Probability density functions of the involved random variables are essential for the reliability-based design of offshore structures. The objective of present research was the derivation of probability density function (PDF) for the local joint flexibility (LJF) factor, fLJF, in two-planar tubular DK-joints commonly found in jacket-type offshore structures. A total of 162 finite element (FE) analyses were carried out on 81 FE models of DK-joints subjected to two types of axial loading. Generated FE models were validated using available experimental data, FE results, and design formulas. Based on the results of parametric FE study, a sample database was prepared for the fLJF values and density histograms were generated for respective samples based on the Freedman-Diaconis rule. Nine theoretical PDFs were fitted to the developed histograms and the maximum likelihood (ML) method was applied to evaluate the parameters of fitted PDFs. In each case, the Kolmogorov-Smirnov and chi-squared tests were used to evaluate the goodness of fit. Finally, the Inverse Gaussian model was proposed as the governing probability distribution function for the fLJF. After substituting the values of estimated parameters, two fully defined PDFs were presented for the fLJF in tubular DK-joints subjected to two types of axial loading.

Background and Aim: Evaluation is the final part of almost all educational development systems. Because it provides techniques and methods for determining effectiveness and ways to improve educational interventions. The purpose of this study is to design an evaluation model for the implementation of joint police exercises that is appropriate to the characteristics and local culture of the education system of Amin Police University. Method: The present study is mixed in nature (qualitative-quantitative) in which the participants were all experts, professors, commanders, managers and those involved in the design and implementation of exercises of Amin Police University and experts of the NAJA and Tehran Police. In the qualitative section, 6 people were purposefully selected in the focus group and in the quantitative section, 75 people were randomly selected. To identify the dimensions, components and indicators of the model, the focus group was interviewed and to confirm the extracted model, the confirmatory factor analysis method was used in PLS software. Results: Based on interviews with experts and review of texts and data analysis, the results showed that the final model of the exercise has 3 dimensions: before, during and after the exercise and 6 components: design, coordination, implementation, achievement of goals and summary and 20 indicators. Conclusion: The findings of this study show that this model is practical and has the ability to measure, monitor all the effective elements in the stages before, during and after the implementation of joint police exercises.

It is usually supposed that the training (source domain) and test (target domain) data follow a similar distribution and feature space in most pattern recognition tasks. However, in many real-world applications, particularly in visual recognition, this hypothesis has been frequently violated. This problem is known as domain shift problem. Domain adaptation and transfer learning are promising techniques to learn an effective and robust classifier to tackle shift problem. In this paper, we propose a novel scheme for domain adaptation, entitled as Joint Distribution Adaptation via Feature and Model Matching (JDAFMM), in which feature transform and model matching are jointly optimized. Due to joint optimization, we can have a robust model with feasible feature transformation and model parameter adaptation. By introducing regularization operated between the marginal and conditional distributions’ shifts across domains, we can successfully adapt data drift as much as possible along with empirical risk minimization and rate of consistency maximization between manifold and prediction function. We conduct extensive experiments to evaluate the performance of the proposed model against those of other machine learning and domain adaptation methods in three types of visual benchmark datasets. Our experiments illustrate that our JDAFMM significantly outperforms other baseline and state-of-the-art methods.

Alarm systems are essential in safe operation of industrial plants. Since many process variables are interacting with each other, so in this paper, an approximate method is introduced to design and analysis of a multivariate alarm system. In this method, the alarm system is designed base on joint indices. The Joint FAR and Joint MAR are defined for a m-variable alarm system thanks to multivariate Markov scheme. In proposed method, the alarm joint indices are defined by solving a Linear Programing (LP) optimization problem. By defining joint indices, tuning of the alarm parameters (like, threshold and etc. ) can be done by these indices instead of correlation analysis. In this paper, penalty scenario and Genetic algorithm are used for alarm generation, and parameter optimization in Tennessee Eastman (TE) Process. The results of proposed method are compared with other methods.

Background: Degenerative Joint Disease (DJD) of the first Metacarpophalangeal (MCP) joint, can severely impair hand function due to pain, stiffness, and reduced range of motion. Anatomical variations in metacarpal head morphology may play a critical role in altering joint biomechanics and stress distribution, potentially accelerating cartilage wear and osteoarthritis progression.Objective: This study aimed to evaluate the biomechanical impact of different first metacarpal head morphologies on stress distribution within the MCP joint under various positions using computer simulation.Material and Methods: In this computer simulation study, three-dimensional models of the thumb MCP joint were reconstructed from Computed Tomography (CT) scans of healthy subjects. The models were adjusted to represent flat, biplanar, and convex metacarpal head morphologies and were simulated in three positions: neutral, 20° flexion, and 20° extension. Computer simulation was performed using Analysis System (ANSYS) to calculate von Mises stress distributions. Descriptive statistics and one-way Analysis of Variance (ANOVA) were applied to compare stress values between groups.Results: The flat metacarpal head exhibited the highest stress concentrations, peaking at 138 MPa in 20° extension. Biplanar morphology showed moderate stresses, while the convex shape demonstrated the lowest stress, with a maximum of 58 MPa. The analysis confirmed significant differences between groups (P-value=0.039). Stress increased notably in flexion and extension positions compared to neutral across all morphologies. Conclusion: Metacarpal head morphology and joint positioning significantly influence MCP joint biomechanics. Flat and biplanar shapes increase stress concentration, potentially elevating DJD risk. Convex morphology offers better stress dispersion, indicating a biomechanical advantage.

Dynamic behavior of a structure is significantly affected by the properties of its joints. This effect is more important in light weight structures with low damping such as airspace structures. In this paper two models for joint inteiface are presented and their properties are identified using modal testing results. In the identification procedure the characteristic equation of motion is used to obtain joint parameters. A good correlation between identified model and the modal testing results is obtained.