Search Results (253)

Within the framework of animal learning theory, information transfer refers to the generalization or transmission of acquired knowledge to novel situations, individuals, or environments. In pollinators, this process is critical for adaptive foraging and ecological success. Although olfactory information transfer has been demonstrated in bumblebees through classical conditioning or colony exposure, the influence of reward type (nectar vs. pollen) remains poorly understood. Here, we evaluated olfactory information transfer and decision-making in the South American bumblebee Bombus pauloensis using complementary laboratory and colony-based experiments. Under laboratory conditions, bees were either classically conditioned through the proboscis extension reflex (PER) paradigm using scented sucrose solution or exposed overnight to scented pollen and subsequently tested in a four-arm arena. In a more natural context, colonies were exposed to scented sucrose solution or scented pollen through in-nest feeders, after which foraging choices were assessed in a flight cage. Sucrose-conditioned bees exhibited an odor-contingent preference in the four-arm arena, consistent with associative learning under our conditions. In contrast, pollen exposure produced odor-associated trends. Linalool exhibited a bias, although airborne volatile concentrations were not directly quantified, representing a methodological limitation. Moreover, linalool-scented pollen induced intense feeder manipulation and increased mortality during overnight exposure, highlighting substantial physiological costs. Under colony conditions, 24-h exposure to scented pollen promoted significant odor-guided search preferences, particularly with linalool, whereas no significant effect of scented sucrose was detected under our 24 h in-nest exposure; individual feeder contact was not verified. Overall, behavioral responses varied jointly with odor identity and the combined operational framework; the salience of pollen-associated odors observed here is consistent with reward-specific effects but cannot be fully disentangled from differences in protocol structure between the sucrose and pollen experiments. Full article

Agile outcome models commonly map Jira variables directly to a retrospective label and then explain the prediction through fragmented feature attributions; they rarely separate domain concepts, team clustering, unresolved work, and concept-label coupling. This study evaluates a domain-informed, concept-bottleneck-style explainable AI architecture for retrospective diagnosis of Agile Epic outcomes. A frozen Jira export of 10,000 unique issue-level records was linked to a pre-specified analytical cohort of 180 Epics across 14 teams. Six experts rated efficiency, effectiveness, sustainability, and contextual risk, while outcomes were recorded as Successful, Challenged, or Unsuccessful. Because the outcome labels and concept ratings were informed by the same Jira evidence, the models estimate consistency with an expert labelling procedure, rather than independent project success. Under five-fold group-aware cross-validation, the fixed-configuration flat LightGBM achieved macro-F1 = 0.864 ± 0.053 and the fixed-configuration HMXAI/CBM-style model achieved 0.843 ± 0.084. These descriptive primary scores are not a joint nested-model-selection comparison. The proposed method, therefore does, not demonstrate a performance improvement; its contribution is an inspectable diagnostic structure. Performance fell materially on the resolved-only subset (LightGBM macro-F1 = 0.645), and model-specific nested, leave-one-team-out, calibration, uncertainty, correlation, and intervention analyses further bound the claims. Concept interventions were not uniformly monotone, so the concept layer is domain-interpretable in form but not yet user-validated as actionable. The study contributes a transparent audit of when concept-level diagnosis can complement flat classification and when circularity, censoring, and shortcut learning restrict interpretation. Full article

The success of HIV treatment is primarily measured through viral load suppression. However, little is known regarding the association between sexual union status and viral suppression among older adults. This study aimed to assess the association between sexual union status and HIV viral load suppression among adults aged 40 years and older living with HIV in rural Mpumalanga, South Africa. An observational analytical study design was followed, utilising data from the HIV After 40 study in Mpumalanga. The study focused on 4637 older men and women aged 40 years and above, living with HIV. Four nested unadjusted and adjusted binary logistic regression models assessed the association between sexual union status and HIV viral load suppression. All statistical analyses were done using SPSS Version 29.0; the significance level was set at p < 0.05. Viral suppression rates significantly differed by sexual union status (p = 0.003), with married/cohabiting participants showing lower suppression (40.6%) than single (45.1%) and divorced/widowed participants (45.9%). The fully adjusted logistic regression model showed a non-significant association between viral load suppression and sexual union status (p = 0.066); however, a significant association was found in the unadjusted (p = 0.020) and partially adjusted models. Having a primary education (p < 0.001), South African citizenship (p < 0.001), being aged ≥66 years (p < 0.001), and being female (p = 0.003) were independently associated with lower odds of being virally suppressed. Sexual union status was not significantly associated with viral suppression in the fully adjusted regression model. This underscores the need for gender-responsive, age-sensitive HIV treatment interventions in rural South Africa. Full article

Amid the well-researched negative effects of road congestion and increased private car use, there is a need for more sustainable modes of transport. The literature points towards trains as being a vital part of the solution to the current problems, but their success depends on a number of variables, especially when it comes to the main railway stations in the largest cities. The first goal of this study was to identify the relationship between passenger numbers at major railway stations in Poland and selected parameters characterizing public transport services; the second was to assess the usefulness of the NOAH (Nest of Apes Heuristic) method for data analysis. In Poland, the number of major transfer hubs is limited, and there is a lack of an existing method allowing comparison of variables in such small datasets in a way that infers statistical significance. This is a research gap that the authors aimed to address using the NOAH algorithm combined with an analysis of regression. The initial dataset had been successfully expanded in a way that dependencies could be observed, with both goals being met. Passenger numbers relied most on the number of trains departing at each station daily, while walking distance during transfers impacted that number most negatively. The results point towards other variables influencing the passenger numbers, which were not considered in this study but could form the basis of further research. The utilized method could also be applied to a different group of cities, and in other countries. Additionally, the study added to the development of the NOAH algorithm itself, improving the method. Full article

Spring conditions may influence reproduction in the Gyrfalcon, Falco rusticolus, but their effects are difficult to separate from prey variation. We compared 66 nesting territories in 2024 and 69 in 2025 across natural tree nests, artificial nests mounted in trees, rock nests, and raven-built bridge nests on the Yamal Peninsula, Russia. Occupancy did not differ between years (35/66 vs. 40/69; Fisher’s exact test, p = 0.606). The proportion of occupied territories with a confirmed nesting attempt was higher in 2025 but not statistically significant (18/35 vs. 30/40; p = 0.053). Hatching success was higher in 2025 (7/15 vs. 26/29; p = 0.003), as was apparent nesting success among attempts with known outcomes (3/11 vs. 19/24; p = 0.007). Exact paired comparisons of permanent quadrats detected no interannual difference in ptarmigan pellet occurrence. The reproductive contrast coincided with earlier warming and snowmelt in 2025. Direct observations of eggs laid in snow or ice accumulated within raven-built bridge nests illustrated a nest-scale pathway to early failure. Although this two-year comparison documents an association between spring conditions and breeding performance rather than demonstrating causality, the results show why spring conditions, prey, and functional nest-site availability should be evaluated together in Gyrfalcon monitoring and conservation. Full article

First-year students in STEM programs face significant academic and personal challenges that can undermine retention and success, particularly for those navigating new institutional environments without prior college experience. While self- regulated learning (SRL) theory offers a well-established framework for understanding how students plan and reflect, less attention has been paid to the performance phase, the stage where students must translate plans into action amid real academic and social demands. This qualitative study examines the experiences of 15 first-year life science students across three institution types, a Hispanic-Serving Institution, a predominantly white institution, and a liberal arts college, to investigate what plans students formed at the end of their first semester and what factors facilitated or hindered implementation during their second semester. Using thematic analysis of semi-structured interviews, three major plan themes emerged: help-seeking, internal academic adjustments, and managing social and emotional well-being. Facilitating factors for these plans included small class sizes, anonymized participation tools, approachable instructors, peer and family support, counseling services, and structured planning tools, while hindering factors included fear of judgment, high instructor-student ratios, scheduling conflicts, academic burnout, and unsupportive living environments. The findings reveal that plan implementation depended on the interplay of intersecting psychological, social, and structural factors, which created unique conditions that influenced whether students were able to enact their plans. Importantly, the findings reveal that plan implementation unfolded not as a linear process but through nested micro-cycles of forethought, performance, and reflection within the performance phase, triggered by specific events throughout the semester. These findings have implications for how institutions design learner-centered support for STEM students not only at key transition points, but also throughout the semester, to address the conditions that influence whether students are able to successfully implement, adapt, or abandon their regulatory efforts. Full article

This study investigates how the integration of artificial intelligence reshapes the professional identity of university English teachers within higher education. Against the backdrop of global digital transformation, AI presents both disruptive potential and a significant ‘adaptation crises’ for educators. Using Bronfenbrenner’s Ecological Systems Theory as an analytical framework, this research systematically reviews the existing literature to address three objectives: (1) identify directions and typologies of teacher identity transformation; (2) analyse multilayered ecological influential factors; and (3) examine core challenges teachers face and their corresponding coping strategies. The findings indicate that professional identity is dynamically reconstructed across nested ecosystems, from micro-level classroom interactions to chronological level sociocultural contexts. This study advances an integrative perspective on the complex technology–teacher relationship, highlighting that successful identity transformation requires coordinated support across all ecological levels. Theoretical and practical implications are discussed to facilitate sustainable teacher development in the face of AI. Full article

The nondestructive detection of sodium chloride in mural plaster layers is important for assessing salt-related deterioration in cultural heritage materials. However, the weak and indirect spectral response of sodium chloride makes accurate hyperspectral detection challenging. This study developed a hyperspectral regression framework centered on Sparrow Search Algorithm (SSA) optimization, in which continuous wavelet transform (CWT) was used to construct multiscale spectral representations and Pearson correlation analysis combined with the Successive Projections Algorithm (PCC-SPA) was used for compact variable selection. Partial least squares regression (PLSR), support vector regression (SVR), extreme gradient boosting (XGBoost), SSA-optimized SVR, and SSA-optimized XGBoost were evaluated under nested stratified specimen-grouped five-fold cross-validation. Feature selection and hyperparameter optimization were independently performed within each outer training fold, whereas the held-out specimens were reserved for performance evaluation. SVR-SSA maintained high predictive capability across both conventional and multiscale spectral representations. SG + SNV yielded an R² of 0.8167 ± 0.0690 and an RMSE of 0.3701 ± 0.0636 percentage points. Scale 6 CWT achieved closely comparable R² and RMSE values of 0.8100 ± 0.0812 and 0.3731 ± 0.0767 percentage points, respectively, together with a lower MAE of 0.2788 ± 0.0620 percentage points. Among the ten CWT scales, Scale 6 achieved the highest mean prediction accuracy, whereas Scale 2 provided the best comprehensive balance between predictive accuracy and fold-to-fold stability. These results demonstrate that the effectiveness of SSA optimization depends on the input feature representation and that CWT provides scale-resolved information beyond a single conventional spectral representation. The proposed framework provides methodological support for the nondestructive quantitative assessment of NaCl-related deterioration in mural plaster materials and establishes a basis for further application in mural conservation. Full article

Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators necessitates a large vertical space, which directly conflicts with the height constraints of trellis canopies. This paper presented a hollow telescopic Cartesian manipulator with a belt-driven cascaded differential transmission for single-degree-of-freedom kiwifruit operations. The two-stage nested carbon fiber structure offers an extension ratio of 1.83:1 and a 549 mm retracted length, requiring 45.36% less vertical space than its single-stage architecture. The hybrid model–vision error compensation (HMVEC) strategy is proposed to address nonlinear positioning errors inherent in the cantilever telescopic configuration. It combines a polynomial–Fourier kinematic model for feedforward correction with YOLO11n AprilTag detection. The results showed the HMVEC strategy reduced the positioning root mean square error (RMSE) by 42.28% (from 7.90 mm to 4.56 mm). The designed manipulator achieved a 95% kiwifruit-transfer success rate at a mean cycle time of 4.8 s per fruit. These results demonstrate the feasibility of the proposed structural design and HMVEC strategy for precise manipulator positioning and post-detachment fruit transfer. Full article

AI-enabled simulation platforms are increasingly used for talent development, offering scalable, low-risk practice and algorithmic feedback for interpersonal HR capabilities such as difficult conversations, negotiation, and consultation. Yet HRD evaluation cautions that within-platform improvement may not transfer to authentic interactions once dashboards and metrics disappear. We conceptualize AI-enabled simulation training as metricized scaffolding and integrate scaffolding theory with exploration–exploitation learning dynamics to explain when algorithmic supports fade into self-regulation rather than produce metric adaptation (score chasing). We introduce Algorithmic Feedback Literacy (AFL)—learners’ capability to interpret algorithmic feedback, calibrate its authority, and translate cues into portable self-scaffolds—as a mechanism linking metricized practice to transfer. We test this model in an AI-enabled virtual internship and career development program that combines repeated simulation attempts with an authentic consultation assessment. Using platform traces (592 attempts nested within 90 learners), instructor-rated consultation performance as a transfer outcome, and 102 learner reflections, we examine how distributed practice span and practice dose related to simulation gains and transfer, and how consultation complexity changes, which interactional behaviors predicted success. Distributed practice span predicted transfer beyond within-platform gains, while practice dose operated primarily through measurable simulation improvement. Complexity shifted performance relevance toward governance and boundary-setting behaviors. Qualitative evidence identified two developmental pathways: productive internalization, where learners converted metric cues into routines and mode-switching strategies, and metric adaptation, where learners optimized scores without internalizing broader professional judgment. We discuss design implications for AI-enabled HRD and retention-oriented capability building: cultivate AFL through transparency and calibration supports, design for deliberate fading, and pair metricized refinement with authentic assessments to strengthen transfer. Full article

Background: Language outcomes among DHH children are widely variable. Equally variable is the diversity of factors involved in fostering healthy language development in this population. Some DHH children use cochlear implants and hearing aids and reach expected milestones. Others exhibit delays, ranging from mild to severe. Regardless of how “success” is defined for individual children who use these technologies, recognizing the ecological contexts in which DHH children develop language, and the relationships among those contexts, can support the identification of contextual predictors and points of intervention that lie beyond the individual DHH child. Purpose: The purpose of this article is to articulate Bronfenbrenner’s Ecological Systems Theory and how it relates to language development in this population of children. We discuss the original conceptualization of the theory, portraying the levels of ecosystems depicted as nested, and explore how a networked version of the theory might provide a complementary lens for elucidating the ecological variables associated with language development. Implications for research: We describe implications for research including expanding the ecological variables examined, examining interactions between ecosystems, use of diverse methodologies, and shifting beyond a deficit model towards an ecological perspective. Full article

In this study, daily reference evapotranspiration (ET₀) values for the period 2025–2030 for Lake Burdur, located in the Mediterranean climate zone and within the Burdur closed basin, were estimated using nested architecture focused on high accuracy. The ET₀ target corresponds to the FAO-56 Penman–Monteith reference evapotranspiration variable provided by the Open-Meteo Historical Weather API, and it is treated throughout as a standardized measure of atmospheric evaporative demand rather than as actual lake-surface evaporation or basin water loss. For this purpose, daily mean air temperature, relative humidity, shortwave surface radiation, and evapotranspiration data for the period 1984–2024 were obtained from the Open-Meteo platform. In the first stage of the study (Model 1), separate SARIMAX (statistical), XGBoost (machine learning), and LSTM (deep learning) models were applied for temperature, relative humidity, and radiation series; the model with the highest validation mean for each variable was selected. Accordingly, LSTM (Mean $R^2$ = 0.967) was determined to be the most successful model for temperature, SARIMA(X) (Mean $R^2$ = 0.812) for relative humidity, and XGBoost (Mean $R^2$ = 0.845) for the radiation variable, which is non-linear, has strong autocorrelation, and exhibits distinct seasonality. In the second stage (Model 2), these best climate predictions were used as independent variables for evapotranspiration, and LSTM provided the highest success for evapotranspiration (Mean $R^2$ = 0.941). Trend analyses revealed that the increase in temperature and evapotranspiration and the decrease in relative humidity observed in the past period will continue in the near future. The uncertainty analysis conducted using the Monte Carlo/resampling approach on historical data showed that the 95% prediction intervals largely protected the upward trend in evapotranspiration against random fluctuations. These intervals reflect residual-based uncertainty under the fitted model rather than the full predictive uncertainty of future basin evapotranspiration. The findings indicate that designing model selection appropriate to the structure of the variables within a nested prediction framework significantly improves forecast accuracy and can provide a viable decision support input for sustainable water management in Mediterranean basins experiencing water scarcity. Full article

The current global population trend is towards greater concentration in cities, which presents a potential negative risk to wetlands and the species that inhabit them, especially birds, when they are encroached upon by urban growth that destroys the vegetation used for perching, roosting, nesting, and feeding. Bird species capable of adapting to urban environments are the ones that ensure their permanence and success over other species with less adaptive capacity. Therefore, in the present study, the abundance and distribution of the Mexican Grackle Quiscalus mexicanus were obtained in two urban wetlands with different levels of urbanization and land-use policies. The point count method was used, and the behavior of the species, types of vegetation, urban infrastructure, and human activities were also recorded. The results showed that the lowest abundance occurred in the wetland that was the most urbanized, with a land-use policy for urban development, intensive recreational use, and a preference for street lighting and sidewalks. In contrast, the highest abundance was found in the wetland with a land-use policy focused on protection, showing habitat preference for areas with trees and guided visit activities for environmental education. It can therefore be concluded that this species develops better in less urbanized environments with abundant trees. Full article

Detecting rotated parabolic shapes in digital images remains a significant challenge in computer vision, especially in embedded environments constrained by computational and memory resources. This study introduces a novel field-programmable gate array (FPGA)-based genetic algorithm (GA) architecture specifically tailored for rotated parabola detection, implemented as an intellectual property (IP) core on a PYNQ-Z1 system-on-chip (SoC) platform. The architecture encodes four parabola parameters into fixed-length chromosomes, assesses their geometric consistency with a 640 × 480 binary edge image using a hardware fitness function, and executes the entire evolutionary process in programmable logic. Image pre-processing is executed on an external CPU, using Canny edge detection for synthetic images and Holistically Nested Edge Detection (HED). For real images, post-processing and result visualization are conducted on the ARM processor using the PYNQ framework. Experimental results on synthetic images demonstrate mean accuracies of 98.47% and 95.23%, with detection success rates of up to 96%. For real images, since manually annotated ground truth is not available, results are presented as qualitative observations of convergence consistency across 100 independent runs. These findings demonstrate the feasibility of detecting rotated parabolas on resource-constrained embedded platforms and indicate promising applications in domains where parabolic patterns are prevalent, such as structural inspection, biomedical imaging, and perception modules for autonomous vehicles and driver-assistance systems. Full article

The diversity of mating systems in fish is unparalleled among vertebrates. This variability is shaped by a long evolutionary history associated with differences in selection pressures and plasticity within species. However, there is also significant intraspecific variability within species, often related to differences in environments among populations. Herein, I explore how habitat features (temperature, oxygen, turbidity and vegetation) and availability of mates or mating resources (nest sites, population density, sex ratio and alternative mating strategies) can affect the distribution of reproductive success in a population. The literature reviewed here indicates that differences in the intensity of sexual selection and variation in mating-system structure can be directly related to differences in breeding habitat. The way in which habitat affects mating-system structure is complex, with both abiotic and biotic factors interacting to influence different aspects of breeding behavior and success. Thus far, our understanding of variation in mating systems in fishes is based on very well-studied species, and more exploration is needed to provide an overview of habitat and mating-system structure. This is critical as we face human-induced changes in breeding habitats that can alter mating systems and potentially affect variation and viability of fish populations. Full article