Subjective Variables Research Articles

Gender differences in clinical characteristics and influencing factors of suicide attempts in first-episode and drug-naïve major depressive disorder patients with comorbid metabolic syndrome.

Patients with major depressive disorder (MDD) have a high rate of metabolic syndrome (MetS), which could worsen disease progression. One of the most serious progressions in MDD is suicide attempts (SAs). Previous studies have found gender differences in MetS and SAs among MDD patients respectively. Therefore, we aimed to explore gender differences of SAs in first-episode and drug-naïve (FEDN) MDD patients with comorbid MetS. 1718 outpatients with FEDN MDD were recruited. Depression, anxiety and psychotic symptoms were evaluated using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA) and Positive and Negative Syndrome Scale (PANSS) positive subscale, respectively. Blood sugar, blood fat, blood pressure and body mass index (BMI) were measured to evaluate MetS. 34.4% patients with FEDN MDD were diagnosed as MetS and those subjects with or without MetS differed in the distribution of SAs and gender. In MetS subgroup, 29.5% and 29.7% of male and female subjects had SAs respectively, without significant differences. However, compared with non-suicide attempters, suicide attempters had higher level of blood pressure in female subjects, while there are no differences in any clinical variables in male subjects. Additionally, the influencing factors for SAs differed by gender. The HAMA scores and BMI were variables associated with SAs in male patients while HAMA scores, marital status and systolic blood pressure (SBP) were associated with SAs in female patients. Furthermore, the receiver operating characteristics (ROC) curves, demonstrating the combination all influencing factors by gender, showed good performance and model accuracy. In FEDN MDD patients with comorbid MetS, there were no gender differences in SAs. However, clinical characteristics and influencing factors of SAs differed in different gender groups.

Suicide risk among adult subjects hospitalized in an acute psychiatric ward: 6-yearretrospective investigation.

The phenomenon of suicide risk (SR) represents a psychiatric, social and environmental emergency. The acute psychiatric ward as the Italian Service for Psychiatric Diagnosis and Care (SPDC) represents the place where SR is high due to the acute and serious conditions of people hospitalized. The objective of this study was to evaluate the characteristics of subjects admitted to a SPDC over a 6-year period for SR represented by: suicidal ideation, attempted and failed suicide. With a retrospective single-center observational design, we collected hospitalizations from 01/01/2017 to 31/12/2022 in the SPDC of AUSL-Modena for SR, analyzed the modality of SR and compared the demographic and clinical variables of subjects with SR with those hospitalized for other clinical reasons in the same period. Data were statistically analyzed. In the 6-year of study period, we collected 2,930 hospitalizations in the SPDC of AUSL- Modena and among them, 68% (n = 528) were carried out due to SR, which represented the second leading cause of hospitalization (18%), in particular among females (Pearson Chi2 = 17.41, p < 0.001). Individuals with SR were more frequently voluntary admitted (Pearson Chi2 = 215.41, p < 0.001) for a shorter period (7.36 ± 8.16 vs 11.66 ± 15.93, t = 6.03, t-test, p < 0.001) and less frequently repeated the hospitalization during the study period for the same reason (Pearson Chi2 = 6.0, p = 0.014). The most frequent psychiatric disorders associated with SR were depressive, personality and adjustment disorders, which represented the most common factor associated with SR (68%), followed by family/relationship problems (12%) and alcohol/substance abuse (8%). We highlighted three kinds of suicidal risk: suicidal ideation (40%), often associated with personality disorders and substance use disorders, drug ingestion (30%) concomitant with conflicting family relationships and use of violent means (30%) associated with depressive disorders (Pearson Chi2 = 42.83, p = 0.002). Our study provides a real-world setting evaluation of subjects hospitalized for SR and, in accordance with literature, suggests that suicidal behavior is the product of many clinical and social factors' interaction, that occurred in a crucial moment of life in vulnerable individuals. The identification of subjects at SR represents the first step of preventive multi-professional interventions.

Subjective variability of the “just-right feeling”: Effectiveness of social media advertising design

With the rapid advancement of AI and algorithmic technologies, social media platforms have gained the ability to identify consumer personality traits. However, developing compelling advertising strategies for individuals with different self-construals remains challenging. Based on construal level theory, this research investigates the interactions between shot scale, advertising appeals, and self-construal on social media. The results of the four empirical studies indicated that independent individuals prefer advertisements with long-shot images and desirability appeals. In contrast, interdependent individuals favor advertisements with close-up images and feasibility appeals. Furthermore, the findings reveal that long-shot images match with desirability appeals or close-up images paired with feasibility appeals significantly increase the click-through rate and foster more positive advertising attitudes. The above findings are central to the feeling right during information processing, which plays a crucial role in advertising acceptance. Therefore, this research constructs a new framework for personalized advertisement design in social media and offers a practical guide for businesses seeking to optimize their advertising strategies.

Mediating role of self-esteem on university students’ physical activity attitude and frequency

Background and Study Aim. The health benefits of physical activity are well-known. Despite this, there is an ongoing decline in total physical activity (PA), especially among adolescents and young women. This decline points to the need to find more effective solutions. This study aimed to determine the relationship between physical activity and attitudes toward physical activity, and also between self-esteem body image (as subjective variables) and body mass index (as an objective variable). Material and Methods. This cross-sectional study measured and investigated 152 female students from various faculties in their first and second years of study at the Bucharest University of Economic Studies. The research utilized direct anthropometrical measurements and several self-reported instruments. These included questionnaires on self-esteem body image, physical activity attitude, and a report on the frequency of physical activity. Self-esteem was assessed using the Rosenberg Self-Esteem Scale. Body dissatisfaction was evaluated using the Contour Drawing Rating Scale. Results. The mean Body Mass Index (BMI) value of 20.89 kg/m² indicates a healthy, normal BMI. The mean self-esteem score (31.48) falls within the normal range, but 60% of respondents could not match their actual body shape with their ideal or desired body image. 54% of respondents reported engaging in 30 minutes of physical activity, three times a week, outside the university program. As a result, the mean value of the attitude towards physical activity is slightly positive. 33% of young women's self-esteem is related to physical factors such as body image, frequency of physical activity, and attitude towards physical activity. Conclusions. The study indicates that targeted physical education programs and consistent, independent physical activity can mitigate the challenges associated with the investigated variables. Moreover, self-esteem significantly mediates the relationship between body image and physical activity frequency, as well as students’ attitudes toward physical activity.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar functional connectivity characteristics through clustering, thereby enhancing the classification process. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.&#xD.

IEHT vs. Regular Treatment - Are there Differences between the Two forms of Treatment in the Retrospective Course with regard to Individual Objective and Subjective Outcome Variables?

This retrospective follow-up study over one year (after discharge from the index treatment), compares the effects of inpatient-equivalent home treatment (IEHT) and regular psychiatric treatment using subjective and objective quality indicators. Using a 1:1 matching of cohorts (diagnosis, age, gender, number of previous treatments and severity of illness), 732 IEHT patients were assigned to a comparison group. Outcome data was taken from the routine data of the clinic database and the in-house basic documentation. In the subjective quality measure, patients and practitioners in regular psychiatric treatment reported a significantly higher improvement in condition. In terms of the objective quality measure, the number of further inpatient treatment days in the observation period, the two groups did not differ significantly, which can be interpreted as encouragement for the use of this new, increasingly established form of treatment.

Camera-Based Respiratory Imaging System for Monitoring Infant Thoracoabdominal Patterns of Respiration.

Existing respiratory monitoring techniques primarily focus on respiratory rate measurement, neglecting the potential of using thoracoabdominal patterns of respiration for infant lung health assessment. To bridge this gap, we exploit the unique advantage of spatial redundancy of a camera sensor to analyze the infant thoracoabdominal respiratory motion. Specifically, we propose a camera-based respiratory imaging (CRI) system that utilizes optical flow to construct a spatio-temporal respiratory imager for comparing the infant chest and abdominal respiratory motion, and employs deep learning algorithms to identify infant abdominal, thoracoabdominal synchronous, and thoracoabdominal asynchronous patterns of respiration. To alleviate the challenges posed by limited clinical training data and subject variability, we introduce a novel multiple-expert contrastive learning (MECL) strategy to CRI. It enriches training samples by reversing and pairing different-class data, and promotes the representation consistency of same-class data through multi-expert collaborative optimization. Clinical validation involving 44 infants shows that MECL achieves 70% in sensitivity and 80.21% in specificity, which validates the feasibility of CRI for respiratory pattern recognition. This work investigates a novel video-based approach for assessing the infant thoracoabdominal patterns of respiration, revealing a new value stream of video health monitoring in neonatal care.

Alcohol and cannabis co-use: Probing subjective response in eliciting cross-substance craving

The co-use of alcohol and cannabis is rising in prevalence, yet the mechanisms driving individuals to co-use are not well understood. Subjective response to alcohol or cannabis may predict the desire to use either substance. However, which specific facets of subjective response predict cross-substance craving remains unclear. The present study investigated whether acute administration of alcohol or cannabis facilitates cue-induced craving for the other substance, with an emphasis on the underlying subjective response mechanisms contributing to co-use. This is a secondary analysis of a behavioral pharmacology study that combined alcohol/cannabis administration with a cross-substance cue-reactivity paradigm in individuals who were heavy alcohol and heavy cannabis co-users. Over two sessions, twenty-nine individuals (17M/12F) self-administered alcohol or cannabis (in a crossover design), and then completed a cue-reactivity exercise with the other substance. Analyses tested how changes in subjective response variables following substance administration predicted cross-substance cue-induced craving. Following alcohol administration, greater subjective ratings of positive mood predicted significantly greater cue-induced cannabis craving (β = 1.14, SE = 0.41, t = 2.80, p = 0.010). Following cannabis administration, lower subjective effects ratings of positive mood/arousal predicted significantly greater cue-induced alcohol craving (β = −1.08, SE = 0.38, t = −2.85, p = 0.009; β = −2.38, SE = 1.13, t = −2.10, p = 0.047). This study identified subject response mechanisms contributing to cross-substance cue induced craving. These mechanisms include increases in positive mood following alcohol use and decreases in positive mood and arousal, akin to increases in relaxation, following cannabis use.

Influence of punishments associated with task outcomes in physical and mental efforts performed by semi-elite soccer players during training tasks

ABSTRACT Punishing application associated with the task outcomes is a familiar strategy used by soccer coaches during training, but it has been mainly remained in the research background. To date, no previous studies have addressed the effects of punishments on soccer players’ physical and mental efforts during on-field training sessions. Voluntarily, 40 semi-elite male soccer players from two Spanish teams of the same club participated in the study (M age = 22.90 yrs, SD = 5.60 yrs), performing two training sessions with the same tasks, but different in the use or non-use of punishments associated with the task outcomes. Physical load was registered using Polar Team and Ratio of Perceived Exertion. Mental load was estimated by the NASA-Task Load Index and mental fatigue was collected using a Visual Analogue Scale. A portable eye tracker was used to seek the effects of punishment manipulation on ocular metrics. After training, players achieved higher values of peak and mean heart rate, peak and mean speed, distance/minute, and number of sprints at the session with punishments, compared to the session without them. The punishments also influenced participants’ eye movement metrics because changes were found in pupil diameter, latency and velocity of saccades, and number of saccades and blinks when compared to the two training sessions. However, no differences were found between sessions for subjective mental load and fatigue variables. These findings have practical implications to the design of soccer training tasks because this emotional constraint influenced the physical load and objective mental load of the soccer players, but not their subjective measures of mental load and fatigue.

A Deep Cascade Architecture for Stroke Lesion Segmentation and Synthetic Parametric Map Generation over CT St

Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role in patient prognosis. Nowadays, computed tomography (CT) is the most utilized diagnostic imaging for early analysis and lesion stroke detection. Nonetheless, acute lesions are not visible on CT, and it is only possible to use this modality in screening analysis, discarding other neurological affectations. Expert radiologists can observe stroke lesions in advanced stages (subacute and chronic), as hypodense regions, but with limited sensibility and remarked subject variability. Computational strategies have been addressed to support lesion segmentation, following deep autoencoders and multimodal inputs. However, these strategies remain limited due to the high variability in the appearance and geometry of stroke lesions. This work introduces a novel deep representation that uses multimodal inputs from CT studies and parametric maps, computed from perfusion (CTP), to retrieve stroke lesions. The architecture follows an autoencoder deep representation, that forces attention on the geometry of stroke through additive cross-attention modules. Besides, a cascade train is herein proposed to generate synthetic perfusion maps that complement multimodal inputs and help with stroke lesion refinement at each stage of processing. The proposed approach brings saliency maps that support observational expert analysis, about lesion localization, but also lead with automatic shape estimation of the stroke. The proposed approach was validated on the ISLES 2018 public dataset with a total of 92 studies that include the annotation of an expert radiologist. The proposed approach achieves a Dice score of 0.66 and a precision of 0.67, outperforming classical autoencoder approximations.

Review of deep learning-based pathological image classification: From task-specific models to foundation models

Pathological diagnosis is considered the gold standard in cancer diagnosis, playing a crucial role in guiding treatment decisions and prognosis assessment for patients. However, achieving accurate diagnosis of pathology images poses several challenges, including the scarcity of pathologists and the inherent subjective variability in their interpretations. The advancements in whole-slide imaging technology and deep learning methods provide new opportunities for digital pathology, especially in low-resource settings, by enabling effective pathological image classification. In this article, we begin by introducing the datasets, which include both unimodal and multimodal types, as essential resources for advancing pathological image classification. We then provide a comprehensive overview of deep learning-based pathological image classification models, covering task-specific models such as supervised, unsupervised, weakly supervised, and semi-supervised learning methods, as well as unimodal and multimodal foundation models. Next, we review tumor-related indicators that can be predicted from pathological images, focusing on two main categories: indicators that can be recognized by pathologists, such as tumor classification, grading, and region recognition; and those that cannot be recognized by pathologists, including molecular subtype prediction, tumor origin prediction, biomarker prediction, and survival prediction. Finally, we summarize the key challenges in digital pathology and propose potential future directions.

Elastic Bands Improve Oral Appliance Treatment Effect on Obstructive Sleep Apnoea: A Randomised Crossover Trial.

Oral appliances (OAs) that limit mouth opening during sleep, such as monobloc appliances, have shown superior treatment effects in subgroups of patients with obstructive sleep apnoea. The application of elastic bands on bibloc appliances may resemble these benefits. The primary objective was to investigate if application of elastic bands to bibloc appliances improves treatment success (> 50% reduction of respiratory event index (REI)), in addition to other subjective variables. Furthermore, we aimed to identify variables predicting the need for elastic bands in OA treatment. Included patients (n = 69) were randomly assigned to OA treatment with or without elastic bands. After 3 weeks, treatment effect was investigated with home respiratory polygraphy and questionnaires. Thereafter, patients changed treatment modality, with identical follow-up regime. Statistical analyses were performed using Student's t-test and Pearson's chi-squared test to investigate differences between the two treatment modalities, and logistic regression analysis was conducted to investigate variables tentatively associated with treatment success. Based on REI, the success rate with OA treatment was in favour of elastic bands (53.9% vs. 34.6%, p = 0.002). Male sex and larger maximum mouth opening were identified as predictors for increased treatment success with elastic bands. The main benefit with elastic bands seemed to be greater reduction of REI when supine. However, patients seem to prefer OA without elastic bands. Elastic bands improved OA treatment effect by reducing the REI in supine position. Patient groups that seemed to benefit from elastic bands in OA treatment were men with large maximum mouth openings.

The repeatability of a cycling exercise-heat stress test in a male population

IntroductionThere is considerable inter-individual variability in the physiological responses to environmental stressors and so to accurately assess and monitor changes in an individual's ability to cope with exercise-heat stress, a reliable protocol is required. The aim of this study was to examine the repeatability of a 90-min steady-state heat exercise bout with physiological and subjective variables, and performance during an incremental test to exhaustion post 90-min steady-state exercise. MethodSixteen mixed ability males (Age: 39 ± 15yrs; Height: 176.5 ± 4.8 cm; BM: 79.7 ± 10.3 kg; V˙O2peak: 46.2 ± 8.6 ml/kg/min; PPO: 309 ± 39W) who trained at least three times a week undertook two 90-min steady-state – followed by an incremental protocol to exhaustion – cycling heat stress tests (HSTs) in a hot-humid environment (35 °C, 60%RH). Heart rate (HR), rectal (T‾re) and skin temperature (T‾sk), rating of perceived exertion (RPE), thermal sensation (TS), and thermal comfort (TC) were measured throughout. Data was analysed using Intraclass Correlation Coefficients (ICC), technical error of measurement (TEM), Bland-Altman plots, t-tests, and Cohen's d to indicate magnitude of change. ResultsPhysiological variables indicated good repeatability evident through moderate to strong ICC ratings, low magnitudes of change (d), lower mean biases compared to their respective calculated TEMs, and statistical non-significance, except HR90, ₸sk90, and ₸sk. Hydration status showed good repeatability except for urine osmolality (osmu90) and resting urine colour (colu). Perceptual variables showed encouraging repeatability apart from resting TS and mean TS. Performance data showed good repeatability overall, however 11 participants progressed to the incremental test to exhaustion in the second visit compared to 7 in the first. ConclusionCurrent data demonstrated favourable physiological, perceptual, and performance repeatability during repeated cycling HSTs in hot-humid conditions. However, given more participants progressed to the incremental trial to exhaustion protocol in the second visit, at least one familiarisation trial may improve the reliability of exercise capacity assessment.

Learning to Score: A Coding System for Constructed Response Items via Interactive Clustering

Constructed response items that require the student to give more detailed and elaborate responses are widely applied in large-scale assessments. However, the hand-craft scoring with a rubric for massive responses is labor-intensive and impractical due to rater subjectivity and answer variability. The automatic response coding method, such as the automatic scoring of short answers, has become a critical component of the learning and assessment system. In this paper, we propose an interactive coding system called ASSIST to efficiently score student responses with expert knowledge and then generate an automatic score classifier. First, the ungraded responses are clustered to generate specific codes, representative responses, and indicator words. The constraint set based on feedback from experts is taken as training data in metric learning to compensate for machine bias. Meanwhile, the classifier from responses to code is trained according to the clustering results. Second, the experts review each coded cluster with the representative responses and indicator words to score a rating. The coded cluster and score pairs will be validated to ensure inter-rater reliability. Finally, the classifier is available for scoring a new response with out-of-distribution detection, which is based on the similarity between response representation and class proxy, i.e., the weight of class in the last linear layer of the classifier. The originality of the system developed stems from the interactive response clustering procedure, which involves expert feedback and an adaptive automatic classifier that can identify new response classes. The proposed system is evaluated on our real-world assessment dataset. The results of the experiments demonstrate the effectiveness of the proposed system in saving human effort and improving scoring performance. The average improvements in clustering quality and scoring accuracy are 14.48% and 18.94%, respectively. Additionally, we reported the inter-rater reliability, out-of-distribution rate, and cluster statistics, before and after interaction.

Sleep Health among Community-Recruited Opioid-Using People Who Inject Drugs in Los Angeles, CA and Denver, CO.

Chronic insufficient and poor-quality sleep are linked to hypertension, diabetes, depression, heart attack, and stroke. While studies on substance use and sleep typically focus on people in or entering treatment, there is a lack of research on sleep health among community-recruited people who inject drugs (PWID). To address this literature gap, we examined factors associated with insufficient and poor-quality sleep among community-recruited PWID. We recruited and interviewed 472 active opioid-using PWID (injected within the last 30days) in Los Angeles, CA and Denver, CO between 2021 and 2022. Participants completed computer-assisted interviews covering demographics, subsistence measures, drug use patterns, injection-related behaviors, health risks, and sleep duration and quality in the last 3months. Descriptive statistics were used to analyze all variables for subjects with complete responses to sleep items (n = 464). Bivariate analyses determined factors associated with sleep measures using chi-square and t-tests. Collinear variables were removed, and binomial linear multivariable regression calculated risk ratios (RR) for insufficient and poor-quality sleep in the last 3months. Participants exhibited low sleep duration (mean = 4.99, standard deviation (SD) = 2.70), with 76% reporting insufficient sleep and 62% poor-quality sleep. Bivariate analyses associated both sleep measures with drug use, high subsistence scores, violent victimization, and poor health outcomes. Multivariable analyses showed a high subsistence score predicting insufficient (RR = 1.31) and poor-quality sleep (RR = 1.69) compared to low subsistence. Poor sleep health is common among structurally vulnerablecommunity-recruited PWID, asmeasured by subsistence index associated with adverse sleep outcomes. Further research on structural interventions to address sleep and subsequent health outcomes among PWID is imperative.

Analysis of the Behavior of Young Couples in Buying a House Using Sharia Contracts

This study aims to analyze the direct and indirect influence among variables such as attitudes, subjective norms and behavioral control perceptions on the decision-making of young couples in buying houses based on sharia contracts with interest variables as the intervening variable. This study used a quantitative method used by distributing questionnaires to 250 young couples. This study used a side purposive sampling method. This study applied a a path analysis to examine the direct and indirect influence of independent variables on dependent variables through intervening variables. The results of the study showed that attitudes, subjective norms and perception of behavioral control influenced people’s interests to buy a house using sharia-based contracts. Furthermore, attitudes affect decision-making, while subjective norm variables and behavioral control perceptions do not have a significant effect on decision-making. Nevertheless, interests have a positive and significant effect on decision-making. Based on the results of the path analysis, interests could mediate attitudes, subjective norms and perception of behavior control in making decisions in taking houses based on sharia contracts. The results of this study imply that sharia-based banks in Indonesia need to provide a continuous literacy program toward sharia-based mortgage products to increase customers’ interests so that they know the advantages of using sharia-based banks, especially in terms of house mortgage financing products.

A 10-week multicomponent outdoor exercise program improves hemodynamic parameters and physical fitness in cardiovascular disease adult and elderly patients

AbstractThis study aimed to assess the effects of a 10-week multicomponent outdoor exercise program on hemodynamic, physical fitness and psychological variables in subjects with CVDs. Twenty participants (12 M and 8 F; age 68.5 ± 8.6 years; BMI 28.49 ± 4.98 kg/m2) suffering from CVDs participated in this randomized controlled study and were allocated into two groups: an experimental group (EG; 6 M, 5 F) who underwent a multicomponent training (i.e., cardiorespiratory, resistance, mobility and breathing exercises; 60’, 2d·wk-1), or a wait-list control group (CG; 6 M, 3 F) who did not engage in any PA. Hemodynamic parameters were assessed through resting heart rate (RHR) and peripheral blood pressure (P-SBP/P-DBP). Physical fitness was assessed via a 30″ chair stand test (30CST), timed up and go (TUG) test, handgrip strength (HGS) test, and 2’ step test (TMST). The health status and quality of life (QoL) were evaluated with short form-12 (SF-12) and World Health Organization QoL (WHOQoL-bref), respectively. After the intervention, EG showed significant reduction in hemodynamic parameters (RHR p &lt; 0.001, ES = 0.98; P-SBP p &lt; 0.001, ES = 0.53; P-DBP: p &lt; 0.001, ES = 0.78), physical fitness (30CST p &lt; 0.001, ES = 0.92; TUG p = 0.001, ES = 0.67; TMST p &lt; 0.05, ES = 0.79) and physical component of SF-12 (PSC-12 p &lt; 0.05, ES = 0.42) compared to CG; no significant changes were observed in HGS, QoL and mental health component of the SF-12 (p &gt; 0.05). The findings showed significant improvements in hemodynamic parameters and physical fitness suggesting the effectiveness of the multicomponent outdoor exercise program in adults and elderly with stabilized CVDs.

A Systematic Review: How Is Urban Vulnerability in Fragmented European Cities Measured?

Urban vulnerability defines a situation of socio‐spatial fragility that precedes exclusion and generates a growing social fragmentation in European cities. The psychosocial and multidimensional nature of urban vulnerability determines the interaction among complex socioeconomic, sociodemographic, residential, and subjective variables. The main objective of the article is to explore the comprehensive treatment of this concept within the European framework. A systematic review of the literature allowed for the analysis of over 190 published articles drawn from the Web of Science and Scopus databases from 2002 to 2024. The systematic review is grouped into three main areas: (a) theoretical support for the concept and official variables used for measuring these, (b) classification of the articles reviewed into thematic categories, and (c) identification of changes in the conceptualization and measurement of urban vulnerability. Finally, based on the reflection and review undertaken, this article proposes a conceptual basis and a battery of indicators of urban vulnerability, all of which refer to common areas of vulnerability within the European context. In particular, this proposal includes a new approach for conceptualizing and measuring urban vulnerability based on the results of this subjective review. The findings of this comparative effort form the basis for developing a systematic approach to measuring this concept key to the area of territorial sciences within the European context.

Mechanics- and Behavior-Related Temporomandibular Joint Differences

Fatigue of temporomandibular joint (TMJ) tissues reflects the effects of magnitude (energy density; ED) and frequency of loading (jaw muscle duty factor; DF). This observational study measured these variables and tested for differences in mechanobehavior scores (MBS = ED2 × DF) and component variables in subjects with and without TMJ disc displacement (±D). In accordance with Institutional Review Board and STROBE guidelines, written informed consent was obtained, and examination and imaging protocols identified eligible adult subjects. Specifically, magnetic resonance imaging was used to assign subjects’ TMJs to ±D groups. Subjects were trained to record in-field jaw muscle activities, from which DFs (percentage of recording time) were determined. EDs (mJ/mm3) were estimated using modeled TMJ loads and in vivo dynamic stereometry. Multivariate analysis of variance, post hoc independent t tests, and K-means cluster analysis identified significant group differences (P < 0.05). Of 242 individuals screened, 65 females (TMJs: 78 +D, 52 −D) and 53 males (TMJs: 39 +D, 67 −D) participated. Subjects produced 312 daytime and 319 nighttime recordings of average duration 6.0 ± 0.2 h and 7.6 ± 0.1 h, respectively, and 219 (114 right, 105 left) intact dynamic stereometry recordings. Average EDs were 2-fold and significantly larger in +D than −D TMJs (P < 0.0001). DFs were on average 3-fold larger during the daytime versus nighttime for both masseter and temporalis muscles and 1.8- and 3.0-fold larger for the masseter versus temporalis muscle during the daytime and nighttime, respectively. Daytime masseter MBSs for +D TMJs in females were the largest overall at 621 ± 212 (mJ/mm3)2% and 2- to 43-fold larger versus −D TMJs in both sexes during daytime and nighttime. Cluster analysis (P < 0.0001) identified groups 2 and 3, which comprised 87% +D TMJs and had average MBSs 21-fold larger than group 1. The results show MBS as a potential biomarker to predict homeostasis versus progression or reversal of degenerative TMJ structural changes.

Extending speech emotion recognition systems to non-prototypical emotions using mixed-emotion model

In the conventional approach to speech emotion recognition (SER), the classifier is usually trained on acted emotional speech data to predict individual basic emotions. In this work, we extend the SER systems with the realistic assumption of the coexistence of multiple basic emotions in an utterance. We utilize the MSP-Podcast database for developing the SER system, which contains spontaneous speech utterances. From the primary and secondary emotion annotations of this database, we organize six prototypical and seven non-prototypical emotions. We then propose the mixed-emotion model and express the non-prototypical emotions as a linear combination of prototypical emotions. The combination weights of the mixed-emotion model are computed using a normalized dominance scale based algorithm inspired by the integration of basic emotion theory and dimensional emotion theory in human psychology. We first train a prototypical SER model using the ECAPA-TDNN architecture. The softmax predictions from this model serve as emotion profile inputs to the mixed-emotion model, which then predicts the non-prototypical emotions. Assuming the coexistence of multiple emotions, we only apply the utterances with uniform emotion profiles to the mixed-emotion model. The developed system continues with the conventional SER model if the emotion profile tends to delta function owing to the probable occurrence of a single prototypical emotion. We develop the proposed mixed-emotion model based SER framework using MFCC and wav2vec 2.0 extracted features. Further, we show that due to human perception variations, there exist prominent annotation variations in the non-prototypical emotion ground truths. To address that, we extend the supervised evaluation protocols in four different formulations that capture the subjective variability at different levels. The proposed system shows a best-case performance improvement of 7.10% and 8.39% over the conventional prototypical SER model for the MFCC and wav2vec 2.0 features, respectively.