Search Results (243)

Europe has experienced a growing divergence in trends of population change across the urban hierarchy. A key driver of this divergence is internal migration, which underpins the efficient functioning of the economy by enhancing labor market flexibility and allowing people to choose the most desired locations. Internal migration in Latvia is of increasing importance, as the propensity to change residence within national borders has become the primary mechanism of demographic change, shaping population redistribution across regions and the urban hierarchy. We used Latvia as a case study, exemplified by the monocentric urban system with Riga City at its center, as well as a relatively dense network of small towns spread across all regions. Small towns in Latvia, although not characterized by high levels of internal migration, exhibit notable changes in their demographic and socioeconomic composition. Our analysis uses administrative data on registered migration for each year from 2011 to 2021 to characterize migration patterns, as well as data from the 2011 and 2021 census rounds on 1-year migration to analyze the composition of the migrant population. The results showed sociodemographic variations in the characteristics of individuals migrating to small towns. Understanding the temporal and spatial dynamics of internal migration patterns and compositional effects is vital for effective local and regional development policies to plan essential services and infrastructure. Full article

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics. Full article

This paper examines the impact of import competition on skill premium and the moderating effect of labor market flexibility on it, using panel data from 30 provinces in China from 2010 to 2019. A dynamic panel threshold model with instrumental variables is employed to address the endogeneity problem and to identify the nonlinear moderating effect of labor market flexibility. The results show the following: (1) Import competition has a promoting effect on skill premium, and this effect declines from eastern to western regions in China. (2) The import competition increases the skill premium through the channels of enhancing regional innovation capacity and promoting industrial upgrading and rationalization. (3) There exists a significant threshold effect in the moderating effect of labor market flexibility. When labor market flexibility surpasses the threshold value of 1.330, the enhancing effect of import competition on the skill premium is alleviated, facilitating labor reallocation and wage adjustment. The integration of labor market flexibility into the globalization–inequality debate extends the existing literature for providing a new understanding of the mechanisms behind the skill premium. The policy implications are that targeted labor market reforms are essential for mitigating wage differentials between skilled and unskilled workers arising from intensified import competition. Full article

Handling delicate agricultural products, such as tomatoes, requires careful attention from workers during harvesting, sorting, and packaging processes. This labor-intensive approach is often inefficient and susceptible to human error. A potential solution to improve efficiency is the development of automated systems capable of replacing manual labor. However, such systems face significant challenges due to the irregular shapes and fragility of these products, requiring specialized adaptable and soft gripping mechanisms. In this context, this paper introduces a parametric design methodology for 3D-printed flexible fingers in soft grippers, tailored for agricultural applications. The approach was tested in a case study that targeted soft agricultural products with diameters between 45 and 75 mm. Three finger topologies were modeled and compared to identify an optimal configuration. A prototype was then developed using 3D printing with Z-SemiFlex. Experimental tests confirmed that the prototype could grasp different fruits reliably and without surface damage. It achieved an Average Precision (AP) of 87.5% for tomatoes and 92.5% for mandarins across 80 trials. These results validate the feasibility of the proposed design methodology for fingers in soft grippers. Full article

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technology, favored for its design flexibility and suitability for low-volume production. However, precise cost estimation remains a critical challenge, particularly in industrial environments where decision-making depends on accurate financial assessments. This study proposes a comprehensive, parameter-based cost calculation model for FDM processes, with a special focus on the wear of machine tooling. Unlike conventional methods, the model separates tooling costs from general machine operation costs and introduces a novel approach to nozzle wear estimation based on extruded material volume rather than printing time. The framework incorporates key cost components—including material usage, support removal, machine operation, tooling degradation, and labor—and links them to quantifiable parameters such as part volume, build time, and energy consumption. The methodology was tested across multiple scenarios with different geometries and production volumes, revealing significant differences between time- and volume-based wear calculations. The results demonstrate that the proposed model provides more accurate and adaptable cost predictions, especially in varied production settings. This approach enhances the financial transparency of FDM workflows and supports better-informed decisions in both prototyping and small-batch manufacturing contexts. Full article

Concrete infrastructure inspection is essential for maintaining the safety and longevity of urban environments. Traditional manual crack detection methods are labor-intensive, inconsistent, and difficult to scale. Recent advancements in deep learning and computer vision offer automated alternatives, particularly when deployed via unmanned aerial vehicles (UAVs) for enhanced coverage and flexibility. However, achieving real-time performance on embedded systems requires models that are not only accurate but also lightweight and computationally efficient. This study presents CrackDetect-Lite, a comparative analysis of three deep learning architectures for binary crack detection using the SDNET2018 benchmark dataset: CNNSimple (a custom lightweight model), RSNet (a shallow residual network), and MobileVNet (a fine-tuned MobileNetV2). Class imbalance was addressed using a weighted cross-entropy loss function, and models were evaluated across multiple criteria including classification accuracy, crack-class F1-score, inference latency, and model size. Among the models, MobileVNet achieved the best balance between detection performance and deployability, with an accuracy of 90.5% and a crack F1-score of 0.73, while maintaining a low computational footprint suitable for UAV-based deployment. These findings demonstrate that carefully selected lightweight CNN architectures can deliver reliable, real-time crack detection, supporting scalable and autonomous infrastructure monitoring in smart city systems. Full article

Increasing labor costs, labor shortage, high environmental impact, and low productivity levels are the main reasons that have led the construction industry to search for sustainable alternatives to conventional traditional construction techniques, such as Additive Construction. Large-scale concrete 3D printing has emerged as a viable alternative, which can address these major challenges. Through the high material efficiency, design flexibility, and automation levels provided, 3D printing can revolutionize the way buildings are designed and built. The seismic behavior of 3D-printed load bearing elements remains generally underexplored. To that scope, the structural design of a two-story building is investigated. The proposed methodology involves finite element models and stress analysis of critical structural members. The performance of the studied walls is further investigated using 3D solid element models and nonlinear constitutive laws to validate structural adequacy. Different printing patterns and structural details of unreinforced and reinforced 3D-printed concrete walls are analyzed through parametric analyses. The results indicate the acceptable response of 3D-printed load bearing elements, under certain construction configurations, as required by the existing regulatory framework. The proposed methodology could be applied for the design of such structures and for the optimization of printing patterns and reinforcing details. Full article

Generative Artificial Intelligence (GenAI) is widely recognized for its profound impact on labor market demand, supply, and skill dynamics. However, due to its transformative nature, GenAI increasingly overlaps with traditional AI roles, blurring boundaries and intensifying the need to reassess workforce competencies. To address this challenge, this paper introduces KANVAS (Kolmogorov–Arnold Network Versatile Algorithmic Solution)—a framework based on Kolmogorov–Arnold Networks (KANs), which utilize B-spline-based, compact, and interpretable neural units—to distinguish between traditional AI roles and emerging GenAI-related positions. The aim of the study is to develop a reliable and interpretable labor market classification system that differentiates these roles using explainable machine learning. Unlike prior studies that emphasize predictive performance, our work is the first to employ KANs as an explanatory tool for labor classification, to reveal how GenAI-related and European Skills, Competences, Qualifications, and Occupations (ESCO)-aligned skills differentially contribute to distinguishing modern from traditional AI job roles. Using raw job vacancy data from two labor market platforms, KANVAS implements a hybrid pipeline combining a state-of-the-art Large Language Model (LLM) with Explainable AI (XAI) techniques, including Shapley Additive Explanations (SHAP), to enhance model transparency. The framework achieves approximately 80% classification consistency between traditional and GenAI-aligned roles, while also identifying the most influential skills contributing to each category. Our findings indicate that GenAI positions prioritize competencies such as prompt engineering and LLM integration, whereas traditional roles emphasize statistical modeling and legacy toolkits. By surfacing these distinctions, the framework offers actionable insights for curriculum design, targeted reskilling programs, and workforce policy development. Overall, KANVAS contributes a novel, interpretable approach to understanding how GenAI reshapes job roles and skill requirements in a rapidly evolving labor market. Finally, the open-source implementation of KANVAS is flexible and well-suited for HR managers and relevant stakeholders. Full article

Heritage landscapes endure not through the preservation of fixed forms but through the capacity to adapt to changing social, political, economic, and environmental conditions. Conservation policies that privilege static ideals of authenticity risk undermining the very systems they aim to protect. This paper advances a model of shared stewardship that links conservation of heritage to support for livelihoods, functional flexibility, and community authority in decision-making. Using the Ifugao Rice Terraces of the Philippine Cordillera as a case study, we integrate archaeological, ethnographic, spatial, and agricultural economic evidence to examine the terraces as a dynamic socio-ecological system. Archaeological findings and oral histories show that wet-rice agriculture expanded in the 17th century, replacing earlier taro-based systems and incorporating swidden fields, managed forests, and ritual obligations. Contemporary changes such as the shift from heirloom tinawon rice to commercial crops, the impacts of labor migration, and climate variability reflect long-standing adaptive strategies rather than cultural decline. Comparative cases from other UNESCO and heritage sites demonstrate that economic viability, adaptability, and local governance are essential to sustaining long-inhabited agricultural landscapes. We thus argue that the Ifugao terraces, like their global counterparts, should be conserved as living systems whose cultural continuity depends on their ability to respond to present and future challenges. Full article

The compressive strength (Fc) of cement mortar (CM) is a key parameter in ensuring the mechanical reliability and durability of cement-based materials. Traditional testing methods are labor-intensive, time-consuming, and often lack predictive flexibility. With the increasing adoption of machine learning (ML) in civil engineering, data-driven approaches offer a rapid, cost-effective alternative for forecasting material properties. This study investigates a wide range of supervised linear and nonlinear ML regression models to predict the Fc of CM. The evaluated models include linear regression, ridge regression, lasso regression, decision trees, random forests, gradient boosting, k-nearest neighbors (KNN), and twelve neural network (NN) architectures, developed by combining different optimizers (L-BFGS, Adam, and SGD) with activation functions (tanh, relu, logistic, and identity). Model performance was assessed using the root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Among all models, NN_tanh_lbfgs achieved the best results, with an almost perfect fit in training (R² = 0.9999, RMSE = 0.0083, MAE = 0.0063) and excellent generalization in testing (R² = 0.9946, RMSE = 1.5032, MAE = 1.2545). NN_logistic_lbfgs, gradient boosting, and NN_relu_lbfgs also exhibited high predictive accuracy and robustness. The SHAP analysis revealed that curing age and nano silica/cement ratio (NS/C) positively influence Fc, while porosity has the strongest negative impact. The main novelty of this study lies in the systematic tuning of neural networks via distinct optimizer–activation combinations, and the integration of SHAP for interpretability—bridging the gap between predictive performance and explainability in cementitious materials research. These results confirm the NN_tanh_lbfgs as a highly reliable model for estimating Fc in CM, offering a robust, interpretable, and scalable solution for data-driven strength prediction. Full article

Globally, the accelerating aging of the rural labor force is profoundly impacting the economic resilience of the labor-intensive forestry sector. However, the intrinsic connection between the two has not been fully understood and requires further exploration. As the most populous nation globally and a top producer, trader, and consumer of forest products, China stands out as a perfect case study for this issue. Based on this, this study utilizes panel data from 30 provinces in China from 2012 to 2022 and employs a dual machine learning model to empirically examine the impact and mechanisms of rural labor force aging on forestry economic resilience from the perspective of production factors. The findings indicate: (1) overall, the increase in rural labor force aging significantly inhibits forestry economic resilience; (2) rural labor force aging enhances forestry economic resilience by promoting large-scale forest land management, driving forestry technological innovation, and increasing government capital investment; it also inhibits forestry economic resilience by reducing educational human capital and health human capital; (3) the rural force aging exerts a marked adverse effect on the resilience of the forestry economy in the eastern and central regions, major grain-producing areas, and major grain-consuming areas. Based on this, this study proposes policy recommendations in three areas: building a flexible and diversified labor supply and replacement system, exploring a “scale and technology” integration path suited to national conditions, and implementing differentiated regional strategies. The aim is to provide a reference for government departments in formulating strategies to enhance the resilience of the forestry economy in the era of aging. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are time-dependent or event-driven, narrowing the opportunity window to capture representative observations. Yet, accelerating the deployment of deep learning (DL) models is crucial to support timely, data-driven decision-making in operational settings. To tackle such an issue, this paper explores the use of 2D synthetic data grounded in real-world patterns to train initial DL models in contexts where annotated datasets are scarce or can only be acquired within restrictive time windows. Two complementary approaches to synthetic data generation are investigated: rule-based digital image processing and advanced text-to-image generative diffusion models. These methods can operate independently or be combined to enhance flexibility and coverage. A proof-of-concept is presented through a couple case studies in precision viticulture, a domain often constrained by seasonal dependencies and environmental variability. Specifically, the detection of Lobesia botrana in sticky traps and the classification of grapevine foliar symptoms associated with black rot, ESCA, and leaf blight are addressed. The results suggest that the proposed approach potentially accelerates the deployment of preliminary DL models by comprehensively automating the production of context-aware datasets roughly inspired by specific challenge-driven operational settings, thereby mitigating the need for time-consuming and labor-intensive processes, from image acquisition to annotation. Although models trained on such synthetic datasets require further refinement—for example, through active learning—the approach offers a scalable and functional solution that reduces human involvement, even in scenarios of data scarcity, and supports the effective transition of laboratory-developed AI to real-world deployment environments. Full article

Robotic waste sorting systems offer a scalable and consistent alternative to manual sorting for Construction and Demolition Waste (CDW) by reducing labor-intensive tasks and exposure to hazardous conditions, while enabling the extraction of high-purity materials (e.g., polymers) from the waste streams. Despite advancements in perception systems, manipulation and planning remain significant bottlenecks, limiting widespread adoption due to high complexity and cost. This paper introduces RaapWaste, a robot- and application-agnostic planning framework specifically designed for waste sorting, addressing challenges in motion planning, scheduling, and real-world integration. Built on open-source resources, RaapWaste employs a modular and flexible architecture, enabling integration of diverse planning techniques and scheduling strategies. The framework aims to simulate the performance of real-world sorting equipment (e.g., robots, grippers). To evaluate its effectiveness, we conducted simulations with articulated and delta robots, as well as real-world tests on CDW sorting. Metrics such as the Sorting Throughput (ST) and Sorting Ratio (SR) reveal the RaapWaste’s capability across different waste sorting cases. In simulation, the delta robot achieved an SR exceeding 95%, while the UR5e showed consistent performance. In real-world CDW experiments, the system achieved a peak SR of 99% and maintained 80% using the SPT scheduler. Full article

This paper presents an innovative multi-criteria visual quality control algorithm designed for deployment on cost-effective Edge devices within the Industrial Internet of Things environment. Traditional industrial vision systems are typically associated with high acquisition, implementation, and maintenance costs. The proposed solution addresses the need to reduce these costs while maintaining high defect detection efficiency. The developed algorithm largely eliminates the need for time- and energy-intensive neural network training or retraining, though these capabilities remain optional. Consequently, the reliance on human labor, particularly for tasks such as manual data labeling, has been significantly reduced. The algorithm is optimized to run on low-power computing units typical of budget industrial computers, making it a viable alternative to server- or cloud-based solutions. The system supports flexible integration with existing industrial automation infrastructure, but it can also be deployed at manual workstations. The algorithm’s primary application is to assess the spread quality of thick liquid mold filling; however, its effectiveness has also been demonstrated for 3D printing processes. The proposed hybrid algorithm combines three approaches: (1) the classical SSIM image quality metric, (2) depth image measurement using Intel MiDaS technology combined with analysis of depth map visualizations and histogram analysis, and (3) feature extraction using selected artificial intelligence models based on the OpenCLIP framework and publicly available pretrained models. This combination allows the individual methods to compensate for each other’s limitations, resulting in improved defect detection performance. The use of hybrid metrics in defective sample selection has been shown to yield superior algorithmic performance compared to the application of individual methods independently. Experimental tests confirmed the high effectiveness and practical applicability of the proposed solution, preserving low hardware requirements. Full article