Search Results (1,961)

High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

(1) Background: This article aims to understand the forms and elements through which the inhabitants of the city of Medellin have configured their subjectivity in the context of the application of neoliberal policies in the last two decades. In this way, we can approach the frameworks of understanding that constitute a fundamental part of the individuation processes in which the incorporation of their subjectivities is evidenced in neoliberal contexts that, in the historical process, have been converging with authoritarian, antidemocratic and neoconservative elements. (2) Method: A qualitative approach with a hermeneutic-interpretative paradigm was used. In-depth semi-structured interviews were conducted with 41 inhabitants of Medellín who were politically identified with right-wing or center-right positions. Data analysis included thematic coding to identify patterns of thought and points of view. (3) Results: Participants associate success with individual effort and see state intervention as an obstacle to development. They reject redistributive policies, arguing that they generate dependency. In addition, they justify authoritarian models of government in the name of security and progress, from a moral superiority, which is related to a negative and stigmatizing perception of progressive sectors and a negative view of the social rule of law and public policies with social sense. (4) Conclusions: The naturalization of merit as a guiding principle, the perception of themselves as morally superior based on religious values that grant a subjective place of certainty and goodness; the criminalization of expressions of political leftism, mobilizations and redistributive reforms and support for policies that establish authoritarianism and perpetuate exclusion and structural inequalities, closes roads to a participatory democracy that enables social and economic transformations. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

This article examines the role of managed ecosystems, and particularly forests, in achieving carbon neutrality in Russia. The range of estimates of Russia’s forests’ net carbon balance in different studies varies by up to 7 times. The. A comparison of Russia’s National GHG inventory data for 2023 and 2024 (with the latter showing 37% higher forest sequestration) is presented and explained. The possible changes in the Long-Term Low-Emission Development Strategy of Russia (LT LEDS) carbon neutrality scenario due to new land use, land use change and forestry (LULUCF) data in National GHG Inventory Document (NID) 2024 are discussed. It is demonstrated that the refined net carbon balance should not impact the mitigation ambition in the Russian forestry sector. An assessment of changes in the drafts of the Operational plan of the LT LEDS is presented and it is concluded that its structure and content have significantly improved; however, a delay in operationalization nullifies efforts. The article highlights the problem of GHG emissions increases in forest fires and compares the gap between official “ground-based” and Remote Sensing approaches in calculations of such emissions. Considering the intention to increase net absorption by implementing forest carbon projects, the latest changes in the regulations of such projects are discussed. The limitations of reforestation carbon projects in Russia are provided. Proposals are presented for the development of the national forest policy towards increasing the net forest carbon absorption, including considering the projected decrease in annual net absorption by Russian forests by 2050. The role of government and private investment in improving the forest management of structural measures to adapt forestry to modern climate change and the place of forest climate projects need to be clearly defined in the LT LEDS. Full article

This article highlights the importance of the discourse on sound media for the development of so-called “cosmic music” in Germany in the late 1960s and early 1970s. Already the slogan of the Ohr record label “Macht das Ohr auf” (Open up your ears) testifies to the awareness of Rolf-Ulrich Kaiser, the founder of the label, and the bands gathered around him about the impact of media on everyday practices and the reflection on the physiological effect of sound. In particular, this article focuses on the figure of Kaiser and his Buch der neuen Pop-Musik (1969), where the author stresses the emancipatory potential of popular music starting from the considerations put forward by H. Marcuse, T. W. Adorno and M. McLuhan. On the basis of these suggestions, Kaiser envisages the possibility of a ‘functional transformation’ of sound media, placing himself in a long German tradition of reflections on the relationship between man and technology, in which it is possible to identify a line that proposes a progressive and socialist use of technical reproduction apparatuses (Benjamin, Brecht, Enzensberger) and another line that questions the connection between media and mystical experience (Mann, Hesse). In this sense, this paper explores the intellectual and literary context of the media anthropology on which the sound aesthetics of German cosmic music was founded. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

This study aimed to create and evaluate an optimization-based sensor fusion algorithm that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to provide a more efficient and reliable gap-filling process for OMC measurements to be used for future research. The proposed algorithm takes the first and last frame of OMC data and fills the rest with gyroscope data from the IMC. The algorithm was validated using data from twelve participants who performed a hand cycling task with an inertial measurement unit (IMU) placed on their hand, forearm, and upper arm. The OMC tracked a cluster of reflective markers that were placed on top of each IMU. The proposed algorithm was evaluated with simulated gaps of up to five minutes. Average total root-mean-square errors (RMSE) of <1.8° across a 5 min duration were observed for all sensor placements for the cyclic upper limb motion pattern used in this study. The results demonstrated that the fusion of these two sensing modalities is feasible and shines light on the possibility of more field-based studies for human motion analysis. Full article

The global food industry is facing growing pressure to enhance food safety, extend shelf life, minimize waste, and adopt environmentally sustainable packaging solution. Nanotechnology offers innovative ways to meet these demands by enabling the creation of smart and sustainable food packaging systems. Due to their unique properties, nanomaterials can significantly enhance the functional performance of packaging by boosting mechanical strength, barrier efficiency, antimicrobial activity, and responsiveness to environmental stimuli. This review provides a comprehensive overview of nanomaterials used as smart and sustainable food packaging, focusing on their role in active and intelligent packaging systems. By integrating nanomaterials like metal and metal oxide nanoparticles, carbon-based nanostructures, and nano-biopolymers, packaging can now perform real-time sensing, spoilage detection, and traceability. These systems improve food quality management and supply chain transparency while supporting global sustainability goals. The review also discusses potential risks related to nanomaterials’ migration, environmental impact, and consumer safety, as well as the current regulatory landscape and limitations in industrial scalability. Emphasis is placed on the importance of standardized safety assessments and eco-friendly design to support responsible innovation. Overall, nano-enabled smart packaging represents a promising strategy for advancing food safety and sustainability. Future developments will require collaboration across disciplines and robust regulatory frameworks to ensure the safe and practical application of nanotechnology in food systems. Full article

Falkland Islands English (FIE) began its development in the first half of the 19th century. In part, as a consequence of its youth, FIE is an understudied variety. It shares some morphosyntactic features with other anglophone countries in the Southern Hemisphere, but it also shares lexical features with regional varieties of Spanish, including Rioplatense Spanish. Che is one of many South American words that have entered FIE through Spanish, with its spelling ranging from “chay” and “chey” to “ché”. The word has received some marginal attention in terms of its meaning. It is said to be used in a similar way to the British dear or love and the Australian mate, and it has been compared to chum or pal, and is taken as an equivalent of the River Plate, hey!, hi!, or I say!. In this work, we explore the hypothesis that che entered FIE through historical contact with Rioplatense Spanish, drawing on both linguistic and sociohistorical evidence, and presenting survey, corpus, and ethnographic data that illustrate its current vitality, usage, and social meanings among FIE speakers. In situ observations, fieldwork, and an online survey were used to look into the vitality of che. Concomitantly, by crawling social media and the local press, enough data was gathered to build a small corpus to further study its vitality. A thorough literature review was conducted to hypothesise about the borrowing process involving its entry into FIE. The findings confirm that the word is primarily a vocative, it is commonly used, and it is indicative of a sense of belonging to the Falklands community. Although there is no consensus on the origin of che in the River Plate region, it seems to be the case that it entered FIE during the intense Spanish–English contact that took place during the second half of the 19th century. Full article

This study conducts a systematic qualitative review of empirical research on sense of place within housing contexts, employing the tripartite model of place identity, place attachment, and place dependence. The study employs an expanded model that captures the internal complexity of each indicator by integrating its cognitive, affective, and conative components, which represent the dimensions of human–place interaction. This model conceptualizes sense of place as a multidimensional construct, facilitating thematic synthesis and cross-study comparisons. A structured search of Scopus and Web of Science identified 10 studies that met predefined inclusion criteria. Additionally, eight studies with divergent conceptualizations of sense of place were narratively analyzed to explore the diversity of interpretations across disciplinary perspectives in housing research. The review yields three key findings: (1) The expanded tripartite model provides a framework for understanding the relationships between residents and housing. (2) Sense of place is both a criterion and a catalyst for housing sustainability. (3) The development of a sense of place is influenced by the interaction of physical, spatial, environmental, social, cultural, economic, and institutional housing factors. Sense of place provides insight into how housing becomes home, informing context-dependent strategies that enhance place-based connections and contribute to housing sustainability. Full article

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

The structure and interpersonal interactions of traditional residential communities have also been impacted and recreated as a result of the fast development of urban space and related communities. This study explores the interrelationship between neighborhood social cohesion and the life satisfaction of urban adult residents through the mediating effect of place attachment. A comprehensive theoretical model was constructed to analyze the action mechanism among these variables. Data were collected through an online questionnaire platform (n = 301), and structural equation modeling (PLS-SEM) was employed for analysis. The findings revealed a significant positive relationship between neighborhood social cohesion and residents’ place attachment. Place attachment appeared to play a mediating role between neighborhood social cohesion and life satisfaction, in which place dependence was also a potential effective mediator between the three dimensions of neighborhood social cohesion (neighborliness, sense of community, and neighborhood attractiveness) and life satisfaction. The results suggest that enhancing community cohesion may contribute to urban adult residents’ well-being by strengthening their functional dependence on the community. Full article