Search Results (4,224)

Background: Next-generation sequencing (NGS) is the most commonly used method for determining BRAF mutational status in patients with advanced melanoma. Automated PCR-based methods, such as the Idylla^TM system, are increasingly used for mutation diagnostics, but it is unclear what impact the choice of diagnostic method has on the management of melanoma. Objectives: To compare the concordance rate of BRAF V600 mutational analysis using Idylla^TM and NGS and to analyze the technical and clinical turnaround time. The clinical relevance is compared by analyzing the impact on the treatment decision. Methods: In this monocentric prospective cohort study, the BRAF mutation status of 51 patients was determined using both methods in parallel. Results: BRAF V600 mutation was detected in 23/51 cases (45%). Idylla^TM showed a 100% concordant result with a faster turnaround time (0.2 days) compared to NGS (12.2 days). In general, less tumor material was required for Idylla^TM than for NGS. Most patients received immunotherapy as a first-line therapy regardless of the BRAF V600 status. Conclusions: Idylla^TM testing proved to be a reliable and rapid alternative to NGS in the determination of BRAF V600 mutation. Although BRAF. status was available earlier, this had no influence on the treatment decision in most cases. Full article

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas. Full article

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the `r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data. Full article

The rapid and accurate detection of weeds is the prerequisite and foundation for precision weeding, automation, and intelligent field operations. Due to the wide variety of weeds in the field and their significant morphological differences, most existing detection methods can only recognize major crops and weeds, with a pressing need to enhance accuracy. This study introduces a novel weed detection approach that integrates the GFPN (Green Feature Pyramid Network), Slide Loss, and multi-SEAM (Spatial and Enhancement Attention Modules) to enhance accuracy and improve efficiency. This approach recognizes crop seedlings utilizing an improved YOLO v8 algorithm, followed by the reverse detection of weeds through graphics processing technology. The experimental results demonstrated that the improved YOLO v8 model achieved remarkable performance, with an accuracy of 92.9%, a recall rate of 87.0%, and an F1 score of 90%. The detection speed was approximately 22.47 ms per image. And when shooting from a height ranging from 80 cm to 100 cm in the field test, the crop detection effect was the best. This reverse weed detection method addresses the challenges posed by weed diversity and complexities in image recognition modeling, thereby contributing to the enhancement of automated and intelligent weeding efficiency and quality. It also provides valuable technical support for precision weeding in farmland operations. Full article

Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable $k\text{-}\epsilon$ turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. Full article

Short-circuit impedance is an important economic and technical index to test the cost, efficiency and operation safety of transformers. Increasing the short-circuit impedance of the transformer can reduce the influence of the transformer fault current on the system. The short-circuit impedance of a general power transformer is 4~7%. When the short-circuit impedance is too small, the short-circuit current is too large, which will cause harm to electrical equipment. This paper proposes a method to adjust the short-circuit impedance by adding magnetic shunts of different thicknesses between the high and low voltage windings of the transformer. Compared with other methods, this method does not change the structure of the transformer core and winding, and is simple and efficient. In this paper, a three-dimensional simulation model of a single-phase multi-winding transformer is established by Altair Flux to study the influence of the thickness of magnetic shunts on the short-circuit impedance of a transformer. The feasibility of the proposed method is verified by comparing the simulation with the measured values. The magnetic shunt is also introduced into the three-phase transformer. The result shows that adding magnetic shunts of different thicknesses between the high and low voltage windings of the transformer will change the distribution and size of the leakage of the magnetic field. The short-circuit impedance increases significantly with the increase in the thickness of the magnetic shunt, but a certain number of magnetic shunts have minimal effects on the efficiency of the transformer. Full article

Phenol–formaldehyde (PF) resins remain the preferred adhesive for exterior plywood, as they confer these boards their extreme weather resistance. However, their high price and toxicity has made phenol alternatives, such as technical lignins, increasingly more attractive. While many works report the use of kraft lignin, the most commercially available form are lignosulphonates (LS). However, these lack industrial success and are associated with low moisture resistance. In the current study, lignosulphonate–phenol–formaldehyde (LPF) resoles were synthesized considering a phenol replacement of 30% (w/w). Two LS samples of softwood (SLS) and hardwood (HLS) origin were compared. These samples were previously methylolated to increase their reactivity. The effectiveness of the treatment was confirmed through the Automated Bonding Evaluation System. Plywood was manufactured and tested according to EN 314 class 3 for exterior conditions, which is seldom found in the literature. Although a 35% increase in shear strength is still necessary to comply with the standard, methylolated SLS was the most promising substitute, as it resulted in the highest board performance. Notably, when this sample was used without previous methylolation, the plywood boards suffered delamination during immersion in boiling water prior to shear testing. These results reinforce the need for the methylolation of LS to increase the weather resistance of plywood. Full article

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. Full article

Digital learning environments create a dynamic and engaging learning and teaching context that promotes a deeper understanding of complex concepts, eases the teaching process and fosters a passion for learning. Moreover, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting their teaching strategies accordingly. The teachers can also receive valuable insights into students’ strengths and weaknesses, allowing them to provide targeted support and intervention when needed. For students from the defence and security fields, digital learning environments can create realistic simulations and virtual training scenarios that allow students to practise their skills in a controlled and safe environment, develop hands-on experience, and enhance their decision-making abilities without the need for real-world training exercises. In this context, the purpose of this paper is to introduce an approach for solving mathematical problems embedded in technical scenarios within the defence and security fields with the aid of digital technology using different software environments such as Python, Matlab, or SolidWorks. In this way, students can visualise abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. At the same time, the use of didactic and interactive materials can increase the interest among students and teachers for utilising mathematical models and digital technologies in the educational process. This paper also helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity and creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, based on survey analysis, good practices are presented, and advice for improvement is collected while having a wide range of users giving feedback, and participating in discussions and testing (pilot) short-term learning/teaching/training activities. Full article

The roll bonding of an experimental Al-2%Cu-2%Mn alloy with technically pure 1050A aluminum at true deformations of 0.26, 0.33 and 0.40 has been simulated using the QForm 10.3.0 FEM software. The flow stress of the Al-2%Cu-2%Mn alloy has been measured in temperature and strain rate ranges of 350–450 °C and 0.1–20 s⁻¹, respectively. The simulation results suggest that the equivalent strain in the cladding layer is more intense than that in the base layer, reaching 1.0, 1.4 and 2.0 at strains of 0.26, 0.33 and 0.40, respectively. The latter fact favors a decrease in the difference between the flow stresses of the rolled sheet layer contact surfaces by an average of 25% at the highest strain. The experimental roll bonding has achieved good layer adhesion for all the test samples. The average peeling strength of the samples produced at strains of 0.26 and 0.33 proves to be 12.6 and 18.4 N/mm, respectively, and at a strain of 0.40, it has exceeded the flow stress of the 1050A alloy cladding layer. The change in the rolling force for different rolling routes has demonstrated the best fit with the experimental data. Full article

This study emphasizes the influential role of rheology in decoding the viscoelastic properties of pressure-sensitive adhesives (PSAs) vital to predicting key application features such as shear, tack, and peel, depending on the flow characteristics of PSAs during bonding and debonding processes. By applying the principle of time–temperature superposition (TTS), we extend the scope of our frequency analysis, surpassing the technical constraints of the available apparatus. Our exploration aims to uncover the general correlations between PSAs’ viscoelastic properties and their performance in end-use applications. Initially, the adhesive performance and viscoelastic properties of a UV-crosslinkable styrene-butadiene-styrene (SBS) model adhesive prior and subsequent to UV irradiation were examined. The subsequent crosslinking reaction increased cohesive strength and heat resistance, although tack and peel strength observed a substantial decline. We successfully demonstrated these effects by logging the viscoelastic properties, specifically the storage modulus G′ at lower frequencies, which mirrors the shear strength at higher temperatures and the shift in the tan δ peak to represent each PSA’s tack. These correlations were partially reflected in three commercial UV crosslinkable acrylic PSA products, although the effect of UV irradiation was less distinctive. This study also revealed the challenges in predicting tack and peel strength, which result from a complex interplay of bonding and debonding processes. Our findings reinforce the necessity for more sophisticated analysis techniques and models that can accurately predict the end-use performance of PSAs across different physical structures and chemical compositions. Further research is needed to develop these predictive models, which may reduce the need for labor-intensive testing under real-life conditions. Full article

Visibility is one of the key meteorological parameters with special importance in aviation meteorology and the transportation industry. Nevertheless, it is not a straightforward task to automatize visibility observations, since the assistance of trained human observers is still inevitable. The current paper attempts to make the first step in the process of automated visibility observations: it examines, by the approaches of artificial intelligence (AI), whether light points in the target area can or cannot be automatically detected for the purposes of night-time visibility observations. From a technical point of view, our approach mimics human visibility observation of the whole circular horizon by the usage of camera imagery. We evaluated the detectability of light points in the camera images (1) based on an AI approach (convolutional neural network, CNN) and (2) based on a traditional approach using simple binary thresholding (BT). The models based on trained CNN achieved remarkably better results in terms of higher values of statistical metrics, and less susceptibility to errors than the BT-based method. Compared to BT, the CNN classification method indicated greater stability since the accuracy of these models grew with increasing pixel size around the key points. This fundamental difference between the approaches was also confirmed through the Mann–Whitney U test. Thus, the presented AI-based determination of key points’ detectability in the night with decent accuracy has great potential in the objectivization of everyday routines of professional meteorology. Full article

The paper analyses the state of the issue related to the reliability of power supply for selected electronic security systems employed in buildings and over vast areas constituting so-called state critical infrastructure. The authors conducted operational tests covering power supply systems, developed power supply system models, executed a functional safety reliability analysis for such technical facilities, and worked out graphs, as well as drew conclusions arising from the conducted computer simulation. The article also contains element (fuse) redundancy tests, which are the fundamental components of each security system power supply device. In addition, the operation process analysis covering power supply devices functioning within a given environment was conducted for selected representative electronic security systems operated in buildings. Analysis results enabled determining basic operation process indices for selected power supply systems, i.e., failure rate λ and recovery rate μ. Then, reliability models for devices powering electronic security systems were developed, and a computer simulation to work out reliability parameters was conducted for the determined operation process indices (λ, μ). Basic reliability indices for electronic security systems responsible for the life, health and property accumulated within the buildings and vast areas in question were determined for power supply models developed this way. Data for reliability computer simulations were developed on the basis of proprietary system tests. The authors also tested selected activation times of redundant components protecting power supplies. Full article

In order to meet the demand of the intelligent and efficient picking of fresh citrus fruit in a natural environment, a flexible and independent picking method of fresh citrus fruit based on picking pattern recognition was proposed. The convolutional attention (CA) mechanism was added in the YOLOv7 network model. This makes the model pay more attention to the citrus fruit region, reduces the interference of some redundant information in the background and feature maps, effectively improves the recognition accuracy of the YOLOv7 network model, and reduces the detection error of the hand region. According to the physical parameters of the citrus fruit and stem, an end-effector suitable for picking citrus fruit was designed, which effectively reduced the damage during the picking of citrus fruit. According to the actual distribution of citrus fruits in the natural environment, a citrus fruit-picking task planning model was established, so that the adaptability of the flexible handle can make up for the inaccuracy of the deep learning method to a certain extent when the end-effector picks fruits independently. Finally, on the basis of integrating the key components of the picking robot, a production test was carried out in a standard citrus orchard. The experimental results show that the success rate of the citrus-picking robot arm is 87.15%, and the success rate of picking in the natural field environment is 82.4%, which is better than the success rate of 80% of the market picking robot. In the picking experiment, the main reason for the unsuccessful positioning of citrus fruits is that the position of citrus fruits is beyond the picking range of the end-effector, and the motion parameters of the robot arm joint will produce errors, affecting the motion accuracy of the robot arm, leading to the failure of picking. This study can provide technical support for the exploration and application of the intelligent fruit-picking mode. Full article

For the application of poly(lactic acid) (PLA) and PLA/starch composites in technical components such as toys, it is essential to know their degradation behavior under relevant application conditions in a hydrothermal environment. For this purpose, composites made from PLA and native potato starch were produced using twin-screw extruders and then processed into test specimens, which were then subjected to various one-week ageing processes with varying temperatures (23, 50, 70, 90 °C) and humidity levels (10, 50, 75, 90%). This was followed by mechanical characterization (tensile test) and identification of degradation using Gel Permeation Chromatography (GPC), Thermogravimetric Analysis (TGA), Fourier Transform Infrared Spectroscopy (FTIR), and Nuclear Magnetic Resonance spectroscopy (NMR). With increasing temperature and humidity, there was a clear degradation of the PLA, which could be reduced or slowed down by adding 50 wt.% starch, due to increased crystallinity. Hydrolysis was identified as the main degradation mechanism for PLA and PLA/starch composites, especially above the glass transition temperature, with thermo-oxidative degradation also playing a subordinate role. Both hydrolytic degradation and thermo-oxidative degradation led to a reduction in mechanical properties such as tensile strength. Full article