Non-uniform Patterns Research Articles

Amplification of Higher-Order Salivary Gland Volume Effects from External Beam Radiotherapy in Normal Tissue Complication Probability Modeling of Radiopharmaceutical Therapy

Salivary glands are common organs at risk in both head and neck external beam radiotherapy (EBRT) and radiopharmaceutical therapy (RPT), but incidences of xerostomia in RPT are inconsistent with the EBRT Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) limits. In EBRT, salivary glands are usually assumed to be parallel organs, with QUANTEC guidelines based on Dmean, but this is known to be a gross over-simplification of the full complexity of the underlying functional organization. The goal of this work is to combine machine learning of EBRT dose–outcome data with stylized small-scale RPT dosimetry to discover more reliable normal tissue complication probability (NTCP) models of xerostomia across both modalities. A retrospective cohort of 211 EBRT patients was analyzed using a custom-designed in-house machine learning workflow. From this, a hierarchy of three models of increasing complexity was trained, evaluated for performance and generalization, and coupled with stylized small-scale salivary gland dosimetry to assess the influence of model complexity on the predicted NTCP for plausible patterns of RPT dose nonuniformity. The three models in the hierarchy (A, B, C), in increasing order of complexity, associate xerostomia with the following: the mean dose to the whole contralateral parotid (model A), the mean dose to a ductally localized region (model B) and a serial interaction dose term between two ductal sub-compartments (model C). While the difference between the three models for EBRT p-values and AUCs is rather marginal, for physiologically driven ductal dose distributions in RPT, the predicted reduction in TD50 can be as large as a factor of 10. These results provide hints towards a plausible reconciliation of the observed inconsistency of xerostomia in RPT with EBRT dose limits.

Distribution and Maturity of Medial Collagen Fibers in Thoracoabdominal Post-Dissection Aortic Aneurysms: A Comparative Study of Marfan and Non-Marfan Patients.

Thoracoabdominal aortic aneurysms (TAAAs) are rare but serious conditions characterized by dilation of the aorta characterized by remodeling of the vessel wall, with changes in the elastin and collagen content. Individuals with Marfan syndrome have a genetic predisposition for elastic fiber fragmentation and elastin degradation and are prone to early aneurysm formation and progression. Our objective was to analyze the medial collagen characteristics through histological, polarized light microscopy, and electron microscopy methods across the thoracic and abdominal aorta in twenty-five patients undergoing open surgical repair, including nine with Marfan syndrome. While age at surgery differed significantly between the groups, maximum aortic diameter and aneurysm extent did not. Collagen content increased from thoracic to infrarenal segments in both cohorts, with non-Marfan patients exhibiting higher collagen percentages, notably in the infrarenal aorta (729.3 nm vs. 1068.3 nm, p = 0.02). Both groups predominantly displayed mature collagen fibers, with the suprarenal segment containing the highest proportion of less mature fibers. Electron microscopy revealed comparable collagen fibril diameters across segments irrespective of Marfan status. Our findings underscore non-uniform histological patterns in TAAAs and suggest that ECM remodeling involves mature collagen deposition, albeit with lower collagen content observed in the infrarenal aorta of Marfan patients.

Eye-Inspired Single-Pixel Imaging with Lateral Inhibition and Variable Resolution for Special Unmanned Vehicle Applications in Tunnel Inspection.

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system's ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust.

Do polluters outperform non-polluters?

ABSTRACT This paper investigates whether industrial companies with more toxic emissions outperform those with less toxic emissions. First, firms that pollute the most do not consistently outperform those that pollute the least in an almost stochastic dominance sense. Moreover, firms with average amounts of emissions occasionally outperform firms that pollute the least due to the non-uniform pattern in mean returns across emission portfolios. Second, although a hedge portfolio that is long (short) in the most (least) brown firms earns significantly positive alphas based on standard asset pricing models, these abnormal returns can become statistically insignificant when factors from multiple models are combined. Third, although the highest-emission quintile generates superior reward-to-risk ratios and manipulation-proof performance measures than the lowest-emission quintile, the pattern across portfolios is once again not uniform.

Spatial-spectral feature mining in hyperspectral corn leaf venation structure and its application in nitrogen content estimation

The growth of corn plants requires a significant amount of nitrogen nutrient supply to ensure the yield. Meanwhile, overfertilization could result in adverse environmental impacts, making it crucial to monitor the nitrogen supply condition in corn plants precisely. Hyperspectral imaging (HSI) could be the solution to this challenge, as HSI plant phenotyping technology has been widely utilized to nondestructively identify plant traits, such as mineral nutrient deficiencies in corn plants. However, limitations persist where the conventional HSI nitrogen prediction models mainly relied on the overall colors of the plant tissue extracted from the spectral domain but rarely included the spatially distributed information on the leaf level. This study aimed to examine if including new information from the spatial domain could potentially improve the performance of HSI models. Because the venation structure plays a crucial role in the distribution of various nutrients across the leaf, a series of physiological responses caused by nitrogen deficiency could result in non-uniform color patterns related to the veins. The recent advancement of HSI techniques such as LeafSpec, whose imaging quality and spatial-spectral resolution have been significantly improved, made it possible to extract the venation structure together with high-resolution spatial-spectral features. In this study, the hypothesis was that utilizing high-resolution spatial-spectral features extracted based on the venation structure could yield a better-performed nitrogen content prediction model compared to the averaged spectrum of the corn leaf. First, a venation structure segmentation algorithm was developed using the Local Binary Patterns (LBP) method for hyperspectral corn leaf images scanned with LeafSpec. Second, for every hyperspectral leaf image, 131 spectral index heatmaps were generated, and 30 leaf regions were picked based on the venation structure, resulting in 3930 spatial-spectral features. Third, the features were picked using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. As the results tested with a field assay involving two different corn genotypes and two nitrogen treatments, the Partial Least Square Regression (PLS-R) model built with selected spatial-spectral features outperformed the model built with the averaged leaf spectra in terms of the Root Mean Squared Error (RMSE) of predictions. The results provided encouraging proof and guidance for the potential benefit of using high-resolution spatial-spectral features derived from hyperspectral leaf images to improve the accuracy and robustness of the nitrogen content prediction models for corn plants.

Inter-farm wake effect on layout optimization: Case study of standardized two-phase wind farms

Due to the limited availability of suitable sites, wind farms are now being constructed closer to each other or in phases, highlighting the importance of understanding the wake effects between adjacent farms. To address this, a study case is standardized from two adjacent wind farms, Rønland I and II. The standardized case incorporates variations in spatial and scale factors of wind farms. To investigate the wake effects between wind farms, this study is conducted under the inflow of a single directional sector of 30 degrees. When inter-farm spacing increases, the distribution of potential power undergoes a transition from a non-uniform stripe pattern to a gradient pattern, ultimately approaching a uniform pattern. The pattern of power potential appropriately explains the different trends of optimized layouts. Moreover, a 95 % unwaked potential power region (UPPR) is defined and enclosed by the contour line of 95 % of power potential without wake effects. The optimized layouts demonstrate different patterns depending on the proportion of downstream wind farms shaded by 95 % UPPR. When the 95 % UPPR predominantly covers the design region, wind turbines tend to concentrate near the downwind edge. As the region moves further away, the layout transitions to a double-column staggered distribution.

A new approach for characterizing concrete deterioration under sulfate attack and its application: Insights from elastic modulus variation using indentation technology

Assessing the safety of concrete structures subjected to sulfate attack requires a thorough understanding of concrete deterioration degree and the depth of the deteriorated surface layer. However, the existing techniques either solely consider the depth of concrete deterioration or rely on macroscopic mechanical properties to assess the degree of deterioration of concrete, disregarding the non-uniform distribution pattern where deterioration initiates at the surface and diminishes with depth. To address this limitation, an approach utilizing indentation technology is proposed for determining the degree and depth of concrete deterioration caused by sulfate attack concurrently. Indentation technology, known for its efficacy in characterizing local mechanical properties, such as the elastic modulus of concrete, offers a promising solution. By measuring the variation of elastic modulus along the erosion direction in the deteriorated concrete, valuable insights into the degree and deterioration depth can be gained. This study employed a sulfate attack test on concrete specimens immersed in a 5 % NaSO4 solution, with the proposed method employed to determine the deterioration degree and depth. The results demonstrated that, with increasing exposure time (51, 86, 121, and 180 days), the deterioration depths reached 4.750, 7.425, 11.791, and 14.007 mm, respectively, while the corresponding deterioration degrees at a depth of 2 mm were 6.832 %, 18.806 %, 41.501 %, and 55.116 %. Comparing these findings with existing methods, the proposed method quantitatively characterizes the deterioration depth using micro-indentation technology, overcoming some of the limitations (e.g. immaturity or inapplicability). And, the evolution of the deterioration degree aligns with conclusions presented in published research. Furthermore, the results obtained through the new proposed method offers more accurate insights into the distribution of deterioration degree in concrete structures at various depths. Engineers can identify specific areas of deterioration accurately, targetting maintenance efforts more effectively, leading to improved longevity and durability of concrete structures.

Groundwater Dynamics in the Middle Brahmaputra River Basin: A Case Study of Shallow Aquifers in Inner Guwahati City, Assam, India

This study investigated the hydrogeological characteristics and groundwater dynamics in the shallow aquifer zones of inner Guwahati city, Assam, India. Sixteen dug wells spread across the city, specifically used for domestic purposes, were selected for this study. Additionally, ten wells were selected for trend analysis. The borehole lithology reveals predominant compositions of clay, sand, and granules, with thin clay cappings indicating significant groundwater potential. Depth-to-water level analysis revealed varying water levels across the study area, with shallow levels in the northern and western regions and gradual deepening toward the eastern and southern parts. The groundwater flow directions show nonuniform patterns and reflect the influence of topography and domestic pumping in urban residential zones. The general groundwater flow direction is toward the Brahmaputra River. Trends in groundwater level, assessed using the Mann–Kendall test and Sen’s slope, suggest both falling and rising trends across different locations, indicating complex groundwater dynamics influenced by factors such as recharge, extraction, and topography. However, the long-term rainfall data indicate no significant trend over the studied period, suggesting limited natural influence on groundwater level trends. These findings may contribute to a comprehensive understanding of groundwater dynamics in the study area and are essential for sustainable water resource management and mitigation of groundwater depletion risks.

Prescriber Phenotypes: Variability in Topical Rosacea Treatment Patterns among United States Dermatologists

Background/Objectives: Aggregate prescribing behavior for inflammatory lesions of rosacea has been described, but individual physician behavior has not been characterized. This study aims to assess the modern state of topical rosacea drug selection by analyzing prescribing patterns among individual dermatologists. Methods: We assessed utilization patterns of four topical papulopustular rosacea agents in 2021 Medicare Part-D data. K-means cluster analysis identified prescriber phenotypes based on the proportion of claims for each drug by physician. Results: Cluster analysis identified four prescriber phenotypes for topical rosacea agents, with the majority favoring metronidazole. In each of the other clusters, metronidazole was co-prescribed alongside the primary agent. Significant predictors of phenotype included patient ages, patient risk scores, and a group practice setting. Conclusions: The study reveals nonuniform prescribing patterns for topical papulopustular rosacea treatments among U.S. dermatologists. While aggregate data indicate diverse drug utilization, cluster analysis suggests that individual prescribers tend to use a limited selection of agents.

Evaluating the spatiotemporal patterns of drought characteristics in a semi‐arid region of Limpopo Province, South Africa

Drought is a complex phenomenon resulting from below-average rainfall and is characterized by frequency, duration, and severity, occurring at a regional scale with dire consequences, especially in semiarid environments. This study used the Reconnaissance Drought Index (RDI) to assess drought severity in two district municipalities in Limpopo Province. Rainfall and air temperature data from 12 stations covering 1970–2020 were obtained from the Agricultural Research Council. The calculation of RDI relies on the monthly accumulation ratio of total rainfall to potential evapotranspiration (PET). For this study, PET was estimated using the Hargreaves and Samani temperature-based approach. The RDI results showed a high spatial–temporal variation in drought characteristics over the study area. All stations experienced extreme drought conditions in different years, with the maximum drought severity (-3.40) occurring from 2002–2003 in the western parts of the study area, indicating extreme drought. Furthermore, the results revealed continuous drought conditions over various periods, including severe droughts between 1995 and 1998 and between 2014 and 2016, with the severity varying between mild and moderate drought conditions. The results reveal notable but nonuniform drought patterns as the climate evolves, with potential implications for water availability and livelihoods. The study's findings underscore the significance of adopting multidimensional approaches to drought assessment that encompass meteorological and hydrological factors to inform strategies for adaptive water management and policy formulation in the face of a changing climate.

Free vibration and buckling behavior of porous orthotropic doubly-curved shallow shells subjected to non-uniform edge compression using higher-order shear deformation theory

Due to the continuous enhancement of manufacturing technology for porous materials, it is expected to be increasingly used in aerospace, aviation, and other engineering applications, where the necessity for lightweight structures is paramount. Also, the non-uniform edge loads caused by service conditions, complex adjacent structures, and external loads lead to localized stress concentrations within the shells. Localized stress concentrations result in a loss of load-carrying capacity in specific regions, causing step-by-step failure of the shell. This study investigates the buckling and free vibration behavior of porous orthotropic doubly-curved shallow shells with four different porosity distribution patterns subjected to non-uniform edge compressive loads. The equations of motion of doubly-curved shallow shells are established by using higher-order shear deformation theory and Hamilton’s principle. Then, the pre-buckling in-plane stress distributions of doubly-curved shallow shells are determined and appended to the equations of motion. These fundamental partial differential equations are solved by Galerkin’s method, and the critical buckling load and natural frequency formulations are achieved. By comparing with the results in published literature, the feasibility and accuracy of present formulations are validated. Finally, systematic parametric studies are carried out to discuss the effects of different non-uniform edge compression patterns, porosity distribution patterns, porosity coefficients, aspect ratios, arc length-to-thickness ratios, radius-to-arc length ratios, orthotropy ratios, and shell types on the buckling and free vibration responses of porous orthotropic doubly-curved shallow shells. Parametric studies indicate that the reduction or increment in critical buckling loads (CBLs) and fundamental natural frequencies (FNFs) of spherical shells (SSs) are more sensitive than those of hyperbolic paraboloidal shells (HPSs). The CBLs and FNFs of SSs are larger than those of HPSs. The maximum and minimum CBLs are achieved for the triangular and uniform edge compression, respectively. The porosity effect is independent of edge compression pattern types.

The grain-scale signature of isotopic diffusion in ice

Abstract. Diffusion limits the survival of climate signals on the water stable isotopes in ice sheets. Diffusive smoothing acts not only on annual signals near the surface, but also on long-timescale signals at depth as they shorten to decimetres or centimetres. Short-circuiting of the slow diffusion in crystal grains by fast diffusion along liquid veins can explain the “excess diffusion” found on some ice-core isotopic records. But experimental evidence is lacking as to whether this mechanism operates as theorised; theories of the short-circuiting also under-explore the role of diffusion along grain boundaries. The non-uniform patterns of isotopic deviation δ across crystal grains induced by short-circuiting offer a testable prediction of these theories. Here, we extend the modelling for grain boundaries (and veins) and calculate these patterns for different grain-boundary diffusivities and thicknesses, temperatures, and vein-water flow velocities. Two isotopic patterns are shown to prevail in ice of millimetre grain size: (i) an axisymmetric “pole” pattern with excursions in δ centred on triple junctions, in the case of thin, low-diffusivity grain boundaries, and (ii) a “spoke” pattern with excursions around triple junctions showing the impression of grain boundaries, when these are thick and highly diffusive. The excursions have widths ∼ 10 %–50 % of the grain radius and variations in δ ∼ 10−2 to 10−1 times the bulk isotopic signal for oxygen and deuterium, which set the minimum measurement capability needed to detect the patterns. We examine how the predicted patterns vary with depth through a signal wavelength to suggest an experimental procedure, based on laser ablation mapping, of testing ice-core samples for these signatures of isotopic short-circuiting. Because our model accounts for veins and grain boundaries, its predicted enhancement factor (quantifying the level of excess diffusion) characterises the bulk-ice isotopic diffusivity more comprehensively than past studies.

Multi-segmented tube design and multi-objective optimization of deep coaxial borehole heat exchanger

Deep coaxial borehole heat exchanger (DCBHE) is a key component to extract medium-deep geothermal energy for low-carbon building heating. It is a convention that both center tube and annuals tube are respectively using a single material. However, a basic idea behind this study is using non-linear design to ask for performance improvement. It is assumed that non-uniform tube design pattern can meet different heat exchanger demand at different depth under a geothermal gradient dominated underground environment. The whole study is to prove this conjecture and make it useful for engineering application. A new numerical model is put forward to considered multi-segmented structure in heat transfer computation and a nondominated sorting genetic algorithm (NSGA-Ⅱ) is adopted in optimization process. The results of this study show that the optimized designed case can increase outlet temperature by 3 °C while reducing investment of 10000RMB. In addition, a home-made software is developed to perform the thermal and economic evaluation of DCBHE as well as automatically optimization of this multi-segmented structure. This study can provide a new model, algorithm, results and software tool for better utilizations of deep geothermal energy.

Regional morphological adaptations of vastus lateralis muscle in response to different progressive resistance training programs: A randomised controlled trial.

Resistance training often increases muscle size, a phenomenon known as muscle hypertrophy. These morphological adaptations were typically documented to occur in a non-uniform pattern. Investigating the specific morphological adaptations to different training programs was of interest. This study aimed to investigate two resistance training programs, a high-intensity program (HI) and a combined high-intensity with low-intensity blood flow restriction program (MIX), on morphological adaptations of vastus lateralis muscle in healthy young men. Eighteen active participants were recruited and randomly assigned to the HI (n = 10) or MIX (n = 8) groups, undergoing different 6-week resistance training programs. The training volume set was equated and progressively increased from three sets in weeks 1 and 2 to six sets, and eight sets in weeks 3-4 and 5-6, respectively. Three specific regions of vastus lateralis were assessed by magnetic resonance imaging (MRI) and ultrasound imaging (US) during pre-and post-intervention. Statistical analysis revealed statistically significant increases in muscle area at the proximal (HI: Δ12%, MIX: Δ9.2%), middle (HI: Δ8.7%, MIX: Δ9.0%), and distal (HI: Δ14%, MIX: Δ13%) regions. Additionally, both HI and MIX groups showed statistically significant increases in the sum of muscle thickness post-intervention (HI: Δ12%, MIX: Δ19%) and in the sum of fascia thickness post-intervention (HI: Δ27%, MIX: Δ54%). Despite the MIX group training with higher volume load, no statistical differences were observed between groups for any week. These findings suggested that both HI and MIX programs effectively induced increases in muscle area and sums of muscle and fascia thickness in healthy young men, allowing practitioners to choose either program based on individual preferences and constraints.

Effects of non-uniform perforated solar screen on daylighting and visual comfort performance

Perforated solar screens are extensively utilized as a shading solution for fully glazed buildings and transparent envelope systems. While uniform perforated solar screens have been traditionally employed, the growing trend towards non-uniform screens highlights their potential to enhance architectural aesthetics through varied perforation patterns on building facades. However, the effects of non-uniform screens in daylighting performance have been rarely investigated. This study aims to assess the effects of non-uniform perforated solar screens on daylighting performance, encompassing daylight availability, uniformity, and annual glare probabilities. A comparative analysis was conducted using simulation studies for eight distinct non-uniform perforation patterns against a uniform perforation scenario. Orthogonal experiment and data envelopment analysis were employed to identify the influential factors and evaluate the overall performance. The results suggest that non-uniform perforated solar screens with specific perforation patterns significantly outperform uniform screens in terms of daylighting performance, at equal perforation ratios. Specifically, two non-uniform prototypes, namely (h) periphery-center and (f) bottom-top, demonstrated exceptional performance. Furthermore, the perforation pattern was identified as the most significant geometrical factor affecting the overall performance of daylighting and glare discomfort. The results of this study may inform the design of perforation patterns to enhance daylighting performance in non-uniformly perforated solar screens. The novelty of this research lies in establishing a framework for evaluating the influence of non-uniform perforation patterns on indoor daylighting and visual comfort.

Stress Response of Citrus Leaves under Mechanical Damage and Huanglongbing Disease Infection Using Plasmonic TiO2 Nanotube Substrate-Based Imprinting Mass Spectrometry Imaging

Mapping the molecular signatures and metabolic regulation of plant tissues under biotic/abiotic stresses and defensive responses has become a subject of increasing interest in plant biology and systems biology, but determining when and where specialized metabolites are produced and accumulated currently remains a somewhat elusive goal. Herein, we demonstrated the use of a TiO2 nanotube-based composite substrate modified with plasmonic gold nanoparticles and hydrophobic polydopamine (AuNP-hPDA-TDNT) for surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) analysis of a wide range of pesticides and for visualizing the stress-responsive metabolites of citrus leaves during various plant defense processes. This method enabled the visualization of non-uniform and tissue-specific distribution patterns of functional metabolites of citrus leaves that were mechanically damaged, fed to larvae, and infected by Huanglongbing disease. Interestingly, some specialized metabolites exhibited different accumulation and regulation patterns for mechanical damage and larval feeding, suggesting that plant-derived secondary metabolites exercise specific defensive functions with respect to various damage processes. Moreover, the early diagnosis and detection of HLB disease-associated biomarkers can facilitate the prevention of citrus HLB diseases. Overall, this imprinting MS imaging strategy will expand the scope of MS techniques in plant biology, providing more biologically relevant insights into the biosynthesis, accumulation, and defensive role of bioactive metabolites in economically important plants.

Finite element modeling of active cracking in actively reinforced concrete pavement slab exposed to fluctuating temperature

The continuously reinforced concrete pavement (CRCP) system grapples with challenges such as non-uniform transverse crack patterns and the need for substantial reinforcement. Field research on the Belgian CRCP sections along motorway E313 indicates that active cracking induced by partial surface saw-cuts consistently leads to transverse crack patterns. This study introduces an innovative modification to the CRCP: the actively reinforced concrete pavement design (ARCP). The ARCP leverages partial surface saw-cuts to reduce reinforcement needs by replacing continuous-length steel bars with partial-length counterparts. The main objective of the present study is to develop a 3D finite element (FE) model capturing the active cracking behavior of ARCP under realistic external temperature variations. Comparative analysis with CRCP considers early-age crack patterns, crack strain development, and the distribution of maximum steel stress for different steel ratios (0.67%, 0.75%, and 0.85%). FE simulation results align with field data, indicating that ARCP exhibits similar early-age cracking behavior to CRCP but with a significant 24 to 42% reduction in total reinforcement. This innovation presents a promising avenue for addressing CRCP challenges while optimizing material usage in pavement construction.

Investigating the bending and buckling behaviors of composite porous beams reinforced with carbon nanotubes and graphene platelets using a TRPIM path following mesh-free approach

The aim of the present work consists in investigating the nonlinear behavior of porous beams reinforced with graphene platelets (GPL) and supported carbon nanotubes (CNT), termed functionally graded graphene platelets reinforced composite beam (FG-GPLRC) and functionally graded nanotube carbon reinforced composite beam (FG-CNTRC), respectively. Notably, the distribution of GPL/CNT is explored in both uniform and non-uniform patterns across the beam's thickness. What sets this research apart is its utilization of a refined beam model as enhanced FSDT incorporating nonlinear shear terms which is a crucial advancement in accurately capturing the post-buckling response in certain boundary conditions, a feature lacking in the existing FSDT literature. Innovatively, the post-buckling load-deflection relationship is derived through the solution of governing equations incorporating cubic nonlinearity. This is achieved by employing Galerkin's method alongside a non-iterative high-order continuation technique based on the asymptotic numerical method coupled with the Tchebychev-radial point interpolation method (TRPIM), using a path-following where the solutions are obtained branch-by-branch by eliminating the need for iterative processes. In essence, this research underscores the pivotal role of porosity and GPL/CNT reinforcement in shaping the post-buckling configuration of both perfect and imperfect nanocomposite beams, thereby advancing our understanding of structural behavior in porous nanocomposite materials. The findings of this study illuminate the significant influence of parameters such as porosity coefficient, porosity distribution, GPL/CNT distribution, and GPL-weight/CNT-volume fraction on the nonlinear buckling behavior of porous beams.

Anti-distortion bioinspired camera with an inhomogeneous photo-pixel array

The bioinspired camera, comprising a single lens and a curved image sensor—a photodiode array on a curved surface—, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096 silicon photodiodes arrayed on a curved surface in a nonuniform pattern assimilated to the distorting optics are the key to anti-distortion engineering. That is, the photo-pixel distribution pattern itself is warped in the same manner as images are warped, which correctively reverses distortion. Acquired images feature no appreciable distortion across a 120° horizontal view, as confirmed by their neural-network recognition accuracies. This distortion correction via photo-pixel array reconfiguration is a form of in-sensor computing.

Real-time Bayesian inversion in resin transfer moulding using neural surrogates

In Resin Transfer Moulding (RTM), local variations in reinforcement properties (porosity and permeability) and the formation of gaps along the reinforcement edges result in non-uniform resin flow patterns, which may cause defects in the produced composite component. The ensemble Kalman inversion (EKI) algorithm has previously been used to invert in-process data to estimate local reinforcement properties. However, implementation of this algorithm in some applications is limited by the requirement to run thousands of computationally expensive resin flow simulations. In this study, a machine learning approach is used to train a surrogate model which can emulate resin flow simulations near-instantaneously. A partition of the flow domain into a low-dimensional representation enables an artificial neural network (ANN) surrogate to make accurate predictions, with a simple architecture. When the ANN is integrated within the EKI algorithm, estimates for local reinforcement permeability and porosity can be achieved in real time, as was verified by virtual and lab experiments. Since EKI utilises the Bayesian framework, estimates are given within confidence intervals and statements can be made on-line regarding the probability of defects within sections of the reinforcement. The proposed framework has shown good predictive capabilities for the set of laboratory experiments and estimates for reinforcement properties were always computed within 1 s.