Search Results (703)

After an earthquake, legislation tends to permit the rapid demolition of damaged buildings, including the built heritage, for safety reasons, as was the case for many small historic centers after the 2016 earthquake in central Italy. A balance should, of course, be struck between safety and preservation. There must be a willingness to engage in continuous interaction with the various bodies involved in post-earthquake management, particularly in the preventive phase of the complex activities regarding the issues of the seismic vulnerability of historic built. The widespread historical built heritage in Italy requires fast and reliable assessment procedures that allow a large-scale evaluation of the vulnerability of historical buildings before a seismic event. To this end, a proposal is presented here for the inverse use of the protocol for the seismic vulnerability survey of historic centers by means of a system called CARTIS form, coordinated since 2015 by the Italian consortium of Seismic and Structural Engineering Laboratories (ReLUIS). This rapid assessment is compared with an equally fast method for constructing fragility curves, based only on the information available in the ReLUIS–CARTIS database, defining the relationship between the probability of reaching a level of loss of structural safety or a vulnerability index as a function of the seismic acceleration PGA and the ground orography. The methodology outlined could be considered to be progress in cultural heritage diagnostics on a large scale, considering cultural heritage to be the diffuse historical residential masonry buildings that form the historic centers. Full article

Invasive plant species have been recognized as adversely affecting native ecosystems. Some of these plant species become problematic in disturbed environments such as urbanized, agricultural, and abandoned developed or farmed land. In some cases, they can dominate the invaded ground, preventing a transition back to the native plant community. In Tenerife (Canary Islands), the invasive plant species Ulex europaeus L. has established dense infestations in abandoned agricultural lands. Removing such invasive species in Tenerife through ecological restoration is crucial for restoring ecosystem functionality and promoting biodiversity. This study evaluates various management methods for U. europaeus in abandoned fields, assessing their impact on species richness, diversity, composition, and regeneration. The findings can inform management strategies to combat this invasive species, contributing to biodiversity conservation and ecosystem resilience. The study was conducted in two highly invaded areas, evaluating chemical (C), mechanical (M), mechanical and chemical (MC), and mechanical, chemical, and plantation treatments (MCP), along with a control, to analyze changes in species richness, diversity, and plant community. Results indicate that U. europaeus remains capable of reoccupying treated areas. The different treatments led to differences in species richness and composition. The MCP treatment yielded the best results if the planted native species grew faster than Ulex europaeus, preventing its establishment due to its shade-intolerant nature. However, continual control is required to eliminate U. europaeus regeneration from seeds that can persist for at least 30 years. Full article

To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study proposes an end-to-end adaptive multi-scale feature extraction and fusion detection network, named AMFEF-DETR. Specifically, to extract target features from complex backgrounds more accurately, we propose an adaptive backbone network, FADC-ResNet, which dynamically adjusts dilation rates and performs adaptive frequency awareness. This enables the convolutional kernels to effectively adapt to varying scales of ground targets, capturing more details while expanding the receptive field. We also propose a HiLo attention-based intra-scale feature interaction (HLIFI) module to handle high-level features from the backbone. This module uses dual-pathway encoding of high and low frequencies to enhance the focus on the details of dense small targets while reducing noise interference. Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. The Inner-Shape-IoU loss function, designed to focus on bounding box shapes and incorporate auxiliary boxes, is introduced to accelerate convergence and improve regression accuracy. When evaluated on the VisDrone dataset, the AMFEF-DETR demonstrated improvements of 4.02% and 16.71% in mAP50 and FPS, respectively, compared to the RT-DETR. Additionally, the AMFEF-DETR model exhibited strong robustness, achieving mAP50 values 2.68% and 3.75% higher than the RT-DETR and YOLOv10, respectively, on the HIT-UAV dataset. Full article

The two near-identical pushbroom Thermal Infrared Sensors (TIRS) aboard Landsat 8 and 9 are currently imaging the Earth’s surface at 10.9 and 12 microns from similar 705 km altitude, sun-synchronous polar orbits. This work validates the consistency in the imaging data quality, which is vital for harmonization of the data from the two sensors needed for global mapping. The overlapping operation of these two near-identical sensors, launched eight years apart, provides a unique opportunity to assess the sensitivity of the conventionally used metrics to any unexpectedly found nuanced differences in their spatial performance caused by variety of factors. Our study evaluates spatial quality metrics for bands 10 and 11 from 2022, the first complete year during which both TIRS instruments have been operational. The assessment relies on the straight-knife-edge technique, also known as the Edge Method. The study focuses on comparing the consistency and stability of eight separate spatial metrics derived from four separate water–desert boundary scenes. Desert coastal scenes were selected for their high thermal contrast in both the along- and across-track directions with respect to the platforms ground tracks. The analysis makes use of the 30 m upsampled TIRS images. The results show that the Landsat 8 and Landsat 9 TIRS spatial performance are both meeting the spatial performance requirements of the Landsat program, and that the two sensors are consistent and nearly identical in both across- and along-track directions. Better agreement, both with time and in magnitude, is found for the edge slope and line spread function’s full-width at half maximum. The trend of averaged modulation transfer function at Nyquist shows that Landsat 8 TIRS MTF differs more between the along- and across-track scans than that for Landsat 9 TIRS. The across-track MTF is consistently lower than that for the along-track, though the differences are within the scatter seen in the results due to the use of the natural edges. Full article

Three-dimensional printing of cementitious materials for construction has been extensively investigated in recent years, with several demonstration projects successfully carried out. These efforts aim to leverage the printing process to achieve more efficient production of components compared to conventional concrete technologies. This includes both the process itself (eliminating the formwork stage) and the flexibility in producing complexly shaped elements. To maximize the potential of 3D printing in the construction industry, additional steps must be taken, grounded in a holistic view of the entire process. This involves integration of the production chain, including design, materials, and manufacturing of components, to create elements with optimal performance, encompassing structural, environmental, and architectural aspects. Such multi-functionality requires the viewing of 3D printing not just as a production technology but as a platform enabling the integration of all these components. To advance this approach, quantitative tools are developed to optimize the following three key components: material composition; manufacturing parameters to ensure buildability; and design tools to optimize multiple performance criteria, particularly structural and architectural shape. A demonstration component, namely a post-tensioned beam, featuring two multi-functional characteristics—structural and architectural—is designed, produced, and evaluated. The scientific concepts and research tools used to develop these quantitative design tools are multidisciplinary, including rheological characterization, control of the internal structure and composition of granular materials, simulation of the mechanical behavior of green material during printing, and the hardened properties of the components, all utilizing structural optimization to enhance performance. Full article

The sustainability of cities is a challenge due to the growth, functioning, and needs of the population. In this context, the concept of the smart city has gained prominence worldwide in the last decades. In Brazil, it has also drawn attention driven by technological advances and the search for innovative solutions to urban challenges. Several different reports were created aiming to assess and categorize the advancement of cities in this regard. Some of them established their own indicators for this evaluation, whereas others are based on standards ISO 37120 and ISO 37122. The focus of this study revolves around the examination of indicators presently utilized, which may be influenced by initiatives grounded in the principles of green chemistry (PGCs). Furthermore, it explores how these principles can aid in the realization of the targets outlined in the Sustainable Development Goals (SDGs) set forth by the United Nations. Two case studies are presented, the first pointing out how PGCs and SDGs can correlate with smart city reporting indicators. The second is a case study centered on the Brazilian city of Curitiba (PR). We aim to exemplify how the city’s action plans underscore the significance of synergy among the principles of green chemistry, the objectives of sustainable development, and indicators for smart cities. Assessing how chemically green a city can be is a relevant argument for future industrial installations and stakeholders and the influence of this index on the quality of life of its population. Such an approach not only fosters innovation and efficiency but also fosters environmental stewardship, thus contributing to overall sustainability. Full article

Evidence supporting a link between gait and cognition is accumulating. However, the relation between executive functioning and spatiotemporal gait parameters has received little attention. This is surprising since these gait variables are related to falls. The goal of this preliminary study was to determine whether performance on measures of inhibition, reasoning, and fluency is related to variability in stride length and step width during dual-task treadmill walking in a sample of healthy adults. Nineteen healthy adults averaging 40 years of age were evaluated. Results indicated that processing speed was reduced, t(18) = 6.31, p = 0.0001, step width increased, t(18) = −8.00, p = 0.0001, and stride length decreased, t(18) = 3.06, p = 0.007, while dual tasking, but variability in gait parameters did not significantly change, consistent with a gait/posture-first approach. As hypothesized, better performance on a visual design fluency task which assesses cognitive flexibility was associated with less dual-task stride length variability, r_s(17) = −0.43, p = 0.034, and step width variability, r = −0.56, p = 0.006. The results extend previous findings with older adults walking over ground and additionally suggest that cognitive flexibility may be important for gait maintenance while dual tasking. Full article

The transformation of spent coffee grounds (SCGs) into hydrochars has been extensively studied in recent years to explore their potential in biofortifying foods and mitigating the plant toxicity associated with SCGs. This study aimed to evaluate the effects of adding activated (ASCG and AH160) and functionalized SCGs, as well as SCG-derived hydrochars (ASCG-Fe and AH160-Fe), on cucumber production and plant iron content. To achieve this, SCGs and SCG-derived hydrochars activated and functionalized with Fe were incorporated into cucumber crops grown in a greenhouse over multiple harvests. Among the treatments, SCG-Fe proved to be the most promising for cucumber production, yielding an average of 25 kg of cumulative production per treatment across three harvests. Regarding iron content, the average results across all harvests showed that SCGs and functionalized SCG-hydrochars matched the performance of the commercial chelate (0.108 vs. 0.11 mg Fe/100 g fresh weight). However, in subsequent harvests, iron appeared to leach out, with the activated bio-products (ASCG and AH160) leaving the highest iron reserves in the soil. Additionally, the hydrochar activated at 160 °C demonstrated the highest utilization efficiency. In conclusion, the incorporation of SCG residues and second-generation residues (hydrochars) shows promise as agents for biofortifying cucumbers. Full article

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 mV, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services. Full article

The concept of mixed-use urban planning is gaining recognition as a crucial element in the development of sustainable and vibrant urban environments. In contrast, many 20th-century cities were designed with segregated land uses and monofunctional zones, following the principles set out in the 1933 Athens Charter. Over time, this approach has been widely criticized, and in the present era, mixed-use environments are praised for fostering social interaction, generating economic synergies, and reducing environmental impacts. This article explores the complex relationship between urban activities, morphology, and planning, with a particular focus on the Barcelona metropolitan area. Utilizing GIS mapping and morphological drawings, this research offers innovative perspectives by analyzing a series of selected urban fragments, highlighting the differences and similarities among various urban fabrics. After a review of the evolution of mixed-use planning regulations and plans since the mid-20th century, a threefold analysis was conducted: examining planning standards and codes, assessing the ground floor activities in promoting urban mixticity, and defining the characteristics of urban patterns’ vitality. Through mapping and indexes, the research offers both qualitative and quantitative evaluations, uncovering new tools to better understand functional mix as a critical element in addressing the challenges of contemporary urbanization. Full article

Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in the model are the key to improving the accuracy of APC. However, the conventional APC method first performs phase unwrapping and then removes the APS based on the least-squares method (LSM), and the general phase unwrapping method is prone to introducing unwrapping error. In particular, the LSM is difficult to apply directly due to the phase wrapping of permanent scatterers (PSs). Therefore, a novel methodology for estimating parameters of the APC model based on the maximum likelihood estimation (MLE) and the Gauss-Newton algorithm is proposed in this paper, which first introduces the MLE method to provide a suitable objective function for the parameter estimation of nonlinear far-end and near-end correction models. Then, based on the Gauss-Newton algorithm, the parameters of the objective function are iteratively estimated with suitable initial values, and the Matthews and Davies algorithm is used to optimize the Gauss-Newton algorithm to improve the accuracy of parameter estimation. Finally, the parameter estimation performance is evaluated based on Monte Carlo simulation experiments. The method proposed in this paper experimentally verifies the feasibility and superiority, which avoids phase unwrapping processing unlike the conventional method. Full article

The lack of vertical observation of reactive nitrogen oxides in agricultural areas has posed a significant challenge in fully understanding their sources and impacts on atmospheric oxidation. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations were conducted in the agricultural regions of the North China Plain (NCP) during the summer of 2019 to measure the vertical distributions of aerosols, nitrogen dioxide (NO₂), and nitrous acid (HONO). This study aimed at revealing the spatiotemporal distribution, sources, and environmental effects of reactive nitrogen oxides in the NCP agricultural areas. Our findings indicated that the vertical profiles of aerosols and NO₂ exhibited a near-Gaussian distribution, with distinct peak times occurring between 8:00–10:00 and 16:00–18:00. HONO reached its maximum concentration near the surface around 8:00 in the morning and decreased exponentially with altitude. After sunrise, the concentration of HONO rapidly decreased due to photolysis. Additionally, the potential source contribution function (PSCF) was used to evaluate the potential sources of air pollutants. The results indicated that the main potential pollution sources of aerosols were located in the southern part of the Hebei, Shanxi, Shandong, and Jiangsu provinces, while the potential pollution sources of NO₂ were concentrated in the Beijing–Tianjin–Hebei region. At altitudes exceeding 500 m, the heterogeneous reactions of NO₂ on aerosol surfaces were identified as one of the important contributors to the formation of HONO. Furthermore, we discussed the production rate of hydroxyl radicals (OH) from HONO photolysis. It was found that the production rate of OH from HONO photolysis decreased with altitude, with peaks occurring in the morning and late afternoon. This pattern was consistent with the variations in HONO concentration, indicating that HONO was the main contributor to OH production in the agricultural regions of the NCP. This study provides a new perspective on the sources of active nitrogen in agricultural regions and their contribution to atmospheric oxidation capacity from a vertical perspective. Full article

Tetragonal and hexagonal hybrid sp³/sp² carbon allotropes C₅ were proposed based on crystal chemistry and subsequently used as template structures to identify new binary phases of the B–N system, specifically tetragonal and hexagonal boron nitrides, B₂N₃ and B₃N₃. The ground structures and energy-dependent quantities of the new phases were computed within the framework of quantum density functional theory (DFT). All four new boron nitrides were found to be cohesive and mechanically (elastic constants) stable. Vickers hardness (H_V), evaluated by various models, qualified all new phases as superhard (H_V > 40 GPa). Dynamically, all new boron nitrides were found to be stable from positive phonon frequencies. The electronic band structures revealed mainly conductive behavior due to the presence of π electrons of sp²-like hybrid atoms. Full article

Soil–structure interaction (SSI) leads to a modification in the dynamic properties of structure, but due to the complexity of analysis, it is traditionally assumed in seismic designs that the structure is fixed-supported on the ground, which brings about potential risks to the seismic performances of structure. The study works on the possibility of SSI having detrimental effects by comparing the dynamic responses of the SSI system to a fixed-base structure, and presents charts for an evaluation of the system frequency of SSI for the purpose of engineering practice. In order to reveal the physical nature, the SSI model is reduced to its simplest form, consisting of a SDOF oscillator, a three-dimensional rectangular foundation, and a multi-layered half-space. The energy dissipation in the soil is achieved by foundation impedances and the substructure method. Previously, the foundation impedances are usually acquired by two-dimensional or axisymmetric three-dimensional models in uniform half-space to avoid the high cost of the more realistic, fully 3D models, while a high-precision indirect boundary element method is employed, combined with the non-singular Green’s functions of distributed loads to calculate the foundation impedances. Although SSI dampens the peak amplitude of structure response in the frequency domain, case studies on four buildings’ responses to 42 earthquakes in the time history show a possibility of 15–20% that SSI amplifies the dynamic responses of structures, such as the maximum and the mean values in the time history, depending on the properties of the structures and the site, as well as the frequency component of incident waves. Full article

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F₁-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas. Full article