Search Results (791)

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management. Full article

Geisha coffee (Coffea arabica L. cv. Geisha) is internationally recognized for its exceptional sensory quality; however, its functional properties and bioactive composition remain insufficiently explored. This study evaluated the phytochemical profile, antioxidant capacity, vascular bioactivity, and toxicological safety of an aqueous extract of roasted Geisha coffee (AErGC) from the Chiriquí highlands, Panama. The chemical composition was determined using HPLC-PDA. Antioxidant activity was assessed using DPPH, ABTS, and lipid peroxidation assays. Vascular effects were studied in rat aortic rings, and safety was evaluated through Artemia salina and a single-dose acute oral toxicity model in rats (OECD 423). Chemical characterization was performed by HPLC-PDA, revealing notably elevated levels of caffeine (69.5 ± 6.4 mg/g) and 5-O-caffeoylquinic acid (74.5 ± 6.9 mg/g). The extract exhibited strong free radical scavenging capacity, with an IC₅₀ value of 14.7 ± 4.9 µg/mL in the DPPH assay, and inhibited lipid peroxidation by 72.71 ± 1.63% at 15.6 µg/mL. In endothelium-intact rings, AErGC induced a concentration-dependent vasorelaxant effect, reaching a maximum relaxation of 70.84 ± 2.9%. Toxicological results showed an LC₅₀ > 1000 µg/mL in A. salina and an oral LD₅₀ > 2000 mg/kg, classifying the extract as Category 5 (low toxicity). These findings highlight Panamanian Geisha coffee as a promising functional beverage with antioxidant and vascular protective properties, supporting its potential as a nutraceutical. Full article

Background/Objectives: Arboviruses including dengue virus (DENV), Zika virus (ZIKV), chikungunya virus (CHIKV), yellow fever virus (YFV), and West Nile virus (WNV) co-circulate across the Americas, generating overlapping febrile syndromes that challenge etiological diagnosis based solely on clinical criteria. Cartridge-based multiplex molecular platforms offer potential for decentralized testing in hyperendemic settings, yet independent real-world evaluations of their clinical and analytical performance remain limited. Methods: A retrospective two-phase analytical study was conducted. Phase 1 assessed clinical diagnostic accuracy for dengue using 163 de-identified serum samples classified using a composite reference standard consisting of Panbio NS1 ELISA reactivity (≥11 Panbio units) combined with compatible clinical and epidemiological data, operationalized in accordance with the PAHO 2023 laboratory confirmation algorithm for dengue; RT-qPCR was not routinely available for all archived samples, and reported sensitivity should therefore be interpreted as a conservative lower-bound estimate; Phase 2 evaluated analytical sensitivity across all eight panel targets using characterized arboviral reference strains in serial dilution experiments, with reference RT-qPCR assays as the comparator; this phase was incorporated to characterize detection thresholds for targets not represented by clinical specimens. Results: In Phase 1, the M10 demonstrated sensitivity of 96.0% (96/100), specificity of 100% (63/63), overall accuracy of 97.5%, and near-perfect agreement with the reference standard (Cohen’s κ = 0.95). DENV-3 was the predominant serotype (74/96; 77.1%), followed by DENV-1 (16.7%) and DENV-4 (6.3%); DENV-2 was not detected. In Phase 2, operational LoDs (defined as the lowest concentration yielding a detectable Ct in all triplicate reactions for the RT-qPCR, and from a single cartridge per dilution point for the STANDARD M10) were equivalent or superior to reference RT-qPCR for six targets (DENV-1, DENV-3, DENV-4, ZIKV, WNV, YFV; range 1–5 PFU/mL), while DENV-2 and CHIKV showed 20-fold higher operational LoDs (20 PFU/mL vs. 1 PFU/mL for the reference RT-qPCR); formal LoD₉₅ estimates were not determined. Conclusions: The STANDARD M10 Arbovirus Panel shows high clinical accuracy for dengue and adequate analytical sensitivity for most targets, supporting its use as a complementary decentralized molecular tool. Reduced sensitivity for DENV-2 and CHIKV and the absence of formal LoD₉₅ estimates remain key limitations to be addressed in future validation studies. Full article

Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual environments where process variability and environmental noise are inherent. This study proposes a monitoring approach for classifying SMAW process parameters using airborne acoustic signals generated by the welding arc. Welding experiments were conducted on carbon steel plates of different thicknesses (3, 6, and 12 mm) using E6010, E6011, E6013, and E7018 electrodes under Alternating Current (AC) and Direct Current (DC) configurations; acoustic signals were recorded in real time and processed using Mel-Frequency Cepstral Coefficients (MFCCs) and deep audio embeddings from pre-trained VGGish and YAMNet models as inputs to artificial neural network classifiers for multi-output classification of welding process parameters. Model performance was evaluated using per-target metrics (accuracy and macro F1-score) and joint multi-output metrics (Exact Match and Hamming Accuracy). MFCC-based models significantly outperformed embedding-based approaches, achieving up to 94.51% Exact Match and 97.88% Hamming Accuracy, while reducing computational costs. These results demonstrate the feasibility of SMAW monitoring using arc sound, suggesting that spectral features are an effective solution for welding-process monitoring and a promising foundation for future weld-quality monitoring systems. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

This study evaluated spatial and seasonal patterns of physicochemical water quality in the Chiriquí Viejo River basin (western Panama), a tropical watershed characterized by strong seasonal variability. A total of 90 water samples were collected at ten stations during the rainy season (May to October 2024) and dry season (January to March 2025). Dissolved oxygen (DO), turbidity, potential of hydrogen (pH), apparent color, total dissolved solids (TDS), and electrical conductivity (EC) were analyzed following ISO/IEC 17025:2017 accredited methods, and precipitation patterns were characterized using spatial interpolation of meteorological data. Spatio-temporal variability was assessed using linear mixed-effects models, with season and basin position as fixed effects and sampling site as a random factor. Results showed a spatial and seasonal structuring of water quality, with the upper basin exhibiting high and stable DO concentrations and low turbidity and apparent color. In contrast, the middle and lower basin showed rainy-season increases in turbidity and apparent color, supported by a significant season × basin interaction, indicating that precipitation driven impacts are heterogeneous along the basin. EC and TDS displayed spatial gradients, while DO remained relatively stable across seasons and basin levels. These findings highlight turbidity and apparent color as sensitive indicators of precipitation-driven impacts. Full article

Characterizing the specific interactions of Leishmania species with different host systems is essential for the development and validation of experimental infection models and for identifying potential therapeutic targets. Leishmania parasites elicit diverse host immune responses that result in different levels of disease severity. Here, we developed a murine model of L. panamensis infection and compared the responses of BALB/c and C57BL/6 mice following intradermal ear inoculation. BALB/c mice developed progressive ulcerative lesions associated with high parasite burden, whereas C57BL/6 mice exhibited a transient edema and maintained low parasite levels detected only at early stages of infection. C57BL/6 mice displayed early production of IL-13, IL-4, and IL-10, followed by delayed IFN-γ secretion. In contrast, BALB/c mice showed a mixed Th1/Th2 response at later stages of infection. Humoral responses also differed between strains, with BALB/c mice developing an early and sustained IgG1-dominated response, while C57BL/6 mice exhibited weak and delayed antibody production. These findings suggest that resistance to L. panamensis infection in C57BL/6 mice is associated with an early and transient Th2/regulatory response accompanied by a weak and delayed antibody production. Full article

Monitoring ambient air quality is essential for assessing environmental conditions and examining relationships among pollution indicators. This study presents a cross-sectional comparative analysis of key air quality indicators (PM_2.5, O₃, NO₂, SO₂, CO, and volatile organic compounds), together with a contextual variable related to pollution exposure (household solid fuels), across countries in Latin America and the Caribbean for the year 2024. The objective is to characterize air quality profiles by analyzing the interrelationships among indicators and the relative positioning of countries, integrating both elements within a multivariate framework. Multivariate statistical techniques, including HJ-Biplot and cluster analysis, were applied to provide an integrated representation of the data. The results indicate differences in the configuration of air quality indicators across countries, with some Caribbean countries associated with lower levels of pollution indicators, while several South and Central American countries are associated with higher levels. These results also suggest associations between air quality indicators and factors such as industrial activity proxies, population density, and the use of household solid fuels. Given the cross-sectional nature of the data, these findings should be interpreted as associations rather than causal relationships. Full article

Background/Objectives: This paper presents a bi-objective mathematical programming model for sizing hybrid renewable energy systems (HRESs) in isolated mini-grids and compares the optimized solutions with the first-year operation of a real system deployed in La Miel, Panama. Methods: The model minimizes the levelized cost of energy (LCOE) and the expected energy not served (EENS), using an $\epsilon$-constraint approach over a one-year time series (8760 h) of measured demand. For La Miel, the annual demand is 132,578 kWh with a peak load of 28.4 kW. Four configurations are evaluated: (A) diesel-only, (B) photovoltaic (PV)+diesel, (C) PV+batteries, and (D) PV+diesel+batteries. The results are compared with the installed plant (E) including 107 kWp PV, a 40 kVA diesel generator, and lead-acid battery banks (4560 Ah nominal capacity). Results: The optimized hybrid configuration (D) achieves near-zero EENS with an LCOE of 41.4–41.8 cts-USD/kWh, compared to 56.6 cts-USD/kWh for diesel-only. The real system achieves EENS = 0% with LCOE = 48.3 cts-USD/kWh and an annual renewable penetration of 53.2% (up to 68.4% in March 2020), while the optimized case reaches 79.6% on average (up to 95.3% in March). Conclusions: The distinctive contribution of the study is the direct ex ante versus ex post comparison between optimized planning outcomes and the documented first-year operation of the installed system. Operational constraints observed on site (e.g., minimum battery SoC of 60% to comply with voltage quality limits) and demand growth explain part of the LCOE gap between optimized and real performance. Full article

The Petaquilla gold mine in Panama was abruptly closed without restoring the site. The objective of this study is to assess mine soils from a geochemical perspective, identify potential contaminants, and conduct a human health risk assessment (HHRA). Soil samples were analysed to determine pH, EC, OM, texture, hydrocarbons (TPHs), enzymatic activity (DHA), and the following potentially toxic elements (PTEs): As, Ba, Cd, Cu, Hg, Sb, Pb and Zn. The Igeo, PLI and HHRA indexes were evaluated. The Igeo indicates that the processing zone has atypical values of Cu (1.47), indicating moderate pollution (1 < Igeo ≤ 2), Zn (3.80), indicating strong pollution (3 < Igeo ≤ 4), and Pb (7.62), indicating extreme pollution (Igeo > 5), with enrichment due to mining activity. The PLI map shows that the affected areas are surrounding the Molejon River (1.62) and the processing zone (1.21), which are slightly contaminated (1 ≤ PLI < 2), and one site in the processing zone with moderate to considerable contamination (PLI ≥ 3) at the warehouse (6.07). Regarding TPHs, the processing area in front of transformer (54,844.47 mg kg⁻¹) and the workshop entrance (2045.26 mg kg⁻¹) have values above industrial use (620 mg kg⁻¹) due to visible hydrocarbon spills. In terms of HHRA, the non-carcinogenic risk associated with exposure to PTEs exceeds the reference threshold for both children and adults under a residential exposure scenario, whereas the non-carcinogenic risk for TPHs remains below the acceptable limit. Regarding carcinogenic risk, exposure to Pb and As remains within acceptable limits for both receptors. With a view to restoring the mine’s soil, the processing area and the workshop entrance are the first areas that need to be addressed. Full article

Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems, particularly with the increasing integration of renewable energy and the transition toward smart grids. However, most existing approaches rely on a single forecasting model, despite evidence that model performance varies across datasets and forecasting horizons. To address this limitation, this paper proposes an explainable meta-learning framework for adaptive model selection in STLF. Unlike conventional methods that aim to identify a universally optimal model, the proposed approach learns to select the most suitable model based on dataset characteristics and forecasting conditions. The framework integrates cross-dataset evaluation, meta-feature extraction, and a Random Forest-based meta-learner to dynamically determine the best-performing model. The proposed approach is evaluated on three benchmark power systems—Panama, PJM, and Spanish datasets—under both single-step and multi-horizon forecasting settings. The results provide initial evidence of adaptability across multiple datasets. Specifically, LSTM achieves the best single-step performance on the Panama (MAPE = 2.88%) and PJM (MAPE = 7.71%) datasets, while XGBoost outperforms other models on the Spanish dataset (MAPE = 1.07%). Statistical analysis suggests meaningful performance differences, although these findings should be interpreted with caution due to the limited sample size. Furthermore, SHapley Additive exPlanations (SHAP) are employed to enhance interpretability, revealing that forecasting horizon, data variability, and dataset characteristics are the most influential factors in model selection. Overall, the proposed framework improves forecasting accuracy, robustness, and transparency, while promoting a shift from model-centric design to adaptive, data-driven model selection. The framework offers a structured and explainable approach with potential for practical deployment in smart grid applications. Full article

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. Full article

Many people experienced difficulties grieving the death of a loved one during the COVID-19 pandemic. The main objective of this research was to examine the psychological manifestations of grief for people who experienced the death of a loved one between March 2020 and March 2022 in Panama. A sample of 110 participants completed an online survey including sociodemographic questions and psychological questionnaires. A subsample of twenty-six participants was interviewed about their experience of loss leading up to death, at the time of death, and after death. Results indicated that 43.6% of participants suffered from complicated grief (CG). Participants who experienced CG had more post-traumatic stress symptoms, somatic symptomatology, anxiety/insomnia, social dysfunction, severe depression, use of avoidant coping mechanisms, and more anxiety about the pandemic than participants who did not experience CG. A logistic regression analysis indicated that anxiety/insomnia symptoms, denial as a coping mechanism, and post-traumatic stress symptoms increased the likelihood of CG. For qualitative analyses, the most relevant themes that emerged were distress associated with contagion and illness, hospitalization and access to healthcare services, communication with medical staff, the impact of the news of death, inability to view the body, emotions following the loss, farewell rituals, and coping mechanisms. Full article

Chemical differentiation of coffee species is essential for quality control, authenticity verification, and the prevention of mislabeling in the coffee industry. This is particularly relevant in Panama, a recognized producer of specialty coffees such as Geisha, where studies on chemical characterization remain limited. This study investigated the phenolic composition, antioxidant activity, and caffeine content of roasted coffee as potential chemical markers for differentiating Coffea arabica and Coffea canephora. Ultrasound-assisted hydroethanolic extraction was optimized using the response surface methodology, with temperature, ethanol concentration, and extraction time as the experimental variables. The optimized extraction parameters were established at 75 °C, 44% ethanol, and 25 min, resulting in high recovery of the total phenolic content, total flavonoid content, and antioxidant activity (ABTS and DPPH assays). Under these conditions, C. canephora samples obtained higher levels of phenolic compounds, antioxidant activity, and caffeine content than C. arabica. Pearson’s correlation analysis showed significant associations among phenolic compounds, caffeine content, and antioxidant activity. PCA explained 90.5% of the total variance and clearly discriminated between the two species, mainly based on differences in caffeine content and antioxidant activity. HCA confirmed this classification and revealed subgroups within C. arabica, particularly among the Geisha varieties. PLS-DA achieved complete separation between species, with zero classification error under cross-validation. These results indicated that combined chemical and multivariate approaches can be used to differentiate and assess the authenticity of coffee, particularly in underexplored production regions such as Panama. Full article

University cafeterias generate solid waste as a result of high user turnover and routine food service operations. While waste characterization studies are common in higher education institutions, data-driven predictive modeling remains limited, particularly in Latin American contexts. This study addresses this gap by integrating physical waste generation with behavioral surveys to develop predictive tools for operational decision-making. The findings should be interpreted as a single-site operational demonstration; broader generalization requires replication and local recalibration in cafeterias with different operational and social characteristics. Waste generation was characterized in a Panamanian university cafeteria by shift over 20 consecutive working days, separating organic and inorganic fractions, and collecting 705 user surveys on consumption habits. Two complementary predictive approaches were developed: a rule-based classification model and a Monte Carlo simulation framework. Organic waste exhibited a stable pattern throughout the study period, with clear concentration during lunch hours and a strong dependence on user volume. In contrast, inorganic waste showed higher day-to-day variability and increased during evening service, reflecting changes in service practices rather than attendance alone. Statistical analysis indicated that waste generation was more closely associated with food type purchased and faculty affiliation than with self-reported environmental awareness. Overall, the results demonstrate that straightforward predictive approaches can support shift-level planning and operational waste management decisions in university cafeterias. Full article