Personality Features Research Articles

Subtypes of borderline personality features in adolescence: Insights from cross-lagged panel network analysis.

Borderline personality disorder (BPD) is a complex and severe psychiatric condition characterized by emotional, self-image, behavior, and relational instability. While adult BPD heterogeneity has been extensively studied, the phenomenological borderline personality features (BPFs) in adolescence remain uninvestigated. This study aimed to explore the potentially dynamic causal relationships between BPFs in adolescence and identify the subtypes through cross-lagged panel network (CLPN) analysis. Two independent Chinese adolescent samples were followed over 18 months (N₁ = 1,056, M1age = 15.37, SD1 = 1.86) and 6 months (N₂ = 723, M2age = 16.84, SD₂ = 0.48) to track BPFs. CLPN modeling was employed to investigate the stability, potential causal relations, and subtypes of adolescent BPFs. The results revealed a relatively stable overall adolescent BPF network structure with some subtle changes over time. Impulsivity emerged as the BPF with the highest out-expected influence, indicating its predictive role for other BPFs. A strong reciprocal causal relationship was observed between impulsivity and affective instability. Based on the CLPN estimation, two distinct BPFs subgroups were spontaneously clustered: externalized-dysregulation subtype (impulsivity, affective instability, and self-harm/suicide) and introjective-disturbance subtype (identity disturbance, chronic emptiness, and stress-related dissociation). The present study tentatively explores a potential typology for adolescent BPF based on these two clusters, which possibly have different pathological mechanisms, and moreover offer insights into the essential construct and clinical intervention of adolescent BPD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS)metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Productivity, the humanization of work, and the future of labor: insights from industrial psychology in late socialist Yugoslavia and Romania

ABSTRACT Industrial psychology in socialist Yugoslavia and Romania is a relevant source for both labor history and the history of science/intellectual history. In this paper, we lay down the intellectual and institutional foundations of the discipline in the two contexts, including the envisaged roles for industrial psychologists working in factories and plans for expanding the profession. We argue that industrial psychologists defined the pursuit of labor productivity on the shopfloor as not just a matter of economic calculation, but also as a matter of workers’ self-realization. Their perspective on workers went beyond the measurement of their abilities and skills, taking into consideration personality features as well as inter-personal relations. In research conducted to support creativity and battle monotony at work, industrial psychologists also broadened the understanding of labor efficiency to encompass the pursuit of workers’ satisfaction. We show that research in industrial psychology was part of a broader drive to theorize work and the future of labor under socialism. Discussions of issues such as the scientific-technological revolution, the second industrial revolution, AI, cybernetization, automation, and narcomania, which we analyze in the final part of the paper, represent a rich source of Marxist intellectual reflection on the topic of labor in late socialist Eastern Europe.

MITIGATION OF FOOD INSECURITY IN ETHIOPIA: PERSONAL, HOUSEHOLD, AND REGIONAL DETERMINANTS

Food insecurity has plagued Ethiopia for decades. The objective of this study is determining which personal attributes, household features, income-generating undertakings, and regional location change the food insecurity of Ethiopian households updating past studies. Furthermore, the study quantifies the probability changes in a household being food insecure in response to changes in personal attributes, household features, income-generating undertakings, and regional location. The data from the 2021/2022 Ethiopian Rural Socioeconomic Survey have been applied to estimate coefficients using the logit technique and calculate the effects of explanatory variables. Results show that being a male household head, education, having non-farm enterprise, and access to electricity lower the likelihood of a household being food insecure. In contrast, the likelihood of being food insecure has been associated with the region where a household was located, or dependence on raising livestock. The regional discrepancies have been substantial. Broadening access to education, stimulating non-farm enterprise, and promoting economic development while accounting for regional differences will reduce the risk of household food insecurity.

Borderline Personality Features and Basic Psychological Needs during Adolescence

Objectives: Anchored in self-determination theory, this study investigates the associations between borderline personality disorder (BPD) features and basic psychological need frustration (BPNF) in adolescents. Methods: Participants (N = 270; M age = 15.3) assessed their own BPD features and BPNF through an online questionnaire. Results: Regression analyses revealed that frustrations of relatedness, autonomy, and competence were jointly and uniquely associated with more BPD features in adolescents, controlling for their gender and parents’ education. Implications: These results suggest the importance of taking BPNF into account to better understand the early signs of BPD among adolescents.

NGHIÊN CỨU TỔNG QUAN VIỆC SỬ DỤNG ỨNG DỤNG TRÍ TUỆ NHÂN TẠO TRONG VIỆC XÂY DỰNG VIDEO HỌC LIỆU SỐ

Developing digital learning materials, especially video lectures, is becoming an important issue in education. The emergence of artificial intelligence (AI) has made the creation of videos easier compared to the traditional approach. However, the education sector has yet to benefit from these advanced technologies fully. This research explores the application of Artificial Intelligence (AI) in creating digital learning videos. The study uses 43 selected articles and the PRISMA analysis model to search, classify, and content-filter the results. The findings show a noticeable growth in using AI to create digital learning videos, with 33 topics formed from keywords and articles published in various journals. The most influential works include automatic content extraction from videos and virtual teachers, and there is significant interest in ChatGPT. The main research topics include • developing effective AI models to convert text to video, • integrating personalization and interaction features, and • applying these digital learning videos in education and training. However, many issues remain to be researched, such as enhancing the context-understanding capabilities of AI models and building frameworks to evaluate the effectiveness of videos. AI-generated videos' quality, reliability, and flexibility are significant unresolved challenges, opening up research gaps for future scholars. Keywords: Artificial Intelligence (AI); video; education; learning; PRISMA.

Identity Diffuser: Preserving Abnormal Region of Interests While Diffusing Identity

To release medical images that can be freely used in downstream processes while maintaining their utility, it is necessary to remove personal features from the images while preserving the lesion structures. Unlike previous studies that focused on removing lesion structures while preserving the individuality of medical images, this study proposes and validates a new framework that maintains the lesion structures while diffusing individual characteristics. In this framework, we apply local differential privacy techniques to provide theoretical guarantees of privacy protection. Additionally, to enhance the utility of protected medical images, we perform denoising using a diffusion model on the noise-contaminated medical images. Numerous chest X-rays generated by the proposed method were evaluated by physicians, revealing a trade-off between the level of privacy protection and utility. In other words, it was confirmed that increasing the level of personal information protection tends to result in relatively lower utility. This study potentially enables the release of certain types of medical images that were previously difficult to share.

A study on the identification of factors related to depression in a population with an increasing number of chronic diseases in the short term in China based on a health ecology model

BackgroundThe rapid increase in the number of patients with chronic diseases and depression, as well as the rapid spread of their effects, have led to these two health problems gradually developing into major public health issues in China and around the world. Currently, many individuals with chronic diseases are experiencing depressive symptoms one after another. Therefore, it is imperative to conduct research on how to prevent depression in this growing population of individuals with chronic diseases in a timely manner. MethodsBased on the data of the 2015 and 2018 national follow-up surveys of the China Health and Retirement Longitudinal Study, a total of 7641 patients with short-term increase in the number of chronic diseases were selected as the study objects, and a binary logistic regression model was constructed according to the five dimensions of the health ecology model. The neural network model was used to explore the main (first two) factors affecting the increase in the number of chronic diseases in China in the short term, and the random forest and extreme value gradient lifting algorithm were used to verify them, and effective suggestions were put forward. ResultsThe detection rate of depression in the population with increasing number of chronic diseases from 2015 to 2018 was 42.13 %. The model was established based on five dimensions of the health ecology model: Model 1 (Personal trait layer), Model 2 (Personal trait layer plus Behavioral feature layer), Model 3 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer), Model 4 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer plus Networking layer) and Model 5 (Personal trait layer plus Behavioral feature layer plus Living and working conditions layer plus Networking layer plus Policy environment layer).The prediction accuracy of the five models was 66.4 %, 68.3 %, 70.7 %, 71.6 % and 71.6 %, respectively, and Model 5 showed that the P values of gender, self-rated health, night's sleep time (h), disability, life satisfaction, child satisfaction, place of residence and highest level of education were all <0.05, life satisfaction and self-rated health importance were 0.249 (100 %) and 0.226 (90.8 %). ConclusionGender, self-rated health, night sleep duration, disability, satisfaction with life, satisfaction with children, place of residence and highest level of education were the main influencing factors for the increase of depressive symptoms in the population with chronic diseases in the short term, among which life satisfaction and self-rated health have the greatest impact on depressive symptoms, and there is an interaction between the two.

A COMPREHENSIVE REVIEW OF AGNI AND ITS ROLE IN THE ETIOPATHO-GENESIS OF THYROID DYSFUNCTION: A HIGHLY WIDESPREAD DISEASE

Ayurveda is an alternative medicine system that originated in the Indian subcontinent. Ayurveda believes that the universe is made up of five elements: Vayu (air), Jala (water), Aakash (space or ether), Prithvi (earth), and Teja (fire). Ayurveda emphasises the maintenance and promotion of health and the prevention of diseases through various dietary and lifestyle regimens and the treatment of diseases through various therapeutic measures, substantiating the principle “Swasthasya Swasthya Rakshanam,” i.e., preserving the health of healthy persons, and “Aturashya Vikar Prasamanam,” i.e., curing the diseases of the diseased. Sushruta has described the features of a healthy person, i.e., doshas must be in equilibrium, the digestive fire (Agni) must be in a balanced state, and the tissues (Dhatus) and Malas (wastes) must work in a normal state. Agni regulates numerous metabolic reactions, including digestion and cell growth. It is related to a stance or entity that brings about transformation in any form. It supports healthy digestion, joy, and happiness and preserves body tempera-ture and complexity. Thyroid hormones play a crucial part in maintaining homeostasis in the body and function in food digestion at the GI level, which assists in biochemical reactions at the tissue level and helps convert het-erogeneous chemicals to homogeneous ones. An imbalance in Thyroid hormones plays a crucial role in metabol-ic processes. Thyroid hormone functioning can be normal or disordered, similar to Agni in Ayurveda. Agni leads to various metabolic disorders like thyroid disorders. So here, an attempt is made to address the essential func-tions of thyroid hormones as a part of Agni.

Neural signatures of motor imagery for a supernumerary thumb in VR: an EEG analysis

Human movement augmentation is a rising field of research. A promising control strategy for augmented effectors involves utilizing electroencephalography through motor imagery (MI) functions. However, performing MI of a supernumerary effector is challenging, to which MI training is one potential solution. In this study, we investigate the validity of a virtual reality (VR) environment as a medium for eliciting MI neural activations for a supernumerary thumb. Specifically, we assess whether it is possible to induce a distinct neural signature for MI of a supernumerary thumb in VR. Twenty participants underwent a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements. Spectral power and event related desynchronization (ERD) analyses at the group level showed that the MI signature associated with the supernumerary thumb was indeed distinct, significantly different from both the baseline and the MI signature associated with the natural thumb, while single-trial classification showed that it is distinguishable with a 78% and 69% classification accuracy, respectively. Furthermore, spectral power and ERD analyses at the group level showed that the MI signatures associated with directional movement of the supernumerary thumb, flexion and extension, were also significantly different, and single-trial classification demonstrated that these movements could be distinguished with 60% accuracy. Fine-tuning the models further increased the respective classification accuracies, indicating the potential presence of personalized features across subjects.

The digital cumulative complexity model: a framework for improving engagement in digital mental health interventions.

Mental health disorders affect a substantial portion of the global population. Despite preferences for psychotherapy, access remains limited due to various barriers. Digital mental health interventions (DMHIs) have emerged to increase accessibility, yet engagement and treatment completion rates are concerning. Evidence across healthcare where some degree of self-management is required show that treatment engagement is negatively influenced by contextual complexity. This article examines the non-random factors influencing patient engagement in digital and face-to-face psychological therapies. It reviews established models and introduces an adapted version of the Cumulative Complexity Model (CuCoM) as a framework for understanding engagement in the context of digital mental health. Theoretical models like the Fogg Behavior Model, Persuasive System Design, Self-Determination Theory, and Supportive Accountability aim to explain disengagement. However, none adequately consider these broader contextual factors and their complex interactions with personal characteristics, intervention requirements and technology features. We expand on these models by proposing an application of CuCoM's application in mental health and digital contexts (known as DiCuCoM), focusing on the interplay between patient burden, personal capacity, and treatment demands. Standardized DMHIs often fail to consider individual variations in burden and capacity, leading to engagement variation. DiCuCoM highlights the need for balancing patient workload with capacity to improve engagement. Factors such as life demands, burden of treatment, and personal capacity are examined for their influence on treatment adherence. The article proposes a person-centered approach to treatment, informed by models like CuCoM and Minimally Disruptive Medicine, emphasizing the need for mental healthcare systems to acknowledge and address the unique burdens and capacities of individuals. Strategies for enhancing engagement include assessing personal capacity, reducing treatment burden, and utilizing technology to predict and respond to disengagement. New interventions informed by such models could lead to better engagement and ultimately better outcomes.

Loan Defaults Prediction Based on Stacked Models Trained by Personalized Features

Training one machine learning model with features that all clients have will result in a waste of features, which is likely to adversely affect the model’s performance. To solve the problem, the study attempts a new method, which it to train an individual stacked model for each loan client based on personalised features. Data used contains information of about fifteen million loan applicants, their default status, and 468 features in all. 41 of the features that can be quantitively analysed are selected according to the feature importance output by a Random Forest model. Default prediction of every client is made by a stacked model trained with all selected features he/she has. The stacked model consists of two layers, in which a Light Gradient-Boosting Machine (LGBM) classifier is the base learner, and a Logistic Regression model is the meta learner. As the defaulters account for only 3.14%, which is significantly unbalanced, Area Under the Curve (AUC) and F1 scores are employed to evaluate the method, instead of accuracy. Test results show that models trained by personalised features perform better than the ones trained by shared features. Additionally, the stacked model outperforms individual Logistic Regression model, but performs nearly the same as individual LGBM Classifier. In detailed, the stacked models trained with personalised features result in AUC=0.772 and F1=0.188. Due to data unbalance, although the method’s F1 score is relatively low, it’s considered to be passable. In the future, stacked models combining different models will be attempted.

Правовий статус страховика як учасника договірних страхових відносин

Despite the regulation of insurance relations by Ukrainian legislation, due attention from both scientists and legislators requires the issue of improving the legal status of participants in insurance relations, determining their place in such relations in order to effectively protect their rights and interests. The purpose of the article is to study the legal status of the insurer as a participant in insurance contractual relations, analyze existing approaches to its determination, establish the features of the insurer's legal personality and provide proposals for further improvement of legislation in this area. A characteristic feature of an insurance contract as an independent type of civil legal obligations within the group of contracts for the provision of services is that the conclusion of insurance contracts is directly the subject of activity of specially authorized participants in civil relations – insurance organizations (insurers). It has been established that the insurer, as a participant in insurance contractual relations, is a legal entity that, being specially created to provide insurance services and having received a license for this in the prescribed manner, undertakes the obligation, in accordance with the terms of the contract or an indication of the law, to provide insurance to the insured for a certain fee, to carry out an insurance payment (insurance compensation) to the insured or beneficiary as a result of the occurrence of a certain event (insured event). In insurance contractual relations, the insurer can only have a legal entity on its side, which is empowered to carry out insurance activities only after receiving a special permit (license), which indicates that legal entities - insurers have special legal personality. An insurance organization is endowed with the general legal capacity of a legal entity with a special scope of legal capacity as a financial institution. Attention is drawn to the fact that a significant factor in the modernization of the civil legislation of Ukraine today is the global trend towards unification and harmonization of private law regulation, the formation of optimal legal norms for many areas of private law relations, including insurance. In this regard, the need is justified to determine at the legislative level the requirements for the insurer when it carries out certain types of compulsory insurance, which have their own specifics and provide for the creation of increased guarantees of protection for those persons who, due to objective circumstances, are exposed to increased risk and the extension of insurance protection to cases possible violation of public interests by the method of guaranteeing receipt of insurance payment (insurance compensation). It is concluded that the recodification of the civil legislation of Ukraine opens up prospects for a qualitative update of the legal regulation of insurance contractual relations and will contribute to the development of the provision of insurance services, strengthening of legal institutions in the field of insurance, raising the standards of providing insurance services and ensuring adequate protection of the rights of participants in insurance relations

The Role of Personality Features and Cognitive Avoidance Strategies in Cigarette Smoking Behavior

The Role of Personality Features and Cognitive Avoidance Strategies in Cigarette Smoking Behavior

USE OF TECHNOLOGIES OF HIGHER NERVOUS ACTIVITY AND PSYCHO-PHYSIOLOGICAL HUMAN FUNCTIONS STUDYING IN EXTREME CONDITIONS

Applied techniques and some results of studies the higher nervous activity (HNA) of the people, their psychophysiological functions (PFF) by Ukrainian scientists and doctors were described, as well as influences of extreme conditions on HNA. Aim. Results of the influence of extreme mountain conditions at high altitudes (hypoxia, etc.) demonstration and further research of changes in the psychophysiological functions of the brain under these conditions; description of the developed methods of professional selection. Methods. Comparative analysis of a large number of the data from experiments and observations. Physical, mathematical, program modeling. Methods of HNA, brain research: electroencephalography, diagnosis of personal neurodynamic features, etc. Three methods of PFF research: parameters of the neurodynamic level, parameters of the neuropsychological level, and personal specifics. The adapted questionnaire scheme (QS) based on Cattell's 16-factor personality tests was applied. Results. The influence of hypoxia and other factors of mountain conditions on HNA were studied deeply. The results were presented. Also, there were presented results of registered changes of EEG in such extreme conditions. The influence of a number of stress factors in extreme conditions (hypoxia, etc.) has been demonstrated. Some basic methods of HNA researches used in such conditions were described as well as results of investigations of individual typological features of the nervous systems were presented. Methods for human psychophysiological functions studying were described. Studies of human personal characteristics were described in details. Data recorded by the PNN-3 device were taken for the diagnosis of personal neurodynamic features. The parameters of the neuropsychological level were determined using the following methods: "triangles" (the level of development of short-term visual memory), "instrument scales" (working memory, attention functions, etc.), and "red and black tables" (allocation and switching of attention). Personality structures, features, and types of behavior of operators in stressful situations were estimated basing on adapted QS by Cattell. Mathematical models of the reliability of human nervous system functioning under extreme conditions were developed. Conclusions. The obtained results were taken as the basis for the developed professional selection methods for special contingents, pilots, operators, etc. Physical, mathematical, and program models of the reliability of the human nervous system functioning in these conditions were proposed.

Neurofeedback Training in Children with ADHD: A Systematic Review of Personalization and Methodological Features Facilitating Training Conditions.

Objective. Current research on the effectiveness of neurofeedback (NFB) in children with attention-deficit/hyperactivity disorder (ADHD) is divided. Personalized NFB (pNFB), using pre-recorded individual electroencephalogram (EEG) features, is hypothesized to provide more reliable results. Our paper reviews available evidence on pNFB effectiveness and its methodological quality. Additionally, it explores whether other methodological features implying personalization are related to successful NFB. Methods. We conducted a systematic literature review on PubMed, PSYNDEX, PsycInfo and PsycArticles until November, 30, 2023. Studies that focused on pNFB in children with ADHD were selected, deviant studies excluded. Quality ratings by independent raters using Loney's1 criteria were conducted. Pooled effect sizes for NFB effects and methodological features were calculated. Results. Three of 109 studies included personalization and were reviewed in the full-text. In two studies, theta/beta-NFB was personalized using individual alpha peak frequencies (iAPF), whereas in one study, individual beta rhythms were trained. All three studies demonstrated significant short- and long-term improvements in ADHD symptoms, as assessed by questionnaires and objective performance tests, when compared to standard protocols (SP), sham-NFB, and control conditions. Twelve of 111 studies reported methodological features consistently related to NFB effectiveness. These features, including self-control instructions, feedback animations, timing of feedback presentation, behavioral performance, pre-recorded individual ERP-components and stimulant medication dosage, can be used to personalize NFB and enhance training success. Conclusion. Personalizing NFB with iAPF appears promising based on the existing -albeit small- body of research. Future NFB studies should include iAPF and other personalized features facilitating implementation consistently associated with treatment success.

Personality Traits of Academic Librarians and Their Knowledge Acquisition and Application Behavior

Abstract This study investigated the effect of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness) on knowledge acquisition and application by Pakistani university librarians. To increase their impact in academic institutions, it is imperative to examine how people acquire and apply knowledge within the field of librarianship. This study employed a quantitative research approach based on the survey research method. The Big Five personality inventory designed by John and Srivastava was adopted, however, measurement items related to knowledge acquisition and application behavior were developed with the help of the existing literature. The study population comprised academic librarians working in the university libraries of Pakistan, with a census sampling method used to collect data and 357 completed questionnaires received. The results indicated that extraversion, agreeableness, and openness to experience positively affect the university librarian’s knowledge acquisition, while neuroticism has a negative effect. The findings also revealed that extraversion and openness to experience positively affects academic librarians’ knowledge application behavior while neuroticism has a negative effective, however, conscientiousness and agreeableness personality traits were found to be insignificant predictors of the same. This study highlights the significant personality features contributing to knowledge acquisition and application behavior in academic libraries from a developing country’s perspective.

Psychometric Properties of Korean Version of Nonsuicidal Self-injury Inventory: Validation Study of Deliberate Self-harm Inventory.

This study aimed to develop a Korean version of the Nonsuicidal Self-injury Inventory (K-NSSI) through the Deliberate Self-harm Inventory (DSHI) developed by Gratz for the Korean context and confirm its reliability and validity for clinical application. A total of 188 participants were analyzed utilizing the DSHI, the Korean version of the Self-harm Inventory, the Personality Assessment Inventory-Borderline Features (PAI-BOR), and the DSM-5 Level-2-Depression scale (Level-2-Dep). Cronbach's α assessed their reliability, while frequency analysis examined the items of the K-NSSI scales. Validity of the K-NSSI was confirmed through correlation analyses between K-NSSI (tendency for and frequency of nonsuicidal self-injury [NSSI] behavior) and SHI, PAI-BOR total scale, four subscales of PAI-BOR, and Level-2-Dep scale. Polyserial correlations analyzed the correlation between the presence of NSSI behavior and other scales. Confirmatory factor analysis and structural equation modeling explored the relationship between borderline personality features and self-harm. Cronbach's α was 0.71, indicating an "acceptable" level of reliability. Statistically significant correlations were observed between the presence of NSSI behavior and the total scores of SHI and PAI-BOR, and the four subscales of PAI-BOR. The frequency of NSSI showed statistically significant correlations with the total score of SHI and PAI-BOR, and its four subscales. Notably, 13.8% of participants reported engaging in self-harm behaviors, 26.9% reported a single occurrence, and 73.1% reported two or more instances. Structural equation modeling demonstrated that self-harm adequately predicted borderline personality traits. This study successfully validated the adaptation of DSHI into K-NSSI. The K-NSSI can facilitate interventions for self-harm incidents.

Revolutionizing Learning Management Systems: Architecture of an AI-Based LMS with Instructor-driven Personalized Content Generation

The increasing advancement of machine learning (ML) and artificial intelligence (AI) technologies forces the modern practice of e-learning platforms to provide advanced services for their users. E-learning is no longer focused only on delivering high-quality learning resources, but also requires an advanced Learning Management System that is capable of understanding specific user needs. This paper introduces a new design for an AI-enhanced Learning Management System (LMS) that includes an automated content generation, translation, and personalization feature, significantly enhancing the core educational characteristics of such systems.

The Impact of Inflammatory Bowel Diseases on Patients' Quality of Life and Work Functionality: An Observational Study.

This study aimed to assess the employment status, quality of life, and work functionality in patients affected by inflammatory bowel diseases (IBDs). Patients (N = 216) were enrolled between June 2020 and October 2021. Personal, clinical, and occupational features were investigated. The Short Inflammatory Bowel Disease Questionnaire and the Work Productivity and Activity Impairment questionnaire were administered. Among the enrolled patients, 114 (53%) were employed. The mean absenteeism and presenteeism percentages of 10% and 37% were determined, with 44% and 39% of daily and work activity impairment, respectively. A poor Health-Related Quality of Life (HRQoL) was retrieved (47 ± 12). Extraintestinal manifestations, disease activity, and HRQoL were associated with occupational outcomes. IBD can impact patients' work functionality. The association between clinical, psychological, and occupational issues suggests the relevance for a multidisciplinary management of the disease.