Search Results (26)

Specklinia is an orchid genus composed of approximately 120 species. Although it has only been recognized recently as a monophyletic group, the presence of several poorly understood species complexes has led in the application of numerous names to taxa not occurring in Mexico and northern Central America. This study presents an updated and comprehensive systematic revision of Specklinia in Mexico, based on recent taxonomic and phylogenetic evidence. We examined Specklinia specimens housed in Mexican and international herbaria, as well as cultivated specimens and observations recorded on iNaturalist Mexico. For each species, we provided a description, taxonomic history, potential distribution model, conservation status assessment, photographic plate or botanical illustration are provided, along with information on distribution, habitat, phenology and comparisons with morphologically similar species. Twenty-two Specklinia species are accepted for Mexico, including five described as new (S. orbiculata, S. perez-garciae, S. solanoi, S. wendtii, and S. xoconochcana.) and two newly recorded for the country (S. acicularis and S. juddii). The identity of Mexican taxa morphologically similar to S. digitale, S. endotrachys, and S. grobyi are re-evaluated here. New combinations in Specklinia are proposed for Pleurothallis choconiana and P. hagsateri, which are recognized as distinct species. Lectotypes are designated for Epidendrum lanceola, E. tribuloides, Pleurothallis marginata, P. pergarcilis, P. microphylla, P. lateritia and P. spathulata. An identification key to the Mexican species of Specklinia is also provided. This study establishes a robust taxonomic framework for Specklinia in Mexico and highlights the need for continued systematic and conservation research, particularly in light of the proposed inclusion of 11 species under a risk category. Full article

Passive solar shading must balance energy saving, daylight availability, glare control, and thermal comfort under contrasting climates. This study develops and validates a lightweight, interpretable light-thermal-energy coupling framework for early-stage shading optimization in building retrofits and new construction. It addresses two questions: how shading geometry, envelope performance, and thermal inertia should adapt to hot low-latitude and cold high-latitude regions, and how their coupled performance can be quantified. The framework combines solar geometry, the Perez radiation model, surface irradiance calculation, indoor ray tracing/voxel illuminance simulation, daylight glare probability (DGP) assessment, and a 6R3C (6-Resistance, 3-Capacitance) transient thermal network. A full-factorial matrix of 120 design combinations was evaluated using shading scale, glazing performance, envelope thermal resistance, and thermal mass as variables, with energy use, thermal response, daylight availability, and DGP as objectives. Results show climate-dependent thermal inertia: it stabilizes indoor temperature in cold regions but increases heat accumulation in hot regions. The optimal schemes satisfy visual comfort (DGP < 0.40) and achieve energy savings up to 44.2% for retrofits and 50.0% for new buildings in hot regions, and 14.6% in cold regions. The framework provides transparent decision support for climate-adaptive, low-carbon building design and complements EnergyPlus, TRNSYS, and Radiance. By supporting energy-efficient retrofits and climate-responsive new construction, the proposed approach contributes to sustainability by reducing dependence on mechanical heating and cooling, improving operational resource efficiency, and maintaining indoor thermal and visual comfort. Full article

Accurate and spatially representative estimation of Global Horizontal Irradiance (GHI) is critical for solar energy planning in tropical regions characterized by strong atmospheric variability and complex topography. This study aims to evaluate the performance and robustness of four semi-empirical satellite-derived GHI models, Beyer, Perez, Hammer, and Rigollier, under heterogeneous tropical conditions in West Java, Indonesia. Hourly GHI data for 2022 were derived from GK2A satellite observations and validated against ground measurements from eight stations representing coastal, lowland, and mountainous areas. Model performance was assessed at annual and seasonal scales using relative Root Mean Square Error (rRMSE) and relative Mean Bias Error (rMBE). The results show significant variability in model performance across locations, with the average annual rRMSE computed per model and averaged over the eight stations being similar among models: 41.10% (Perez), 41.18% (Beyer), 42.44% (Hammer), and 42.49% (Rigollier). Perez showed the most consistent performance, with station-level rRMSE values ranging from 35.36% to 43.32% and rMBE ranging from −18.20% to 22.09%. Seasonal analysis indicates higher errors during the rainy season, 41.16% (Perez), 45.23% (Beyer), 42.74% (Hammer), and 46.34% (Rigollier), while lower errors were observed during the dry season, particularly for Beyer (36.16%) and Rigollier (36.29%). Spatial analysis indicates higher irradiance in coastal and lowland areas compared to mountainous regions. These findings emphasize the importance of climate- and topography-aware model selection for reliable solar resource assessment in tropical environments. Full article

Semi-empirical models remain among the most widely used tools for analyzing proton exchange membrane fuel cells (PEMFCs) because they provide a practical balance between physical interpretability and computational efficiency. Existing reviews primarily focus on optimization techniques, system-level modeling, or data-driven approaches, with comparatively limited attention to the structural evolution of semi-empirical voltage models and their analytical reformulation. This paper presents a structured review of semi-empirical PEMFC models, tracing their development from classical formulations, including the Larminie–Dicks, Amphlett, and Mann models, to recent reduced-parameter models such as the Perez model and its variants. Emphasis is placed on the transition toward reduced formulations, highlighting their potential advantages in terms of numerical stability, reduced parameter coupling, and suitability for parameter-estimation and control-oriented applications. In addition, analytical reformulation approaches based on the Lambert W function and the g-function are reviewed and discussed, showing how these methods can enable partial analytical inversion of nonlinear model equations and support more systematic numerical solution procedures. A comparative benchmark analysis using Ballard Mark V and BCS 500 W PEMFC datasets is presented based on root mean square error, mean absolute error, voltage-error trends, and parameter-sensitivity analysis. The results show that reduced models can provide competitive accuracy and favorable robustness in the considered cases, while physically detailed models may remain advantageous when a richer physical interpretation is required. The review clarifies the trade-offs among physical fidelity, parameter identifiability, model complexity, and computational practicality, and provides guidance for model selection, parameter estimation, and optimization in PEMFC engineering applications. Full article

An accurate estimate of sun irradiance is important for solar energy management in urban areas with complicated atmospheric conditions. The urban solar irradiance (USI) can be predictively researched with a variety of models; however, basing this entirely on one model often leads to other important conditions being omitted. A hybrid framework is suggested in this study, integrating the Perez–Ineichen PI model with a Deep Neural Network (DNN) model for predicting USI in Wuhan, China. The PI model predicts clear-sky irradiance labels based on atmospheric parameters normalized against the National Solar Radiation Database for greater accuracy. The model is trained on the Clear Sky Index with real-time atmospheric parameters gained from ground station measurements and satellite images. Following correlation analysis using bands from Sentinel-2 to find suitable bands for the model, the algorithm was prepared for atmospheric parameters, including cloud cover, aerosol concentration, and surface reflectance, all of which impact solar radiation. The architecture incorporates attention methods for important atmospheric parameters and skip connections for greater training stability. Results from the Deep Neural Network-Selected bands (DNN-S) and Deep Neural Network-All bands (DNN-A) models gave different performances, with the DNN-S model yielding better accuracy with a RMSE of 69.49 W/m² clear-sky, 87.60 W/m² cloudy-sky, and 72.57 W/m² all-sky. The results were validated using hyperspectral imagery, along with cloud mask, solar area, and surface albedo-derived products, confirming that the USI estimates are supported by the high precision and consistency of Sentinel-2-derived irradiance estimates. Full article

The performance of large-scale photovoltaic (PV) power plants is strongly influenced by array layout parameters including module tilt angle, azimuth angle, and row spacing. These geometric variables jointly determine solar irradiance geometry, shading losses, and land-use efficiency, affecting annual energy yield and levelized cost of electricity. To achieve multi-objective comprehensive optimization of array layout parameters for a PV power generation system, a collaborative optimization strategy for PV array layout based on the lemur optimization (LO) algorithm is proposed in this paper. The method couples the Perez anisotropic irradiance model with a dynamic shading irradiance geometric model to simulate the effective insolation, incorporating land availability, shading thresholds, and maintenance access requirements. In addition, the LO algorithm is employed to solve resulting nonlinear and constrained problems, enabling an efficient global search across large parameter spaces. The case studies in Lianyungang, Dalian, and Fuzhou City show that the proposed scheme based on the LO algorithm improves annual energy yield compared with the existing optimization schemes, providing new theoretical methods and engineering application paths for the optimal layout of PV arrays. Full article

Accurate estimation of global horizontal irradiance (GHI) is essential for optimizing photovoltaic (PV) systems, particularly in regions with distinct climatic characteristics. Geostationary satellites, such as GK2A and COMS, provide consistent and spatially extensive data, offering a practical alternative to ground-based measurements. However, the performance of semi-empirical GHI models has been sparsely evaluated across diverse geographic zones. This study aimed to conduct a comparative analysis of four semi-empirical models—Beyer, Rigollier, Hammer, and Perez—applied to two contrasting locations: Seoul, South Korea (temperate) and Jakarta, Indonesia (tropical). Using satellite-derived cloud indices and ground-based pyranometer data, model performance was evaluated via RMSE, MBE, and their relative metrics. Results indicate that the Hammer model achieves the best performance in Seoul (RMSE: 103.92 W/m²; MBE: 0.09 W/m²), while the Perez model outperforms others in Jakarta with the lowest relative RMSE of 58.69%. The analysis outlines the limitations of transferring models calibrated in temperate climates to tropical settings without regional adaptation. This study provides critical insights for improving satellite-based GHI estimation and supports the development of region-specific forecasting tools essential for expanding solar infrastructure in Southeast Asia. Full article

How can we address the rapid advancement and widespread adoption of Artificial Intelligence (AI) across various sectors of society? This paper aims to provide insights through a cybernetic-systemic approach, which is well-suited to tackle the complexities of this new landscape. We will utilize the framework of Organizational Cybernetics (OC) and the Viable System Model (VSM), along with Perez Rios’s Taxonomy of Organizational Pathologies (TOP). By examining the key risks and potential challenges posed by AI through the lens of OC, this paper seeks to contribute to the development of more effective strategies for responsible innovation and governance in AI. Full article

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables. Full article

Plant stomata regulate transpiration (T) and CO₂ assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance and hydro-metrological observations from FLUXNET sites with four plant functional types and using three widely applied methods to estimate ecosystem T from eddy covariance data, namely uWUE, Perez-Priego, and TEA, we quantified the regulation effect of environmental factors on canopy stomatal conductance (G_s). The environmental factors considered here include radiation (net radiation and solar radiation), water (soil moisture, relative air humidity, and vapor pressure deficit), temperature (air temperature), and atmospheric conditions (CO₂ concentration and wind speed). Our findings reveal variation in the influence of these factors on G_s across biomes, with air temperature, relative humidity, soil water content, and net radiation being consistently significant. Wind speed had the least influence. Incorporating the leaf area index into a Random Forest model to account for vegetation phenology significantly improved model accuracy (R² increased from 0.663 to 0.799). These insights enhance our understanding of the primary factors influencing stomatal conductance, contributing to a broader knowledge of vegetation physiology and ecosystem functioning. Full article

Declines in commercial crustacean species (such as lobsters, king crab, etc.) have caused an increased interest in the harvest of the red deep-sea crab Chaceon quinquedens. The red deep-sea crab is a federally managed fishery; however, little is known about the species’ general biology, especially the conditions required for larval survival. We aimed to answer two main questions about the life history of the red deep-sea crab. First, is there a common larval hatching pattern between adult female crabs? Specifically, our inquiries are about the duration of the hatching process, daily peak hatching time, and the relationship between female morphometry and the total larvae hatched. Second, which are the factors affecting the survival and development of larval red deep-sea crabs? In order to answer these research questions, we studied the effects of diet (rotifers, Artemia sp., algae, and unfed), temperature (9 °C, 15 °C, and 20 °C), and aquaculture settings. Ovigerous females were obtained from commercial traps and transported to the NOAA James J. Howard Laboratory, NJ. They were placed in the Females Husbandry and Hatching Collection System (FHCS), where the larvae hatched. Hatching of adult females was monitored and measured by volume. A simple linear regression (SLR) was calculated to predict the number of larvae hatched based on the measured volumes, and it was significant (F = 1196; df = 1, 13; R² = 0.9892, p = 3.498 × 10⁻¹⁴). Duration of hatching period showed an approximate 30 days for adult females red deep-sea crabs, with a common daily maximum hatching time at 22:00 hrs (hatching time seem to follow the sun cycle and the first hours after sunset, Perez, pers. observation). Linear polynomial quadratic regressions were conducted for both years with an interaction term for the two continuous variables (diet and temperature), and were used to model the proportion of larval survival through time. In both years, a highly significant difference was obtained (F = 56.15; df = 4, 2134; R² = 0.09353; p = < 2.2 × 10⁻¹⁶). There is an effect of diet and temperature in the survival of red deep-sea crabs, but not a combined effect of them. Full article

This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot–Bourges–Campana–Gicquel (ABCG), Berger–Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R²), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the Bird clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models’ inputs are not available. Full article

Socio-emotional competencies play an essential role in personal development as they are associated with highly prosocial behavior and low aggressiveness. An individual who is online manages his/her emotions in a specific manner. Thus, it is highly relevant to analyze and evaluate online socio-emotional competencies. Until now, however, no instruments had been defined or developed for that purpose. This study’s objective was thus to design and validate a questionnaire for the evaluation of socio-emotional competencies in virtual contexts, and to analyze eventual differences according to gender and academic year. Using the model developed by Bisquerra and Pérez (2007) as a theoretical framework, the competencies posited therein were transferred to an online environment. The questionnaire was filled out by 888 adolescents ages 12 to 17 (48% males, M = 13.83 years old, DT = 1.27), all residents of Aragón, Spain. On the basis of their responses, structure analysis, validation, and reliability were carried out. Confirmatory factor analysis (CFA) yielded a five-dimensional structure with good fit and internal consistency. The five resulting dimensions evaluate (1) emotional e-conscience, (2) emotional e-autonomy, (3) emotional e-regulation, (4) e-self-control of impulsiveness, and (5) social e-competency. Differences among genders were observed in the categories of emotional e-conscience and social e-competency. Furthermore, the results of this study show that online emotional expression does not imply emotional competency. These results represent an advance in the field of emotional education. Full article