Search Results (2,404)

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems. Full article

Polygenic adaptation in response to natural selection on a quantitative trait has become an important topic in population genetics and evolution. We modeled a scenario in which a population was assumed to be in equilibrium between mutation, selection and genetic drift, when a sudden shift in the fitness optimum occurred. It is well known that after an environmental shift the trait mean may approach the new optimum very quickly at a rate proportional to the equilibrium genetic variance. Here, we analyze the dynamics of the allele frequencies at individual loci, using diffusion theory. We show that genetic drift slows down the speed of polygenic adaptation. We also found that, while the frequencies of rare and very common alleles decrease during the adaptive phase, alleles starting at intermediate equilibrium frequencies at the time of the optimum shift change most quickly and thus may substantially modify the shape of the allele frequency distribution. Finally, we explain how these properties of the allele frequency spectrum may be utilized in statistical tests of polygenic selection. Full article

Despite decades of studies on symmetric impinging-jet atomization, the combined role of controlled pre-impingement asymmetry and viscosity in setting the instability pathways and droplet statistics of laminar microjets remains insufficiently quantified. The effects of pre-impingement jet-length difference and liquid viscosity on the flow morphologies, instability dynamics, and atomization behavior of laminar impinging microjets are investigated experimentally using high-speed imaging. By systematically varying the jet-length asymmetry and viscosity over a range of Weber numbers, the evolution of liquid-sheet motion and breakup is resolved from synchronized front- and side-view observations. Specifically, the scientific objective of this work is to elucidate how pre-impingement jet-length asymmetry and liquid viscosity jointly regulate the dynamical behavior of laminar impinging microjets, with particular emphasis on regime transitions of liquid-sheet morphologies, the coupling between upper-sheet oscillations and rim instabilities revealed by synchronized multi-view imaging and POD-based frequency analysis and the resulting droplet-size statistics. These aspects address physical questions that have not been systematically resolved in classical impinging-jet studies, which predominantly focus on symmetric configurations or performance-oriented atomization. With increasing Weber number, the flow undergoes a sequence of regime transitions, including merged-jet, liquid-chain, wavy-rim, fishbone, closed-rim, open-rim, and arc-shaped atomization states. The presence and extent of the closed-rim regime depend sensitively on both jet-length asymmetry and liquid viscosity. Increasing jet-length difference accelerates transitions between these regimes, whereas increasing liquid viscosity stabilizes the liquid sheet and shifts the onset of unsteady breakup to higher Weber numbers. Proper orthogonal decomposition is applied to time-resolved image sequences to extract dominant oscillatory modes and their characteristic frequencies. Within the fishbone regime, the oscillation frequency of rim deformation either coincides with that of the upper region of the liquid sheet or appears as its subharmonic, indicating period-doubling behavior under specific combinations of Weber number and jet-length asymmetry. These frequency characteristics govern the spatiotemporal organization of ligament formation and detachment along the sheet rim. In the arc-shaped atomization regime, droplet-size distributions follow a log-normal form, and at sufficiently high Weber numbers, the mean droplet diameter shows only a weak dependence on jet-length asymmetry. These findings provide microscale-regime guidance for tunable droplet formation in open microfluidic jetting and related small-scale multiphase flows. The innovation of this study lies in the systematic use of synchronized multi-view imaging combined with POD-based frequency analysis and droplet statistics to directly connect liquid-sheet oscillations, rim instability dynamics, and breakup organization under controlled geometric asymmetry and viscosity variations. This approach enables a unified physical interpretation of regime transitions and instability mechanisms that cannot be resolved from single-view observations or morphology-based classification alone. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces stochastic perturbations into the step-size evolution; (2) a mirror opposition-based learning strategy to actively inject structured population diversity; and (3) an adaptive adjustment mechanism for the Lévy flight parameter $\beta$ to enable phase-sensitive optimization behavior. The effectiveness of EPIMO is validated through a multi-stage experimental framework. Systematic evaluations on the CEC 2017 and CEC 2022 benchmark suites, alongside four classical engineering optimization problems (Himmelblau function, step-cone pulley design, hydrostatic thrust bearing design, and three-bar truss design), demonstrate its comprehensive superiority. The Wilcoxon rank-sum test confirms statistically significant performance improvements over its predecessor (PIMO) and a range of state-of-the-art and classical algorithms. EPIMO exhibits exceptional performance in convergence accuracy, stability, robustness, and constraint-handling capability, establishing it as a highly reliable and efficient metaheuristic optimizer. This research contributes a systematic, adaptive enhancement framework for projection-based metaheuristics, which can be generalized to improve other swarm intelligence systems when facing complex, constrained, and high-dimensional engineering optimization tasks. Full article

Data loss caused by sensor malfunctions in bridge Structural Health Monitoring (SHM) systems poses a critical risk to structural safety assessment. Although deep learning has advanced data imputation, standard “black-box” models often fail to capture the underlying deterioration mechanisms governed by physical laws. To address this limitation, we propose SP-VMD-CNN-GRU, a prior-knowledge-guided framework that integrates environmental thermal mechanisms with deep representation learning for bridge crack data imputation. Deviating from empirical parameter selection, we utilize the Granger causality test to statistically validate temperature as the primary driver of crack evolution. Leveraging this prior knowledge, we introduce a Shared Periodic Variational Mode Decomposition (SP-VMD) method to isolate temperature-dominated annual and daily periodic components from noise. These physically validated components serve as inputs to a hybrid CNN-GRU network, designed to simultaneously capture spatial correlations across sensor arrays and long-term temporal dependencies. Validated on real-world monitoring data from the Luo’an River Grand Bridge, our framework achieves the highest coefficient of determination ($R^2$) of 0.9916 and the lowest Mean Absolute Percentage Error (MAPE) of 12.95%. Furthermore, statistical validation via Diebold–Mariano and Model Confidence Set tests proves that our physics-guided approach significantly surpasses standard baselines (TCN, LSTM), demonstrating the critical value of integrating prior knowledge into data-driven SHM. Full article

This paper reviews constitutive models used to predict concrete spalling under elevated temperatures, with emphasis on fire exposure and concrete linings in deep geological repositories for spent fuel and nuclear waste. The review synthesizes (1) how material composition (ordinary Portland cement concrete, geopolymer concrete, and fiber-reinforced systems using polypropylene and steel fibers) affects spalling resistance; (2) how coupled environmental and mechanical actions (temperature, moisture, stress state, chloride ingress, and radiation) drive damage initiation and spalling; and (3) how constituent-scale characteristics (microstructure, porosity, permeability, elastic modulus, and water content) govern thermal–hydro–mechanical–chemical (THMC) transport and damage evolution. We compare major constitutive modeling frameworks, including plasticity–damage models (e.g., concrete damage plasticity), statistical damage approaches, and fully coupled THM/THMC formulations, and highlight how key parameters (e.g., water-to-binder ratio, temperature-driven pore-pressure gradients, and crack evolution laws) control predicted spalling onset, depth, and timing. Several overarching challenges emerge: lack of standardized experimental protocols for spalling tests and assessments, which limits cross-study benchmarking; continued debate on whether spalling is dominated by pore pressure, thermo-mechanical stress, or their interaction; limited integration of multiscale and constituent-level material characteristics; and high data and computational demands associated with advanced multi-physics models. The paper concludes with targeted research directions to improve model calibration, validation, and performance-based design of concrete systems for high-temperature and repository applications. Full article

The Collatz map is investigated from a nonlinear-dynamics perspective with emphasis on the structure of its iterative orbits. By embedding integers within Sharkovsky’s ordering, odd initial values are shown to be sufficient for a complete characterization of dynamics. A “direction-phase” decomposition is introduced to separate iterative orbits into upward and downward phases, yielding a family of recursive functions parameterized by the number of upward phases. This formulation reveals a logarithmic scaling relation between the total iteration count and the initial value, confirming finite-time convergence to the period-three orbit. The Collatz dynamics is further shown to be dynamically equivalent to a binary shift map, whose ergodicity implies inevitable evolution toward attractors, thereby reinforcing convergence. Numerical analysis indicates that attraction basins follow a power-law distribution and display pronounced self-similarity. Moreover, odd integers grouped by upward-phase counts are found to follow Gamma statistics. Beyond its research implications, the framework provides a concise pedagogical case study illustrating how nonlinear dynamics, symbolic dynamics, and statistical characterization can be integrated to analyze a classical discrete problem. Full article

Background/Objectives: Human papillomavirus (HPV) infection remains the principal etiologic factor for cervical intraepithelial neoplasia (CIN) and cervical cancer. This longitudinal cohort study aimed to characterize the dynamics of cytological and histopathological changes over a two-year follow-up, focusing on post-treatment reduction in lesion grade, persistence, and progression in relation to HPV genotype distribution and smoking status. Methods: A total of 351 women aged 20–76 years were included, with cervical samples collected at the “Elena Doamna” Clinical Hospital, Iași, Romania. Cytology was categorized according to the Bethesda System, while colposcopy and conization served as diagnostic confirmation methods. HPV genotyping identified both high-risk (HR) and low-risk (LR) viral subtypes. Longitudinal assessments were performed at baseline, one-year, and two-year intervals to evaluate temporal patterns of disease evolution. Results: At baseline, HSIL represented the predominant cytologic category (51.3%, n = 180), followed by ASC-US (19.1%), ASC-H (15.1%), and LSIL (14.5%). Negative cytology increased from 62.4% at one year to 71.8% at two years, indicating substantial post-treatment reduction in lesion grade. Downgrading of lesion severity after treatment occurred in 26.2%, persistence in 11.1%, and progression in 11.1% of cases. Concordance between colposcopy and conization was moderate but statistically significant (κ = 0.345), with the highest agreement observed for HSIL with equivocal features between CIN II and CIN III lesions. Smoking showed a significant association with lesion persistence at two years (OR = 3.07; 95% CI: 1.16–8.08) but no statistically significant association with HR-HPV persistence. HR-HPV genotypes 16, 18, 31, and 33 were most frequently linked to progression, whereas HPV 35, 59, and 68 were associated with persistence. Conclusions: Over two years, most cervical lesions regressed or normalized, demonstrating effective management and follow-up. Persistent infection with HR-HPV types and smoking were the primary determinants of unfavorable outcomes. These findings highlight the clinical relevance of sustained surveillance, HPV genotyping, and smoking cessation as integral components of evidence-based cervical disease prevention and management strategies. Full article

Prostate cancer (PCa) progression involves dynamic interactions between neoplastic cells and the reactive stroma (RS). Although myofibroblasts are established components of the RS, the role of other stromal populations, such as telocytes, remains poorly understood. This study investigated the presence and distribution of a telocytic stromal phenotype (CD34⁺/Vimentin⁺) in PCa across different histological grades and acinar patterns. We used digital image analysis and standardized immunohistochemistry to assess biopsy samples from 120 patients with confirmed PCa. The telocytic phenotype showed a heterogeneous distribution and was significantly enriched in high-grade tumors and specific acinar architectures, particularly Patterns B and D. In contrast, well-differentiated regions exhibited lower telocyte density, resembling non-neoplastic prostate tissue. Although the myofibroblastic phenotype (α-SMA⁺/Vimentin⁺/CD34⁻) also increased overall with tumor grade and varied across acinar patterns, this association was comparatively weaker and less statistically robust than that observed for telocytes. These results suggest that stromal remodeling encompasses a spectrum of cellular phenotypes influenced by local architectural constraints. It is proposed that telocytes serve as key mediators of tissue organization and biomechanical signaling, contributing to a feedback loop that promotes tumor progression. Combining acinar architecture with stromal phenotyping provides a refined framework for understanding epithelial–stromal co-evolution in PCa. Full article

Quality of life, as an essential component of sustainable development, is particularly relevant in transnational contexts characterized by deep inequalities in human development, equity, and social well-being. The objective of this paper is to analyze the temporal and spatial changes in transnational patterns of quality of life observed between 2018 and 2025, taking into account levels of human development. To this end, multivariate statistical techniques were applied: partial triadic analysis, which allows the identification of both the common structure of the data and the temporal evolution of the indicators, together with the HJ-Biplot and cluster analysis, which provide a multidimensional and interpretable visualization of country profiles. The results reveal consistent configurations of quality of life, largely aligned with levels of human development, and highlight persistent inequalities in environmental quality, economic accessibility, and objective well-being. These findings are relevant for the formulation of policies aimed at enhancing population well-being, particularly in countries facing structural constraints despite their high levels of development. The contribution of this research lies in its three-dimensional, dynamic, and reproducible approach, which makes it possible to identify regional contrasts that are not visible through traditional methods based on unidimensional indicators or cross-sectional analyses. Full article

Background/Objectives: Cephalosporins represent one of the most important classes of β-lactam antibiotics, widely used in clinical practice due to their broad-spectrum activity and favorable safety profile. As generations evolved, structural modifications were introduced to expand antimicrobial coverage and overcome β-lactamase resistance. This study aimed to analyze the drug-like properties of cephalosporins across different generations using molecular descriptors to identify structural and pharmacokinetic patterns influencing bioavailability and oral administration profiles. Methods: Thirty-eight cephalosporins representative of different generations were selected. Molecular data were obtained from PubChem, and SMILES were extracted and validated. Molecular descriptors (including MW, logP, TPSA, HBA, HBD, rotatable bonds, and global complexity indices) were calculated using the SwissADME and ChemDes platforms. Statistical analysis included ANOVA followed by post hoc tests, and principal component analysis (PCA). Results: A progressive increase in molecular weight, polarity, and TPSA was observed across generations, with fourth-generation cephalosporins showing significantly higher values compared to first-generation compounds (p < 0.0001). LogP decreased significantly in fourth-generation agents (p < 0.0001), reflecting increased polarity. PCA revealed that most compounds from generations 1–2 cluster in regions consistent with Lipinski’s and Veber’s rules, whereas fourth- and fifth generation - cephalosporins deviated substantially, prioritizing antimicrobial efficacy over oral bioavailability. Recurrent structural modifications such as oximes, tetrazoles, and aminothiazoles were identified, with increasing frequency in modern generations. Conclusions: The evolution of cephalosporins reflects a strategic shift toward enhanced antimicrobial potency and β-lactamase stability at the expense of oral bioavailability. Understanding these structural transitions provides valuable insights for rational drug design, aiming to balance antimicrobial effectiveness with favorable pharmacokinetic profiles essential for therapeutic success. Full article

Knowledge tracing is a methodological framework focused on modeling and predicting learners’ future performance on tasks involving related concepts, while also tracking the dynamic evolution of their knowledge over time. The current study aims to assess the effectiveness of an intelligent learning system (ILS) based on the Knowledge Tracing Model in improving academic passion, self-efficacy, and achievement among 100 students enrolled in a Special Care course. A quasi-experimental design was employed via a single experimental group without a control group. Three instruments—achievement test, self-efficacy, and academic passion—were administered pre- and post-intervention. A statistically significant improvement was observed across all three domains. The findings suggest a positive association between the use of the ILS and gains in academic achievement, self-efficacy, and academic passion. In conclusion, the results support the use of knowledge-tracing-based learning systems for academic performance enhancement and university students’ motivation. Full article

Classical earth pressure theories often struggle to account for the complex coupling effects of wall displacement and spatial non-uniformity under non-limit states. This study presents an interpretable machine learning framework designed to extract universal mechanical laws from heterogeneous experimental datasets. Using a multi-source database of rigid retaining walls with sandy backfill, a three-stage feature refinement strategy is proposed that incorporates Recursive Feature Elimination, Collinearity Analysis, and Interpretability Comparison to identify a parsimonious set of five fundamental physical parameters. A SHapley Additive exPlanations-Categorical Boosting (CatBoost-SHAP) framework is established to predict the active earth pressure coefficient (K) and interpret the underlying mechanisms across various movement modes (RB, RT, and T). Results demonstrate that the model effectively captures the progressive evolution of shear bands and the soil arching effect. Specifically, a critical displacement threshold of Δ/H ≈ 0.006 is identified, marking the transition from mode-dominated stress non-uniformity to magnitude-driven limit states. Leave-One-Dataset-Out Cross-Validation (LODOCV) confirms the model’s ability to maintain physical consistency over purely statistical fitting despite significant inter-literature heterogeneity. Finally, a Graphical User Interface (GUI) is developed to facilitate rapid, displacement-based design in engineering practice. This research bridges the gap between empirical laboratory observations and generalized mechanical logic, providing a data-driven foundation for refined geotechnical design. Full article

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies. Full article