Reliable Model Research Articles

Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model

<p>Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.</p>

Human umbilical cord mesenchymal stem cells reverse depression in rats induced by chronic unpredictable mild stress combined with lipopolysaccharide.

Inflammation and oxidative stress are considered crucial to the pathogenesis of depression. Rat models of depression can be created by combined treatments of chronic unpredictable mild stress (CUMS) and lipopolysaccharide (LPS). Behaviors associated with depression could be improved by treatment with mesenchymal stem cells (MSCs) owing to immunomodulatory functions of the cells. Therapeutic potentials of the MSCs to reverse pro-inflammatory cytokines, proteins, and metabolites were identified by transcriptomic, proteomic, and metabolomic analysis, respectively. A depression model was established in male SD rats by 2 weeks of CUMS combined with LPS. The models were verified by behavioral tests, namely SPT, OFT, EPM, and qRT-PCR for pro-inflammatory cytokines. Such depressed rats were administered human umbilical cord MSCs (hUC-MSCs) via the tail vein once a week for 2 and 4 weeks. The homing capacity was confirmed by detection of the fluorescent dye on day 7 after the hUC-MSCs were labeled with CM-Dil and administered. The expression of GFAP in astrocytes serves as a biomarker of CNS disorders and IBA1 in microglia serves as a marker of microglia activation were detected by immunohistochemistry at 2 and 4 weeks after final administration of hUC-MSCs. At the same time, transcriptomics of rat hippocampal tissue, proteomic and metabolomic analysis of the serum from the normal, depressed, and treated rats were also compared. Reliable models of rat depression were successfully induced by treatments of CUMS combined with LPS. Rat depression behaviors, pro-inflammatory cytokines, and morphological disorders of the hippocampus associated with depression were reversed in 4 weeks by hUC-MSC treatment. hUC-MSCs could reach the hippocampus CA1 region through the blood circulation on day 7 after administration owing to the disruption of blood brain barrier (BBB) by microglial activation from depression. Differentiations of whole-genome expression, protein, and metabolite profiles between the normal and depression-modeled rats, which were analyzed by transcriptomic, proteomics, and metabolomics, further verified the high association with depression behaviors. Rat depression can be reversed or recovered by treatment with hUC-MSCs.

Modeling Land–Atmosphere Interactions over Semiarid Plains in Morocco: In-Depth Assessment of GCM Stretched-Grid Simulations Using In Situ Data

Abstract Land surface–atmosphere interactions are a key component of climate modeling. They are particularly critical to understand and anticipate the climate and the water resources over the semiarid and arid North African regions. This study uses in situ observations to assess the ability of the IPSL-CM global climate model to simulate the land–atmosphere interactions over the Moroccan semiarid plains. A specific configuration with a grid refinement over the Haouz Plain, near Marrakech, and nudging outside Morocco has been performed to properly assess the model’s performances. To ensure reliable model–observation comparisons despite the fact that station measurements are not representative of a mesh-size area, we carried out experiments with adapted vegetation properties. Results show that the CMIP6 version of the model’s physics represents the near-surface climate over the Haouz Plain reasonably well. Nonetheless, the simulation exhibits a nocturnal warm bias, and the wind speed is overestimated in tree-covered meshes and underestimated in the wheat-covered region. Further sensitivity experiments reveal that LAI-dependent parameterization of roughness length leads to a strong surface wind drag and to underestimated land surface atmosphere thermal coupling. Setting the roughness heights to the observed values improves the wind speed and, to a lesser extent, the nocturnal temperature. A low bias in latent heat flux and soil moisture coinciding with a pronounced diurnal warm bias at the surface is still present in our simulations. Including a first-order irrigation parameterization yields more realistic simulated evapotranspiration flux and daytime skin surface temperatures. This result raises the importance of accounting for the irrigation process in present and future climate simulations over Moroccan agricultural areas.

Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: Implications for climate change.

The increase of environmental temperature due to current global warming is not only favouring the expansion of the distribution range of many insect species, but it is also changing their phenology. Insect phenology is tightly linked to developmental timing, which is regulated by environmental temperatures. However, the degree to which the effects of developmental temperatures extend across developmental stages and their inter-stage relationships have not been thoroughly quantified in mosquitoes. Here, we used the mosquito Aedes albopictus, which is an aggressive invasive species and an arboviral vector, to study how developmental temperature influences fitness across developmental stages, thermal traits, energy reserves, transcriptome and Wolbachia prevalence in laboratory-reared populations originally collected from either temperate or tropical regions. We show that hatchability, larval and pupal viability and developmental speed are strongly influenced by temperature, and these effects extend to wing length, body mass, longevity and content of water, protein and lipids in adults in a population-specific manner. On the contrary, neither adult thermal preference nor heat resistance significantly change with temperature. Wolbachia density was generally lower in adult mosquitoes reared at 18°C than at other tested temperatures, and transcriptome analysis showed enrichment for functions linked to stress responses (i.e. cuticle proteins and chitin, cytochrome p450 and heat shock proteins) in mosquitoes reared at both 18 and 32°C. Our data showed an overall reduced vector fitness performance when mosquitoes were reared at 32°C, and the absence of isomorphy in the relationship between developmental stages and temperature in the laboratory population deriving from larvae collected in northern Italy. Altogether, these results have important implications for reliable model projections of the invasion potentials of Ae. albopictus and its epidemiological impact.

Degradation and Reliability Modeling of EM Robustness of Voltage Regulators Based on ADT: An Approach and a Case Study

Degradation and Reliability Modeling of EM Robustness of Voltage Regulators Based on ADT: An Approach and a Case Study

A finite failure software reliability model using extended log-logistic distribution

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models.

A Compositional Semantics of Boolean-Logic Driven Markov Processes

Boolean-logic driven Markov processes (BDMPs) is a prominent dynamic extension of static fault trees to model repairable and complex dynamic systems. While BDMPs are intensively used in an industrial context for dependability analysis of energy systems, its formal semantics has not been systematically treated. To date, BDMPs are defined as a library of the domain-specific dependability-modelling language Figaro, a library that is neither open source nor publicly available. A rigorous semantic underpinning of BDMPs is indispensable for (1) developing BDMP analysis tools and (2) comparing its expressive power to other related reliability modelling languages. This paper presents a formal semantics to BDMPs using Markov automata (MA), an extension of continuous-time Markov chains (CTMCs) with action transitions that can be used to compose complex MA from smaller MA. This enables us to provide a compositional semantics. That is, we express the semantics of each individual BDMP element as an MA and obtain the MA for the entire BDMP by combining the MA of its elements. This makes the semantics comprehensible, for those who are familiar with automata theory, and easily extensible with new BDMP elements, e.g., to model security aspects. After the entire BDMP is considered, the actions in its MA that were used to “glue” the MA of BDMP elements, are ignored. This results in a CTMC that is amenable to exact numerical analysis by, e.g., efficient probabilistic model-checking techniques. We report on a prototypical implementation of our semantics and empirically show that our semantics yields dependability metrics that correspond to the interpretation by the Figaro knowledge base of BDMPs.

Reliability model for key components of urban rail transit train based on improved hunter-prey optimization

The reliability of key components of urban rail transit (URT) plays an important role in the maintenance plans of URT. It is necessary to establish the reliability model of URT trains. In the current research, the reliability model has a limited scope of application and fails to accurately depict the reliability of key components in URT trains. To solve the above problem, a multi-peak type mixture Weibull distribution model is established using several three-parameter Weibull distributions based on fault modes of components sourced from historical lifetime data. Due to the complexity of this model, parameter estimation is challenging. For this purpose, an improved hunter-prey optimization (IHPO) was proposed to improve parameter estimation accuracy. Firstly, an improved Hénon chaos map was introduced to improve the distribution of the initial population. Secondly, the Lévy flight was introduced to increase the probability of the individual spreading to the whole range at the late stage. Lastly, a nonlinear balance factor was proposed to enhance the algorithm’s global search capability. The simulation experiment was carried out with examples of the balanced pressing wheel and the wheelset. The IHPO algorithm-based parameter estimation method shows the highest R-square with values of 0.996 and 0.999, respectively, and the lowest root mean square error with values of 0.019 and 0.008, respectively. The simulation results demonstrate that the stability and optimization of the HPO are improved, and the multi-peak mixture Weibull distribution model based on the IHPO can accurately depict URT trains’ reliability.

Data-Driven Fatigue Failure Probability Updating of OSD by Bayesian Backward Propagation

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

Temporal Analysis Of COVID-19 Cases And Recovery Trends In Panvel City: A Spline Curve Approach

The spline curve method in mathematical modeling, was assessed across degrees for its efficacy in fitting daily cured and COVID-19 positive patient data of Panvel City. This study highlights the critical role of selecting an appropriate degree in spline interpolation for reliable disease-related dataset modeling.

Experimental and mesoscopic modeling numerical researches on steel fiber reinforced concrete slabs under contact explosion

This paper presents experimental research on the damage characteristics of steel fiber reinforced concrete slabs (SCS), steel fiber reinforced concrete slabs mixed with fly ash (CFS), and steel fiber reinforced concrete slabs mixed with fly ash and slag (CFSS) under contact explosion loads. The findings reveal that SCS and CFS exhibit crater damage on the top surface, spalling failure on the bottom surface, and certain damage cracks under the same contact explosion load. Additionally, the CFSS displays crater and spalling damage along with a through-hole at the center. Comparative analysis demonstrates that CFS offers the best explosion resistance properties. A reliable three-dimensional mesoscopic modeling using PYTHON and ABAQUS is established to analyze the dynamic response of the CFS, emphasizing its dependence on fiber volume. Furthermore, the study evaluates energy changes in the matrix and steel fiber to understand their impact on the slab's damage dimensions under contact explosion. Results indicate that the mesoscopic modeling effectively reflects structure damage and failure behavior under explosive super dynamic loads. Specifically, for CFS, with a steel fiber volume of 1.3%, the internal energy and stress generated by steel fiber are maximized, resulting in the smallest damage dimension under contact explosion and the best explosion-resistant effect. This research provides valuable insights for guiding the design of explosion-resistant structures and their deployment in concrete engineering applications.

Сравнительная оценка применения микроэлектромеханических акселерометров для измерения крутильных колебаний судовых машинно-движительных комплексов

Microelectromechanical accelerometers are investigated, and their applicability to measuring torsional vibrations occurring in marine propulsion systems is evaluated. The calculated and experimental assessment of the applicability of accelerometers of the ADXL345, ADXL375 and H3LIS331DL models for measuring torsional vibrations of marine shaft lines has been carried out and recommendations for their practical application have been developed. The materials used were the rules of the Russian Classification Society, scientific and technical literature in the field of torsional vibrations and the authors' own research. The methods used were analysis, numerical and physical experiment. Recommendations on the use of accelerometers of the ADXL345, ADXL375 and H3LIS331DL models depending on the rotational speed and diameter of the torsiographed shaft are obtained. The results obtained give grounds to recommend the ADXL375 accelerometer as the basic basic model for measuring torsional vibrations of most marine propulsion systems with rotation frequencies up to 1,025 min–1. At rotational speeds of less than 135 min–1, the use of the ADXL345 accelerometer with a measuring range of ±2g is recommended, and when setting the range of ±16g, it can be used at rotational speeds up to 380 min–1. If it is necessary to measure torsional vibrations at a rotational speed of more than 1,025 min–1, it is recommended to use a less accurate, but more reliable accelerometer model H3LIS331DL in terms of overload shutdown threshold.

Structural and Transport Properties of Hydrophilic and Hydrophobic Modified Ionomers in Proton Exchange Membrane Fuel Cells.

The sluggish commercial application of proton exchange membrane fuel cells (PEMFCs) with low Pt loading is chiefly hindered by concentration polarization loss, particularly at high current density regions. Addressing this, our study concentrates on the ionomer membranes in the cathode catalyst layer (CCL) and explores the potential of incorporating additional hydrophilic or hydrophobic components to modify these ionomers. Therefore, an all-atom model was constructed and for the ionomer and hydrophilic and hydrophobic modifications were implemented via incorporating SiO2 and PTFE, respectively. The investigation was conducted via molecular dynamics (MD) simulations to predict the morphology and structure of the ionomer and analyze the kinetic properties of oxygen molecules and protons. The simulation results elaborate that the hydrophilic and hydrophobic modifications favor the phase separation and the self-diffusion coefficients of oxygen molecules and protons are enhanced. Considering the hydration level of the ionomer films, hydrophilic modification facilitates mass transfer under low-hydration-level conditions, while hydrophobic modification is more effective in optimizing mass transfer as the hydration level increases. The optimal contents of SiO2 and PTFE for each hydration level in this work are 9.6% and 45%, respectively. This work proposes a reliable model and presents a detailed analysis of hydrophilic and hydrophobic modifications, which provides theoretical guidance for quantitative preparations of various composite membranes.

딥러닝 기반 시계열 분석 모델의 불확실성 정량화 비교 연구

With the advancement of artificial intelligence, machine learning, and deep learning, their applications in various industries, particularly finance, have increased. However, interpreting predictions from deep learning models poses challenges, especially in finance where result interpretation is important. This study aims to determine the uncertainty of stable deep learning models, despite changes in the model like dropout, to establish standards for reliable models and identify those detecting anomal data through model uncertainty. In the experiment, the traditional statistical model ARIMA and deep learning models mainly used for time series analysis, CNN, LSTM, MLP, and CNN-LSTM. Uncertainty was measured from a Bayesian perspective using MC Dropout. The experimental results confirmed that deep learning models performed better than statistical models for various patterns of time series data. It was observed that, even if the performance was not the best, LSTM based models exhibited low uncertainty, indicating stability. Consequently, this study highlights the importance of considering uncertainty along with accuracy in model selection. Moreover, it was confirmed that models with higher uncertainty are suitable for anomaly detection, making CNN based models particularly fitting for this purpose.

Predicting cheating among married individuals based on sexual satisfaction and personality traits: A review

This article discusses the prediction of cheating among married individuals based on two significant factors: sexual satisfaction and personality traits. Sexual satisfaction is an essential component of a healthy and fulfilling relationship, and individuals who experience a lack of fulfillment in their sexual lives may seek satisfaction outside their marriage. Personality traits play a crucial role in determining an individual's propensity for cheating, with several traits commonly associated with a higher likelihood of engaging in infidelity, including low levels of conscientiousness, high levels of impulsivity, narcissism, and a desire for novelty and excitement. While sexual satisfaction and personality traits each contribute to the prediction of cheating, their interaction can further enhance our understanding. Developing reliable predictive models for cheating based on sexual satisfaction and personality traits is a complex task, with researchers using various methods to discern patterns and make accurate predictions. Data analysis techniques, such as logistic regression and machine learning algorithms, have been applied to collected data on married individuals and their tendencies to cheat, achieving moderate success in predictive accuracy. However, it is essential to acknowledge the limitations of these predictions, such as individual circumstances, relationship dynamics, and other external variables that are not accounted for in these models. Privacy concerns and ethical considerations surrounding the monitoring of individuals' personal lives must also be taken into account. These predictive models should be used with caution and in conjunction with comprehensive counseling and support systems to maintain the integrity of relationships.

Impacts of aquifer heterogeneity on subsidence resulting from engineering dewatering in the Lower Yangtze River plains

Study regionLower Yangtze River alluvial plain, China. Study focusThe uneven subsidence caused by dewatering in underground engineering constructions in densely populated areas of alluvial plains with an unevenly distributed medium has caused increased concerns. This study applied the transition probability geostatistical software (T-PROGS) geostatistical models based on data from 31 boreholes to characterize a three-dimensional (3D) heterogeneous aquifer (8.8×105 m2 in area and 56 m in depth) in an underground construction area in the alluvial plain of the lower Yangtze River. The SUB model was constructed to simulate the pumping-induced subsidence processes based on a reliable MODFLOW transit flow model. New hydrological insightsThe simulation results indicated that surface subsidence increased with pumping and reached a maximum of 33 mm by the end of the extraction (the 14th day). Surface subsidence development exhibited spatial anisotropy around the pumping center, consistent with the observations. Furthermore, the proposed model indicates that hydraulic head evolution and soil compressibility distributions significantly influence the spatial–temporal development of subsidence, implying the significance of the aquifer heterogeneity. The compressible soft soil overlying the pumping section was identified as a vulnerable subsidence zone, suggesting its significance for engineering geological surveys and dewatering designs in underground construction in alluvial plains.

Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach

Abstract The current work assesses the availability and reliability of repairable 1-out-of-3 cold standby systems, undergoing switching failure, by the Markov models, where a finite set of differential equations govern the repairable system. A numerical approach based on KAOS programming in MATLAB is employed to solve the differential equations. Thus, numerical solutions for the time-dependent availability can then be obtained. The Laplace transform method is used for the reliability model to find the explicit form of the reliability function and the mean time to failure (MTTF) of the system. An availability estimation model for a 1-out-of-3 cold standby system is developed and the main electrical power network, local electricity generator, and domestic electricity generator are studied. Finally, the model is analyzed to illustrate the effect of both failures and repair rates as well as component capacity partitioning on system availability and MTTF.

Scalable BIM based open workflow for structural analysis of masonry building aggregates

Masonry building aggregates are complex historical structures comprising interconnected buildings. The safety of each building depends not only on its structural integrity but also on the interactions with neighbouring structures and their collective performance. The structural assessment of such complex constructions can benefit from BIM methodology. The latter eases modelling complicated solid geometries and assigning mechanical properties information essential for developing a reliable finite element model. Thus far, BIM to finite element analysis procedures applied to historic constructions have remained laborious, semi-automatic, and based on proprietary software. This paper introduces an open-source automated solid finite element analysis method using OpenBIM data (IFC). The tool implemented automatically creates tetrahedron mesh and assigns mechanical properties, self-weights, and fixities at the structure's base. The methodology is applied to an actual masonry building aggregate to validate its capability of working with complex geometries and scalability.

Efficient AI-Physics hybrid model with productive capabilities to reduce the time of history matching and scenario assessment; a case study: Minagish oil field

This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary objective is to reduce the time of history match and achieve the best match between model and observation data using our hybrid model. We trained our model using historical data and created a reliable forecasting model that predicts field behavior and proposes new scenarios to improve production in the upcoming years. To test our model, we combined AI physics history training with blind test prediction calculations of the remaining oil map, and the AI physics model created a forecast scenario definition based on this map. Our proposed simulation method can reduce the time of history matching and scenario evaluation by 90-95%. We created three improved forecast scenarios based on predefined scenarios that can produce millions of standard barrels more oil over three years than the original development scenario. The results demonstrate the potential of our AI-Physics hybrid model in revolutionizing reservoir simulation in the oil and gas industry. Our approach can significantly reduce simulation time, improve forecasting accuracy, and optimize production strategies, leading to significant economic benefits.

Exploring representative samples for modeling of wave buoy motion behavior

To collect sufficient data to describe buoy motion behavior in extreme environment conditions is intractable. The conventional data-driven models constructed with randomly selected and limited training samples are unable to perform adequately for exhibiting complex dynamic characteristics. In this work, an active sample exploring strategy is developed to build a reliable prediction model with limited samples. First, an evaluated index is designed to depict both the data distribution characteristics and estimation uncertainty of all test data using a probabilistic model. The representative data are then captured and integrated into the initial training set based on this index, and the updated uncertainty information is used as the criterion to judge the exploring termination. Finally, the initial training set is supplemented to enhance the model performance. Experimental and comparative results demonstrate the superiority of the proposed sample exploring strategy.