Search Results (1,277)

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

Air pollution from fine particulate matter (PM2.5) is a recurring crisis in Northern Thailand, largely driven by seasonal biomass burning. This study investigates how socioeconomic and individual characteristics shape labor allocation during high-exposure periods. Using survey data from 400 individuals across eight provinces in April–May 2024, we applied a logit model to estimate the probability of reducing work hours. Results show heterogeneous and non-linear patterns of avoidance. The probability of work reduction rose across higher income strata but peaked in the third stratum before declining in the fourth, reflecting the trade-off between avoidance and the opportunity cost of foregone earnings. Education exhibited a strong awareness effect, with each additional year increasing avoidance behavior. Outdoor workers and individuals with respiratory conditions were most likely to reduce work, indicating rational prioritization under greater exposure risks. Together, these findings demonstrate environmental inequality: lower-income and less-educated groups remain disproportionately exposed due to limited coping capacity. The regional context of Northern Thailand further amplifies these vulnerabilities. Policy interventions should prioritize protective measures for vulnerable groups while promoting long-term alternatives to biomass burning. By highlighting nuanced behavioral responses, this study extends evidence on environmental inequality in developing-country contexts. Full article

This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection. Full article

A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows. Full article

This article traces the trajectory of China’s dominant literary field as it shifted from proletarian to intellectual literature in the early reform era. It examines the conditions and cultural logic underlying the striking phenomenon whereby former industrial workers, once incorporated into the literary field, rapidly distanced themselves from the very genre historically rooted in their own industrial experiences, namely, worker literature. Focusing on writers emerging from factories and on Shanghai Literature—a journal once known for publishing worker literature. The article analyzes the reconfiguration of class and identity that accompanied China’s transition from its high socialist past. I argue that socialist worker literature never fully reconciled the structural antagonism between manual and mental labor. In the early reform era, factory-based writers appropriated literature as a mode of symbolic escape and ideological critique. Hence, literature itself became a site where the contradictions of socialist and capitalist modernity were negotiated and contested. Full article

Stunning of farmed fish prior to slaughter is increasingly recognized as a key animal welfare priority, yet uptake remains limited in the EU aquaculture sector. While the effects of different stunning methods on fish welfare are the subject of significant recent research, the effect on aquaculture businesses remains unclear. Therefore, this study assesses the economic feasibility of implementing electrical stunning for four species where it is not currently routine: carp, trout, seabass, and seabream. Using a granular cost model across 17 country–species–system combinations, and cost data from 2018 to 2020, the impact of introducing in-water and dry electrical stunning systems under various cost pass-through and sensitivity scenarios is evaluated. Results show that while stunning increases the production costs, under realistic assumptions, 16 out of 17 segments remain profitable, with the one unprofitable segment already being unprofitable under business-as-usual conditions. Three trout systems even experience cost savings due to reduced labor requirements. Sensitivity analyses confirm the robustness of these findings across plausible increases in operating costs and financing assumptions. Even under a 0% cost pass-through, 16 segments still remain profitable. These results provide timely, policy-relevant evidence to support species-specific welfare legislation, while identifying segments that may require targeted support for compliance. Full article

The emigration of highly skilled individuals has become a critical concern for many countries amid increasing global labor mobility. This study employs the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) method within a fuzzy multi-criteria decision-making (FMCDM) framework to identify and prioritize the key drivers of skilled migration. Drawing on opinions from sixteen Turkish emigrants currently residing abroad, the study captures firsthand perspectives on the structural factors influencing their migration decisions. The results indicate that the most influential factors are workplace conditions, living standards, and academic standards. These findings underscore the multifaceted nature of brain drain and highlight the necessity for comprehensive policy approaches that address both push and pull dynamics. By systematically ranking these determinants, the study contributes to the growing body of evidence-based research on international human capital flows. Full article

The swift advancement of the digital economy presents new pathways toward achieving common prosperity in China. Based on microdata derived from the China Family Panel Studies (2010–2022), this study employs the “Broadband China” pilot policy as a quasi-natural experiment to explore how digital economy development influences multidimensional relative poverty. We develop a multidimensional relative poverty index encompassing economic, health, education, and living condition aspects utilizing the Alkire–Foster dual cutoff method and employ a staggered Difference-in-Differences design for empirical analysis. Results show that the policy leads to an average decrease of 1.8 percentage points in the probability of multidimensional relative poverty across households. The effect is more pronounced in central and western regions, rural households, and those with a high proportion of non-labor force, particularly in the dimensions of economic, health, and living conditions dimensions. Mechanism analysis via interaction term regression indicates that increased population mobility and improved informal employment are key channels. These findings suggest that enhancing digital infrastructure and tailoring mobility and employment policies to fit regional and urban–rural contexts can effectively alleviate multidimensional relative poverty. This study contributes empirical evidence connecting the advancement of the digital economy to poverty alleviation and aligns with the United Nations Sustainable Development Goal 1 (No Poverty). Full article

Agricultural drainage canals provide critical habitats for fish species that are highly sensitive to agricultural practices. However, conventional monitoring methods such as capture surveys are invasive and labor-intensive, which means they can disturb fish populations and hinder long-term ecological assessment. Therefore, there is a strong need for effective and non-invasive monitoring techniques. In this study, we developed a practical method using the YOLOv8n deep learning model to automatically detect and quantify fish occurrence in underwater images from a canal in Ibaraki Prefecture, Japan. The model showed high performance in validation (F1-score = 91.6%, Precision = 95.1%, Recall = 88.4%) but exhibited reduced performance under real field conditions (F1-score = 61.6%) due to turbidity, variable lighting, and sediment resuspension. By correcting for detection errors, we estimated that approximately 7300 individuals of Pseudorasbora parva and 80 individuals of Cyprinus carpio passed through the observation site during a seven-hour monitoring period. These findings demonstrate the feasibility of deep learning-based monitoring to capture temporal patterns of fish occurrence in agricultural drainage canals. This approach provides a promising tool for sustainable aquatic ecosystem management in agricultural landscapes and emphasizes the need for further improvements in recall under turbid and low-visibility conditions. Full article

A necessary condition for the success of the 7 October 2023 Hamas deadly incursion into Israel was the absence of the Israel Defense Forces (IDF) from that region. The IDF was involved in helping the settlers in their conflicts with Palestinians on the West Bank, many miles from the Gaza border. Absent the settlers, it is likely that either the Hamas attack might not have occurred or would have been blunted at the outset, yielding a much more measured Israeli response. Hence it is imperative that we understand the origins of the settler movement. It is to be found in Biblical injunctions that were to be amplified considerably by the outcomes of the extraordinarily successful Six-Day war of 1967 and its sequel the Yom Kippur war of 1973. In the third chapter of the Book of Genesis, that is, of the entire Hebrew Bible, God commands Abraham to leave his current domicile and travel to Canaan where a great nation would be formed. Effectively, this is the religious foundation of the connection between the people of Israel and the land of Israel, then called Canaan. The contrast between the outcomes of 1967 and 1973 was striking. Instead of a lopsided victory in the earlier war, the human losses in 1973 were surprising, even terrifying. This intense ephemeral gain combined with a world view defense engendered by mortality salience established the basis for later religious Zionist extremism. The vastly increased number of casualties in 1973 set the stage for the victory of Likud, much more amenable to West Bank settlements than the ousted Labor government had been. Religious Zionists leaped at this opportunity, justifying this activity by referring to God’s commandment to settle the entire land of Israel in the West Bank territories using their Biblical Hebrew names: Yehuda (Judea) and Shomron (Samaria), whatever the cost in violent Palestinian land dispossession. Full article

The integrity of urban water supply pipelines, an essential element of municipal infrastructure, is frequently undermined by internal defects such as corrosion, tuberculation, and foreign matter. Traditional inspection methods relying on CCTV are time-consuming, labor-intensive, and prone to subjective interpretation, which hinders the timely and accurate assessment of pipeline conditions. This study proposes YOLOv8-VSW, a systematically optimized and lightweight model based on YOLOv8 for automated defect detection in in-service pipelines. The framework is twofold: First, to overcome data limitations, a specialized defect dataset was constructed and augmented using photometric transformation, affine transformation, and noise injection. Second, the model architecture was improved on three levels: a VanillaNet backbone was adopted for lightweighting, a C2f-Star module was introduced to enhance multi-scale feature fusion, and the WIoUv3 dynamic loss function was employed to improve robustness under complex imaging conditions. Experimental results demonstrate the superior performance of the proposed YOLOv8-VSW model. This study validates the framework on a curated, real-world image dataset, where YOLOv8-VSW achieved mAP@50 of 83.5%, a 4.0% improvement over the baseline. Concurrently, GFLOPs were reduced by approximately 38.9%, while the inference speed was increased to 603.8 FPS. The findings validate the effectiveness of the proposed method, delivering a solution that effectively balances detection accuracy, computational efficiency, and model size. The results establish a strong technical basis for the intelligent and automated control of safety in urban water supply systems. Full article

Over the last few years, the number of Brazilian immigrants living in Portugal has risen significantly, motivated by expectations of safety, prosperity, and professional success. However, the integration into the labor market frequently involves adversities such as professional devaluation, precarious working conditions, and experiences of social exclusion. This qualitative study aims to explore the work experiences of Brazilian immigrants in Portugal, with a special focus on how community support and collective resilience shape their ability to cope with adversity. Based on minority stress theory and intersectionality, we conducted 24 semi-structured interviews with Brazilian immigrants from diverse professional backgrounds. Thematic analysis revealed four main themes: (1) precarious integration into the labor market and underemployment, (2) experiences of xenophobia, racism, and discrimination, (3) mental health challenges and emotional exhaustion, and (4) community support and collective resilience. Participants emphasized the importance of informal solidarity networks to overcome institutional barriers and maintain emotional well-being. These results suggest that resilience is not only an individual resource, but a relational process rooted in everyday acts of care and connection. The study highlights the protective role of community in contexts of structural vulnerability and contributes to current discussions on migrant integration and well-being. Full article

Grassland ecosystems are vital for global biodiversity, yet traditional wildlife monitoring methods are often labor-intensive and costly. Although drone-based aerial surveys provide a scalable alternative, they face significant challenges such as detecting extremely small targets, handling complex backgrounds, and operating under strict computational constraints. To address these issues, this study proposes IECA-YOLOv7, a lightweight detection model that incorporates three key innovations: an Improved Efficient Channel Attention (IECA) module for enhanced feature representation, a content-aware CARAFE upsampling operator for improved detail recovery, and a Normalized Wasserstein Distance (NWD) loss function for robust small-target localization. Evaluated on a dedicated grassland wildlife dataset (GWAID), the model achieves a mAP@0.5 of 86.6% and a mAP@0.5:0.95 of 47.2%, outperforming the baseline YOLOv7-tiny by 2.9% in Precision and 1.8% in Recall. Furthermore, it surpasses non-YOLO architectures such as RetinaNet, EfficientDet-D0, and DETR by significant margins, demonstrating superior performance in small-object detection under complex conditions. Cross-dataset validation on VisDrone, CARPK, and DOTA demonstrates a strong generalization capability. With a model size under 5 MB, IECA-YOLOv7 effectively balances accuracy and efficiency, offering a practical solution for real-time wildlife monitoring via drones under challenging environmental constraints such as variable lighting, occlusion, and limited computational resources, thereby supporting broader conservation efforts. Full article