Search Results (1,519)

The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article

Objective: The material properties of craniofacial sutures significantly influence the outcomes of orthodontic treatment, particularly with newer appliances. This study specifically investigates how the Young’s modulus of craniofacial sutures impacts the anterosuperior protraction achieved using a recently developed extraoral appliance. Our goal is to identify the patterns by which suture properties affect skull deformation induced by this device. Materials and Methods: We conducted four finite element (FE) simulations to evaluate the Right Angle Maxillary Protraction Appliance (RAMPA) when integrated with an intraoral device (gHu-1). We tested Young’s moduli of 30 MPa, 50 MPa, and 80 MPa for the sutures, drawing on values reported in previous research. To isolate RAMPA’s effects on craniofacial deformation, we also performed an additional simulation with rigid sutures and a separate model that included only the intraoral device. Results: Simulations with flexible sutures showed consistent displacement and stress patterns. In contrast, the rigid suture model exhibited substantial deviations, ranging from 32% to 76%, especially in the maxillary palatine suture and orbital cavity. Both displacements and von Mises stresses were proportional to the Young’s modulus, with linear variations of approximately 15%. Conclusions: Our findings demonstrate that RAMPA effectively achieves anterosuperior protraction across a broad spectrum of suture material properties. This positions RAMPA as a promising treatment option for patients with long-face syndrome. Furthermore, the observed linear relationship (with a fixed slope) between craniofacial deformation and the Young’s modulus of sutures provides a crucial foundation for predicting treatment outcomes in various patients. Full article

Classical spectral analysis characterizes linear systems effectively but often fails to reveal the nonlinear temporal structure of chaotic dynamics. We introduce the ordinal spectrum, a frequency-domain characterization derived from the ordinal-pattern representation of a time series. Applied to both synthetic and real-world datasets—including periodic, stochastic, and chaotic signals from physical, biological, and astronomical sources—the ordinal spectrum identifies the temporal scales implied in a possible chaotic behavior. By providing an interpretable, data-driven view of symbolic dynamics in the frequency domain, this approach complements state–space reconstructions and enhances the detection of nonlinear temporal organization that classical spectra may obscure. Its ability to distinguish between qualitatively different dynamics make it a useful tool for exploring complex time series across diverse scientific domains. Full article

In this study, we explore the soliton solutions of the truncated M-fractional fifth-order Korteweg–de Vries (KdV) equation by applying the improved modified extended tanh function method (IMETM). Novel analytical solutions are obtained for the proposed system, such as brigh soliton, dark soliton, hyperbolic, exponential, Weierstrass, singular periodic, and Jacobi elliptic periodic solutions. To validate these results, we present detailed graphical representations of selected solutions, demonstrating both their mathematical structure and physical behavior. Furthermore, we conduct a comprehensive linear stability analysis to investigate the stability of these solutions. Our findings reveal that the fractional derivative significantly affects the amplitude, width, and velocity of the solitons, offering new insights into the control and manipulation of soliton dynamics in fractional systems. The novelty of this work lies in extending the IMETM approach to the truncated M-fractional fifth-order KdV equation for the first time, yielding a wide spectrum of exact analytical soliton solutions together with a rigorous stability analysis. This research contributes to the broader understanding of fractional differential equations and their applications in various scientific fields. Full article

Background: Mitral annular plane systolic excursion (MAPSE) is a simple and widely used M-mode echocardiographic marker of left-ventricular longitudinal function that correlates well with left ventricular ejection fraction (LVEF). Conventional chronic right ventricle (RV) pacing is associated with left ventricle (LV) dysfunction, inducing heart failure (HF) and leading to the development of pacing-induced cardiomyopathy (PiCM). The aim of this study is to ascertain the clinical usefulness of MAPSE in the assessment of LV function in patients with permanent RV pacing. Methods: We performed a cross-sectional association analysis, enrolling consecutive patients with pacemakers and chronic RV pacing burdens over 20% (Vp > 20%) from 2021 to 2024. All patients were assessed by standard transthoracic echocardiography (TTE) with LVEF and MAPSE among other parameters being assessed. We performed a correlation test using linear regression and plotted an ROC curve. Results: 409 patients (mean age = 68.7 year) were included, 225 men (55%) and 245 (59.9%) with dual-chamber pacemakers. The mean follow-up period was 18 ± 2 months, with HF incidence in the study group being 23.2%. The results showed that average, septal, and lateral MAPSE all correlate well with LVEF, but septal values seemed to provide the strongest correlation (r = 0.90, p < 0.001), and that a septal MAPSE cut off value of <10 mm (sensitivity 99.4, specificity 42.1, AUC = 0.89) was associated with impaired LVEF (<50%). Conclusions: MAPSE seems to corelate well with LVEF across the spectrum of HF in pts with chronic RV conventional pacing. Septal MAPSE shows the strongest correlation with LVEF, and a value of <10 mm is a cut-off for altered LVEF, making it a potentially useful marker of cardiac function in these pts. Full article

To investigate the flow characteristics of movable water in coal under the influence of micro-nano pore fractures with multiple fractal structures, this study employed nuclear magnetic resonance (NMR) and multifractal theory to analyze gas–water seepage under different injection pressures. Then, the scale threshold for mobile water entering coal pores and fractures was determined by clarifying the relationship among “injection pressure-T₂ dynamic multiple fractal parameter seepage resistance-critical pore scale”. The results indicate that coal samples from Yiwu (YW) and Wuxiang (WX) enter the nanoscale pore size range at an injection pressure of 8 MPa, while the coal sample from Malan (ML) enters the nanoscale pore size range at an injection pressure of 9 MPa. During the water injection process, there is a significant linear relationship between the multiple fractal parameters log X(q, ε) and log(ε) of the sample. The generalized fractal dimension D(q) decreases monotonically with increasing q in an inverse S-shape. This decrease occurs in two distinct stages: D(q) decreases rapidly in the low probability interval q < 0; D(q) decreases slowly in the high probability interval q > 0. The multiple fractal singularity spectrum function f(α) has an asymmetric upward parabolic convex function relationship with α, which is divided into a rapidly increasing left branch curve and a slowly decreasing right branch curve with α₀ as the boundary. Supporting evidence indicates the feasibility of a methodology for identifying the variation in multiple fractal parameters of gas–water NMR seepage and the critical scale transition conditions. This investigation establishes a methodological foundation for analyzing gas–water transport pathways within porous media materials. Full article

The energy deposition process for the main components of SIC Schottky diodes is simulated based on Geant4. Particle bombardment results were simulated under different angles, target materials and doping concentrations on the same target material for different light particles and heavy ions, and then the Linear Energy Transfer of SiC materials and external conditions that affect LET are obtained. The results show that the LET value of protons exhibits significant oscillations at low energy incidence, gradually decreasing exponentially after 10⁻¹ MeV. Alpha particles have a LET peak near 1 MeV, while beta particles show an exponential decrease. The LET values at low energy levels increase exponentially, while at high energy levels, the LET values show a similar linear relationship with energy. For different incident angles, the average LET value of protons in the low-level region gradually increases as the incident angle increases. The average LET value of protons in the remaining energy ranges is less affected by angle; the incident angle has no significant effect on the LET distribution of alpha particles within the full spectrum range. The results provide important references for understanding the energy deposition process and LET distribution of silicon carbide devices under single-particle interaction. Full article

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

We identified Streptomyces globisporus bja209 through a targeted screen of actinomycetes from natural habitats using an E. coli JW5503 ΔtolC DualRep2(c) reporter strain. This strain produced antibacterial compounds whose action depended on the growth medium. HPLC-MS and genomic analysis revealed two metabolites: albomycin δ2 (a translation inhibitor) and desferrioxamine E. The latter induced the SOS response. Desferrioxamine E exhibited a narrow spectrum of antagonistic activity against carbapenem-resistant A. baumannii and C. michiganensis, and its production was critically regulated by iron concentration. Notably, the structurally similar desferrioxamine B was inactive. Contrary to previous reports, pangenome analysis of published GenBank genomes revealed that albomycin BGC is restricted to specific S. globisporus strains and not present in other Streptomycetes phylogenetic clades. The C-1027 BGC was found in a large linear plasmid (165.5 kb) of the S. globisporus bja209 strain and also found exclusively on linear plasmids in some of the published S. globisporus genomes. Full article

An optical fiber sensor based on a HEMA/AM/SA interpenetrating polymer network (IPN) hydrogel is proposed for monitoring the concentration of Pb²⁺. The Fabry–Perot interference cavity is constructed from a single-mode fiber, a ceramic ferrule, and an IPN hydrogel layer. P (HEMA co AM)/SA IPN hydrogel films were prepared by a step-by-step crosslinking method, which had good mechanical properties, swelling properties, and Pb²⁺ adsorption capacity. The Pb²⁺ concentration changes cause the interference spectrum shift of the sensor. By monitoring the wavelength shift under different Pb²⁺ concentrations, the sensor sensitivity in the range of 0~1 ppm Pb²⁺ concentration in solution is 5.0743 nm/ppm with 0.994 linearity. The influence of different proportions of IPN hydrogel on the performance of the sensor was studied. In the range of 10–90% HEMA, higher sensitivity is obtained by a small weight ratio of HEMA/AM. The sensor stability, repeatability, selectivity, dynamic response, and temperature response are also investigated in experiments. Experimental results demonstrate that the proposed sensor exhibits good stability, sensitivity, repeatability, and selectivity. Owing to its compact structure, straightforward fabrication, low cost, and good sensing performance, this sensor shows strong potential for application in monitoring Pb²⁺ concentrations. Full article

We present pbhstat, a publicly available Python package designed to compute the mass function and total abundance of primordial black holes (PBHs) from a given primordial power spectrum. The package offers a modular framework using multiple statistical approaches, including Press–Schechter theory, peaks theory, and formalisms based on the non-linear compaction function. Currently, the implementation is limited to scenarios with nearly Gaussian initial conditions. Full article

Viscoelastic relaxation time and frequency spectra are useful for describing, analyzing, comparing, and improving the mechanical properties of materials. The spectra are typically obtained using the stress or oscillatory shear measurements. Over the last 80 years, dozens of mathematical models and algorithms were proposed to identify relaxation spectra models using different analytical and numerical tools. Some models and identification algorithms are intended for specific materials, while others are general and can be applied for an arbitrary rheological material. The identified relaxation spectrum model always depends on the identification method applied and on the specific measurements used in the identification process. The stress relaxation experiment data consist of the sampling times used in the experiment and the noise-corrupted relaxation modulus measurements. The aim of this paper is to build a model of the spectrum that asymptotically does not depend on the sampling times used in the experiment as the number of measurements tends to infinity. Broad model classes, determined by a finite series of various basis functions, are assumed for the relaxation spectra approximation. Both orthogonal series expansions based on the Legendre, Laguerre, and Chebyshev functions and non-orthogonal basis functions, like power exponential and modified Bessel functions of the second kind, are considered. It is proved that, even when the true spectrum description is entirely unfamiliar, the approximate sampling-times-independent spectra optimal models can be determined using modulus measurements for appropriately randomly selected sampling times. The recovered spectra models are strongly consistent estimates of the desirable models corresponding to the relaxation modulus models, being optimal for the deterministic integral weighted square error. A complete identification algorithm leading to the relaxation spectra models is presented that requires solving a sequence of weighted least-squares relaxation modulus approximation problems and a random selection of the sampling times. The problems of relaxation spectra identification are ill-posed; solution stability is ensured by applying Tikhonov regularization. Stochastic convergence analysis is conducted and the convergence with an exponential rate is demonstrated. Simulation studies are presented for the Kohlrausch–Williams–Watts spectrum with short relaxation times, the uni- and double-mode Gauss-like spectra with intermediate relaxation times, and the Baumgaertel–Schausberger–Winter spectrum with long relaxation times. Models using spectrum expansions on different basis series are applied. These studies have shown that sampling times randomization provides the sequence of the optimal spectra models that asymptotically converge to sampling-times-independent models. The noise robustness of the identified model was shown both by analytical analysis and numerical studies. Full article

Earthquakes remain among the most destructive natural hazards, causing severe loss of life and property in seismically active regions such as Nepal. Major events such as the 1934 Nepal–Bihar earthquake (M_w 8.2), the 2015 Gorkha earthquake (M_w 7.8), and the 2023 Jajarkot earthquake (M_L 6.4) have repeatedly exposed the vulnerability of Nepal’s built environment. In response, the Ready-to-Use Detailing (RUD) guideline (NBC 205:2024) was introduced to provide standardized structural detailing for low-rise reinforced concrete buildings without masonry infill, particularly for use in areas where access to professional engineering services is limited. This study was motivated by the need to critically assess the structural performance of buildings designed according to such rule-of-thumb detailing, which is widely applied through owner–builder practices. Nonlinear pushover analyses were carried out using finite element modelling for typical configurations on soil types C and D, under peak ground accelerations of 0.25 g, 0.30 g, 0.35 g, and 0.40 g. The response spectrum from NBC 105:2020 was adopted to determine performance points. The analysis focused on global response, capacity curves, storey drift, and hinge formation to evaluate structural resilience. The maximum story drift for the linear static analysis is found to be 0.56% and 0.86% for peak ground acceleration of 0.40 g, for both three and four-storied buildings. Also, from non-linear static analysis, it is found that almost all hinges formed in the beams and columns are in the Immediate Occupancy (IO) level. The findings suggest that the RUD guidelines are capable of providing adequate seismic performance for low-rise reinforced concrete buildings, given that the recommended material quality and construction standards are satisfied. Full article

The inherent high dimensionality of hyperspectral imagery presents both opportunities and challenges for agricultural crop classification. This study offers a rigorous comparative evaluation of three U-Net-based architectures, i.e., U-Net, U-Net++, and Atrous U-Net, applied to EnMAP hyperspectral data over the heterogeneous agricultural region of Lake Vegoritida, Greece. To address the spectral redundancy, we integrated multiple dimensionality-reduction strategies, including Linear Discriminant Analysis, SHAP-based model-driven feature selection, and unsupervised clustering approaches. Results reveal that model performance is contingent on (a) the network’s architecture and (b) the features’ space provided by band selection. While U-Net++ consistently excels when the full spectrum or ACS-derived subsets are employed, standard U-Net achieves great performance under LDA reduction, and Atrous U-Net benefits from SHAP-driven compact representations. Importantly, band selection methods such as ACS and SHAP substantially reduce spectral dimensionality without sacrificing accuracy, with the U-Net++–ACS configuration delivering the highest F1-score (0.77). These findings demonstrate that effective hyperspectral crop classification requires a joint optimization of architecture and spectral representation, underscoring the potential of compact, interpretable pipelines for scalable and operational precision agriculture. Full article

Rett syndrome (RTT) and MECP2 duplication syndrome, a subtype of autism spectrum disorder (ASD), are neurodevelopmental disorders caused by MeCP2 loss and gain of function, respectively. While MeCP2 is known to regulate transcription through its interaction with methylated DNA and chromatin-associated factors such as topoisomerase IIβ (TOP2β), the downstream transcriptional consequences of MeCP2 dosage imbalance remain partially characterized. Here, we present a transcriptome-centered analysis of mouse primary cortical neurons subjected to MeCP2 knockdown (KD) or overexpression (OE), which model RTT and ASD-like conditions in parallel. Using a robust computational pipeline integrating generalized linear models with quasi-likelihood F-tests and Magnitude–Altitude Scoring (GLMQL-MAS), we identified differentially expressed genes (DEGs) in KD and OE relative to wild-type (WT) neurons. This study represents a computational analysis of secondary transcriptomic data aimed at nominating candidate genes for future experimental validation. Gene Ontology enrichment revealed both shared and condition-specific biological processes, with KD uniquely affecting neurodevelopmental and stress-response pathways, and OE perturbing extracellular matrix, calcium signaling, and neuroinflammatory processes. To prioritize robust and disease-relevant targets, we applied Cross-MAS and further filtered DEGs by correlation with MeCP2 expression and regulation directional consistency. This yielded 16 high-confidence dosage-sensitive genes that were capable of classifying WT, KD, and OE samples with 100% accuracy using PCA and logistic regression. Among these, RTT-associated candidates such as Plcb1, Gpr161, Mknk2, Rgcc, and Abhd6 were linked to disrupted synaptic signaling and neurogenesis, while ASD-associated genes, including Aim2, Mcm6, Pcdhb9, and Cbs, implicated neuroinflammation and metabolic stress. These findings establish a compact and mechanistically informative set of MeCP2-responsive genes, which enhance our understanding of transcriptional dysregulation in RTT and ASD and nominate molecular markers for future functional validation and therapeutic exploration. Full article