Accurate, near real-time soybean phenology information is critical for crop management and breeding. Previous approaches relying on satellite remote sensing time-series data suffer from temporal delays, limiting their usefulness for in-season decision-making. To overcome this limitation, this study reframes phenology identification as a near real-time classification task using single-timepoint Unmanned Aerial Vehicle (UAV) imagery collected from 420 soybean germplasm resources across three experimental sites, and proposes an innovative multi-modal dynamic Gating Fusion Model that integrates two optimized pathways. one based on machine learning (ML) and the other on deep learning (DP). In the ML branch, systematic benchmarking of tabular-feature models identified the Soft Voting ensemble as the best classifier. In the DL branch, an enhanced BC-ConvNeXt model equipped with BiFPN and CBAM modules was developed to strengthen visual feature extraction. Building on these two optimal classifiers, the dynamic gating fusion model achieved the highest F1-score of 94.3% across seven key growth stages (V1, V2, R1, R2, R6, R7, R8). This result represents a significant improvement of 1.5% and 10.6% over the best performing ML and DL models, respectively. The superior performance arises from the intelligent arbitration of complementary strengths, with gating-weight analysis revealing a strategy that prioritizes ML predictions while leveraging DL for error correction. This work establishes a complete framework for near real-time crop phenology detection and demonstrates the strong potential of intelligent multi-modal fusion in high-throughput phenotyping.
Roly Gutarra Romero, Alma Gabriela Valente Mercado, Luis Ramírez Sirgo
In recent years, the topic of dynamic capabilities has acquired new content. As higher-order competencies, they allow one to constantly update oneself with new knowledge, flexibly recombine resources, and adapt to a rapidly changing environment. A key part of dynamic capabilities is working with the future, starting with basic skills - futures literacy (FL). Since this competence is key to the human resources of organizations, its development seems important, starting with university programs. For a long time, there were no objective tools for measuring the degree of their mastery. The authors of this article attempt to fill this problem by offering an innovative approach to identifying and standardizing the assessment of FL competence. Six theoretical dimensions of FL are proposed as a basis for grouping assessment criteria and compiling final assessments and their interpretation. The corresponding dimensions, such as FL sub-competencies that include foresight, the assessment of future scenarios, and decision-making under uncertainty, can be assessed independently of each other. The ability to measure the initial level of FL will allow for the development of more effective educational programs for the development of this competence.
Saumya Malik, Valentina Pyatkin, Sander Land
et al.
Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from the development of benchmarks that test capabilities in specific skill areas to others that test agreement with human preferences. At the same time, progress in evaluation has not been mirrored by the effectiveness of reward models in downstream tasks -- simpler direct alignment algorithms are reported to work better in many cases. This paper introduces RewardBench 2, a new multi-skill reward modeling benchmark designed to bring new, challenging data for accuracy-based reward model evaluation -- models score about 20 points on average lower on RewardBench 2 compared to the first RewardBench -- while being highly correlated with downstream performance. Compared to most other benchmarks, RewardBench 2 sources new human prompts instead of existing prompts from downstream evaluations, facilitating more rigorous evaluation practices. In this paper, we describe our benchmark construction process and report how existing models perform on it, while quantifying how performance on the benchmark correlates with downstream use of the models in both inference-time scaling algorithms, like best-of-N sampling, and RLHF training algorithms like proximal policy optimization.
National statistical institutes are beginning to use non-traditional data sources to produce official statistics. These sources, originally collected for non-statistical purposes, include point-of-sales(POS) data and mobile phone global positioning system(GPS) data. Such data have the potential to significantly enhance the usefulness of official statistics. In the era of big data, many private companies are accumulating vast amounts of transaction data. Exploring how to leverage these data for official statistics is increasingly important. However, progress has been slower than expected, mainly because such data are not collected through sample-based survey methods and therefore exhibit substantial selection bias. If this bias can be properly addressed, these data could become a valuable resource for official statistics, substantially expanding their scope and improving the quality of decision-making, including economic policy. This paper demonstrates that even biased transaction data can be useful for producing official statistics for prompt release, by drawing on the concepts of density ratio estimation and supervised learning under covariate shift, both developed in the field of machine learning. As a case study, we show that preliminary statistics can be produced in a timely manner using biased data from a Japanese private employment agency. This approach enables the early release of a key labor market indicator that would otherwise be delayed by up to a year, thereby making it unavailable for timely decision-making.
The overwhelming majority of homeless individuals are jobless, despite many expressing a willingness to work. While this strong individual-level link between homelessness and unemployment is well-documented, the broader impact of labor market dynamics on homelessness remains largely unexplored. To fill this gap, this paper investigates the impact of local labor market conditions on the duration of homelessness, using individuals' homeless shelter usage records as a proxy for measuring their homelessness duration. Specifically, drawing on Canada's National Homelessness Information System data from 2014 to 2017, we analyze how local employment growth and changes in the local employment rate affect shelter usage duration. Our findings reveal that a 1% increase in local employment is associated with a 0.11-quarter (approximately 0.33-month) reduction in the average duration of shelter usage, while a 1% rise in the local employment rate leads to a 0.34-quarter (approximately 1.02-month) reduction. These changes correspond to decreases of 2.9% and 8.9%, respectively, in the average duration of shelter stays. The findings underscore the critical role of employment opportunities in reducing homelessness and lend support to job-oriented policy interventions for the homeless. In addition, the results suggest that demographic disparities-particularly the overrepresentation of Indigenous people and men among the homeless-are partially explained by slower exit rates from homelessness within these groups.
Island endemism is shaped by complex interactions among environmental, ecological, and evolutionary factors, yet the relative contributions of topography, climate, and land cover remain incompletely quantified. We investigated the drivers of endemic plant richness across Crete, a Mediterranean biodiversity hotspot, using spatially explicit data on species distributions, topographic complexity, climatic variability, land cover, and soil characteristics. Artificial Neural Network models, a machine learning tool, were employed to assess the relative importance of these predictors and to identify hotspots of endemism. We found that total species richness, elevation range, and climatic variability were the strongest predictors of endemic richness, reflecting the role of biodiversity, topographic heterogeneity, and climatic gradients in generating diverse habitats and micro-refugia that promote speciation and buffer extinction risk. Endemic hotspots only partially overlapped with areas of high total species richness, indicating that total species richness was the optimal from the ones examined, yet an imperfect surrogate. These environmentally heterogeneous areas also provide critical ecosystem services, including soil stabilization, pollination, and cultural value, which are increasingly threatened by tourism, renewable energy development, land-use change, and climate impacts. Our findings underscore the importance of prioritizing mountainous and climatically variable regions in conservation planning, integrating ecosystem service considerations, and accounting for within-island spatial heterogeneity. By explicitly linking the environmental drivers of endemism to both biodiversity patterns and ecosystem function, this study provides a framework for evidence-based conservation planning in Crete and other Mediterranean islands with similar geological and biogeographic contexts.
Marina Morgado Garcia, Mariana Michel Barbosa, Augusto Afonso Guerra Júnior
et al.
Introdução: A avaliação de serviços de saúde é compreendida como fator qualificador de gestão e tem papel fundamental como indicador de melhorias. A satisfação é entendida como a percepção do usuário, e quando utilizada para ponderar o acesso aos medicamentos, pode ser considerada um componente da avaliação da qualidade dos serviços, representando uma importante ferramenta de estratégias de gestão pública. Objetivo: avaliar a satisfação dos usuários em relação ao acesso aos medicamentos, na região metropolitana de Belo Horizonte, nas suas 5 dimensões: disponibilidade, acessibilidade geográfica, adequação, capacidade aquisitiva e aceitabilidade. Metodologia: Foi realizado estudo epidemiológico descritivo do tipo inquérito. O questionário foi aplicado em locais de ampla circulação com distintos públicos na região metropolitana de Belo Horizonte. A coleta de dados foi realizada com consumidores, com 18 ou mais anos, que utilizavam serviços privados e/ou públicos de saúde. Para mensurar a satisfação em relação às dimensões de acesso, foi utilizada a escala de Likert. Resultados e Discussão: Foram entrevistados 580 indivíduos, dos quais pouco mais da metade tinha plano de saúde e relatavam utilizar exclusivamente os serviços privados. Com uma diferença estatisticamente significante, um maior número de usuários que acessavam exclusivamente o serviço público estavam insatisfeitos ou muito insatisfeitos quando comparados aos usuários que utilizavam exclusivamente o setor privado, em relação a todas as dimensões de acesso a medicamentos: disponibilidade (31,4% versus 14,9%), acomodação (22,9% versus 9,09%) e acessibilidade geográfica (17,2% versus 10,0%), capacidade aquisitiva (31,5% versus 20,91%) e aceitabilidade (11,4% versus 8,79%), respectivamente. Considerações finais: Os resultados revelam que os indivíduos que acessam exclusivamente os serviços públicos, estão em sua maioria mais insatisfeitos que os indivíduos que acessam exclusivamente os serviços privados. Além disso, os dados indicam o nível mediano de satisfação em relação às diferentes dimensões que constituem acesso a medicamentos.
Pharmacy and materia medica, Pharmaceutical industry
The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for many ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We implement a spatial mixture model, separating the spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process and within each class the process is governed by a separate spatial model. Model predictions take the form of scalar predictions as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty. We demonstrate the method using GEDI data over Wollemi National Park. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required.
This paper presents a Neural Networks (NNs) based approach for designing the Fuel-Optimal Powered Descent Guidance (FOPDG) for lunar pinpoint landing. According to Pontryagin's Minimum Principle, the optimality conditions are first derived. To generate the dataset of optimal trajectories for training NNs, we formulate a parameterized system, which allows for generating each optimal trajectory by a simple propagation without using any optimization method. Then, a dataset containing the optimal state and optimal thrust vector pairs can be readily collected. Since it is challenging for NNs to approximate bang-bang (or discontinuous) type of optimal thrust magnitude, we introduce a regularisation function to the switching function so that the regularized switching function approximated by a simple NN can be used to represent the optimal thrust magnitude. Meanwhile, another two well-trained NNs are used to predict the thrust steering angle and time of flight given a flight state. Finally, numerical simulations show that the proposed method is capable of generating the FOPDG that steers the lunar lander to the desired landing site with acceptable landing errors.
In agricultural landscape management, the conventional top-down approaches that primarily focus on market-led responses struggle to preserve the landscape elements essential for environmental sustainability. To address this deficiency, land use and land cover change (LUCC) scenarios promote an integrated understanding of landscape dynamics and highlight the inconsistency between the compartmentalisation of the public sector (“siloisation”) and the necessity for management that reflects the interdependencies of socio-ecological systems. This study investigates the extent to which the creation and dissemination of LUCC scenarios lead to modifications in the values, attitudes, and behaviours of local actors engaged in land management, giving particular emphasis to the role of these scenarios in encouraging integrated management. To accomplish this objective, we interviewed local actors who actively participated in the co-construction of the scenario narratives or learned about the scenarios during dissemination workshops. We then analysed the data via a thematic and lexicometric analysis. The findings highlighted the dual function of these scenarios as a catalyst for pre-existing political will to promote integrated management and as a tool for raising awareness about major environmental challenges. At the group level, the outcomes encompassed aspects such as basing political decisions on the results of scenarios and fostering collaboration between institutions. These outcomes were observed among the actors involved in co-constructing scenarios or those with pre-existing motivations to pursue integrated management initiatives. Additional personal outcomes included an increased awareness of environmental challenges and the consolidation of non-formalised knowledge. We argue that combining co-construction and dissemination enhances the outcomes of scenarios considerably.
Fatema Al Saba, Charilaos Mertzanis, Ilias Kampouris
Purpose: This paper hopes to examine the effect of staff empowerment on jobs that fall inside the travel and tourism industry across eighty-four nations from the years 2000 to 2021 using yearly cross-country information gathered by the World Tourism Organization (WTO).
Methods:The purpose of this study is to provide an approximation of the level of employee empowerment according to the limit to which companies that are active in the economic reality provide employees with training opportunities. The analysis accounts for the effect of economic situations, the development of infrastructure, and policy frameworks by controlling for the impact of several social, economic, and institutional variables. This allows the analysis to take into account the influence held by economic circumstances, growth in infrastructure, and policies and frameworks.
Results:Our research shows that there is a substantial beneficial correlation involving employee training and employment in tourism-related industries across the board in every country. The robustness of these results is demonstrated by the fact that they are not affected by a variety of tests for sensitivity and endogeneity analyses. According to the findings of our research, modifications to employee training could not have a quick or solely linear effect on employment rates in the tourism sector. It has been observed that nonlinear effects can occur, in addition to the possibility of delays in the impact that training programs have on employment. In addition, a wide variety of social, economic, environmental, and geopolitical factors all have the potential to have an impact on the link between employee training and job placement in this sector.
Implications: Employee training programs in the economy appear to be important tools in enhancing employee skills and therefore empower them to seek employment in the tourism sector.
Management. Industrial management, Marketing. Distribution of products
To keep up with today's dense metropolitan areas and their accompanying traffic problems, a growing number of towns are looking for more advanced and swift urban taxi drones. The safety parameters that must be taken into consideration may be the most important element in the widespread use of such technology. Most recent aviation mishaps have happened during the landing phase, making this a particularly important safety consideration for Vertical and/or Short Take-Off and Landing (V/STOL) drones. In this study, we focused on improving the fault tolerance of the processor architectures used by the predecessors of Autonomous Landing Guidance Assistance Systems (ALGAS), which in turn improves their decision-making capabilities. Furthermore, this is achieved by proposing a fault-tolerant processing architecture that depends on the Gamma Distribution Sliding Window Unit (GDSWU). This proposed GDSWU has been designed completely using VHDL, and the targeted FPFA was the Intel Cyclone V 5CGXFC9D6F27C7 chip. The GDSWU could operate at a maximum frequency of 369.96 MHz, as calculated by the synthesis results of the INTEL Quartus Prime program. The suggested GDSWU core only requires 20.36 mW for dynamic core and I/O power consumption.
František Bartoš, Alexandra Sarafoglou, Henrik R. Godmann
et al.
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. We collected $350{,}757$ coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (DHM; 2007). The model asserts that when people flip an ordinary coin, it tends to land on the same side it started -- DHM estimated the probability of a same-side outcome to be about 51\%. Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, $\text{Pr}(\text{same side}) = 0.508$, 95\% credible interval (CI) [$0.506$, $0.509$], $\text{BF}_{\text{same-side bias}} = 2359$. Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias. Our data also confirmed the generic prediction that when people flip an ordinary coin -- with the initial side-up randomly determined -- it is equally likely to land heads or tails: $\text{Pr}(\text{heads}) = 0.500$, 95\% CI [$0.498$, $0.502$], $\text{BF}_{\text{heads-tails bias}} = 0.182$. Furthermore, this lack of heads-tails bias does not appear to vary across coins. Additional analyses revealed that the within-people same-side bias decreased as more coins were flipped, an effect that is consistent with the possibility that practice makes people flip coins in a less wobbly fashion. Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started.
Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.
O artigo analisa a atuação do empresariado no Novo Ensino Médio. No processo de recomposição burguesa, compreendemos que o empresariado tem atuado no sentido de direcionar as políticas educacionais e o Estado, como por exemplo, o movimento Todos Pela Educação. Utilizamos uma pesquisa de cunho documental, calcada no materialismo histórico-dialético para apontar que a atuação do capital privado tem sido no sentido de conformar a classe trabalhadora para as demandas de um mercado flexível e pragmático.
Palavra-chave: Novo Ensino Médio; Empresariado; Reforma Educacional; Todos Pela Educação.
Special aspects of education, Labor market. Labor supply. Labor demand