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Promoción del bienestar integral: Una intervención desde el diagnóstico y adherencia al tratamiento mediante la implementación de grupos de apoyo.
No full textEl objetivo del proyecto es fortalecer la adherencia al tratamiento oncológico mediante la mejora del bienestar psicológico de pacientes con diagnóstico de cáncer, se abordará la falta de acceso al acompañamiento psicológico dentro de sistemas de salud. Con base en literatura revisada, se evidencia que el diagnóstico de cáncer actúa como factor
principal de estrés crónico, incrementando niveles de ansiedad y depresión, afectando la calidad de vida, factores que actúan como motivación al abandono del tratamiento. Se propone una investigación-acción con enfoque mixto, combinando métodos cualitativos y
cuantitativos mediante una metodología cuasiexperimental incluyendo pacientes oncológicos
de 20 a 45 años, con diagnostico reciente, una muestra estimada de 60 a 80 participantes correspondientes a grupo de acción y grupo control. Se propone implementar grupos de apoyo con enfoque en psicoeducación, aceptación y afrontamiento, liderados por los pacientes, supervisados y guiados por profesionales de la salud mental. Como resultados se espera la reducción de síntomas de ansiedad y depresión, mejora de la calidad de vida, fortalecimiento de afrontamiento emocional y mayor adherencia al tratamiento oncológico
MANUSCRIPT OPEN ACCESS for https://doi.org/10.1016/j.socec.2020.101613.
No full textThis is a private preprint version for Social preferences across different populations (published version available at: https://doi.org/10.1016/j.socec.2020.101613
ESCP_PriorityRecord_v0.1
No full textThis registration contains a private priority record for an unpublished theoretical framework.
Its sole purpose is to establish the existence and authorship of specific concepts, definitions, and theoretical structures as of the registration date. It is not intended as a preregistration, does not describe a study design, data collection, or analysis plan, and does not supersede or differ from any prior registrations.
No claims are made regarding completeness, correctness, or final form. Any future public or peer-reviewed work will be developed separately
ARVANORA (WOMEN'S CLOTHES FOR WEDDINGS)
No full textArvanora merupakan brand busana wanita yang berfokus pada pakaian kondangan dengan konsep modern, elegan, dan eksklusif. Brand ini hadir untuk menjawab kebutuhan wanita Indonesia akan busana yang tidak hanya menonjolkan keindahan visual, tetapi juga mengutamakan kenyamanan, kualitas bahan, dan kesempurnaan detail. Terinspirasi dari dinamika gaya berpakaian kondangan yang terus berkembang, Arvanora memadukan unsur desain kontemporer dengan sentuhan budaya lokal sehingga menghasilkan busana yang sopan, berkelas, dan relevan dengan tren masa kini.
Melalui filosofi nama ARV yang merepresentasikan keunggulan kualitas dan ketelitian pengerjaan, serta NORA yang melambangkan cahaya, keanggunan, dan pesona, Arvanora membangun identitas merek yang kuat dan bermakna. Setiap koleksi dirancang untuk meningkatkan rasa percaya diri pemakainya dan memberikan pengalaman emosional yang positif saat dikenakan. Dengan positioning sebagai brand lokal premium, Arvanora berkomitmen mendukung perkembangan industri fashion Indonesia sekaligus menjadi pilihan utama wanita yang ingin tampil memukau di setiap acara spesial
Ontotectonics: A Foundational Domain Declaration
No full textCanonical domain declaration.
DOI-registered on Zenodo: https://doi.org/10.5281/zenodo.1827144
Tilapia-Chromium_2009
No full textThe Effects of Source and Level of Dietary Chromium Supplementation
on Humoral Antibody Response and Blood Chemical Parameters in
Hybrid Tilapia Fish (Oreochromis niloticus × O.aureus).
Research Journal of Biological Sciences 4 (7): 821-827,2009
ISSN: 1815-8846
Medwell Journals, 2009
Pathogen Intelligence Layer (C): An Adaptive Framework for Real-Time Disease Surveillance and Contextual Risk Assessment in the BioShield-Integration Cascade
No full textThis registration documents the Pathogen Intelligence Layer (C), a critical component of the unified BioShield-Integration framework (A→B→C→D cascade). This research project addresses the urgent need for proactive disease surveillance systems capable of detecting emerging pathogen threats before they escalate into widespread outbreaks.
PURPOSE:
The Pathogen Intelligence Layer integrates environmental monitoring data (Layer A - HydroNet), agricultural biosecurity signals (Layer B - BioShield), epidemiological surveillance feeds, genomic data, and social media signals to provide comprehensive, real-time pathogen risk assessment. The system employs machine learning ensemble methods to generate early warning alerts with high precision while minimizing false positives.
RESEARCH OBJECTIVES:
1. Validate the hypothesis that multi-source integrated surveillance can detect disease outbreaks 3-7 days earlier than conventional reporting mechanisms
2. Develop and optimize machine learning models achieving >85% sensitivity and >80% precision in outbreak detection
3. Demonstrate the added value of environmental and agricultural data in improving epidemiological risk assessment accuracy
4. Establish scalable deployment models suitable for resource-constrained settings
METHODOLOGY:
The system architecture comprises five core modules:
- Data Ingestion: Multi-source integration with real-time validation and preprocessing
- Risk Assessment Engine: Ensemble machine learning combining Isolation Forest (anomaly detection), Random Forest (classification), Gradient Boosting (severity regression), and LSTM networks (temporal prediction)
- Alert Management: Intelligent filtering with adaptive thresholds learned from user feedback
- Monitoring Dashboard: Real-time geospatial visualization and trend analysis
- Reporting System: Automated daily, weekly, and on-demand report generation
VALIDATION APPROACH:
A 24-month prospective validation study (January 2024 - December 2025) across three geographic regions in Morocco, analyzing 47 confirmed disease outbreaks with comparison against traditional indicator-based surveillance systems.
EXPECTED OUTCOMES:
1. Early warning capability: Ability to detect pathogen outbreaks 3-7 days earlier than conventional reporting systems
2. High-accuracy risk assessment: ML ensemble models achieving >85% sensitivity and >80% precision
3. Demonstrated integration benefit: Evidence that combining environmental, agricultural, genomic, and social signals improves outbreak prediction
4. Operational insights: Dashboards and automated reporting enhancing decision-making for agricultural biosecurity
5. Scalable deployment: Proof-of-concept implementations suitable for resource-limited and edge-device environment
Educación científica para estudiantes con trastorno del espectro autista en el contexto chileno y sudamericano.
No full textEl artículo analiza la enseñanza de las ciencias naturales en estudiantes con Trastorno del Espectro Autista (TEA) desde una perspectiva neuroeducativa e inclusiva. A partir de una revisión crítica de literatura científica y de la experiencia docente del autor, se examinan las particularidades neurocognitivas del TEA, las estrategias pedagógicas más efectivas y los principales desafíos que enfrentan los sistemas educativos en Chile y América Latina. El trabajo destaca la necesidad de superar modelos tradicionales de enseñanza, promoviendo metodologías activas, personalizadas y multisensoriales que permitan una educación científica equitativa, significativa y de calidad para estudiantes dentro del espectro autista
The design of neutrality: Evaluations and biases in the perception of male, female, and neutral avatar faces
No full textThis study investigates how participants perceive avatars designed to evoke male, female, or gender-neutral impressions. While previous research has shown that users often project gendered stereotypes onto artificial agents, few studies have directly examined how neutral designs are perceived across diverse gender identities. Our aim is to explore whether gender-neutral avatars elicit more balanced evaluations and reduce the activation of stereotypical associations in human–agent interaction.
To do so, we conducted an experiments involving participants identifying as women, men, and non-binary. All participants evaluated avatars created and previously validated to represent feminine, masculine, and neutral appearances.
In the experiment, we adopted a mixed design to increase statistical power and generalizability. Participants evaluated a set of three avatars per gender category (female, male, neutral), allowing within-subject comparisons. Additionally, individual differences in gender stereotypes were measured to assess how these beliefs influenced participants’ responses to avatar gender