Search Results (156)

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. Full article

Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitation, we developed the U-smile method, a residual-based, post hoc evaluation approach for assessing prediction improvements and worsening separately for events and non-events. The U-smile method produces three families of interpretable BA-RB-I coefficients at three levels of generality and a standardized graphical summary through U-smile and prediction improvement–worsening (PIW) plots, enabling transparent, interpretable, and explainable VS. Validated in balanced and imbalanced BC scenarios, the method proved robust to class imbalance and collinearity, and more sensitive than traditional metrics in detecting subtle but meaningful effects. Moreover, the method’s intuitive visual output (U-smile plot) facilitates the rapid communication of results to non-technical stakeholders, bridging the gap between data science and applied decision-making. The U-smile method supports both local and global evaluations and complements existing explainable machine learning (XML) and artificial intelligence (XAI) tools without overlapping in their functions. The U-smile method offers a transparency-enhancing and human-oriented approach for ethical and fair VS, making it highly suited for high-stakes domains, e.g., healthcare and public health. Full article

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data. Full article

The valine glutamine (VQ) proteins are transcription cofactors involved in various aspects of plant biology, including growth, development, and stress resistance, making them an attractive target for genetic engineering aimed at enhancing plant resilience and productivity. However, comprehensive reports or systematic studies on VQ cofactors in Salix suchowensis remain lacking. In this study, we analyzed SsVQ genes using bioinformatics methods based on the Salix suchowensis genome database. Expression profiles were further investigated through qRT-PCR under six treatments: PEG, NaCl, 40 °C, ABA, SA, and MeJA. A total of 39 SsVQ genes were identified, with phylogenetic analysis classifying them into seven groups. Collinearity analysis suggested that SsVQ gene amplification primarily resulted from whole genome duplication (WGD) or segmental duplication events. Ka/Ks ratios indicated that willow VQ genes have undergone predominantly purifying selection. Gene structure analysis revealed that SsVQ genes are intronless. Multiple sequence alignment showed that SsVQ19 shares similarity with PtVQ27, containing a hydrophilic threonine (T) residue preceding the VQ amino acid residues. Furthermore, genes within each group exhibited conserved structures and VQ motifs. Promoter and expression analyses suggested the potential roles of SsVQ genes in regulating willow responses to environmental stresses and hormonal signals. Most SsVQ genes displayed differential expression at specific time points, with six members (SsVQ2, SsVQ9, SsVQ12, SsVQ23, SsVQ32, and SsVQ34) showing sustained high-amplitude expression profiles across treatments. Notably, SsVQ34 demonstrated pronounced transcriptional induction under PEG stress, with expression levels upregulated by 62.29-fold (1 h), 49.21-fold (6 h), 99.9-fold (12 h), and 201.50-fold (24 h). Certain SsVQ genes showed co-expression under abiotic/hormonal stresses, implying synergistic functions. Paralogous gene pairs exhibited stronger co-expression than non-paralogous pairs. This study provides novel insights into the structural and functional characteristics of the VQ gene family in Salix suchowensis, establishing a foundation for future research on the stress-resistance mechanisms of willow VQ genes. Full article

Background: Smilax glabra (S. glabra) and Smilax zeylanica (S. zeylanica), two medicinally important species within the genus Smilax, have been widely used in Traditional Chinese Medicine (TCM) for the treatment of rheumatism, traumatic injuries, and related ailments. Despite their medicinal significance, research on the mitochondrial DNA (mtDNA) of Smilax species remains limited. Methods: We utilized NovaSeq 6000 and PromethION sequencing platforms to assemble the complete mitochondrial genomes of Smilax glabra and Smilax zeylanica, and conducted in-depth comparative genomic and evolutionary analyses. Results: The complete mitochondrial genomes of S. glabra and S. zeylanica were assembled and annotated, with total lengths of 535,215 bp and 471,049 bp, respectively. Both genomes encode 40 unique protein-coding genes (PCGs), composed of 24 core and 16 non-core genes, alongside multiple tRNA and rRNA genes. Repetitive element analysis identified 158 and 403 dispersed repeats in S. glabra and S. zeylanica, respectively, as well as 123 and 139 simple sequence repeats (SSRs). RNA editing site predictions revealed C-to-U conversions in both species. Additionally, chloroplast-to-mitochondrial DNA migration analysis detected 34 homologous fragments in S. glabra and 28 homologous fragments in S. zeylanica. Phylogenetically, S. glabra and S. zeylanica cluster within the Liliales order and Smilacaceae family, closely related to Lilium species. Collinearity analysis indicated numerous syntenic blocks between Smilax and three other Liliopsida species, though gene order was not conserved. Conclusions: This study presents high-quality mitochondrial genome assemblies for S. glabra and S. zeylanica, providing valuable insights into molecular identification and conservation efforts of these traditional medicinal plants. Full article

Basic principles of ferroelectric activity induced by the noncollinear alignment of spins are reviewed. There is a fundamental reason why the inversion symmetry can be broken by certain magnetic order. This situation occurs when the magnetic order simultaneously involves ferromagnetic ($\mathit{F}$) and antiferromagnetic ($\mathit{A}$) counterparts, transforming under the spatial inversion $\mathcal{I}$ and time reversal $\mathcal{T}$ as $\mathcal{I}\mathit{F}=\mathit{F}$ and $\mathcal{IT}\mathit{A}=\mathit{A}$, respectively. The incompatibility of these two conditions results in breaking the inversion symmetry, which manifests itself in the electric polarization $\overrightarrow{P}$. The noncollinear alignment of spins is one of examples of such coexistence of $\mathit{F}$ and $\mathit{A}$. This coexistence principle imposes a constraint on possible dependencies of $\overrightarrow{P}$ on the directions of spins, which can include only “antisymmetric coupling” in the bond, ${\overrightarrow{\mathcal{P}}}_{ij}·[{\mathit{e}}_i\times {\mathit{e}}_j]$, and “single-ion anisotropy”, ${\mathit{e}}_i·$ $\overrightarrow{\mathbb{\Pi }}$ ${\mathit{e}}_i$. Microscopically, ${\overrightarrow{\mathcal{P}}}_{ij}$ can be evaluated in the framework of superexchange theory. For the single Kramers doublet, this theory yields ${\overrightarrow{\mathcal{P}}}_{ij}\sim {\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$, where ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ is the spin-dependent part of the position operator induced by the relativistic spin-orbit coupling. ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ remains invariant under spatial inversion, providing the microscopic reason why noncollinear alignment of spins can induce $\overrightarrow{P}$ even in centrosymmetric crystals. The symmetry properties of ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ can be rationalized from the viewpoint of symmetry of Kramers states. Particularly, the commonly used Katsura–Nagaosa–Balatsky (KNB) rule $\overrightarrow{P}\propto {\overrightarrow{ϵ}}_{ji}\times [{\mathit{e}}_i\times {\mathit{e}}_j]$ (${\overrightarrow{ϵ}}_{ji}$ being the direction of the bond $ij$) can be justified only for relatively high symmetry of the bonds. The single-ion anisotropy vanishes for the spin ${\displaystyle \frac{1}{2}}$ or if magnetic ions are located in inversion centers, thus severely restricting the applicability of this microscopic mechanism. The properties of multiferroic materials are reconsidered from the viewpoint of these principles. A particular attention is paid to complications caused by possible deviations from the KNB rule. Full article

The influence of acoustic field distribution and temperature variations on the full-aperture diffraction efficiency of non-collinear acousto-optic tunable filters (AOTFs) was investigated based on tellurium dioxide crystals. The strong acoustic anisotropy of the crystal induces non-uniform acoustic energy distribution, limiting the overall diffraction efficiency. To analyze this effect, the acoustic field distribution within a large-aperture AOTF was simulated, and the diffraction efficiency across different aperture regions was evaluated and experimentally validated. The results demonstrate that sound beam contraction and acoustic energy non-uniformity significantly reduce the peak diffraction efficiency and increase the power required to achieve high diffraction efficiency. Additionally, temperature-induced variations in acoustic velocity alter the acoustic field structure, leading to spatially non-uniform changes in diffraction efficiency. Both simulations and experimental measurements confirm that while the overall impact of temperature on full-aperture diffraction efficiency remains relatively small, localized variations are pronounced, highlighting potential inaccuracies in single-beam-based efficiency measurements. Full article

This study numerically explores the dynamics of the photogravitational circular restricted three-body problem, where an infinitesimal particle moves under the gravitational influence of two primary bodies connected by a massless rod. These primary masses revolve in circular orbits around their common center of mass, which remains fixed at the origin of the coordinate system. The distance between the two masses remains constant, independent of their rotation period. The third body, being infinitesimally small compared to the primary masses, has a negligible effect on their motion. The primary mass is considered as a radiating body, while the secondary is modeled as an elongated one comprising two hypothetical point masses separated by a fixed distance. The analysis focuses on determining the number, location, and stability of equilibrium points, as well as examining the structure of zero-velocity curves under the influence of system parameters such as mass and force ratio, radiation pressure and geometric configuration of the secondary body. The system is found to allow up to six equilibria: four collinear and two non-collinear. Their number and positions are significantly affected by variations in the system’s parameters. Stability analysis reveals that the two non-collinear equilibrium points can exhibit stability under specific parameter configurations, while the four collinear points are typically unstable. An exception is the innermost collinear equilibrium point, which can be stable for certain parameter values. Our numerical investigation on periodic orbits around the collinear equilibrium points of the asteroid triple-system 2001SN₂₆₃ show that a variation, either to the values of radiation or the force ratio parameters, influence their special characteristics such as period and stability. Also, their continuation in the space of initial conditions shows that all families terminate naturally at collision orbits with either the primary or the secondary. Full article

Three-phase induction motors are widely applied in industrial systems due to their durability and efficiency. However, electrical faults such as inter-turn short circuits can compromise performance, leading to unplanned downtime and maintenance costs. Traditional fault detection methods rely on stator current or vibration analysis, each with limitations regarding sensitivity to specific failure modes and dependence on motor power ratings. Despite advancements in non-invasive sensing, challenges remain in balancing fault detection accuracy, computational efficiency, and adaptability to real-world conditions. This study proposes a stray flux-based method for detecting inter-turn short circuits using an externally mounted search coil sensor, eliminating the need for intrusive modifications and enabling fault detection independent of motor power. To account for variations in fault manifestation, the method was evaluated with three different relative positions between the search coil and the faulty winding. Feature extraction and selection are performed using a hybrid strategy combining random forest-based ranking and collinearity filtering, optimizing classification accuracy while reducing computational complexity. Two classification tasks were conducted: binary classification to differentiate between healthy and faulty motors, and multiclass classification to assess fault severity. The method achieved 100% accuracy in binary classification and 99.3% in multiclass classification using the full feature set. Feature reduction to eight attributes resulted in 92.4% and 85.4% accuracy, respectively, demonstrating a trade-off between performance and computational efficiency. The results support the feasibility of deploying stray flux-based fault detection in industrial applications, ensuring a balance between classification reliability, real-time processing, and potential implementation in embedded systems with limited computational resources. Full article

Aldehyde dehydrogenases (ALDHs) are a gene family that relies on NAD +/NADP + proteins to oxidize toxic aldehydes to non-toxic carboxylic acids, and they play a crucial role in the growth and development of plants, as well as in their ability to withstand stress. This study identified 26 ALDH genes from six Glycyrrhiza uralensis gene families distributed on six chromosomes. By analyzing the phylogeny, gene structure, conserved motifs, cis-regulatory elements, collinearity of homologs, evolutionary patterns, differentiation patterns, and expression variations under drought stress, we found that the ALDH gene is involved in phytohormones and exhibits responsiveness to various environmental stressors by modulating multiple cis-regulatory elements. In addition, GuALDH3H1, GuALDH6B1, GuALDH12A2, and GuALDH12A1 have been identified as playing a crucial role in the response to drought stress. By analyzing the expression patterns of different tissues under drought stress, we discovered that GuALDH3I2 and GuALDH2B2 exhibited the most pronounced impact in relation to the drought stress response, which indicates that they play a positive role in the response to abiotic stress. These findings provide a comprehensive theoretical basis for the ALDH gene family in Glycyrrhiza uralensis and enhance our understanding of the molecular mechanisms underlying ALDH genes in licorice growth, development, and adaptation to drought stress. Full article

Chalcone isomerase (CHI) is not only an enzyme related to flavonoid biosynthesis, but also one of the key enzymes in the flavonoid metabolic pathway. In this study, members of the CHI gene family were identified in the whole genome of sweet potato. Bioinformatics methods were used to analyze the physical and chemical properties, systematic evolution, conserved domain, chromosome location, cis-acting elements of the promoter, and so on, of CHI gene family members. In addition, the tissue site-specific expression of CHI gene family members and their expression patterns under three kinds of abiotic stress were analyzed. The results showed that five members of IbCHI gene family were identified in sweet potato, which were unevenly distributed on four chromosomes. The protein secondary structure and tertiary structure were consistent, and there was a conservative domain related to chalcone isomerase. The prediction of subcellular localization showed that it was mainly located in cytoplasm and chloroplast. Systematic evolution showed that the members of sweet potato CHI gene family could be divided into Type I-IV, and the Type I gene IbCHI1 showed CHI catalytic activity in transgenic callus. The collinearity gene pairs were identified between sweet potato and allied species. Its promoter contains light response elements, hormone response elements, and stress response elements. The results of real-time fluorescence quantitative PCR (qRT-PCR) analysis showed that the expression of the IbCHI gene was tissue-specific and that the catalytic genes IbCHI1 and IbCHI5 serve as primary responders to abiotic stress, while the non-catalytic members IbCHI3 and IbCHI4 may fine-tune metabolic flux or participate in low-temperature, salt, and drought stress signaling. This study can provide a theoretical basis for a follow-up functional genomics study of the chalcone isomerase gene family in sweet potato. Full article

Background/Objectives: Sterol regulatory element-binding transcription factor 2 (SREBF2) is a key transcription factor involved in regulating cholesterol homeostasis. However, its role in buffalo mammary gland lipid metabolism remains unclear. Methods: To address this, we isolated and characterized the SREBF2 gene from buffalo mammary glands and performed an in-depth analysis of its molecular characteristics, tissue-specific expression, and functional roles in buffalo mammary epithelial cells (BuMECs). Additionally, we investigated the single nucleotide polymorphisms (SNPs) of SREBF2 in both river and swamp buffalo. Results: The coding sequence (CDS) of buffalo SREBF2 is 3327 bp long and encodes a protein of 1108 amino acid residues. Bioinformatics analysis revealed that the molecular characteristics of buffalo SREBF2 were highly similar across Bovidae species, with collinearity being observed among them. An expression profile analysis revealed that SREBF2 is expressed in all 11 tested tissues of buffalo, with its expression level in the mammary gland being higher during lactation than in the dry period. The knockdown of SREBF2 in BuMECs during lactation led to a significant reduction in the expression of genes involved in triglyceride (TAG) and cholesterol synthesis, including PI3K, AKT, mTOR, SREBF1, PPARG, INSIG1, ACACA, SCD, DGAT1, LPL, CD36, HMGCR, and SQLE. This knockdown led to a 23.53% and 94.56% reduction in TAG and cholesterol levels in BuMECs, respectively. In addition, a total of 22 SNPs were identified in both buffalo types, of which four non-synonymous substitutions (c.301G>C, c.304A>T, c.1240G>A, and c.2944G>A) were found exclusively in the SREBF2 CDS of swamp buffalo, and the assessment revealed that these substitutions had no impact on SREBF2 function. Conclusions: These findings emphasize the critical role of SREBF2 in regulating both triglyceride and cholesterol biosynthesis, providing valuable insights into its functions in buffalo mammary glands. Full article

Parental concerns about the education of children usually reflect deep-seated anxieties. This study identifies the most influential factors shaping these global concerns based on World Values Survey (WVS) data spanning several decades. Using advanced techniques, including feature selection (Adaptive and Gradient Boosting, Pairwise Correlations, LASSO, Bayesian Model Averaging), mixed-effects modeling, cross-validation procedures, different regressions and overfitting, collinearity, and reverse causality checks together with two-way graphical representations, this study identified three enduring predictors: fear of job loss, fear of war, and respondent age. These findings mainly underline the role of socio-economic and geopolitical stability and security and, in addition, that of generational perspectives in shaping global parental priorities. All three predictors were consistent across seven dataset versions, various subsets considering random (ten-folds) or non-random criteria (different values for socio-demographic variables in mixed-effects models), and distinct feature selection approaches. Secondary influences, including opinions regarding the priority of work in life, other fears, and socio-demographic variables (e.g., gender, number of children, marital and professional status, income, education level, community size, etc.) provided more nuances to this study and additional explanatory power. The findings have implications for designing socio-economically sensitive educational policies that address parental priorities and anxieties in diverse global contexts. Full article