bluuebunny/crossref_metadata_2025 huggingface dataset:
Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation
Authors: Sprint Gina , Schmitter-Edgecombe Maureen , Cook Diane
Published: November 2024

Background

Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model.

Objective

This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences.

Methods

HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, speech, and baseline experimenter testing session markers. Data are converted into text prompts for a large language model. The system then combines these inputs with relevant external knowledge from scientific literature to construct a predictive model. The model’s performance is validated using data from 3 studies involving 124 participants, comparing its diagnostic accuracy with baseline machine learning classifiers.

Results

HDTwin achieves a peak accuracy of 0.81 based on the automated selection of markers, significantly outperforming baseline classifiers. On average, HDTwin yielded accuracy=0.77, precision=0.88, recall=0.63, and Matthews correlation coefficient=0.57. In comparison, the baseline classifiers yielded average accuracy=0.65, precision=0.86, recall=0.35, and Matthews correlation coefficient=0.36. The experiments also reveal that HDTwin yields superior predictive accuracy when information sources are fused compared to single sources. HDTwin’s chatbot interface provides interactive dialogues, aiding in diagnosis interpretation and allowing further exploration of patient data.

Conclusions

HDTwin integrates diverse cognitive health data, enhancing the accuracy and explainability of cognitive diagnoses. This approach outperforms traditional models and provides an interface for navigating patient information. The approach shows promise for improving early detection and intervention strategies in cognitive health.
Exploring the Needs of People With Chronic Low Back Pain and Health Care Professionals for mHealth Devices to Support Self-Managed Physical Activity and Pain: User-Centered Design Approach (Preprint)
Authors: Berger Mathilde , Deblock-Bellamy Anne , Chèze Laurence , Robert Thomas , Desrosiers Julie J , Christe Guillaume , Bertrand Anne Martine
Published: November 2024

BACKGROUND

Chronic low back pain (CLBP) is a major economic and social problem worldwide. Despite the variety of recommended treatments, long-term self-management of this condition is complex and requires the development of innovative interventions. Mobile health (mHealth) technologies hold great promise for the management of chronic pain, particularly to support physical activity. However, their implementation is challenged by a lack of user compliance and limited engagement, which may be due to insufficient consideration of the needs of potential users during development.

OBJECTIVE

This study aims to explore the needs of people with CLBP and health care professionals regarding mHealth technologies to support self-managed physical activity, and to delineate design recommendations based on identified needs.

METHODS

A participatory study was conducted using a 3-phase, user-centered design approach: needs investigation with a group of experts in a workshop (phase 1), needs exploration with end users in focus groups (phase 2), and validation of needs using Delphi questionnaires followed by the development of a set of recommendations (phase 3).

RESULTS

A total of 121 people with CLBP, expert patients, health care professionals, rehabilitation researchers, and biomechanical engineers participated in this study. The results indicated how technology could help people with CLBP overcome their difficulties with managing physical activity. Specific needs were formulated concerning device objectives, expected strategies, functionalities, technical features, conditions of use, and potential facilitators and barriers to use. These needs were validated by consensus from the potential end users and translated into design recommendations.

CONCLUSIONS

This study provides design recommendations for the development of an mHealth device specifically adapted for people with CLBP.
Virtual Reality in Obesity Management: Addressing Key Limitations in Clinical Application (Preprint)
Authors: Hu Xin , Wei Bin
Published: November 2024

UNSTRUCTURED

no
Binaural Pulse Modulation (BPM) as Adjunctive Treatment for Anxiety: A Pilot Study (Preprint)
Authors: Prof. Dr. Gerry , Wallach Joseph , Machado-Ferrer Yanin , Chinchilla-Acosta Mauricio , Meyer Abraham Gérard , Lebovits Robert , Donkin Scott
Published: November 2024

BACKGROUND

Several decades of feedback research with EEG signals have shown that participants can be trained to influence the amplitude or topography of specific components of scalp electric activity. However, it has been very difficult to influence specific mental states or treat psychiatric disorders with EEG-based neurofeedback, probably because of its low spatial specificity and difficulties associated with the poor signal-to-noise ratio provided by single trial-based EEG. For this reason, tuned sound may be a viable alternative.

OBJECTIVE

Our objective in this pilot study was to examine the possibility of using a Binaural Pulse-Mode-Modulation-type (BPM) device to restore an effective psycho-emotional state by activating the endogenous methods of self-regulation to activate the processes of recovery from anxiety and mood disorders. We desired to evaluate if emotional distress would be altered (reduced) or regulated by means of BPM-type systems.

METHODS

Sixty adult participants were studied with self-reported measures of distress (Generalized Anxiety Disorder 7, Covid-Stress Scale, PTSD Checklist for DSM-5, Beck Depression Inventory-II) were completed before, after, four weeks, and 12 weeks post-treatment. This BPM produced two frequencies combined to create a binaural pulse through differential auditory tone presentations, and the user response adjusted to the appropriate target tone for effective treatment. Each individual calibrated the binaural pulse to increase the level of emotion experienced while imagining an experience with a similar emotional valence or while engaged in a cognitive function while also listening to the sound. “Treatment” was based on the individual's control of the binaural pulses to achieve the desired state. The training focused on specific aspects of their psychological difficulties while listening to an auditory tone, turning a knob until the sound increased the desired emotional state. Another knob was turned to intensify the emotional state associated with distress reduction.

RESULTS

On self-report measures, the BPM treatment group was significantly better than the sham treatment and control groups. These findings indicate that over the four-week intervention period, BPM was similarly effective to standard treatment approaches for anxiety, PTSD, and stress.

CONCLUSIONS

BPM appears to result in a temporary change in self-reported levels of distress during treatment. Limitations of the study are reviewed and directions for further research are offered.

CLINICALTRIAL

Not a clinical trial
Virtual patients using large language models: Scalable, contextualized simulation of clinician-patient dialog with feedback (Preprint)
Authors: Cook David , Overgaard Joshua , Pankratz V. Shane , Del Fiol Guilherme , Aakre Chris A.
Published: November 2024

BACKGROUND

Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.

OBJECTIVE

Show proof-of-concept that VPs powered by large language models (LLMs) generate authentic dialogs, accurate representations of patient preferences, and personalized feedback on clinical performance; and explore LLMs for rating dialog and feedback quality.

METHODS

We conducted an intrinsic evaluation study rating 60 VP-clinician conversations. We used carefully engineered prompts to direct OpenAI Generative Pre-trained Transformer (GPT) to emulate a patient and provide feedback. Using 2 outpatient medicine topics (chronic cough [diagnosis] and diabetes [management]), each with permutations representing different patient preferences, we created 60 conversations (dialogs plus feedback): 48 with a human clinician, and 12 "self-chat" dialogs with GPT role-playing both the VP and clinician. Primary outcomes were dialog authenticity and feedback quality, rated using novel instruments meticulously grounded in empirical and conceptual work. Each conversation was rated by 3 physicians and also by GPT. Secondary outcomes included patient preferences represented in the dialogs, cost, and user experience.

RESULTS

The average cost per conversation was $0.51 for GPT-4.0-turbo and $0.02 for GPT-3.5-turbo. Conversation ratings (maximum 6) were mean (SD) overall authenticity 4.7 (0.7); overall user experience 4.9 (0.7); and average feedback 4.7 (0.6). For dialogs created using GPT-4.0-turbo, physician ratings of patient preferences aligned with intended preferences in 20-47 of 48 dialogs (42-98%). Subgroup comparisons revealed higher ratings for dialogs using GPT-4.0-turbo vs GPT-3.5-turbo, and for human-generated vs self-chat dialogs. Feedback ratings were similar for human-generated vs GPT-generated ratings, whereas authenticity ratings were significantly lower.

CONCLUSIONS

LLM-powered VPs can simulate patient-clinician dialogs, demonstrably represent patient preferences, and provide personalized performance feedback. This approach is scalable, globally-accessible, and inexpensive. LLM-generated ratings of feedback quality are similar to human ratings.

CLINICALTRIAL

None
Comparing text classification algorithms with n-grams for mediation prediction
Authors: Lewu Retzi Y. , Kusrini Kusrini , Yaqin Ainul
Published: November 2024

Tingkat keberhasilan mediasi perkara perdata di pengadilan negeri dari tahun ke tahun sangat rendah dan menyebabkan penumpukan perkara yang harus ditangani dengan persidangan. Sementara itu, pendaftaran perkara baru dengan klasifikasi perkara serupa terus bermunculan dan wajib dimediasi. Penelitian ini dilakukan dengan memanfaatkan data mediasi perkara terdahulu sebagai dataset untuk memprediksi hasil mediasi perkara baru. Ketika n-gram digunakan pada dataset yang telah di-preprocessing, hanya ditemukan nilai pada unigram (n=1). Pada penerapan model menggunakan algoritma machine learning, dihasilkan akurasi yang sama sebesar 0.6875 pada Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine (SVM), sedangkan algoritma Decision tree menghasilkan akurasi paling rendah sebesar 0,375. Rendahnya nilai dikarenakan Decision Tree lebih cenderung overfit untuk digunakan dengan teks berbahasa Indonesia. Pola kalimat formal pada dokumen mediasi berbahasa Indonesia tidak memenuhi unsur – unsur kata majemuk, imbuhan, variasi susunan kata, dan semantik leksikal. Untuk penelitian selanjutnya direkomendasikan penggunaan algoritma klasifikasi lain, pemanfaataannya pada dokumen – dokumen lain seperti putusan pengadilan, penentuan rangking mediator berdasarkan keberhasilan mediasi serta implementasi model pada aplikasi e-mediasi yang terintegrasi dengan sistem informasi manajemen perkara
STUDENT VIRTUAL CLASS ATTENDANCE BASED ON FACE RECOGNITION USING CNN MODEL
Authors: Nursantika Dian , Piantari Erna , Huseani Dwi Fitria Al , Husaeni Dwi Novia Al , Urwah Mushfani Ainul
Published: November 2024

Attendance records are an important tool that can be used to include and broadcast member participation in an activity, including the learning process. In online learning classrooms, the process of recording attendance becomes challenging to do manually, thus an automatic attendance recording system is needed. The authentication process is important in developing an existing recording system to guarantee the correctness of the recorded data. In this research, a face authentication system was built to create a system for recording online class attendance to help integrate participant activities and participation in online class learning. The face recognition approach uses a Convolutional Neural Network (CNN) model specifically designed to automate student attendance in virtual classes. Student image data is taken from virtual classroom sessions and used to train a CNN model. This model can recognize and verify student identity in various lighting conditions and head positions. This research consists of several stages, namely data collection, artificial neural networks, use of facial recognition, dataset application stage, and facial recognition in video frames. The experimental results showed that there were 11193 samples studied and of these 11193 samples the distribution was even, namely 6.7%. In addition, the model performance results show an accuracy of 76.28%.
Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification
Authors: Adnyana I Gede , Sugiartawan Putu , Hartawan I Nyoman Buda
Published: November 2024

Abstract The proliferation of cyber security attacks necessitates advanced and efficient detection methods. This study explores the application of Convolutional Neural Networks (CNNs) for classifying cyber security attacks using a comprehensive dataset containing various attack types and network traffic features. Emphasizing the role of hyperparameter optimization (HPO) techniques, this research aims to enhance the CNN model's performance in accurately detecting and classifying cyber attacks. Traditional machine learning approaches often need to catch up in capturing the complex patterns in such data, whereas CNNs excel in automatically extracting hierarchical features. Using the provided dataset, which includes attributes such as packet length, source and destination ports, protocol, and traffic type, we implemented various (HPO) techniques, including Grid Search, Random Search, and Bayesian Optimization, to identify the optimal CNN configurations. Our optimized CNN model significantly improved classification result. to baseline models without hyperparameter tuning. The results underline the importance of HPO in developing robust CNN models for cybersecurity applications. This study provides a practical framework for leveraging deep learning and optimization techniques to enhance cyber defense mechanisms, paving the way for future advancements in the field.
Strategy Selection to Enhance Customer Data Quality using AHP: Case Study of General Insurance Company (PT XYZ)
Authors: Syafira Adinda Rizkita , Hidayanto Achmad Nizar , Nugroho Widijanto Satyo , Samik-Ibrahim Rahmat Mustafa
Published: November 2024

This study examines strategies to enhance customer data quality at PT XYZ, a general insurance company, which is crucial for strategic decision-making and revenue growth. The importance of this topic lies in its potential to significantly improve operational efficiency and customer satisfaction. The Analytical Hierarchy Process (AHP) is utilized to select the optimal strategy from available alternatives. The research begins by establishing criteria and identifying alternative strategies to improve customer data quality. Expert evaluations are conducted based on four criteria: cost, time, security, and availability. Four experts are involved, chosen for their expertise in technology and organizational impact. The experts include a Senior IT Business Intelligence, a Data Analyst, a Unit Head of IT Infrastructure & Security Department, and a Division Head of Digital. The findings highlight that the strategy of Strengthening Collaboration Across Departments, with a value of 0.455, is the most effective. This strategy emphasizes interdepartmental cooperation to enhance data quality. The results underscore the long-term benefits of improved operational efficiency and customer satisfaction despite initial investment challenges, demonstrating the practical application of AHP in organizational settings.
Offensive Language and Hate Speech Detection using BERT Model
Authors: Amalia Fadila Shely , Suyanto Yohanes
Published: November 2024

Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.
Research on the Academic Growth and Engineering Ability Cultivation of Students in Private Universities Based on the Concept of “CDIO-F”
Authors: Yang Yufan
Published: November 2024

The CDIO concept is an advanced engineering education concept. Private universities are an important component of higher education in China, and academic growth and engineering ability are key indicators for cultivating engineering college students. However, in private universities, the academic growth and engineering ability cultivation of engineering students cannot fully meet the needs of society. Therefore, the cultivation of students’ academic growth and engineering ability has become an urgent issue to be solved. On the basis of the CDIO concept, the paper adds a feedback link and proposes a CDIO-F engineering education model. By demonstrating the compatibility of the CDIO-F concept with students’ academic growth and engineering ability cultivation, a CDIO-F based model for private university students’ academic growth and engineering ability cultivation is established. This provides new ideas for the cultivation of engineering majors in private universities.
The Issues and Optimization Strategies in the Modernization of Vocational Education in the Era of Chat GPT
Authors: Fu Ping , Zhang Penghao , Teng Huazi
Published: November 2024

The emergence of Chat GPT technology represents a significant breakthrough in natural language processing, revolutionizing human-machine interaction and presenting profound challenges to vocational education modernization in China. In the Chat GPT era, vocational education undergoes transformative changes, including personalized learning reaching new heights, big data-driven intelligent decision-making entering a new phase, and the integration of education resources on unprecedented scales. However, this modernization effort also encounters several critical issues, such as overreliance on technology and the associated risks of unemployment, threats to data privacy and security, erosion of ethical values, and the widening digital divide in education. To ensure the high-quality development of vocational education modernization with the support of artificial intelligence tools like Chat GPT, it is crucial to enhance supervision and regulation of Chat GPT technology applications, promoting its safe and responsible use. Moreover, prioritizing technology ethics and moral education becomes imperative to nurture students’ ethical awareness and moral compass in the utilization and development of technology. Lastly, optimizing the distribution of digital educational resources is essential to promote educational equity and bridge the digital divide among students.
Conclusiones generales
Authors: Soria-Barreto Karla
Published: November 2024

En relación al análisis de la dinámica empresarial, desde el modelo de natalidad y mortalidad de empresas para la región de Coquimbo, se determinaron varios indicadores que describen el comportamiento de la creación y destrucción de empresas, como son la tasa de entrada (bruta y neta) y la tasa de salida de empresas; tasa de riesgo y supervivencia, matriz de transición. Una vez calculados y analizados los indicadores anteriores se procedió a aplicar una regresión de modo de determinar las variables explicativas del fenómeno de entrada y salida de empresas a nivel sectorial. El análisis se hizo en base a datos del sII durante los años 1999-2004. recordar que la región contribuye con cerca del 3% de número de empresas a nivel nacional, y donde su componente principal, cercano al 90%, son micro y pequeñas empresas.
Léxico k’iche’ colonial y su persistencia en el tiempo: verbos transitivos e intransitivos
Authors: López Ixcoy Saqijix Candelaria
Published: November 2024

El objetivo de este artículo es identificar, analizar y describir la gramática del manuscrito k’iche’ Theologia Indorum, particularmente en relación con dos tipos de verbos, transitivos e intransitivos. Asimismo, se pretende mostrar cómo estos verbos han permanecido a lo largo del tiempo y su evolución, relacionada con la estructura morfológica desde la época colonial hasta la actualidad. Se concluye que las formas lingüísticas persisten tanto en forma como en significado, y que la continuidad del léxico coadyuva al fortalecimiento del idioma actual. Los ejemplos que se presentan evidencian la riqueza de vocabulario de este manuscrito antiguo y cómo su estudio contribuye a revitalizar el idioma frente al peligro de pérdida paulatina, así como a reforzar la identidad lingüística.
Territorialidad tentacular religiosa desde el culto guadalupano en Pocolum, Tenejapa, Chiapas
Authors: Paniagua Barrios Lucero del Carmen
Published: November 2024

El objetivo de este artículo es describir la organización religiosa que regula la vida en el santuario de Sibaniljá Pocolum, Tenejapa, Chiapas. Se parte de la propuesta de Rita Segato, quien plantea nuevas formas de reconfiguración territorial para administrar y aglutinar a la feligresía católica frente a la transformación religiosa. En primer lugar, se presenta una breve descripción del lugar que ocupaba la cabecera municipal en la vida religiosa de los católicos tenejapanecos en la década de los sesenta; en segundo lugar, se señalan algunos cambios que influyeron en las dimensiones sociorreligiosa y económica de esta región; en tercer lugar, se emplea la noción de configuración «tentacular» para enfatizar la importancia que guarda este santuario guadalupano en la región; finalmente, se describe la relevancia de la Virgen de Guadalupe como emblema o icono que da sentido a la ritualidad católica en la zona. Estos datos surgen de entrevistas y de la observación de campo realizada entre 2021 y 2024 en la región.
INVESTIGATING THE HYPERBOLIC AND HYBRID SCALAR FIELD COSMOLOGIES WITH VARYING COSMOLOGICAL CONSTANT IN f(R,T) GRAVITY
Authors: Ahmed N. , Kamel T. M.
Published: November 2024

This paper investigated two scalar field cosmological models in f(R,T) gravity with cosmic transit and varying cosmological constant Λ(t). The cosmological constant tends to have a tiny positive value at the current epoch. The scalar field pressure pϕ shows a sign flipping for normal scalar field. For the phantom field, the scalar potential V(ϕ) is negative and the energy density ρϕ = Ek ± V takes negative values when the equation of state parameter ωϕ is less than -1. The WEC, ρ = ∑i ρi ≥ 0 and pi + ρi ≥ 0, is not violated but with an instability for the second model at late times. For a scalar field ϕ, the condition ρϕ + pϕ = ρϕ (1 + ωϕ) = 2Ek ≥ 0 allows for ρϕ < 0 if ωϕ < -1. The causality and energy conditions have been discussed for both models. The cosmology in both models was studied using a given function a(t) derived from the desired cosmic behavior, which is the opposite of the traditional view.
NEW CATALOG OF DISTANCES TO PLANETARY NEBULAE BASED ON GAIA PARALLAXES AND STATISTICAL DISTANCES
Authors: Hernández-Juárez D. , Rodríguez M. , Peña M.
Published: November 2024

We have developed a method to determine the most reliable distances for a large group of planetary nebulae. For this purpose, we analyze the distances obtained from Gaia parallaxes and three determinations of statistical distances. The most reliable distance is derived for 2211 objects, and uncertainties for these distances are calculated in a homogeneous way. Using our most reliable distances, we compare the distributions of Galactic heights of hydrogen-poor and hydrogen-rich central stars of planetary nebulae. We find that [WR] central stars are closer to the Galactic plane than hydrogen-rich central stars and than other hydrogen-poor central stars. The latter have a similar distribution to hydrogen-rich central stars, which is significantly different from the one of [WR] central stars. This result disagrees with the proposed evolutionary sequence for hydrogen-poor central stars.
FEED-FORWARD NEURAL NETWORKS TO ESTIMATE STOKES PROFILES
Authors: Raygoza-Romero Joan Manuel , Lopez Nava Irvin Hussein , Ramírez-Vélez Julio Cesar
Published: November 2024

Magnetic fields are believed to play a crucial role in stellar evolution. To better understand this evolution, it is essential to measure the magnetic fields on the stellar surface. These measurements can be achieved through spectropolarimetric observations, using the polarized radiative transfer equation. Magnetic field properties are inferred by adjusting the Stokes profiles. In this study, we propose a deep learning approach using a feed-forward neural network to estimate the Stokes profiles based on eight input parameters that describe the magnetic field configuration. To achieve this, we conducted scaling experiments on the data, explored different configurations of the FNN architecture, and compared two approaches. A model capable of accurately estimating the Stokes profiles I, Q and V was obtained. However, we encountered difficulties in estimating Stokes profiles Q and U when they have low amplitudes.
THE VARIABLE STARS POPULATION OF THE EXTENDED YOUNG GLOBULAR CLUSTER NGC 1851
Authors: Arellano Ferro A. , Pérez Parra C. E. , Yepez M. A. , Bustos Fierro I. , Prudil Z. , Zerpa Guillen L. J.
Published: November 2024

We report VI CCD photometry of the globular cluster NGC 1851. We aim to study the membership of the variable stars detected in the field of the cluster as listed in the Catalogue of Variable stars in Globular Clusters (CVSGC; Clement et al. 2001) and reported by the Gaia mission. We cross match the two sets of variables to produce light curves that lead to the estimation of physical parameters. The resulting colour-magnitude diagram (CMD), free of likely field stars, enables us to confirm the position of the variables, their type and evolutionary stage. We provide new estimations of the period using data acquired on a long time-base. The Fourier decomposition of cluster member RR Lyrae light curves lead to a mean metalicity and distance of [Fe/H]ZW = -1.35 ± 0.22 dex and 11.9 ± 0.6 kpc. The variability and membership of stars reported by Gaia-DR3 as variables in the field of the cluster is discussed.
ML and AI to Predict Climatic Conditions
Authors: Vimalsubramanian Mr. G.
Published: November 2024

AI-driven system developed to predict the effects of climate change by leveraging advanced machine learning techniques. By integrating diverse datasets including atmospheric measurements, oceanic data, satellite imagery, and socioeconomic indicators, the system aims to uncover complex relationships influencing climate dynamics. Machine learning algorithms such as regression, classification, and clustering are applied to analyze and predict climate patterns and trends. Deep learning models such as CNNs and RNNs are utilized for time-series analysis and forecasting, enhancing the system's ability to provide accurate predictions. Ultimately, this AI-based approach seeks to support informed decision-making and adaptive strategies in mitigating the impacts of climate change on ecosystems, agriculture, water resources, and human health.
Facial Expression Recognition for Mental Health Monitoring
Authors: Garg Inakshi
Published: November 2024

Anxiety, depression, and stress are examples of mental health issues that have become major global health concerns, impacting millions of people globally. Early identification and ongoing monitoring of mental health issues are now essential due to the rising need for prompt and efficient mental health care. Conventional techniques for evaluating mental health, such selfreport questionnaires and clinical interviews, frequently depend on subjective information and are therefore open to bias or inconsistent results. More objective, timely, and non-invasive techniques for monitoring mental health are therefore required. As per WHO Mental health issues encompass a range of mental states and illnesses, including psychosocial impairments and mental disorders, that are linked to considerable suffering, impaired functioning, or self-harm risk. 9 million individuals worldwide suffered from a mental illness in 2019, with anxiety and depression being the most prevalent. Facial expression recognition (FER) has drawn a lot of interest in this area as a potentially useful method for tracking psychological and emotional states. Facial expressions may be useful indications of an individual's emotional state because they are intimately related to it. An extensive analysis of contactless sensing techniques for tracking mental health is provided in this article. It looks at published studies that employ contactless sensing techniques to forecast mental health conditions.
A Study on Environmental Management & Ecological System Development
Authors: Choudhary Dr. Rajendra Kumar
Published: November 2024

Abstract: Changes in the environment due to environmental degradation due to various reasons are creating adverse conditions in the natural system of the biological world. The tension in the relationship between man and nature is being held responsible for this. The decisions taken by the modern society in the name of economic-social development have proved to be suicidal because it has led to the decline in the quality of the environment, which is the main cause of the crisis. The increasing pressure of human responses, especially resource exploitation, use of inappropriate technology, unwise use of energy, luxurious lifestyle, inappropriate settlement expansion and political aloofness, etc. is crippling the organs of nature, due to which the crisis for the biological world including humans is increasing. Ignoring the elements of nature is the biggest tragedy of this era. Such environmental distortions are being born due to unwise decisions to accelerate cultural development, which are proving difficult for nature to compensate. Instead of taking from nature, the developed society has started plundering the resources. Naturally, its harmful effects can be seen in the form of pollution of various life-giving elements, natural calamities and social distortions
Dejar atrás el católico-centrismo: la ambivalencia y el humanismo etnográfico como herramientas para construir una mirada compleja en el estudio de los fenómenos religiosos
Authors: Gervasi Francesco
Published: November 2024

En el presente artículo me propongo demostrar que, para poder construir una mirada compleja para estudiar los fenómenos religiosos, necesitamos renunciar al paradigma del católico-centrismo y cambiarlo por uno capaz de reconocer y valorizar sus diversas formas de expresión (la diversidad religiosa). Para lograr lo anterior, en el primer apartado aclararé en qué sentido la religión representa un fenómeno complejo. Después, explicaré en qué consiste el católico-centrismo, destacando de qué manera este problema tiende a afectar a las investigaciones sobre la religión. En la segunda parte del texto, presentaré dos reflexiones de carácter epistemológico que, desde mi punto de vista, pueden ayudarnos a acercarnos a los fenómenos religiosos con una mirada que tenga en cuenta su diversidad de expresiones y, por lo tanto, su complejidad, a saber: la “ambivalencia como recurso” de Anna Rita Calabró y el “humanismo etnográfico” de Ernesto De Martino. La propuesta de Calabró se basa en la idea según la cual, para alcanzar una comprensión compleja y multilineal de los fenómenos sociales (incluyendo los de tipo religioso), necesitamos concebirlos como campos dinámicos en los cuales hay que tomar en cuenta (en lugar de ocultarlas y fingir que no existen) las dimensiones contradictorias que los caracterizan. El “humanismo etnográfico”, en cambio, consiste en una actitud epistemológica de “comparación sistemática y explícita entre lo propio y lo ajeno” (De Martino, 2002b: 391), que puede ser alcanzada solo a través de un proceso de reconocimiento continuo de los límites de nuestra cultura y, simultáneamente, de apertura hacia aquellas ajenas. En las conclusiones, finalmente, después de haber sintetizado los aspectos más importantes abordados en el texto, destacaré las características principales de esta nueva mirada compleja sobre los fenómenos religiosos, explicando sus implicaciones metodológicas y teóricas para la investigación de campo.
Sobre el problema de la definición del objeto de estudio en Psicología y disciplinas afines
Authors: López Corral Alfonso
Published: November 2024

Transcurridos más de cien años de la publicación del influyente artículo “Psychology as the behaviorist views it” por John B. Watson, más conocido en español como “El manifiesto conductista”, donde se establecía que el objeto de estudio de la psicología era la conducta y se hacía una crítica a la psicología mentalista, se sigue cuestionando qué es lo que estudia la psicología y si debe considerársele una ciencia. El objetivo de este trabajo es señalar algunos aspectos del estatus de la psicología como ciencia, referidos, principalmente, a su objeto, u objetos, de estudio. Se resalta su indeterminación con un ejemplo de la relación entre cerebro y conducta y se mencionan algunos problemas tanto para la psicología como para las neurociencias a partir de sus objetivos de investigación. Finalmente, se retoman las sugerencias de J.R. Kantor para avanzar hacia una ciencia de la psicología con carácter naturalista.
Foto-reactores para el tratamiento de contaminantes emergentes en aguas: avances realizados
Authors: MORALES María Gisela , OYARZÚN Nadia , BARÚA María Gloria , ESCALADA Juan Pablo , BREGLIANI Mabel , PAJARES Adriana
Published: November 2024

El desarrollo de las ciudades radicadas en la costa atlántica patagónica, tanto la vida diaria como las actividades económicas, pueden aportar sustancias potencialmente capaces de alterar la vida acuática. Entre ellas, se pueden mencionar fármacos, hormonas, drogas ilícitas, bactericidas, plaguicidas y pesticidas. Estos compuestos, denominados contaminantes emergentes, actualmente no se encuentran reguladas a pesar de que sus efectos son potencialmente nocivos para el hombre y el ambiente acuático. Los estudios referidos a los productos de degradación muestran que pueden ser más tóxicos que los compuestos originales. En los últimos años, se han propuesto procesos de oxidación fotoquímicos como alternativa para el tratamiento de aguas contaminadas. Los procesos de oxidación avanzada (POAs) se emplean en el tratamiento terciario de aguas, tanto para eliminar compuestos difícilmente biodegradables como para disminuir la contaminación microbiológica. En este proyecto, para la aplicación de estos procesos se diseñó y construyó un foto-reactor del tipo Bach empleando LEDs para su iluminación artificial. Los resultados que se obtuvieron con algunas metodologías para la determinación de parámetros cinéticos en reacciones de fotodegradación sensibilizada a esta escala se compararon con los obtenidos a escala laboratorio. Se seleccionó como compuesto de estudio 3 hidroxipiridina (3-OHP), sustrato muy conocido por nuestro grupo de investigación. Asimismo, se trabajó en la puesta a punto de técnicas que permitan analizar la toxicidad del efluente tratado. Los resultados son promisorios, se pudieron detectar algunas diferencias significativas al trabajar en esta escala superior a la de laboratorio.
La Ley Corta y su impacto en el territorio de Santa Cruz. Retrospectiva a 15 años de su promulgación
Authors: SALAZAR Alberto Nicolás
Published: November 2024

La promulgación de la Ley Nacional 26.197, conocida como "Ley Corta", tuvo como finalidad responder a una demanda recurrente entre los actores provinciales en las provincias productoras de hidrocarburos. La ley convirtió a las administraciones provinciales en autoridad de aplicación y fiscalización de la actividad hidrocarburífera. Sin embargo, no logró revertir la tendencia decreciente en la producción, al menos en la Provincia de Santa Cruz.
El objetivo de este informe es analizar el impacto de la Ley Corta y sus derivaciones en la provincia, evaluando indicadores económicos y las consecuencias de la reforma en el sector. El análisis incluye la revisión de hitos institucionales, como la creación del Instituto de Energía de Santa Cruz y la Ley Provincial 3.117, que regula la extensión de concesiones. También se examinan los volúmenes de hidrocarburos extraídos, la inversión realizada, los pozos perforados y otros indicadores económicos relevantes.
Los resultados muestran que, aunque la reforma facilitó cambios institucionales y la transferencia del dominio, no logró detener la disminución de la producción. Sin embargo, se observó una mejora en las condiciones de inversión y un aumento en el porcentaje de regalías que deben pagar las operadoras.
En conclusión, la implementación de la Ley Corta ha generado cambios en el marco regulatorio que benefician a la provincia, pero no han sido suficientes para revertir la tendencia declinante en la actividad. Se subraya la importancia de un análisis profesionalizado continuo y la necesidad de políticas que incentiven la inversión y promuevan el uso eficiente de los recursos.
Teachers’ Learning Experiences: Typology of Transformative Learning during the Covid-19 Pandemic
Authors:   University of Latvia, Goba-Medne Līva , Rubene Zanda
Published: November 2024

The destabilizing force of the COVID-19 pandemic served as a catalyst for intricate change processes at both societal and individual levels. This study investigates the experiences of Latvian schoolteachers, examining the substantial changes they have undergone in their professional roles. The study seeks to illuminate the diverse ways in which teachers have engaged in transformative learning processes during three years of pandemic and to identify the challenges they encountered in the process. By delving into the transformative nature of teachers’ learning experiences, as a result a typology was developed to illustrate the trajectories of this change. The aim of the study was to investigate how transformative learning manifests in teachers’ experiences during the COVID-19 pandemic and to identify the orientation frames of the change teachers have undergone. Employing a qualitative design grounded in an ethnomethodology approach, the study utilized narrative interviews with 23 Latvian general education schoolteachers, adhering to the principles of maximum variation and theoretical saturation in sampling. The documentary method was employed for data analysis and interpretation. The reported outcome of this study is a two-dimensional typology of teachers’ transformative learning experiences, reflecting five axes of transformation: Digital Methodological, Relational, Self-Efficacy, Personal Boundary, Organizational Transformations. The study provides valuable insights into how educators adapt and evolve in response to the challenges posed by the pandemic, while also identifying valuable support mechanisms.
Teachers’ Feedback on the Use of Supplementary Materials for Developing Students’ Self-Regulated Learning and Problem Solving Skills
Authors:   University of Latvia,, Linde Inga , Hačatrjana Liena , Daniela Linda
Published: November 2024

Transversal skills play an important role in the educational process worldwide and have been paid particular attention in the system of education in Latvia with the introduction of the new competence-based curriculum. Self-regulated learning (SRL), critical thinking and problem solving (PS) skills remain among the most crucial skills at all levels of formal and informal education, as well as in lifelong learning. This requires knowledgeable teachers, equipped with methodological support, guidelines and supplementary materials, that would enable them to professionally advance these skills in their students. The purpose of this study is to analyse teachers’ feedback on the use of supplementary materials aimed at developing students’ PS and SRL skills. The feedback was provided by 57 teachers of Grades 1–6 attending an online teacher professional development (TPD) course on developing students’ SRL skills. The paper also presents a comparative analysis between the results of the current study and the results of an earlier piloting of the supplementary materials. The findings show that students frequently lack sufficient metacognitive skills and highlight that teachers consider supplementary materials to be a useful tool for developing these skills, particularly when accompanied by an appropriate TPD course that deepens teachers’ knowledge of SRL and enhances their competence in developing students’ SRL and PS skills.
Model for Pedagogical-Digital Competence Development in e-Learning
Authors:   University of Latvia, Sarva Edīte
Published: November 2024

In this study requirements and preferences of educators regarding the implementation of technology-enhanced learning approaches were determined. Learning materials were developed, and instructional formats were selected based on the gathered insights. Participants then took part in an online course and were required to participate in synchronous meetings, engage in self-directed learning tailored to their individualized learning objectives and utilizing provided support materials, collaborate within learning support groups, and apply acquired knowledge and skills in practical contexts with their students or colleagues over a span of up to two consecutive years. The developed online course underwent evaluation with 1347 educators across four distinct cohorts, each consisting of 173–501 educators, with minor adjustments made for subsequent cohorts. Data on participant learning experiences was collected through evaluation forms, learning reflection exercises, and participant contributions within a customized virtual learning environment. The effectiveness of the e-learning components was assessed based on participant feedback and the extent to which acquired knowledge and skills were implemented in practice. Leveraging the accumulated data a model for organizing e-learning to enhance educator pedagogical-digital competence in an online setting is proposed.
In-Service Teachers’ Education Field Preferences in their Final Papers
Authors:   University of Latvia, Aboltina Liga , Helmane Ineta
Published: November 2024

Education is essential to keep up with the constantly developing world. The quality of education systems relies heavily on teachers. Teachers need training, resources and support to do their jobs effectively. It is crucial to train and recruit qualified teachers to ensure the success of the education system. In Latvia, the necessities for the professional development of teachers are determined by the Education Development Guidelines 2021–2027. The article analyses theoretical materials concerning the essence of in-service teacher training. Within the framework of in-service teacher training implemented in the Faculty of Education Sciences and Psychology at the University of Latvia, one of the main priorities of which is to promote the professional development of pre-school and primary school teachers, it was possible to analyse empirical data on teachers’ preferences in the context of in-service teacher training. The main findings reveal teachers’ need to engage in professional development and their preferences when choosing an educational field, including teaching and research. Empirical research can inform in-service teacher training practices and help develop programmes that better meet the needs of teachers in the classroom. In-service teacher training is crucial to enhance the education workforce’s professional development and to improve the quality and effectiveness of the education system.
Educators’ Initial Reactions to Universal Design Principles and Implementation Prospects During Online Professional Development
Authors:   University of Latvia, Margevica-Grinberga Ieva , Rektina Andra , Sarva Edite
Published: November 2024

This study investigates the implementation of Universal Design for Learning (UDL) principles through an online professional development module designed for educators in Latvia. The research focuses on the emotions and perceptions of 264 educators as they experience UDL during a four-hour training session. Utilizing a comprehensive methodology that includes unstructured surveys, the study explores how educators feel about the flexibility and choices provided by UDL principles. The findings reveal that educators responded positively to the ability to choose between different task formats and ways of answering tasks. This flexibility heightened their satisfaction and interest during professional development. The study underscores the significance of offering choice and modeling UDL principles during professional development, which fosters a supportive and inclusive teaching environment. Moreover, the results highlight the role of experience exchange and knowledge sharing among educators as critical components in the professional development process. These elements contribute to a more engaging and effective learning experience for educators. This research adds insights to the existing literature on UDL, emphasizing the importance of practical application and emotional engagement in professional development. Overall, the study suggests that integrating UDL principles into professional development can positively influence educators’ perceptions of inclusive teaching practices. The positive emotional responses to choice and flexibility underscore the need for adaptable instructional strategies that cater to diverse learner needs, ultimately promoting a more equitable and accessible educational landscape.