Search Results (12,718)

Semantic labeling of very high-resolution remote sensing images (VHRRSI) has emerged as a crucial research area in remote sensing image interpretation. However, challenges arise due to significant variations in target orientation and scale, particularly for small targets that are more prone to obscuration and misidentification. The high interclass similarity and low intraclass similarity further exacerbate difficulties in distinguishing objects with similar color and geographic location. To address this concern, we introduce a self-cascading multiscale network (ScasMNet) based on a fully convolutional network, aimed at enhancing the segmentation precision for each category in remote sensing images (RSIs). In ScasMNet, cropped Digital Surface Model (DSM) data and corresponding RGB data are fed into the network via two distinct paths. In the encoder stage, one branch utilizes convolution to extract height information from DSM images layer by layer, enabling better differentiation of trees and low vegetation with similar color and geographic location. A parallel branch extracts spatial, color, and texture information from the RGB data. By cascading the features of different layers, the heterogeneous data are fused to generate complementary discriminative characteristics. Lastly, to refine segmented edges, fully conditional random fields (DenseCRFs) are employed for postprocessing presegmented images. Experimental findings showcase that ScasMNet achieves an overall accuracy (OA) of 92.74% on two challenging benchmarks, demonstrating its outstanding performance, particularly for small-scale objects. This demonstrates that ScasMNet ranks among the state-of-the-art methods in addressing challenges related to semantic segmentation in RSIs. Full article

The accurate and extensive monitoring of soil salinization is essential for sustainable agricultural development. It is difficult for single remote sensing data (satellite, unmanned aerial vehicle) to simultaneously meet the requirements of wide-scale and high-precision soil salinity monitoring. Therefore, this paper adopts the upscaling method to upscale the unmanned aerial vehicle (UAV) data to the same pixel size as the satellite data. Based on the optimally upscaled UAV data, the satellite model was corrected using the numerical regression fitting method to improve the inversion accuracy of the satellite model. The results showed that the accuracy of the original UAV soil salinity inversion model (R² = 0.893, RMSE = 1.448) was higher than that of the original satellite model (R² = 0.630, RMSE = 2.255). The satellite inversion model corrected with UAV data had an accuracy of R² = 0.787, RMSE = 2.043, and R² improved by 0.157. The effect of satellite inversion correction was verified using a UAV inversion salt distribution map, and it was found that the same rate of salt distribution was improved from 75.771% before correction to 90.774% after correction. Therefore, the use of UAV fusion correction of satellite data can realize the requirements from a small range of UAV to a large range of satellite data and from low precision before correction to high precision after correction. It provides an effective technical reference for the precise monitoring of soil salinity and the sustainable development of large-scale agriculture. Full article

This paper examines the impact of ten microeconomic factors on hotel business success. This research encompassed a sample of 115 small, family-operated hotels situated in rural regions of the Western Balkan countries (WBC). This research was based on the assumption that factors such as the size of the hotel, age, solvency, liquidity, labour productivity, capital productivity, CSR, and reduction of CO₂ emissions exhibit a positive influence on business success, whereas leverage, indebtedness, and energy consumption have a negative effect on the business success of hotels. The findings revealed that business success from the previous period, size, liquidity, and CSR exhibit a positive influence on business success, whereas leverage, capital productivity, and indebtedness demonstrate a negative effect. Conversely, the age of the hotel and labour productivity were not found to significantly influence business success, as did energy consumption. In the context of sustainable development, a positive CSR impact means that tourists value this behaviour of the hotel, while a lack of a statistically significant impact of energy consumption implies either that hotels do not implement efficient measures of energy efficiency or that energy efficiency may not be a crucial factor in attracting guests or influencing their loyalty. The findings also show that labour productivity expressed conventionally does not have a statistically significant impact on hotel business success. However, when expressed in a way that respects the concept of sustainable development and CSR, workforce productivity is a significant factor in hotel business success. Due to the problem of multicollinearity, the influence of CO₂ emissions was not examined. The findings suggest the following two groups of key measures: 1. Policymakers must work on ensuring more favourable conditions under which hotels can borrow, as well as on ensuring adequate infrastructure; 2. They must work on improving the strategy for maintaining liquidity to avoid the high costs of short-term loans and increasing size in order to further utilise economies of scale. These two microeconomic factors have the greatest impact on the business success of hotels. Full article

(1) Background: Industry 4.0 and the COVID-19 pandemic have resulted in an acceleration of digital transformation, primarily in production systems and logistics. This raises the need to assess where a company is in its digital transformation today and what measures must be taken to improve logistics processes. This article aims to present the results of a study assessing the digital maturity of logistics processes in a group of selected enterprises located in Poland. The research was conducted among companies that are business partners of the Poznan School of Logistics. (2) Methods: The DMM-OP digital process maturity assessment model was used in the study. Digital maturity was assessed on a five-point scale in four areas of company activity: process management, performance measurement, employee support, and technology. The research procedure included four stages. (3) Results: The results indicate that companies in the process management and performance measurement dimensions achieved the highest level of digital maturity. In commercial enterprises, the level of digital transformation is at the lowest level. Large enterprises achieved the best results, but there were also very good results in the group of small enterprises. (4) Conclusions: The results presented in the article can be used by industry and academia. The research was not statistical but can form the basis for benchmarking analyses. Full article

Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, which integrates both local and non-local bidirectional features. Additionally, a local feature extraction branch is introduced to enhance target information by applying local magnification at the feature extraction layer allowing for the capture of more detailed features. To address the challenge of target and background blending, we propose a strategy involving multi-scale fusion of the local branch and global feature extraction. Additionally, the use of a 1 × 1 convolution structure and concat operation reduces model computation. Compared to the baseline, our method shows a 2.9% improvement in mAP₅₀ on a real infrared dataset, with the detection rate reaching 93.84%. These experimental results underscore the effectiveness of our method in extracting relevant features while suppressing background interference in infrared dim and small target detection (IDSTD), making it more robust. Full article

Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel. Full article

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm’s robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion. Full article

Steppe pastures are characteristic of the Armenian landscape and play an important role in supporting livelihoods and biodiversity conservation. The productivity and biodiversity of steppe pastures depend on grazing management, soil types, and climatic and topographical characteristics. As a whole, they form local small-scale sites. Our data on five study sites located on the southeast slope of Mt. Aragats summarized the impact of sites and grazing on canopy height; productivity; grass, legume, and forb biomass; nitrogen, phosphorus, and potassium concentrations; pH; and litter contents in the soil. Five grazed and ungrazed plots (ca. 600–800 m²) were established at each study site. Within each plot, two permanent 40 m long transects were installed. The canopy height was recorded in ungrazed and freely grazed plots. The aboveground biomass was cut at the soil surface from May to June; grouped into grass, legumes, and forbs; dried; and weighed. Soil samples were collected in every ungrazed and freely grazed plot. The results indicated that grazing decreased the plant parameters and nitrogen and litter content across all sites, while it had no effect on the phosphorus and potassium content or the pH. It seems that plant parameters, as well as soil parameters, were more affected by the management strategy than by the topographical and climatic features of the sites, as revealed via redundancy analysis. Our results suggest that it is important to introduce livestock rotation practices for sites with respect to the sustainable management of steppe pastures. This management strategy could ensure ecosystem services, high forage quality, and soil fertility. Full article

Technological advancements in nanofibers and production technologies have led to nanofibers being applied in various applications. Nanofibers are produced by a variety of techniques such as electrospinning, drawing, self-assembly, phase separation, and others. Electrospinning is widely used due to its versatility and scalability. Nanofiber production by other techniques is still limited to the laboratory scale, hence the dominance of electrospinning. The versatility of nanofibers has seen them being used in various applications such as health, protection, clothing, filtration, packaging, and electronics. Their large surface area, small diameters, and porous structures make them good materials in these diverse fields. Nanofibers are incorporated with nanoparticles to enhance stability. In biomedical applications, nanofibers are used in drug delivery systems, wound healing, and tissue engineering because of their biocompatibility and biodegradability. In fields like protection, clothing, and packaging, nanofibers are used due to their large surface area, porosity, and flexibility. These properties also make nanofibers highly effective in filtration, where their small size and large surface area allow them to efficiently remove a significant number of contaminants. Additionally, nanofibers are utilized in the production of flexible electronics, enhancing comfort in wearable devices. Biopolymers are being adopted to address the environmental and health concerns of traditional nanofiber materials. Biopolymers are biodegradable and biocompatible; however, their stability can be affected by production and environmental conditions. This work highlights the applications of nanofibers, especially the environmentally friendly nanofiber applications in health, packaging, water treatment, protection, electronics, clothing, and technical textiles. Full article

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels. Full article

The real implementation of biogas reforming at an industrial scale to obtain interesting products (like hydrogen or syngas) is a developing research field where multidisciplinary teams are continuously adding improvements and innovative technologies. These works can contribute to the proliferation of green technologies where the circular economy and sustainability are key points. To assess the sustainability of these processes, there are different tools like life cycle assessment (LCA), which involves a complete procedure where even small details count to consider a certain technology sustainable or not. The aim of this work was to review works where LCA is applied to different aspects of biogas reforming, focusing on the role of catalysts, which are essential to improve the efficiency of a certain process but can also contribute to its environmental impact. In conclusion, catalysts have an influence on LCA through the improvement of catalytic performance and the impact of their production, whereas other aspects related to biogas or methane reforming could equally affect their catalytic durability or reusability, with a subsequent effect on LCA. Further research about this subject is required, as this is a continuously changing technology with plenty of possibilities, in order to homogenize this research field. Full article

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network’s spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods. Full article

Suspension bridges are the most common type of bridge used to cross the ocean. The cable clamps in suspension bridges clamp the main cables by bolt preload, but the elastic interaction of the bolts reduces the preload, which is detrimental to the force in suspension bridges. However, research on the factors influencing the elastic interaction of cable clamp bolts in suspension bridges is currently limited. This paper aims to explore the law of influence of external factors on the elastic interaction of bolts through a combined approach of theoretical analysis, full-scale experiment, and finite element simulation. The results indicate that the average preload loss was reduced by about 27% when the elastic modulus was increased by about 110%. The average preload loss was reduced by about 45% when the bolt center distance was increased by 75%. The number of bolts has a small effect on the elastic interaction, which can be ignored. When the preload of bolt installation was increased by 133%, the average preload loss was reduced by approximately 125%, which was almost a linear relationship. Tightening the bolt from the center bolt creates greater elastic interaction. The conclusions can provide suggestions for reducing the elastic interaction of bolts in the design and construction of suspension bridge cable clamps. Full article

A small-scale (up to 5 kWe) biogas-solid oxide fuel cell (SOFC) energy system is an envisioned system, which can be used to meet both electrical and thermal energy demand of off-grid settlements. SOFC systems are reported to be more efficient than alternatives like internal combustion engines (ICE). In addition to energy recovery, implementation of biogas-SOFC systems can enhance sanitation among these settlements. However, the capital investment costs and the operation and maintenance costs of a biogas-SOFC energy system are currently higher than the existing alternatives. From previous works, H₂S removal by biochar was proposed as a potential local cost-effective alternative. This research demonstrates the techno-economic potential of locally produced biochars made from cow manure, jackfruit leaves, and jack fruit branches in rural Uganda for purifying the biogas prior to SOFC use. Results revealed that the use of biochar from cow manure and jack fruit leaves can reduce H₂S to below the desired 1 ppm and substitute alternative biogas treatments like activated carbon. These experimental results were then translated to demonstrate how this biochar would improve the economic feasibility for the implementation of biogas-SOFC systems. It is likely that the operation and maintenance cost of a biogas-SOFC energy system can in the long run be reduced by over 80%. Also, the use of internal reforming as opposed to external reforming can greatly reduce the system capital cost by over 25% and hence further increase the chances of system economic feasibility. By applying the proposed cost reduction strategies coupled with subsidies such as tax reduction or exemption, the biogas-SOFC energy system could become economically competitive with the already existing technologies for off-grid electricity generation, like solar photovoltaic systems. Full article

While most studies focused on the impact of intensive agriculture on the barn owl’s diet, little is known about the effect of cropland abandonment. We compared the taxon composition/evenness and feeding guild structure of small mammal prey identified in pellets collected before (2004) and after (2012) the abandonment of 9% of cultivated fields within a cultural landscape. Data on prey abundance per pellet were analysed through non-metric multidimensional scaling and permutational, paired tests. Prey taxon evenness in 2012 was significantly lower than in 2004. That induced a shift in prey taxon composition as indicated by the significantly lower dietary similarity compared with the random expectation. The increasing and declining abundance of Murinae and Crocidurinae, respectively, had the largest contribution to the differentiation of the diet spectrum. Insectivorous prey was significantly more abundant in 2004 compared to 2012, while the opposite was true for omnivorous prey. Our results suggest that even a small fraction of abandoned crops in the landscape might induce a detectable shift in the barn owl’s food niche. The dietary effects are similar to those observed after agricultural intensification, that is, an increase in the abundance of generalists to the detriment of specialist mammal prey. Full article