Search Results (234)

CaO-Al₂O₃-Ce₂O₃ is a potential new-type basic metallurgical slag system for rare earth steel. To investigate the effects of CaF₂ on the melting point and equilibrium phase types of this slag system, the phase equilibrium relationships and extent of the liquid phase region of CaO-Al₂O₃-Ce₂O₃-CaF₂ slag system at 1300 °C, 1400 °C, and 1500 °C in C/CO were determined by the high-temperature phase equilibrium experiment, Scanning Electron Microscope-Energy Dispersive X-ray Spectrometer (SEM-EDX) and X-ray Diffraction (XRD), and the isothermal phase diagram was plotted. The experimental results show that within the composition range in this study, the slag system has five, seven, and six liquid–solid equilibrium coexistence regions at 1300 °C, 1400 °C, and 1500 °C. The involved multiphase equilibrium regions include five two-phase regions (i.e., Liquid + CaO, Liquid + CaO·2Al₂O₃, Liquid + 2CaO·Al₂O₃·Ce₂O₃, Liquid + 2CaO·3Al₂O₃·Ce₂O₃, Liquid + 11CaO·7Al₂O₃·CaF₂), 4 three-phase regions (i.e., Liquid + CaO + 2CaO·Al₂O₃·Ce₂O₃, Liquid + 11CaO·7Al₂O₃·CaF₂ + 2CaO·Al₂O₃·Ce₂O₃, Liquid + CaO·2Al₂O₃ + 2CaO·3Al₂O₃·Ce₂O₃, Liquid + 11CaO·7Al₂O₃·CaF₂ + 2CaO·3Al₂O₃·Ce₂O₃), and 1 four-phase region (i.e., Liquid + CaO + 11CaO·7Al₂O₃·CaF₂ + 2CaO·Al₂O₃·Ce₂O₃). Meanwhile, based on liquid phase compositions under liquid–solid multiphase equilibrium, the slag system’s liquid phase ranges at the experimental temperatures were determined as follows: at 1300 °C: w(CaO)/w(Al₂O₃) = 0.42~0.92, w(Ce₂O₃) = 1.63%~8.02%, w(CaF₂) = 9.17%~21.46%; 1400 °C: 0.28~1.18, 0.9%~12.62%, 1.04%~23.34%, respectively; 1500 °C: 0.23~1.21, 0~14.42%, 0~26.32%, respectively. Full article

The mechanically activated transition (MAT) from linear viscoelasticity to yielding is considered an essential part of the operational behavior of ductile materials. The MAT region is restricted by proportional limit at σ₀ and ε₀ and the yield point at σ_y and ε_y, or, in terms of this paper, $E_0={\sigma }_0/{\epsilon }_0$ and ε₀ and $E_y={\sigma }_y/{\epsilon }_y$ and ε_y, respectively. This stage precedes yielding and controls the parameters of the yield point. For bulk plastic (co)polymers and cellular polymeric foams, the quantitative correlations between $E_0$, ε₀, $E_y,$ and ε_y were determined. The ratios ${\displaystyle \raisebox{1ex}{$E_0$}\!\left/ \!\raisebox{-1ex}{$E_y$}\right.}=1.55\pm 0.15$ and ${\displaystyle \raisebox{1ex}{${\epsilon }_y$}\!\left/ \!\raisebox{-1ex}{${\epsilon }_0$}\right.}=2.1\pm 0.2$ were specified as yielding criteria. For all the samples studied, their mechanical response within the MAT region was unified in terms of master curve constructed via re-calculation of the experimental “stress–strain” diagrams in the reduced coordinates ${\displaystyle \frac{\mathit{lg} E-\mathit{lg} E_0}{\mathit{lg} E_0-\mathit{lg} E_y}}=f\left({\displaystyle \frac{\mathit{lg} \epsilon -\mathit{lg} {\epsilon }_0}{\mathit{lg} {\epsilon }_y-\mathit{lg} {\epsilon }_0}}\right),$ where $E=\sigma /\epsilon$ and ε are the current modulus and strain, respectively. To generalize these regularities found for bulk plastics and foams, our earlier experimental results concerning the rheology of soil-based pastes and data from the literature concerning the computer simulation of plastic deformation were invoked. Master curves for (1) dispersed pastes, (2) bulk plastics, (3) polymeric foams, and (4) various virtual models were shown to be in satisfactory coincidence. For the materials analyzed, this result was considered as the unification of their mechanical response within the MAT region. An algorithm for the express analysis of the mechanical response of plastic systems within the MAT region is proposed. The limitations and advances of the proposed methodological approach based on correlation studies followed by construction of master curves are outlined. Full article

We propose a strictly causal early–warning framework for financial crises based on topological signal extraction from multivariate return streams. Sliding windows of daily log–returns are mapped to point clouds, from which Vietoris–Rips persistence diagrams are computed and summarised by persistence landscapes. A single, interpretable indicator is obtained as the $L_2$ norm of the landscape and passed through a causal decision rule (with thresholds $\alpha ,\beta$ and run–length parameters $s,t$) that suppresses isolated spikes and collapses bursts to time–stamped warnings. On four major U.S. equity indices (S&P 500, NASDAQ, DJIA, Russell 2000) over 1999–2021, the method, at a fixed strictly causal operating point $(\alpha =\beta =3.1,s=57,t=16)$, attains a balanced precision–recall ($F_1\approx 0.50$) with an average lead time of about 34 days. It anticipates two of the four canonical crises and issues a contemporaneous signal for the 2008 global financial crisis. Sensitivity analyses confirm the qualitative robustness of the detector, while comparisons with permissive spike rules and volatility–based baselines demonstrate substantially fewer false alarms at comparable recall. The approach delivers interpretable topology–based warnings and provides a reproducible route to combining persistent homology with causal event detection in financial time series. Full article

Context: Modern software systems have become increasingly complex, making it difficult to interpret raw requirements and effectively utilize traditional tools for software design and analysis. Unified Modeling Language (UML) class diagrams are widely used to visualize and understand system architecture, but analyzing them manually, especially for large-scale systems, poses significant challenges. Objectives: This study aims to automate the analysis of UML class diagrams by assessing their complexity using a machine learning approach. The goal is to support software developers in identifying potential design issues early in the development process and to improve overall software quality. Methodology: To achieve this, this research introduces a Visual Question Answering (VQA)-based framework that integrates both computer vision and natural language processing. Vision Transformers (ViTs) are employed to extract global visual features from UML class diagrams, while the BERT language model processes natural language queries. By combining these two models, the system can accurately respond to questions related to software complexity, such as class coupling and inheritance depth. Results: The proposed method demonstrated strong performance in experimental trials. The ViT model achieved an accuracy of 0.8800, with both the F1 score and recall reaching 0.8985. These metrics highlight the effectiveness of the approach in automatically evaluating UML class diagrams. Conclusions: The findings confirm that advanced machine learning techniques can be successfully applied to automate software design analysis. This approach can help developers detect design flaws early and enhance software maintainability. Future work will explore advanced fusion strategies, novel data augmentation techniques, and lightweight model adaptations suitable for environments with limited computational resources. Full article

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification. Full article

Differential equations have demonstrated significant practical effectiveness across diverse fields, including physics, chemistry, biological engineering, computer science, electrical power systems, and security cryptography. This study investigates the dynamics of a Caputo fractional discrete-time modified Brusselator model. Conditions for the existence and local stability of the fixed point $FP$ are established. Using bifurcation theory, the occurrence of both period-doubling and Neimark–-Sacker bifurcations is analyzed, revealing that these bifurcations occur at specific values of the bifurcation parameter. Maximum Lyapunov characteristic exponents are computed to assess system sensitivity. Two-dimensional diagrams are presented to illustrate phase portraits, local stability regions, closed invariant curves, bifurcation types, and Lyapunov exponents, while the 0-1 test confirms the presence of chaos in the model. Finally, MATLAB simulations validate the theoretical results. Full article

In chili cultivation, obstacles to continuous cropping significantly compromise crop yield and soil health, whereas crop rotation can enhance the microbial environment of the soil and reduce disease incidence. However, its effects on the diversity of rhizosphere soil microbial communities are not clear. In this study, we analyzed the composition and characteristics of rhizosphere soil microbial communities under chili continuous cropping (CC) and chili–cotton crop rotation (CR) using high-throughput sequencing technology. CR treatment reduced the alpha diversity indices (including Chao1, Observed_species, and Shannon index) of bacterial communities and had less of an effect on fungal community diversity. Principal component analysis (PCA) revealed distinct compositional differences in bacterial and fungal communities between the treatments. Compared with CC, CR treatment has altered the structure of the soil microbial community. In terms of bacterial communities, the relative abundance of Firmicutes increased from 12.89% to 17.97%, while the Proteobacteria increased by 6.8%. At the genus level, CR treatment significantly enriched beneficial genera such as RB41 (8.19%), Lactobacillus (4.56%), and Bacillus (1.50%) (p < 0.05). In contrast, the relative abundances of Alternaria and Fusarium in the fungal community decreased by 6.62% and 5.34%, respectively (p < 0.05). Venn diagrams and linear discriminant effect size analysis (LEfSe) further indicated that CR facilitated the enrichment of beneficial bacteria, such as Bacillus, whereas CC favored enrichment of pathogens, such as Firmicutes. Fusarium solani MG6 and F. oxysporum LG2 are the primary chili root-rot pathogens. Optimal growth occurs at 25 °C, pH 6: after 5 days, MG6 colonies reach 6.42 ± 0.04 cm, and LG2 5.33 ± 0.02 cm, peaking in sporulation (p < 0.05). In addition, there are significant differences in the utilization spectra of carbon and nitrogen sources between the two strains of fungi, suggesting their different ecological adaptability. Integrated analyses revealed that CR enhanced soil health and reduced the root rot incidence by optimizing the structure of soil microbial communities, increasing the proportion of beneficial bacteria, and suppressing pathogens, providing a scientific basis for microbial-based soil management strategies in chili cultivation. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Groundwater in the Qingshui River Plain of southern Ningxia is one of the main water sources for local domestic and agricultural use. However, due to the geological background of the area, 33.94% of the groundwater samples had fluoride concentrations that exceeded the WHO drinking water standards. To examine the spatial patterns and formation processes of fluoride in groundwater, researchers gathered 79 rock samples, 2618 soil samples, 21 sediment samples, 138 groundwater samples, and 82 surface water samples across the southern Qingshui River Plain. The collected data were analyzed using statistical approaches and hydrogeochemical diagrams. The findings reveal that fluoride levels in groundwater exhibit a gradual increase from the eastern, western, and southern peripheral sloping plains toward the central valley plain. Vertically, higher fluoride concentrations are found within 100 m of depth. Over a ten-year period, fluoride concentrations have shown minimal variation. Fluoride-rich rocks, unconsolidated sediments, and soils are the primary sources of fluoride in groundwater. The primary mechanisms governing high-fluoride groundwater formation are rock weathering and evaporative concentration, whereas cation exchange adsorption promotes fluoride (F⁻) mobilization into the aquifer. Additional sources of fluoride ions include leaching of fluoride-rich sediments during atmospheric precipitation infiltration and recharge from fluoride-rich surface water. Full article

Ca₃(BO₃)₂ microwave dielectric ceramics with space group R-3c (#167) were prepared by cold sintering, and their properties were systematically investigated. Phonon density of state diagrams for the Ca₃(BO₃)₂ lattice were obtained based on first-principles calculations to provide a more comprehensive understanding of the lattice vibrational properties of the material. Raman scattering and infrared reflectance spectroscopy were employed to investigate the lattice vibrational characteristics, identifying two types of vibrational modes: internal modes associated with the planar bending and symmetric stretching vibrations of the [BO₃] group, and external modes linked to the vibrations of the [CaO₆] octahedron. The intrinsic dielectric properties were determined by fitting the experimental data using a four-parameter semi-quantum model. The results demonstrate that the dielectric properties of Ca₃(BO₃)₂ ceramics are primarily influenced by the external vibrational modes. The sample under 800 MPa exhibits optimal dielectric performance, with a dielectric constant (ε_r) of 5.95, a quality factor (Q × f) of 11,836 GHz, and a temperature coefficient of resonant frequency (τ_f) of −39.89 ppm/°C. A simulation of this Ca₃(BO₃)₂ sample as a dielectric substrate was conducted using HFSS to fabricate a microstrip patch antenna operating at 14.97 GHz, which exhibits a return loss (S₁₁) of −25.5 dB and a gain of 7.15 dBi. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

The trematodes Fasciola hepatica and F. gigantica are transmitted by lymnaeid snails and cause fascioliasis in livestock and humans. Human infection is emerging in southern and southeastern Asia. In India, the number of case reports has increased since 1993. This multidisciplinary study analyzes the epidemiological scenario of human infection. The study reviews the total of 55 fascioliasis patients, their characteristics, and geographical distribution. Causes underlying this emergence are assessed by analyzing (i) the climate change suffered by India based on 40-year-data from meteorological stations, and (ii) the geographical fascioliasis hotspots according to archeological–historical records about thousands of years of pack animal movements. The review suggests frequent misdiagnosis of the wide lowland-distributed F. gigantica with F. hepatica and emphasizes the need to obtain anamnesic information about the locality of residence and the infection source. Prevalence appears to be higher in females and in the 30–40-year age group. The time elapsed between symptom onset and diagnosis varied from 10 days to 5 years (mean 9.2 months). Infection was diagnosed by egg finding (in 12 cases), adult finding (28), serology (3), and clinics and image techniques (12). Climate diagrams and the Wb-bs forecast index show higher temperatures favoring the warm condition-preferring main snail vector Radix luteola and a precipitation increase due to fewer rainy days but more days of extreme rainfall, leading to increasing surface water availability and favoring fascioliasis transmission. Climate trends indicate a risk of future increasing fascioliasis emergence, including a seasonal infection risk from June–July to October–November. Geographical zones of high human infection risk defined by archeological–historical analyses concern: (i) the Indo-Gangetic Plains and corridors used by the old Grand Trunk Road and Daksinapatha Road, (ii) northern mountainous areas by connections with the Silk Road and Tea-Horse Road, and (iii) the hinterlands of western and eastern seaport cities involved in the past Maritime Silk Road. Routes and nodes are illustrated, all transhumant–nomadic–pastoralist groups are detailed, and livestock prevalences per state are given. A baseline defining areas and seasons of high infection risk is established for the first time in India. This is henceforth expected to be helpful for physicians, prevention measures, control initiatives, and recommendations for health administration officers. Full article

The Zaghouan Fluorite Province (ZFP) encloses F-Ba(Pb-Zn) ores hosted within Jurassic carbonate series, in northeastern Tunisia. Critical breakthroughs on the Jebel Stah fluorite deposits, an MVT-style F-mineralization, have been made within the Lower Jurassic limestones along the Zaghouan Fault, which is a major target for mineralization. This study presents the first REE-Y analyses conducted by LA-ICP-MS on fluorites in Tunisia, and specifically on the fluorites of Jebel Stah deposit. This analytical technique provides highly accurate insights into the geochemical regime of mineralizing fluids and the related scavenging sources. Distinct geochemical characteristics between two fluorite generations (G1 and G2) were revealed. Fluorites (Fl2) from the early generation (G1) showed low ΣREE + Y (36.3 and 39.73 ppm, respectively). When normalized to chondrites, early fluorite G1 displayed a bell-shaped REE + Y pattern with a depletion in LREE relative to HREE and a slight MREE hump. Late fluorite (Fl3) generation (G2) displayed higher ΣREE + Y concentrations (77.43 ppm), but an almost similar REE pattern. Ce/Ce* ratios demonstrated strong negative Ce anomalies in all fluorites, while Eu/Eu* ratios indicated weak negative Eu anomalies. The positive Y anomaly observed in the REE + Y patterns of fluorites G1 and G2 suggests Y-Ho fractionation in the fluid system. Moreover, significant degrees of differentiation between terbium (Tb) and lanthanum (La) have been observed in all fluorite samples. The plot of fluorites from both fluorite generations on the Tb/La–Tb/Ca diagram gives evidence of the sedimentary hydrothermal origin of the ore-forming fluids in the Jebel Stah F-deposit. Sr isotopes show that the mineralizing fluids are radiogenic and deeply sourced basinal brines, whereas the small variation in ⁸⁷Sr/⁸⁶Sr ratios suggests a similar source for Sr in fluorites G1 and G2. These results allow us to conclude that the economic fluorite (G1) ore of Jebel Stah was deposited due to the interaction of the deeply sourced hydrothermal fluid with the carbonated host rocks (dolomitization, an increase in pH, and Ca activity), whereas the late fluorite (G2) is an accessory and could have resulted from the mixing of the hydrothermal fluid with shallow meteoric waters. Full article

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code to be used as input image datasets to train, validate, and test the proposed CNN model. Two scenarios were adopted from PAWS transducers. First, two sensors were positioned in two corners of the plate, while, in the second scenario, four sensors were used to monitor and collect the signals. Eight datasets were collected and reshaped from these two scenarios. These datasets presented the signals of two, three, four, and five impacts. The model’s performance was evaluated using four metrics: confusion matrix, accuracy, precision, and F1 score. The proposed model demonstrated exceptional performance by accurately localizing all of the impact points of the first scenario and 99% of the second scenario. The main limitation of the proposed model is how to differentiate the data samples that have similar features. From our point of view, the similarity challenge arose from two factors: the segmentation interval and the impact distance. First, applying the segmenting procedure to the PWAS signals led to an increase in the number of data samples. The procedure segmented each PWAS signal to 30 samples with equal intervals, regardless of the features of the signal. Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. Second, some of the impacts had a close distance to the PWAS sensors, which resulted in similar segmented signals. Therefore, the second scenario was more challenging for the proposed model. Full article

We prove a conjecture on the second minimal odd periodic orbits with respect to Sharkovski ordering for the continuous endomorphisms on the real line. A $(2k+1)$-periodic orbit $\{{\beta }_1<{\beta }_2<\cdots <{\beta }_{2k+1}\}$, ($k\ge 3$) is called second minimal for the map f, if $2k-1$ is a minimal period of ${f|}_{[{\beta }_1,{\beta }_{2k+1}]}$ in the Sharkovski ordering. Full classification of second minimal orbits is presented in terms of cyclic permutations and directed graphs of transitions. It is proved that second minimal odd orbits either have a Stefan-type structure like minimal odd orbits or one of the $4k-3$ types, each characterized with unique cyclic permutations and directed graphs of transitions with an accuracy up to the inverses. The new concept of second minimal orbits and its classification have an important application towards an understanding of the universal structure of the distribution of the periodic windows in the bifurcation diagram generated by the chaotic dynamics of nonlinear maps on the interval. Full article