Search Results (6,344)

An efficient and robust solution for 3D indoor reconstruction is crucial for various managerial operations in the Architecture, Engineering, and Construction (AEC) sector, such as indoor asset tracking and facility management. Conventional approaches, primarily relying on SLAM and deep learning techniques, face certain limitations. With the recent emergence of radiance field (RF)-inspired methods, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS), it is worthwhile to evaluate their capability and applicability for reconstructing built environments in the AEC domain. This paper aims to compare different RF-inspired methods with conventional SLAM-based methods and to assess their potential use for asset management and related downstream tasks in indoor environments. Experiments were conducted in university and laboratory settings, focusing on 3D indoor reconstruction and semantic asset segmentation. The results indicate that 3DGS and Nerfacto generally outperform other NeRF-based methods. In addition, this study provides guidance on selecting appropriate reconstruction approaches for specific use cases. Full article

The diagnosis of post-stressed anchor rods is essential for maintaining the service and ensuring the safety of Electricité de France (EDF) structures. These rods are critical for the mechanical strength of structures and electromechanical components. Currently, the standard method for estimating the effective tension of post-stressed tie rods with a free length involves measuring the residual force using a hydraulic jack. However, this method can be costly, impact the structure’s operation, and pose risks to employees. Until now, there has been no reliable on-field approach to estimating residual tension using a lightweight setup. This research introduces a nondestructive method using multimodal ultrasonic guided waves to evaluate the residual tension of anchor rods with a few centimeters free at one end. The methodology was developed through both laboratory experiments and simulations. This new method allows for the extraction of dispersion curves for the first three modes, bending, torsional, and longitudinal, using time–frequency analysis and enables the estimation of the steel bar’s properties. Future work will focus on applying this methodology in the field. Full article

The third-generation instrument era is approaching, and the Einstein Telescope (ET) giant interferometer is becoming a reality, with the potential to be installed at an underground site where seismic noise is about 100 times lower than at the surface. Moreover, new available technologies and the experience acquired from operating advanced detectors are key to further extending the detection bandwidth down to 2–3 Hz, with the possibility of suspending a cryogenic payload. The New Generation of Super-Attenuator (NGSA) is an R&D project aimed at the improvement of vibration isolation performance for thirrd-generation detectors of gravitational waves, assuming that the present mechanical system adopted for the advanced VIRGO interferometer (second generation) is compliant with a third-generation detector. In this paper, we report the preliminary results obtained from a simulation activity devoted to the characterization of a mechanical system based on a multi-stage pendulum and a double-inverted pendulum in a nested configuration (NIP). The final outcomes provide guidelines for the construction of a reduced-scale prototype to be assembled and tested in the “PLANET” laboratory at INFN Naples, where the multi-stage pendulum—equipped with a new magnetic anti-spring (nMAS)—will be hung from the NIP structure. Full article

Due to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method based on a neural network is applied to implement a long-term structural assessment, with the input being monitored strain data; it is named the jellyfish search algorithm-optimized BP neural network (JS-BP) model. Considering the structural characteristics, three key parameters are selected as the key parameters to implement the assessment, namely, the bending and tensile modulus, reaction modulus at top of the subgrade, and seam equivalent modulus. To implement the method, the databases are established first with the simulation results from some finite element models of prefabricated concrete pavement. Then, the proposed JS-BP neural network model is trained and checked with the established database. The simulation results verify an excellent accuracy of the proposed method as the difference between the predicted value and the true value is smaller than 1%. Moreover, the aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the proposed JS-BP model, the accuracy of the traditional BP model is not so high, as the largest error can be up to 25%. Lastly, the proposed method is verified with some experiments using laboratory models. From the test results it is indicated that the prediction accuracy of the proposed method for the three parameters is still good enough, as the prediction error is within 5%. Full article

Background/Objective: Attention should be paid to the introduction of more functional training methods during the second stage of cardiac rehabilitation, which imitate everyday activities to some extent. The main purpose of this research was to analyze the effects of a 22-day training program carried out in normobaric hypoxic conditions corresponding to the altitude of 3000 m a.s.l. in patients after myocardial infarction and to compare it with the same training conducted in normoxic conditions. Material and Methods: This study included 36 patients after myocardial infarction who underwent percutaneous angioplasty with stent implantation. They were examined before and after 2 days of training sessions: day one, spiroergometric exercise test on a mechanical treadmill, blood collection for laboratory tests; day two, echocardiography of the heart. Than patients underwent 22 days of training in hypoxic conditions. At the end of experiment patients had the same examinations as day one and two. Results: Training conducted in hypoxic conditions had a wider impact on spiroergometrical parameters. Significant, beneficial changes were demonstrated in relation to test duration, distance covered, energy expenditure MET, respiratory exchange ratio RER, as well as resting values of systolic and diastolic blood pressure. There were no changes in parameters for morphology, cytokines, and fibrinogen. There were some differences in relation to echocardiography examinations. Conclusions: The conditions in which the rehabilitation training was conducted affect the level of exercise tolerance. The hypoxic conditions in which the training was conducted affected only two hemodynamic parameters: LVESd and e’ septal. Rehabilitation training conducted in various environmental conditions had an impact only on the IL-10 value. Full article

Within-field soil physical and chemical heterogeneity may affect spatio-temporal crop performance. Managing this heterogeneity can contribute to improving resource use and crop productivity. A simulation experiment based on comprehensive soil and crop data collected at the patchCROP landscape laboratory in Tempelberg, Brandenburg, Germany, an area characterized by heterogeneous soil conditions, was carried out to quantify the impact of within-field soil heterogeneities and their interactions with interannual weather variability on crop yield variability in summer and winter crops. Our hypothesis was that crop–soil water holding capacity interactions vary depending on the crop, with some crops being more sensitive to water stress conditions. Daily climate data from 1990 to 2019 were collected from a nearby station, and crop management model inputs were based on the patchCROP management data. A previously validated agroecosystem model was used to simulate crop growth and yield for each soil auger profile over the 30-year period. A total of 49 soil auger profiles were classified based on their plant available soil water capacity (PAWC), and the seasonal rainfall by crop was also classified from lowest to highest. The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. Spatial variability ranged from 17.3% for rapeseed to 45.8% for lupine, while temporal variability ranged from 10.4% for soybean to 36.8% for sunflower. Maize and sunflower showed a significant interaction between soil PAWC and seasonal rainfall, unlike legume crops lupine and soybean. As for winter crops, the interaction was also significant, except for wheat. Grain yield variations tended to be higher in years with low seasonal rainfall, and crop responses under high seasonal rainfall were more consistent across soil water categories. The simulated results can contribute to cropping system design for allocating crops and resources according to soil conditions and predicted seasonal weather conditions. Full article

Navigable waterways play a vital role in the efficient transportation of millions of tons of cargo annually. Inland traffic must pass through a lock, which consists of miter gates. Failures and closures of these gates can significantly disrupt waterborne commerce. Miter gates often experience fatigue cracking due to their loading and welded connections. Repairing every crack can lead to excessive miter gate downtime and serious economic impacts. However, if the rate of crack growth is shown to be sufficiently slow, e.g., using Paris’ law, immediate repairs may be deemed unnecessary, and this downtime can be avoided. Paris’ law is often obtained from laboratory testing with detailed crack measurements of specimens with relatively simple geometry. However, Paris’ law parameters for an in situ structure will likely deviate from those predicted from physical testing due to variations in loading and materials and a far more complicated geometry. To improve Paris’ law parameter prediction, this research proposes a framework that utilizes (1) convenient vision-based tracking of crack evolution both in the laboratory and the field and (2) numerical model estimation of stress intensity factors (SIFs). This study’s methodology provides an efficient tool for Paris’ law parameter prediction that can be updated as more data become available through vision-based monitoring and provide actionable information about the criticality of existing cracks. Full article

It is difficult to meet environmental requirements via the coarse treatment methods of landfilling and open-air storage of construction waste. At the same time, the consumption of building materials in highway engineering is enormous. Using construction waste as a filling material for proposed roads has become a research hotspot in recent years. This paper starts with basic performance tests of recycled construction waste materials, and then moves on to laboratory experiments conducted to obtain the road performance of the recycled materials, the testing of key indicators of post-construction filling quality of the roadbed, and analyses of the deformation pattern of roadbed filled with construction waste. Additionally, the ABAQUS finite element software was used to establish a numerical model for roadbed deformation and analyze the roadbed deformation under different compaction levels and vehicle load conditions. The experimental results show that the recycled material has a moisture content of 8.5%, water absorption of 11.73%, and an apparent density of 2.61 g/cm³, while the liquid limit of fine aggregates is 20% and the plasticity index is 5.4. Although the physical properties are slightly inferior to natural aggregates, its bearing ratio (25–55%) and low expansion characteristics meet the requirements for high-grade highway roadbed filling materials. The roadbed layer with a loose compaction of 250 mm, after eight passes of rolling, showed a settlement difference of less than 5 mm, with the loose compaction coefficient stabilizing between 1.15 and 1.20. Finite element simulations indicated that the total settlement of the roadbed stabilizes at 20–30 mm, and increasing the compaction level to 96% can reduce the settlement by 2–4%. Vehicle overload causes a positive correlation between the vertical displacement and shear stress in the base layer, suggesting the need to strengthen vehicle load control. The findings provide theoretical and technical support for the large-scale application of recycled construction waste materials in roadbed engineering. Full article

In this paper, the control structure for a crane system is proposed. It is designed to achieve fast cargo transfer with minimum cargo sway. The proposed control structure reduces the cargo sway generated by the position controller, which accelerates and decelerates cargo to transfer it with minimum time from the start to the desired location. A comparison between results achieved by simulation and experiments in the laboratory is given. Each segment of the proposed control structure is analyzed, and reasons for their use in this control structure are explained. The laboratory model’s parameters are identified to parameterize the position controller and sway-reduction control structure. This control structure uses only the cargo’s position feedback because the main reason for cargo sway, for which a sway reduction is needed, is crane movement, which is controlled by the position controller. Other control structures use two types of feedback, while this proposed control structure uses only one. Because of this, it is also economical. Full article

Medical diagnostics is an important step in the identification and detection of any disease. Generally, diagnosis requires expert supervision, but in recent times, the evolving emergence of machine intelligence and its widespread applications has necessitated the integration of machine intelligence with pathological expert supervision. This research aims to mitigate the diagnostics of urinary tract infections (UTIs) by visual recognition of Colony-Forming Units (CFUs) in urine culture. Recognizing the patterns specific to positive, negative, or uncertain UTI suspicion has been complemented with several neural networks inheriting the Multi-Layered Perceptron (MLP) architecture, like Vision Transformer, Class-Attention in Vision Transformers, etc., to name a few. In contrast to the fixed model edge weights of MLPs, the novel Kolmogorov–Arnold Network (KAN) architecture considers a set of trainable activation functions on the edges, therefore enabling better extraction of features. Inheriting the novel KAN architecture, this research proposes a set of three deep learning models, namely, K²AN, KAN-C-Norm, and KAN-C-MLP. These models, experimented on an open-source pathological dataset, outperforms the state-of-the-art deep learning models (particularly those inheriting the MLP architecture) by nearly $7.8361\%$. By rapid UTI detection, the proposed methodology reduces diagnostic delays, minimizes human error, and streamlines laboratory workflows. Further, preliminary results can complement (expert-supervised) molecular testing by enabling them to focus only on clinically important cases, reducing stress on traditional approaches. Full article

The influence of supercritical CO₂ on the properties of petroleum has become the focus of academic and industrial attention internationally. CO₂ has been shown in laboratory studies and in field applications of shale oil to be an effective oil displacement agent. In this paper, the research progress of the interaction between CO₂ and crude oil is investigated from three perspectives: (i) the research methods of the interaction experiment between CO₂ and crude oil; (ii) the influence of CO₂ on oil property and the primary controlling factors; and (iii) the cause, influence, and harm of CO₂-induced asphaltene precipitation. Our current knowledge on this topic is as follows: (1) Physical simulation can investigate the effects of various variables on CO₂ displacement, which is in situ and intuitive. Numerical simulation can investigate the displacement principle at the microscopic molecular level and also scale up the results of physical simulation to the macroscopic scale of oilfield production to explore the long-term large-scale injection rules; (2) after entering the formation, CO₂ dissolves in crude oil, expands the volume of crude oil, reduces the viscosity, improves the oil–water mobility ratio, reduces the oil–water interfacial tension, and extracts light hydrocarbons to form a miscible displacement zone; (3) after CO₂ is injected into the formation and dissolves in crude oil, it occupies the surface space of asphaltenes and causes asphaltenes to precipitate. Under the combined influence of internal and external factors, the precipitation of asphaltenes has a significant impact on the physical properties of the reservoir. Clarifying the influencing factors of CO₂ on the property of crude oil has reference significance for understanding the reaction characteristics between supercritical CO₂ and formation fluids, providing a theoretical basis for CO₂ injection enhanced oil recovery technology, and has reference value for carbon storage research. Full article

Chronic graft-versus-host disease (cGVHD) is a prognostically negative event following hematopoietic stem cell transplant (HSCT). While cGVHD mainly affects the muscles, skin, oral mucosa, eyes, lungs, gastrointestinal tract, and liver, central nervous system (CNS) involvement remains possible and, moreover, is rare when it occurs isolated. CNS-cGVHD can manifest with a wide spectrum of CNS disorders, including cerebrovascular diseases, autoimmune demyelinating diseases, and immune-mediated encephalitis. We present a case of 65-year-old man previously treated with HSCT presenting with progressive cerebrovascular disorder and optic neuropathy without any clear alternative causal processes except for immune-mediated CNS microangiopathy in the context of possible CNS-cGVHD, along with suggestive imaging and instrumental and laboratory findings. Starting one year after HSCT for acute myeloid leukemia, when the first cerebral ischemic event occurred and was then associated with a reduction in visual acuity, an extensive diagnostic work-up had remained inconclusive over many years, leading us to the hypothesis of CNS-cGVHD and, therefore, to the start of immunosuppressive therapy. Our experience highlighted not ignoring the possibility of cGVHD as the underlying mechanism of CNS disorder, even in the absence of other systemic presentations, once more common etiologies of CNS pathological processes have been ruled out. Full article

This review examines the degradation of refractory materials in aluminum reduction cells, focusing specifically on contamination caused by the cryolite-based bath. Aluminosilicate refractories, particularly Ordinary Refractory Bricks, play a vital role in maintaining the structural integrity and thermal balance of these cells under demanding operational conditions. The interaction between the molten bath and refractory linings leads to chemical reactions and mineralogical changes that modify the mechanical and thermal properties of the material over time. The study integrates findings from industrial autopsies, laboratory experiments, and a comprehensive review of the existing literature to identify and analyze the mechanisms of degradation. By analyzing the findings obtained from these methodologies, this review explores how cryolitic infiltration triggers transformations that compromise performance and reduce the lifespan of refractory linings. Covering a broad temperature range (665–960 °C), the study addresses key challenges in understanding bath-induced contamination and provides insights into how to improve the durability and efficiency of refractory materials in aluminum production. Full article

The rapid advancement of artificial intelligence (AI) has spurred innovation across various domains—information technology, medicine, education, and the social sciences—and is likewise creating new opportunities in architecture for understanding human–environment interactions. This study aims to develop a fine-tuned AI model that leverages electroencephalography (EEG) data to analyse users’ emotional states in real time and apply these insights to architectural spaces. Specifically, the SEED dataset—an EEG-based emotion recognition resource provided by the BCMI laboratory at Shanghai Jiao Tong University—was employed to fine-tune the ChatGPT model for classifying three emotional states (positive, neutral, and negative). Experimental results demonstrate the model’s effectiveness in differentiating these states based on EEG signals, although the limited number of participants confines our findings to a proof of concept. Furthermore, to assess the feasibility of the proposed approach in real architectural contexts, we integrated the model into a 360° virtual reality (VR) setting, where it showed promise for real-time emotion recognition and adaptive design. By combining AI-driven biometric data analysis with user-centred architectural design, this study aims to foster sustainable built environments that respond dynamically to human emotions. The results underscore the potential of EEG-based emotion recognition for enhancing occupant experiences and provide foundational insights for future investigations into human–space interactions. Full article

A low-grade copper ore from an Australian mine was processed under continuous steady state conditions using the REFLUX^™ Flotation Cell (RFC^™), and the performance was quantified with reference to a batch mechanical cell and the plant circuit, at the plant feed concentration. In the RFC^™, the variation in the copper grade and the recovery were determined using feed fluxes ranging from 0.5 to 3.0 cm/s, with a strong positive bias flux to achieve cleaning. The RFC^™ experiments showed an increasing product grade with increasing feed flux, increasing to 23% copper in a single stage. The result exceeded the grade of 14% produced by a laboratory-scale, two-stage mechanical cell and was comparable to the multi-stage plant circuit. The RFC^™ recoveries increased with increasing feed flux, peaking at 81.7% for a feed flux of 2.0 cm/s before declining. Moreover, for equivalent copper recovery, the laboratory-scale RFC^™ throughput performance was more than five times higher than for the rougher circuit of the industrial plant. It is noted the RFC^™ product grade was nearly three times higher than for the rougher cells. For similar recoveries and product grades, the RFC^™ throughput was about eight times higher than that observed for the rougher and cleaner circuits of the industrial plant. This work demonstrates the potential for the process footprint to be significantly minimised. Full article