Hasil untuk "Labor systems"

U. Mogili, B. Deepak

Abstract In the present era, there are too many developments in precision agriculture for increasing the crop productivity. Especially, in the developing countries like India, over 70% of the rural people depends upon the agriculture fields. The agriculture fields faces dramatic losses due to the diseases. These diseases came from the pests and insets, which reduces the productivity of the crops. Pesticides and fertilizers are used to kill the insects and pests in order to enhance the crop quality. The WHO (World Health Organization) estimated as one million cases of ill effected, when spraying the pesticides in the crop filed manually. The Unmanned aerial vehicle (UAV) – aircrafts are used to spray the pesticides to avoid the health problems of humans when they spray manually. UAVs can be used easily, where the equipment and labors difficulty to operate. This paper reviews briefly the implementation of UAVs for crop monitoring and pesticide spraying.

Erik Brynjolfsson, L. Hitt

Nada O. Eissa, Jeffrey B. Liebman

G. Robinson

B. Sianesi

T. Piketty, Emmanuel Saez, Stefanie Stantcheva

N. Gomez‐Lopez, Derek StLouis, Marcus A Lehr et al.

Labor resembles an inflammatory response that includes secretion of cytokines/chemokines by resident and infiltrating immune cells into reproductive tissues and the maternal/fetal interface. Untimely activation of these inflammatory pathways leads to preterm labor, which can result in preterm birth. Preterm birth is a major determinant of neonatal mortality and morbidity; therefore, the elucidation of the process of labor at a cellular and molecular level is essential for understanding the pathophysiology of preterm labor. Here, we summarize the role of innate and adaptive immune cells in the physiological or pathological activation of labor. We review published literature regarding the role of innate and adaptive immune cells in the cervix, myometrium, fetal membranes, decidua and the fetus in late pregnancy and labor at term and preterm. Accumulating evidence suggests that innate immune cells (neutrophils, macrophages and mast cells) mediate the process of labor by releasing pro-inflammatory factors such as cytokines, chemokines and matrix metalloproteinases. Adaptive immune cells (T-cell subsets and B cells) participate in the maintenance of fetomaternal tolerance during pregnancy, and an alteration in their function or abundance may lead to labor at term or preterm. Also, immune cells that bridge the innate and adaptive immune systems (natural killer T (NKT) cells and dendritic cells (DCs)) seem to participate in the pathophysiology of preterm labor. In conclusion, a balance between innate and adaptive immune cells is required in order to sustain pregnancy; an alteration of this balance will lead to labor at term or preterm.

Andrea Della Valle, Sara De Carlo, Gregorio Sonsini et al.

Abstract The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5500 scientific articles reporting the use of the FST have been published. Despite its widespread use, the FST behaviours are still manually scored, resulting in a labor-intensive and time-consuming process that is prone to human bias and variability. Despite eliminating some biases, existing automated systems are costly and typically only able to distinguish between immobility and active behaviours. Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. Our ML model was validated against manual scoring in rats treated with fluoxetine and desipramine, two antidepressants known to induce distinct behavioural patterns. The ML model successfully differentiated among swimming, climbing, and immobility behaviours, demonstrating its potential as a standardized and unbiased tool for automatized behavioural analysis in the FST. Subsequently, we successfully validated our model by testing its ability to distinguish between drugs that predominantly evoke climbing (i.e., amitriptyline), those that preferentially facilitate swimming (i.e., paroxetine), and those that evoke both in a more balanced manner (i.e., venlafaxine). This approach represents a significant advancement in preclinical research, providing a more accurate and efficient method to analyze forced swimming data in rodents. We anticipate that in addition to the FST, our model and approach could be extended for application to various behavioural tests in laboratory animals, by training with specific datasets.

Ruby Putt, Hubert Brouwers, Peter John Groves et al.

Cage-free egg production is now the predominant system in Australia. However, the occurrence of floor eggs (FE), which are eggs laid outside designated nest boxes, presents a major challenge for these producers. To understand factors that may be associated with the laying of FE, a national scoping survey of cage-free egg-laying flocks was undertaken. Forty-three flocks across multiple farms were surveyed via a phone-based interview using predetermined questions. Floor egg levels ranged from 0.01–17%. There was no difference in floor egg levels between the breeds of brown-egg-laying hens. Age at peak lay did not alter the level of FE, but higher rate of peak lay had a weak association with fewer FE (r = −0.31, <i>p</i> = 0.049). Larger flocks had a lower percentage of FE (r = −0.5, <i>p</i> = 0.002), and farmers of larger sized flocks considered a lower level of floor eggs to be acceptable. Farms with tunnel-ventilated sheds reported fewer FE compared to those using other ventilation systems (<i>p</i> = 0.013). Higher floor egg levels were associated with increased labor costs (<i>p</i> = 0.023). These findings suggest that shed design and environmental management may be leveraged to reduce floor egg occurrence and improve operational efficiency in cage-free systems.

Gunhee Kim, Junsik Shin, Jongbin Won et al.

Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability.

Rahmawati Nur, Musta'anah Himmayatul

This study aims to analyze the economic feasibility of organic and conventional rice farming in Sleman Regency, Indonesia. The analysis compared production costs, revenues, income, profits, and overall economic feasibility between the two farming systems. The research was conducted purposively in Widodomartani and Sumberharjo Villages. A census method was employed to include all 30 organic rice farmers, while 33 conventional farmers were selected using a proportional random sampling method. Data were analyzed using a quantitative descriptive approach on a 1,000 m² land basis. The results showed that organic rice farming incurred higher production costs than conventional farming but also generated greater revenue, income, and profit. The analysis revealed that both systems were economically feasible, as reflected by R/C ratios greater than one, with values of 1.55 for organic and 1.50 for conventional rice farming. In terms of capital, land, and labor productivity, both systems outperformed local economic references, such as interest rates, land rent, and minimum wage, with organic farming achieving relatively higher values across all indicators. Therefore, encouraging the broader adoption of organic farming through policy support, farmer training, and sustainable agricultural initiatives is essential to enhance profitability while maintaining the environment and promoting long-term agricultural sustainability.

Yongqing Ben, Yongqing Ben, Yu'e Zhang et al.

IntroductionReconciling food security with economic development amid rapid industrialization and urbanization presents a critical global challenge. This study investigates the spatiotemporal dynamics of grain production and its spatial interaction with economic development in Jiangsu Province, China—an economically advanced region exemplifying this tension.MethodsWe integrate the Gini coefficient, concentration index, standard deviational ellipse, spatial exploratory analysis (global/local Moran's I), and a Spatial Durbin Model (SDM) to quantify spatial differentiation patterns and spillover effects.Results(1) Pronounced spatial polarization emerged: Northern Jiangsu consolidated as a High-High grain production cluster, while Southern Jiangsu evolved into a Low-Low cluster. The spatial divergence between economic and grain production centroids expanded to 125.4 km. (2) Spatial econometrics confirmed localized suppression of grain output by economic development, alongside positive spillovers to neighboring regions—validating core-periphery complementarity. Urbanization drove sown area contraction via labor migration and cropland conversion. (3) Cultivated land endowment and rural labor were fundamental pillars of food security. Industrial restructuring indirectly enhanced production through land efficiency gains.DiscussionThe findings validate core-periphery theory and reveal complex spatial spillovers. Policy prescriptions include: spatial governance mechanisms coordinating regional specialization; industrial feedback systems reinvesting economic gains into agriculture; a Technology-Driven Resource Breakthrough strategy; and institutional safeguards for cropland. This establishes a replicable paradigm for food security-economic growth synergies in developing economies.

Criscent Birungi, Cody Hyndman

We consider the problem of optimal annuitization with labour income, where an agent aims to maximize utility from consumption and labour income under age-dependent force of mortality. Using a dynamic programming approach, we derive closed-form solutions for the value function and the optimal consumption, portfolio, and labor supply strategies. Our results show that before retirement, investment behavior increases with wealth until a threshold set by labor supply. After retirement, agents tend to consume a larger portion of their wealth. Two main factors influence optimal annuitization decisions as people get older. First, the agent's perspective (demand side); the agent's personal discount rate rises with age, reducing their desire to annuitize. Second, the insurer's perspective (supply side); insurers offer higher payout rates (mortality credits). Our model demonstrates that beyond a certain age, sharply declining survival probabilities make annuitization substantially optimal, as the powerful incentive of mortality credits outweighs the agent's high personal discount rate. Finally, post-retirement labor income serves as a direct substitute for annuitization by providing an alternative stable income source. It enhances the financial security of retirees.

Giacomo Baggio, Marco Fabris

This paper leverages linear systems theory to propose a principled measure of complexity for network systems. We focus on a network of first-order scalar linear systems interconnected through a directed graph. By locally filtering out the effect of nodal dynamics in the interconnected system, we propose a new quantitative index of network complexity rooted in the notion of McMillan degree of a linear system. First, we show that network systems with the same interconnection structure share the same complexity index for almost all choices of their interconnection weights. Then, we investigate the dependence of the proposed index on the topology of the network and the pattern of heterogeneity of the nodal dynamics. Specifically, we find that the index depends on the matching number of subgraphs identified by nodal dynamics of different nature, highlighting the joint impact of network architecture and component diversity on overall system complexity.

Anais Galdin, Jesse Silbert

Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.

Jens-Joris Decorte, Jeroen Van Hautte, Chris Develder et al.

Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose \textbf{ConTeXT-match}, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. \textbf{ConTeXT-match} significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce \textbf{Skill-XL}, a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present \textbf{JobBERT V2}, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.

M. Carmen Cobo, William J. Farris, Chandler J. Olson et al.

Solenogastres is a group of mollusks with evolutionary and ecological importance. Nevertheless, their diversity is underestimated and knowledge about the distribution of the approximately 300 formally described species is limited. Factors that contribute to this include their small size and frequent misidentification by non-specialists. Recent deep-sea explorations have resulted in the collection of numerous specimens through effective methods such as epibenthic sledges. However, this is a costly, labor-intensive, and destructive methodology. In contrast, Autonomous Reef Monitoring Structures (ARMS) offer a novel, non-destructive approach, by providing a substrate for benthic organism colonization. This study is the first to describe Solenogastres collected using ARMS, demonstrating that they are an effective tool for biodiversity assessment and characterizing rare marine invertebrates. Following an integrative taxonomic approach, two new solenogaster species are described: Dondersia tweedtae Farris, Olson &amp;amp; Kocot, sp. nov. (Dondersiidae) and Eleutheromenia bullescens Cobo, sp. nov. (Pruvotinidae). The diagnosis of the family Dondersiidae is amended and the necessity of reassessing the validity of the current diagnostic characters for Pruvotinidae, and its classification is emphasized. The two newly described species exhibit distinct external characteristics; D. tweedtae sp. nov. has a striking pink color with a bright yellow dorsal keel and E. bullescens sp. nov. has a unique, discontinuous dorsal keel with nearly spherical protrusions. The presence of cnidocytes in the digestive systems of both species indicate that they feed on cnidarians. It is hypothesized that, like in some nudibranchs, their coloration and body features reflect defensive adaptations related to their diet. This study shows that while habitus alone is typically insufficient for accurate identification in solenogasters, it can sometimes simplify the process. For this, live observations and photographs are essential.

Jiefei Liu, Derek W. Bailey, Huiping Cao et al.

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

Samsuzzaman, Md Nasim Reza, Sumaiya Islam et al.

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R² up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity.

Eric Yang, Laura Katz, Sushila Shenoy

<h4>Objective</h4>The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches.<h4>Methods</h4>Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field.<h4>Results</h4>With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods.<h4>Conclusion</h4>Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.