Complex Shapes Research Articles

Aluminum Electrolysis Fire-Eye Image Segmentation Based on the Improved U-Net Under Carbon Slag Interference

To solve the problem of low segmentation model accuracy due to the complex shape of carbon slag in the aluminum electrolysis fire-eye image and the blurring of the boundary between the slag and the surrounding electrolyte, this paper proposes a segmentation model of the fire-eye image based on an improved U-Net. The model reduces the depth of the traditional U-Net to four layers and uses the multiscale dilated convolution module (MDCM) in the down-sampling stage. Second, the Convolutional Block Attention Module (CBAM) is embedded in the skip connection part of the network to improve the ability of the model to extract contextual features from images of multiple scales, enhance the guidance of high-level features to low-level features, and make the model pay more attention to the critical regions. To alleviate the negative impact of the imbalance of positive and negative examples in the dataset, the weighted binary cross-entropy loss and the Dice loss are used to replace the traditional cross-entropy loss. The experimental results show that the segmentation accuracy of the improved model on the fire-eye dataset reaches 88.03%, which is 5.61 percentage points higher than U-Net.

Three-Dimensional Multi-Morphology TPMS-Based Microchannel Design Method for Conformal Cooling in Injection Molding

Abstract Triply periodic minimal surface (TPMS) possesses diverse morphological characteristics, such as pore sizes, porosity, and structural types. Integrating TPMS-based microchannels into micro-cellular cooling structures is advantageous for designing and controlling fluid characteristics within mold cooling channels. However, it is still difficult to design multi-morphology TPMS-based cooling microchannels that conform to the external shapes of injection molds. This work proposes a 3D multi-morphology TPMS-based design method that transforms 3D constraints into a combination of 2D constraints. First, a beta growth algorithm based on closed-loop constraints is proposed to transition different morphologies smoothly on the plane. Subsequently, a transition optimization algorithm along the normal direction of the plane is introduced to smoothly transition multi-morphology TPMS-based microchannels in all directions. With this layered approach, multi-morphology microchannels can be obtained with first-order geometric continuity under complex shape constraints. Finally, several TPMS-based conformal cooling structures are designed for an automotive hood cover. The results of finite element simulations show that the cooling structures generated by the proposed method have a better cooling effect than the conformal channels. It can be concluded that multi-morphology TPMS-based structures perform better by contrast with conformal channels as the average temperature of the cooling surface decreases by 8.48 K, and the standard deviation of temperature distribution decreases by 24.65%.

Predictive value of CT-based and AI-reconstructed 3D-TAPSE in patients undergoing transcatheter tricuspid valve repair

BackgroundThe tricuspid annular plane systolic excursion (TAPSE) assessed by echocardiography has failed in predicting outcomes in patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve intervention (TTVI). Considering the complex shape of the tricuspid annulus and right ventricle, as well as the difficult echocardiographic image acquisition of the right heart, cardiac computed tomography (CT) might be superior for the analysis of the annular excursion. Thus, this study aimed to analyze whether CT-captured TAPSE provides additional value in predicting outcomes after TTVI.Methods and resultsFor TTVI procedure planning, 75 patients (mean age, 77 ± 8 years; 61% female) with severe TR underwent full cardiac cycle CT. Septal, lateral, anterior, and posterior TAPSE, as well as TAPSE- volume, were analyzed. Indexed anterior and posterior (iTAPSE) and TAPSE volume were reduced in patients with right ventricular ejection fraction <45%. At 1 year after TTVI (mean follow-up, 193 ± 146days), the combined endpoint of death and rehospitalization occurred in significantly fewer patients with posterior iTAPSE >4.5 mm/m2 (17.2% vs. 63.6%; HR 0.225, CI 0.087–0.581; P < 0.001) and in patients with iTAPSE volume >9 ml/m2 (16.4% vs. 57.1%; HR: 0.269 CI 0.105–0.686; P = 0.003). Echocardiographic TAPSE correlated best with lateral CT-based TAPSE, although both failed in predicting outcomes after TTVI. In multivariate Cox regression, posterior iTAPSE was found to be a significant predictor of outcome 1 year after TTVI.ConclusionsPosterior iTAPSE is an independent predictor of cardiovascular outcomes among patients undergoing TTVI. Furthermore, CT-measured TAPSE has incremental value and refines risk stratification for clinical outcomes in patients undergoing TTVI.

Development of Novel Monolithic Catalyst for BTEX Catalytic Oxidation Using 3D Printing Technology

Four differently shaped monolithic catalyst supports were made using 3D printing technology. Two catalytically active mixed oxides, MnFeOx and MnCuOx, were applied to the monolithic supports using the impregnation technique. Catalysts were characterized using an adhesion test, field emission scanning electron microscopy, X-ray diffraction, and Raman spectroscopy in a manner similar to the density functional theory model. Excellent mechanical stability of the catalyst layer was obtained, with catalyst mass loss under 2% after 30 min of ultrasound exposure. SEM analysis revealed that the catalyst layer was rough but homogeneous in appearance and ~6 μm thick. The presence of double oxides—FeMnO3 and CuMn2O4—as well as single oxides of Mn, Fe, and Cu was established via XRD and Raman spectroscopy. Additional theoretical calculations of Raman spectra for FeMnO3 and CuMn2O4 were performed in order to aid in the interpretation of Raman spectra. The catalytic activity of the prepared catalysts for the catalytic oxidation of a gaseous mixture of benzene, toluene, ethylbenzene, and o-xylene (BTEX) was investigated. The monolithic support with the most complex shape and, consequently, the greatest surface area proved to enable the highest efficiency, while both catalysts performed well having similar conversions.

Elastic robotic structures: a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications

The design of adaptive structures and objects takes place at the intersection of design, architecture, robotics and engineering. Evolving from 1960s cybernetics to today’s interactive projects, technological advancements shape new visions for adaptive systems. A key challenge in this field is developing human-scale, shape-morphing structures. Elastic materials offer a promising solution for creating lightweight systems capable of large transformations with minimal components and energy, unlike conventional rigid systems. This approach requires methodologies for designing and controlling complex material deformations. While architectural and structural design methods focus on large-scale but static elastic structures, soft robotics explores dynamic behaviors. However these approaches are limited for complex shapes and large-scale, as their focus is on specialized applications. To address these issues, this research introduces a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications. It also presents the concept of elastic robotic structures (ERS), which refers to a body of work developed with the framework. ERS are defined as large-scale elastic systems that are robotically actuated and can achieve multiple geometrical states, interacting with humans and adapting to diverse conditions. The multidisciplinary framework is presented for ERS design, characterization and control, showing how it leverages the integration of architecture, engineering and robotics to overcome the limitations of discipline-specific traditional approaches. The framework is applied in the realization of different types of ERS, which are presented and categorized. Combining the flexibility and interactivity of design methodologies with the reliability of robotic solutions will enable designers and engineers to develop innovative elastic shape-changing systems and program their behaviors.

A method for enhancing the accuracy of industrial bearing surface defect detection

ABSTRACT Deep learning models often encounter challenges when detecting surface defects on industrial bearings, including inaccurate feature extraction caused by random shapes and multi-scale defects, as well as high leakage rates of small objects in complex multi-object scenes. To address these challenges, a YOLOv8n-based method (YOLOv8-IWDD) is proposed for detecting bearing surface defects. Firstly, the ImplicitHead detection head is introduced, which utilises implicit addition and multiplication to highlight key features. This enhancement improves feature extraction, resulting in improved detection accuracy for small objects, especially in complex multi-object scenarios, and effectively reduces missed detections. Secondly, the Inner-WIoU loss function optimises bounding box localisation, enabling the model to better handle randomly shaped defects, accelerate convergence, and enhance localisation accuracy. In addition, dynamic label assignment and dynamic sampling strategies are proposed to achieve flexible adjustment of sample assignment and sampling method, which further improves the detection accuracy and robustness. These strategies enhance the model’s robustness in handling multi-scale and complex shape defects. The experimental results show that YOLOv8-IWDD improves mAP0.5 by 2.9%, mAP0.5:0.95 by 9.4%, precision by 2.2%, and recall by 6.5% compared with the YOLOv8n model. The method achieves promising results in detecting surface defects on industrial bearings.

Influence of Processing Parameters on Flow Behaviour of Ultra-Large-Section Beam Blank Continuous Casting Mould

The complex cross-sectional shape of oversized beam blanks and the size effect of ultra-large-section beam blanks create severe issues related to the surface and internal quality of the castings. To ensure quality and control in the production of ultra-large-section beam blanks, a numerical and physical model of molten steel flow in the three-port submerged entrance nozzle (SEN) mould, with section dimensions of 1300 × 510 × 140 mm, was established. This model was created using numerical simulations and NSGA-II genetic algorithm optimisation, and the impact of the casting speed and SEN immersion depth on the mould’s flow behaviour was investigated. The results showed that a deeper SEN immersion depth resulted in, a greater impact depth of the molten steel, and the surface flow velocity decreased. Both the impact depth and the surface flow velocity of the molten steel increased with increasing casting speed. The physical simulation and numerical simulation of the molten steel flow form and flow velocity distribution in the mould were in good agreement with each other, thus verifying the accuracy of the numerical simulation. The process parameters derived from this study were all within an appropriate range, which can help to improve the quality of continuously cast beam blanks. This also provides guidance for selecting optimal parameters for actual continuous casting production processes.

Analysis of the spiking dynamics of a diode laser with dual optical feedback

In a complex dynamical system, noise, feedback, and external forces shape behavior that can range from regularity to high-dimensional chaos. Multiple feedback sources can significantly alter its dynamics, potentially even suppressing the system’s output. This study investigates the impact of competing feedback sources on a stochastic complex dynamical system using a photonic neuron—a diode laser with external optical feedback. By varying the feedback intensities from two external reflectors, we explore how dual feedback influences the system’s behavior. Using ordinal analysis and advanced measures of complexity, we quantify the system’s dynamics and uncover underlying symmetries. Our findings reveal that the interaction between the two feedback sources induces a more intense deterministic behavior, distinct from the dynamics produced by each feedback source individually. Additionally, clear temporal symmetries emerge across all dynamical regimes. By employing a novel entropy-vector representation, we are able to identify a unique signature that characterizes the system’s dynamics.

Amplifying the Sensitivity of Electrospun Polyvinylidene Fluoride Piezoelectric Sensors Through Electrical Polarization Process for Low-Frequency Applications

Piezoelectric sensors convert mechanical stress into electrical charge via the piezoelectric effect, and when fabricated as fibers, they offer flexibility, lightweight properties, and adaptability to complex shapes for self-powered wearable sensors. Polyvinylidene fluoride (PVDF) nanofibers have garnered significant interest due to their potential applications in various fields, including sensors, actuators, and energy-harvesting devices. Achieving optimal piezoelectric properties in PVDF nanofibers requires the careful optimization of polarization. Applying a high electric field to PVDF chains can cause significant mechanical deformation due to electrostriction, leading to crack formation and fragmentation, particularly at the chain ends. Therefore, it is essential to explore methods for polarizing PVDF at the lowest possible voltage to prevent structural damage. In this study, a Design of Experiments (DoE) approach was employed to systematically optimize the polarization parameters using a definitive screening design. The main effects of the input parameters on piezoelectric properties were identified. Heat treatment and the electric field were significant factors affecting the sensor’s sensitivity and β-phase fraction. At the highest temperature of 120 °C and the maximum applied electric field of 3.5 kV/cm, the % β-phase (F(β)) exceeded 95%. However, when reducing the electric field to 1.5 kV/cm and 120 °C, the % F(β) ranged between 87.5% and 90%. The dielectric constant (ɛ′) of polarized PVDF was determined to be 30 at an electric field frequency of 1 Hz, compared to a value of 25 for non-polarized PVDF. The piezoelectric voltage coefficient (g33) for polarized PVDF was measured at 32 mV·m/N at 1 Hz, whereas non-polarized PVDF exhibited a value of 3.4 mV·m/N. The findings indicate that, in addition to a high density of β-phase dipoles, the polarization of these dipoles significantly enhances the sensitivity of the PVDF nanofiber mat.

How Do Vegetation Biomass, Area, and Shape Attributes Influence the Cooling Effect of Urban Green Spaces?

Despite the increasing volume of research exploring the impact of various characteristics of urban green spaces (UGS) on land surface temperature (LST), findings remain inconsistent, particularly in arid climatic zones. In this research, we examined UGS change and their temperature and analyzed the relationship between pertinent variables of vegetation biomass, area, and shape of green patches and LST in Karaj city, an Iranian semi-arid urban area in 2000 and 2020. Linear regressions were used to model the relationship between green patches’ variables and LST. The results showed that vegetation biomass of green patches was more effective in reducing temperature in comparison with area and shape complexity. Moreover, larger patches with more vegetation biomass and higher shape complexity showed lower temperatures. These results can guide urban landscape optimization by providing a clear understanding of which factors contribute most significantly to temperature mitigation in arid and semi-arid urban areas. For instance, areas identified as green but thermally not significantly cold need to be prioritized for improvements such as planting denser vegetation or introducing more heat resilient species.

Improved electrical and thermoelectric properties of electrodeposited Bi1-x Sb x nanowire networks by thermal annealing.

Arrays of thermoelectric nanowires embedded in organic films are attracting increasing interest to fabricate flexible thermoelectric devices with adjustable dimensions and complex shapes, useful for sustainable power sources of portable electronic devices and wireless sensor networks. Here, we report the electrochemical synthesis of interconnected bismuth-antimony (Bi1-x Sb x ) nanowires (with 0.06 < x < 0.15) within the branched cylindrical nanopores of polycarbonate membranes. The influence of temperature and magnetic field on the electrical and thermoelectric properties was studied by considering electric and thermal currents flowing in the plane of the films. We show that short annealing times with temperature up to 250 °C of the nanowire-based nanocomposite lead to a large increase in the thermoelectric power, reaching values up to -80 μV K-1 at room temperature, which are comparable to those of bulk Bi-Sb alloys. In addition, we report Hall effect measurements on crossed nanowires, made possible for the first time by the remarkable electrical connectivity of the nanowire network. These measurements, combined with variations in temperature and under the magnetic field of the electrical resistance, indicate that the interconnected networks of Bi1-x Sb x nanowires after thermal annealing behave like n-type, narrow band gap semiconductors. Overall, the electrical and thermoelectric properties near the ambient temperature of the heat-treated Bi1-x Sb x nanowire networks are similar to those of bulk polycrystalline Bi-Sb alloys, which are well-known thermoelectric materials exhibiting optimal performance near and below room temperature.

Synthesis and characterization of dextran palmitate for extrusion 3D printing.

The fabrication of objects with complex shape and geometry has been greatly facilitated with the advancements in additive manufacturing. While synthetic polymers like ABS and PLA have found widespread use in extrusion 3D printing, other biobased thermoplastics that are both biodegradable and biocompatible could offer strategic advantages over traditional synthetic materials. In this work dextran of low (20kDa) and medium (40kDa) molecular weight (MW) was modified with palmitic acid to obtain meltable polymers for extrusion 3D printing/fused deposition modeling additive manufacturing. The dextran derivatives were characterized by FTIR, NMR and elemental analysis. The degree of substitution (DS) varied between 0.94±0.31 and 1.36±0.16. Our findings reveal a melting temperature near 40°C, independent of the DS or MW. Extrudability varied depending on the DS. Cubes with dimensions of 15×15×10mm3 were printed from the dextran palmitate. The swelling degree of the cubes in water remained limited, up to 0.17±0.02g/g. This work demonstrates the great potential of dextran palmitate as biobased and biodegradable polymers for 3D printing for future applications in human health.

Attribution of alterations in coastal processes in the southern and eastern Baltic Sea to climate change-driven modifications of coastal drivers

The main drivers of coastal processes, such as wave activity, variations in the water level, ice cover, and wind drift, may act differently in different segments of marginal seas with complex shapes. We analyse how the relative role of these drivers on the evolution of sedimentary shores changes along the southern and eastern Baltic Sea. While changes in the average water level have a strong impact along the southern shores of the Baltic Sea, rapid increases in the water level extremes affect most of the eastern subbasins of the Gulf of Finland and Gulf of Riga. The presence of a two-peak structure of predominant winds creates a fragile balance in alongshore sediment transport on the northeastern part of the Baltic proper and the Gulf of Riga. This balance could be changed by a rotation of predominant wave directions by a few degrees. Severe waves usually occur on the southern shores of the sea during water levels that are close to the long-term mean, while synchronisation of strong waves and high-water level is common on the eastern shore. The presence of sea ice is uncommon and insignificantly damps coastal processes in the southern part of the sea but the frequent presence of ice cover and freezing temperatures during the windy season stabilise the beaches of the north-eastern shores. Climate driven changes in ice cover duration may lead to erosion of many beaches in this part of the sea. The core message is that the impact of a single manifestation of climate change vary greatly in different parts of the Baltic Sea and the reaction of coastal processes to this impact is substantially site-specific.

Negative Staining Electron Microscopy of a Highly Flexible Sec1/Munc18 Protein Complex Stabilized by Glutaraldehyde Crosslinking.

Negative staining electron microscopy is one of the easiest ways to determine the shape and dimensions of multimeric protein complexes over 100kDa molecular weight. This method requires small volumes (< 10μL) of dilute protein (0.01-0.1mg/mL). Here we describe a method for quickly crosslinking a protein sample and preparing negative stained grids, and we also describe how to label a biotinylated protein subunit with avidin to determine its position within a complex using negative staining EM. This method should be generally applicable for most soluble protein complexes.

Tracking the optimal watershed landscape pattern for driving pollutant transport: Insights from the integration of mechanistic models and data-driven approaches.

Identifying landscape patterns conducive to pollutant transport control is of vitally importance for water quality protection. However, it remains unclear which landscape patterns can weaken the transport capacity of pollutants entering water bodies. To fill this gap, this study proposes a new framework. This framework quantifies the contribution of landscape patterns to pollutant migration; it also identifies the optimal landscape patterns capable of reducing pollutants entering rivers. Furthermore, it analyzes the impact pathways of landscape patterns on pollutant migration by integrating mechanism models, machine learning techniques, and structural equation models (SEM). The results showed that on cultivated land and urban land, when the slope reached 35%, the terrestrial transport intensity of NH₃-N peaked at 34kg/km2 and 45kg/km2 respectively, with more pollutants entering the receiving water bodies. Meanwhile, in the forest with a DEM of 900m, the terrestrial transport intensity of NH₃-N was the highest (50kg/km2). The complexity of the landscape boundary shape in areas dominated by cultivated land and forest was verified to have a significant impact on the terrestrial migration intensity of NH₃-N, with a contribution rate of over 65%. From the comparison results of multiple land use combinations, it can be seen that the combination of woodland and grassland indirectly weakens the transport capacity of pollutants entering water bodies by directly influencing the connectivity among landscape units. In particular, when the proportion of woodland and grassland reaches 75%, it has a positive effect on improving river pollution and is the optimal landscape combination pattern for reducing the pollution load of the river. The outcomes can be used to develop more efficacious optimization and regulation tactics for landscape patterns and offer a decision - making foundation for the control of pollutant transport in large basins.

Non-coding RNA Networks in Infection.

In the face of global health challenges posed by infectious diseases and the emergence of drug-resistant pathogens, the exploration of cellular non-coding RNA (ncRNA) networks has unveiled new dimensions in infection research. Particularly microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have emerged as instrumental players in ensuring a balance between protection against hyper-inflammatory conditions and the effective elimination of pathogens. Specifically, ncRNAs, such as the miRNA miR-155 or the lncRNAs MaIL1 (macrophage interferon-regulatory lncRNA 1), and LUCAT1 (lung cancer-associated transcript 1) have been recurrently linked to infectious and inflammatory diseases. Together with other ncRNAs, discussed in this chapter, they form a complex regulatory network shaping the host response to pathogens. Additionally, some pathogens exploit these ncRNAs to establish and sustain infections, underscoring their dual roles in host protection and colonization. Despite the substantial progress made, the vast majority of ncRNA loci remains unexplored, with ongoing research likely to reveal novel ncRNA categories and expand our understanding of their roles in infections. This chapter consolidates current insights into ncRNA-mediated regulatory networks, highlighting their contributions to severe diseases and their potential as targets and biomarkers for innovative therapeutic strategies.

New modeling framework for describing the effects of landscape pattern changes on nutrient pollution transport.

Landscape pattern plays a crucial role in regulating hydrological and pollutant migration processes. However, there is a lack of quantitative tools to describe the nutrient pollution transport process under the influence of different landscape patterns. To fill this gap, this study presents a new modeling framework, namely the Landscape Pattern-Source Flow Sink model (LP-SFS). The model consists of three modules: nutrient pollution emission, land transport, and river transport. Each module is implemented using a separate calculation program. It characterizes the transport path of pollutants on landscape units conceptually and operationally and focuses on quantifying the blocking effect of grid-scale landscape units on nutrients. The framework takes the Luanhe River Basin in the North China Plain as an exemplary case. The simulation results of the new framework indicated that in regions predominantly occupied by forest and urban (FU), the intensity of terrestrial pollutant migration attained the highest level. In the slope zone ranging from 25 to 35°, owing to the relatively strong responsiveness of water flow and gravity to the slope, soil erosion is inclined to occur, thereby causing the intensity of terrestrial nutrient pollution transport in this slope zone to reach the maximum value of 4.55kg/km2. Furthermore, in the elevation zone <700m, urban and cultivated land was concentratedly distributed, which leads to more pollutants entering surface water bodies and increases the intensity of terrestrial migration of pollutants. The complex boundary shape of forestland and grassland in the watershed weakened the transport capacity of nutrients, resulting in pollutants remaining in the soil and being difficult to be transported to surface water bodies. This new method is applicable to large-scale watersheds with strong spatial heterogeneity and severe landscape fragmentation and can provide technical support for nutrient pollution control and optimization of land resource allocation in large-scale watersheds.

FRACTAL GEOMETRIES DESIGN FOR THREE-DIMENSIONAL PRINTING: ANALYSIS OF PERFORMANCE AND VIBRATION RESPONSE FOR PRODUCT DESIGN

Additive Manufacturing (AM) has enhanced the development of complex designs and geometries that are difficult or impossible to achieve with conventional manufacturing methods. Therefore, research focused on the analysis of the functional properties related to the behavior conferred by the filling material and the internal structure became especially relevant. In this sense, fractal geometry is one of the most established theories to characterize complex geometries based on repetitive self-similar patterns that allow complex shapes to be described and relate them to mechanical, rheological and morphological properties. The design of fractal microstructures promises the development of advanced materials with improved mechanical properties and multiple functions related to vibration absorption, weight reduction, among others. In relation to the compatibility of elastic materials with three-dimensional printing processes, the study of fractal geometries in terms of geometric design and its correlation with improved mechanical behavior is established as an objective. Several TPU-based fractal samples were designed and tested using compression processes to characterize the behavior of elastic fractal geometries as a function of fractal dimension. Specifically, Sierpinski pyramids-based specimens were designed to explore the influence of fractal dimensions and iterations on the elasticity of the samples. Additionally, a case study was carried out to by using Finite Element Method (FEA) in order to analyze the stress distribution and vibrational behavior based on fractal order. The results demonstrated a notable variation in compression behavior and vibrational behavior depending on the fractal order, highlighting the potential of fractal geometries to enhance mechanical performance in AM applications. Keywords: Fractal Geometries, Product Design, Three-Dimensional Printing, Performance, Thermoplastic Polyurethane.

Photocrosslinking of hyaluronic acid-based hydrogels through biotissue barriers.

Photocrosslinkable hydrogels based on hyaluronic acid are promising biomaterials high in demand in tissue engineering. Typically, hydrogels are photocured under the action of UV or blue light strongly absorbed by biotissues, which limits prototyping under living organism conditions. To overcome this limitation, we propose the derivatives of well-known photosensitizers, namely chlorin p6, chlorin e6 and phthalocyanine, as those for radical polymerization in the transparency window of biotissues. Taking into account the efficiency of radical generation and dark and light cell toxicity, we evaluated water miscible pyridine phthalocyanine as a promising initiator for the intravital hydrogel photoprinting of hyaluronic acid glycidyl methacrylate (HAGM) under irradiation near 670 nm. Coinitiators (dithiothreitol or 2-mercaptoethanol) reduce the irradiation dose required for HAGM crosslinking from ∼405 J cm-2 to 80 J cm-2. Patterning by direct laser writing using a scanning 675 nm laser beam was performed to demonstrate the formation of complex shape structures. Young's moduli typical of soft tissue (∼270-460 kPa) were achieved for crosslinked hydrogels. The viability of human keratinocytes HaCaT cells within the photocrosslinking process was shown. To demonstrate scaffolding across the biotissue barrier, the subcutaneously injected photocomposition was crosslinked in BALB/c mice. The safety of the irradiation dose of 660-675 nm light (100 mW cm-2, 15 min) and the non-toxicity of the hydrogel components were confirmed by histomorphologic analysis. The intravitally photocrosslinked scaffolds maintained their shape and size for at least one month, accompanied by slow biodegradation. We conclude that the proposed technology provides a lucrative opportunity for minimally invasive scaffold formation through biotissue barriers.

Building Detection and Outlining in Multi-Modal Remote Sensor Data: A Stratified Approach

Exploitation of aerial sensor data for vectorized representation of urban structures is of great importance for urban modeling. Even if elevation data is available, extracting complex and irregular building shapes would become challenging. In this paper, we present a stratified approach for the extraction of building outlines consisting of two steps: Classification and vectorization. For classification, we use training data transfer to evaluate a dataset with no or little labeled data. Since the available reference data is also flawed, we use a self-developed interactive tool to adjust and improve the building shape before contrasting it with the classification results. Initial building polygons are slightly generalized and refined considering building shape characteristics. Hereby, Least-Squares adjustment is implemented to solve the best-fit problem of building edges with the input data by applying the Gauss-Helmert and Gauss-Markov Models. On the raster level, the resulting polygons achieved an accuracy of over 99% in the Potsdam dataset and almost 98% in the Munich dataset while on the vector level, the median building-wise deviations lie in sub-meter range. Although there were few mis-detections and, sometimes, considerable peak deviations for the Hausdorff distance, the compelling qualitative results attest to the robustness and validity of the proposed procedure.