Valid Comparisons Research Articles

An evaluation of measurement invariance of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition borderline personality disorder criteria across heterosexual, lesbian, gay, and bisexual adults.

There is a robust, yet poorly understood relationship between nonheterosexual orientation and borderline personality disorder (BPD), with lesbian, gay, and bisexual individuals evidencing greater BPD symptoms compared to heterosexual individuals. Recent evidence suggests possible psychometric bias in BPD diagnostic criteria leading to greater endorsement among sexual minority individuals, which hinders researchers' ability to make valid group comparisons. The present study utilized an epidemiological sample of 35,995 men and women to evaluate the extent of differential item functioning (DIF) among BPD criteria across sexual orientation groups using a multiple indicators multiple causes approach. All criteria except affective instability and emptiness indicated DIF for at least one sexual minority focal group, although both demonstrated DIF in sensitivity analyses. DIF was most consistently indicated for suicidality, efforts to avoid abandonment, and impulsivity. Contrary to predictions, DIF was mostly nonuniform with greater item discrimination for sexual minority groups compared to their heterosexual counterparts. Finally, all estimated effect sizes were small, suggesting that DIF was not practically meaningful and unlikely to impact the validity of group comparisons for BPD criteria across heterosexual and nonheterosexual men and women. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Critical Assessment on the Stability and Convergence of the Conventional Gear Tooth Contact Analysis

Mathematical modeling of gear engagement is crucial during design to ensure optimal performance in manufacturing. This study reproduces the conventional tooth contact analysis (TCA) model, highlighting convergence issues in parallel-axis gears and limitations in local synthesis methods. The research critically analyzes the TCA method, which solves five nonlinear equations to assess performance and accuracy. Simulations replicate the conditions of previous studies to ensure valid comparisons. Initial guess values are randomly generated within a specific range to guide the iterative process toward convergence, with this range progressively narrowed to improve computational efficiency and accuracy. Results indicate that the TCA approach is highly sensitive to initial guess values, particularly the starting angular position. Convergence issues arise from the complexity of nonlinear equations and multiple roots. This can lead to divergence or reverting to the initial guess when values deviate significantly from the true solution.

Cross-institutional evaluation of deep learning and radiomics models in predicting microvascular invasion in hepatocellular carcinoma: validity, robustness, and ultrasound modality efficacy comparison

PurposeTo conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation.MethodsThis retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS). For radiomics, we constructed models with enlarged vs. original regions of interest (ROIs). A cross-validation approach was performed by training models on one center’s dataset and validating the other, and vice versa. This allowed assessment of the validity of different ultrasound modalities and the cross-center robustness of the models. The optimal model combined with alpha-fetoprotein (AFP) was also validated. The head-to-head comparison was based on the area under the receiver operating characteristic curve (AUC).ResultsThirteen DL models and 25 radiomics models using different ultrasound modalities were constructed and compared. B + CEUS was the optimal modality for both DL and radiomics models. The DL model achieved AUCs of 0.802–0.818 internally and 0.667–0.688 externally across the two centers, whereas radiomics achieved AUCs of 0.749–0.869 internally and 0.646–0.697 externally. The radiomics models showed overall improvement with enlarged ROIs (P < 0.05 for both CEUS and B + CEUS modalities). The DL models showed good cross-institutional robustness (P > 0.05 for all modalities, 1.6–2.1% differences in AUC for the optimal modality), whereas the radiomics models had relatively limited robustness across the two centers (12% drop-off in AUC for the optimal modality). Adding AFP improved the DL models (P < 0.05 externally) and well maintained the robustness, but did not benefit the radiomics model (P > 0.05).ConclusionCross-institutional validation indicated that DL demonstrated better robustness than radiomics for preoperative MVI prediction in patients with HCC, representing a promising solution to non-standardized ultrasound examination procedures.

A new validated TRNSYS module for phase change material-filled multi-glazed windows

Incorporating phase change materials (PCM) into multi-glazed windows (MW) has been recognized as an effective way to regulate the indoor thermal environment and reduce the energy consumption of buildings. However, the design and calculation of PCM-filled multi-glazed windows (MW + PCM) remain challenging due to the lack of general transient simulation methods and integrated tools. To address this issue, the present study developed a dynamic coupled thermal and optical model for MW + PCM based on the enthalpy-temperature and interface energy balance methods. Then, the model was implemented in C++ and integrated into the TRNSYS software as a new module (Type 2602). In parallel, a test platform comprising two test chambers was established to evaluate the effect of PCM on the thermal and optical properties of the window and to validate the newly proposed module. It was found that compared to the traditional triple-glazed window (TW), the PCM-filled triple-glazed window (TW + PCM) effectively reduces the irradiance through the window by 34.17 % and the maximum temperature of the inner surface by approximately 5.56 °C. While the increase in external surface radiation flux density from 600 to 1200 W/m2 has a diminishing effect on the heat flux variation rate during the heat dissipation process for TW + PCM, it effectively shortens the latent heat absorption time by 0.50 h and increases the temperature difference between the inner and outer surfaces by 3.02 °C. The new module demonstrates satisfactory accuracy through experimental validation and simulation comparisons, with the maximum heat flux coefficient of variance of the root mean square error (CV(RMSE)) less than 6.35 % and 11.25 % under TW and TW + PCM configurations.

Exploring the Learning Curve of Dental Implant Placement Using a Task-Autonomous Robotic System Among Young Dentists From Different Specialties-A Pilot Module Study.

The learning curve effect of dynamic computer-assisted implant surgery (D-CAIS) was observed among inexperienced novice surgeons. The learning curves can provide valuable information for novice surgeons and valid comparisons between new and conventional techniques. Recently, robotic computer-assisted implant surgery (R-CAIS) has shown promise as a novel dental implant surgical technique for both partially and edentulous patients. However, its learning curve remains unknown. The aim of this study was to explore the learning curve of dental implant placement surgery with a task-autonomous robotic system among young dentists with different specialties. Four young dentists (mean age: 25.3 ± 1.5 years at the beginning of their first attempt) with equal representation of males and females and with different specialties participated in this study. None of the participants had prior experience in R-CAIS. Each operator placed eight implants over eight attempts using a semi-active task-autonomous robotic system. Among the eight implants, four were straight lateral incisor implants, and four were 30°-tilted premolar implants. The implants were placed in each dental quadrant of the maxillary and mandibular jaw modules. The operation time was recorded. Coronal, apical, and angular deviations between the planned and actual sites of implant placement were measured by merging preoperative and postoperative cone-beam computed tomography (CBCT) scans. The data were analyzed with repeated-measures ANOVA (α = 0.05). The mean time for implant placement was associated with the number of attempts (p < 0.01). The time taken for the second attempt was significantly shorter than that of the first attempt (33.26 vs. 30.47 min; p < 0.001) then it plateaued. Three-dimensional (3D) angular (p = 0.31), coronal deviation (p = 0.26), and apical deviation (p = 0.06) did not differ significantly among attempts. The mean values and standard deviations of 3D coronal deviation, 3D apical deviation, and 3D angular deviation were 0.71 ± 0.31 mm, 0.72 ± 0.30 mm, and 0.94 ± 0.58°, respectively. Neither the position of the jaw (p > 0.59) nor the tilt angle of the implant (straight or 30°-tilted, p > 0.85) was related to implant placement accuracy. Dentists quickly learned the basic workflow of R-CAIS and thus facilitated the clinicians in the mastery of implant placement on edentulous jaw modules, leading to a comparable operating speed and high precision. Moreover, the accuracy of placement of straight and tilted implants in both the maxilla and mandible with R-CAIS was satisfactory.

Empirical validation and engineering model comparison of outdoor long-range 3D GPU FDTD acoustic simulations with turbulence

We build upon previous work which introduced an acoustic GPU-accelerated 3D Finite-Difference Time-Domain solver, Empapu, equipped with a dynamic moving frame and turbulence modeled as a position-dependent refractive index for long-range outdoor sound propagation simulations. This physics-based model would serve as a benchmark to assess different engineering models, e.g., ISO 9613-2, CNOSSOS-EU and sonX, in the presence of uneven terrain with different ground properties and an inhomogeneous atmosphere. The solver now integrates an improved model of turbulence reproducing the kinetic spectral energy of velocity fluctuations, derived from wind velocity fields, with an eddy (quasiwavelet) approach, relevant in long-range propagations. This study validates our model by comparing level differences in third-octave band spectra at receiver points up to 300 m to analytical methods and measurements in a mostly flat topography comprised of asphalt and grassland. Further analysis is done by comparing results in different locations in Switzerland in the presence of uneven ground and barriers. This work will provide a physics-based basis for evaluating and comparing engineering models for outdoor sound propagation in lieu of measurements.

Predicting undergraduate OSCE performance using traditional and construct-driven situational judgment tests at admission.

Situational Judgement Tests (SJTs) are popular to screen for social skills during undergraduate medical admission as they have been shown to predict relevant study outcomes. Two different types of SJTs can be distinguished: Traditional SJTs, which measure general effective behavior, and construct-driven SJTs which are designed to measure specific constructs. To date, there has been no comparison of the predictive validity of these two types of SJTs in medical admission. With the present research, we examine whether the HAM-SJT, a traditional SJT, and the CD-SJT, a construct-driven SJT with an agentic and a communal scale, administered during undergraduate medical admission can predict OSCE (i.e., objective structured clinical examination) performance in a low-stakes (nLS = 159) and a high-stakes (nHS = 160) sample of medical students. Results showed a moderate positive relation between the communal scale of the CD-SJT and performance in OSCE stations with trained patients in the high-stakes sample (r =.20, p =.009). This SJT had also an incremental value in predicting the OSCE performance above and beyond GPA (i.e., grade point average), a science test (i.e., HAM-Nat), and gender (ß = 0.18, 95% CI [0.03; 0.33], p =.020). That is, individuals who chose more communal behavioral responses in the SJT were rated more favorably in interactions with trained patients in the OSCE. A comparable correlation coefficient was observed for the HAM-SJT when controlling for range restriction due to admission (rraw = 0.14 vs. rcontrolled = 0.20). Our research provides a first indication for the predictive validity of construct-driven SJTs in high-stakes undergraduate medical admission.

Use of puberty-blocking hormones for gender dysphoria in New Zealand: descriptive analysis and international comparisons.

To investigate use of puberty-blocking hormones (gonadotropin-releasing hormone analogues [GnRHa]) for gender dysphoria in New Zealand. Specifically, to describe demographic characteristics and time trends in the prevalence and incidence of prescribing, and to calculate cumulative incidence (proportion) of first prescribing of GnRHa for gender dysphoria in order to make valid international comparisons. The national Pharmaceutical Collection was used to identify all dispensing from 2006 to 2023 to those aged <18, by sex/gender and age. Cumulative incidence of first prescriptions between ages 12 and 17 (which largely excludes prescribing for other indications) was calculated and compared with the Netherlands and England and Wales. In New Zealand, prescription of GnRHa for gender dysphoria started around 2011; prevalence of use increased to 2014, then more steeply to 2022, followed by a decline. Incidence data show the decline started from 2021. New Zealand, compared to the Netherlands (which started prescribing in the 1990s), had 1.7 times the cumulative incidence of first prescriptions by 2018. Compared to England and Wales up to 2020, New Zealand had 3.5-6.9 times the cumulative incidence. The high rate of prescribing for probable gender dysphoria in New Zealand, and the decline after 2021, require further investigation.

Mathematical modeling of osmotic membrane bioreactor process for oily wastewater treatment

ABSTRACT To evaluate the disposal effluent from the Aldura refinery in Iraq, which comprises oily wastewater, a mathematical model has been developed for both forward osmosis (FO) and osmotic membrane bioreactor (OsMBR). The procedure is explained mathematically, accounting for both the concentration and polarization aspects. As a result of mathematical modeling, the water flux was determined by the osmotic pressure, the concentration, and the polarization of the feed and draw solutions. Based on traditional methods of predicting water flux using external and internal concentration polarizations, it is determined that water flux will occur in the first model (Model-1). To increase the accuracy of Model-1, the resistivity (K) of the solute has been modified to be independent of the diffusivity of the solute. The old model (Model-1) and the updated model (Model-2) overestimated water flux by 17 and 25%, respectively. It was possible to make a valid comparison between the experiment and theory based on the results of both experiments.

Design and Psychometric Evaluation of Nurses' Mobile Health Device Acceptance Scale (NMHDA-Scale): Application of the Expectation-Confirmation Theory.

The use of mobile tools in nursing care is indispensable. Given the importance of nurses' acceptance of these tools in delivering effective care, this issue requires greater attention. This study aims to design the Mobile Health Tool Acceptance Scale for Nurses based on the Expectation-Confirmation Theory and to evaluate it psychometrically. Using a Waltz-based approach grounded in existing tools and the constructs of the Expectation-Confirmation Theory, the initial version of the scale was designed and evaluated for face and content validity. Construct validity was examined through exploratory factor analysis, concurrent validity, and known-group comparison. Reliability was assessed using measures of internal consistency and stability. The initial version of the scale consisted of 33 items. During the qualitative and quantitative content validity stage, 1 item was added and 1 item was removed. Exploratory factor analysis, retaining 33 items, identified 5 factors that explained 70.53% of the variance. A significant positive correlation was found between the scores of the designed tool and nurses' attitudes toward using mobile-based apps in nursing care (r=0.655, P<.001). The intraclass correlation coefficient, Cronbach α, and ω coefficient were 0.938, 0.953, and 0.907, respectively. The 33-item scale developed is a valid and reliable instrument for measuring nurses' acceptance of mobile health tools.

B - 140 Comparison of Performance Validity, Symptom Validity, Cognitive, and Psychological Symptom Report Measures in an Active-Duty Military Sample

Abstract Objective Performance and symptom validity (PVT/SVT) are distinct but related constructs (Nelson et al., 2007; Ruocco et al., 2008). However, studies have rarely examined PVT and SVT via factor analytic techniques, which offer stronger understanding of concurrent performance patterns across tests. Ord et al (2021) conducted one such study and concluded that PVT/SVT each “contribute unique information about examinees’ performance.” This and previous studies, however, have not included cognitive or symptom measures along with SVTs/PVTs, which limits interpretation. Methods In a large sample of Active-Duty (ad) personnel (n = 629), EFA and CFA modeling evaluated underlying patterns between different forms of testing (PVT, SVT, symptom report, and cognitive report). Measures include the MSVT, NV-MSVT, RBANS, and PAI. Results EFA (Maximum Likelihood and Geomin rotation) evidenced shared method variance patterns across PVT, SVT, and both cognitive/symptom scales without good fit (X2 = 4643.0, RMSEA = 0.15, CFI = 0.58). When modeled with CFA, these domains provided good fit, X2(145) = 634.92, CFI = 0.94, RMSEA = 0.07, SRMR = 0.05. Standardized loadings were large for PVT (0.40–0.97), SVT (0.62–0.98), symptom report (0.43–0.91), and cognitive ability (0.48–0.87). Higher order factor structure suggests convergence of PVT and Cognitive Testing (0.70); however, SVT and were distinct from cognitively-focused testing (−0.43). Conclusions Validity testing in neuropsychology has accelerated (Sweet et al., 2021), and understanding relationships between validity and symptom domains is critical to this effort. Our study suggests differentiation between domains, with weaker relationships between validity and clinical symptoms.

Visualizing Neighborhood COVID-19 Levels, Trends, and Inequities in Wastewater: An Equity-Centered Approach and Comparison to CDC Methods.

Monitoring neighborhood-level SARS-CoV-2 wastewater concentrations can help guide public health interventions and provide early warning ahead of lagging COVID-19 clinical indicators. To date, however, U.S. Centers for Disease Control and Prevention's (CDC) National Wastewater Surveillance System (NWSS) has provided methodology solely for communicating national and state-level "wastewater viral activity levels." In October 2022, the Boston Public Health Commission (BPHC) began routinely sampling wastewater at 11 neighborhood sites to better understand COVID-19 epidemiology and inequities across neighborhoods, which vary widely in sociodemographic and socioeconomic characteristics. We developed equity-centered methods to routinely report interpretable and actionable descriptions of COVID-19 wastewater levels, trends, and neighborhood-level inequities. To produce these data visualizations, spanning October 2022 to December 2023, we followed four general steps: (1) smoothing raw values; (2) classifying current COVID-19 wastewater levels; (3) classifying current trends; and (4) reporting and visualizing results. COVID-19 wastewater levels corresponded well with lagged COVID-19 hospitalizations and deaths over time, with "Very High" wastewater levels coinciding with winter surges. When citywide COVID-19 levels were at the highest and lowest points, levels and trends tended to be consistent across sites. In contrast, when citywide levels were moderate, neighborhood levels and trends were more variable, revealing inequities across neighborhoods, emphasizing the importance of neighborhood-level results. Applying CDC/NWSS state-level methodology to neighborhood sites resulted in vastly different neighborhood-specific wastewater cut points for "High" or "Low," obscured inequities between neighborhoods, and systematically underestimated COVID-19 levels during surge periods in neighborhoods with the highest COVID-19 morbidity and mortality. Our methods offer an approach that other local jurisdictions can use for routinely monitoring, comparing, and communicating neighborhood-level wastewater levels, trends, and inequities. Applying CDC/NWSS methodology at the neighborhood-level can obscure and perpetuate COVID-19 inequities. We recommend jurisdictions adopt equity-focused approaches in neighborhood-level wastewater surveillance for valid community comparisons.

Lectin-Based Fluorescent Comparison of Glycan Profile-FDA Validation to Expedite Approval of Biosimilars.

Glycan profile comparisons are one of the most tedious analytical exercises for establishing compliance with recombinant therapeutic protein batches. Based on its intensive research, the FDA has confirmed that lectin array binding with fluorescent monitoring is the fastest and most reliable method for profile comparisons. Using a database of over 150 biological products expressed in nine diverse mammalian cell systems, the FDA immobilized 74 lectins to study their binding using fluorescently labeled glycoproteins. The FDA identified nine distinct lectins from a custom-designed lectin microarray: rPhoSL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHALs, which detect core fucose, terminal GlcNAc, terminal β-galactose, high mannose, α-2,3-linked sialic acids, α-2,6-linked sialic acids, bisecting GlcNAc, terminal α-galactose, and triantennary structures, respectively. This method can be used for screening and routine testing and to monitor batch-to-batch variability of therapeutic proteins, including establishing analytical similarity as a crucial part of biosimilar development.

ANALISIS PERBANDINGAN SILHOUETTE COEFFICIENT DAN METODE ELBOW PADA PENGELOMPOKKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR IPM DENGAN K-MEDOIDS

Development is a process of change that is carried out consistently with the aim of improvement in all aspects of life with the prevailing values in society to achieve predetermined life goals. The quality of life of the community is measured by the Human Development Index (HDI) at the provincial level through three indicators, including: economic level, health, and education. K-medoids is a method used to group objects that contain outliers. In determining the optimal number of clusters using the Silhouette Coefficient method which has the advantage of determining the best number of clusters that can measure how close the relationship between objects and measure how far a cluster is separated from other clusters. The technique called the Elbow method is employed to ascertain the optimal quantity of clusters, which is done by examining the percentage outcomes derived from comparing the quantity of clusters that form an elbow at a certain point. Then cluster evaluation is carried out using the Davies Bouldin Index (DBI) as a comparison for cluster validation. The results of this study using the Silhouette Coefficient method produced the best cluster, namely 3 clusters with a Silhoutte Coefficient value of 0,3129 and a DBI value of 1,3184. Meanwhile, using the Elbow method produced the best cluster, namely 4 clusters with an SSE value of 54,5548 with a DBI value of 1,1754. So that the best cluster is 4 clusters with the Elbow method with the smallest DBI value.

Bayesian Adaptive Lasso for Detecting Item-Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models.

In multidimensional tests, the identification of latent traits measured by each item is crucial. In addition to item-trait relationship, differential item functioning (DIF) is routinely evaluated to ensure valid comparison among different groups. The two problems are investigated separately in the literature. This paper uses a unified framework for detecting item-trait relationship and DIF in multidimensional item response theory (MIRT) models. By incorporating DIF effects in MIRT models, these problems can be considered as variable selection for latent/observed variables and their interactions. A Bayesian adaptive Lasso procedure is developed for variable selection, in which item-trait relationship and DIF effects can be obtained simultaneously. Simulation studies show the performance of our method for parameter estimation, the recovery of item-trait relationship and the detection of DIF effects. An application is presented using data from the Eysenck Personality Questionnaire.

A Comparison of Statistical Validity of In-Situ Hydraulic Conductivity Prediction Models of Rock Mass Inferred from Borehole Logs and Lugeon Test Data

In-situ hydraulic conductivity is a vital property in rock engineering for jointed rock mass. Understanding its correlation with rock mass parameters is crucial for water circulation. Therefore, a study was carried out to develop statistically significant empirical relationships between hydraulic conductivity and various rock mass parameters to estimate in-situ hydraulic conductivity from Lugeon test and various rock mass parameters obtained from borehole logs.The study initially aimed to establish a correlation between hydraulic conductivity and Rock Quality Designation (RQD). However, the outcomes were unsatisfactory, prompting further research. Later, two more robust models were developed, namely the HC-model and modified HC-model. The HC-model incorporated four rock mass parameters, including Rock Quality Designation Index, Depth Index, Gouge Content Designation Index, and Lithology Permeability Index, achieving a maximum coefficient of determination (R2) of 0.46. The modified HC-model included six parameters, encompassing fracture frequency and theoretical aperture, resulting in an improved R2 of 0.69. Both models significantly outperformed RQD-alone predictions (R2 &lt; 0.10), highlighting the need for incorporating multiple rock mass parameters in predicting hydraulic conductivity due to a complex interplay of various factors. However, the effects of joint persistence and roughness are limiting in the present analysis.

Risk factors associated with re-revision following revision total knee arthroplasty: a systematic review.

The aim of this study was to perform a systematic review and bias evaluation of the current literature to create an overview of risk factors for re-revision following revision total knee arthroplasty (rTKA). A systematic search of MEDLINE and Embase was completed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The studies were required to include a population of index rTKAs. Primary or secondary outcomes had to be re-revision. The association between preoperative factors and the effect on the risk for re-revision was also required to be reported by the studies. The search yielded 4,847 studies, of which 15 were included. A majority of the studies were retrospective cohorts or registry studies. In total, 26 significant risk factors for re-revision were identified. Of these, the following risk factors were consistent across multiple studies: age at the time of index revision, male sex, index revision being partial revision, and index revision due to infection. Modifiable risk factors were opioid use, BMI > 40 kg/m2, and anaemia. History of one-stage revision due to infection was associated with the highest risk of re-revision. Overall, 26 risk factors have been associated with an increased risk of re-revision following rTKA. However, various levels of methodological bias were found in the studies. Future studies should ensure valid comparisons by including patients with identical indications and using clear definitions for accurate assessments.

Revisiting the English Short Measure for Adult Playfulness (SMAP): An Investigation of Reliability, Validity, and Cross-Cultural Comparisons

Adult playfulness describes the personality of the quick initiation and strong intensity of enjoyable experiences coupled with the frequency of engaging in playful behaviors. In addition to examining the reliability and validity of the Short Measure for Adult Playfulness (SMAP), we compared the psychometric properties of the SMAP across (a) 4- and 7-point answer formats; (b) German and English language versions; and (c) gender. The SMAP and criterion validity measures were distributed across three independent samples from Canada (N total = 1,177) and a German sample (N = 660). Results supported a unidimensional solution with strong factor loadings for all items (>.50 across all samples) and high test-retest reliability (r = .67, BF10>100). In item response theory analyses, high discrimination parameters were found across the latent continuum. Although both the four-point and seven-point Likert scale versions showed robust item properties, the seven-point Likert scale version yielded more precise measurement of higher playfulness (θ > 1.50). No significant differential item functioning was located across gender or language. Finally, results supported external construct validity via associations with broad personality traits, humor, and orientations to happiness. The present investigation provides support for the reliability and validity of the English SMAP for concise adult playfulness measurement.

Measuring sexual behavior stigma among cisgender men who have sex with men: an assessment of cross-country measurement invariance.

Globally, cisgender men who have sex with men experience sexual stigma, but limited investigation of cross-population scale performance hinder comparisons. As measurement invariance is a necessary but seldom-established criterion of valid cross-cultural comparisons, we assessed invariance in scales of stigma related to sexual behavior across 9 countries. This secondary analysis used data collected from adult (mean age=29.6, standard deviation=12.5) cisgender men who have sex with men (n=8,669) in studies from 6 West African, 2 Southern African, and 1 North American country from 2012-2016. A common item set assessed 2 sexual behavior stigma domains. A sequential process was used to test the factor structure and measurement invariance, which included multigroup confirmatory factor analyses (CFA). Individual countries, items, living with HIV, and disclosure were explored as possible sources of noninvariance. Goodness-of-fit statistics indicated adequate fit of the same 2-factor model in 7 of the 9 countries. The chi2 difference test comparing a constrained and unconstrained 7-country model in which loadings and thresholds were freely estimated was significant (p<0.001), indicating metric and scalar noninvariance, but removing the US provided evidence of invariance and freeing certain items led to a finding of partial invariance. Sexuality disclosure exhibited a direct relationship with select stigma items in several countries. Our findings point to the utility of the two stigma scale dimensions in making cross-country comparisons, but also to the necessity of assessing invariance with explicit attention to several factors including differential disclosure of sexuality across contexts to ensure valid comparisons.

Time-series classification in smart manufacturing systems: An experimental evaluation of state-of-the-art machine learning algorithms

Manufacturing is transformed towards smart manufacturing, entering a new data-driven era fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, Time-Series Classification (TSC) emerges as a crucial task in this domain. Over the past decade, researchers have introduced numerous methods for TSC, necessitating not only algorithmic development and analysis but also validation and empirical comparison. This dual approach holds substantial value for practitioners by streamlining choices and revealing insights into models’ strengths and weaknesses. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the state-of-the-art Machine Learning (ML) and Deep Learning (DL) algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 state-of-the-art algorithms from both TSC and manufacturing literature. Following this, we methodologically selected the 36 most representative algorithms from this list. To evaluate their performance across various manufacturing classification tasks, we curated a set of 22 manufacturing datasets, representative of different characteristics that cover diverse manufacturing problems. Subsequently, we implemented and evaluated the algorithms on the manufacturing benchmark datasets, and analyzed the results for each dataset. Based on the results, ResNet, DrCIF, InceptionTime, and ARSENAL emerged as the top-performing algorithms, boasting an average accuracy of over 96.6 % across all 22 manufacturing TSC datasets. These findings underscore the robustness, efficiency, scalability, and effectiveness of convolutional kernels in capturing temporal features in time-series data collected from manufacturing systems for TSC tasks, as three out of the top four performing algorithms leverage these kernels for feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve recognition for their effectiveness in capturing features within manufacturing time-series data using RNN-based structures.