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This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

This study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.

The study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.

It is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.

Limited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

Prefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.

A model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.

The IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.

The sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

The purpose of this paper is to test the impact of selected characteristics of jobseekers on employers’ decisions regarding potential hires (direct and probabilistic signals). The main focus of the study is to test the impact of jobseekers’ participation in selected active labour market programmes on employers’ hiring decisions for three positions: unskilled worker, skilled worker and administrative employee. Other characteristics tested include age, gender, presence of children in the household, state of health, experience of short- and long-term unemployment and indebtedness.

This study analyses data from a representative survey of employers with five or more employees in the Czech Republic. The survey was conducted in December 2020 using stratified random sampling, combining online questionnaires and personal interviews. The study includes 1,040 employers and uses the factorial survey experiment (FSE) design.

The results of the FSE suggest that the perceived positive impact of completing one of the activation programmes depends on the position for which the candidate is being recruited. While for the unskilled job category, the completion of any of the tested schemes (training, subsidised jobs or public works) had a positive effect; for the skilled job category, only the training and subsidised jobs schemes had a positive effect; and for the administrative job category, public works programme even had a negative effect.

A somewhat limiting factor in the context of this study seems to be the definitions of the positions tested (unskilled and skilled workers and administrative staff). The decision-making of the respondents was somewhat restricted by such broadly defined categories. Typically, studies with FSE designs have a focus on a specific sector of the economy, which allows for a better definition of the positions or jobs under test. The relationship between position and the impact of individual characteristics is clearly a matter for further research.

The results of the study confirm that completion of the activation programme, as well as other candidate characteristics, constitute differentiating signals for employers that influence their hiring decisions. At the same time, there is evidence that the training programme and the subsidised jobs programme are effective in terms of increasing participants’ chances of employment.

The demand side should be included in the evaluation of activation policies. The design of the FSE provides an appropriate way to test the impact of activation measures on the decision-making of employers.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.