Profile Curvature Research Articles

Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin

Predicting landslides has become a critical global challenge for promoting sustainable development in mountainous regions. This study conducts a comparative analysis of landslide susceptibility maps (L.S.M.s) generated using two GIS-based data-driven bivariate statistical models: (a) Frequency Ratio (F.R.) and (b) Evidential Belief Function (E.B.F). These models are applied and evaluated in the high landslide-prone upper and middle Teesta basin of the Darjeeling-Sikkim Himalaya, leveraging geographic information system (GIS) and remote sensing techniques. We compile a comprehensive landslide inventory map containing 2387 regional landslide points. We use approximately 70% of this dataset for model training and reserve the remaining 30% for validation. In the construction of the Landslide Susceptibility maps (LSMs), a comprehensive set of twenty-one landslide-triggering parameters has been considered. These parameters encompass factors such as elevation, distance from drainage, distance from lineament, distance from roads, geology, geomorphology, lithology, land use, and land cover, normalized difference vegetation index, profile curvature, rainfall, relief amplitude, roughness, slope, slope aspect, slope classes, stream power index, sediment transport index, topographic position index, topographic ruggedness index, and topographic wetness index. An examination of multicollinearity statistics reveals no collinearity issues among the twenty-one causative factors utilized in this research. The final L.S.M.s demonstrate that the combined application of the F.R. and E.B.F. models yields the highest training accuracy at 98.10%. The insights derived from this study hold significant promise as valuable tools for assessing environmental hazards and land use planning.

Порівняння злітних характеристик двох типів крила для літака з можливістю короткого зльоту та посадки

The subject of study in this article is the effect of mechanization of the trailing edge of the wing, located along 95% of the wing console, on the lift coefficient, drag coefficient, and aerodynamic quality of the wing during takeoff operation for a wing with a high curvature profile. The purpose of this study was to determine the effectiveness of using a flap along 95% of the trailing edge of the wing console for profiles with high curvature during takeoff for aircraft with short takeoff and landing capabilities. Objectives: to set the geometric characteristics of the wing, create a mathematical model of the wing for two design cases, determine the control data for the calculation, and compare the data obtained. The method for determining the efficiency is to conduct an analysis by comparing the aerodynamic characteristics of wings with the same mechanization, namely, a single-slot Fowler flap, but the flap has a different length. In the first design case, roll control is performed using an aileron that reduces the length of the flap. In the second design case, the flap is placed along 95% of the trailing edge of the wing cantilever, and roll control is performed using interceptors. The study was conducted using the PANSYM software. A key factor is that during the calculation, the structural elements responsible for roll control are not used, i.e., they are in a non-deflected position. The influence of the screen effect is not considered. The calculation is performed at angles of attack from -5° to +15°, viscosity is considered, and the number of viscosity calculation operations is 8. Conclusions. This paper presents additional studies of the influence of the flap along 95% of the wing console edge on its aerodynamic characteristics. The scientific novelty is that the efficiency of using flaps along 95% of the trailing edge of the wing for aircraft with the possibility of short takeoff and landing in the takeoff flight mode has been proved. After comparing the obtained results of the calculation of the two mathematical models, the following conclusions were drawn: the increase in lift coefficient is 30%–50% and the increase in drag coefficient is 35%–45%, but this leads to a decrease in aerodynamic quality by 11%–21%. Thus, a significant increase in the lift coefficient makes it possible to reduce the required distance for takeoff, which is important for aircraft with the capability of short takeoff and landing.

Evolutions of temperature field and stress field in narrow gap oscillating laser welding process based on equivalent heat source

With the increased demand for high-performance thick high-strength steel structures in high-end offshore equipment, narrow gap oscillating laser welding with the advantage of high power density and satisfied penetration depth has attracted considerable attention. A thermal-elastic-plastic finite element model was established to calculate the narrow gap oscillating laser welding process of 30-mm-thick high-strength steel, which requires a large amount of computational time cost. To increase the computational efficiency, two equivalent heat source models were founded to replace the laser oscillation. The temperature field and stress-strain field were analyzed. The accuracy of the equivalent heat source model was verified from the weld seam profile and residual stress distribution curves. Results showed that the simulated results were in good agreement with the actual results. With the number of welding passes increasing, the cross-section shape of the molten pool changed from an elongated shape to an arc shape, and the groove gap on the top surface was reduced from 4.82 mm before welding to 3.18 mm after welding. The maximum equivalent residual stress, with the value of 669 MPa, was distributed in the middle and upper parts of the weld seam as well as the root. The longitudinal residual stresses exhibited tensile stresses in the weld seam zone. Due to the upward bending of the workpiece, the transverse residual stresses showed compressive stresses on the top surface of the weld seam and maximum tensile stresses with the value of 806 MPa at the bottom of the weld seam.

Plasma melatonin night‐time peak levels and 24‐hour area under the curve profile seem higher in AD patients with epileptiform activity than in those without

AbstractBackgroundPlasma melatonin levels drop in elderly, which might become more pronounced in Alzheimer’s disease (AD) as compared to controls.(1) Melatonin can serve as a potential biomarker for AD, given the inverse correlation between cerebrospinal fluid (CSF) melatonin levels and severity of neuropathology (Braak stages).(2) Due to suggested antiepileptic effects(6), melatonin can potentially slow down disease progression as epileptiform activity may contribute to AD disease progression.(3‐5). We here present a preliminary interim analysis of an ongoing study.Aims1. To evaluate melatonin’s potential as biomarker for AD by comparing blood melatonin levels between patients with mild cognitive impairment (MCI) and dementia due to AD and preclinical AD subjects.2. To study the link between melatonin levels and epileptiform activity in AD.MethodsAll subjects underwent a neuropsychological examination, and lumbar puncture for analysis of core AD CSF biomarkers or amyloid brain PET. Subjects belonging to the AD continuum (dementia [n = 7]; MCI [n = 4]; preclinical AD [n = 1]) were diagnosed according to the NIA‐AA research criteria. A subset of subjects (n = 6) underwent 50‐minute magnetoencephalography to detect epileptiform activity. Blood samples were taken multiple times during a 24‐hour period, in in‐hospital controlled circumstances, and were analysed by use of liquid chromatography‐mass spectrometry.ResultsAD patients with epileptiform activity (n = 2) tended to have a higher melatonin 24‐hour area under the curve (AUC) and peak concentration (AUC 517.9pg/ml.h [+/‐140.1pg/ml.h] versus 204.5pg/ml.h [+/‐115.5pg/ml.h]; peak 65.4pg/ml [+/‐14.6pg/ml] versus 25.8pg/ml [+/‐17.5pg/ml]) than AD patients without (n = 4) (p = 0.064 for both; MWU test). There was no statistical difference regarding the melatonin 24‐hour plasma AUC or peak levels between patients with dementia, MCI, preclinical AD subjects (p = 0.760 for both; Kruskall‐Wallis test). We have to interpret these data with caution since sample sizes are still small and more patients are being recruited.ConclusionOn the basis of very preliminary data, 24‐hour plasma melatonin profile and peak plasma melatonin concentration tended to be higher in AD patients with epileptiform activity than in those without. Whether this could be a potential compensatory mechanism against epileptiform activity in AD or whether melatonin could rather have pro‐epileptic effects, remains to be elucidated in future research.

GPU-accelerated polyenergetic forward projection for 9 MeV industrial CT system

Polyenergetic forward projection has great significance in inspecting hazardous materials, establishing optimal radiographic variables and investigating beam hardening effects. However, it is computationally intensive to perform polyenergetic forward-projection calculations for high-resolution phantoms. To address this issue, a rapid polyenergetic forward-projection algorithm is proposed for a 9 MeV industrial computed tomography (CT) system. The FLUktuierende KAskade (FLUKA) software package is used to generate the 9 MeV X-ray spectrum data. Two voxelised phantoms are used to model scanned objects, one being a multi-material cylinder and the other a single-material turbine blade. An incremental version of Siddon's algorithm is adopted to calculate the intersection lengths between the X-rays and the auxiliary phantoms. Three strategies are utilised to accelerate the calculation, in which: the intersection lengths do not vary with the energy bins and can be used repeatedly until all the energy bins are counted; a graphics processing unit (GPU) is used to accelerate the ray tracing algorithm by utilising a parallel computing technique; and faster memory access is achieved by binding the auxiliary phantoms to texture objects. The simulation results in this paper show that the GPU-based approach not only maintains the image precision but also gains significant speed-ups over the conventional central processing unit (CPU)-based Siddon method. Furthermore, beam hardening artefacts can clearly be seen from the profile curves of the reconstructed slices, indicating that this method is effective.

Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population

ObjectiveIdentifying individuals with subclinical cardiovascular (CV) disease could improve monitoring and risk stratification. While peak left ventricular (LV) systolic strain has emerged as a strong prognostic factor, few studies have analyzed the whole temporal profiles of the deformation curves during the complete cardiac cycle. Therefore, in this longitudinal study, we applied an unsupervised machine learning approach based on time-series-derived features from the LV strain curve to identify distinct strain phenogroups that might be related to the risk of adverse cardiovascular events in the general population.MethodWe prospectively studied 1,185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic data including LV strain traces at baseline and collected adverse events on average 9.1 years later. A Gaussian Mixture Model (GMM) was applied to features derived from LV strain curves, including the slopes during systole, early and late diastole, peak strain, and the duration and height of diastasis. We evaluated the performance of the model using the clinical characteristics of the participants and the incidence of adverse events in the training dataset. To ascertain the validity of the trained model, we used an additional community-based cohort (n = 545) as external validation cohort.ResultsThe most appropriate number of clusters to separate the LV strain curves was four. In clusters 1 and 2, we observed differences in age and heart rate distributions, but they had similarly low prevalence of CV risk factors. Cluster 4 had the worst combination of CV risk factors, and a higher prevalence of LV hypertrophy and diastolic dysfunction than in other clusters. In cluster 3, the reported values were in between those of strain clusters 2 and 4. Adjusting for traditional covariables, we observed that clusters 3 and 4 had a significantly higher risk for CV (28% and 20%, P ≤ 0.038) and cardiac (57% and 43%, P ≤ 0.024) adverse events. Using SHAP values we observed that the features that incorporate temporal information, such as the slope during systole and early diastole, had a higher impact on the model's decision than peak LV systolic strain.ConclusionEmploying a GMM on features derived from the raw LV strain curves, we extracted clinically significant phenogroups which could provide additive prognostic information over the peak LV strain.

Structural Complexity of Coral Reefs in Guam, Mariana Islands

The complexity of tropical reef habitats affects the occurrence and diversity of the organisms residing in these ecosystems. Quantifying this complexity is important to better understand and monitor reef community assemblages and their roles in providing ecological services. This study employed structure-from-motion photogrammetry to produce accurate 3D reconstructions of eight reefs in Guam and quantified the structural complexity of these sites using seven terrain metrics: rugosity, slope, vector ruggedness measure (VRM), multiscale roughness (magnitude and scale), plan curvature, and profile curvature. The relationships between terrain complexity, benthic community diversity, and coral cover were investigated with generalized linear models. While the average structural complexity metrics did not differ between most sites, there was significant variation within sites. All surveyed transects exhibited high structural complexity, with an average rugosity of 2.28 and an average slope of 43 degrees. Benthic diversity was significantly correlated with the roughness magnitude. Coral cover was significantly correlated with slope, roughness magnitude, and VRM. This study is among the first to employ this methodology in Guam and provides additional insight into the structural complexity of Guam’s reefs, which can become an important component of holistic reef assessments in the future.

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.

Load-carrying mechanisms of 3D post-tensioned precast concrete sub-structure under internal column removal scenario

To shed light on structural response of a post-tensioned precast concrete (PTPC) sub-structure under an internal column removal scenario, a large three-dimensional (3D) beam-slab assembly was tested under twenty-four-point quasi-static loading. The specimen was a two-by-two bay floor with primary beams, secondary beams and slabs. The primary and secondary beams were designed with precast concrete using wet-connection joints in which post-tensioned (PT) tendons were used in the primary beams only. Besides, precast half planks were employed for the slab panels. A proper restraint system along the four sides was designed to facilitate mobilization of all load-carrying mechanisms in the sub-structure. The test results, including the load-displacement curve, joint rotation and deflection profile, crack pattern and failure mode, and load redistribution, are presented and discussed in detail. The test results revealed the distinctive roles of different load-carrying mechanisms in the sub-structure at different stages. In addition, a simplified analytical method was developed to predict the flexural and tensile capacities of the 3D sub-structure. The method could predict the respective contributions of individual members such as the primary beams, secondary beams and slabs, thereby allowing structural engineers to control the behaviour of the 3D sub-structure at different stages.

A kind of rotational surfaces with a light-like axis in conformally flat pseudo-spaces of dimensional three

In this work, we define the rotational surface with a light-like axis in conformally flat pseudo-spaces $\left(\mathbb{E}_3^1\right)_\lambda$, where $\lambda$ is a radial-type conformal factor. We relate the principal curvatures of a non-degenerate surface that belongs to conformally equivalent spaces $\left(\mathbb{E}_3^1\right)_\lambda$ and $\mathbb{R}_1^3$, based on the radial conformal factor. Thus, we establish a relationship between the radial conformal factor and the profile curve of the rotational flat surface in $\left(\mathbb{E}_3^1\right)_\lambda$, but also for that of the rotational surface with zero extrinsic curvature.

Performance of carbon nanotubes (CNTs) on the development of radiating hybrid nanofluid flow through an stretching cylinder

In various industries, one of the important aspects is the cooling processes for which the material exhibits its perfect shape and size. Intending to the aforementioned property the current investigation leads to carry out the features of the materialistic property of thermal radiation and the dissipative heat by incorporating these in the water-based hybrid nanofluid. The fluid past a stretching cylinder embedded with a permeable medium and the impact of the magnetic field, and thermal radiation are depicted in momentum and energy profiles. In addition to that, the role of the Hamilton-Crosser conductivity model for the behavior of various shapes of the carbon nanotube (CNT) nanoparticles with their volume concentration is also vital. The transformation of the dimensional form of the governing equations into the non-dimensional form is obtained with the use of proper transformation rules. Further, the proposed designed model is handled by employing the traditional shooting-based Runge-Kutta fourth-order technique. The significant properties of different components are deployed graphically and the validation with earlier study shows a good correlation. Moreover, the important characteristics of the outcomes are; the surface cooling, driven by increased thermal buoyancy, promotes fluid velocity while simultaneously influencing the curvature parameter and profile to slow down the accumulation of nanoparticles.

A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

AbstractLandslides pose a significant threat to human life and infrastructure, underscoring the ongoing need for accurate landslide susceptibility mapping (LSM) to effectively assess risks. This study introduces an innovative approach that leverages multi-objective evolutionary fuzzy algorithms for landslide modeling in Khalkhal town, Iran. Two algorithms, namely the non-dominated sorting genetic algorithm II (NSGA-II) and the evolutionary non-dominated radial slots-based algorithm (ENORA), were employed to optimize Gaussian fuzzy rules. By utilizing 15 landslide conditioning factors (aspect, altitude, distance from the fault, soil, slope, lithology, rainfall, distance from the road, the normalized difference vegetation index (NDVI), land cover, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and distance from the river) and historical landslide events (153 landslide locations), we randomly partitioned the input data into training (70%) and validation (30%) sets. The training set determined the weight of conditioning factor classes using the frequency ratio (FR) approach. These weights were then used as inputs for the NSGA-II and ENORA algorithms to generate an LSM. The NSGA-II algorithm achieved a root-mean-square error (RMSE) of 0.25 during training and 0.43 during validation. Similarly, the ENORA algorithm demonstrated an RMSE of 0.28 in training and 0.48 in validation. The findings revealed that the LSM created by the NSGA-II algorithm exhibited superior predictive capabilities (area under the receiver operating characteristic curve (AUC) = 0.867) compared to the ENORA algorithm (AUC = 0.844). Additionally, a particle swarm optimization (PSO) algorithm was employed to determine the importance of conditioning factors, identifying lithology, land cover, and altitude as the most influential factors.

Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction.

This study aimed to evaluate the ability of deep learning reconstruction (DLR) compared to that of hybrid iterative reconstruction (IR) to depict small vessels on computed tomography (CT). DLR and two types of hybrid IRs were used for image reconstruction. The target vessels were the basilar artery (BA), superior cerebellar artery (SCA), anterior inferior cerebellar artery (AICA), and posterior inferior cerebellar artery (PICA). The peak value, ΔCT values defined as the difference between the peak value and background, and full width at half maximum (FWHM), were obtained from the profile curves. In all target vessels, the peak and ΔCT values of DLR were significantly higher than those of the two types of hybrid IR (p < 0.001). Compared to that associated with hybrid IR, the FWHM of DLR was significantly lower in the SCA (p < 0.001), AICA (p < 0.001), and PICA (p < 0.001). In conclusion, DLR has the potential to improve visualization of small vessels.

Influence of Input Factors on Surface Roughness and Material Removal Speed when Wire-EDM a Hardened SKD11 Steel Curve Profile

Influence of Input Factors on Surface Roughness and Material Removal Speed when Wire-EDM a Hardened SKD11 Steel Curve Profile

Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.

Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.

Dead but not forgotten: complexity of Acropora palmata colonies increases with greater composition of dead coral.

Coral reefs are highly biodiverse ecosystems that have declined due to natural and anthropogenic stressors. Researchers often attribute reef ecological processes to corals' complex structure, but effective conservation requires disentangling the contributions of coral versus reef structures. Many studies assessing the relationships between reef structure and ecological dynamics commonly use live coral as a proxy for reef complexity, disregarding the contribution of dead coral skeletons to reef habitat provision or other biogeochemical reef dynamics. This study aimed to assess the contribution of dead coral to reef complexity by examining structural variations in live and dead Acropora palmata colonies. We used photogrammetry to reconstruct digital elevation models (DEMs) and orthomosaics of the benthic region immediately surrounding 10 A. palmata colonies. These reconstructions were used to quantify structural metrics, including surface rugosity, fractal dimension, slope, planform curvature, and profile curvature, as a function of benthic composition (i.e., live A. palmata, dead A. palmata, or non-A. palmata substrate). The results revealed that dead coral maintained more varied profile curvatures and higher fractal dimensions than live or non-coral substrate. Conversely, A. palmata colonies with a higher proportion of live coral displayed more uniform structure, with lower fractal dimensions and less variability in profile curvature measures. Other metrics showed no significant difference among substrate types. These findings provide novel insights into the structural differences between live and dead coral, and an alternative perspective on the mechanisms driving the observed structural complexity on reefs. Overall, our results highlight the overlooked potential contributions of dead coral to reef habitat provision, ecological processes, and other biogeochemical reef dynamics, and could have important implications for coral reef conservation.

Development of Flexible Tooling for Deformation Sensing Applied to Composite Materials Fabrication Process

Among the existing techniques for composite materials manufacturing, Vacuum Assisted Resin Infusion (VARI) is a liquid composite molding (LCM) process where resin flows through a dry fiber preform to fully impregnate it. This method uses flexible film sealed onto a rigid mold to form the infusion cavity containing the fibers. As the fabric preform is impregnated, its thickness varies due to the changes in the applied compaction pressure. This thickness variation affects the resin flow and the final fiber volume fraction of the manufactured part. This study focuses on the initial steps of developing an integrated acquisition system for thickness variation monitoring during VARI. The conventional flexible tooling is to be replaced by a flexible membrane equipped with optical fiber Bragg grating (FBG) sensors. A prototype was developed by embedding FBG sensors in a silicon rubber material and initial measurements of a cylindrical profile curvature were performed. Preliminary results show satisfactory precision of the device, which opens a gap for a more precise and accurate thickness monitoring process during real part manufacturing.

Magnetic-field induced flattening of evaporating ferro-nanofluid meniscus for enhanced cooling

This work envisages applying magnetic fields to flatten thin ferro-nanofluid film meniscus for enhanced heat transfer during evaporative cooling process. Experiments are conducted to examine the shape of the evaporating extended meniscus of ferro-nanofluid (Fe304 nanoparticles in water suspension 0.01% v/v) under axisymmetric vertical magnetic fields (∼10−2T) generated by solenoid placed below the heated substrate. The study includes measuring the thickness profile of the meniscus evaporating region using high nano-scale resolution reflectometry and theoretically interpreting meniscus shape in terms of flow and film heat transfer. Profile measurements reveal an increasing influence of magnetic fields in thinning of the adsorbed region and stretching of the transition region, which effectively reduce the thermal resistance across meniscus width. Data reduction based on thickness as well as the derived curvature profiles yields a high-pressure gradient driving flow along the evaporating thin film region. The results imply additional suction induced by disjoining pressure of thinner adsorbed layer which sustains replenishment of lost evaporative mass across a longer transition region interface. Subsequent heat transfer analysis reveals up to 156 % enhancement in overall heat flux and 101 % in average heat transfer with increasing magnetic field intensity. The findings establish proof of concept of applying magnetic field to augment film-based cooling.

Behaviour of volatile compounds during batch multi-stage distillation of whisky: experimental and simulation data

This study characterised the behaviour of congeners during the multi-stage discontinuous distillation of whisky using a Stupfler-type still. It also investigated the influence of recycling the head and tail fractions on the composition of the unaged spirit and, for the compounds with known equilibrium data, to validate the capability of software tools to simulate their behaviour during distillation. To this end, profile curves of ethanol (% ABV) and 45 aroma compounds (mg/L) during distillation are reported with 27 compounds, compared to simulated data using ProSim® BatchColumn software. Further, the mass distribution of each compound between the fractions of discarded head, recycled head, heart, tail and residue are estimated. Assuming that this distribution was maintained during each distillation, it was shown that for the wash, a pseudo-steady state was achieved in less than five distillations, and the true recovery rate of each compound in the heart was assessed. This study clearly demonstrates that during distillation, the congeners behave according to the ethanol content of the boiling liquid in the column. It provides real information on the separation of volatile compounds with a batch still with a distillation column above the heater. Additionally, the data highlights the importance to the unaged spirit of recycling the head and tail fractions. These results provide useful information for distillers to choose the appropriate way of collecting the heart fraction.