To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.
This paper extends the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end workflows involving multi-step reasoning, tool invocation, and autonomous decision-making, substantially expanding occupational displacement risk beyond what existing task-level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters--not a regression estimate--incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information-intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43-0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment
This paper looks into the impact of China’s new rural land reform, the three rights separation policy (TRSP), on Chinese farmers’ income. Based on data collected from 360 rural households in Anhui Province, China, 2021, this paper constructed the influence pathways of the TRSP on household income and estimated the effects along different pathways using the structural equation model (SEM) model. It showed that through expanding the planting scale and promoting resource-use efficiency, the new land tenure system can indirectly increase transfer-in household income. However, the TRSP has a significant negative direct effect on transfer-out households’ income, and only a slight impact on transferring rural labor to other industries or relaxing the liquidity constraint. In short, the TRSP’s effect on income gains is more prominent in transfer-in households than transfer-out ones, which in the long run would lead to an increased income gap, more so if transfer-out households lack easy access to non-farm employment. Our findings suggest that public authorities should respect farmers’ autonomy in land transfer decisions and pay special attention to labor transfer in poverty alleviation. Meanwhile, widening income disparities among different groups should be heeded while implementing local governments’ service roles.
Abstract This study analyses the influence of geographical origin and taste on honey consumer behavior. First, we explore the influence of geographical origin on consumers’ hedonic evaluation of honey. We then assess the influence of geographical origin and taste on their willingness to pay (WTP) for honey. We conducted a field experiment at a real supermarket. The participants were exposed to two treatments (blind and informed treatment). The findings showed that knowledge about the geographical origin of honey influences consumers’ hedonic evaluations and that the WTP for honey is more strongly influenced by geographical origin than by taste.
Nutrition. Foods and food supply, Agricultural industries
Alexey S. Tanashkin, Irina G. Tanashkina, Alexander S. Maksimchuik
In this paper, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. There are numerous potential difficulties one could encounter in the effort to build a good model. Their main source is the huge difference between noisy real market data and ideal data usually used in tutorials on machine learning. This paper covers all stages of modeling: collection of initial data, identification of outliers, search and analysis of patterns in the data, formation and final choice of price factors, building of the model, and evaluation of its efficiency. For each stage, we highlight potential issues and describe sound methods for overcoming emerging difficulties on actual examples. We show that the combination of classical linear regression with kriging (interpolation method of geostatistics) allows to build an effective model for land parcels. For flats, when many objects are attributed to one spatial point, the application of geostatistical methods becomes problematic. Instead, we suggest linear regression with automatic generation and selection of additional rules on the base of decision trees, so called the RuleFit method. We compare the performance of our inherently interpretable models with well-proven "black-box" Random Forest method and demonstrate similar results. Thus we show, that despite such a strong restriction as the requirement of interpretability which is important in practical aspects, for example, legal matters, it is still possible to build effective models of real property markets.
As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.
Janneke Pieters, Ana Kujundzic, Rulof Burger
et al.
Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.
<p>This study aims to investigate the unique features of the food supply chain, examining the impact of food supply chain integration, consisting of internal integration, supplier and customer, the quality of food products and product innovation capability. Managers need to understand the importance of supplier and customer integration when responding to supply chain risk and company uncertainty. The data were collected from 168 managers active in the food industry in Tehran province. The partial least squares tool (SmartPLS 3.0) was used to analyze the data using Structural Equation Modeling (SEM) technique. The results show that there is a strong relationship between uncertainty and supply chain integration including customer, supplier and internal integration. The findings indicate that customer integration and supplier integration are critical factors in improving product quality in the food supply chain. The results can be related to the prominent role of customer relations and contact in the development of innovation capabilities in manufactured products, which has also been approved by some previous studies. Additionally, analyzing the various dimensions of supply chain integration separately revealed that internal integration is a capability factor for external integration. This study can help businesses in the food industry understand the value-creating roles of food supply chain integration and provide valuable guidance for them to decide how to meet the various challenges and manage food supply chain integration in order to improve product quality and product innovation capability.</p>
This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.
Ilham Adi Panuntun, Ying-Nong Chen, Ilham Jamaluddin
et al.
In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.
Self-service machines are a form of pseudo-automation; rather than actually automate tasks, they offset them to unpaid customers. Typically implemented for customer convenience and to reduce labor costs, self-service is often criticized for worsening customer service and increasing loss and theft for retailers. Though millions of frontline service workers continue to interact with these technologies on a day-to-day basis, little is known about how these machines change the nature of frontline labor. Through interviews with current and former cashiers who work with self-checkout technologies, we investigate how technology that offsets labor from an employee to a customer can reconfigure frontline work. We find three changes to cashiering tasks as a result of self-checkout: (1) Working at self-checkout involved parallel demands from multiple customers, (2) self-checkout work was more problem-oriented (including monitoring and policing customers), and (3) traditional checkout began to become more demanding as easier transactions were filtered to self-checkout. As their interactions with customers became more focused on problem solving and rule enforcement, cashiers were often positioned as adversaries to customers at self-checkout. To cope with perceived adversarialism, cashiers engaged in a form of relational patchwork, using techniques like scapegoating the self-checkout machine and providing excessive customer service in order to maintain positive customer interactions in the face of potential conflict. Our findings highlight how even under pseudo-automation, workers must engage in relational work to manage and mend negative human-to-human interactions so that machines can be properly implemented in context.
Land use / land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related to topography, ecology, and human development. We identify a close connection between modeling of spatial patterns of land use and the task of image inpainting from computer vision and conduct a study of a modified PixelCNN architecture with approximately 19 million parameters for modeling LULC. In comparison with a benchmark spatial statistical model, we find that the former is capable of capturing much richer spatial correlation patterns such as roads and water bodies but does not produce a calibrated predictive distribution, suggesting the need for additional tuning. We find evidence of predictive underdispersion with regard to important ecologically-relevant land use statistics such as patch count and adjacency which can be ameliorated to some extent by manipulating sampling variability.
Elías Jordan Karmach-Sánchez, Carlos Leandro Delgado-Fuentealba, Paola Giuliana Zerega-Tallia
et al.
Para los jóvenes, la elección acertada de un pro-grama universitario constituye un desafío para ellos, su familia y la sociedad, puesto que el desarrollo personal, el éxito profesional, los valores y el servicio a la comunidad dependen de ella. Considerando que los factores que intervienen en esta elección son variados, el estudio buscó determinar, empíricamente, si el grado de autoestima y moti-vación escolar tiene efecto en la matrícula de un programa universitario, como primera preferencia. La muestra corresponde a los 2626 jóvenes que ingresaron a la Universidad de Concepción, Cam-pus Chillán, en 2016-2021. Se utilizó un modelo pro-bit binario, pues la variable dependiente toma uno de dos valores discretos. El grado de autoestima y motivación se midió a partir de uno de los índices de desarrollo personal y social (IDPS), entregado por la Agencia de Calidad del Ministerio de Educación de Chile para cada establecimiento de egreso de enseñanza media. Se incorporaron variables de control, como las características personales del estudiante y del establecimiento. Los resultados confirman que, cuanto mayor es el índice de autoestima y moti-vación escolar, mayor es la probabilidad de que el estudiante ingrese al programa de su primera preferencia. Este análisis confirma (1) la importancia de este Indicador y (2) que aún faltan avances para fortalecer programas escolares que pongan el énfasis en propiciar condiciones que permitan el desarrollo integral de los estudiantes, destacando las fortalezas por sobre las debilidades.
Enrique González-Plaza, David García, Jesús-Ignacio Prieto
Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.
Engineering machinery, tools, and implements, Technological innovations. Automation
With 70 million dead, World War II remains the most devastating conflict in history. Among the survivors, millions were displaced, returned maimed from the battlefield, or endured years of captivity. We examine the effects of such war exposures on labor market careers, showing that they often become apparent only at certain life stages. While war injuries reduced employment in old age, former prisoners of war prolonged their time in the workforce before retiring. Many displaced workers, especially women, never returned to employment. These responses align with standard life-cycle theory and thus likely hold relevance for other conflicts.
Tech workers -- professional workers in the technology industry including software engineers, product managers, UX designers, etc. -- are not normally associated with labor activism. Yet, since 2017, we have seen a significant rise in labor actions among this group. Using an original dataset, we demonstrate how, in the case of tech workers, periods of intense workplace social activism preceded later periods of heightened labor activism. Regression analysis confirms that participation in social activism increases the likelihood of labor activism six months to one year later at the same company. This finding extends Fantasia's cultures of solidarity argument to professional workers. We find that organizing emerges out of collective action and ensuing conflict with management: first, tech workers, guided by their professional interest in socially beneficial work, engage in workplace social activism. This generates solidarity among employee-participants but also creates conflict with management and leads to the emergence of labor activism among professionals.
Objetivo: Diseñar instrumentos de control documental para la Dirección Municipal de Planificación Física de Santo Domingo, provincia Villa Clara, Cuba, para fortalecer dicha gestión de documentos.
Métodos: El estudio se clasifica como descriptivo. Para la obtención de resultados se utilizaron los métodos inducción-deducción, analítico-sintético, análisis documental y la encuesta.
Principales resultados: Se diseñaron dos instrumentos de control documental para la entidad; estos fueron: cuadro de clasificación documental y tabla de retención; se estableció además una Comisión De Valoración Documental.
Conclusiones: Se evidenciaron condiciones desfavorables para la gestión documental en el Archivo Central de la organización. Se identifica la falta de espacio para los documentos, así como la ausencia de sistemas de gestión para automatizar los procesos de transferencia documental y digitalizar las series documentales. Se diseñaron instrumentos de gestión documental que contribuyen a minimizar los riesgos a los que se expone la organización y mejorar su gestión.