Search Results (26,493)

These days, the use of natural materials is required for sustainable and consequently plus-, zero- and low-energy construction. One of the main objectives of this research was to demonstrate that pelite concrete block masonry can be a structural and thermal insulation material. In order to determine the actual thermal insulation parameters of the building partition, in situ experimental research was carried out in real conditions, taking into account the temperature distribution at different heights of the partition. Empirical measurements were made at five designated heights of the partition with temperature and humidity parameters varying over time. The described experiment was intended to verify the technical parameters of perlite concrete in terms of its thermal insulation properties as a construction material used for vertical partitions. It was shown on the basis of the results obtained that the masonry made of perlite concrete blocks with dimensions of 24 × 24.5 × 37.5 cm laid on the mounting foam can be treated as a building element that meets both the structural and thermal insulation requirements of vertical single-layer partitions. However, it is important for the material to work in a dry environment, since, as shown, a wet perlite block has twice the thermal conductivity coefficient. The results of the measurements were confirmed, for they were known from the physics of buildings, the general principles of the formation of heat and the moisture flow in the analysed masonry of a perlite block. Illustrating this regularity is shown from the course of temperature and moisture in the walls. The proposed new building material is an alternative to walls with a layer of thermal insulation made of materials such as polystyrene or wool and fits into the concept of sustainable construction, acting against climate change, reducing building operating costs, improving living and working conditions as well as fulfilling international obligations regarding environmental goals. Full article

Background: Both lung lobe segmentation and lung fissure segmentation are useful in the clinical diagnosis and evaluation of lung disease. It is often of clinical interest to quantify each lobe separately because many diseases are associated with specific lobes. Fissure segmentation is important for a significant proportion of lung lobe segmentation methods, as well as for assessing fissure completeness, since there is an increasing requirement for the quantification of fissure integrity. Methods: We propose a method for the fully automatic segmentation of pulmonary fissures on lung computed tomography (CT) based on U-Net and PAN models using a Derivative of Stick (DoS) filter for data preprocessing. Model ensembling is also used to improve prediction accuracy. Results: Our method achieved an F1 score of $0.916$ for right-lung fissures and $0.933$ for left-lung fissures, which are significantly higher than the standalone DoS results ($0.724$ and $0.666$, respectively). We also performed lung lobe segmentation using fissure segmentation. The lobe segmentation algorithm shows results close to those of state-of-the-art methods, with an average Dice score of $0.989$. Conclusions: The proposed method segments pulmonary fissures efficiently and have low memory requirements, which makes it suitable for further research in this field involving rapid experimentation. Full article

Abstract: The uniformity of growth and mechanical properties of grafted seedlings affect the quality of mechanical grafting operations. The growth uniformity of grafted seedlings in a greenhouse will be poor due to the uneven and unstable light and temperature conditions. Plant factories can cultivate grafted seedlings in the most suitable environment by regulating environmental parameters such as light and temperature. The aim of this study was to investigate the impact of the light conditions on tomato seedlings in plant factory and to develop an optimal cultivation light formula. The effects of light intensity (50, 100, 150, 200, and 250 μmol m⁻² s⁻²) and photoperiod (10, 12, 14, 16, and 18 h a day(h/d)) on the morphological and mechanical properties of tomato seedlings were experimentally investigated. Orthogonal experiments were conducted involving light quality (R:B = 75:25, R:B = 50:50, and R:B = 25:75), light intensity (150 μmol m⁻² s⁻², 200 μmol m⁻² s⁻², and 250 μmol m⁻² s⁻²), and photoperiod (14, 16, and 18 h/d) as independent variables to determine the optimal combination. Finally, a comparative grafting experiment was conducted between the seedlings cultivated using the optimal light formula and commercially available seedlings. The result showed that increasing light intensity inhibited hypocotyl length and promoted seedling stem growth, and excessive light intensity decreased seedling mechanical properties. The optimal light intensity for rootstocks is 200 μmol m⁻² s⁻², and the optimal light intensity for scions is 250 μmol m⁻² s⁻². Shortening the photoperiod would promote hypocotyl growth and inhibit seedling stem elongation. Different photoperiods had a significant impact on the mechanical properties of tomato seedlings. The most suitable photoperiod for rootstocks was 18 h/d and for scions was 16 h/d. The most suitable light formula was R:B = 50:50, 250 μmol m⁻² s⁻², 18 h/d. By analyzing the experimental results, the mechanical properties of seedlings grown by the regulated light environment were better than those of commercially available seedlings, and the success rate of mechanical grafting was 7% higher. Overall, in plant factories compared to commercially available tomato seedlings, tomato seedlings cultivated by the regulated light environment were more suitable for mechanical grafting. This research result provides theoretical support for subsequent research on grafting machinery. Full article

Experimental methods of single-molecule enzymology allow scientists to determine physicochemical properties of distinct single molecules of various enzymes and to perform direct monitoring of functioning of enzymes at different steps of their catalytic cycle. The approach based on the use of solid-state nanopores is a promising tool for studying the functioning of single-enzyme molecules. Herein, this approach is employed for monitoring the functioning of cytochrome P450 BM3, which represents a very convenient model of cytochrome P450-containing monooxygenase systems. A nanopore of ~5 nm in diameter has been formed in a 40 nm-thick silicon nitride chip by electron beam drilling (EBD), and a single molecule of the BM3 enzyme has been entrapped in the pore. The functioning of the enzyme molecule has been monitored by recording the time dependence of the ion current through the nanopore during the reaction of laurate hydroxylation. In our experiments, the enzyme molecule has been found to be active for 1500 s. The results of our research can be further used in the development of highly sensitive detectors for single-molecule studies in enzymology. Full article

In the current global scenario, it is essential to find more effective and practical solutions to mitigate the problem of particulate emissions from vehicles. In this research, particulate emission characteristics with changing GDI pressure or applying a split GDI strategy with different second injection timings were initially explored in a Dual-Fuel Spark Ignition (DFSI) engine, which employs Ethanol Port Injection (EPI) plus Gasoline Direct Injection (GDI). The experimental results show that by increasing GDI pressure ($P_{GDI}$) from 5.5 MPa to 18 MPa, ignition delay (${\theta }_F$) shows a small decrease of 0.68 degrees. The parameters, such as maximum in-cylinder temperature ($T_{MI}$) and exhaust gas temperature ($T_{EG}$), each increase by 53.75 K and 13.84 K. An apparent reduction of 59.5% and 36.26% was achieved for the concentrations of particulate number ($N_P$) and particulate mass ($M_P$), respectively. Particulate emissions are effectively reduced by a split GDI strategy with an appropriate range of second injection timing ($t_{GDI2}$). Under $t_{GDI2}$ = −260 ºCA, $N_P$ and $M_P$ concentrations exhibit a relatively lower level. However, by delaying $t_{GDI2}$ from −260 ºCA to −140 ºCA, there is an increase of more than 60% in $N_P$ concentration. The research findings help offer new and valuable insights into optimising particulate number and mass emissions from DFSI engines. Moreover, the findings could contribute novel and valuable insights into the optimisation of particulate emission characteristics in DFSI engines. Full article

In order to develop green energy, reduce carbon emissions, and alleviate global warming and the green energy crisis, many researchers focus on wave energy, using a device to convert wave energy into electricity. The three main types of wave energy converters are the overtopping type, the oscillating water column type, and the oscillating body type, and for most of them, the power generation efficiency is low. The research team in this paper proposed a wave energy converter for a wave-induced oscillation heave plate. The plate vibrates up and down under the action of waves, and the captured energy of the vibrating plate transfers the energy to the generator, so as to generate electricity. There is electricity only when there is vibration; therefore, the vibration characteristic of the converter is crucial to power generation. So, the vibration characteristics of the energy capture structure of the converter were studied experimentally. The test results show that the energy harvesting device can vibrate, and the vibration effect is good, which further indicates that the device can generate electricity. The effects of different wave conditions and system stiffnesses on amplitude and corresponding amplitude were studied, and the amplitude increases with the increase in wave height and period and decreases with the increase in system stiffness. The amplitude response decreases with the increase in wave height and system stiffness. Under the test conditions, the maximum amplitude of the system is 6.23 cm (when the wave period is 1.40 s, the wave height is 0.25 m, and the system stiffness is 1735.62 N/m), and the maximum amplitude ratio is 0.34 (when the wave period is 1.1 s, the wave height is 0.10 m, and the system stiffness is 1735.62 N/m). Full article

This article presents the results of an experimental study carried out to assess the possibility of using waste wind turbine blades as retaining wall structures for slopes and trenches. The use of Vestas and LM-type blades as retaining wall components was assumed, based on ‘columns’ made of Vestas-type closed profiles filled with concrete and ‘slabs’ of fragments extracted from LM-type blades. The results of the tests and comparisons of the displacement and strain values of the components obtained using different measurement methods are presented in this paper. The force–strain and force–displacement relationships obtained from the tests were used to validate numerical models of slope protection walls and excavations designed from used wind turbine blades. According to our research, there is a high degree of variability in the strength parameters and deformation of the composite elements made from the wind turbine blades. Therefore, in the case of this type of material, characterized by a significant variation in carrying capacity, deformability, and the nature of the failures, the use of different measurement methods makes it possible to obtain much of the data necessary for assessing the reusability of wind turbine blades in building. Full article

Urolithiasis (UL) is increasingly prevalent due to rising cardiorenometabolic diseases, posing significant management challenges despite advances in urological techniques. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, primarily used for type 2 diabetes mellitus, chronic kidney disease, and heart failure, have emerged as a potential novel approach for UL treatment. These inhibitors may help reduce the risk of urolithiasis, particularly in patients with diabetes, by improving glycemic control and altering urinary chemistry, which are crucial factors in stone formation. However, the changes in urinary composition induced by SGLT2 inhibitors might also increase the risk of uric acid stone formation. This review evaluates the potential of SGLT2 inhibitors in managing UL, highlighting both the benefits and the risks. While these inhibitors show promise in reducing new and recurrent urinary stones in patients with diabetes, data on their effects in patients without diabetes who form stones are limited. Current human evidence largely comes from post hoc analyses of randomized controlled trials (RCTs) and large-scale database studies, with only one study providing detailed stone composition data. Experimental studies in animal models and cell lines have focused on calcium oxalate (CaOx) stones, showing that SGLT2 inhibitors specifically target CaOx stone formation and related renal inflammation. Although primarily studied for CaOx stones, their potential impact on other calcium-containing stones, such as calcium phosphate, remains promising. Further research is needed to explore their therapeutic potential and optimize treatment strategies. Full article

The scientific goal of this study was to investigate the effects of various parameters on cavitation-induced erosion, with the aim to enhance the understanding and assessment of cavitation resistance in hydraulic systems. Cavitation erosion poses significant challenges to the durability and efficiency of hydraulic components, such as those found in hydropower plants and pumping stations. Prompted by the need to improve the reliability of cavitation testing and material assessment, this research conducted a comprehensive sensitivity analysis of a cavitation jet apparatus (CJA). This study employed an experimental platform that consisted of a vertical cylindrical test tank, a submerged nozzle, and an aluminum sample. By examining a range of orifice diameters, this research identified that smaller diameters led to increased erosion intensity, with the most pronounced effects observed at a diameter of 2 mm. Furthermore, various standoff distances (SoDs) were tested, which revealed that shorter distances resulted in greater erosion, with the highest impact noted at an SoD of 5 cm. This study also evaluated different nozzle geometries, where it was found that a 132° conical sharped edges nozzle, combined with an orifice diameter of 2 mm and an SoD of 5 cm, produced the most severe erosion. Conversely, chamfered edges nozzles and a commercial nozzle (MEG2510) with an SoD of 10 cm or greater showed reduced erosion. These results highlight that by standardizing the testing duration to 1200 s, the CJA could reliably assess the cavitation resistance of materials. This study established a clear relationship between increased pressure and higher impact forces, which led to more severe erosion. The findings underscore the effectiveness of the CJA in evaluating material resistance under various cavitation conditions, thus addressing a critical need for reliable cavitation testing tools. Full article

Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the second stage, temporal features are extracted using a transformer encoder. In the final stage, the features are mapped using a deep-learning network. The proposed HCTC method is investigated using the benchmark RadioML database and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method achieves a better performance in modulation signal classification. Additionally, the performance of the proposed method is evaluated when applied to different batch sizes and model configurations. Finally, open issues in modulation recognition research are addressed, and future research perspectives are discussed. Full article

The growing use of titanium alloys has led to the gradual replacement of traditional processing methods by laser cutting technology, making it the preferred method for processing titanium alloy plates due to its high efficiency, precision, and adaptability. In this review, the characteristics of laser cutting technology and its application in titanium alloy plate processing are summarized, outlining several aspects of the cutting process, microstructure, and mechanical properties of the material after cutting, along with simulation predictions. Previous research categorized laser-cutting input parameters into beam parameters and process parameters, with the commonly used parameters being the laser power, cutting speed, and gas pressure. Various parameter combinations can achieve different cutting qualities, and seven indices can be used to evaluate the cutting process, with the surface roughness and slit width serving as the most common indices. Different auxiliary gases have shown a significant impact on the laser cutting quality, with commonly used gases consisting of nitrogen, argon, and air. Argon-assisted cutting generally results in better surface quality. Due to the rapid temperature change, the titanium alloy microstructure will undergo a non-diffusive martensitic phase transformation during laser cutting, producing a heat-affected zone. Experimental studies and simulations of the mechanical properties have shown that the occurrence of a martensitic phase transformation increases the hardness and residual tensile stress of the material, which reduces the fatigue strength and static tensile properties. In addition, studies have found that the more streaks appear on the cut surface, the lower the fatigue strength is, with fatigue cracks arising from the stripes. Hence, the established analytical solution model and three-dimensional finite element model can effectively predict the temperature distribution and residual stress during the cutting process. This can provide a better understanding of the high residual stress characteristics of the cutting edge and the stripe formation mechanism, allowing researchers to better explore the mechanism of laser cutting. Full article

Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. Full article

High-toughness resin concrete steel mesh (HTRCS) composites, as a novel reinforcement material, are extensively employed, yet there has been a lack of comprehensive quantitative studies on them, and our knowledge about them predominantly relies on experimental investigations. To delve into the shear performance of reinforced concrete beams fortified with HTRCS, this research executed four-point bending static load experiments on a benchmark of two standard beams and six HTRCS-reinforced beams. The results demonstrate that the shear bearing capacity of the reinforced concrete beams was notably enhanced with HTRCS, ranging from approximately 10% to 65%. Further examination revealed that the stiffness of the specimens is significantly influenced by the HTRCS thickness, shear–span ratio, and concrete strength, with the shear–span ratio exerting the most notable impact on stiffness. This analysis furnishes a solid theoretical foundation for the utilization of HTRCS. Full article

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R²) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R² of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices. Full article

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition. Full article