Search Results (451)

As China transitions from rapid urbanization to high-quality development, the competition for population among cities has intensified, characterized by a shift from labor-intensive migration to multi-dimensional lifestyle choices. However, traditional migration models often assume global linearity, failing to capture the complex non-linear thresholds and spatial non-stationarity inherent in migration decisions. This study employs a novel Geographically Weighted Random Forest (GWRF) model to analyze net migration flows across 278 Chinese cities using high-granularity mobile signaling data from the 2020 Spring Festival travel rush. The results reveal that GWRF significantly outperforms traditional OLS, GWR, and global Random Forest models, effectively handling spatial heterogeneity and non-linearity. Wage levels are the dominant global driver, exhibiting a distinct “S-curve” non-linear threshold, while population scale shows a significant U-shaped effect, highlighting the transition from agglomeration economies to congestion costs. Migration drivers exhibit profound spatial heterogeneity: western inland cities are “wage-driven,” the Pearl River Delta is “employment-structure driven,” and the northeastern “Rust Belt” is increasingly sensitive to “innovation investment” (technology expenditure). These findings challenge the “one-size-fits-all” approach to population policy, offering precise, spatially targeted strategies for urban planners to mitigate population shrinkage and enhance urban vitality. Full article

Generally, land seismic data acquisition in arid areas is a labor-intensive, costly, and challenging process, often hindered by challenging terrain and safety risks. To overcome these limitations, we propose the integration of autonomous Unmanned Aerial Vehicles (UAVs) into land seismic data acquisition, enabling efficient data collection in difficult, inaccessible terrain. This is a cooperative mission workflow combining a Scouting UAV for high-resolution aerial scouting, followed by the swarm deployment of an Autonomous Seismic Acquisition Device (ASAD) for seismic data recording. The cooperative system allows for precise landing and subsequent deployment of seismic sensors in optimal locations. Previously, we demonstrated the applicability of passive seismic recorded with ASAD drones to near-surface characterization. This study covers the results of a field trial, where both the ASAD and Scouting UAV systems successfully acquired high-resolution seismic data with an active source, comparable to that of a conventional seismic data acquisition system. The results show that the ASAD seismic data exhibit a slightly higher noise level due to coupling variances and the fact that geophones were hardwired into 9-sensor arrays. However, due to its single-point sensing nature, it yields a superior frequency bandwidth, making it suitable for imaging shallow anomalies. The system underwent P-wave refraction tomography modeling and accurately detected a shallow subsurface cavity, showcasing its potential for near-surface characterization and shallow geohazard identification. This heterogeneous robotic system can support seismic data acquisition by enhancing safety, improving efficiency, and streamlining equipment mobilization, while minimizing environmental footprint. Full article

Transport infrastructure serves as a critical physical carrier for constructing a unified national market and promoting coordinated regional economic development. Addressing the practical contradiction between rapid transport network expansion and persistent regional development imbalances, this paper constructs a comprehensive transport infrastructure service efficiency index using panel data from 297 prefecture-level cities in China from 2010 to 2023. We systematically investigate the nonlinear impact and underlying mechanisms of transport infrastructure on inter-city economic disparities. The findings reveal a significant inverted U-shaped relationship between transport infrastructure construction and regional economic disparity. Specifically, in the early stages of transport development, the dominance of the agglomeration effect leads to widening regional gaps; once a specific threshold is crossed (an index value of approximately 0.274), the diffusion effect emerges, facilitating convergence. This nonlinear relationship exhibits significant regional heterogeneity: the eastern region has largely crossed the inflection point into the convergence phase, while the western region remains in the “climbing” period dominated by polarization effects. Mechanism testing indicates that labor factor allocation is the core driver of this inverted U-shaped evolution. This study not only clarifies the dynamic boundaries of transport infrastructure’s impact on regional economic patterns but also provides empirical evidence for formulating differentiated transport and regional coordination policies for regions at different developmental stages. Full article

The growing diversity of anthropogenic chemicals in the environment far exceeds the scope of routine analytical monitoring. Non-target screening (NTS) using high-resolution mass spectrometry (HRMS) has thus emerged to discover unknown organic contaminants. Liquid or gas chromatography (LC/GC) coupled with ion mobility–mass spectrometry (IM-MS) further enhances NTS by providing multidimensional, structurally informative data. Machine learning (ML) offers a powerful solution by efficiently processing high-dimensional data and uncovering patterns. Both supervised and unsupervised learning approaches show strong potential to streamline labor-intensive processes. This review provides an overview of key ML algorithms and representative workflows in LC/GC-(IM-) MS-related NTS, followed by a critical synthesis of recent advances in ML-enabled applications across the entire NTS procedure, from sample analysis to data acquisition, and ultimately risk assessment. Continued advances in ML are expected to transform NTS into a more efficient and robust tool for risk assessment. Full article

Mercury (Hg) is a ubiquitous element in the environment that may pose a threat to human health due to its toxicity, high mobility through the food chain, and long-lasting persistence. Organic Hg compounds, particularly methylmercury, are more toxic than inorganic mercury due to their easy absorption and persistent retention within the organism. Although natural attenuation can occur in soil through various processes, excessive levels of Hg cause pollution that can adversely affect agricultural soil, making remediation necessary to either remove or stabilize Hg within the soil. This review primarily aims to summarize key remediation strategies—chemical, biological, and physical—developed in recent years for agricultural soil remediation. It discusses the influencing factors, advantages, limitations, mechanisms, and practical applications of these soil remediation technologies. The published literature focuses on identifying plant species and microorganisms capable of remediating Hg-contaminated soils. Emerging amendments, such as biochar and nanomaterials, have been tested for treating mercury (Hg)-polluted soils primarily by immobilizing mercury and reducing its bioavailability and methylation. Ex situ remediation technologies are effective for Hg-contaminated soils but are often costly, labor-intensive, detrimental to soil quality, and generate hazardous secondary waste. In contrast, in situ technologies treat Hg directly within the soil, preserving the soil matrix and its biota. According to the literature, remediation of Hg-contaminated agricultural soils can be compatible with food crop production only if the bioavailable Hg fraction is sufficiently reduced and crop uptake remains below food safety limits. The gap between laboratory trials and actual field applications in Hg-contaminated soil remediation mainly arises from differences in scale, complexity, and the uncertainty of real-world conditions, which often reduce the efficiency and predictability of treatments. This review aims to provide a practical reference for improving the effective remediation of Hg-contaminated soils in the future. Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

The reallocation of production factors, particularly labor, is central to understanding economic development and household welfare. This paper investigates how the transfer of farmland, a fundamental shift in factor endowment, affects rural household financial vulnerability, with a specific focus on the mediating role of labor mobility. While factor market liberalization is theorized to enhance efficiency, the micro-level pathways through which land transactions influence financial resilience remain underexplored. Utilizing a unique household survey dataset from Shaanxi Province, China, and employing ordered Probit model alongside propensity score matching (PSM), the impact of farmland transfer-out on the financial vulnerability of rural households is revealed. The results show that farmland transfer-out significantly reduces household financial vulnerability. Mechanism analysis confirms that this effect operates primarily by releasing surplus agricultural labor and promoting its shift into non-farm employment, thereby expanding both the sectoral and geographic scope of household labor supply. Heterogeneity analysis further reveals that the responsiveness of labor mobility to land transfer is more pronounced among households with older heads, higher human capital, and stronger social networks. However, the ultimate mitigating effect on financial vulnerability is consistent across diverse household types. These findings contribute to the literature on factor market integration and household finance in developing economies and offer direct policy implications for designing land institutions and labor policies that synergistically enhance rural economic resilience. Full article

Generative artificial intelligence in higher education is typically framed as either a student productivity tool or an institutional disruption. This agenda-setting essay advances a third position: mobile generative AI functions as relational infrastructure—a persistent communicative presence that mediates identity, meaning-making, and belonging during institutional transition. Focusing on international graduate student onboarding, we abductively “think through” two complementary theoretical lenses. Constitutive Artificial Intelligence Identity Theory (CAIIT) conceptualizes AI as a co-constitutive participant in identity formation through recursive communicative feedback loops. Language Convergence/Meaning Divergence (LC/MD) theory explains how shared institutional language masks interpretive gaps across intercultural and bureaucratic contexts. Reading narrative vignettes through these frameworks, we argue that generative AI is neither simple curricular tool nor personal aid, but both relational and organizational infrastructure, redistributing translational, emotional, and interpretive labor in higher education. We outline four design principles for AI-integrated onboarding: distinguish communicative scaffolding from cognitive replacement; design systems that assume meaning divergence; center equity in AI-mediated transitions; and anticipate ethical risk. Reframing AI as relational infrastructure shifts AI-in-education research toward relational accountability and institutional care. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article

Although high-frequency electromagnetic methods, such as Radio Magnetotellurics (RMT) and Controlled-Source Radio Magnetotellurics (CSRMT), are highly effective for shallow-to-medium depth exploration, deploying traditional transmitter–receiver setups remains labor-intensive and significantly slows down large-scale surveys. To overcome these logistical bottlenecks, we developed a mobile Ultra-Audio Frequency Electromagnetic (UAEM) measurement system. While the hardware is designed with dual-mode capabilities supporting conventional controlled-source operations, this paper specifically focuses on its application in a Signals of Opportunity (SOOP) mode. By utilizing pre-existing, stable anthropogenic signals, including Amplitude Modulation (AM) broadcasts and naval very low frequency communications, the system effectively functions as a broadband RMT receiver. Technical evaluations demonstrate that the instrument operates across a 1 Hz to 1000 kHz bandwidth with a high sampling rate of 2.5 MHz. Furthermore, it achieves a dynamic range of 143 dB and maintains an apparent resistivity measurement accuracy of better than 3%. Thanks to its modular, vehicle-towed design, the UAEM system enables continuous, on-the-move data acquisition wherever ambient field sources are available. This approach eliminates the need for dedicated transmitter deployment, fundamentally reducing exploration costs and boosting overall survey efficiency. Full article

Apple picking is an inherently labor-intensive, time-consuming, and costly task, and robotic harvesting represents a potential alternative to address this challenge. This study presents the development and field evaluation of an integrated robotic system for apple harvesting, which combines machine vision, a dual four-degree-of-freedom (DoF) manipulator, and a mobile platform. The harvesting mechanism employed a streamlined 4-DoF manipulator driven by closed-loop stepper motors, incorporating a differential gear mechanism to execute yaw and pitch motions. Trajectory planning utilized linear interpolation with a harmonic acceleration/deceleration profile to ensure smooth end-effector movement. Fruit detection and localization within the canopy were performed by a stereo vision system running a lightweight deep neural network, achieving a mean hand-eye calibration accuracy of 4.7 ± 2.7 mm. Three negative-pressure driven soft end-effector designs—a suction soft end-effector (SSE), a grasping soft end-effector (GSE), and a suction-grasping soft end-effector (SGSE)—were assessed for their harvesting performance. Field trials conducted in a commercial spindle orchard demonstrated that the GSE achieved the highest performance, with a harvesting success rate of 80.80% among reachable fruits, a full-process success rate (from detection to collection) of 61.59%, an overall fruit damage rate of 10.89%, and an average single-fruit cycle time of 5.27 s. In contrast, the SSE and SGSE showed lower success rates (49.21% and 64.71%, respectively). This work provides a practical robotic harvesting solution. It validates the feasibility of a zoned, multi-manipulator harvesting strategy and delivers comparative data to guide the development of more efficient and robust harvesting robots. Full article

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: ‘Shrubs’, ‘Grasses’, ‘Legumes’, and ‘Others’. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture. Full article

Energy thriftiness and metabolic adaptations have had a crucial role in the emergence and spreading of the Homo lineage in the world. A higher-energy demand was required not only for the growing body mass, encephalization and human proliferation, but also for the survival adaptations to the environmental stresses. Because lean body mass lacks the energy-storage capacity required to supply the body’s demands, dedicated fat-storing cells originated. To feed such fat stores, the hominid evolution developed “meat-adaptive” genes to detect, digest and metabolize higher fat diets, and body-fat stores can be affected by lifestyle through hormonal-controlled daily energy balance. In energy surplus conditions, hypertrophy and hyperplasia of adipocytes can occur, with hypertrophic adipocyte signaling both a neo-adipocyte differentiation (leading to hyperplasia) and a local macrophage density (resident + infiltrated macrophages) for fat surplus scavenging. Adiposity-induced inflammation is caused by fat-overstored (hypertrophied) adipocytes that may operate as an overactive endocrine organ secreting an array of pro-inflammatory adipokines that, in combination with resident-macrophage activity and infiltrated blood-recruited, monocyte-derived macrophages, amplify the inflammatory process by spurting pro-inflammatory cytokines into the bloodstream. From an evolutionary perspective, obese humans represent a natural selection overexpressing the “thrifty” genes evolved for efficient food collection and fat deposition intended to help in survival in prolonged periods of famine. However, genetically speaking, obesity is a polygenic multifactorial disorder. Considering the rapidity of obesity-epidemic growth worldwide, epigenetic sets forth the key assumption of the mismatch between our human genome molded over thousands of generations, coping with the unprecedented dietary and physical conditions. Consequently, obesity would be due to our evolutionary-adapted polygenic-charge expressed by a deteriorated lifestyle characterized by high energy-dense food intake coupled with a reduction in caloric expenditure stemming from new mobility-reducing technologies. As a model of lifestyle change (LiSM), our 28-year on-going longitudinal study (“Moving for Health”) has shown effectiveness in the reduction not only of obesity but especially of its comorbidities, in a (10 week to 3 year) length-dependent LiSM. However, a disappointing progressive decrease in compliance with the study has been observed and attributed to the resistance of people to change their actual “obesogenic” lifestyle, basically represented by the individuals’ demand for labor-saving technologies and convenient, affordable, palatable foods. Full article

This study focuses on the challenges and opportunities of integrating industrial robots into robotic bricklaying systems (RBSs) for automation and digital transformation in the construction industry. A mobile RBS was designed, engineered, manufactured and commercially implemented for the first time in Poland. The RBS is designed to perform robotic bricklaying in situ in municipal, residential, and industrial buildings, where sustainable construction tasks are implemented. The details of the design solutions for the RBS, virtual simulation, and real robotic bricklaying processes are presented. The results of bricklaying using the RBS and the factors that influence the robotic bricklaying process are summarized. A 3D digital building information model (BIM) created using Autodesk Revit tools was used for simulated robotic bricklaying in the ABB RobotStudio 2025.5 program, from which they were transferred to the programming of the ABB IRB 4600 bricklaying robot. The laser programming method for the bricklaying robot, bricklaying procedures, and algorithms are also presented. The costs of human labor and robot construction were compared, and the return on investment (ROI) was calculated. RBS evaluations were performed in laboratory settings, on-site demonstrations, and commercial wall-laying in residential apartments. Full article