Hasil untuk "Labor systems"

Tim Bartley

Deepak K. Datta, J. Guthrie, P. Wright

R. Collier, D. Collier

W. Scott, G. Davis

J. Allmendinger

Irene Solaiman, Zeerak Talat, William Agnew et al.

Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated. In this paper, we present a guide that moves toward a standard approach in evaluating a base generative AI system for any modality in two overarching categories: what can be evaluated in a base system independent of context and what can be evaluated in a societal context. Importantly, this refers to base systems that have no predetermined application or deployment context, including a model itself, as well as system components, such as training data. Our framework for a base system defines seven categories of social impact: bias, stereotypes, and representational harms; cultural values and sensitive content; disparate performance; privacy and data protection; financial costs; environmental costs; and data and content moderation labor costs. Suggested methods for evaluation apply to listed generative modalities and analyses of the limitations of existing evaluations serve as a starting point for necessary investment in future evaluations. We offer five overarching categories for what can be evaluated in a broader societal context, each with its own subcategories: trustworthiness and autonomy; inequality, marginalization, and violence; concentration of authority; labor and creativity; and ecosystem and environment. Each subcategory includes recommendations for mitigating harm.

Mirzayusup Rustambaev, Govkherjan Yuldasheva, Roza Azkhodjaeva et al.

This article examines migrant victimization as a structural outcome of migration governance systems in developed countries. This study demonstrates that variations in enforcement intensity, labor-market segmentation, and procedural safeguards explain cross-regional differences in exposure to harm and access to justice, thereby reframing migrant victimization as a governance-dependent outcome rather than an individual-level vulnerability. Criminal victimization remains an under-explored facet of migration dynamics in developed countries, despite migrants’ overrepresentation as victims. Legal scholarship on this topic is exceptionally limited and primarily focused on refugee protection. The victimization of migrants at the hands of individuals, groups, and authorities is therefore examined with the aim of informing research agendas, policy debates, and international cooperation. The assessment proceeds in six steps. First, migrant victimization patterns are outlined according to crime type, drawing on the European Union, the United States, and the Global South as contextual frames. Next, legal definitions and protections are distinguished from victimization levels, with the international legal framework and the concept of safe access to justice underscored. Then, broader systemic drivers and comparative factors influencing victimization patterns are investigated, again through the lens of the European Union, North America, Western Europe, and Oceania. Fourth, considerations relevant to the study of migrant victimization are reviewed, including methodological challenges, variations in legal terminology, underlying fieldwork orientations, and the availability of prior research. This study argues that migrant victimization is best understood as a structural product of migration governance regimes, where enforcement design, legal stratification, and labor-market segmentation systematically shape exposure to harm and access to justice.

Dario Augusto Borges Oliveira, Luiz Gustavo Ribeiro Pereira, T. Bresolin et al.

In livestock operations, systematically monitoring animal body weight, biometric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs

Eleni Vrochidou, Viktoria Nikoleta Tsakalidou, Ioannis Kalathas et al.

In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors that meet manipulation requirements is necessary. To that end, this work reviews the state-of-the-art robotic end effectors for harvesting applications. Detachment methods, types of end effectors, and additional sensors are discussed. Performance measures are included to evaluate technologies and determine optimal end effectors for specific crops. Challenges and potential future trends of end effectors in agricultural robotic systems are reported. Research has shown that contact-grasping grippers for fruit holding are the most common type of end effectors. Furthermore, most research is concerned with tomato, apple, and sweet pepper harvesting applications. This work can be used as a guide for up-to-date technology for the selection of suitable end effectors for harvesting robots.

Shai Barbut, Emily M. Leishman, Ryley J. Vanderhout et al.

Summary: Improving carcass portion yields (e.g., breast meat) is a major goal of modern turkey breeding and traditionally requires manual collection of portion weights. This can be a labor-intensive process considering the large amount of data needed to be useful for breeding companies. Recently, there has been increasing interest in using computer vision systems to assess parameters such as size, weight, volume, and grade of poultry meat. The present study developed mathematical equations to predict turkeys’ (4,000) meat yield using a non-invasive real-time 2D carcass imaging system. Although our breast meat models proved to be good, the thigh and drum models did not demonstrate a high correlation between observed and predicted weights probably due to the orientation of the image and any potential shifts made during image capture. These results represent a first step in developing prediction models for valuable turkey carcass portions using practical imaging systems. Further investigations need to take place to demonstrate this system can be more fruitful than simply predicting portion weight off live weight and help the industry to better collect phenotypes in a cost-effective manner.

Na Rae Baek, Yeongwook Lee, Dong-hee Noh et al.

Stem removal from harvested fruits remains one of the most labor-intensive tasks in fruit harvesting, directly affecting the fruit quality and marketability. Accurate and rapid fruit and stem segmentation techniques are essential for automating this process. This study proposes an enhanced You Only Look Once (YOLO) model, AppleStem (AS)-YOLO, which uses a ghost bottleneck and global attention mechanism to segment apple stems. The proposed model reduces the number of parameters and enhances the computational efficiency using the ghost bottleneck while improving feature extraction capabilities using the global attention mechanism. The model was evaluated using both a custom-built and an open dataset, which will be later released to ensure reproducibility. Experimental results demonstrated that the AS-YOLO model achieved high accuracy, with a mean average precision (mAP)@50 of 0.956 and mAP@50–95 of 0.782 across all classes, along with a real-time inference speed of 129.8 frames per second (FPS). Compared with state-of-the-art segmentation models, AS-YOLO exhibited superior performance. The proposed AS-YOLO model demonstrates the potential for real-time application in automated fruit-harvesting systems, contributing to the advancement of agricultural automation.

Šutović Milojica M.

Education represents a crucial developmental resource for any society, encompassing both its potential and prosperity. It is also a determinant of its success, an agent of socialization, and a means for learning values. It constitutes a key element of class structure and the stratification pyramid, and serves as an instrument for their maintenance. In order to maintain societal and state order, education plays a key role in institutionalizing value orientations, cementing cultural cohesion, and differentiating social roles, and frames occupations as vocations, responsibilities, obligations, and duties. Consequently, the most effective means of undermining a nation and its state is through the destruction of its educational and upbringing systems. This is achieved through mechanisms of control, surveillance, and repression, directing people towards advertisements, media, and social networks, marketing, and paternalism, all devoid of spirit, imagination, or spirituality. In this context, education within the globalized periphery has become an instrument of colonization and a tool of pedagogy aimed at subjugation and enslavement. It relegates individuals to metaphorical straitjackets, masked as neutral transitions, while enabling the rise of a volatile, gangster-style political capitalism with no alternatives. A system that values means over ends, utility over goods, earnings and profit over intellectual and human values, and relies increasingly on robots and artificial intelligence. In the near future, these technological advancements will displace many diploma-holders from the labor market. This is a new code and permit for inequality, the triumph of poverty, and the establishment of global totalitarianism, in which the classical form will become a mere swan song. This issue necessitates a reimagined discourse and a redefined understanding both theoretical and epistemological, as well as practical and educational. That is how all progress begins.

Тарас Чатченко, Андрій Гриценко

This article explores the impact of digitalization on the labor market within the context of a transforming economy. The purpose of this research is to investigate how digital tools and systems redefine workforce organization, skill relevance, and economic productivity. The results reveal a significant shift from manual and routine labor toward knowledge-intensive, high-skilled positions. Technologies such as automation, artificial intelligence, cloud computing, and platform work are considered as providers of s new forms of employment, such as platform-based work and remote collaboration. Flexibility as a fundamental feature of contemporary labor relations, influencing job types, work schedules, remuneration systems, mobility, and organizational design is analaused and investigated. The practical insights for policymakers, employers, and educators in developing digital competencies and inclusive, future-ready labor systems are offered.

MA Nan, CAO Shanshan, BAI Tao et al.

[Significance]The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.[Progress]Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.[Conclusions and Prospects]To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.

Weiqiao Wang

Through an exploration of meal regulations, dining rituals, and monastic rules of Han Buddhist and Cistercian monks, this article discusses how food affects space formation, layout organization, and site selection in monastic venues using Guoqing Si and Poblet Monastery as detailed case studies. The dining rituals, such as guotang and the Refectory, transform daily routines into acts of worship and practice, particularly within the palace-like dining spaces. Monastic rules and the concept of cleanliness influence the layout of monastic spaces, effectively distinguishing between sacred and secular areas. The types of food, influenced by self-sufficiency and food taboos, impact the formation of monasteries in the surrounding landscape, while the diligent labor of monks in cultivating the wilderness contributes to the sanctity of the venues. By employing anthropology as a tool for field observation and considering architectural design as a holistic mindset, this article concludes that due to the self-sufficiency of monastic lives, monks establish a sustainable agri-food space system. This ensures that food production, waste management, water utilization, food processing, and meal consumption can be sustainable practices. Food taboos are determined by the understanding of purity in both religions, leading to the establishment of a distinct spatial order for food between the sacred and secular realms. Ultimately, ordinary meals are consumed within extraordinary dining spaces, providing monks with a silent and sacred eating atmosphere. Under the overall influence of food, both monasteries have developed their own food spatial systems, and the act of dining has transformed from a daily routine to a sacred worship.

Babakhouya Ayoub, Naji Abdelwahab, Daaif Abdelaziz et al.

Agriculture plays a crucial role in our existence by supplying food, raw materials, and employment opportunities. In Morocco, it serves as the backbone of the economy, employing 40% of the workforce and contributing approximately 13% to the country's GDP [1]. IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in the agri-food industry. The primary focus of this paper is to assess the diverse utilization of Artificial Intelligence in agriculture, specifically in tasks like irrigation, weeding, and spraying. These applications employ sensors and integrated systems in robots and drones, effectively reducing water and chemical usage, preserving soil fertility, optimizing labor, and enhancing productivity and quality. The research identifies the most common AI strategies used in the industry. Furthermore, we conducted an analysis of significant trends and provided researchers and practitioners with valuable insights for future research endeavors in addition to challenges hindering AgriTech applications in Moroccan farms.

G. Distelhorst, Jens Hainmueller, R. Locke

Thijs Bol, H. G. van de Werfhorst

J. Chen, J. Qiu

This article develops the critical concept of digital utility through studying the case of DiDi Chuxing and the platformization of transport services in urban China. By examining DiDi’s business model, its datafication strategies, its relations with the Chinese government, and its labor management systems, the article demonstrates how the platformization of transport is emblematic of a private company becoming a digital utility provider. With technological imagination and practical inconsistency, this process remediates service delivery while reworking infrastructures and redefining the access to public and private services. We argue that platform companies are able to become digital utility suppliers because of their capacity to straddle the public and the private sectors, their aspiration to become “ecosystem builders,” and their heavy reliance on the constant intensive labor of users, particularly drivers, to produce data. However, these factors also make instability a definitive feature of digital utility companies in their present condition. Morphing into the terrain of utilities is a common undertaking by DiDi and similar platform companies. To problematize the logics of digital utility, especially its labor-intensive datafication processes and its complex relations with regulators, provides a conceptual anchor for further debates on the infrastructuralization of platforms and the platformization of society.

Zhaoming Chen, Qiang Wang, Jinchuan Ma et al.

Ammonia (NH3) volatilization losses result in low nitrogen use efficiency (NUE) and various environmental impacts in agroecosystems. Machine-transplanted rice with side-deep fertilization (MRSF) has been recommended as an effective alternative to traditional transplantation with manual broadcasting of fertilizer. Controlled-release nitrogen fertilizer (CRF) can enhance rice yield and NUE in paddy fields. However, there is scarce information about combined effects of MRSF and CRF on NH3 volatilization loss and rice grain yield, NUE, net economic benefit (NEB) in a double rice cropping system. In this study, a field experiment was conducted to evaluate the impact of MRSF with CRF on grain yields, NUE and economic returns of early rice and late rice from 2019 to 2021, as well as NH3 emissions in two rice seasons (2019 and 2021). Six treatments were designed as no N fertilizer (N0), compound fertilizer broadcasting (CFB), compound fertilizer side-deep placement (CFD), CRF broadcasting (CRFB), CRF side-deep placement (CRFD1), and single side-deep placement of CRF (CRFD2). The results showed that the CFD and CRFB treatments decreased NH3 volatilization while enhancing or maintaining rice yield and NUE compared to the CFB treatment. MRSF with CRF (CRFD1 and CRFD2) significantly reduced NH3 emissions of early and late rice by 57.6–67.9% and 62.2–80.9% by decreasing the NH4+–N concentrations in the surface water compared to the CFB treatment, respectively. Rice grain yields in the MRSF with CRF treatments increased by 3.9–17.3% in early rice and 5.4–21.6% in late rice relative to the CFB treatment. In addition, MRSF with CRF treatments improved NUE for early and late rice from 32.1 to 36.2% and 21.3–28.4% in the CFB treatment to 48.4–61.2% and 39.7–62.3%, respectively. The yield-scale NH3 volatilization losses were reduced under the MRSF with CRF treatments by 61.2–71.5% in early rice and 67.4–84.3% in late rice. Furthermore, MRSF with single basal application of CRF reduced time-consuming and labor-intensive while increasing rice yields and net economic benefits. Overall, co-application of MRSF and CRF can reduce NH3 emissions, and improve rice yield, NUE and profitability in double rice cropping systems.