Hasil untuk "Labor systems"

Baohua Zhang, Wenqian Huang, Jiangbo Li et al.

G. Kootstra, Xin Wang, P. Blok et al.

The world-wide demand for agricultural products is rapidly growing. However, despite the growing population, labor shortage becomes a limiting factor for agricultural production. Further automation of agriculture is an important solution to tackle these challenges. Selective harvesting of high-value crops, such as apples, tomatoes, and broccoli, is currently mainly performed by humans, rendering it one of the most labor-intensive and expensive agricultural tasks. This explains the large interest in the development of selective harvesting robots. Selective harvesting, however, is a challenging task for a robot, due to the high levels of variation and incomplete information, as well as safety. This review paper provides an overview of the state of the art in selective harvesting robotics in three different production systems; greenhouse, orchard, and open field. The limitations of current systems are discussed, and future research directions are proposed.

A. Feenberg

Ryan Stevens

Generative AI has the potential to transform how firms produce output. Yet, credible evidence on how AI is actually substituting for human labor remains limited. In this paper, we study firm-level substitution between contracted online labor and generative AI using payments data from a large U.S. expense management platform. We track quarterly spending from Q3 2021 to Q3 2025 on online labor marketplaces (such as Upwork and Fiverr) and leading AI model providers. To identify causal effects, we exploit the October 2022 release of ChatGPT as a common adoption shock and estimate a difference-in-differences model. We provide a novel measure of exposure based on the share of spending at online labor marketplaces prior to the shock. Firms with greater exposure to online labor adopt AI earlier and more intensively following the shock, while simultaneously reducing spending on contracted labor. By Q3 2025, firms in the highest exposure quartile increase their share of spending on AI model providers by 0.8 percentage points relative to the lowest exposure quartile, alongside significant declines in labor marketplace spending. Combining these responses yields a direct estimate of substitution: among the most exposed firms, a \$1 decline in online labor spending is associated with approximately \$0.03 of additional AI spending, implying order-of-magnitude cost savings from replacing outsourced tasks with AI services. These effects are heterogeneous across firms and emerge gradually over time. Taken together, our results provide the first direct, micro-level evidence that generative AI is being used as a partial substitute for human labor in production.

Criscent Birungi, Cody Hyndman

The decision to annuitize wealth in retirement planning has become increasingly complex due to rising longevity risk and changing retirement patterns, including increased labor force participation at older ages. While an extensive literature studies consumption, labor, and annuitization decisions, these elements are typically examined in isolation. This paper develops a unified stochastic control and optimal stopping framework in which habit formation and endogenous labor supply shape retirement and annuitization decisions under age-dependent mortality. We derive optimal consumption, labor, portfolio, and annuitization policies in a continuous-time lifecycle model. The solution is characterized via dynamic programming and a Hamilton-Jacobi-Bellman variational inequality. Our results reveal a rich sequence of retirement dynamics. When wealth is low relative to habit, labor is supplied defensively to protect consumption standards. As wealth increases, agents enter a work-to-retire phase in which labor is supplied at its maximum level to accelerate access to retirement. Human capital acts as a stabilizing asset, justifying a more aggressive pre-retirement investment portfolio, followed by abrupt de-risking upon annuitization. Subjective mortality beliefs are a key determinant in shaping retirement dynamics. Agents with pessimistic longevity beliefs rationally perceive annuities as unattractive, leading them to avoid or delay annuitization. This framework provides a behavior-based explanation for low annuity demand and offers guidance for retirement planning jointly linking labor supply, portfolio choice, and the timing of annuitization.

LI BING

Accelerating digital and intelligent transformation is a crucial measure for oil and gas enterprises to advance industrial transformation and upgrading and foster new productive forces. Sinopec’s upstream sector in China has thoroughly implemented the “Digital and Intelligent Sinopec” initiative, focusing on supporting corporate reform and management. By closely aligning with the development trends of digital and intelligent technologies and the demands of exploration and production operations, the digital and intelligent transformation has been steadily advanced. A group-level Exploration and Development Data Center (EPDC) has been established, aggregating 17.2 PB of various types of exploration and development data, which has enabled centralized data management and shared applications. An Internet of Things network covering oil and gas production sites has been nearly completed, with digital coverage rates for oil, gas, and water wells, and station facilities reaching 94.90% and 92.30%, respectively. This has fundamentally transformed the traditional manual management model of stationing personnel at wells and stations, effectively supporting the reform of production operation modes and labor organization under digital and intelligent conditions. The construction and deepened application of unified systems have been advanced coordinately, continuously improving the digital coverage across all exploration and development business operations. Sinopec has also actively promoted the construction of artificial intelligence (AI) scenarios and their pilot applications, achieving notable results in scenarios such as intelligent seismic processing and interpretation, intelligent rock thin-section identification and analysis, intelligent reservoir numerical simulation, intelligent drilling, intelligent fracturing, and intelligent well condition diagnosis. Looking ahead to the “15th Five-Year Plan”, Sinopec’s upstream sector in China aims to build intelligent oil and gas fields, accelerate the integration of data flow, business flow, value flow, and supervision flow (“four flows in one”), and promote the construction and application of high-value AI scenarios across the entire business chain. These efforts will support the deeper and more substantive integration of digitalization and intellectualization, enhancing the operational efficiency, economic benefits, and management capability of oil and gas exploration, development, and production.

Maurer, Bill, Nelms

Wei Wang, Hui Huang, Xuan Fu

Based on data from Chinese listed firms, this study examines how regional artificial intelligence (AI) development affects supply chain efficiency. The results show that regional AI development significantly improves efficiency by promoting digital transformation and reducing information asymmetry, with stronger effects observed in labor-intensive industries and inland regions. This study makes three key contributions. First, it extends the regional innovation systems perspective to the field of supply chain management by highlighting the spillover effects of regional AI ecosystems. Second, it supports the “latecomer advantage” in technology diffusion by analyzing industry and regional heterogeneity. Third, it reveals how external digital infrastructure and internal supply chain collaboration interact through the dual mechanisms of digitalization and information transparency.

Richard Luan Silva Machado, Rosangela Rodrigues Dias, Mariany Costa Deprá et al.

The exploitation of the Amazon biome in search of net profit, specifically in the production of cocoa (<i>Theobroma cacao</i>) and açaí (<i>Euterpe oleracea</i>), has caused deforestation, degradation of natural resources, and high greenhouse gas (GHG) emissions, highlighting the urgency of improving the environmental, economic and social sustainability of these crops. These species were selected for their rapid expansion in the Amazon, driven by global demand, their local economic relevance, and their potential to either promote conservation or drive deforestation, depending on the production system. This study analyzes the pillars of environmental, social, and economic sustainability of cocoa and açaí production systems in the Amazon, comparing monoculture, agroforestry, and extractivism to support forest conservation strategies in the biome. Analysis of the environmental life cycle, social life cycle, and economic performance were used to determine the carbon footprint, the final point of workers, and the net profit of the activities. According to the results found in this study, cocoa monoculture had the largest carbon footprint (1.35 tCO₂eq/ha), followed by agroforestry (1.20 tCO₂eq/ha), açaí monoculture (0.84 tCO₂eq/ha) and extractivism (0.25 tCO₂eq/ha). In the carbon balance, only the areas outside indigenous lands presented positive carbon. Regarding the economic aspect, the net profit of açaí monoculture was USD 6783.44/ha, extractivism USD 6059.42/ha, agroforestry USD 4505.55/ha, and cocoa monoculture USD 3937.32/ha. In the social sphere, in cocoa and açaí production, the most relevant negative impacts are the subcategories of child labor and gender discrimination, and the positive impacts are related to the sub-category of forced labor. These results suggest that açaí and cocoa extractivism, under responsible management plans, offer a promising balance between profitability and environmental conservation. Furthermore, agroforestry systems have also demonstrated favorable outcomes, providing additional benefits such as biodiversity conservation and system resilience, which make them a promising sustainable alternative.

Enrique Ide, Eduard Talamàs

Current AI systems are better than humans in some knowledge dimensions but weaker in others. Guided by the long-standing vision of machine intelligence inspired by the Turing Test, AI developers increasingly seek to eliminate this "jagged" nature by pursuing Artificial General Intelligence (AGI) that surpasses human knowledge across domains. This pursuit has sparked an important debate, with leading economists arguing that AGI risks eroding the value of human capital. We contribute to this debate by showing how AI capabilities in different dimensions shape labor income in a multidimensional knowledge economy. AI improvements in dimensions where it is stronger than humans always increase labor income, but the effects of AI progress in dimensions where it is weaker than humans depend on the nature of human-AI communication. When communication allows the integration of partial solutions, improvements in AI's weak dimensions reduce the marginal product of labor, and labor income is maximized by a deliberately jagged form of AI. In contrast, when communication is limited to sharing full solutions, improvements in AI's weak dimensions can raise the marginal product of labor, and labor income can be maximized when AI achieves high performance across all dimensions. These results point to the importance of empirically assessing the additivity properties of human-AI communication for understanding the labor-market consequences of progress toward AGI.

Pikkin Lau, Lingfeng Wang, Wei Wei et al.

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.

Xiaoshi Zhou, Yanran Dai, Haidong Qin et al.

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups.

Hadi Santoso, Ilham Hanif, Hilyah Magdalena et al.

The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.

Fumiko Ohori, Satoko Itaya, Toru Osuga et al.

Robotic automation is becoming prevalent in the manufacturing industry, improving productivity and saving labor. Mobile robotic systems such as Automated Guided Vehicles (AGVs) play critical roles in transport and storage of parts and products. Maintaining reliable wireless communications between AGVs and their control system is essential for high productivity, but often difficult in physically complex wireless environments of storage systems. Specifically, signal loss due to shadowing can trigger operational delays due to the need to check the status of storage operations. On the other, avoiding signal loss requires using more wireless resources. So, it is important to analyze and evaluate the wireless resources required to guarantee efficient operation. However, it is difficult to combine existing simulators for wireless systems and factory systems to analyze inter-dependencies of these systems. This paper presents a method for building a simulation model of a 3D storage system with wireless communication based on multi-layer system analysis. The method is applied to the evaluation of the storage performance and wireless channel usage of a 4-level storage system, including a comparison of the effects of fixed and adaptive rate control. Simulation results shown that adaptive rate control based on learning a spatial map of link quality can achieve reliable storage with up to 50 % reduction in channel load ratio compared to the use of fixed rates. The results demonstrate the usefulness of this type of model for evaluating performance in factory sites with complex wireless environments.

R. Kaplan, R. Cooper

Soulaimane Berkane, Dionysis Theodosis, Tarek Hamel et al.

This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable.

Gioele Zardini, Nicolas Lanzetti, Giuseppe Belgioioso et al.

The evolution of existing transportation systems,mainly driven by urbanization and increased availability of mobility options, such as private, profit-maximizing ride-hailing companies, calls for tools to reason about their design and regulation. To study this complex socio-technical problem, one needs to account for the strategic interactions of the heterogeneous stakeholders involved in the mobility ecosystem and analyze how they influence the system. In this paper, we focus on the interactions between citizens who compete for the limited resources of a mobility system to complete their desired trip. Specifically, we present a game-theoretic framework for multi-modal mobility systems, where citizens, characterized by heterogeneous preferences, have access to various mobility options and seek individually-optimal decisions. We study the arising game and prove the existence of an equilibrium, which can be efficiently computed via a convex optimization problem. Through both an analytical and a numerical case study for the classic scenario of Sioux Falls, USA, we illustrate the capabilities of our model and perform sensitivity analyses. Importantly, we show how to embed our framework into a "larger" game among stakeholders of the mobility ecosystem (e.g., municipality, Mobility Service Providers, and citizens), effectively giving rise to tools to inform strategic interventions and policy-making in the mobility ecosystem.

Hirotaka Goto

Individual participants in human society collectively exhibit aggregation behavior. In this study, we present a simple microscopic model of labor force migration based on the active Brownian particles framework. In particular, agent-based simulations show that the model produces clusters of agents from a random initial distribution. Furthermore, two empirical regularities called Zipf's and Okun's laws were observed. To reveal the mechanism underlying the reproduced aggregation phenomena, we use our microscopic model to derive an extended Keller--Segel system, which is a classic model describing the aggregation behavior of biological organisms called taxis. The obtained macroscopic system indicates that the concentration of the workforce in the real world can be explained through a new type of taxis central to human behavior, highlighting the relevance of urbanization to blow-up phenomena in the derived PDE system. We then characterize the transition between the aggregation and diffusion regimes both analytically and computationally. The predicted long-term dynamics of urbanization -- originating in the asymmetric natures of employed and unemployed agents -- are compared with global empirical data, particularly in the realms of labor statistics and urban indicators.

Zijian Feng, Xin Chen, Zijian Lv et al.

Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power grid to other power grids with topology changes is still a challenging task. This paper proposed a transferable DL-based model for STVS assessment by constructing the topology-aware voltage dynamic features from raw PMU data. Since the reactive power flow and grid topology are essential to voltage stability, the topology-aware and physics-informed voltage dynamic features are utilized to effectively represent the topological and temporal patterns from post-disturbance system dynamic trajectories. The proposed DL-based STVS assessment model is tested under random operating conditions on the New England 39-bus system. It has 99.99\% classification accuracy of the short-term voltage stability status using the topology-aware and physics-informed voltage dynamic features. In addition to high accuracy, the experiments show good adaptability to PMU errors. Moreover, The proposed STVS assessment method has outstanding performance on new grid topologies after fine-tuning. In particular, the highest accuracy reaches 99.68\% in evaluation, which demonstrates a good knowledge transfer ability of the proposed model for power grid topology change.

Ningjing Chen, Daniel Yee Tak Fong, Janet Yuen Ha Wong

OBJECTIVE: Occupational ergonomic factors (OEF) include physical exertion, demanding posture, repetitive work, hand-arm vibration, kneeling or squatting, rising, and climbing, which are risk factors for low-back pain (LBP). This study aimed to examine the prevalence, years lived with disability (YLD), healthcare costs, and productivity losses of LBP attributable to OEF by age, sex, World Health Organization region, and country in 2019. METHODS: In this cross-sectional study, prevalence and YLD were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Employment statistics were obtained from the International Labor Organization websites. Health and economic impact was estimated for 192 countries and territories using the population attributable fraction method. RESULTS: Globally, OEF were responsible for 126.1 million prevalent cases of LBP and 15.1 million YLD in the working-age population (aged 15–84 years) in 2019, with the Western Pacific region suffering most. OEF-attributable LBP led to $216.1 billion of economic losses worldwide. Of these, $47.0 billion were paid in healthcare costs, with the public sector serving as the largest contributor (59.2%). High-income countries bore >70% of global economic burden, whereas middle-income countries experienced >70% of global YLD. Generally, more prevalent cases and healthcare costs were found among females, whereas more YLD, productivity losses, and total costs were found among males. CONCLUSIONS: Globally, OEF-attributable LBP presented a heavy burden on health and economic systems. Exercise together with education, active monitoring, evidence-based medical practices, alternative cost-effective solutions, and prioritizing health policies are needed.