Agricultural seeds are sold as commodities yet seed quality can be non-uniform. Despite the extensive literature showing that plasma treatments of seeds provides advantages for many crops, lettuce studies, particularly in indoor farming systems, are limited. This study provides a systematic investigation of the impacts of non-thermal plasma treatments with various feed gases (N<sub>2</sub>, O<sub>2</sub>, dry air, and wet air) on the germination and growth characteristics of four lettuce cultivars (Red Oakleaf (RO), Black Simpson (BS), Valley Heart Romaine (VHR), and Paris Romaine (PR)) under controlled cultivation conditions in an agrivoltaic agrotunnel. Although the germination time was not conclusively affected by the treatments, the results show a complex interaction between germination rate and yield across the different cultivars and plasma treatments. Except for PR seeds (77.8% vs. 65.8% control), wet air plasma treatments increased germination rates by 18.7–100% over controls for all other cultivars. In yield analysis, wet air treatment had the strongest effect, especially for VHR (51.7 vs. 42.5 g/pot). Treatments did not notably affect RO. For BS, N<sub>2</sub> treatment gave the highest increase (54.2 vs. 48.1 g/pot), while PR responded best to O<sub>2</sub> treatment (58.4 vs. 51.8 g/pot). The energy consumption of plasma treatments was negligible for all treatments, while labor costs for small batches of seeds accounted for the largest share of secondary operating costs (839, 622, and 659 h/year, respectively for BS, VHR, and PR). Despite additional expenses, including labor, O&M, and degradation costs, the reduced seed requirements from higher germination rates and higher yield increased net profit by 12.0% compared to untreated cultivation in the most impacted (Valley Heart Romaine) lettuce. There is an opportunity for further cost optimization of the non-thermal plasma treatment for each type of lettuce seed.
Beril Yalcinkaya, Beril Yalcinkaya, Micael S. Couceiro
et al.
Robotic fleet management systems are increasingly vital for sustainable operations in agriculture, forestry, and other field domains where labor shortages, efficiency, and environmental concerns intersect. We present FORMIGA, a fleet management framework that integrates human operators and autonomous robots into a collaborative ecosystem. FORMIGA combines standardised communication through the Robot Operating System with a user-centered interface for monitoring and intervention, while also leveraging large language models to generate executable task code from natural language prompts. The framework was deployed and validated within the FEROX project, a European initiative addressing sustainable berry harvesting in remote environments. In simulation-based trials, FORMIGA demonstrated adaptive task allocation, reduced operator workload, and faster task completion compared to semi-autonomous control, enabling dynamic labor division between humans and robots. By enhancing productivity, supporting worker safety, and promoting resource-efficient operations, FORMIGA contributes to the economic, and environmental dimensions of sustainability, offering a transferable tool for advancing human–robot collaboration in field robotics.
Mechanical engineering and machinery, Electronic computers. Computer science
Digital workers on crowdsourcing platforms (e.g., Amazon Mechanical Turk, Appen, Clickworker, Prolific) play a crucial role in training and improving AI systems, yet they often face low pay, unfair conditions, and a lack of recognition for their contributions. To map these issues in the existing literature of computer science, AI, and related scholarship, we selected over 300 research papers on digital labor published between 2015 and 2024, narrowing them down to 143 on digital gig-labor for a detailed analysis. This analysis provides a broad overview of the key challenges, concerns, and trends in the field. Our synthesis reveals how the persistent patterns of representation and voices of gig workers in digital labor are structured and governed. We offer new insights for researchers, platform designers, and policymakers, helping them better understand the experiences of digital workers and pointing to key areas where interventions and future investigations are promptly needed. By mapping the findings from the past ten years' growth of the domain and possible implications, this paper contributes to a more coherent and critical understanding of digital labor in contemporary and future AI ecosystems.
This paper exploits variation resulting from a series of federal and state Medicaid expansions between 1977 and 2017 to estimate the effects of children's increased access to public health insurance on the labor market outcomes of their mothers. The results imply that the extended Medicaid eligibility of children leads to positive labor supply responses of single mothers and to negative labor supply responses of married mothers. The analysis of mechanisms suggests that extended children's Medicaid eligibility positively affects take-up of Medicaid and health of children.
Extended Abstract
Background: The inadequacy of the supply of agricultural inputs with the demand for various products of this sector reveals the need for the optimal use of resources and increasing productivity. In this regard, addressing the issue of productivity in rice production is very important due to its essential role in feeding different sections of society, providing food security, reducing dependence on imports and foreign exchange, strengthening trade interactions with other countries, generating income, creating employment, creating balance in the business and capital market, and many other issues. In 2022, Mazandaran Province produced 1.6 million tons of paddy as a strategic product, responsible for 44.47% of Iran's paddy production, and in this sense, it has been ranked first in the country. This province has long been known as the hub of rice production, and this user product, having about 76% of Mazandaran's irrigated crop area, has always made an important contribution to the province's employment. For this purpose, the present study aimed to identify factors affecting the prosperity of the rice production business in Mazandaran Province, focusing on measuring the inefficiency of various production inputs, especially the labor force.
Methods: Three institutional, managerial, and policy-market criteria effective in the prosperity of rice production business were extracted in this study. The input criterion includes all production factors affecting the productivity of this product, which includes eight subcriteria as water, labor, land, fertilizer, poison, machinery, capital, and seed. The management criterion is all management actions by relevant organizations and bodies (Jahad Keshavarzi, Regional Water, Room of Commerce), which includes six regulatory, executive, organizational, service, and innovation options. The political-market criterion also covered the macro-government policies that can affect the productivity of rice, and there are six financial, economic, structural, commercial, marketing, and strategic development options. Thus, 19 effective options in the productivity of rice production were considered in this study. In this study, factors affecting the productivity of this product were exracted and prioritized using the Analytical Hierarchy Process (AHP) method, measuring the production efficiency of important cultivars of this product (high-quality rice and high-yielding rice) using the data envelopment analysis method (DEA), and then examining productivity changes over time using the Malmquist Index (MI). The data needed for identifying and prioritizing factors in this research were collected by designing a questionnaire, which was completed based on the opinions of 18 experts, including those from the Agricultural Jihad Organization of Mazandaran Province and Sari City, as well as the academic community. The statistics and information of the Agricultural Jahad Organization of the province were used to complete the data in measuring the productivity of production and efficiency of inputs.
Results: The results indicate that among the eight production factors, water, mechanization, and land are the most important input factors in rice production with weights of 0.36, 0.2, and 0.14, respectively. Among the five management factors, benefiting from the opinions of agricultural experts, implementing the optimal cultivation pattern of crops according to the climatic conditions and the status of water resources in the province, and using new technologies in agricultural operations with weights of 0.40, 0.25, and 0.14, respectively, were known as three important and superior factors for the management of rice production business. In addition, the financial, economic options, and improvement of the structure of the rice product marketing system were determined with the weights of 0.30, 0.22, and 0.19, respectively, as three policy-market subcriteria affecting the rice productivity of this province. Based on the findings in the agricultural year of 2017-2018 in the east of this province, Qaemshahr City, the land, machinary, poison, and fertilizer inputs were inefficient at 52.68%, 48.26%, 34.37%, and 33.16%, respectively. In 2018, the inefficiency rates in the use of land, labor, and poison inputs were 71.36%, 15.09%, and 4.46%, respectively. In the production of high-yielding rice in the east of the province, there has been inefficiency in the use of land, machinary, seed, water, and fertilizer inputs. Accordingly, Behshahr City acted inefficiently in consuming the mentioned inputs by 68.29, 52.60, 16.65, 12.63, and 7.55%, respectively. In 1998, the cities of Behshahr and Neka acted inefficiently in the consumption of all the investigated inputs, except for machinery. The percentages of inefficiency in the labor input are 16.14 and 42.07%, respectively. In addition, the productivity growth index values of Malmquist in the production of high-quality rice and high-yielding rice are 1.155 and 1.094, respectively. Hence, it can be concluded that the production productivity of this product has increased in this province.
Conclusion: The results indicate that the productivity of different rice varieties has increased during the studied period. In the case of high-yielding rice, however, the technical efficiency of producers in newer technology is lower than in older technology. Therefore, it is necessary for trustee organizations and knowledge-based companies to invest in the research, innovation, and promotion of new technology in training to use this technology. In this study, "water" has been determined as the most important input affecting the productivity of this product; therefore, it is recommended to take necessary measures to promote water storage and reduce its consumption. It is also suggested to provide financial support to rice farmers and the development of knowledge-based companies to provide new irrigation systems. Referring to the results of this study, the use of "machinery" is considered the second most effective factor in increasing productivity. In addition to reducing the cost of manpower and saving time, the uniformity and accuracy of the work are increased with mechanized cultivation, and seedlings are exposed to less damage. However, this issue does not mean to ignore the role and importance of the workforce in the production and elimination of job opportunities. Rather, it is recommended to train skilled and specialized human resources to benefit from mechanization for the long-term stability of the rice production business and stable rural employment.
Abstract Pavement crack detection is widely spread over road maintenance, ensuring the longevity and safety of infrastructure. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to errors. In response, automated crack detection systems based on deep learning have emerged, offering more efficient and accurate solutions. However, existing models often face challenges such as large model sizes, slow inference speeds, and limited applicability in real-time applications. In this paper, we propose a novel light-weight Crack Regional Segmentation method based on YOLOv11, which introduces attention mechanisms to address challenges in pavement images, such as varying crack sizes, occlusion, and irregular surface textures. By embedding a region-based attention mechanism into the YOLOv11 network, the method enhances the model’s ability to focus on crack features. Specifically, the model network layers are progressively pruned to reduce the number of parameters and floating-point operations, thereby further improving operational efficiency and refining detection in the target regions. Furthermore, to tackle issues with blurred or indistinct crack boundaries, we present a fuzzy information-guided YOLOv11-based model, FIG-YOLO. This model integrates fuzzy logic and fuzzy membership functions to handle uncertainty in crack detection. The fuzzy membership functions are used to quantify the degree of crack features, allowing the model to better distinguish between crack and non-crack regions, especially in cases where crack boundaries are unclear. This approach significantly improves the accuracy of crack detection and segmentation. Extensive experiments demonstrate that our approach effectively addresses challenges such as complex backgrounds and blurred crack edges in pavement images. This research not only provides a novel solution for the automated detection of pavement cracks but also offers insights into the development of intelligent road maintenance systems. With the expansion of large-scale datasets and the advancement of deep learning models, pavement crack detection algorithms are expected to further enhance their accuracy and efficiency, offering significant support for road infrastructure management.
This article examines how artificial intelligence has become fetishized in contemporary discourse, being imagined as an autonomous force rather than crystallized collective human labor. Drawing on Marx's theory of commodity fetishism and critical AI studies, the paper analyzes how the apparent agency of AI systems masks their dependence on human labor. It thus challenges individualistic models of intelligence as computation, favoring an understanding of cognition as fundamentally social and historically situated, drawing on authors such as Vygotsky and Ilyenkov. The paper argues that the collective intelligence crystallized in these systems belongs to humanity as a whole, and must be reclaimed, since AI is too powerful for society to leave in the hands of the tech oligopoly. It advocates for a public AI under democratic control, recognizing that the struggle over AI is inseparable from broader struggles against exploitation, for climate justice, and for genuine democracy.
Climate change significantly increases risks to power systems, exacerbating issues such as aging infrastructure, evolving regulations, cybersecurity threats, and fluctuating demand. This paper focuses on the utilization of Grid Enhancing Technologies (GETs) to strengthen power system resilience in the face of extreme weather events. GETs are pivotal in optimizing energy distribution, enabling predictive maintenance, ensuring reliable electricity supply, facilitating renewable energy integration, and automating responses to power instabilities and outages. Drawing insights from resilience theory, the paper reviews recent grid resilience literature, highlighting increasing vulnerabilities due to severe weather events. It demonstrates how GETs are crucial in optimizing smart grid operations, thereby not only mitigating climate-related impacts but also promoting industrial transformation. Keywords: Climate change, power systems, grid enhancing technologies (GETs), power system resilience, extreme weather
John Meluso, Amanda Casari, Katie McLaughlin
et al.
Invisible labor is work that is either not fully visible or not appropriately compensated. In open source software (OSS) ecosystems, essential tasks that do not involve code (like content moderation) often become invisible to the detriment of individuals and organizations. However, invisible labor is sufficiently difficult to measure that we do not know how much of OSS activities are invisible. Our study addresses this challenge, demonstrating that roughly half of OSS work is invisible. We do this by developing a cognitive anchoring survey technique that measures OSS developer self-assessments of labor visibility and attribution. Survey respondents (n=142) reported that their work is more likely to be invisible (2 in 3 tasks) than visible, and that half (50.1%) is uncompensated. Priming participants with the idea of visibility caused participants to think their work was more visible, and that visibility was less important, than those primed with invisibility. We also found evidence that tensions between attribution motivations probably increase how common invisible labor is. This suggests that advertising OSS activities as "open" may lead contributors to overestimate how visible their labor actually is. Our findings suggest benefits to working with varied stakeholders to make select, collectively valued activities visible, and increasing compensation in valued forms (like attribution, opportunities, or pay) when possible. This could improve fairness in software development while providing greater transparency into work designs that help organizations and communities achieve their goals.
Furrow irrigation systems have been widely evaluated around the world. However, there is no national data indicating how efficient furrow irrigation is under Uruguayan conditions. The objective of the present work was to evaluate the performance of a system of furrow irrigation in two different soils texture. Seventeen irrigation events were analyzed in sugar-cane cultivation in northern Uruguay during 2016-17 and 2017-18 irrigation seasons. The water advance and recess curves were determined; flow rate during irrigation and runoff were monitored. The maximum furrow length studied was 100 m and the average slope was 0.24%. Application efficiencies in both types of soils were observed above 75%. These field data were compared with data simulated by the WinSRFR model, where high correlations were observed in the results of water application efficiency, distribution uniformity and runoff. These first results encourage to continue working in the efficient use of water, not only thinking about a better use of the resource but also in less loss by run-off, and therefore, less possibility of contamination and lower cost of energy and labor.
We contribute empirical and conceptual insights regarding the roles of digital labor platforms in online freelancing, focusing attention to social identities such as gender, race, ethnicity, and occupation. Findings highlight how digital labor platforms reinforce and exacerbate identity-based stereotypes, bias and expectations in online freelance work. We focus on online freelancing as this form of working arrangement is becoming more prevalent. Online freelancing also relies on the market-making power of digital platforms to create an online labor market. Many see this as one likely future of work with less bias. Others worry that labor platforms' market power allows them to embed known biases into new working arrangements: a platformization of inequality. Drawing on data from 108 online freelancers, we discuss six findings: 1) female freelance work is undervalued; 2) gendered occupational expectations; 3) gendered treatment; 4) shared expectations of differential values; 5) racial stereotypes and expectations; and 6) race and ethnicity as an asset. We discuss the role of design in the platformization and visibility of social identity dimensions and the implications of the reinforced identity perceptions and marginalization in digital labor platforms.
A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate demand. In turn, the long tails are reflected in the goods and services that fulfill those needs. With this background, we propose a theoretical model of economic activity on a long tail distribution, where innovation in demand for new niche outputs competes with innovation in supply automation for mature outputs. Our model yields analytic expressions and asymptotes for the shares of automation and labor in terms of just four parameters: the rates of innovation in supply and demand, the exponent of the long tail distribution and an initial value. We validate the model via non-linear stochastic regression on historical US economic data with surprising accuracy.
Relevance. An analysis of scientific papers on international educational migration shows that most foreign and domestic studies have been carried out at the country level. These studies characterize the volume of exports of national educational services, state policy changes in attracting foreign citizens to study, features of adapting foreign students and integrating graduates in the national labor market. At the same time, the influence of international educational immigration on the processes, taking place in certain territories of states, in regions, districts, cities, is studied to a lesser extent. In these regions, the attraction of foreign citizens to study at local universities would be considered as a development priority and competitive advantage not only of a specific higher educational institution, but also territorial education in general. Therefore, identifying the features of modern development of universities in Russian regions and changing the volumes and directions of educational immigration are important and relevant areas of scientific research. The study aims at identifying the features of development of regional universities and impact of the changes, taking place in them, on educational immigration volume. Methods: statistical analysis, comparison, generalization, content analysis of documents, structural method of presenting the material. Results. The article analyzes the main legal documents of reforming the higher education system in Russian regions, highlights the features and results of implementation of projects and programs to improve the efficiency of regional universities. Particular attention is paid to the characteristics of the volumes and directions of educational immigration in the universities of the Russian regions. Conclusions. The formation of federal universities, national research universities, and the creation of a network of flagship regional universities contributed to the development and strengthening of the system of higher educational institutions in the Russian regions. The positive changes that have taken place in the higher education systems of the regions of the country contributed to an increase in the contingent of foreign students there.
The technique of additive manufacturing has increasing popularity in food research area as well as other scientific fields. However, 3D food printers are expensive options compared to 3D polymer printers. Scientists, that require laboratory scale production capacities, resemble the syringe-pump systems that available in open source and free hardware designs. Present study aimed to develop an exchangeable syringe-pump mechanism (SPM) to demonstrate transformation of conventional 3D printer from polymer to food extrusion. The SPM can print a variety of materials, including miscellaneous foods, pastes, hydrogels and even biopolymers. The complete mechanism relies mostly on 3D printed parts and costs approximately 72$. Therefore, it allows users to obtain a 3D food printer inexpensively and does not require large amounts of technical labor. The SPM uses big volume (60 ml) luer lock syringe and blunt tip needles for greater versatility and user-friendliness. It could also be extended with cooling mechanism, so that the proposed system gains unique attribute among its counterparts. Finally, a standard polymer-printing 3D-Printer was converted into a laboratory-scale food printer, and edible ink was successfully printed in the desired shape.
Antônio Cordeiro de Santana, Ádina Lima de Santana, Ádamo Lima de Santana
et al.
The state of Pará has the largest deforested and degraded area in the Amazon, a result from the expansion of extensive livestock, the projects of rural settlement in the Agrarian Reform, mining, timber extraction, hydroelectric power plants and the pressure from large urban centers for food. Reversing the deforestation and soil degradation can thrive with the adoption of appropriate technologies in systems of higher productivity and bioeconomic efficiency, higher social inclusion, and less impact on the environment. The Agroforestry System, formed by the combination of acai, cocoa and black pepper crops, and African mahogany as a forest species, can be one of the alternatives to recover degraded areas and meet the objectives of sustainable development. According to the bioeconomic criteria of viability, this agroforestry system presented a competitive advantage in relation to monocultures of acai, cocoa, and black pepper; due having a higher net present value of US$ 6,508.94/ha; internal rate of return 13.93%; cost-benefit ratio of 1.104; uniform present value of US$ 764.54/ha; occupy more labor and mitigate the emission of greenhouse gases; reduce erosion, recovers water quality and interaction with biodiversity. The inclusion of the opportunity cost of natural assets such as land, water, and forest, and of the benefits with the green certification in the price of products, contributes to the use of good practices in the production, commercialization and preservation of natural resources.