with no special tuning). Algorithms were trained on a diverse set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluate the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black-box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black-box approaches. We find that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.

One of the primary causes of the mechanical seal failure is rotor vibration. Traditional dynamic seal models often would not be able to fully explaining the failure mechanisms. The seal dynamic models proposed in this paper, including those developed by the authors, prove valuable in predicting seal dynamics during operation in specific turbomachinery and in explaining the causes of seal failure. The single-mass dynamic model is suitable for analyzing contact mechanical seal and simple design dry gas seals dynamics. The two-mass dynamic model is used for investigating operational dynamics processes in classical dry gas seals under complex loading conditions. The three-mass dynamic model is used to study various complex mechanical seal types. This model is able to determine the normal operating condition range and to explain leakage mechanisms in the presence of excessive rotor vibrations.

Signal preprocessing and feature extraction are two important and necessary steps of data analysis. In signal analysis, typically these two steps are implemented through multiple algorithms. Moreover, the significant parameters in most algorithms are set empirically. We have developed an algorithm that can preserve and highlight characteristic waveforms while attenuating non-feature waveforms. The parameters in the algorithm are set through difference analysis. We have conducted experiments on both clinical and open-source ECG data, obtaining effective test results.

Direct competitions involve competitors that mutually contend for the same resource or objective. A controlled, well-documented, and data-rich field involving direct competition is represented by sports. Usually the winner of sports competitions is believed to be the one with the greatest talent, however, there are other factors that contribute to the outcome of a competition, there are in fact random, unpredictable events that can change the outcome of a competition. For this reason, if one wants to understand the properties of a competition is necessary to have a method to evaluate the importance of the talent or aptitude of a competitor, as opposed to the importance of chance, in determining the outcome of the competition itself. In this work we study the sport of tennis using the data obtained from the Association of Tennis Professionals (ATP) tournaments. We construct an agent-based model that is able to produce data analogous to the real one; this model depends on three parameters, the talent, the action of chance, and the weight of talent. In particular, we don't fix the values of these parameters and we fit the model results using a genetic algorithm, in this way, we study all the possible combinations of parameters, in the parameter space, that are able to reproduce the real systems data. We show that the model fits well the real data only for limited regions of the parameter space. On these limited regions of the parameter space are possible further optimization of the model results, limiting the values of parameters. In this way, our agent-based model, by means of this genetic algorithm calibration, is able to provide us with useful information without any a priori constraints.

In this study, a cholera infection model with the bilinear infection rate is developed by considering the perturbation of the infection rate by the mean-reverting process. First of all, we give the existence of a globally unique positive solution for a stochastic system at arbitrary initial value. On this basis, the sufficient condition for the model to have an ergodic stationary distribution is given by constructing proper Lyapunov functions and tight sets. This indicates in a biological sense the long-term persistence of cholera infection. Furthermore, after transforming the stochastic model to an relevant linearised system, an accurate expression for the probability density function of the stochastic model around a quasi-endemic equilibrium is derived. Subsequently, the sufficient condition to make the disease extinct is also derived. Eventually, the theoretical findings are shown by numerical simulations. Numerical simulations show that the impact of regression speed and fluctuation intensity on stochastic systems.

Visual object tracking is an important technology in camera based sensor networks, which has a wide range of practicability in auto drive system. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It makes such transformer-based applications be exposed to security threats. In this work, the security of the transformer model is investigated with the important component of autonomous driving, visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, so adversarial attacks are implemented as the security threats to conduct the investigation. First, adversarial examples are generated on top of video sequences to degrade tracking performance, and the frame-by-frame temporal motion is taken into consideration when generating perturbations over the predicted tracking results. Then, the influence of perturbations on performance is sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrate that the executed adversarial examples are effective on the performance drops of the transformer-based visual tracking.

To address the requirements of sonar imaging, such as high receiving sensitivity, wide bandwidth, wide receiving angle, an AlN PMUT with an optimized ratio of 0.6 for the piezoelectric layer diameter to backside cavity diameter is proposed in this paper. A sample AlN PMUT is designed and fabricated with SOI substrate-based bulk MEMS process. The characterization test result of the sample demonstrates a -6 dB bandwidth of approximately 500 kHz and a measured receiving sensitivity per unit area of at 1.37 V/μPa/mm2, which significantly surpasses the performance of previously reported PMUTs. The -6 dB horizontal angles of the AlN PMUT at 300 kHz and 500 kHz are measured 68.30° and 54.24°, respectively. To achieve an accurate prediction of its characteristics when being packaged and assembled in a receive array, numerical simulations with the consideration of film stress are conducted. The numerical result shows a maximum deviation of ±7% in the underwater receiving sensitivity across the frequency range of 200 kHz to 1000 kHz and a deviation of about 0.33% in the peak of underwater receiving sensitivity comparing to the experimental data. By such good agreement, the simulation method reveals its capability of providing theoretical foundation for enhancing the uniformity of the AlN PMUTs in future studies.

In this report, we present a study demonstrating how machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and obtain robust predictions even with a modest clinical dataset. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pretransplant and posttransplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. The random forest classifier revealed different important predictive factors between ALL and AML in our relapse models consistent with previous knowledge. Furthermore, it also distinguished donor CD34 chimerism as most impactful in relapse prediction compare to donor chimerism of other cellular lineages in our dataset for ALL but CD3 for AML. Our models greatly improved sensitivity and specificity at predicting relapses in cross validation compared to a reference model based on our own institutional incidence of relapse and inferential statistical principles. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool, confirmed our Random Forest Classification result and provided an intuitive explanation of how our machine learning models made the relapse prediction for each individual patient.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

Although researchers have proposed various indentation size effect (ISE) models, these models often involve different form and number of parameters that can make our wonder which is the best in existed ISE models. Three types of ISE test data, namely, normal ISE, reverse ISE and transition of normal to reverse ISE, are used to evaluate the sixteen ISE models. The comparatively study indicates that H-J, N-G-Fs, N-G-H, N-G-A and N-G-Qs models can accurately predict the normal ISE. The reason for this is that the friction stress that is not related to dislocation activities or the indentation size effect of plastic zone has been introduced into these models. Therefore, these two factors should be considered in future ISE models. The sixteen ISE models are originally proposed to describe the normal ISE of different materials. However, to our surprise, some of these models are able to capture the reverse ISE and the transition of normal to reverse ISE of different materials. The determination coefficients (DC) of the sixteen ISE models are also determined for different materials. For reverse ISE, the highest DC value for NiCSi, TC4 and PED Ni are given by the EXP, N-G-Fs and N-G-A models, respectively. For the transition of normal to reverse ISE, the N-G-YC, N-G-Fs, and N-G-H models produce the maximum DC for ZrO2, Cu and Y2O3-ZrO2, respectively. Moreover, the mean DC of the N-G-Fs model is the maximum among the sixteen ISE models, followed by the N-G-H model, but they cannot accurately predict the reverse ISE. Therefore, the N-G-Fs and N-G-H models should be further modified to accurately predict the reverse ISE.

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961-2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state will be chosen. The results show that in the coastal region, there is a tendency towards more saturated soils, and in the semi-arid region, there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable.

Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, the project provides insights into the model workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and at times, unsound ranking of the features to make the predictions. The paper therefore argues that it is important for research in applied machine learning to provide insights into explainability of the models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare. A future direction is to investigate methods to quantify the effectiveness of the machine learning models in terms of the insights from their explainability.

Although there is a vast body of neuroscientific research on cognitive behavioral therapy (CBT), many therapists are confused about how to incorporate the evidence into their clinical practice. This narrative review translates neuroscientific research on CBT for depression into a therapist-friendly formulation model. Four brain networks that are particularly relevant to depression, including the Central Executive Network, Default mode Network, Salience Network, and Affective Network, were identified based on their interactions and described in relation to depressive symptoms. Additionally, it clarified how each CBT technique works on a brain map. Assessment, intervention selection, and patient psychoeducation based on this model will facilitate the clinical application of neuroscience in CBT.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

Lightning is responsible for most annually burned area by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm designed to run on a uniform 20-km grid over the Continental USA and Alaska. The algorithm consists of a large set of logistic equations parameterized via logistic regression using long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 Principal Components (strong predictors) from a list of 611 potential predictors. Our statistical analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: horizontal temperature distribution by pressure levels, amount of low-level atmospheric moisture, and wind vectors. These physical variables impact the vertical separation of electric charges in the lower troposphere during storms causing the voltage potential between ground and the cloud deck to increase to a level that triggers electrical discharges. Results from a forecast verification using independent data showed excellent model skill, thus making this algorithm suitable for inclusion into software systems forecasting the chance of wildfire ignitions.

Introduction: Overuse of antibiotics has been increasing in recent years and is currently the most prescribed drug in the paediatric age. It is estimated that 72% of patients in the neonatal intensive care unit (NICU) are treated with antibiotics. One of the effects observed with their use is the alteration of the microbiota, which is associated with metabolic disorders including obesity. In this study, the aim was to determine in newborn rats, whether most commonly prescribed antibiotics in Mexican NICUs have a differential effect on weight gain, development of adiposity, and modification of carbohydrate and lipid metabolism. Methods: This is an experimental study in newborn rats, comparing Ampicillin/Meropenem (Access/Watch groups) 100 mg/g every 12 h /10 mg/g every 12 h (each of these two antibiotics was adjusted to the required dosages based on weight); Cefotaxime 200 mg/g every 24 h; (Watch group) and Amikacin 15 mg/g every 24 h (Access group) versus Saline Solution as Control. Each antibiotic was intraperitoneal administered daily for 5 days in males (to avoid gender differences) to 14 newborn rats in each group. A comparison of anthropometric and biochemical parameters registered on day 28 was made using ANOVA analysis. Differences were considered if p < 0.05.Results: Amikacin was the antibiotic with the greatest effect on anthropometric measurements and LDL-cholesterol; as well as Cefotaxime on Glucose and triglycerides, while Meropenem plus Ampicillin showed the weakest effect on measured parameters. Conclusion: The administration of antibiotics in the neonatal stage has repercussions on the body composition of adult rats, as well as on lipids and carbohydrates serum levels. Future studies might evaluate the toxicity of antibiotics on the immature neonatal organs.

This study aims to determine the impact of the type of ICT usage on household income from main vegetables, agriculture (including main vegetables), and total income. This study is based on surveys and interviews with vegetable farmers using structured questionnaires conducted in three villages in Indonesia, including Cianjur, Sleman, and Malang in West Java, Yogyakarta, and East Java, respectively. The data were collected from 375 respondents selected in each region from November 2021 to March 2022. An econometric model called the multivariate linear regression (MLR) model is used to assess heterogeneous factors that influence the possibility of increasing the income of vegetable farmers. The study found that variables that use ICT as a primary variable, household, marketing, geographical characteristics, and immediate source information in agriculture have a significant impact on household income (P < 0.01) from primary vegetables, agriculture, and total income, with multiple R squares of 70.5, 72.0, 73.7% and F statistics of 28.48, 30.66, and 33.34, respectively. In summary, this novel study shows that the five categories of information technology used in farming and selling of harvest have a positive impact on household incomes

This paper serves as a seamless continuation of the previous work titled "Special Spirals are Produced by the ROTASE Galactic Spiral Equations with the Sequential Prime Numbers." Upon revisiting the data, a noteworthy observation emerged: the special spirals can be meticulously aligned with the prime spiral on a point-by-point basis through careful scaling and rotation for the parameter ρ remaining a constant greater than one. As ρ increases, both the scaling factor and rotation angle exhibit a corresponding augmentation, albeit with an intriguing caveat—the rotation angle asymptotically approaches its limit of 90°. Notably, the galactic spiral can be perceived as a remarkably close approximation of the Archimedean spiral, with the degree of proximity increasing as ρ grows larger. Furthermore, an intriguing correspondence arises when ρ takes on a value of 1; at this value, the galactic spiral bears a striking resemblance to the Fermat spiral, the space between two consecutive spiral loops undergoes a continuous reduction as the loop's radius expands, yet it never diminishes to zero; simultaneously, the area between two successive loops experiences an augmentation with the value of ρ.

The evaluation of tourism sustainability has become of paramount importance for researchers and decision-makers to better protect tourist destinations, especially with the continuous increase in the number of tourists and visitors that alter the natural components of the environment and degrade ecosystems. Therefore, the use of geographical data is of great significance, particularly in environmental monitoring and land management, due to its ease of use and availability. However, calculating composite indices for sustainability involves several steps and requires a basic understanding of existing models. This article describes the development and design of a package of plugins in QGIS that serves as an easy-to-use tool for processing and automatically calculating economic, environmental, social indices, and the overall index of tourist sustainability. These plugins have been built in Python and designed as QGIS software plugins.

The crab’s fishery of the genus Callinectes is one of the most important fisheries in Mexico. Sonora and Sinaloa are the states in the eastern coast of the Gulf of California, the mainland coast. Sinaloa encompass the greatest production in Mexico of these species, in particular the Bahía Santa Maria La Reforma (BSMR), supply the most important catches. An administrative mandatory document for the fishery is the Management Plan of Crab. One of the main weaknesses found in it for Sonora and Sinaloa is the lack of fisheries assessment in both states. For this reason, a structured size method called CASA (Catch at Size Analysis) was applied in the BSMR, in the seasons 2000, 2011 and 2014 to C. bellicosus. The first catch size (CW50%) estimated for females were 97.5, 102.5 and 100 mm, while in males it was estimated at 117.5, 107.5 and 102.5 mm. In the specific case of fishing mortality (F), the algorithm allowed us to detect that in the larger sizes the greater fishing pressure is applied for both, females and males, yielding a weighted exploitation rate of 0.047 (2000), 0.119 (2011) and 0.426 (2014) for females and 0.045 (2000), 0.295 (2011) and 0.132 (2014) for males, all below 50% (E= 0.50), crab C. bellicosus in BSMR is not at risk of over exploitation.

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To determine a fair price, it is necessary to consider the conditions and needs of both parties: provider and user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The proposed mechanism takes into account three factors including fault possibility, interaction between virtual machines, and energy consumption to reach a fair price. In the proposed game, the provider decides on the price of the virtual machine which can be offered to the user on each physical machine and the user decides between the virtual machines offered to run its application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. According to the results, the proposed mechanism has been able to determine a more reasonable price for the user and the provider which increases the overall profitability of the cloud system.

In this work, quiescence is added to the Susceptible-Infectious-Recovered (SIR) model with demography. In order to investigate consequences of quiescence in the infection process in more depth, we use stochastic simulations on the stochastic version of model that we built. This method provides a more accurate picture of the dynamics of infectious diseases by taking into consideration the inherent randomness in the disease processes. We examine the effects of quiescence on the number of infected people using simulations. The results, presented in histograms depicting the distribution of infected individuals, reveal a notable trend: the mean number of infected individuals is higher when quiescence is incorporated into the dynamics. These finding emphasizes the dynamic influence of quiescence on infectious disease spread. The higher mean number of infections during periods of quiescence highlights the need for public health strategies that are flexible enough to focused interventions during these times to reduce the possibility of an increase in infections.

The crosslinking behavior of polyethylene (PE) determines its exceptional performance and application. In this study, we investigated the crosslinking behaviors of different PE resins through model simulation and rheological methods. Specifically, the mathematical equation of "S" model was established for PE resin. According to this equation, the optimal maximum gel content for high-density polyethylene (HDPE) was found to be around 85%. Moreover, the maximum crosslinking degrees for different PE resins depended largely on their density and molecular weight. The melt viscosities before crosslinking in PE resins were highly influenced by their melt index. The higher melt indexes resulted in the lower storage moduli, improving melt processability during processing. In addition, the crosslinking rates of PE resins were strongly influenced by peroxide concentration, independent of PE resin structures. For high molecular weight and low-density PE resins, they exhibited decreased ti values, increased A0 values, and decreased k6 values. However, there were no noticeable variations in the values of k2 and phi among different PE resins. And all simulated modeling outcomes showed the remarkable consistency with the experimental rheological data. These findings are of utmost significance in the industrial manufacture of PE resin.

The realm of natural language processing has always been fascinated by the intricacies of sentence semantics. The emergence of context-aware word representations, especially from pre-trained models like ELMO and BERT, has revolutionized several semantic tasks including but not limited to, question answering, text categorization, and sentiment analysis. Despite these advancements, the integration of supplementary knowledge, particularly syntactic, to augment semantic comprehension in models remains an area ripe for exploration. This paper introduces the Syntactic Enhancement Network (SEN), a pioneering method for synergizing syntactic elements with established pre-trained language models. Our approach encompasses a dual-phase evaluation: initially, we delve into the syntactic augmentation of both RNN-based and Transformer-based language models; subsequently, we test our SEN's proficiency in two specific domains: the task of sentence completion and the extraction of biological relationships. The results are striking, with SEN achieving a stellar 91.2% accuracy in sentence completion—surpassing baseline models by a substantial 37.8%. In the context of biological relation extraction, SEN demonstrates competitive prowess with a 75.1% F1 score.

Cyberaggression is an important problem today; it can affect adolescents in different ways. Therefore, reliable and valid measures are necessary to better study the phenomenon. The aim of the present study was to generate validity and reliability evidence for a Spanish-language cyberaggression scale in a population of adolescents from northwestern Mexico. The results of this study contribute to the research and focus on cyberaggression in adolescents in Mexico. The measure in this article differs from other measures by detecting the different roles, including the bystander, contributing to the state of knowledge about cyberaggression, ratifying the presence of this actor that has not been sufficiently explored in the cyber context.

Amyotrophic Lateral Sclerosis (ALS) stands as a debilitating neurodegenerative condition characterized by the progressive degeneration of motor neurons. Despite extensive research in various model animals, the cellular signal mechanisms of ALS remain elusive, impeding the development of efficacious treatments. Among these models, a well-characterized and diminutive organism, Caenorhabditis elegans (C. elegans), has arisen as a potent tool for investigating the molecular and cellular dimensions of ALS pathogenesis. This review summarizes the contributions of C. elegans models to our comprehension of ALS, emphasizing pivotal findings pertaining to genetics, protein aggregation, cellular pathways, and potential therapeutic strategies. We analyze both the merits and constraints of C. elegans system in the realm of ALS research, and point towards future investigations that could bridge the chasm between C. elegans foundational discoveries and clinical applications.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

Camera malfunctions or loss of storage elements in imaging devices may lead to loss of important image information or random impulse noise interference. Low rank is one of the important prior information in image optimization processing. This paper uses different low-rank constraint models of the image matrices to recover the impulse interference satellite images. Firstly, an overview of image inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F-norm is presented, and corresponding optimiza-tion iterative algorithms are provided. Then, we conducted experiments under three types of pulse interference and provided visual and numerical results. Finally, it is concluded that the WSVT_ADMM method based on the weighted nuclear norm can obtain the best image inpainting results; The UV_ADMM method based on F norm of matrix factorization has the least computa-tion time and can be used for large-scale low-rank matrix computation; The WSVT_ADMM method based on weighted nuclear norm and the TSVT_ ADMM method based on truncated nuclear norms can significantly improve the repair effect compared to the nuclear norm-based methods such as SVT, SVP, and n_ADMM.

Motivated by the intriguing discovery of $X(6900)$ by LHCb Collaboration, we undertake a comprehensive study of the $s\bar{s}s\bar{s}$ tetraquark system in positive parity, employing the Gaussian expansion within the chiral quark model method. We consider two structures, diquark-antidiquark ($ss$-$\bar{s}\bar{s}$) structure and meson-meson ($s\bar{s}$-$s\bar{s}$)strcuture, encompassing all conceivable color and spin configurations. Despite the absence of bound states in our calculations, we have identified potential resonant states with $J^P=0^{+}$, namely $R(0,2150)$ and $R(0,2915)$, as well as a resonant state with $J^P=1^{+}$ denoted as $R(1,2950)$, and a resonant state with $J^P=2^{+}$ denoted as $R(2,2850)$, utilizing the real-scaling method. Through a comparison of their energies and widths, we propose that $R(0,2915)$ and $R(1,2950)$ may share characteristics with $X(6900)$, while $R(0,2150)$ could be a promising experimental candidate for $f_0(2100)$. We strongly advocate for experimental investigations to shed light on the existence and properties of these resonant states.

Many relevant health social actors have voiced concerns about the danger of self-medication in times of growing use of over-the-counter medicines and, in some contexts, unregulated selling of them. Previous research has examined the incidence of parental self-medication as well as the use and abuse of antibiotics without professional advice. However, these studies have limited evidence on the role of family doctors and the perceived severity of self-medication in the case of parents. Based on Health Belief Model, our research tested the effects of exposure to medical information on the perceived level of self-treating the children without medical advice. Specifically, we aimed to assess whether exposure to information warning about the risks of treating children without a medical prescription influences parents' attitudes toward administering medicines to their children without medical advice. The results showed that our respondents evaluated the practices of self-medication negatively, especially when it came to treating their children without medical advice. However, their attitudes towards self-medication varied depending on their beliefs about administering certain medications. Additionally, the results showed that the perceived level of satisfaction with the family physician could be a significant factor in controlling and avoiding the treatment of children without professional advice.

The precipitation, flocculation, and deposition of asphaltene cause severe formation damage within a reservoir and shorten a well’s productive life. Pressure depletion is one factor that contributes to asphaltene precipitation during production; therefore, the first step in managing asphaltene is to determine the onset pressure of the precipitation. While there are numerous equation of state models that can be used to predict the onset pressure, these models are complex and heavily reliant on tuning parameters. Using multivariate linear regression, this work attempts to develop a simple and accurate thermodynamic model for predicting the upper precipitation onset pressure under pressure depletion above the bubble point pressure (Pb) at various temperatures. A total of 94 experimental data points from 37 published crude oil data sets were compiled from the literature. To develop the model, 59 experimental data points were used as training data and 35 experimental data points as testing data. According to the results of the multicollinearity test, the bubble point pressure, temperature, resins, and saturate-to-aromatic ratio were chosen as predictors. The upper onset pressure data with comparable trends were clustered, and unsupervised recognition of three distinct cluster groups was performed. For each cluster identified, a multivariate linear regression model was developed. The model was chosen based on Mallow’s coefficient of determination (Cp), adjusted R² (statistical measure of fit), and S (standard error of the regression slope). The developed model was tested using a data set, and the results showed an adjusted R² of 96.25%, with a mean absolute error of 4.1%. The model was randomly applied to 15 data points to compare it to perturbed-chain statistical associated fluid theory (PC SAFT) and the Peng-Robinson equation of state models and to the multivariate regression models of Fahim (2007) and Ameli et al. (2016). The results showed that the mean absolute error for predicting the asphaltene precipitation onset pressure was 2.82% using Peng-Robinson, 2.36% using the PC SAFT equation of state, 23.96% using the Fahim model, 24.80% using the model reported by Ameli et al., and 2.39% using the newly developed multivariate regression model. The developed multivariate model appears to be as accurate as the PC SAFT equation of state modeling with tuning parameters. The primary advantage of multivariate regression is that, unlike the PC SAFT equation of state model, it does not require saturates, aromatics, resins, and asphaltenes (SARA)-based characterization methodologies or rigorous parameter tuning. It is simple to use, quick, and it produces results in a short period of time.

Renewable energy research becomes increasingly necessary as it grows in importance. Wind turbines are an excellent method of harnessing wind energy, which is one of the most important renewable energy sources. In light of the importance of studying wind turbine performance, this study describes the background and principles of wind turbine operation. Computational fluid dynamics (CFD) was used to evaluate the hydrodynamic performance of two types of offshore and onshore wind turbines. Mechanical power and thrust force are calculated using the CFD model for both types of turbines in a steady state based on the number of blades and rotors. The results of three-dimensional simulations are compared with those of experimental testing. Based on the method and assumptions used, the offshore turbine's mechanical torque showed an average deviation of about 4%. Increased wind speed increased mechanical power and thrust, while turbulence caused irregular pressure distributions on surfaces as a result of turbulence intensity. Furthermore, as the free wind speed increases, the blade tip and hub speed will also increase, expanding the vortices created by that flow.

A model to rationalize the effects of test temperature and microstructural variables on the creep crack growth (CCG) and creep-fatigue crack growth (CFCG) rates in ferritic steels is described. The model predicts that as the average spacing between grain boundary particles that initiate creep cavities decrease, the CCG and CFCG rates increase. Further, CCG data at several temperatures collapse into a single trend when a temperature compensated CCG rate derived from the model is used. CCG and CFCG behavior measured at different temperatures is used to assess the effects of variables such as differences between basemetal (BM), weldmetal (WM) and the heat-affected zone (HAZ) regions. The model is demonstrated for Grade 22 and Grade 91 steels using data from literature. It is shown that differences between the CCG behavior of Grade 22 steel in new and ex-service conditions are negligible in the BM and WM regions but not in the HAZ region. The CCG behavior of Grade 91 steels can be separated into creep-ductile and creep-brittle regions. The creep-brittle tendency is linked to the presence of excess trace element concentrations in the material chemistry. Significant differences found in the CCG rates between BM and the WM and HAZ regions of Grade 91 steel were explained.

It is known that the source of a tectonic earthquake in the framework of the theory of elasticity and viscoelasticity is considered as a displacement along a certain fault surface. Usually, when describing the source, the geometry of the fault surface is simplified to a flat rectangular area. The displacement vector is assumed to be constant. In this paper, we propose a model of an earthquake source in the form of a displacement with a constant vector along a stochastic surface. A number of standard assumptions were made when modeling. We take into account only the elastic properties of the medium. We consider the Earth’s crust as a half-space and assume that the medium is homogeneous and isotropic. For the mathematical description of the earthquake source, we use the classical force equivalent of displacement along the fault. This is the distribution of double pairs of forces. The field of displacements under the action of body forces is found through a combination of Mindlin nuclei of strain. The paper presents solutions for strike-slip fault. To obtain a stochastic fault surface, we propose to introduce a random deformation of a rectangular flat surface. The paper presents the results of a computational experiment comparing the levels and regions of relative deformations of the Earth’s crust in the case of displacement along a flat rupture surface and along a stochastic one. In the case of a stochastic surface, the regions of relative deformations become asymmetric. The developed model will help to indirectly take into account various mechanical properties of the Earth’s crust when modeling deformations caused by earthquakes.

An attempt has been made to explore the relative contributions of moisture feedback processes on tropical intraseasonal oscillation or Madden Julian Oscillation (MJO). We focused on moisture feedback processes, including evaporation wind feedback (EWF) and moisture convergence feedback (MCF), which integrates the mechanisms of convective interactions in the tropical atmosphere. The dynamical framework considered here is a moisture-coupled, single-layer linear shallow-water model on an equatorial beta-plane with zonal momentum damping. With this simple approach, we aimed to recognize the minimal physical mechanisms responsible for the existence of the essential dispersive characteristics of the MJO, including its eastward propagation(k>0), the planetary-scale (small zonal wavenumbers) instability, and the slow phase speed of about ≈ 5 m/sec. Further, we extended our study to determine each feedback mechanism’s influence on the simulated eastward dispersive mode. Our model emphasized that the MJO-like eastward mode is a possible outcome of the combined effect of moisture feedback processes without requiring additional complex mechanisms such as cloud radiative feedback and boundary layer dynamics. The results substantiate the importance of EWF as an energy source for developing eastward moisture mode with a planter-scale instability. Moreover, our model endorses that the MCF alone could not produce instability without surface fluxes, although it has a significant role in developing deep convection. It is found that the MCF exhibits a damping mechanism by regulating the frequency and growth rate of the eastward moisture mode at shorter wavelengths.

Infectious illnesses have always posed a threat to human health, with tuberculosis (TB) being a major concern. The use of various medications and antibiotics in the fight against such illnesses has led to the emergence of drug-resistant infectious diseases, which have become increasingly dif-ficult to manage as medical and scientific research has advanced. Inadequate medical infrastruc-ture, incorrect treatment practices that exacerbate resistance patterns, and transmission within communities and healthcare facilities are the main factors contributing to the spread of drug-resistant TB. Though treating individuals with multiple drug resistance (MDR) and extreme drug resistance (XDR) strains is challenging, early identification of resistance and the imple-mentation of a well-designed treatment regimen can result in a cure. Community-based inter-ventions that address socioeconomic barriers to adherence can also enhance treatment outcomes. While there have been few studies and proposed conceptual models on how to manage and prevent various drug-resistant TB mutations and lineages, a model aimed at limiting and controlling such mutations has been developed. This paradigm seeks to bridge the gap by facilitating the exchange of knowledge among healthcare professionals (HCP) in healthcare facilities (HCF), diagnostic laboratories (DL), and research institutes (RI), particularly for underprivileged communities in the Eastern Cape. The model guide will also monitor and evaluate TB management plans

The optimization design of buildings is very important the energy consumption, carbon emissions ,and sustainable development of buildings. The low-temperature granary has low grain storage temperature and high energy consumption indexes. The design scheme of roof insulation for low-temperature granary should be determined in actual building design processes by considering economy, carbon emissions, and outdoor climate, comprehensively. In this paper, the low-temperature granary roof insulation for different ecological grain storage zones in China are optimized by using a new low-carbon optimization design method. The low-carbon optimization design method can response to the economical issue, emission reduction issue, and outdoor climate issue, simultaneously. The application results of the optimization design method in ecological grain storage zones in China indicate that outdoor climate has significant impacts on the economic performance and carbon reduction effect of roof insulation. The considering of carbon emission cost can apparently increase economic efficiency of roof insulation. The optimal economic thickness of expanded polystyrene (EPS) in Urumqi, Harbin, Zhengzhou, Changsha, Guiyang and Haikou cities is 0.025 m, 0.037 m, 0.085 m, 0.097 m, 0.072 m and 0.148 m, respectively. The different outdoor climates of seven ecological grain storage areas in China have important influences on the comprehensive economic performances of low-temperature granary roof insulation. The design of low-temperature granary roof insulation in Haikou city has the best economic performances among the seven ecological grain storage zones in China.

The main purpose is to create a model for managing sustainability in the metro and its entities in Gauteng. Within this context, the study will focus on the issue of measures, outcomes, and impacts in the City of Johannesburg (CoJ). The study will clarify a public sector application in municipal entities of the CoJ because sustainability is not well understood or used in the public sector. This article investigates the sustainability phenomena in a metropolitan setting, including the CoJ and its municipal entities. The purpose is rather two-fold. A focus group discussion was held with CoJ senior executive managers to provide feedback on the sustainability model. The information and insights were used to refine the sustainability model to manage sustainability in Coj and its enti-ties. The data was analysed using thematic analysis to refine the sustainability model. The re-sults indicated that sustainability is a function of having a shared understanding of sustainabil-ity, the ability to mainstream and integrate sustainability thinking into all programmes and ac-tivities, leadership commitment, requisite competencies, and monitoring and evaluation of sus-tainability. The implication for leadership is to ensure the alignment of sustainability to the strategic objec-tives of CoJ and to foreground any planning in CoJ with a sustainability lens. Achieving this focus requires a shared understanding, development of competencies, committed leadership to imple-ment measurement and impact framework, and adjusting plans based on monitoring and evalua-tion outcomes.

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system based on the opinion of a neurology expert to classify relapsing-remitting multiple sclerosis patients: high, medium, and low responder to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated to interferon-beta response is proposed for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results shows that the fuzzy system presents a 100% efficiency compared with an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model is evaluated, and a 0.8 testing accuracy is achieved. Hence, a pipeline model including data standardization, data compression, and a learning algorithm, can be a useful tool for getting reliable predictions about the response to interferon-beta.

Prostate cancer is a common cancer among men and typically progresses slowly for several decades before becoming aggressive and spreading to other organs, leaving few treatment options. While large animals have been studied, the dog's prostate is anatomically similar to humans and has been used to study spontaneous prostate cancer. However, most research currently focuses on the mouse as a model organism due to the ability to genetically modify their prostatic tissues for molecular analysis. One hallmark in this research was the identification of the prostate-specific promoter Probasin, allowing for prostate-specific expression of transgenes. This has led to generations of mice with aggressive prostatic tumors through overexpression of the SV40 oncogene. The Probasin promoter is also used to drive Cre expression and allow researcher to generated prostate specific loss-of-function studies. Another landmark in the process of modeling prostate cancer in mice was orthoptic delivery of viral particles. This technology allows selective overexpression of oncogenes from lentivirus or the use of CRISPR to generate complex loss of function studies. These genetic modified models are complemented by classical xenografts of human prostate tumor cells in immune deficient mice. Overall, pre-clinical models have provided a portfolio of model systems to study and address complex mechanisms in prostate cancer for improved treatment options. This review will focus on the advances of each technique.

Aortic aneurysms (AA) occur in 4.8% of people causing 150,000 deaths annually. While endovascular aneurysm repairs reduce surgical morbidity, device-related failures (leak/displacement) are frequent highlighting the need for test models that better represent the mural geometry and compliance changes in human AAs. We aimed to develop and characterise an ex vivo porcine aortic model of AA. The optimal duration of tissue elastase exposure to emulate AA changes in elastin microstructure and content was determined using porcine aortic rings. Elastase-induced changes were quantified morphologically, and mechanical properties assessed via ring tensile testing. Subsequent experiments tested the potential for localised elastase treatment in a 1 cm segment of porcine aorta using a specially designed 3D printed test rig. The effect on pressure-diameter behaviour was investigated via inflation-extension testing. Elastase treatment produced time dependent decreases in elastin, resulting in an increased tensile modulus and circumferential length in the ring samples in the final phase of the J-shaped tissue stress-strain curves. In whole aortic segments, localised elastase-induced luminal degradation was successfully limited to a central region. The degree of elastin degradation achieved was sufficient to cause localised dilation with respect to controls under physiological pressures. Localised elastin degradation in porcine aortic segments is feasible and emulates the changes seen clinically in aortic aneurysms.

Historical and Endowment Properties are different from Heritage and cultural Properties, as Historical and Endowment properties are governed by a unique set of laws that Waqf recipients must abide by. Property that is entrusted is usually in the form of buildings, land or valuables which in preservation is not limited to time as long as the property can be utilized. Reliable information technology is needed to ensure data security both digitally and physically, while the rapid development of information technology demands information openness and this will be a challenge in itself. The objectives of this study include examining the collection of historical databases and endowments, the relationship between digital data and physical data and management organizations. The method of how to design a query model to display data is then analyzed whether the data conforms to the rules in waqf management. The results are expected to bring up accurate data between digital data and physical data and if there are differences into findings for the next analysis.

For the first time in human history the majority of people no longer live in a rural environment. According to the United Nations, over 50% of the global population now lives in cities. The abandonment of the rural environment has grave consequences for society in general, as these areas are healthier places to live, produce the food we all depend on, and are an important factor for preserving biodiversity and environmental sustainability. In this paper we will provide a historical overview of rural repopulation models and analyse a number of models currently being applied in several countries in Europe and Latin America. We will also offer observations on a pilot repopulation project called “Habita Tierra” currently being developed. The common characteristics of different repopulation models prompted us to develop a model based on active listening and accompaniment of people and local agents as well as other important factors. Rural depopulation in Spain involves a number of interrelated problems, one of the most salient being the aging rural population. The average farmer in Spain is 61 years of age. There is also the problem of gender imbalance in rural areas, where women enjoy few opportunities or alternatives. In turn, low fertility rates, increasing stress on limited public healthcare, education and transpor-tation resources, a lack of cultural activities and poor connectivity are also factors driving rural depopulation. The only group which is increasing the rural areas in Spain are immigrants, who now account for 10% of the rural population, often performing the agricultural, fishing and livestock farming ac-tivities abandoned by native residents. This group, along with young people, and women of all ages and the programs which support them may offer a solution to the challenge of depopulation. The paper concludes with a list of the key features of successful repopulation programs, drawn from a comparison of historic and contemporary models.

Mice are one of the frequently used animal models in science research whose behavioral characteristics can provide much valuable information in biology, neuroscience, and pharmacology. Nowadays, artificial intelligence is widely used in mice behavior analysis. Integrated AI systems such as ChatGPT and VisualGPT are already available, and we discuss the feasibility of MiceGPT to help researchers identify and classify mouse behavior more easily. We review the applications of mice behavior analysis, analyze the tasks of deep learning on these applications based on an AI pyramid, and finally summarize the AI approaches to solve these tasks. Based on these summaries, we propose three MiceGPT architectures to demonstrate the theoretical feasibility of MiceGPT.

Background: Aedes albopictus, the Asian Tiger Mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travellers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favour the seasonal development of Ae. albopictus.

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.

Electromagnetic (EM) waves, used for purposes such as geophysical characterization, impact properties to be measured. This paper describes the effects of radio frequency (RF) waves on the hydraulic conductivity of glass beads and natural sand. A series of tests were conducted using a customized, rigid-wall, cylindrical permeameter inside a resonant cavity made of Plexiglas covered with electrically conductive transparent films. Constant head ASTM-D2434 [1] tests were performed to measure the samples’ hydraulic conductivity. RF stimulation was performed using a magnetically coupled loop antenna at a constant frequency of 726 MHz and input power levels of 10, 25, 40, and 50 Watts. The hydraulic conductivity of both natural sand and glass-bead samples increased with RF stimulation. Furthermore, measurement of the electric-field component of RF waves was also performed to illustrate the pattern of the electric field as well as evaluate the RF impact on the hydraulic conductivity tests. The electric field was numerically simulated and validated against experimentally measured electric fields. A finite-difference numerical model was developed in MATLAB to analyze the seepage flow, which was then validated against the experimental results. An optimization scheme was then used to develop a governing equation for the RF impact on hydraulic conductivity.

Background: Pteropodine (PT) is a component of some plants with potentially useful pharmaco-logical activities for humans. This compound has biomedical properties related to the modulation of the immune system, nervous system, and inflammatory processes. This study addresses the an-ti-inflammatory and antioxidant capacity of pteropodin in a murine model of arthritis and induced edema of the mouse ear. Methods: To evaluate the anti-inflammatory activity, we used the reversed passive Arthus reaction (RPAR), which includes the rat paw edema test, the rat pleurisy test, and a mouse ear edema model. The antioxidant effect was evaluated by determining the myeloperoxidase enzyme activity. Results: PT showed an anti-inflammatory effect in the different specific and non-specific tests. We found a 52 to 74% of inhibitory effect of PT in the rat paw edema test, 52% of reduction of the pleural exudate volume, and low neutrophil count (28%), when compared to the control group. We also found a 81% of inflammatory inhibition of the ear edema, and a significant myeloperoxidase enzyme inhibition by the three doses of PT. Conclusions: PT exerted a potent an-ti-inflammatory effect in the acute inflammation model in rodents.

In this work the author utilizes the quantum geometrization of spacetime for describing the gravitational interaction within the framework of quantum theory. This approach allows for the development of an equation of gravity that is mathematically connected to the fermion and boson fields. This achievement is accomplished by incorporating two fundamental principles: covariance of the quantum field equations and the principle of least action. By considering these principles, a theory is established that enables the calculation of gravitational corrections to Quantum Electrodynamics, and potentially, to the standard model of particle physics as well. The theory also provides an explanation for two phenomena: the existence of a cosmological pressure density similar to quintessence, that is compatible with the small value of the observed cosmological constant, and the breaking of matter-antimatter symmetry at high energies offering insights into why there is an imbalance between the two in the early universe. In the cosmological modeling of the theory, there exists a proposal to account for the formation of supermassive black holes that are accompanied by their own surrounding galaxies, without relying on the process of mass accretion. The model, in accordance with recent observations conducted by the James Webb Space Telescope, supports the notion that galactic configurations were established relatively early in the history of the universe, shortly after the occurrence of the Big Bang.

In this paper, we extend the Merton model of investment in discrete time to the cases when there is a finite number of investors and the market is with frictions represented by convex penalty functions defined for each investor. In the main result of this paper, we proved the existence of optimal strategy of investment by using a new approach based on the formulation of an equivalent general equilibrium economy model.

Although ChatGPT promises wide-ranging applications, there is a concern that it is politically biased; in particular, it has a left-libertarian orientation. Nevertheless, in light of recent trends in attempts to reduce such biases, this study re-evaluated the political biases of ChatGPT using political orientation tests and the application programming interface. Moreover, the effects of the languages used in the system as well as gender and race settings were evaluated. The results indicated that ChatGPT had less political bias than previously thought; however, they did not entirely discount the political bias. The languages used in the system and the gender and race settings may induce political biases. These findings enhance our understanding of the political biases of ChatGPT and may be useful for bias evaluation and designing ChatGPT’s operational strategy.

The study considers building a dynamic model of profit maximization for the car-sharing system and its verification on the example of implementing such a system in Astana (Republic of Kazakhstan). The region, bounded by the administrative boundaries of Astana, was divided into subregions by covering the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of trips in the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific sub-region, for which the quantitative parameters necessary for solving the profit maximization problem in the car-sharing system are calculated. Stations with cars will be placed in the cells of the grid, which will be available to car-sharing service customers. Based on the collected data, a dynamic (four periods per day) and static profit maximization model in the car-sharing system was built. Modeling was carried out based on the built models in the example of Astana. It was established that using a dynamic model of profit maximization in the car-sharing system increases the profit by 3.7%.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the epidemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of merging online and onsite visits are already being investigated by researchers, not only to avoid the spread of a disease but also to enable visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. The current work takes into account the Contextual Model of Museum Learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices.

This study used the ECOSSE model (v. 5.0.1) to simulate soil respiration (Rs) flux-es estimated from ecosystem respiration (Reco) for eight European permanent grassland (PG) sites with varying grass species, soils, and management. The main aim was to evaluate the strengths and weaknesses of the model in estimating Rs from grasslands, and to gain a better understanding of the terrestrial carbon cycle and how Rs is affect-ed by natural and anthropogenic drivers. Results revealed that the current version of the ECOSSE model may not be reliable for estimating daily Rs fluxes, particularly in dry sites. However, it could still be a valuable tool for predicting cumulative Rs from PG. Additionally, the model demonstrated accurate simulation of Rs in response to grass cutting and slurry application practices. The sensitivity analyses and attribution tests revealed that increased soil organic carbon (SOC), soil pH, temperature, reduced precipitation, and lower water table (WT) depth could lead to increased Rs from soils. The variability of Rs fluxes across sites and years was attributed to climate, weather, soil properties, and management practices. The study suggests the need for additional development and application of the ECOSSE model, specifically in dry and low input sites, to evaluate the impacts of various land management interventions on carbon sequestration and emissions in PG.

High density of buildings and the large traffic volume of cities give rise to narrow spaces and high passenger flows in most subway stations. When evacuation is required during an emergency, these problems may trigger issues in operational safety. Therefore, it is imperative to comprehensively assess subway station emergency evacuation times and capacities during the operating process to ensure the design is safe, science-based, and rational. To further enhance safe evacuation capacity, this paper proposes an overall evacuation time model that considers the multielement charac-teristics of subway stations in multiple segments. The evacuation route is decomposed into five stages according to the critical nodes in the evacuation process. An overall emergency evacuation time model is established based on the diversity of bottlenecks in the five stages, integrating elements such as the horizontal movement velocity of passengers, subway equipment parameters, and human density. Taking Xi’an Wulukou Subway Station as an example, this paper verifies the outcomes of the theoretical model against the Pathfinder software and conducts additional anal-yses of the evacuation conditions of stairs and exits. The results show that the error between the theoretical emergency evacuation time and simulation evacuation time was 5.4%, The emergency evacuation model established in this study boasts strong robustness and stability.

For the ground-state properties of gas-phase nanomolecules with multi-reference character, thermally-assisted-occupation density functional theory (TAO-DFT) has been recently found to outperform the widely used Kohn-Sham density functional theory, when the conventional exchange-correlation energy functionals are employed. Aiming to explore solvation effects on the ground-state properties of nanomolecules with multi-reference character at a minimal computational cost, we combine TAO-DFT with the polarizable continuum model (PCM). To show its usefulness, TAO-DFT-based PCM (TAO-PCM) is employed to study the electronic properties of linear acenes in three different solvents (toluene, chlorobenzene, and water). According to TAO-PCM, in the presence of these solvents, the smaller acenes should have nonradical nature, and the larger acenes should have increasing polyradical nature, showing remarkable similarities to the past findings in the gas phase.

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep-fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determine an analytical solution for the straightened superheater tube under thermal conditions and compare it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep-fatigue life of the reheater header is estimated by considering flexible operation factors and employing the Coffin-Manson and Larson-Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermal-structure analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

The ongoing emergence of SARS-CoV-2 virus variants remains a source of concern because it is accompanied by the potential for increased virulence as well as evasion of immunity. Here we show that, although having an almost identical spike gene sequence as another Omicron variant (BA.5.2.1), a BA.4 isolate lacked all the typical disease characteristics of other isolates seen in the Golden Syrian hamster model despite replicating almost as effectively. Animals infected with BA.4 had similar viral shedding profiles to that seen with BA.5.2.1 (up to day 6 post infection) but they all failed to lose weight or present with any other significant clinical signs. We hypothesize that this lack of detectable signs of disease during infection with BA.4 was due to a small (nine nucleotide) deletion (∆686-694) in the viral genome (ORF1ab) responsible for production of non-structural protein 1 which resulted in the loss of three amino acids (aa 141-143).

Study aimed to evaluate the impact of integrating family planning with maternal, newborn, and child health (FP-MNCH) on uptake of modern contraceptive methods and related health outcomes in two districts of the province of Sindh, Pakistan. Impact of intervention was evaluated using a quasi-experimental control before-after study design. Intervention included capacity building of healthcare providers and outreach workers, ensuring sustained supplies of family planning commodities, data-driven decision-making, and community engagement activities. Data was collected through household surveys at baseline (December2020) and endline (December 2022). The sample size was estimated as 880 married women of reproductive age (MWRA) in each district. The Difference and Difference(DiD) analytical method was used to estimate the impact of intervention adjusted for potential confounding factors. There was statistically significant increase of 11.3% in the current use of MCM in the intervention group compared to the control group (p value <0.001), with increases observed in the uptake of injection , implants, and condoms. Additionally, there was an increase in the proportion of women who had ANC visits (DiD 10.5% p value 0.003), FP counselling during ANC (DiD 15.6% p value < 0.001), LHW visits during pregnancy (DiD 15.1% p value 0.021), PNC check-ups for mother (DiD 25.2% p value <0.001), LHW visits after delivery (DiD 20.4% p value <0.001), and LHW advised for family planning at PNC visit n (DiD 14.5% p value 0.030). This study provides strong evidence for the scaling-up of integrated interventions through existing health care platforms utilizing human resources deployed by the government. This FP-MNCH model has the potential to be adopted in other provinces and at federally administered areas for both health and population planning. This model should be considered for replication in other districts of Sindh to accelerate current level of integration already existing.

The territory of Serbia is vulnerable to various types of natural and man-made disasters. The risk is not equal across the entire territory, depending on the type of hazard and the expected potential for damage. So far, the level of community disaster resilience has not been determined in Serbia. There are no scientific preconditions for its improvement to reduce the future material and intangible consequences of disasters. Regarding that, the project’s main objective is to develop and validate a predictive model of community disaster resilience based on social identity influences through an investigation impact of social identity indicators on the level of community disaster resilience in Serbia. The project is based on the upcoming research on whether the level of community disaster resilience can be predicted based on social identity indicators, how social identity indicators affect different dimensions of community disaster resilience, and how disasters shape social identity. The project is based on multimethod research in which quantitative (face-to-face interviews in 40 of the 191 municipalities), and qualitative (semi-structured interviews) research methodology will be applied. A developed predictive model with an index of community disaster resilience in Serbia will empower the creation of preconditions for designing public policies, strategies and procedures for improving resilience and reducing the consequences of disasters on people and their property and enhancing citizens’ security. The project will encourage the prediction of community disaster resilience based on social identity indicators, improving disaster foresight and preparing to limit disaster losses. Based on the development of analytical frameworks for understanding community disaster resilience and social identity in disasters, essential preconditions for designing innovative information systems will be created to enable local communities to increase their level of resilience.

The Golden Syrian hamster (Mesocricetus auratus) is now commonly used in preclinical research for the study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the assessment of vaccines, drugs and therapeutics. Here we show that hamsters inoculated via the intranasal route with the same infectious virus dose of prototypical SARS-CoV-2 administered in a different volume present with different clinical signs, weight loss and viral shedding, with a reduced volume resulting in reduced severity of disease similar to that obtained by a 500-fold reduction in challenge dose. The tissue burden of virus and the severity of pulmonary pathology were also significantly affected by different challenge inoculum volumes. These findings suggest that direct comparison between the severity of SARS-CoV-2 variants or studies assessing the efficacy of treatments determined by hamster studies cannot be made unless both the challenge dose and inoculation volume are matched when using the intranasal route. Additionally, analysis of sub-genomic and total genomic RNA PCR data demonstrated no link between sub-genomic and live viral titres and that sub-genomic analyses do not provide any information beyond that provided by more sensitive total genomic PCR.

This paper presents additional results of the generalized bathtub model for urban networks, including a simpler derivation and exact solutions for uniformly distributed trip lengths. It is shown that in steady state this trip-based model is equivalent to the more parsimonious accumulation-based model, and that the trip-length distribution has merely a transient effect on traffic dynamics, which converge to the same point in the macroscopic fundamental diagram (MFD). To understand the statistical properties of the system, a queueing approximation method is proposed to compute the network accumulation variance. It is found that (i) the accumulation variance is much larger than predicted by traditional queueing models, due to the nonlinear dynamics imposed by the MFD, (ii) the trip-length distribution has no effect on the accumulation variance, indicating that the proposed formula for the variance might be universal, (iii) the system exhibits critical behavior near the capacity state where the variance diverges to infinity. This indicates that the tools from critical phenomena and phase transitions might be useful to understand congestion in cities.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

Biology is so complex that it has to be studied in different ways and focuses on processes. Self-regulation skills in learning are important metacognitive skills for students in studying Biology. Learning models that train strategic students in learning are a must so that they gain meaningful learning experiences through the process of exploration, analysis of new information carried out in learning by applying self-questioning strategies to monitor why new information can be trusted. The results of the synthesis between old knowledge and new knowledge are enhanced through peer coaching in class. This research was conducted to develop an EAQD-PC learning model (exploring, analyzing, questioning, defining peer coaching) and tools that are valid, practical, and effective. The model development procedure includes the preliminary research stage, the prototype stage, and the assessment stage. Model validation data was collected using expert validation instruments. While the practicality of the model components is measured by the implementation observation sheet. The effectiveness of the model was measured using a metacognitive skill test and a questionnaire. This model is applied to students majoring in Biology Education at Muhammadiyah University of Bone, even semester 2019/2020. The results showed that the components of the EAQD-PC model (a) with a value of 3.87, and valid learning model supporting tools with a value of 3.83, (b) practicality with a value of 3.89 and (c) effectiveness with a value of 3, 97 . This study proves that the EAQD-PC model is practical and effective for training students' metacognitive skills.

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

During the past few years many projects and initiatives were undertaken deploying and testing automated vehicles for public transportation and logistics. However in spite of their ambition, all of these deployments stayed on the level of elaborated experimentation deploying no more than 4 maximum 5 AVs in rather small sites (few Kms of roads) and never really reached the level of large scale “commercial” deployment of transport services. The reasons for this are many, but the most important being the lack of economically viability and commercially realistic models, the lack of scalability of the business and operating models, and the lack of inclusive citizen/user centric services required for the large end-user acceptation and adoption of the solutions. In this paper, based on the experience gained in the H2020 AVENUE project, we present the missing pieces of the puzzle, ad which will be addressed in the Horizon Europe project ULTIMO.

Objective: We aimed to clarify the differences in the association between re-admission for heart failure (HF) and left atrial (LA) overload indices by sex in heart failure and a preserved ejection fraction (HFpEF). Methods: We analysed 898 HFpEF patients hospitalized for acute decompensated HF. Blood tests and transthoracic echocardiography were performed before discharge. The primary endpoint was re-admission for HF during the first year. Results: The ratio of diastolic elastance to arterial elastance (p=0.014), a relative index of LA pressure overload, in men and LA volume index (LAVI, p=0.020) in women were significant for re-admission for HF during the first year in the multivariable Fine-Gray analysis. Stroke volume (SV)/LA volume (LAV), another index for LAV overload, was not a significant prognostic factor of re-admission for HF during this time. Conclusion: LA overload was an important prognostic factor for HF re-readmission during the first year after enrolment in patients with HFpEF, but the indices relating to LA overload differed by sex.

This study evaluated the efficacy of experimental TEGDMA-functionalized dicalcium phosphate dihydrate (T-DCPD) filler-based resin-based composites (RBC) in preventing caries lesions around the restoration margins (secondary caries”, SC). Standardized Class-II cavities were made in sound molars having the cervical margin in dentin. Cavities were filled with a commercial resin-modified glass-ionomer cement (RMGIC) or experimental RBCs containing a BisGMA-TEGDMA resin blend and one of the following inorganic fractions: 60 wt.% Ba glass (RBC-0); 40 wt.% Ba glass, 20 wt.% T-DCPD (RBC-20); 20 wt.% Ba glass, 40 wt.% T-DCPD (RBC-40). An open-system bioreactor produced S. mutans biofilm-driven SC. Specimens were scanned using micro-CT to evaluate demineralization depths. Scanning Electron Microscopy and Energy-dispersive X-ray Spectroscopy characterized the specimens’ surfaces, while antimicrobial activity, buffering effect, and ion uptake by the biofilms were also evaluated. ANOVA and Tukey’s test were applied at p<0.05. RBC-0 and RBC-20 showed SC development in dentin, while RBC-40 and RMGIC significantly reduced the lesion depth at the restoration margin (p<0.0001). Initial enamel demineralization could be observed only around RBC-0 and RBC-20 restorations. A direct antibiofilm activity could explain SC reduction by RMGIC, while a buffering effect on biofilm’s acidogenicity explained the behavior of RBC-40. Experimental RBC with CaP-releasing functionalized T-DCPD filler could prevent SC with the same efficacy as F-releasing materials.

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in 3 dimensions: emphysema, ground glass opacity and consolidation. This method was evaluated on a 60 patient lot and the results show a clear quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

This article analyzes the perspectives of professional training of the public administration staff, from the viewpoint of the sustainability criteria, starting from the experiences of the online courses run in the last two years. In order to understand the nature, type and magnitude of the changes caused by the Covid-19 pandemic in the field of professional training for public administration, we performed a survey among public institution employees, by applying a questionnaire. The instrument included qualitative elements, to allow the framing of statistical results. The article is grounded on the hypothesis that the professional training activities in the last two years were organized preponderantly online and it aims to analyze the sustainability of the new teaching systems/methods. The limitations of the study are given by the fact that each administrative system has structure particularities and its own legal framework regarding the professional training of public administration staff, what makes that the model proposed cannot be applied in all countries. The study provides a model of online professional training for public administration staff, sustainable, based on the experience accumulated in the last two years.

COVID-19 outbreak started in the Chinese city of Wuhan and spread around the globe in months due to its high contagious level. Hence, this disease posed a great challenge to researchers and mathematicians. Numerous mathematical models have been suggested to visualize the spreading speed and trends of this pandemic. In this paper, a comparison of a widely established and accepted method, the Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model with a newly-proposed fractional-order SEIQRDP is studied. Densely populated Countries of Asia (Pakistan, India, and Bangladesh) have been chosen as data sets and both algorithms have been applied to their data. The same comparison technique has also been used on the data of two polar countries, New Zealand and Russia, that validated our findings. Error comparison of both algorithms has been recorded in a tabulated form which shows multi-fold erroneous trends over WHO data of selected countries.

The potential of social networks for the circulation of disinformation as a strategy of diplomacy has been of great interest to the academic community, but the way in which it is propagated and modelled is still in its beginnings. This article aimed to simulate the propagation of disinformation in social networks derived from the diplomacy strategy, based on the elements of the system. For the design of the simulation model, system dynamics was used as the main technique in the research methodology in conjunction with statistical analysis. Five computational simulations were run for the adoption methods of susceptible and uninformed population, misinformation techniques and echo chamber. The developed model found that the diplomacy disinformation agent is able to spread its message efficiently through the bot outreach mechanism and only a part of the susceptible population unsubscribes to the disinformation agent's account. Significant differences were identified in the absence of paid outreach, bots and trolls in the propagation of information, and in the variation in the timing of disinformation propagation. Consequently, the developed model allows the understanding of the problem of disinformation as a strategy of diplomacy from international rather than local dynamics, as well as the effects of the use of each element in the system.