Search Results (76)

This article presents an interpretable framework for customer segmentation and churn risk detection, integrating fuzzy clustering, explainable AI (XAI), and strategic scoring. The process begins with Fuzzy C-Means (FCM) applied to normalized RFM indicators (Recency, Frequency, Monetary), which were then mapped to a 2-tuple linguistic scale to enhance semantic interpretability. Cluster memberships and centroids were analyzed to identify distinct behavioral patterns. An XGBoost classifier was trained to validate the coherence of the fuzzy segments, while SHAP and LIME provided global and local explanations for the classification decisions. Following segmentation, an AHP-based strategic score was computed for each customer, using weights derived from pairwise comparisons reflecting organizational priorities. These scores were also translated into the 2-tuple domain, reinforcing interpretability. The model then identified customers at risk of disengagement, defined by a combination of low Recency, high Frequency and Monetary values, and a low AHP score. Based on Recency thresholds, customers are classified as Active, Latent, or Probable Churn. A second XGBoost model was applied to predict this risk level, with SHAP used to explain its predictive behavior. Overall, the proposed framework integrated fuzzy logic, semantic representation, and explainable AI to support actionable, transparent, and human-centered customer analytics. Full article

Managers often need to stay motivated and effectively motivate others. Therefore, they should rely on evidence-based interventions to effectively motivate and self-motivate. This research investigated how self-determination theory-based interventions affect employees’ motivation dynamics and motivational consequences within short time frames (i.e., within an hour, within a few weeks or months) in two empirical studies. Study one focused on assessing the effectiveness of a one-day training workshop in helping to improve managers’ work motivation, basic psychological needs satisfaction/frustration, subordinates’ motivation, and perceptions of managers’ needs-supportive/thwarting behaviors within a few weeks. Results support the effectiveness of the training, as managers were rated by their direct subordinates as having fewer needs-thwarting behaviors and reported self-improvement in needs satisfaction and frustration six weeks after completing the training program. Study two used the mean and covariance structure analysis and tested the impact of three types of basic psychological needs-supportive/thwarting and control conditions (3 × 2 × 1 factorial design) on participants’ situational motivation, vitality, and general self-efficacy for playing online word games within 30 min. Multi-group confirmatory factor analysis (CFA) confirmed the scalar measurement invariance, then latent group mean comparison results show consistently lower controlled motivation across the experimental conditions. During a quick online working scenario, the theory-based momentary intervention effectively changed situational extrinsic self-regulation in participants. Supplementary structural equation modeling (SEM; cross-sectional) analyses using experience samples supported the indirect dual-path model from basic needs satisfaction to vitality and general efficacy via situational motivation. We discussed the theoretical implications of the temporal properties of work motivation, the practical implications for employee training, and the limitations. Full article

Mean–geometric mean (MGM) linking compares group differences on a latent variable $\theta$ within the two-parameter logistic (2PL) item response theory model. This article investigates three specifications of MGM linking that differ in the weighting of item difficulty differences: unweighted (UW), discrimination-weighted (DW), and precision-weighted (PW). These methods are evaluated under conditions where random DIF effects are present in either item difficulties or item intercepts. The three estimators are analyzed both analytically and through a simulation study. The PW method outperforms the other two only in the absence of random DIF or in small samples when DIF is present. In larger samples, the UW method performs best when random DIF with homogeneous variances affects item difficulties, while the DW method achieves superior performance when such DIF is present in item intercepts. The analytical results and simulation findings consistently show that the PW method introduces bias in the estimated group mean when random DIF is present. Given that the effectiveness of MGM methods depends on the type of random DIF, the distribution of DIF effects was further examined using PISA 2006 reading data. The model comparisons indicate that random DIF with homogeneous variances in item intercepts provides a better fit than random DIF in item difficulties in the PISA 2006 reading dataset. Full article

Factorial invariance is critical for ensuring consistent relationships between measured variables and latent constructs across groups or time, enabling valid comparisons in social science research. Detecting factorial invariance becomes challenging when varying degrees of heterogeneity are present in the distribution of latent factors. This simulation study examined how changes in latent means and variances between groups influence the detection of noninvariance, comparing Bayesian and maximum likelihood fit measures. The design factors included sample size, noninvariance levels, and latent factor distributions. Results indicated that differences in factor variance have a stronger impact on measurement invariance than differences in factor means, with heterogeneity in latent variances more strongly affecting scalar invariance testing than metric invariance testing. Among model selection methods, goodness-of-fit indices generally exhibited lower power compared to likelihood ratio tests (LRTs), information criteria (ICs; except BIC), and leave-one-out cross-validation (LOO), which achieved a good balance between false and true positive rates. Full article

Background/Objectives: Recently, interest in studying food addiction (FA) in the context of behavioral addictions (BAs) has increased. However, research remains limited to determine the FA prevalence among various BAs. The current study aimed to investigate FA in a clinical sample of patients seeking treatment for gaming disorder, compulsive buying-shopping disorder (CBSD), compulsive sexual behavior disorder, and the comorbid presence of multiple BAs, as well as to determine the sociodemographic characteristics, personality traits, and general psychopathology of this clinical population. In addition, we analyzed whether FA is linked to a higher mean body mass index (BMI). Methods: The sample included 209 patients (135 men and 74 women) attending a specialized behavioral addiction unit. The assessment included a semi-structured clinical interview for the diagnosis of the abovementioned BAs, in addition to self-reported psychometric assessments for FA (using the Yale Food Addiction Scale 2. 0, YFAS-2), CBSD (using the Pathological Buying Screener, PBS), general psychopathology (using the Symptom Checklist-Revised, SCL-90-R), personality traits (using the Temperament and Character Inventory-Revised, TCI-R), emotional regulation (using Difficulties in Emotion Regulation Strategies, DERS), and impulsivity (using Impulsive Behavior Scale, UPPS-P). The comparison between the groups for the clinical profile was performed using logistic regression (categorical variables) and analysis of covariance (ANCOVA), adjusted based on the patients’ gender. The sociodemographic profile was based on chi-square tests for categorical variables and analysis of variance (ANOVA) for quantitative measures. Results: The prevalence of FA in the total sample was 22.49%. The highest prevalence of FA was observed in CBSD (31.3%), followed by gaming disorder (24.7%), and the comorbid presence of multiple BAs (14.3%). No group differences (FA+/−) were found in relation to sociodemographic variables, but the comorbidity between FA and any BA was associated more with females as well as having greater general psychopathology, greater emotional dysregulation, higher levels of impulsivity, and a higher mean BMI. Conclusions: The comorbidity between FA and BA is high compared to previous studies (22.49%), and it is also associated with greater severity and dysfunctionality. Emotional distress levels were high, which suggests that the group with this comorbidity may be employing FA behaviors to cope with psychological distress. However, a better understanding of the latent mechanisms that contribute to the progression of this multifaceted comorbid clinical disorder is needed. One aspect that future studies could consider is to explore the existence of FA symptoms early and routinely in patients with BAs. Full article

Online recommendation agents have demonstrated their value in various contexts by helping users navigate information overload, supporting decision-making, and influencing user behavior. There is a lack of studies focusing on recommendation systems for green hotels that utilize user-generated content from social networking and e-commerce platforms. While numerous studies have explored the use of real-world datasets for hotel recommendations, the development of recommendation systems specifically for green hotels remains underexplored, particularly in the context of Saudi Arabia. This study attempts to develop a new approach for green hotel recommendations using text mining and Long Short-Term Memory techniques. Latent Dirichlet Allocation is used to identify the main aspects of users’ preferences from the user-generated content, which will help the recommender system to provide more accurate recommendations to the users. Long Short-Term Memory is used for preference prediction based on numerical ratings. To better perform recommendations, a clustering technique is used to overcome the scalability issue of the proposed recommender system, specifically when there is a large amount of data in the datasets. Specifically, a spectral clustering algorithm is used to cluster the users’ ratings on green hotels. To evaluate the proposed recommendation method, 4684 reviews were collected from Saudi Arabia’s green hotels on the TripAdvisor platform. The method was evaluated for its effectiveness in solving sparsity issues, recommendation accuracy, and scalability. It was found that Long Short-Term Memory better predicts the customers’ overall ratings on green hotels. The comparison results demonstrated that the proposed method provides the highest precision (Precision at Top @5 = 89.44, Precision at Top @7 = 88.21) and lowest prediction error (Mean Absolute Error = 0.84) in hotel recommendations. The author discusses the results and presents the research implications based on the findings of the proposed method. Full article

Background/Objectives: The Level of Personality Functioning Scale—Brief Form 2.0 (LPFS-BF 2.0) is a self-report screening measure of personality impairments according to the DSM-5 Alternative Model for Personality Disorders and the ICD-11 classification of personality disorders. Nevertheless, reliable cut-off scores that could help in clinical decision making are still lacking for many populations. The aim of this study was to develop severity benchmarks of the LPFS-BF 2.0 for a Polish population based on the item response theory (IRT) approach. Methods: A sample of Polish adults (n = 530) took part in the study. The participants assessed their personality functioning and pathological personality traits and provided information about psychiatric diagnosis and psychotherapy seeking. The severity benchmarks were developed using IRT and validated using mean and frequency comparisons between groups of different personality impairments according to the developed cut-offs. Results: Confirmatory factor analysis (CFA) supported a unidimensional model of the LPFS-BF 2.0. The graded IRT model indicated satisfactory item functioning for all LPFS-BF 2.0 items. The normative observed score thresholds at different latent severity levels of personality impairments were developed, and significant overall differences were found between the LPFS-BF 2.0 norm-based severity benchmarks in pathological personality traits and psychotherapy seeking. Conclusions: The IRT-based cut-offs for the LPFS-BF 2.0 identified individuals high on pathological personality traits (particularly disinhibition) and were predictive of psychotherapy seeking. The developed severity benchmarks allow for the interpretation of LPFS-BF 2.0 scores, supporting clinical diagnosis and relevant decision making in the Polish population. Practical implications for healthcare practice and research are being discussed. Full article

Analysis of turbulent heat fluxes in urban forests is crucial for understanding structural variations in the urban sub-surface boundary layer. This study used data captured by an unmanned aerial vehicle (UAV) and an improved semi-empirical triangle method to estimate small-scale turbulent heat fluxes in the sub-surface of an urban forest. To improve the estimation accuracy, the surface temperature (T_S) of the UAV-based remote sensing inversion was corrected using the hot and cold spot correction method, and the process of calculating ${\varphi }_{max}$ using the traditional semi-empirical triangle method was improved to simplify the calculation process and reduce the number of parameters in the model. Based on this method, latent heat fluxes (LE) and sensible heat fluxes (H) were obtained with a horizontal resolution of 0.13 m at different time points in the study area. A comparison and validation with the measured values of the eddy covariance (EC) system showed that the absolute error of the LE estimates ranged from 4.43 to 23.11 W/m², the relative error ranged from 4.57% to 25.33%, the correlation coefficient (r) with the measured values was 0.95, and the root mean square error (RMSE) was 35.96 W/m², while the absolute error of the H estimates ranged from 3.42 to 15.45 W/m², the relative error ranged from 7.51% to 28.65%, r was 0.91, and RMSE was 9.77 W/m². Compared to the traditional triangle method, the r of LE was improved by 0.04, while that of H was improved by 0.06, and the improved triangle method was more accurate in estimating the heat fluxes of urban mixed forest ecosystems in the region. Using this method, it was possible to accurately track the LE and H of individual trees at the leaf level. Full article

This study evaluates nine Planetary Boundary Layer (PBL) turbulence parameterization schemes from the Weather Research and Forecasting (WRF) mesoscale meteorological model, comparing hourly values of meteorological variables observed and simulated at the surface of the Metropolitan Region of São Paulo (MRSP). The numerical results were objectively compared with high-quality observations carried out on three micrometeorological platforms representing typical urban, suburban, and rural land use areas of the MRSP, during the 2013 summer and winter field campaigns as part of the MCITY BRAZIL project. The main objective is to identify which PBL scheme best represents the diurnal evolution of conventional meteorological variables (temperature, relative and specific humidity, wind speed, and direction) and unconventional (sensible and latent heat fluxes, net radiation, and incoming downward solar radiation) on the surface. During the summer field campaign and over the suburban area of the MRSP, most PBL scheme simulations exhibited a cold and dry bias and overestimated wind speed. They also overestimated sensible heat flux, with high agreement index and correlation values. In general, the PBL scheme simulations performed well for latent heat flux, displaying low mean bias error and root square mean error values. Both incoming downward solar radiation and net radiation were also accurately simulated by most of them. The comparison of the nine PBL schemes indicated the local Mellor-Yamada-Janjic (MYJ) scheme performed best during the summer period, particularly for conventional meteorological variables for the land use suburban in the MRSP. During the winter field campaign, simulation outcomes varied significantly based on the site’s land use and the meteorological variable analyzed. The MYJ, Bougeault-Lacarrère (BouLac), and Mellor-Yamada Nakanishi-Niino (MYNN) schemes effectively simulated temperature and humidity, especially in the urban land use area. The MYNN scheme also simulated net radiation accurately. There was a tendency to overestimate sensible and latent heat fluxes, except for the rural land use area where they were consistently underestimated. However, the rural area exhibited superior correlations compared to the urban area. Overall, the MYJ scheme was deemed the most suitable for representing the convectional and nonconventional meteorological variables on the surface in all urban, suburban, and rural land use areas of the MRSP. Full article

In social sciences, multiple groups, such as countries, are frequently compared regarding a construct that is assessed using a number of items administered in a questionnaire. The corresponding scale is assessed with a unidimensional factor model involving a latent factor variable. To enable a comparison of the mean and standard deviation of the factor variable across groups, identification constraints on item intercepts and factor loadings must be imposed. Invariance alignment (IA) provides such a group comparison in the presence of partial invariance (i.e., a minority of item intercepts and factor loadings are allowed to differ across groups). IA is a linking procedure that separately fits a factor model in each group in the first step. In the second step, a linking of estimated item intercepts and factor loadings is conducted using a robust loss function $L_{0.5}$. The present article discusses implementation alternatives in IA. It compares the default $L_{0.5}$ loss function with $L_p$ with other values of the power p between 0 and 1. Moreover, the nondifferentiable $L_p$ loss functions are replaced with differentiable approximations in the estimation of IA that depend on a tuning parameter $\mathsf{\epsilon }$ (such as, e.g., $\mathsf{\epsilon }=0.01$). The consequences of choosing different values of $\mathsf{\epsilon }$ are discussed. Moreover, this article proposes the $L_0$ loss function with a differentiable approximation for IA. Finally, it is demonstrated that the default linking function in IA introduces bias in estimated means and standard deviations if there is noninvariance in factor loadings. Therefore, an alternative linking function based on logarithmized factor loadings is examined for estimating factor means and standard deviations. The implementation alternatives are compared through three simulation studies. It turned out that the linking function for factor loadings in IA should be replaced by the alternative involving logarithmized factor loadings. Furthermore, the default $L_{0.5}$ loss function is inferior to the newly proposed $L_0$ loss function regarding the bias and root mean square error of factor means and standard deviations. Full article

Deep learning (DL) methods have become the trend in predicting feasible solutions in a shorter time compared with traditional computational fluid dynamics (CFD) approaches. Recent studies have stacked numerous convolutional layers to extract high-level feature maps, which are then used for the analysis of various shapes under differing conditions. However, these applications only deal with predicting the flow around the objects located near the center of the domain, whereas most fluid-transport-related phenomena are associated with internal flows, such as pipe flows or air flows inside transportation vehicle engines. Hence, to broaden the scope of the DL approach in CFD, we introduced a stream-wise bidirectional (SB)-LSTM module that generates a better latent space from the internal fluid region by additionally extracting lateral connection features. To evaluate the effectiveness of the proposed method, we compared the results obtained using SB-LSTM to those of the encoder–decoder(ED) model and the U-Net model, as well as with the results when not using it. When SB-LSTM was applied, in the qualitative comparison, it effectively addressed the issue of erratic fluctuations in the predicted field values. Furthermore, in terms of quantitative evaluation, the mean relative error (MRE) for the x-component of velocity, y-component of velocity, and pressure was reduced by at least 2.7%, 4.7%, and 15%, respectively, compared to the absence of the SB-LSTM module. Furthermore, through a comparison of the calculation time, it was found that our approach did not undermine the superiority of the neural network’s computational acceleration effect. Full article

When comparison among groups is of major importance, it is necessary to ensure that the measuring tool exhibits measurement invariance. This means that it measures the same construct in the same way for all groups. In the opposite case, the test results in measurement error and bias toward a particular group of respondents. In this study, a new approach to examine measurement invariance was applied, which was appropriately designed when a large number of group comparisons are involved: the alignment approach. We used this approach to examine whether the factor structure of a cognitive ability test exhibited measurement invariance across the 26 universities of the Kingdom of Saudi Arabia. The results indicated that the P-GAT subscales were invariant across the 26 universities. Moreover, the aligned factor mean values were estimated, and factor mean comparisons of every group’s mean with all the other group means were conducted. The findings from this study showed that the alignment procedure is a valuable method to assess measurement invariance and latent mean differences when a large number of groups are involved. This technique provides an unbiased statistical estimation of group means, with significance tests between group pairs that adjust for sampling errors and missing data. Full article

Soil moisture (SM) is an indicator of the moisture status of the land surface, which is useful for monitoring extreme weather events. Representative global SM datasets include the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP), the Global Land Data Assimilation System (GLDAS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), but due to their low spatial resolutions, none of these datasets well describe SM changes in local areas, and they tend to have a low accuracy. Machine learning (ML)-based SM predictions have demonstrated high accuracy, but obtaining semi-real-time SM information remains challenging, and the dependence of the validation accuracy on the data sampling method used, such as random or yearly sampling, has led to uncertainties. In this study, we aimed to develop an ML-based model for real-time SM estimation that can capture local-scale variabilities in SM and have reliable accuracy, regardless of the sampling method. This study was conducted in South Korea, and satellite image data, numerical weather prediction (NWP) data, and topographic data provided within one day were used as the input data. For SM modeling, 13 input variables affecting the surface SM status were selected: 10- and 20-day cumulative standardized precipitation indexes (SPI10 and SPI20), a normalized difference vegetation index (NDVI), downward shortwave radiation (DSR), air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), latent heat flux (LE), slope, elevation, topographic ruggedness index (TRI), and aspect. Then, SM models based on random forest (RF) and automated machine learning (AutoML) were constructed, trained, and validated using random sampling and leave-one-year-out (LOYO) cross-validation. The RF- and AutoML-based SM models had significantly high accuracy rates based on comparisons with in situ SM (mean absolute error (MAE) = 2.212–4.132%; mean bias error (MBE) = −0.110–0.136%; root mean square error (RMSE) = 3.186–5.384%; correlation coefficient (CC) = 0.732–0.913), while the AutoML-based SM model tended to have a higher accuracy than the RF-based SM model, regardless of the data sampling method used. In addition, when compared to in situ SM data, the SM models demonstrated the highest accuracy, outperforming both GLDAS and ERA5 SM data and well representing changes in the dryness/wetness of the land surface according to meteorological events (heatwave, drought, and rainfall). The SM models proposed in this study can, thus, offer semi-real-time SM data, aiding in the monitoring of moisture changes in the land surface, as well as short-term meteorological disasters, like flash droughts or floods. Full article

A partial least squares (PLS) quantitative chemometric method based on the analysis of the mid-Fourier transform infrared spectroscopy (MID-FTIR) spectrum of polymer inclusion membranes (PIMs) used for the extraction of Cr(VI) from aqueous media is developed. The system previously optimized considering the variables membrane composition, extraction time, and pH, is characterized in terms of its adsorption isotherm, distribution coefficient, extraction percent, and enrichment factor. A Langmuir-type adsorption behavior with K_L = 2199 cm³/mmol, q_max = 0.188 mmol/g, and 0 < R_L < 1 indicates that metal adsorption is favorable. The characterization of the extraction reaction is performed as well, showing a 1:1 Cr(VI):Aliquat 336 ratio, in agreement with solvent extraction data. The principal component analysis (PCA) of the PIMs reveals a complex pattern, which is satisfactorily simplified and related to Cr(VI) concentrations through the use of a variable selection method (iPLS) in which the bands in the ranges 3451–3500 cm⁻¹ and 3751–3800 cm⁻¹ are chosen. The final PLS model, including the 100 wavelengths selected by iPLS and 10 latent variables, shows excellent parameter values with root mean square error of calibration (RMSEC) of 3.73115, root mean square error of cross-validation (RMSECV) of 6.82685, bias of −1.91847 × 10⁻¹³, cross-validation (CV) bias of 0.185947, R² Cal of 0.98145, R² CV of 0.940902, recovery% of 104.02 ± 4.12 (α = 0.05), sensitivity% of 0.001547 ppb, analytical sensitivity (γ) of 3.8 ppb, γ⁻¹: 0.6 ppb⁻¹, selectivity of 0.0155, linear range of 5.8–100 ppb, limit of detection (LD) of 1.9 ppb, and limit of quantitation (LQ) of 5.8 ppb. The developed PIM sensor is easy to implement as it requires few manipulations and a reduced number of chemical compounds in comparison to other similar reported systems. Full article

Hydrolytic reactions taking place at the surface of a silicon nitride (Si₃N₄) bioceramic were found to induce instantaneous inactivation of Human herpesvirus 1 (HHV-1, also known as Herpes simplex virus 1 or HSV-1). Si₃N₄ is a non-oxide ceramic compound with strong antibacterial and antiviral properties that has been proven safe for human cells. HSV-1 is a double-stranded DNA virus that infects a variety of host tissues through a lytic and latent cycle. Real-time reverse transcription (RT)-polymerase chain reaction (PCR) tests of HSV-1 DNA after instantaneous contact with Si₃N₄ showed that ammonia and its nitrogen radical byproducts, produced upon Si₃N₄ hydrolysis, directly reacted with viral proteins and fragmented the virus DNA, irreversibly damaging its structure. A comparison carried out upon testing HSV-1 against ZrO₂ particles under identical experimental conditions showed a significantly weaker (but not null) antiviral effect, which was attributed to oxygen radical influence. The results of this study extend the effectiveness of Si₃N₄’s antiviral properties beyond their previously proven efficacy against a large variety of single-stranded enveloped and non-enveloped RNA viruses. Possible applications include the development of antiviral creams or gels and oral rinses to exploit an extremely efficient, localized, and instantaneous viral reduction by means of a safe and more effective alternative to conventional antiviral creams. Upon incorporating a minor fraction of micrometric Si₃N₄ particles into polymeric matrices, antiherpetic devices could be fabricated, which would effectively impede viral reactivation and enable high local effectiveness for extended periods of time. Full article