Wind power is the leader in electricity production among the standing RES technologies, both in Poland and in Europe/World. In Poland, so far there are only onshore wind turbines. Their dynamic increase in installed capacity has been observed, especially between 2011 and 2017. This study analyzed the impact of offshore wind energy on the ability of the Polish power system to meet power demands. For this purpose, methods of statistical analysis (of existing onshore and planned offshore technologies) for the determination of wind turbine productivity based on wind speed components data from the ERA5 service were used. For onshore wind turbines, the value of the capacity factor CF(P) in Poland was 25.5% in 2021 and 30.1% in 2022. As a result of the simulation, it was calculated that for the planned offshore wind farms, the capacity factor CF(B) would be 55.6% under 2022 wind speed conditions. The 2022 peak load demands in the Polish system were also analyzed. The quantitative impact of installing 6 GW of offshore wind turbine capacity on the national power system was also identified. Full article

This work developed a method that uses a single monochrome high-speed camera without sacrificing the spatial resolution to measure both in-plane and out-of-plane full-field vibrations. By using the high-speed camera and a two-dimensional digital image correlation (2D-DIC) algorithm, the method first extracts the out-of-plane displacement field from the measured virtual in-plane strains. Then it retrieves the in-plane displacement field after eliminating the out-of-plane motion-induced virtual component. For validation, in-plane and out-of-plane translation tests and single-frequency vibration experiments were carried out. The measurement results show good agreement with the reference values, indicating the effectiveness of the proposed high-speed 2D-DIC (HS-2D-DIC). Further, the natural frequencies and mode shapes of a rectangular cantilever panel were also measured successfully, exhibiting the method’s effectiveness in practical applications. Since the HS-2D-DIC requires only a single monochrome camera, no complex optical setup, and no complicated calibration process, the method can be developed as a competitive tool for full-field vibration characterizations. Full article

To disclose influences of ultrasonic vibration agitation on the carbonation resistance of cement-based materials after absorption of CO₂, the variation laws in internal carbonization zone were explored by the testing carbonization depth and carbonization range (pH variation range) of cement mortar after CO₂ absorption at different ages. Results demonstrated that when CO₂ absorption volumes of the cement mortar before carbonization were 0.44%, 0.88%, 1.32%, 1.76%, and 2.20% (28 d), the carbonization depth under ultrasonic vibration decreased by 5.5%, 12.3%, 21.7%, 20.7%, and 26.7% compared to those under mechanical stirring, respectively. When the ultimate CO₂ absorption volume increased to 2.2% of cement mass, the extended degree of cement mortar was 103.23 mm, which decreased by 5.4% compared to that before CO₂ absorption. pH variation values of the carbonization range under ultrasonic vibration presented a rising trend with the increase of CO₂ absorption volume of cement mortar before carbonation. This indicated that, with the increase of CO₂ absorption volume of cement mortar before carbonation increases under ultrasonic vibration, the carbonization process of the hardened body of cement mortar might be decelerated to some extent. Additionally, changes in internal composition and physical images of cement-based materials after absorption of CO₂ were analyzed through microtest means like SEM and XRD. A carbonation resistance model was constructed, thus enabling disclosure of the variation mechanism of carbonation resistance of cement-based materials after absorption of CO₂ under mechanical stirring and ultrasonic vibration. Results demonstrated that the higher CO₂ absorption volume of fresh slurry generated more “nano-level” CaCO₃ crystal nucleus. Accordingly, it could improve the porous structure of the cement mortar, decrease the quantity of capillary tubes significantly, improve the compaction degree of cement-based materials effectively, and lower the diffusion rate of CO₂ in the cement paste base, thus improving the carbonation resistance. Research conclusions have important significance to decrease CO₂ emissions and improve carbonation resistance of concrete. Full article

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography. Full article

The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. However, traditional methods for hyperparameter configuration, such as grid searches and random searches, are time consuming and labor intensive. The optimization of CNN hyperparameters is a complex problem involving multiple local optima that poses a challenge for traditional particle swarm optimization (PSO) algorithms, which are prone to getting stuck in the local optima and achieving suboptimal results. To address the above issues, we proposed an adaptive dimensional Gaussian mutation PSO (ADGMPSO) to efficiently select the optimal hyperparameter configurations. The ADGMPSO algorithm utilized a cat chaos initialization strategy to generate an initial population with a more uniform distribution. It combined the sine-based inertia weights and an asynchronous change learning factor strategy to balance the global exploration and local exploitation capabilities. Finally, an elite particle adaptive dimensional Gaussian mutation strategy was proposed to improve the population diversity and convergence accuracy at the different stages of evolution. The performance of the proposed algorithm was compared to five other evolutionary algorithms, including PSO, BOA, WOA, SSA, and GWO, on ten benchmark test functions, and the results demonstrated the superiority of the proposed algorithm in terms of the optimal value, mean value, and standard deviation. The ADGMPSO algorithm was then applied to the hyperparameter optimization for the LeNet-5 and ResNet-18 network models. The results on the MNIST and CIFAR10 datasets showed that the proposed algorithm achieved a higher accuracy and generalization ability than the other optimization algorithms, such as PSO-CNN, LDWPSO-CNN, and GA-CNN. Full article

Although experiments on intratracheal intubation for animals are essential for research, it remains challenging. This study aimed to validate an animal model using a flexible imaging guide system that can be conveniently and safely used as a new method to provide easy access to organs in small animals. PBS (Phosphate Buffered Saline) and PHMG (Polyhexamethylene guanidine) were administered by intratracheal intubation to 20 rodents (10 mice and 10 rats), and the changes in the lungs were observed. Results were verified using lung tissue histopathologic staining through the intratracheally administered material, which confirmed that 100% of changes in lung tissue occurred in the PHMG-injected group, where intubation was facilitated using the flexible imaging guide. The drug was conveniently and safely administered. The flexible-imaging-fiber-guide-based intratracheal drug injectable system may be conveniently used by researchers. It allows drugs to be administered quantitatively, suggesting its potential wide use in drug development and toxicity evaluation. Full article

Unfettered agricultural activities have severely degraded vast areas of grasslands over the last decade. To rehabilitate and restore the productivity in affected grasslands, rangeland management practices still institute vast nitrogen-based fertilization regimes. However, excessive fertilization can often have damaging environmental effects. Over-fertilization can lead to nitrogen saturation. Although early indicators of nitrogen saturation have been documented, research detailing the near-real-time nitrogen saturation status of grasslands is required to better facilitate management protocols and optimize biomass production within degraded grasslands. Hence, the aim of this study was to discriminate nitrogen-saturated tropical grasses grown under a diverse fertilization treatment trial, using Worldview-3 satellite imagery and decision tree techniques. To accomplish this, nitrogen-saturated plots were first identified through specific physiological-based criteria. Thereafter, Worldview-3 satellite imagery (400–1040 nm) and decision tree techniques were applied to discriminate between nitrogen-saturated and -unsaturated grassland plots. The results showed net nitrate (NO₃⁻-N) concentrations and net pH levels to be significantly different (α = 0.05) between saturated and non-saturated plots. Moreover, the random forest model (overall accuracy of 91%) demonstrated a greater ability to classify saturated plots as opposed to the classification and regression tree method (overall accuracy of 79%). The most important variables for classifying saturated plots were identified as: the Red-Edge (705–745 nm), Coastal (400–450 nm), Near-Infrared 3 (838–950 nm), Soil-Adjusted Vegetation Index (SAVI) and the Normalized Difference Vegetation Index 3 (NDVI3). These results provide a framework to assist rangeland managers in identifying grasslands within the initial stages of nitrogen saturation. This will enable fertilization treatments to be adjusted in near-real-time according to ecosystem demand and thereby maintain the health and longevity of Southern African grasslands. Full article

This study investigates the effects of blockchain technology scalability limitations on the performance of Blockchain-Based Shared Manufacturing (BBSM), an innovative smart-manufacturing paradigm aimed at enhancing the utilization of global manufacturing resources via peer-to-peer (P2P) collaboration of self-organized manufacturing assets. Despite the prevalence of research highlighting blockchain technology’s scalability limitations as the main barrier for adoption, few studies have explored their effects on the operation of blockchain-based systems. The primary goal of the presented research work is to explore the implications of blockchain technology scalability limitations on the BBSM system’s performance and user behavior. To obtain realistic behavior, an experiment is conducted using an online game played by human participants. Analysis of the players’ strategy is used for implementation of a multi-agent simulation model, which is then employed to assess the influence of varying blockchain network configurations on the BBSM concept’s performance. Preliminary experimental findings reveal that a congested blockchain network leads to increased transaction costs and reduced service prices, consequently devaluing the manufacturing role in the BBSM system and causing underutilization of existing maximum production capacities. Moreover, allocating funds to financial activities rather than manufacturing activities yields superior outcomes for system users. Simulation results indicate that the BBSM system’s response to alterations in blockchain network throughput is contingent upon the production function. The findings of this study reveal that the scalability limitations of blockchain technology impair the performance of the BBSM system and affect user behavior in the system, underscoring the necessity for future research to concentrate on incorporating scalable solutions within blockchain-based manufacturing systems. Full article

Context features are mostly used to determine the boundary of a target, which allows one to better locate an object. In this paper, we propose the fusion of the spatial attention mechanism and contextual features to simulate the recognition of objects based on the human eye, thereby improving the detection accuracy of detectors. We chose the PASCAL VOC2007+2012 general dataset to test the generality of our method and examined the improved accuracy of our proposed detector on various targets. Our method showed improved accuracy for small targets and partially overlapping targets. Our proposed model improved the detector’s accuracy by 3.34%. Full article

The ongoing re-equipment of electric power systems is based on the use of smart grid technologies. Among the key tasks that are solved on this basis are increasing the capacity of power transmission lines, reducing losses, and improving power quality. To address these issues, one can use compact power transmission lines. Such lines are notable for their complex split-phase designs and close together placement of current-conducting parts, so as to keep the distance to a permissible minimum, which is achieved by the use of insulating spacers. This article reports the results of computer-aided simulations performed for a standard railroad power supply system, the traction substations of which were connected to 220 kV networks through compact overhead lines (COHLs). The purpose of the study was to calculate the values of quantitative metrics that measure power quality and energy efficiency as well as electromagnetic safety. Modeling was performed in the three-phase reference frame with the use of techniques and algorithms implemented in the Fazonord software package. We considered a power supply system with 25 kV overhead contact systems. It was assumed that the external network used three different designs of COHLs: with coaxial, linear, and sector-shaped arrangements of conductors. Based on the results obtained, we concluded that (1) when using COHLs, the voltages on bow collectors of electric rolling stock were stabilized and did not exceed permissible limits; (2) losses in the traction network were reduced; and (3) the parameters of power quality and electromagnetic safety conditions in external power supply systems of railroads were improved, if judged in terms of electric and magnetic field strengths. Out of the eight types of COHLs considered, compact lines with the three-segment and concentric arrangement of conductors had the best performance, and the use of COHLs with the vertical arrangement of conductors made it possible to reduce electric field strengths. However, the designs of such transmission lines are quite complex and entail higher construction costs. Full article

High-performance grinding has been converted from traditional manual grinding to robotic grinding over recent years. Accurate material removal is challenging for workpieces with complex profiles. Over recent years, digital processing of grinding has shown its great potential in the optimization of manufacturing processes and operational efficiency. Thus, quantification of the material removal process is an inevitable trend. This research establishes a three-dimensional model of the grinding workstation and designs the blade back arc grinding trajectory. A prediction model of the blade material removal depth (MRD) is established, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Experiments were carried out using the Taguchi method to investigate how certain elements might affect the outcomes. An Analysis of Variance (ANOVA) was used to study the effect of abrasive belt grinding characteristics on blade material removal. The mean absolute percent error ($MAPE$) of the established ANFIS model, after training and testing, was 3.976%, demonstrating superior performance to the reported findings, which range from 4.373% to 7.960%. ANFIS exhibited superior outcomes, when compared to other prediction models, such as random forest (RF), artificial neural network (ANN), and support vector regression (SVR). This work can provide some sound guidance for high-precision prediction of material removal amounts from surface grinding of steam turbine blades. Full article

Natural gas pipeline networks used for long-distance transportation are expanding quickly, and the construction of special valves with large diameters has especially increased since 2022. The design and manufacturing of the flow control equipment is carried out on a case-by-case basis, in accordance with the parameters required by the beneficiary. In this paper, results obtained by fluid flow simulation with SolidWorks2023 software for a 500 mm diameter trunnion ball valve lead to important information regarding how the fluid flow develops in the intermediary and fully closed positions. The large inner space of the ball allows the development of high-amplitude vortices; thus, the simulation demonstrates that the shut-on/off operation of large-diameter ball valves is mandatory to avoid fast destruction following partial opening. This paper also demonstrates why the metal–metal (MM) sealing with a double-piston effect (DPE) design for seats produces low leakage rates, including for the shut-off position; the pressure field reveals that few gas particles succeed in crossing the upstream sealing zone, and even fewer cross the downstream sealing zone. Additionally, the interpretation of the results explains and highlights the importance of using seats with a DPE design to achieve fire safety, which is required for natural gas pipeline applications. Full article

The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model. Full article

The transmission tower is an important infrastructure for transmission lines. To secure the operation of the power grid, it is particularly important to evaluate the safety of the in-service transmission tower under the action of random wind loads throughout their entire life cycle. Thus, this paper firstly establishes the time-varying equivalent performance function of the in-service transmission tower under the action of random wind loads. Then, in order to address the shortcomings of the traditional maximum entropy method, the high-order moments-based improved maximum entropy method (HM-IMEM) is proposed and extended to assess the wind resistance global reliability of the in-service transmission tower. Finally, the effectiveness of the proposed method is demonstrated evaluating the wind resistance global reliability of an in-service transmission tower in an engineering setting. Analytic results indicate that: (1) The proposed method can ensure a balance between calculation accuracy and efficiency. Compared with Monte Carlo simulation (MCS) method, the relative error is only 0.11% and the computational cost is much lower than that of the MCS method. (2) The reliability of the in-service transmission tower significantly decreased over time. In order to guide maintenance and reinforcement by predicting the time-varying performance of in-service transmission towers, it is of great engineering value to evaluate the wind resistance global reliability of the in-service transmission tower. Full article

Automated techniques to detect Alzheimer’s Dementia through the use of audio recordings of spontaneous speech are now available with varying degrees of reliability. Here, we present a systematic comparison across different modalities, granularities and machine learning models to guide in choosing the most effective tools. Specifically, we present a multi-modal approach (audio and text) for the automatic detection of Alzheimer’s Dementia from recordings of spontaneous speech. Sixteen features, including four feature extraction methods (Energy–Time plots, Keg of Text Analytics, Keg of Text Analytics-Extended and Speech to Silence ratio) not previously applied in this context were tested to determine their relative performance. These features encompass two modalities (audio vs. text) at two resolution scales (frame-level vs. file-level). We compared the accuracy resulting from these features and found that text-based classification outperformed audio-based classification with the best performance attaining 88.7%, surpassing other reports to-date relying on the same dataset. For text-based classification in particular, the best file-level feature performed 9.8% better than the frame-level feature. However, when comparing audio-based classification, the best frame-level feature performed 1.4% better than the best file-level feature. This multi-modal multi-model comparison at high- and low-resolution offers insights into which approach is most efficacious, depending on the sampling context. Such a comparison of the accuracy of Alzheimer’s Dementia classification using both frame-level and file-level granularities on audio and text modalities of different machine learning models on the same dataset has not been previously addressed. We also demonstrate that the subject’s speech captured in short time frames and their dynamics may contain enough inherent information to indicate the presence of dementia. Overall, such a systematic analysis facilitates the identification of Alzheimer’s Dementia quickly and non-invasively, potentially leading to more timely interventions and improved patient outcomes. Full article