Distinct Layers Research Articles

New Layered Boride NiPtB2-x (x = 0.5) with a Ternary Derivative Structure of MoB.

A novel ternary boride, NiPtB2-x (x = 0.5), was obtained by argon-arc melting of the elements followed by annealing at 750 °C. It exhibits a new structure type with the space group Imma (a = 2.9835(3) Å, b = 3.0470(3) Å, c = 15.3843(3) Å; Z = 4; single-crystal X-ray data) and displays distinct layers of condensed [BNi6] and [BPt6] (and [Pt6]) trigonal prisms with mutually perpendicular axes. Atoms of Pt and Ni from adjacent layers interlink to form empty tetragonal pyramids and tetrahedra. Two boron atom positions form two orthogonal zigzag chains; however, one boron position exhibits a partial boron occupancy. Considering B-deficiency, the platinum boride substructure in NiPtB2-x quantitatively corresponds to a trigonal prismatic slab in the Pt2B structure, while the nickel boride partial structure is consistent with the CrB-type NiB binary. Cell parameters and atomic coordinates of NiPtB2-x and Pt2B were refined in the scope of generalized gradient approximation. Chemical bonding analysis by means of the electron localizability approach, supported by Bader charge analysis, reveals a strong electron contribution of Ni atoms for stabilization of the boron zigzag chains, wherein boron atoms are bonded covalently. Bonding within the platinum boride partial structures in the studied compounds varies depending on the atom coordination of boron: from covalent in both the NiPtB2-x structure and trigonal prismatic slabs in Pt2B to mixed metallic with covalent contributions in [BPt6] octahedra in Pt2B. Electrical resistivity measurements characterize NiPtB2-x as a metal with no phase transitions in the temperature range from 2 to 300 K, in concord with electronic band structure calculations and specific heat measurements. The compound is characterized by a positive Hall coefficient at 20 K. This work unveils a new elemental space on realizing novel layered boride structural arrangements and provides a reference for future experiments.

Microstructural Evolution and Mechanical Properties of Cu–Ag Alloy via Different Severe Plastic Deformation Processes

The field of artificial intelligence and integrated circuits is experiencing rapid development, particularly in the area of highly integrated and miniaturized components, in which Cu-Ag alloys, as a typical lead frame material, play a crucial role. However, current research is primarily focused on low Ag content alloys, and there are few studies on the regulation of the microstructure and mechanical properties of high Ag content Cu-Ag alloys. This limitation hindered the development and utilization of the high Ag content Cu-Ag alloys. In this study, the microstructure and mechanical properties of Cu-28Ag (wt. %) alloy after room temperature and cryogenic rolling were investigated. It was demonstrated that the cryogenic rolling yielded better surface quality, an enhanced dendrite refinement effect, and a more distinct layer structure compared to room temperature rolling. The conductivity of the alloy decreased after cryogenic rolling due to increased electron scattering within the Cu matrix. The tensile strength improved, but the elongation decreased. Specifically, at a deformation amount of 95%, the alloy exhibited an ultimate tensile strength, yield strength, and elongation of 640 MPa, 631 MPa, and 1.9%, respectively. This strengthening was mainly attributed to the refinement of grains, the presence of dislocations, and precipitation. Furthermore, the samples subjected to liquid nitrogen rolling at a deformation amount of 95% exhibited improved homogeneous deformation capacity, which was attributed to grain size refinement, uniform distribution of high-density dislocations, deformation structure, and heterogeneity-induced deformation enhancement.

Human Skin Burn Intensity Resulting From Various Incidents Utilizing Bioheat Transfer Model: A Comparative Analogy

ABSTRACTUnderstanding how human skin reacts to heat is vital for effective burn prevention and treatment. This study uses a bioheat transfer model to develop a comparative analogy to differentiate burn intensity from hot dishes, hot fluids, radiation, and flash fires, aiming to differentiate the burn profiles of each incident type. The finite element method is employed to solve the time‐dependent Pennes' bioheat transport equation concerning the three distinct layers of human skin. The Arrhenius equation is implemented to quantify the damage fraction associated with thermal burns. The burn intensities for the degrees of burns (first, second, and third) are evaluated by applying Henriques burn integral, considering various burning conditions and the corresponding exposure times required for each burn. The numerical results are displayed in various formats, including volume temperature plots and line graphs of damage fraction. The findings reveal that first‐degree burns occur the fastest, followed by second‐degree burns, with third‐degree burns taking the longest to develop. Notably, burns from direct contact with a hot dish are more severe than those from freely flowing heated fluids. The results highlight that exposure time, temperature, and thermal conductivity are key factors in burn depth and tissue damage, offering valuable insights for burn treatment and risk management. The outcomes will effectively predict burn consequences, making it useful in burn injury treatment and safety engineering.

P0040 Measurement of collagen concentration throughout the intestinal layers using Deep Learning: implications in Inflammatory Bowel Disease (IBD)

Abstract Background IBD, particularly Crohn’s disease (CD), is marked by recurrent episodes of inflammation, leading to fibrosis and collagen deposition throughout the intestinal layers.1,2 In this study, we aim to quantify collagen levels across the distinct layers of the gut and examine their correlation with clinical characteristics and disease outcomes. Methods A total of 190 IBD patients were enrolled, including 98 with Ulcerative Colitis (UC) and 92 with CD (60% male; median age: 42 years, IQR: 29–48; follow-up: 110 months, IQR: 39-181), plus 73 controls. Biopsies were collected, stained with Sirius Red, and digitized. A total of 612 biopsies were included. Intestinal layers in biopsy images from the derivation group were manually annotated to train a mask Region-based Convolution Neural Network (mask R-CNN), which uses ResNet-101 backbone. After the detection of intestinal layers by the trained model, K-means was employed at the pixel level, to segment and quantify Collagen Proportionate Area (CPA) regions in each layer. Follow-up biopsies (>6 months) were available for 85 patients. Multivariate Cox regression was used to assess the risk of adverse outcomes, including variables with p<0.05 from the univariate analysis. Results Submucosal collagen was significantly correlated with mucosal CPA (r=0.36; p<0.001) and CPA in the muscular layer (r=0.54; p<0.001), as well as with C-reactive protein (r=0.38; p=0.001) and erythrocyte sedimentation rate (r=0.53; p=0.005). In CD, submucosal CPA showed a positive association with disease activity as measured by the Crohn’s Disease Activity Index (CDAI) (r=0.31; p=0.005), endoscopic activity assessed by the Simple Endoscopic Score for CD (SES-CD) (r=0.32; p=0.004), and Nancy histologic activity score (r=0.25; p=0.046). In UC, the Full Mayo score showed a trend with submucosal CPA, but it did not reach significance (r=0.13; p=0.09). Patients requiring treatment with biologics had higher baseline submucosal CPA levels (mean difference: 12; 95%CI: -0.6 to 24.6; p=0.06). Patients treated with biologics at follow-up biopsies (19/85) experienced a significantly greater reduction in submucosal CPA (-21.1; 95% CI: -38.3 to -3.9) compared to those not receiving biologics (-1.7; 95% CI: -9.4 to 6.1; p = 0.039). Baseline submucosal CPA was independently associated with an increased risk of surgery in CD patients during follow-up (aHR=1.03; 95%CI: 1.002–1.06; p=0.048), along with B2/B3 disease behavior (p=0.003) and elevated CDAI (p=0.004) (Table I). Conclusion Submucosal collagen levels correlate with clinical, endoscopic, and histologic activity in CD, potentially serving as a prognostic marker for long-term outcomes. Emerging therapies may help reduce collagen accumulation and its associated complications.

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis.

Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20-1500 rpm with 0-100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques.

DMSO-Assisted Control Enables Highly Efficient 2D/3D Hybrid Perovskite Solar Cells.

Building 2D/3D heterojunction is a promising approach to passivate surface defects and improve the stability of perovskite solar cells (PSCs). Developing effective methods to build high-quality 2D/3D heterojunction is in demand. The formation of 2D/3D heterojunction involves both the diffusion of 2D spacer molecules and phase transition from 3D to 2D structure. Herein, a DMSO-assisted method is demonstrated to simultaneously regulate both the 2D/3D formation kinetics, yielding high-quality 2D top layer with continuous coverage, which enhances the photovoltaic performance of PSCs. It is found that the presence of DMSO significantly facilitates the diffusion of 2D spacer cation. Meanwhile the residual DMSO may partially dissolve the surface perovskite, facilitating the reaction between 2D molecules with 3D perovskite and ultimately leading to sequential secondary crystal growth and the appearance of a distinct 2D layer. The formation of high-quality 2D layer effectively passivates the surface defects thus suppresses the interfacial charge recombination. As a result, the champion PSC based on optimal 2D/3D heterojunction exhibits a high fill factor of 85% and a power conversion efficiency of 24%. The work offers a novel perspective for the construction of 2D/3D perovskite heterojunctions.

ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements

Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with air-mass hysteresis. Here, we describe ARMTRAJ (https://doi.org/10.5439/2309851, Silber, 2024a; https://doi.org/10.5439/2309849, Silber, 2024b; https://doi.org/10.5439/2309850, Silber, 2024c; https://doi.org/10.5439/2309848, Silber, 2024d), a set of multipurpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25°) and are initialized using ARM datasets. The trajectory datasets include information about air-mass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported air-mass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM's Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ will soon become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.

In situ swelling of low-friction, high load-bearing self-bending bilayer hydrogels inspired by articular cartilage.

The articular cartilage is characterized by its gradient hierarchical structure, which exhibits excellent lubrication and robust load-bearing properties. However, its inherent difficulty in self-repair after damage presents numerous formidable challenges for cartilage repair. Inspired by the unique structure of articular cartilage, a biomimetic bilayer hydrogel composed of PAM (polyacrylamide) and PAM/SA (sodium alginate) is prepared using a two-step in-situ swelling method. The bilayer hydrogel demonstrates exceptional structural stability due to the interlayer in-situ chemical cross-linking. Compared to monolayer hydrogels, the PAM-PAM/SA bilayer hydrogel demonstrates superior mechanical attributes, exhibiting a compressive strength of 1 MPa and a compressive modulus of 0.22 MPa. Furthermore, exploration of the tribological performance of the PAM-PAM/SA bilayer hydrogel have revealed its low-friction performance under high loads, with a coefficient of friction as low as 0.032. Finally, leveraging the differential swelling properties between the distinct layers of the PAM-PAM/SA bilayer hydrogel, a self-bending biomimetic cartilage capable of conforming to complex joint surfaces is fabricated. This highly lubricating, mechanically robust, and conformal biomimetic cartilage provides an effective means for addressing cartilage defects and joint diseases.

Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework

This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field.

Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum

Recent developments in conductive and dielectric multi-material additive manufacturing have generated significant interest for their potential to enhance the performance of electronic devices. Lights-out digital manufacturing, a sophisticated multi-material printing technique, facilitates the seamless sintering of silver nanoparticles and acrylate inks, enabling the creation of functional electronic devices. In this study, the LDM process is utilized to prototype intricate 3D metasurface designs. A comprehensive analysis of the properties of the printed materials confirms the practicality of this multi-material printing approach. The research examines various element distributions within multi-metal layer structures, including carbon, phosphorus, oxygen, sulfur, and fluorine, to investigate the interfaces between conductive and dielectric structures. In addition, the conductivities of silver nanoparticle inks at different curing temperatures are studied to identify optimal printing conditions. Both the conductive and dielectric inks exhibit precisely controlled particle sizes and exceptional stability, which are critical for accurate additive manufacturing. Using this sophisticated printing solution, the paper presents a broadband 3D metasurface engineered to independently manipulate two distinct orbital angular momentum states in the V-band (60–70 GHz) and W-band (79–90 GHz), suitable for high-speed wireless communications. The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. The structure of the dual-band metasurface features four distinct silver layers yet is fabricated on a single ultra-thin planar substrate, demonstrating its potential for applications in future wireless communication systems.

The swelling mechanism of ethylene-vinyl acetate polymer in different solvents via molecular dynamics and experimental studies.

Ethylene-vinyl acetate (EVA) film is the predominant encapsulation material in crystalline silicon photovoltaic modules, the efficient and eco-friendly processing of which is essential for the recycling of the modules. Among the various existing techniques, the chemical approach uses solvents to induce swelling and dissolution on the EVA film to facilitate the separation of distinct layers. This method demonstrates the potential for achieving low-energy consumption and minimal-damage retrieval of the diverse materials within the components. Nonetheless, the mechanism underlying the swelling of the EVA polymer and the consequent delamination of various layers remains elusive, hindering the proper choice of solvents. In this study, the swelling behaviors of the EVA polymer in water, ethanol, and D-limonene solvents were analyzed via molecular dynamics (MD) simulations. The influence of intermolecular interactions on the swelling degree of the EVA polymers had been examined to determine the dominant factors leading to the discrepancies in the diffusion state of EVA in different solvents. The molecular electrostatic potential (MEP) maps were utilized to explain the differences in interactions from a molecular structure perspective. Furthermore, the results of MD simulations were experimentally verified by solvent-immersion experiments and testing methods. Based on the results, the swelling mechanism of the EVA polymer in the selected solvents was proposed and illustrated, providing theoretical support for chemically-driven photovoltaic module recycling processes.

Turbulent mixing layers and associated diffusive fluxes across the epilimnion and metalimnion in stratified large Lake Biwa

AbstractStratification and turbulent mixing have an immense impact on biogeochemical regimes and ecosystem dynamics in stratified large lakes. This study investigates the physical properties of the epi‐ and metalimnion of Lake Biwa (Japan), focusing on the persistent vertical diffusive fluxes due to turbulent mixing in the lower metalimnion and their effects on upward nitrate transport. We conducted 24‐h observations at an offshore station in the summer for three consecutive years to examine the temporal and vertical variations of stratification and turbulence. A mooring system provided long‐term data on stratification and current velocity, while thermal profiles along two offshore transects were collected to investigate lateral changes. Our findings revealed strong stratification in the upper metalimnion, where vertical diffusive fluxes were essentially suppressed. Distinct turbulent mixing layers were identified near the water surface and within the moderately stratified lower metalimnion. Field data and numerical model suggested that the turbulence in the lower metalimnion was driven by shear instability, straining, and/or wave–wave interactions linked with the persistent internal waves that occur even during weak winds. Vertical eddy diffusivity of > 10−5 m2 s−1 in the lower metalimnion was associated with rather strong turbulence, which has not been reported in other large lakes. The elevated turbulence resulted in an upward nitrate flux of 0.2 mmol N m−2 d−1 across the lower metalimnion, indicating that the upward nutrient transport could support primary productivity and play an important ecological role in deep stratified lakes.

Harnessing Raman spectroscopy and multimodal imaging of cartilage for osteoarthritis diagnosis

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the ‘fingerprint’ region from 700 to 1720 cm− 1 and high-frequency stretching region from 2500 to 3300 cm− 1. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm− 1 and 921 cm− 1) and hydroxyproline (877 cm− 1 and 938 cm− 1), sulphated glycosaminoglycan (sGAG) (1064 cm− 1 and 1380 cm− 1) frequencies for both control and OA as well as the 1245 cm− 1 and 1272 cm− 1, 1320 cm− 1 and 1345 cm− 1, 1451 cm− 1 collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500–3300 cm− 1) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic.

Investigating the Structural Framework Beneath Riyadh Region for Potential Geothermal Exploration Utilizing Geological and Geophysical Data

With growing electricity demand and an increasing focus on renewable energy, geothermal energy exploration is gaining prominence in Saudi Arabia. This study investigates the geothermal potential of the Riyadh region by integrating various geophysical data with geological information, providing a detailed image of the structural framework and thermal distribution. We generated a 2D density and susceptibility model using gravity and aeromagnetic data, constrained by well-logs information for sedimentary rocks. Distinct salt layers with low densities (2.15 g/cm3) were introduced in the model overlying the basement rocks, as well as unexposed faults, which may play a crucial role in the thermal regime in the study area. We produced a comprehensive thermal model extracted from the density and susceptibility model, along with temperature measurements from the Minjur Aquifer. The presence of the salt layer not only affects the geothermal gradient, but also suggests the potential for enhanced surface heat flow in specific areas. The results highlight the presence of a promising geothermal reservoir, the Minjur Formation, which exhibits significant potential for geothermal energy extraction due to its porosity, permeability, and sufficient thermal gradient ranging from 80 °C to 120 °C at depths of 2 to 3 km. These findings offer significant implications for Saudi Arabia’s transition to cleaner energy sources and support the future development of geothermal infrastructure in urban areas like Riyadh.

Stable Fabrication of Chitosan/Polycaprolactone (CS/PCL) Core-Shell Nanofibers by Electrospinning

Chitosan (CS), with its non-toxic and antibacterial properties, and Poly(ε-caprolactone) (PCL), known for its biodegradability and biocompatibility, are crucial materials in medical applications. This study proposed a solution utilizing electrospinning to manufacture fibers with nanometer-sized core-shell structures from these materials, with CS serving as the fiber core and PCL forming the shell. The influence of manufacturing technology parameters on fiber size and morphology was thoroughly researched and investigated. The solvent system used, Chloroform/Dimethylformamide (DMF), ensured complete solubility, viscosity control, conductivity, and proper solvent evaporation. Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) results clearly demonstrated the morphology and internal structure of the nanofibers. The water contact angle (WCA) measurements of the fiber membrane showed almost no significant change over time, indicating strong hydrophobicity of the nanofibers. Additionally, the FTIR spectrum revealed distinct core-shell layers without any blending between them, while DSC analysis showed the thermal properties of fiber membranes. In summary, the electrospinning of nanofibers proved to be stable, producing thin fibers with an average diameter of approximately 400 nm. These findings are expected to significantly enhance the applicability of CS/PCL nanofibers across various fields, including healthcare and smart agriculture.

Lithospheric Foundering in Progress Imaged Under an Extinct Continental Arc

AbstractA long‐standing question is how felsic continental crust is differentiated from its mafic parent mantle magmas. One currently proposed fundamental mechanism is lithospheric foundering and loss of dense material into the mantle. A type locality is the young extinct arc forming the Sierra Nevada, California. Here, we image a distinct anisotropic shear layer below the crust‐mantle boundary using receiver functions. The sense of shear is consistent with west‐ to southwestward removal of lithosphere. The shear signal is strongest in the southern Sierra, where lithospheric foundering was proposed to have concluded several million years ago, and is deeper and less pronounced in the central Sierra, where ongoing lithospheric foundering is corroborated by a band of unusually deep (40+ km) seismicity along the western foothills. Our observations provide progressive snapshots of a lithospheric foundering process spanning several million years and hundreds of kilometers, illuminating a fundamental differentiation process by which continents are built.

A Study of Historic Urban Landscape Change Management Based on Layered Interpretation: A Case Study of Dongxi Ancient Town

In the face of external shocks from urbanization and the inherent needs of economic development, it is essential for urban and rural heritage to adapt timely to achieve sustainability in development. Employing Historic Urban Landscape (HUL) methodologies for change management holds significant implications for the sustainable preservation and utilization of heritage. This study used Dongxi Ancient Town as a case study, characterized by a distinct evolutionary trajectory and diverse layers of accumulation throughout its historical progression, making it an exemplary instance for change analysis. This paper analyzed the processes and outcomes of historic urban landscape changes through a layered historical approach. Combining historical data translation methods with ArcGIS spatial analysis, we documented and mapped the cultural and natural characteristics of Dongxi Ancient Town. The layered process of the town’s historical landscape was categorized into four stages: the primary formative period from the Western Han to the Ming dynasties, the rapid development during the Qing dynasty, the prosperous period of the Republic of China, and the transitional expansion period following the establishment of the People’s Republic of China. The study analyzed the morphological changes and values of the historical landscape throughout these periods. Based on the analysis results, we suggest three transformation management strategies for historical landscapes oriented towards economic development: (1) converting cultural heritage into cultural assets, (2) implementing moderate and controlled quantitative changes, and (3) enhancing operational feasibility through collaborative efforts among multiple stakeholders. These strategies aim to establish a sustainable model that balances heritage conservation with economic growth.

Mapping Subsurface and Surface Characteristics of the Recent Pesanggrahan Landslide, Central Java, Indonesia, for Landslide Hazard Management

Regions prone to deep landslides are characterized by thick materials exceeding 10 m and frequent natural disasters. This research aims to analyze the recent occurrence of Pesanggrahan landslide in Central Java, Indonesia, which is a region consisting of both settlements and rice fields. Surface mapping was conducted using aerial photos and direct field observations. Additionally, subsurface conditions to identify the unconsolidated material layers below the landslide surface have been analyzed using the seismic refraction method. The primary velocity (Vp) values are represented in 2D subsurface cross sections. Differences in Vp values corresponded to different geological layers. There were four distinct layers: Top Soil (TS), Clay (CL), Weathered Bedrock (WB), and Tuff Breccia (TB) within the 2D seismic refraction cross-section with Vp values ranging from 150 to 1,800 m/s. The ranges of Vp are: 150-600 m/s for Top Soil (TS), 600-1,200 m/s for Clay (CL), 1,200-1,800 m/s for Weathered Bedrock (WB), and values exceeding 1,800 m/s for Tuff Breccia (TB). The material layer is critical for sustainable land management strategies aimed to control landslides. Furthermore, the potential depth of the sliding plane was managed through effective environmental management practices, including proper disposal of household waste and minimizing the cutting of steep slopes.

Lack of TYK2 signaling enhances host resistance to Candida albicans skin infection

Candida albicans is the most common human fungal pathogen, causing diseases ranging from local to life-threating systemic infections. Tyrosine kinase 2 (TYK2), a crucial mediator in several cytokine signaling pathways, has been associated with protective functions in various microbial infections. However, its specific contribution in the immune response to fungal infections has remained elusive. In this study, we show that mice lacking TYK2 or its enzymatic activity exhibit enhanced resistance to C. albicans skin infections, limiting fungal spread and accelerating wound healing. Impaired TYK2-signaling prompted the formation of a distinctive layer of necrotic neutrophils around the fungal pathogens. Transcriptomic analysis revealed TYK2’s pivotal role in regulating interferon-inducible genes in neutrophils, thereby impacting their antifungal capacity during infection. Furthermore, we show that TYK2-dependent interferon-gamma (IFNγ) production contributes to fungal dissemination from the skin to the kidneys. Our study uncovers a hitherto unrecognized detrimental role of TYK2 in cutaneous C. albicans infections.

A novel tape-free sample preparation method for human osteochondral cryosections for high throughput hyperspectral imaging.

Understanding the osteochondral junction, where non-mineralised cartilage and mineralised bone converge, is crucial for joint health. Current sample preparation techniques are insufficient for detailed spatial hyperspectral imaging analysis. Using the enhanced Kawamoto method, we used the super cryo embedding medium's temperature-dependent properties to transfer high-quality tissue samples onto slides for spatial imaging analysis. We transferred osteochondral samples using a tape-free system and successfully tested them in hematoxylin and eosin (HE), Safranin-O, nanomechanical assessments and nano-Fourier transform infrared (FTIR) mapping. This protocol elucidates the structural and elemental gradients, mechanical characteristics and distinctive biochemical layering, making it a useful tool for analysing biochemical properties' co-distribution in healthy and diseased situations.