Textured Layer Research Articles

Improved transfer learning using textural features conflation and dynamically fine-tuned layers.

Transfer learning involves using previously learnt knowledge of a model task in addressing another task. However, this process works well when the tasks are closely related. It is, therefore, important to select data points that are closely relevant to the previous task and fine-tune the suitable pre-trained model's layers for effective transfer. This work utilises the least divergent textural features of the target datasets and pre-trained model's layers, minimising the lost knowledge during the transfer learning process. This study extends previous works on selecting data points with good textural features and dynamically selected layers using divergence measures by combining them into one model pipeline. Five pre-trained models are used: ResNet50, DenseNet169, InceptionV3, VGG16 and MobileNetV2 on nine datasets: CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Stanford Dogs, Caltech 256, ISIC 2016, ChestX-ray8 and MIT Indoor Scenes. Experimental results show that data points with lower textural feature divergence and layers with more positive weights give better accuracy than other data points and layers. The data points with lower divergence give an average improvement of 3.54% to 6.75%, while the layers improve by 2.42% to 13.04% for the CIFAR-100 dataset. Combining the two methods gives an extra accuracy improvement of 1.56%. This combined approach shows that data points with lower divergence from the source dataset samples can lead to a better adaptation for the target task. The results also demonstrate that selecting layers with more positive weights reduces instances of trial and error in selecting fine-tuning layers for pre-trained models.

Robust SrTiO3 Passivation of Silicon Photocathode by Reduced Graphene Oxide for Solar Water Splitting.

Development of a robust photocathode using low-cost and high-performing materials, e.g., p-Si, to produce clean fuel hydrogen has remained challenging since the semiconductor substrate is easily susceptible to (photo)corrosion under photoelectrochemical (PEC) operational conditions. A protective layer over the substrate to simultaneously provide corrosion resistance and maintain efficient charge transfer across the device is therefore needed. To this end, in the present work, we utilized pulsed laser deposition (PLD) to prepare a high-quality SrTiO3 (STO) layer to passivate the p-Si substrate using a buffer layer of reduced graphene oxide (rGO). Specifically, a very thin (3.9 nm ∼10 unit cells) STO layer epitaxially overgrown on rGO-buffered Si showed the highest onset potential (0.326 V vs RHE) in comparison to the counterparts with thicker and/or nonepitaxial STO. The photovoltage, flat-band potential, and electrochemical impedance spectroscopy measurements revealed that the epitaxial photocathode was more beneficial for charge separation, charge transfer, and targeted redox reaction than the nonepitaxial one. The STO/rGO/Si with a smooth and highly epitaxial STO layer outperforming the directly contacted STO/Si with a textured and polycrystalline STO layer showed the importance of having a well-defined passivation layer. In addition, the numerous pinholes formed in the directly contacted STO/Si led to the rapid degradation of the photocathode during the PEC measurements. The stability tests demonstrated the soundness of the epitaxial STO layer in passivating Si against corrosion. This study provided a facile approach for preparing a robust protection layer over a photoelectrode substrate in realizing an efficient and, at the same time, durable PEC device.

Synergetic Effects of Nanoscale ALD-HfO2 Coatings and Bionic Microstructures for Antiadhesive Surgical Electrodes: Improved Cutting Performance, Antibacterial Property, and Biocompatibility.

The high temperature induced by surgical electrodes is highly susceptible to severe surface adhesion and thermal damage to adjacent tissues, which is a major challenge in improving the quality of electrosurgery. Herein, we reported a coupled electrode with micro/nano hierarchical structures fabricated by depositing nanoscale hafnium oxide (HfO2) coatings on bionic microstructures (BMs) via laser texturing, acid washing, and atomic layer deposition (ALD) techniques. The synergistic effect of HfO2 coatings and BMs greatly enhanced the hemophobicity of the electrode with a blood contact angle of 162.15 ± 3.16°. Furthermore, the coupled surface was proven to have excellent antiadhesive properties to blood when heated above 100 °C, and the underlying mechanism was discussed. Further experiments showed that the coupled electrode had significant advantages in reducing cutting forces, thermal damage, and tissue adhesion mass. Moreover, the antibacterial rates against Escherichia coli and Staphylococcus aureus were 97.2% and 97.9%, respectively. In addition, the noncytotoxicity levels of HfO2 coatings were verified by cell apoptosis and cycle assays, indirectly endowing the coupled electrode with biocompatibility. Overall, the coupled electrode was shown to have broad potential for application in the field of electrosurgery, and this work could provide new insights into antiadhesion properties under high-temperature conditions.

Graph laplacian regularization with sparse coding in secure image restoration and representation for Internet of Things

Image restoration (IR) attempts to recreate the original (ideal) scene from a degraded observation. The goal of image restoration is to avert the process of image deterioration that happens during image acquisition and processing. Blurring and noise are two common types of picture capture degradation. When the blurring function is unknown, the image restoration issue is referred to as blind image restoration. Existing methods of image restoration do not provide efficient results due to time complexity, computational complexity, large-scale scaling factor and limited input. Therefore, in order to overcome those problems, an Improved Graph Laplacian Regularization with Sparse Coding by integrating Internet of Things (IoT) is developed in this research. The process of removing de-noised portions of image to texture layer and cartoon layer is carried out by Morphological Component Analysis (MCA). An improved Graph Laplacian regularized method and Simultaneous Sparse Coding with Gaussian Scale Mixture (SSC-GSM) methods is performed on texture layer, cartoon layer and restored image. A Levin’s dataset and a real-time dataset such as industrial manufacturing products are analyzed in this research. The proposed Graph Laplacian algorithm and sparse coding model are better efficient in identifying sharper texture information and getting a restored image. The proposed method proves that it has better performance in terms of Peak Signal-to-Noise Ratio (PSNR) of 35.82 and Structural Similarity Index Measure (SSIM) of 0.94 than the existing methods.

Nighttime Image Stitching Method Based on Image Decomposition Enhancement.

Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

Dendritic ridges in Antoniadi basin, Mars: Fluvial or volcanic landforms?

Antoniadi basin displays dark-toned dendritic ridges previously interpreted as inverted fluvial channels. Detailed observations of these dark-toned ridges as well as the geological units in the central region of Antoniadi basin are provided emphasizing images from the Colour and Stereo Surface Imaging System (CaSSIS), the High Resolution Stereo Camera (HRSC) and the High Resolution Imaging Science Experiment (HiRISE) instruments. Results show that the dark-toned ridges are part of the most recent geological unit as they overlie, and thus postdate all plains of the central Antoniadi basin, which is Early Amazonian based on its crater size-frequency distribution. Our observations of the dark-toned ridges are not consistent with inverted fluvial channels: they do not widen in the expected downstream direction, they display a rubbly texture and lack layering at high resolution, and have lobes with local levees in place of channel heads. In addition, the branched ridges are more mafic in composition and display a relatively higher thermal inertia than their surroundings. This suite of characteristics is better explained by volcanic flows developed as distributary channels rather than fluvial tributary channels. The occurrence of dikes in the east and west of the studied region supports that these flows were formed by lava, perhaps a'a like flows as suggested by the rubbly texture, but with an unusually high degree of digitation. Alternatively, such a geometry could be explained by the emplacement of the lava along pre-existing fluvial valleys, but neither the underlying topography, nor two nearby older craters, exhibit signs of fluvial erosion.

Formation of Peripheral Coarse Grain in Thin-Walled Al–Mg–Si Extrusion Profiles

Investigations on microstructure and texture development during processing of Al–Mg–Si sheet and extrusion profiles focused on describing and modelling the changes in the bulk. However, understanding the evolution of microstructure in the sub-surface region is highly relevant, as this governs material performance under corrosion and bending deformation, which are crucial applications in mobility and transportation. The aim of this study was to correlate the effect of alloy composition and extrusion parameters to the formation of texture and peripheral coarse grain (PCG) layer. For this, five alloys of the AA6xxx class with varying content of dispersoid forming elements were extruded at increasing extrusion speeds into thin-walled hollow profiles. The microstructure at a plane section was investigated by means of EBSD and characterized in terms of distinct texture component distribution. It was shown that with increasing grain boundary mobility, through adapting dispersoid content and extrusion speed, the PCG layer grows into a fine-grained bulk. This growth of the PCG layer is only counter-acted upon, when the grain boundary mobility in the bulk is high enough, so that extensive growth of Cube oriented grains occurs. The PCG layer shows distinct orientations related to langle 101rangle ||rm{TD}, so that a possible mechanism for the orientation selection during grain growth is proposed.

A CSR-based visible and infrared image fusion method in low illumination conditions for sense and avoid

AbstractMachine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.

High-temperature fracture behaviour of layered alumina ceramics with textured microstructure

Mimicking the damage tolerance of biological materials such as nacre has been realised in textured layered alumina ceramics, showing improved reliability as well as fracture resistance at room temperature. In this work, the fracture behaviour of alumina ceramics with textured microstructure and laminates with embedded textured layers are investigated under uniaxial bending tests at elevated temperatures (up to 1200°C). At temperatures higher than 800°C monolithic textured alumina favours crack deflection along the basal grain boundaries, corresponding to the transition from brittle to more ductile behaviour. In the case of laminates, the loss of compressive residual stresses is counterbalanced by the textured microstructure, effective up to 1200°C. This study demonstrates the potential of tailoring microstructure and architecture in ceramics to enhance damage tolerance within a wide range of temperatures.

Fabrication of high performance Pr4Ni3O10 cathode based upon magnetic orientation behavior for intermediate-temperature solid oxide fuel cell

A textured layer of a highly conductive mixed ion–electron conductor, Pr4Ni3O10, with a layer structure is applicable as a cathode to intermediate-temperature solid oxide fuel cells because of the large anisotropy in its conduction properties. In the present work, an ab-plane preferentially oriented Pr4Ni3O10 cathode was successfully fabricated on a Gd2O3-doped CeO2 electrolyte using a magnetic orientation process in a 0.9 T magnetic field. The orientation of the Pr4Ni3O10 cathode effectively reduced not only the ohmic loss but also the polarization loss.

Surface Textured Double Layer Triboelectric Nanogenerator for Autonomous and Ultra‐Sensitive Biomedical Sensing

AbstractTriboelectric nanogenerators (TENGs) have demonstrated great promise especially for the realization of self‐powered biomedical sensors. Nevertheless, developing TENG sensors able to detect the broad range of biomechanical movements experienced on the human body is still a challenge. Herein, a unique ridge‐structured device sensitive to wide range of forces is reported (i.e., low‐forced pulse monitoring to high‐forced gait monitoring). The device is composed of thermoplastic polyurethane layer sandwiched between two textured silicon elastomeric layers. Compared to non‐textured surface configurations, the proposed ridged‐structure provides an increased frictional contact area between the triboelectric materials, while also acting as a spacer between the triboelectric materials. The influence of ridge dimensions on the output performance is investigated by mechanical simulations and electromechanical experimental tests. The optimized device shows a maximum peak output power and current densities of 490 mW m−2 and 1750 µA m−2, respectively at 30 N and 7 Hz of compressive forces. The proposed device exhibits stable electrical output for 10000 cycles. As a proof of concept, the proposed device is used as wearable sensors for monitoring pulse rate, breath patterns, and gait movements. The study suggests the possibility of utilization of novel‐structured sandwich‐type elastomer ridge‐based TENG in different aspects of biomedical sensing and smart wearable application.

Insights in α-Al2O3 degradation in multilayer CVD coated carbide tools when turning IN718

The degradation behaviour of textured CVD α-alumina has received significant attention within academic literature with regard to steel turning. In contrast, the research performed on Ni-based superalloys is yet to be brought up to the same level of understanding. Hence, the objective of this report will be to offer insights with regard to the degradation mechanisms of the textured alumina layer of CVD Ti(C,N)/α- Al2O3 coated carbides when turning a Ni-based superalloy, and to explore the role of different wear mechanisms that contribute to cutting tool failure. Adhesive wear was observed to be the primary wear mechanism responsible for coating failure, which can result in sudden mechanically driven grain pull-out and delamination events. Grain-pull out was observed across the entire contact zone; delamination was observed to occur most frequently around the trailing edge and depth-of-cut notches, and towards the end of the contact zone as the chip separates from the tool. Evidence of characteristic plastic deformation induced ridges in the alumina coating surface were also revealed within the sliding zone, leading to gradual wear of the alumina layer. These findings demonstrate that there are both similarities and differences in alumina degradation behaviour when compared to previous studies conducted on steel turning.

MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy

Existing image fusion algorithms have difficulty in effectively preserving valuable target features in infrared and visible images, which easily introduces blurry edges and unremarkable notable targets during their fusion process. We propose the MGFuse algorithm as a solution to this problem, which is a novel fusion algorithm that utilizes multiscale decomposition optimization and gradient-weighted local energy. Initially, non-subsampled shearlet transform (NSST) is applied to partition both the infrared and visible images into several high-frequencies and low-frequencies components. Subsequently, the acquired low frequencies continue to be decomposed via proposed optimization function to get base layers and texture layers, which can optimize the quality of image edges and preserve fine-grained details, respectively. In addition, we have formulated an intrinsic attribute-based energy (IAE) fusion scheme to merge the two base layers. The texture layers and high-frequencies are extracted by gradient-weighted local energy (GE) operator based on structure tensor, which are employed to construct the fusion strategy for these parts. At last, the acquired texture and base parts are linearly combined to get the integrated low-frequency layer on which the final image is acquired using inverse NSST. Numerous experimental observations demonstrate that our MGFuse algorithm achieve superior fusion capability than the reference nine advanced algorithms in both qualitative and quantitative assessment, and robustness to noisy images with different noise levels.

Electrolytic Co-Deposition Mechanisms, Texture Layers, and Residual Stresses in Nanocomposite Coatings Processes: A Review

The composite coating has gained wider attention due to its property to protect materials used in energy, bridges, offshore platforms, underground pipelines, and the aviation industry from corrosion and deterioration. In this work, a literature review was conducted about the processes of nanocomposite coating, the mechanisms of electrolytic co-deposition, the texture of layers, and the residual stresses. An important aspect, residual stress, was emphasized, which represents the persistent stress after removing the external force affecting a metal in the plastic region. Because it cannot be measured directly and may be determined by measuring strain and indirect methods, the sources and methods for measuring residual stresses (XRD, SEM, TEM, EDS) were described in the last section to provide a comprehensive overview. Based on the thorough analysis of the published literature, it was concluded that nanoparticles could be electrodeposited with Ni on an Al substrate using a direct current and Ni sulfamate as an electrolytic solution, and Nickel will not reside on the oxide layer covering Al, so chemical changes are needed to prepare the Al surface. In addition, texture changes with the thickness of the coated layer must be investigated.

Musical Time-Space of F. Schubert as a Factor of the Formation of the Executive Concept (Using the C-Moll Sonata D958 as an Example)

The peculiarities of F. Schubert's musical space-time are considered as a preverbal semantic system that determines the typology and content-stylistic qualities of his composer's thinking. It was outlined that the space-time phenomenon of F. Schubert belongs to the boundary type with its characteristic semantics of threshold, meeting and road. Features of the transformation of the romantic two-world with the formation of a space of timelessness on the edge of the worlds, which is manifested in the music of the so-called "Schubert Longitudes". The compositional and semantic features of F. Schubert's cyclical forms, which are related to the radical rethinking of musical time and its awareness as a staged sequence of time intervals, are understood in relation to closed states as opposed to drama at the basis of conflict genesis. It is proved that the principles of contrasting shape formation with the fixation of small time, the ambivalence of the expression, the individualization of textural layers, a certain stadia sequence of the disclosure of the sonata idea, are a special dialogic essence of F. Schubert's thinking. The features of the combination of features of the suite and sonata chronotopes in the Sonata D958, the compositional and semantic affinity of the finale of the sonata and the jig are analyzed. It was found that the baroque genre markers in the structure of the sonata cycle organically fit into the spatio-temporal characteristics of the boundary type. It was determined that the contrasting principle of stage formation, which is based on the fixation of a musical fragment, is a peculiar embodiment of the existential-romantic idea of "traveling through the worlds" and an individual stylistic constant of F. Schubert. The main trends and directions of interpretation of the c-moll sonata are clarified. The main characteristics of the performance concepts of prominent Schubertians - A. Shnabel, P. Badur-Skoda, A. Brendel, S. Richter, V. Kempf, M. Pollini, M. Yudina - are outlined. Emphasis is placed on the fundamental incompleteness of the process of decoding the semantic constants of Schubert's piano style, the semantics of its space-time, and the openness of ways to search for the most authentic concept of the performance of the c-moll sonata in the unity and interaction of the scientific-analytical and performing components of interpretation.

Comparison of Binding Properties of a Laccase-Treated Pea Protein–Sugar Beet Pectin Mixture with Methylcellulose in a Bacon-Type Meat Analogue

A bacon-type meat analogue consists of different structural layers, such as textured protein and a fat mimetic. To obtain a coherent and appealing product, a suitable binder must glue those elements together. A mixture based on pea protein and sugar beet pectin (r = 2:1, 25% w/w solids, pH 6) with and without laccase addition and a methylcellulose hydrogel (6% w/w) serving as benchmark were applied as binder between textured protein and a fat mimetic. A tensile strength test, during which the layers were torn apart, was performed to measure the binding ability. The pea protein-sugar beet pectin mixture without laccase was viscoelastic and had medium and low binding strength at 25 °C (F ≤ 3.5 N) and 70 °C (F ≈ 1.0 N), respectively. The addition of laccase solidified the mixture and increased binding strength at 25 °C (F ≥ 4.0 N) and 70 °C (F ≈ 2.0 N), due to covalent bonds within the binder and between the binder and the textured protein or the fat mimetic layers. Generally, the binding strength was higher when two textured protein layers were glued together. The binding properties of methylcellulose hydrogel was low (F ≤ 2.0 N), except when two fat mimetic layers were bound due to hydrophobic interactions becoming dominant. The investigated mixed pectin-pea protein system is able serve as a clean-label binder in bacon-type meat analogues, and the application in other products seems promising.

Samuel Barber’s chamber and vocal lyrics in the mirror of performance interpretation (on the example of «Three songs», op. 10 to the words by J. Joyce)

Samuel Barber’s chamber and vocal lyrics in the mirror of performance interpretation (on the example of «Three songs», op. 10 to the words by J. Joyce)

Ultrasonic surface post-processing of hot isostatic pressed and heat treated superalloy parts manufactured by laser powder bed fusion

• IN 718 alloy parts were fabricated by laser powder bed fusion (L-PBF). • Hot isostatic pressing (HIP) was applied to enhance material density. • Homogenization and aging were applied to provide the required structure and grain size. • Surface improvement was implemented by ultrasonic impact treatment (UIT). • Surface texture, roughness, waviness, structure, and residual stress were studied. Inconel 718 alloy parts were fabricated by the laser powder bed fusion (L-PBF) technique using a multidirectional stripe scanning strategy. Subsequently, the L-PBF-built parts were hot isostatic pressed (HIP), homogenized, and aged. Ultrasonic surface mechanical treatment was applied to modify the surface texture and microstructural layer of the L-PBF-built and heat-treated Inconel 718 alloy parts. Ultrasonic impact treatment (UIT) was implemented using a forcedly rotated multi-pin impact head. The surface texture, roughness, waviness, porosity, microstructure, phase composition, hardness, and residual stresses of the L-PBF-built and thermo-mechanical post-processed specimens were studied and compared. The results revealed that the UIT post-processing leads to work hardening, induces compressive residual stresses, and ∼20% increase in the surface microhardness in the near-surface layer of the heat-treated L-PBF-built 718 alloy parts. The surface roughness and waviness were significantly reduced after UIT, providing a flattened surface microrelief formation.

Semi‐supervised learning dehazing algorithm based on the OSV model

Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real-world images. To address the aforementioned problems, a semi-supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi-supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real-world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real-world hazy images.

Forest landscape restoration: Spectral behavior and diversity of tropical tree cover classes

Forest landscape restoration (FLR) commitments have been established in the past years to restore over 200 million hectares, as part of the global Bonn Challenge goal, mostly through the implementation of several different restorative practices in degraded lands, ranging from commercial tree monocultures to restoration plantings. The potential of such contrasting restorative practices to support biodiversity conservation and ecosystem services provision vary over space and time, making the monitoring of FLR programs an emerging challenge. Remote sensing techniques, together with innovative technologies for data acquisition, treatment, and analysis have proven to be strategic for planning and monitoring FLR, yet there are still important unresolved questions. Here, we evaluated the potential of multispectral orbital images of the high spatial (5 m) and spectral (12 bands) resolution VENμS microsatellite, joint project of the Israeli Space Agency and CNES, to classify the spectral behavior and diversity of six tree cover classes (savanna woodlands, old- and second-growth semi-deciduous forests, young restoration plantings, and eucalyptus and pine tree monocultures) commonly found in FLR programs in tropical regions. We assessed how these tree cover classes located in a study landscape in southeastern Brazil differ according to their spectral response (winter and summer bands, and vegetation indices), canopy variability (textural features), seasonal behavior (delta layers - difference between summer and winter vegetation indexes), and spectral diversity, and used these attributes as variables to the model. We used the Random Forest algorithm to generate the models and evaluate how the tree cover classes differ in the classification and how the variables supported the model. We achieved high values of global accuracy (91.9%) and “F1 score” (above 0.8) for all tree cover classes, in which second-growth forest presented the lowest accuracy. The textural layers, delta layers, and the spectral diversity layers were the most important attributes to discriminate among tree cover classes. We demonstrate here the potential of using VENμS or similar sensor images together with different image processing and machine learning algorithms to monitor FLR programs, allowing further remote sensing approaches and in-deep field assessments to advance evaluation of FLR benefits for nature and people. We demonstrated how the fusion of all these types of data and innovative approaches to data processing, can result in novel ways to assess restoration performance and open new avenues to upscale monitoring, bridging the gap between FLR expectations and achieved goals.