Hasil untuk "Labor systems"

P. R, S. P, B. M et al.

Agricultural productivity hinges on soil fertility, influenced by key factors like nitrogen, phosphorus, potassium, pH level, and soil moisture. Yet, achieving optimal crop growth is challenging due to limited farmer knowledge and difficulties in determining precise fertilizer quantities. Conventional soil analysis methods involve manual sampling andcostly lab tests, which are subjective. To address this, aproposed solution integrates IoT-enabled soil nutrient monitoring with machine learning algorithms for croprecommendations. Sensors collect data on crucial parameters like nitrogen, phosphorus, and soil temperature, transmitting it to a cloud-based database. Machine learning analyzes this data to suggest ideal crops, minimizing fertilizer use, reducing labor, and enhancing overall productivity. This innovative approach streamlines crop selection, minimizing unnecessary inputs while maximizing yields. By harnessing IoT and machine learning, farmers gain valuable insights into soil health, enabling precise fertilization and crop selection. This not only boosts agricultural productivity but also contributes to economic growth by fostering sustainable practices andincreased yields.

Bruce D. Meyer, Dan Rosenbaum

T. Li

T. Jayne, J. Chamberlin, D. Headey

Evidence assembled in this special issue of Food Policy shows that rising rural population densities in parts of Africa are profoundly affecting farming systems and the region’s economies in ways that are underappreciated in current discourse on African development issues. This study synthesizes how people, markets and governments are responding to rising land pressures in Africa, drawing on key findings from the various contributions in this special issue. The papers herein revisit the issue of Boserupian agricultural intensification as an important response to land constraints, but they also go further than Boserup and her followers to explore broader responses to land constraints, including non-farm diversification, migration, and reduced fertility rates. Agricultural and rural development strategies in the region will need to more fully anticipate the implications of Africa’s rapidly changing land and demographic situation, and the immense challenges that mounting land pressures pose in the context of current evidence of unsustainable agricultural intensification, a rapidly rising labor force associated with the region’s current demographic conditions, and limited nonfarm job creation. These challenges are manageable but will require explicit policy actions to address the unique development challenges in densely populated rural areas.

H. Teklewold, M. Kassie, B. Shiferaw et al.

Xin Meng

Dimitra V. Liagka, Antonis P. Politis, Maria Spilioti et al.

The aim of this study was the economic comparison of two equivalent sheep farms with different reproductive management systems. Financial data were selected from a farm that applied artificial insemination (AI) and from one that applied natural mating (NM). The main objective of the analysis was to estimate the cost of each farm’s products and then to calculate their economic indicators. The AI farm had higher production costs, as a result of higher labor and fixed capital costs. On the other hand, the invested capital for the equipment and buildings of the NM farm was lower. Furthermore, the invested livestock capital based on the genetic value of the animals was higher in the AI farm. The AI farm produced milk, replacement ewe lambs and replacement ram lambs as its primary products, whereas the NM farm produced only milk as its primary product. The production costs for milk were 0.08 EUR/kg lower in the AI farm compared with the NM farm. The AI farm had a higher gross revenue and net and gross profit, resulting from the higher genetic value of the AI farm’s livestock. As indicated, the breeding and sale of genetically improved animals can increase the financial results of a farm and offer alternative sources of income. In conclusion, AI results in more sustainable and economically efficient sheep farming. In this regard, training for farmers and governmental economic support could promote AI application. Finally, the fortification of farmer group initiatives that facilitate the trade of dairy sheep products can accelerate AI utilization in dairy sheep farms in Greece.

Sandra Barteit, Windpanga Aristide Ouédraogo, Charlotte Müller et al.

Rising temperatures in Africa present an increasing threat to agricultural productivity and public health, particularly among subsistence farming communities reliant on rain-fed agriculture. Heat exposure can impair farmers’ work capacity, disrupt harvests, and heighten health risks, especially for young children vulnerable to undernutrition. The Heat to Harvest (H2H) study investigates how environmental heat exposure influences farmers’ physiological and behavioral responses, and how these in turn affect harvest yields and child nutrition. It also examines differences in labor performance and recovery between households with and without cool roof coatings, although this intervention is not the central focus. H2H is designed as a prospective cohort study nested within two Health and Demographic Surveillance Systems (HDSS) in Nouna, Burkina Faso, and Siaya, Kenya. The study integrates environmental monitoring (temperature and humidity sensors used to compute Wet Bulb Globe Temperature), biometric data (via wearables tracking heart rate, temperature, physical activity, energy expenditure, and sleep), and GPS tracking (capturing spatial mobility and labor duration). The study is embedded within a larger cluster-randomized controlled trial, facilitating comparative analysis under varying thermal conditions. Findings will provide evidence-based insights into how climate-related heat stress affects health and agricultural outcomes, supporting the development of targeted adaptation strategies to enhance resilience, health, and food security in vulnerable farming communities.

Geonwoo Kim, Junkee Jeon

We develop a continuous-time model of optimal consumption, portfolio allocation, and early retirement that, to our knowledge, is the first to incorporate an implementation delay —a fixed lag <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula> between the retirement decision and the actual cessation of labor and income. Using a dual-martingale approach, we obtain closed-form solutions and quantify how <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula> affects optimal behavior. For example, when <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula> increases from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.5</mn></mrow></semantics></math></inline-formula> to 2 years (baseline parameters: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>β</mi><mo>=</mo><mn>0.04</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>r</mi><mo>=</mo><mn>0.02</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>μ</mi><mo>=</mo><mn>0.08</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>0.2</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>γ</mi><mo>=</mo><mn>3</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>k</mi><mi>B</mi></msub><mo>=</mo><mn>0.3</mn></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ε</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>), optimal pre-retirement consumption rises by approximately 7%, the risky asset share falls by about 5 percentage points, the expected retirement time increases by over 1 year, and the retirement wealth threshold <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>x</mi><mi>R</mi></msub></semantics></math></inline-formula> grows by roughly 10%. These results provide policy-relevant insights for retirement systems where procedural lags can distort incentives and reduce welfare.

Sook-Hyun Nam, Homin Kye, Juwon Lee et al.

Pharmaceuticals and personal care products (PPCPs) are recognized as emerging contaminants of concern, even at ultra-trace concentrations. However, the current detection systems are prohibitively expensive and typically rely on labor-intensive, lab-based workflows that lack automation in sample pretreatment. In this study, we developed a robotic and on-flow solid-phase extraction (ROF-SPE) system, fully integrated with online liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the on-site and real-time monitoring of 16 PPCPs in wastewater effluent. The system automates the entire pretreatment workflow—including sample collection, filtration, pH adjustment, solid-phase extraction, and injection—prior to seamless coupling with LC–MS/MS analysis. The optimized pretreatment parameters (pH 7 and 10, 12 mL wash volume, 9 mL elution volume) were selected for analytical efficiency and cost-effectiveness. Compared with conventional offline SPE methods (~370 min), the total analysis time was reduced to 80 min (78.4% reduction), and parallel automation significantly enhanced the throughput. The system was capable of quantifying target analytes at concentrations as low as 0.1 ng/L. Among the 16 PPCPs monitored at a municipal wastewater treatment plant in South Korea, only sulfamethazine and ranitidine were not detected. Compounds such as iopromide, caffeine, and paraxanthine were detected at high concentrations, and seasonal variation patterns were also observed This study demonstrates the feasibility of a fully automated and on-site SPE pretreatment system for ultra-trace environmental analysis and presents a practical solution for the real-time monitoring of contaminants in remote areas.

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.

A. A. Nikonova

The study aims to identify the reasons for the inefficiency of research and development (R&amp;D) in Russia. It is guided by the principles of system economic theory, developed at the Central Economics and Mathematics Institute of the Russian Academy of Sciences. The study presents empirical data to demonstrate the significant impact of three main sources of R&amp;D inefficiency: 1. The diverse structure of the Russian economy, which divides the science sector according to various characteristics, leads to low internal demand for domestic research and innovation. 2. The poor institutional quality of governance and dysfunctional governance systems, including regulatory instruments, income distribution, and criteria for evaluating scientific output, result in structural economic, scientific, and educational policies that are ineffective, leading to decreased motivation and reduced potential for science.3. The negative impact of the science sector, researchers, and the educational community on the socio-economic system is due to their depletion during periods of reform and their economic and institutional dependence on authorities, state policies, and recognition of researcher labor value. These three factors are closely related and contribute to the continued reproduction of problems, both in the creation of knowledge and its transfer to the economy. There is an ambiguity in the understanding of the term “efficiency of science” and the methods used to measure it, which prevents comparative analysis and decision-making to motivate actors. When discussing the fundamental factors of innovation activity efficiency, three pillars are identified: demand, the potential of science, and human potential, as well as qualified public administration. The direct and indirect connections between innovation activities and their impact on efficiency are discussed. A flexible approach to combining three different types of innovation models is recommended – linear, project-based, and ecosystem – as all three models rely on the same principles for achieving the best results in R&amp;D and innovation.

Ashley-Dejo Samuel Segun, Oyetunji Opeyemi Temitope, Sule Shakiru Okanlawon et al.

Technology is one of the crucial factor influences the aquaculture production; hence it is essential to encourage fish farmers to embrace modern methods of fish production by utilizing improved fisheries and aquaculture technologies This study assessed the factors that influence adoption of improved fish production technologies in Ogun State, Nigeria. The study adopted survey design and data were collected through structured questionnaire administered to 150 fish farmers by adopting multistage sampling procedure. It was observed that majority of the respondents were male, married, literate and made use of family labor. The study also found that fish enclosure technologies, neutralizers/fertilizers, and fish stock selectivity/harvesting gear systems were distributed among fish farmers in the study area, with earthen fish ponds being the most widely adopted technology. Furthermore, the study revealed that fish farmers generally had positive attitudes towards the use of improved technologies. The study also found that high production costs were the most significant constraint to the adoption of improved technologies, followed by inadequate contact with extension agents and non-availability of input. The study conclude that the adoption of improved fish production technologies could increase fish production in Ogun State, Nigeria, but there is a need for interventions to address the constraints hindering the adoption of improved fisheries technologies in the study area.

MHD Anas Alsakkal, Jayawan Wijekoon

This paper presents a novel FPGA-based neuromorphic cochlea, leveraging the general-purpose spike-coding algorithm, Spiketrum. The focus of this study is on the development and characterization of this cochlea model, which excels in transforming audio vibrations into biologically realistic auditory spike trains. These spike trains are designed to withstand neural fluctuations and spike losses while accurately encapsulating the spatial and precise temporal characteristics of audio, along with the intensity of incoming vibrations. Noteworthy features include the ability to generate real-time spike trains with minimal information loss and the capacity to reconstruct original signals. This fine-tuning capability allows users to optimize spike rates, achieving an optimal balance between output quality and power consumption. Furthermore, the integration of a feedback system into Spiketrum enables selective amplification of specific features while attenuating others, facilitating adaptive power consumption based on application requirements. The hardware implementation supports both spike-based and non-spike-based processors, making it versatile for various computing systems. The cochlea's ability to encode diverse sensory information, extending beyond sound waveforms, positions it as a promising sensory input for current and future spike-based intelligent computing systems, offering compact and real-time spike train generation.

África Periáñez, Ana Fernández del Río, Ivan Nazarov et al.

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Hieu M. Tran, Kien T. Pham, Thanh M. Vo et al.

Dimensions, volume, and mass of agricultural goods are essential physical features to build sizing, grading, and packaging systems. Manual property measurements are time-consuming, costly, and labor-intensive due to traditional technologies while various devices need to be installed to adapt process requirements. Furthermore, it is very challenging to accurately measure product with irregular or specific shapes, such as starfruit (Averrhoa carambola). Recently, there have been several research results on estimating features of the irregularly shaped object, they are either get inaccurate results or need repeated captures, computational resources, and time to rebuild the three-dimensional representation of the goods. The starfruit has not been studied completely in this size and weight measurements. This paper focuses on new techniques which exhibit simple installation to generate multiple functions for estimating the dimensions, volume, and mass with high accuracy. In this proposed method, we separated the process into two main phases. In the first phase, a camera is used to capture a top-view image of a starfruit, then image processing and machine vision techniques are applied to process the acquisition image before the process slices numerically the starfruit into several pieces along the longitudinal axis and estimates the physical attributes of each pieces using disc method and conical frustum method. Its volume is the summation of the volume of each partial slice. In the next phase, the density is used to estimate the mass of starfruit since the correlation coefficient (R-squared) between the volume and mass of starfruit is nearly linear with 0.9205. The validated results are highly competitive with accuracy of about 99&#x0025; for the volume and mass in 300 testing samples.

Benjamin Habib, Elvin Isufi, Ward van Breda et al.

Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS$^2$), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and for the physical governing power flow equations. DSS$^2$ leverages hypergraphs to represent the heterogeneous components of the distribution systems and updates their latent representations via a node-centric message-passing scheme. A weakly supervised learning approach is put forth to train the DSS$^2$ in a learning-to-optimize fashion w.r.t. the Weighted Least Squares loss with noisy measurements and pseudomeasurements. By enforcing the GNN output into the power flow equations and the latter into the loss function, we force the DSS$^2$ to respect the physics of the distribution system. This strategy enables learning from noisy measurements, acting as an implicit denoiser, and alleviating the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS$^2$ outperforms by a margin the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time, while being more robust to noisy, erroneous, and missing measurements. The DSS$^2$ achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.

Yiting Chen, Liliaokeawawa Cothren, Jorge Cortes et al.

This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem. To solve this regulation task, we design a state feedback controller based on a continuous approximation of the projected gradient flow. We first show that the equilibria of the interconnection between the plant and the proposed controller correspond to critical points of the constrained optimization problem. We then derive sufficient conditions to ensure that, for the closed-loop system, isolated locally optimal solutions of the optimization problem are locally exponentially stable and show that input constraints are satisfied at all times by identifying an appropriate forward-invariant set.

Daniel Hert, Tomas Baca, Pavel Petracek et al.

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.

Lan Chen, Xi Chen, Shiyu Wu et al.

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills.