Search Results (58)

Thermal algae are extremophilic organisms that live in one of the harshest environments in the world and thrive in waters with temperatures of up to 90 °C. They have gained attention due to their special ecological adaptations, their great biotechnological potential and their recently recognised role in combating global climate change and achieving sustainable development. However, the biodiversity of these algae is far from being fully explored. The article presents the first finding of the prokaryotic genus Asterocapsa (Chroococcales, Cyanophyceae, Cyanoprokaryota/Cyanobacteria) in thermal waters and describes a new species from the fountain basins in the thermal system of the only continental European geyser (101 °C) in the town of Sapareva Banya (south-west Bulgaria). This species is not only one of the few aquatic representatives of this generally aeroterrestrial genus, but is also characterised by its extremophilic lifestyle and differs clearly from the type species and other aquatic species of the genus due to its morphological characteristics. These include the smaller dimensions of the cells and colonies, as well as the colourless, transparent, but always lamellar and regularly verrucous mucilage envelopes. The unique locality of this alga is highly endangered and was included in the first Red List of Bulgarian wetlands. Due to human activities and changes in the geyser system, we have detected some unfavourable changes in the algal habitat and therefore propose to add the newly described species to the Red List of Bulgarian Microalgae with the status Critically Endangered. Full article

In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R² values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks. Full article

The object of this study is welded joints of AISI 321 and Ti-6Al-4V, obtained by diffusion welding on developed conical surfaces. The problem of creating bimetallic joints of AISI 321 and Ti-6Al-4V with developed conical contact surfaces, using diffusion welding through an intermediate Electrolytic Tough Pitch Copper (Cu-ETP) copper layer, was solved. The joints were studied using micro-X-ray spectral analysis, microstructural analysis, and mechanical tests. High mutual diffusion of copper and titanium, along with increased concentrations of Cr and V in copper, was detected. The shear strength of the obtained welded joints is 250 MPa and 235 MPa at 30 min and 15 min, respectively, which is higher than the copper layer’s strength (180 MPa). The obtained results are explained by the dislocation diffusion mechanism in the volume of grains and beyond, due to thermal deformations during welding. Under operating conditions of internal pressure and cryogenic temperatures, the strength of the connection is ensured by the entire two-layer structure, and tightness is ensured by a vacuum-tight diffusion connection. The obtained strength of the connection (250 MPa) is sufficient under the specified operating conditions. Analysis of existing solutions in the literature review indicates that industrial application of technology for manufacturing bimetallic adapters from AISI 321 stainless steel and Ti-6Al-4V titanium alloy is limited to butt joints with small geometric dimensions. Studies of the transition zone structure and diffusion processes in bimetallic joints with developed conical contact surfaces enabled determination of factors affecting joint structure and diffusion coefficients. The obtained bimetallic adapters, made of Ti-6Al-4V titanium alloy and AISI 321 stainless steel, can be used to connect titanium high-pressure vessels with stainless steel pipelines. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Wind turbines’ volume and power generation capacity have increased worldwide. Consequently, their inspection, maintenance, and repair are garnering increasing attention. Structural defects are common in turbine blades, but their detection is difficult due to the relatively large size of the blades. Therefore, engineers often use nondestructive testing. This study employed an unmanned aerial vehicle (UAV) to simultaneously capture visible-light and infrared thermal images of wind power blades. Subsequently, instant neural graphic primitives and neural radiance fields were used to reconstruct the visible-light image in three dimensions (3D) and generate a 3D mesh model. Experiments determined that after converting parts of the orthographic-view images to elevation- and depression-angle images, the success rate of camera attitude calculation increased from 85.6% to 97.4%. For defect measurement, the system first filters out the perspective images that account for 6–12% of the thermal image foreground area, thereby excluding most perspective images that are difficult to analyze. Based on the thermal image data of wind power generation blades, the blade was considered to be in a normal state when the full range, average value, and standard deviation of the relative temperature grayscale value in the foreground area were within their normal ranges. Otherwise, it was classified as abnormal. A heat accumulation percentage map was established from the perspective image of the abnormal state, and defect detection was based on the occurrence of local minima. When a defect was observed in the thermal image, the previously reconstructed 3D image was switched to the corresponding viewing angle to confirm the actual location of the defect on the blade. Thus, the proposed 3D image reconstruction process and thermal image quality analysis method are effective for the long-term monitoring of wind turbine blade quality. Full article

Microfluidic technology is an emerging interdisciplinary field that uses micropipes to handle or manipulate tiny fluids in chemistry, fluid physics, and biomedical engineering. As one of the rapid prototyping methods, the three-dimensional (3D) printing technique, which is rapid and cost-effective and has integrated molding characteristics, has become an important manufacturing technology for microfluidic chips. Polymethyl-methacrylate (PMMA), as an exceptional thermoplastic material, has found widespread application in the field of microfluidics. This paper presents a comprehensive process study on the fabrication of fused deposition modeling (FDM) 3D-printed PMMA microfluidic chips (chips), encompassing finite element numerical analysis studies, orthogonal process parameter optimization experiments, and the application of 3D-printed integrated microfluidic reactors in the reaction between copper ions and ammonium hydroxide. In this work, a thermal stress finite element model shows that the printing platform temperature was a significant printing parameter to prevent warping and delamination in the 3D printing process. A single printing molding technique is employed to fabricate microfluidic chips with square cross-sectional dimensions reduced to 200 μm, and the microchannels exhibited no clogging or leakage. The orthogonal experimental method of 3D-printed PMMA microchannels was carried out, and the optimized printing parameter resulted in a reduction in the microchannel profile to Ra 1.077 μm. Finally, a set of chemical reaction experiments of copper ions and ammonium hydroxide are performed in a 3D-printed microreactor. Furthermore, a color data graph of copper hydroxide is obtained. This study provides a cheap and high-quality research method for future research in water quality detection and chemical engineering. Full article

Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, distribution line insulators often present detection challenges due to their compact dimensions, diverse flaw types, and frequent installation in populated areas with visually cluttered environments. The combination of these factors, including small defect sizes, varying failure patterns, and complex background interference, in both urban and rural settings, creates significant difficulties for precise defect identification in these critical components. In response to these challenges, this paper proposes a defect recognition algorithm for distribution line insulators based on the improved YOLO11 model. Firstly, the algorithm combines the detection head of the original model with the Adaptively Spatial Feature Fusion (ASFF) module to effectively fuse defect features at different resolution levels and improve the model’s ability to recognize multi-scale defect features. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) replaces the FPN + PAN structure of the original model to achieve a more effective transfer of contextual information in order to facilitate the model’s efficiency in performing defect feature fusion, and the Convolutional Block Attention Module (CBAM) Attention mechanism is embedded in the BiFPN output so that the model is able to give priority attention to defective features on insulators in complex recognition environments. Finally, the ShuffleNetV2 module is used to reduce the parameters of the improved model by replacing the large-parameter C3k2 module at the end of the backbone network for easy deployment on lightweight and small devices. The experimental results show that the improved model performs well in the distribution line insulator defect detection task, with an accuracy precision (AP) and mean accuracy precision (mAP) of 97.0% and 98.1%, respectively, which are 1.4% and 0.7% higher than the original YOLO11 model. Full article

The laser pyrolysis technique was used in the synthesis of magnetic iron oxide nanopowders in the presence of ethanol vapors as a sensitizer. This technique uses the energy from a continuous-wave CO₂ laser operating at a 9.25 μm wavelength, which is transferred to the reactive precursors via the excited ethanol molecules, inducing a rapid heating of the argon-entrained Fe(CO)₅ vapors in the presence of oxygen. For a parametric study, different samples were prepared by changing the percentages of sensitizer in the reactive mixture. Moreover, the raw samples were thermally treated at different temperatures and their morpho-structural and magnetic properties were investigated. The results indicated a high degree of crystallinity (mean ordered dimension) and enhanced magnetic properties when high percentages of ethanol vapors were employed. On the contrary, at low ethanol concentrations, due to a decrease in the reaction temperature, nanoparticles with a very low size were synthesized. The raw particles have a dimension in the range of 2.5 to 10 nm (XRD and TEM). Most of them exhibited superparamagnetic behavior at room temperature, with saturation magnetization values up to 60 emu/g. The crystalline phase detected in samples is mainly maghemite, with a decreased carbon presence (up to 8 at%). In addition to the expected Fe-OH on the particles surfaces, C (and O) bearing functional groups such as C-OH or C=O that act as a supplementary hydrophilic agent in water-based suspension were detected. Using the as-synthesized and thermally treated nanopowders, water suspensions without or with hydrophilic agents (CMCNa, L-Dopa, chitosan) were prepared by means of a horn ultrasonic homogenizer at 0.5 mg/mL concentrations. DLS analyzes revealed that some powder suspensions maintained stable agglomerates over time, with a mean size of 100 nm, pH values between 4.8 and 5.3, and zeta-potential values exceeding 40 mV. All tested agents greatly improved the stability of 250–450 °C thermally treated NPs, with L-Dopa and Chitosan inducing smaller hydrodynamic sizes. Full article

This study addresses the task of ecologically assessing the consequences of natural fires. Statistical data are presented on the carbon dioxide emissions in millions of tons and analytical data on the locations of peat fires, as well as modern methods of detection and control of peat and forest fires, divided into groups. An analysis of the works of leading Russian and international scientists and research organizations engaged in the search for methods of peat fire forecasting is also presented. Our aim was to develop a more effective method of preventing peat soil ignition by changing its physical and moisture characteristics. To that end, peat samples were selected in the Tver region. The laboratory equipment and the methodology of our experimental studies are described in detail, in which we simulated the natural climatic conditions in the center of the Russian Federation. This study provides a mathematical description of the process of spontaneous ignition, which occurs according to the following steps: a heat flow heats the surface to the ignition temperature, creating a self-heating zone; eventually, a wave of ignition (smoldering) capable of self-propagation is formed. We experimentally determined the spontaneous thermal ignition conditions in our experimental studies of the fire hazards of selected peat samples, where the test material was loaded in a cylindrical container made of brass net with a 0.8 mm mesh, of the dimensions 30 × 30 mm. Thermocouple elements were placed inside the container, fixing the temperature of the surface and the center of the sample, where the smoldering or ignition zone of the test material formed. We analyzed the results of our experimental studies on peat samples’ self-heating chemical reaction, leading us to draw conclusions about the possibility of fires on peat soil depending on its physical and chemical characteristics. We also offer recommendations that will improve peat soils’ fire safety, permitting agricultural crop production without a peat fire risk. Full article

A carbon dioxide (CO₂) gas sensor based on non-dispersive infrared (NDIR) technology has been developed and is suitable for use in portable devices for high-precision CO₂ detection. The NDIR gas sensor comprises a MEMS infrared emitter, a MEMS thermopile detector with an integrated optical filter, and a compact gas cell with high optical coupling efficiency. A dual-ellipsoid mirror optical system was designed, and based on optical simulation analysis, the structure of the dual-ellipsoid reflective gas chamber was designed and optimized, achieving a coupling efficiency of up to 54%. Optical and thermal simulations were conducted to design the sensor structure, considering thermal management and light analysis. By optimizing the gas cell structure and conditioning circuit, we effectively reduced the sensor’s baseline noise, enhancing the overall reliability and stability of the system. The sensor’s dimensions were 20 mm × 10 mm × 4 mm (L × W × H), only 15% of the size of traditional NDIR gas sensors with equivalent detection resolution. The developed sensor offers high sensitivity and low noise, with a sensitivity of 15 μV/ppm, a detection limit of 90 ppm, and a resolution of 30 ppm. The total power consumption of the whole sensor system is 6.5 mW, with a maximum power consumption of only 90 mW. Full article

Mechanical vibration in the high-temperature forging production line often causes large forging thermal dimensional measurement error in the detection task, so a vibration point cloud compensation method based on an acceleration sensor is proposed in this study. First, the vibration signal is obtained through the built-in acceleration sensor in the laser camera. After the acceleration of the camera vibration is detected, the displacement of the camera in three directions is solved by secondary integration. Subsequently, the coordinate value of the corresponding point is obtained by the rotation matrix transformation so as to compensate and correct the point cloud deviation caused by the camera vibration. Finally, the forging point cloud is matched using the surface matching algorithm in Halcon. An automatic forging production line for wheel hubs has been built, and the key dimensions of high-temperature forging products have been measured online using the developed method. After the forging point cloud is compensated, the average measurement error of dimensions is reduced from ±0.9 mm to ±0.1 mm, and the standard deviation is reduced from 0.52 mm to 0.056 mm. Using the vibration point cloud compensation method based on the acceleration sensor, as well as using silica aerogel insulation, vibration structural parts, heat insulation and constant temperature, a blue-violet 3D laser camera, and other measures, the dimensional detection accuracy of high-temperature forgings in the forging production line can be improved, and the instability of dimensional detection can be reduced. Full article

Graphene oxide (GO) nanosheets, as one of the most studied graphene derivatives, have demonstrated an intrinsically strong physisorption-based gas–matter behavior, owing to its enhanced volume–surface ratio and abundant surface functional groups. The exploration of efficient and cost-effective synthesis methods for GO is an ongoing task. In this work, we explored a novel approach to upcycle inexpensive polyethylene terephthalate (PET) plastic waste into high-quality GO using a combination of chemical and thermal treatments based on a montmorillonite template. The obtained material had a nanosheet morphology with a lateral dimension of around ~2 µm and a thickness of ~3 nm. In addition, the GO nanosheets were found to be a p-type semiconductor with a bandgap of 2.41 eV and was subsequently realized as a gas sensor. As a result, the GO sensor exhibited a fully reversible sensing response towards ultra-low-concentration NO₂ gas with a limit of detection of ~1.43 ppb, without the implementation of an external excitation stimulus including elevating the operating temperature or bias voltages. When given a thorough test, the sensor maintained an impressive long-term stability and repeatability with little performance degradation after 5 days of experiments. The response factor was estimated to be ~11% when exposed to 1026 ppb NO₂, which is at least one order of magnitude higher than that of other commonly seen gas species including CH₄, H₂, and CO₂. Full article

The safe operation of marine diesel engines (MDEs) is an important safeguard for ships and engine crews at sea. In this paper, a combined neural network prediction model (PCA-CNN-BiLSTM) is proposed for the problem of multi-parameter prediction and fault warning for MDEs. PCA is able to reduce the data dimensions and diminish the redundant information in the data, which helps to improve the training efficiency and generalization ability of the model. CNN can effectively extract spatial features from data, assisting in capturing local patterns and regularities in signals. BiLSTM works to process time series data and capture the temporal dependence in the data, enabling prediction of the failure conditions of MDE, condition monitoring, and prediction of a wide range of thermal parameters with more accuracy. We propose a standardized Euclidean distance-based diesel engine fault warning threshold setting method for ships combined with the standard deviation index threshold to set the diesel engine fault warning threshold. Combined with experimental verification, the method can achieve real-time monitoring of diesel engine operating condition and abnormal condition warning and realize diesel engine health condition assessment and rapid fault detection function. Full article

Carbon fiber laminates have become popular in the manufacturing industry for their many desirable properties, like good vibration damping, high strength-to-weight ratio, toughness, high dimensional stability, and low coefficient of thermal expansion. During the manufacturing process, undesirable foreign objects, such as peel-ply strips, gloving material, and Kapton film, can be introduced into the part which can lead to a localized weakness. These manufacturing defects can function as stress concentration points and oftentimes cause a premature catastrophic failure. In this study, a method using high-resolution pulse-echo ultrasound testing is employed for the detection and quantification of the dimensions of foreign object debris (FOD) embedded within carbon fiber laminates. This research presents a method to create high-resolution C-scans using an out of immersion tank portable housing ultrasound scanning system, with similar capabilities to that of a full immersion system. From the full-waveform dataset, we extract the FOD depth and planar dimensions with an automatic edge detection technique. Results from several carbon fiber laminates are investigated with embedded foreign objects that are often considered undetectable. Results are presented for FOD identification for two different shapes: circles with diameters ranging from 7.62 mm to 12.7 mm, and 3-4-5 triangles with hypotenuses ranging from 7.6 mm to 12.7 mm. CT imaging is used to confirm proper FOD placement and that the FOD was not damaged or altered during manufacturing. Of importance for the ultrasound inspection results, in every single case studied, the FOD is detected, the layer depth is properly identified, and the typical error is less than 1.5 mm for the primary dimension. Full article

Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the environment. The environment can generate occlusion, complicating the timely detection of people. There are currently numerous RGB image datasets available that are used for person detection tasks in urban and wooded areas and consider the general characteristics of a person, like size, shape, and height, without considering the occlusion of the object of interest. The present research work focuses on developing a thermal image dataset, which considers the occlusion situation to develop CNN convolutional deep learning models to perform detection tasks in real-time from an aerial perspective using altitude control in a quadcopter prototype. Extended models are proposed considering the occlusion of the person, in conjunction with a thermal sensor, which allows for highlighting the desired characteristics of the occluded person. Full article