Although lightweight deep learning models have shown promise for livestock monitoring, there is still limited evidence regarding their comparative performance and practical deployment under real broiler production conditions characterized by high stocking density, severe occlusion, and constrained computational resources. In this context, the present study aimed to evaluate three lightweight object detection architectures for broiler monitoring and to determine their suitability for low-cost edge deployment in settings relevant to small and medium-sized producers. A novel dataset, publicly released through Zenodo to support reproducibility, was constructed from images acquired in both a prototype farm and a high-density commercial facility. These environments captured the visual complexity of intensive broiler production, where overlapping individuals and frequent occlusion challenge detection performance. YOLOv10s, Faster R-CNN, and EfficientDet-D0 were trained and evaluated for detection accuracy and computational efficiency. YOLOv10s achieved the best results, with a mean Average Precision (mAP) of 0.95, whereas Faster R-CNN and EfficientDet-D0 were less suitable for crowded scenes due to region proposal saturation and limited feature-extraction capacity. The selected model was further implemented on a Raspberry Pi 5, achieving a stable latency of 392.17 ms. These results demonstrate that YOLOv10s provides a robust balance between accuracy and efficiency for local broiler monitoring on affordable hardware, while also indicating that active thermal management is necessary to maintain operational stability under real-world conditions.
Depression and anxiety disorder (GAD) symptoms are common among people living with HIV (PLHIV). In Panama, despite a growing epidemic and 20 antiretroviral clinics, only eight have integrated institutional mental healthcare. This study examined depression and GAD symptom levels and psychosocial correlates among PLHIV who attended two urban clinics. From August-November 2024, participants self-administered a questionnaire with PHQ-9 and GAD-2 instruments. Hierarchical logistic regression analyses identified associations with demographics, HIV care, psychosocial stressors and health-related functioning variables. Of the 317 participants, 31.0% identified as women, 50.8% men, and 17.4% non-binary/another gender. Moderate to severe depressive symptoms were reported by 16.1%, and high GAD symptoms by 20.4%. Depressive symptoms were associated with younger age (AOR = 0.89, 95%CI[0.81,0.98]), drug use (AOR = 7.77,95%CI[1.28,46.98]), discrimination (AOR = 5.86,95%CI[1.33, 25.79]), chronic pain (AOR=15.20,95%CI[2.44,94.37]) and difficulties with activities (AOR = 51.41,95%CI[4.98,530.61]). GAD symptoms were associated with insufficient resources (AOR = 5.02,95%CI[1.60, 15.77]), ART clinic (0.23[0.06, 0.82]), discrimination (AOR = 5.84,95%CI[1.86,18.29]), chronic pain (AOR = 8.80, 95%CI[1.92,40.39]) and difficulties with activities (AOR = 14.48,95%CI[1.79,116.92]). The findings highlight the mental health burden among PLHIV in Panama. Discrimination, chronic pain and activity limitations are associated with depression and GAD symptoms. Our results underscore the need for enhanced integrated mental healthcare in all clinics and community-based psychosocial support.
This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO2 is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior.
Real-time vessel tracking and environmental assessment in developing regions face significant challenges due to the high cost and proprietary constraints of commercial Automatic Identification System (AIS) services. We introduce MAGI, an open-source, low-cost, IoT-distributed architecture that integrates Orange Pi 5 edge nodes with software-defined radio (SDR) AIS receivers and containerized microservices to capture, preprocess, and stream AIS messages. During a ten-day field campaign in Panama, our decentralized deployment processed over 500,000 AIS transmissions, achieving 99% uptime and delivering vessel position and speed updates with sub-second latency. Based on the collected data, we also evaluated system scalability, energy consumption, and per node cost, demonstrating that a complete coastal network can be deployed for under USD 1200 per site. These results confirm that MAGI is a scalable, secure, and affordable IoT solution for AIS-based vessel tracking and environmental monitoring in resource-constrained settings.
This article examines the perceived development of didactic suitability among in-service mathematics teachers in Panama, drawing on the Onto-Semiotic Approach to Mathematical Knowledge and Instruction (OSA; Spanish: Enfoque Ontosemiótico, OSA). Using a mixed-methods, interpretative design, we integrate a post-training self-assessment survey (five-point Likert scale) with territorial participation metadata from the national EDEM program (2017–2022). The sample comprises 420 in-service teachers from all ten provinces and three indigenous comarcas. Visual analytics—radar charts, violin plots, boxplots, and time-series/heat maps—were used to map variability across the six interrelated dimensions of didactic suitability: epistemic, cognitive, interactional, affective, ecological, and mediational. Mean perceived growth was highest in the epistemic and affective dimensions, while greater dispersion appeared in the ecological and mediational dimensions, particularly in underserved regions. The expanded discussion interprets these patterns vis-à-vis didactic suitability didactic suitability criteria and place-responsive teacher education, highlighting tensions between centralized policies and local constraints. Implications include strengthening context-sensitive resource ecologies, scaffolding dialogic classroom practices, and prioritizing territorial targeting for equity. Methodological transparency is enhanced by detailing the instrument, sampling frame, visualization pipeline, and ethics safeguards. This paper presents the first nationwide, OSA-aligned territorial map of teachers’ didactic suitability in Panama, operationalizes the six OSA dimensions with a reproducible instrument and visual analytics, and provides policy-ready indicators to monitor teacher development and steer equity-oriented e-learning and professional learning that balances conceptual rigor with affect, mediation, and context.
