Search Results (345)

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision ([email protected]) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work. Full article

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud. Full article

This study employed unsupervised machine learning clustering algorithms to systematically analyze the spatial layout characteristics of residential buildings in villages along the Miao Frontier Wall and Miao Frontier Corridor in Western Hunan. The results indicated significant differences between the two regions in terms of the number of building clusters, distribution patterns, and compactness. A comparative analysis of the K-means and DBSCAN algorithms revealed that K-means is more effective in uncovering the internal spatial layout characteristics of settlements. Further analysis showed that villages along the Miao Frontier Wall exhibited greater diversity and complexity, whereas those along the Miao Frontier Corridor demonstrated higher clustering efficiency and denser internal building distribution. These differences can be attributed to variations in historical functions, geographical environments, planning concepts, and social structures. This research uncovers the spatial layout patterns of traditional settlements and proposes a machine learning-based approach to cultural heritage preservation, providing a theoretical foundation for future heritage conservation and spatial optimization, thereby promoting the sustainable development and protection of traditional cultural heritage. Full article

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn’s index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM. Full article

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm’s engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions. Full article

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions. Full article

Fabrication aspects of PdO thin films and coatings are reviewed here. The work provides and organizes the up-to-date information on the methods to obtain the films. In recent years, the interest in Pd oxide for different applications has increased. Since Pd can be converted into PdO, it is instructive to pay attention to the preparation of the pure and the alloyed Pd films, heterostructures, and nanoparticles synthesized on different substrates. The development of PdO films is presented from the early reports on coatings’ formation by oxidation of Pd foils and wires to present technologies. Modern synthesis/growth routes are gathered into chemical and physical categories. Chemical methods include hydrothermal, electrochemical, electroless deposition, and coating methods, such as impregnation, precipitation, screen printing, ink jet printing, spin or dip coating, chemical vapor deposition (CVD), and atomic layer deposition (ALD), while the physical ones include sputtering and cathodic arc deposition, laser ablation, ion or electron beam-induced deposition, evaporation, and supersonic cluster beam deposition. Analysis of publications indicates that many as-deposited Pd or Pd-oxide films are granular, with a high variety of morphologies and properties targeting very different applications, and they are grown on different substrates. We note that a comparative assessment of the challenges and quality among different films for a specific application is generally missing and, in some cases, it is difficult to make a distinction between a film and a randomly oriented, powder-like (granular), thin compact material. Textured or epitaxial films of Pd or PdO are rare and, if orientation is observed, in most cases, it is obtained accidentally. Some practical details and challenges of Pd oxidation toward PdO and some specific issues concerning application of films are also presented. Full article

Laminar flow offers significant potential for increasing the energy efficiency of future transport aircraft. At the Cluster of Excellence SE²A—Sustainable and Energy-Efficient Aviation—the laminarization of the wing by means of hybrid laminar flow control (HLFC) is being investigated. The aim is to maintain the boundary layer as laminar for up to 80% of the chord length of the wing. This is achieved by active suction on the leading edge and the rear part of the wing. The suction panels are constructed with a thin micro-perforated skin and a supporting open-cellular core structure. The mechanical requirements for this kind of sandwich structure vary depending on its position of usage. The suction panel on the leading edge must be able to sustain bird strikes, while the suction panel on the rear part must sustain bending loads from the deformation of the wing. The objective of this study was to investigate the energy absorption properties of a triply periodic minimal surface (TPMS) structure that can be used as a bird strike-resistant core in the wing leading edge. To this end, cubic-sheet-based gyroid specimens of different polymeric materials and different geometric dimensions were manufactured using additive manufacturing processes. The specimens were then tested under quasi-static compression and dynamic crushing loading until failure. It was found that the mechanical behavior was dependent on the material, the unit cell size, the relative density, and the loading rate. In general, the weight-specific energy absorption (SEA) at 50% compaction increased with increasing relative density. Polyurethane specimens exhibited an increase in SEA with increasing loading rate, as opposed to the specimens of the other investigated polymers. A smaller unit cell size induced a more consistent energy absorption, due to the higher plateau force. Full article

The dynamic distribution of urban population density and the interaction with land use elements involve mutual constraints and guidance. However, in the existing research on the relationship between urban population density and land use, the discussion on the distribution patterns of urban population density typically spans long time periods and uses large spatial units, lacking analysis of the dynamic changes in population density within high granularity land parcels over a day. In studies related to the urban built environment, the complex relationships between different-dimensional land use elements and the dynamic distribution of population density also need further exploration. To address these bottlenecks, this study takes Shanghai’s central urban area as an example. Based on 24 h mobile signaling data on weekdays, weekends, and typical holidays, as well as urban land use data, clustering algorithms are used to summarize patterns of dynamic population density distribution. Pearson correlation analysis is then employed to study the correlation between dynamic population density distribution patterns and different land use elements. The results indicate that various urban land use factors such as locational centrality, functional diversity, transportation accessibility, compactness, and landscape quality have different impacts on the dynamic distribution of population density in spatial units, and the dynamic distribution patterns of population density in different land use types also vary. This research contributes to guiding the optimization of spatial quality and formulating planning and management measures that more effectively match construction intensity with population activity density. Full article

This study describes the first genome sequence and analysis of Coniella granati, a fungal pathogen with a broad host range, which is responsible for postharvest crown rot, shoot blight, and canker diseases in pomegranates. C. granati is a geographically widespread pathogen which has been reported across Europe, Asia, the Americas, and Africa. Our analysis revealed a 46.8 Mb genome with features characteristic of hemibiotrophic fungi. Approximately one third of its genome was compartmentalised within ‘AT-rich’ regions exhibiting a low GC content (30 to 45%). These regions primarily comprised transposable elements that are repeated at a high frequency and interspersed throughout the genome. Transcriptome-supported gene annotation of the C. granati genome revealed a streamlined proteome, mirroring similar observations in other pathogens with a latent phase. The genome encoded a relatively compact set of 9568 protein-coding genes with a remarkable 95% having assigned functional annotations. Despite this streamlined nature, a set of 40 cysteine-rich candidate secreted effector-like proteins (CSEPs) was predicted as well as a gene cluster involved in the synthesis of a pomegranate-associated toxin. These potential virulence factors were predominantly located near repeat-rich and AT-rich regions, suggesting that the pathogen evades host defences through Repeat-Induced Point mutation (RIP)-mediated pseudogenisation. Furthermore, 23 of these CSEPs exhibited homology to known effector and pathogenicity genes found in other hemibiotrophic pathogens. The study establishes a foundational resource for the study of the genetic makeup of C. granati, paving the way for future research on its pathogenicity mechanisms and the development of targeted control strategies to safeguard pomegranate production. Full article

For advanced aircraft, the temperature environment inside the cabin is very severe due to the high flight speed and the compact concentration of the electronic equipment in the cabin. Accurately predicting the temperature environment induced inside the cabin during the flight of the aircraft can determine the temperature environment requirements of the onboard equipment inside the cabin and provide an accurate input for the thermal design optimization and test verification of the equipment. The temperature environment of the whole aircraft is divided into zones by the cluster analysis method; the heat transfer mechanism of the aircraft cabin is analyzed for the target area; and the influence of internal and external factors on the thermal environment is considered to establish the temperature environment prediction model of the target cabin. The coefficients of the equations in the model are parameterized to extract the long-term stable terms and trend change terms; with the help of the measured data of the flight state, the model coefficients are determined by a stepwise regression method; and the temperature value inside the aircraft cabin is the output by inputting parameters such as flight altitude, flight speed, and external temperature. The model validation results show that the established temperature environment prediction model can accurately predict the change curve of the cabin temperature during the flight of the aircraft, and the model has a good follow-up performance, which reduces the prediction error caused by the temperature hysteresis effect. For an aircraft, the estimated error is 2.8 °C at a confidence level of 95%. Engineering cases show that the application of this method can increase the thermal design requirements of the airborne equipment by 15 °C, increase the low-temperature test conditions by 17 °C, and avoid the problems caused by an insufficient design and over-testing. This method can accurately predict the internal temperature distribution of the cabin during the flight state of the aircraft, help designers determine the thermal design requirements of the airborne equipment, modify the thermal design and temperature test profile, and improve the environmental worth of the equipment. Full article

The decline in the role of agriculture as the basis for the livelihood of rural residents has led to the development of new directions for rural transformation. In Poland, the concept of multifunctional development has gained the most popularity. However, it does not have a defined spatial development model. There has also been no research into how the development of non-agricultural functions affects spatial development and to what extent this development is sustainable. Therefore, the aim of this study is to show and compare the changes that have taken place over the last 40–50 years in the spatial arrangements of development in traditional agricultural villages and in villages with different non-agricultural functions (tourist, industrial, and service functions). At the same time, we want to indicate which of these functions have contributed to the development of the most sustainable spatial arrangements. To this end, we selected three indicators of sustainable development of rural space: compactness of buildings, continuation of traditional rural layouts, and availability of services, and then carried out an analysis of changes in these indicators on the basis of archival and current cartographic materials and data on service facilities. We conducted the research for four municipalities in eastern Poland (50 villages). The results indicate the predominance of negative spatial phenomena such as the deterioration of the accessibility of services and spatial development contrary to historical spatial layouts. There is a spillover of development in the form of discontinuous, chaotic clusters of buildings often having the character of suburbia and, consequently, the disappearance of village centres, worsening walkability, and blurring of village boundaries. The only positive change is an increase in the compactness of buildings—mainly in villages that previously had a dispersed character. It is difficult to identify village functions that would unequivocally favour spatial sustainability. The service villages showed slight advantages in terms of social (availability of services) and environmental (compactness of buildings) factors. In contrast, the development of agricultural villages was more favourable in cultural terms (traditional village layouts). In all aspects, negative changes were recorded in industrial villages and (the worst results) in tourist villages. However, the trends were similar in all municipalities, which draws attention primarily to the lack of a rational spatial policy related to multifunctional village development. Full article

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (T_air) networks, leading to classification confusion and gaps in T_air data. Satellite imagery used in surface UHI (SUHI) applications overcomes the gaps which befall AUHI, thus making it the primary focus of UHI studies in areas with limited T_air stations. Consequently, we used Landsat 30 m imagery to analyse SUHI patterns using Land Surface Temperature (LST) data. Local climate zones (LCZ) as a UHI study tool have been documented to not result in distinct thermal environments at the surface level per LCZ class. The goal in this study was thus to explore relationships between LCZs and LST patterns, aiming to create a building height (BH)-independent LCZ framework capable of creating distinct thermal environments to study SUHI in African cities where LiDAR data are scarce. Random forests (RF) classified LCZ in R, and the Single Channel Algorithm (SCA) extracted LST via the Google Earth Engine. Statistical analyses, including ANOVA and Tukey’s HSD, assessed thermal distinctiveness, using a 95% confidence interval and 1 °C threshold for practical significance. Semi-Automated Agglomerative Clustering (SAAC) and Automated Divisive Clustering (ADC) grouped LCZs into thermally distinct clusters based on physical characteristics and LST data internal patterns. Built LCZs (1–9) had higher mean LSTs; LCZ 8 reached 37.6 °C in Spring, with a smaller interquartile range (IQR) (34–36 °C) and standard deviation (SD) (1.85 °C), compared to natural classes (A–G) with LCZ 11 (A–B) at 14.9 °C/LST, 17–25 °C/IQR, and 4.2 °C SD. Compact LCZs (2, 3) and open LCZs (5, 6), as well as similar LCZs in composition and density, did not show distinct thermal environments even with building height included. The SAAC and ADC clustered the 14 LCZs into six thermally distinct clusters, with the smallest LST difference being 1.19 °C, above the 1 °C threshold. This clustering approach provides an optimal LCZ framework for SUHI studies, transferable to different urban areas without relying on BH, making it more suitable than the full LCZ typology, particularly for the African context. This clustered framework ensures a thermal distinction between clusters large enough to have practical significance, which is more useful in urban planning than statistical significance. Full article

Pollen carries extensive genetic information, which may provide clues regarding the kinship of Malus, whose genetic relationships are complex. In this study, the phenotypic variation of pollen from 107 Malus taxa was investigated using combined methods of intraspecific/interspecific uniformity testing, cluster analysis, and Pearson correlation analysis. The family aggregation distributions in Malus sections, species, and cultivars were analyzed to infer their pedigree relationships. The results showed that (1) compared with pollen size and morphology, aberrant pollen rate and ornamentation were highly interspecifically differentiated, but ornamentation was also intraspecifically unstable, especially perforation densities ($\overline{c.v.}$ > 15%). (2) The pollen alteration direction from the original to the evolutionary population of Malus was large to small, with elliptic to rectangular morphologies, large and compact to small and sparse ridges, and low to high perforation densities. However, there was no significant change in pollen size. (3) The 107 studied taxa were divided into four groups. Malus species were relatively clustered in the same section, while homologous cultivars showed evidence of family aggregation distribution characteristics (92.60% of cultivars were clustered with their parents). (4) M. baccata and M. pumilar var. neidzwetzkyana were high-frequency parents, participating in 38.7% and 20.7% of cross-breeding, respectively. Overall, this study provides a reference for identifying Malus’ pedigree relationship. Full article

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model’s classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method’s effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation. Full article