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We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

This paper aims to express a mathematical model that predicts the effect of mineral additives on the physical–mechanical properties of high-performance sand concrete (HPSC), using SAS's JMP7 statistical analysis software.

A mixture design modeling approach is applied to sand concrete (SC) for optimizing mixtures without being obliged to do a lot of experiments, where the cement is partially replaced with two mineral additives silica fume (SF) and blast furnace slag (BFS) in proportions as high as 20% of the mass. A total of 15 mixtures of sand concrete is prepared in the laboratory using this analytical technique in combinations with binary and ternary systems to estimate the workability and the compressive strength (CS) of sand concrete at 7 and 28 days.

The results obtained showed that the use of derived models based on the experimental design approach greatly assisted in understanding the interactions between the various parameters of the studied mixtures; the mathematical models present excellent correlation coefficients (R² = 0.96 for CS7 days, R² = 0.93 for CS28 days and R² = 0.95 for slump) for all studied responses. Moreover, it was also found that the inclusion of additives (SF and BFS) in binary mixture SC12 and ternary mixtures SC8 leads to a significant improvement in mechanical strength compared to reference sand concrete SC15. These results give the possibility to obtain a formulation of HPSC.

This paper shows the possibility of manufacturing high-performance sand-concrete with good compressive strength; the developed mathematical model by using SAS's JMP7 statistical analysis software allowed us to reach a strength compression value of about 60 MPa, in 28 days, by replacing 10% of the cement weight with silica fume. Furthermore, with partial replacement of the cement weight (15%) with two additions such as silica fume (10%) and blast furnace slag (5%), a 58 MPa of compressive strength can be achieved, without overlooking the fact that this can be a key economic and environmental alternative.

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).

The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).

Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.

The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.

Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.

A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.

Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.

The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

Preventative health services are keen to identify how to engage men and increase their participation, thus improving health, well-being and life expectancy over time. Prior research has shown general gender norms are a key reason for men’s avoidance of these services, yet there is little investigation of specific gender norms. Furthermore, masculinity has not been examined as a factor associated with customer vulnerability. This paper aims to identify the relationship between gender norm segments for men, likely customer vulnerability over time and subjective health and well-being.

Adult males (n = 13,891) from an Australian longitudinal men’s health study were classified using latent class analysis. Conditional growth mixture modelling was conducted at three timepoints.

Three masculinity segments were identified based on masculine norm conformity: traditional self-reliant, traditional bravado and modern status. All segments had likely customer experience of vulnerability. Over time, the likely experience was temporary for the modern status segment but prolonged for the traditional self-reliant and traditional bravado segments. The traditional self-reliant segment had low subjective health and low overall well-being over time.

Practitioners can tailor services to gender norm segments, enabling self-reliant men to provide expertise and use the “Status” norm to reach all masculinity segments.

The study of customer vulnerability in a group usually considered privileged identifies differential temporal experiences based on gender norms. The study confirms customer vulnerability is temporal in nature; customer vulnerability changes over time from likely to actual for self-reliant men.

The purpose of the study is to propose a novel implementation of twisted tape in sinusoidal wavy-walled tubes to enhance the rate of heat transfer without compromising thermal efficiency. The study numerically investigates the fluid flow characteristics and analyzes the effect of different geometrical configurations, including wall wave amplitude, tape twist angles and nanoparticle volume fractions, on heat transfer improvement and performance factor.

This problem is numerically investigated using computational fluid dynamics, and the method is the finite volume method. A two-phase mixture model is used for nanofluid modeling.

The study investigated the effect of wall waviness, twisted tape, and nanoparticles on forced convective heat transfer and friction factor behavior in laminar pipe flow in three different Reynolds number regimes. The results showed that implementing twisted tape in wavy tubes significantly increased the rate of heat transfer and the performance factor, with the best twist ratio between 90 and 180°. Adding nanoparticles also enhanced heat transfer and performance factor, but to a lesser extent than wavy wall-twisted tape combinations. The study suggests selecting a proper combination of wavy wall and twisted tape at each Reynolds number to achieve an optimum solution.

To the best of the authors’ knowledge, the implementation of the selected passive methods in sinusoidal wavy tubes has not been studied before, and no previous studies have taken into account such a mix of heat transfer improvement techniques.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

A large part of the most recent studies in innovation focuses on the need to investigate the cultural differences between countries. Many of the approaches used focus on Hofstede as the most recognized perspective in international business. The Hofstede perspective requires a deeper analysis of the most profound components in the countries, such as values and beliefs. The purpose of this study is identify the drivers in creating innovation trajectories over time, focusing on different values that influence the innovation processes. These trajectories allow investigating the comparability in the innovation performance of the countries.

Time-series analysis is performed to achieve the research’s goal, considering the innovation inputs and people’s values and influence on innovation output. The Global Innovation Index and the World Value Survey (WVS), which comprise data from countries on different continents, were used in this investigation. The trajectories analysis technique examines differences in innovation trajectory among countries with cultural orientations toward traditional, secular, survival and self-expression values.

In the literature, it can be found that in more socially open societies, the results in innovation are higher than in societies with opposite values, as is the case of traditional vs secular rationale. On the other hand, societies with a tendency toward self-expression will be characterized by a constant search for individual liberties that promote the search for scientific and technological alternatives for problem-solving, contrary to those with survival characteristics.

Considering the results obtained in the study about the values and their relationship with innovation at the country level, the main limitation is the WVS information. This limitation is based on the need to complement the information obtained with other sources of information that allow comparisons to be made from the different cultural approaches that exist.

The findings allow us to contemplate a more general vision of the cultural factors that affect the social dynamics and, therefore, the industrial and commercial dynamics of a country. The managers can use this type of results in the design of strategies that allow them to contemplate adaptation processes that are more appropriate to the cultural contexts in which they operate are worked on in this research.

One of the main contributions is related to the possibility of understanding the relationship between the cultural dynamics of a country and the results in innovation, especially in the time.

The trajectory analysis, specifically Inglehard’s perspective concerning cultural dimensions and innovation, has not been used in the literature. This type of analysis will make it possible to have studies that allow subsequent comparisons to be made with other perspectives, especially at the country level.